Raffaele CARLI


Short bio

Raffaele Carli received the Laurea degree in electronic engineering with honours in 2002 and the Ph.D. in electrical and information engineeringin 2016, both from Politecnico di Bari, Italy.From 2003 to 2004, he was a Reserve Officer with Italian Navy. From 2004 to 2012, he worked as System and Control Engineer and Technical Manager for a space and defense multinational company.Dr. Carli is now a research fellow at Politecnico di Bari, and his research interests include the formalization, simulation, and implementation of decision andcontrol systems, as well as modeling and optimization of complex systems.

Research interests

  • automation;
  • optimization;
  • discrete event industrial systems;
  • decision and control systems;
  • modeling and optimization of complex systems;
  • energy systems.

Publications

2021

  • Cavone, G., Carli, R., Troccoli, G., Tresca, G. & Dotoli, M. (2021) A MILP approach for the multi-drop container loading problem resolution in logistics 4.0 IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 687-692. doi:10.1109/MED51440.2021.9480359
    [BibTeX] [Abstract] [Download PDF]
    This paper addresses the multi-drop container loading problem (CLP), i.e., the problem of packing multiple bins -associated to multiple deliveries to one or more customers- into a finite number of transport units (TUs). Differently from the traditional CLP, the multi-drop CLP has been rarely handled in the literature, while effective algorithms to automatically solve this problem are needed to improve the efficiency and sustainability of internal logistics. To this aim, we propose a novel algorithm that solves a delivery-based mixed integer linear programming formulation of the problem. The algorithm efficiently determines the optimal composition of TUs by minimizing the unused space, while fulfilling a set of geometric and safety constraints, and complying with the delivery allocation. In particular, the proposed algorithm includes two steps: the first aims at clustering bins into groups to be compatibly loaded in various TUs; the latter aims at determining the optimal configuration of each group in the related TU. Finally, the proposed algorithm is applied to several realistic case studies with the aim of testing and analysing its effectiveness in producing stable and compact TU loading configurations in a short computation time, despite the high computational complexity of the multi-drop CLP. © 2021 IEEE.
    @CONFERENCE{Cavone2021687,
    author={Cavone, G. and Carli, R. and Troccoli, G. and Tresca, G. and Dotoli, M.},
    title={A MILP approach for the multi-drop container loading problem resolution in logistics 4.0},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={687-692},
    doi={10.1109/MED51440.2021.9480359},
    art_number={9480359},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113699292&doi=10.1109%2fMED51440.2021.9480359&partnerID=40&md5=734929101ce82724d3629ddb1e06f4a4},
    abstract={This paper addresses the multi-drop container loading problem (CLP), i.e., the problem of packing multiple bins -associated to multiple deliveries to one or more customers- into a finite number of transport units (TUs). Differently from the traditional CLP, the multi-drop CLP has been rarely handled in the literature, while effective algorithms to automatically solve this problem are needed to improve the efficiency and sustainability of internal logistics. To this aim, we propose a novel algorithm that solves a delivery-based mixed integer linear programming formulation of the problem. The algorithm efficiently determines the optimal composition of TUs by minimizing the unused space, while fulfilling a set of geometric and safety constraints, and complying with the delivery allocation. In particular, the proposed algorithm includes two steps: the first aims at clustering bins into groups to be compatibly loaded in various TUs; the latter aims at determining the optimal configuration of each group in the related TU. Finally, the proposed algorithm is applied to several realistic case studies with the aim of testing and analysing its effectiveness in producing stable and compact TU loading configurations in a short computation time, despite the high computational complexity of the multi-drop CLP. © 2021 IEEE.},
    author_keywords={Container loading problem; Logistics; MILP; Multi-drop; Optimization},
    keywords={Bins; Drops; Integer programming, Container-loading problems; Effective algorithms; Internal Logistics; Loading configuration; Mixed integer linear programming; Multiple deliveries; Optimal composition; Safety constraint, Clustering algorithms},
    references={Strandhagen, J.O., Vallandingham, L.R., Fragapane, G., Strandhagen, J.W., Stangeland, A.B.H., Sharma, N., Logistics 4. 0 and emerging sustainable business models (2017) Advances in Manufacturing, 5 (4), pp. 359-369; Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Minimizing and balancing ergonomic risk of workers of an assembly line by job rotation: A minlp model (2018) International Journal of Industrial Engineering and Management, 9 (3), pp. 129-138; Bischoff, E.E., Ratcliff, M., Issues in the development of approaches to container loading (1995) Omega, 23 (4), pp. 377-390; Facchini, F., Digiesi, S., Mossa, G., Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making (2020) International Journal of Production Economics, 219, pp. 164-178; Wäscher, G., Haußner, H., Schumann, H., An improved typology of cutting and packing problems (2007) European Journal of Operational Research, 183 (3), pp. 1109-1130; Bortfeldt, A., Wäscher, G., Constraints in container loading-a stateof-the-art review (2013) European Journal of Operational Research, 229 (1), pp. 1-20; Scheithauer, G., Terno, J., Riehme, J., Sommerweiss, U., A new heuristic approach for solving the multi-pallet packing problem (1996) Dresden: Technische Universität Dresden; Pisinger, D., Heuristics for the container loading problem (2002) European Journal of Operational Research, 141 (2), pp. 382-392; Moura, A., Oliveira, J.F., A grasp approach to the container-loading problem (2005) IEEE Intelligent Systems, 20 (4), pp. 50-57; Jin, Z., Ohno, K., Du, J., An efficient approach for the threedimensional container packing problem with practical constraints (2004) Asia-Pacific Journal of Operational Research, 21 (3), pp. 279-295; Lin, J.-L., Chang, C.-H., Yang, J.-Y., A study of optimal system for multiple-constraint multiple-container packing problems (2006) International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 1200-1210. , Springer; Ceschia, S., Schaerf, A., Local search for a multi-drop multicontainer loading problem (2013) Journal of Heuristics, 19 (2), pp. 275-294; Alonso, M., Alvarez-Valdes, R., Iori, M., Parreño, F., Mathematical models for multi container loading problems with practical constraints (2019) Computers & Industrial Engineering, 127, pp. 722-733; Do Nascimento, O.X., De Queiroz, T.A., Junqueira, L., Practical constraints in the container loading problem: Comprehensive formulations and exact algorithm (2021) Computers & Operations Research, 128, p. 105186; Dotoli, M., Epicoco, N., Falagario, M., Seatzu, C., Turchiano, B., A decision support system for optimizing operations at intermodal railroad terminals (2016) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (3), pp. 487-501; Dotoli, M., Epicoco, N., A technique for the optimal management of containers' drayage at intermodal terminals (2016) 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 000566-000571; Junqueira, L., Morabito, R., Yamashita, D.S., Mip-based approaches for the container loading problem with multi-drop constraints (2012) Annals of Operations Research, 199 (1), pp. 51-75; Lai, K., Xue, J., Xu, B., Container packing in a multi-customer delivering operation (1998) Computers & Industrial Engineering, 35 (1-2), pp. 323-326; Christensen, S.G., Rousøe, D.M., Container loading with multidrop constraints (2009) International Transactions in Operational Research, 16 (6), pp. 727-743; Bemporad, A., Morari, M., Control of systems integrating logic, dynamics, and constraints (1999) Automatica, 35 (3), pp. 407-427},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Helmi, A. M., Carli, R., Dotoli, M. & Ramadan, H. S. (2021) Harris hawks optimization for the efficient reconfiguration of distribution networks IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 214-219. doi:10.1109/MED51440.2021.9480179
    [BibTeX] [Abstract] [Download PDF]
    Improving the efficiency of distribution networks (DNs) is nowadays a challenging objective for modern power grids equipped with distributed generation and storage. In this context, the so-called network reconfiguration problem can be solved to obtain the optimal DN topology that minimizes the total power losses, while ensuring the voltage profile enhancement. The DN reconfiguration problem has NP-hard complexity; hence, finding near-optimal solutions in reasonable time is still an open research need. Facing this issue, this paper proposes a novel metaheuristic approach, where the recent Harris Hawks optimization algorithm is used to efficiently obtain near-optimal configurations. The effectiveness of the proposed method is validated through numerical experiments on the IEEE 85-bus system, comparing the achieved performance with the results obtained by other related techniques. © 2021 IEEE.
    @CONFERENCE{Helmi2021214,
    author={Helmi, A.M. and Carli, R. and Dotoli, M. and Ramadan, H.S.},
    title={Harris hawks optimization for the efficient reconfiguration of distribution networks},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={214-219},
    doi={10.1109/MED51440.2021.9480179},
    art_number={9480179},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113690851&doi=10.1109%2fMED51440.2021.9480179&partnerID=40&md5=ae29f6caf996f43c23ab7874ded72442},
    abstract={Improving the efficiency of distribution networks (DNs) is nowadays a challenging objective for modern power grids equipped with distributed generation and storage. In this context, the so-called network reconfiguration problem can be solved to obtain the optimal DN topology that minimizes the total power losses, while ensuring the voltage profile enhancement. The DN reconfiguration problem has NP-hard complexity; hence, finding near-optimal solutions in reasonable time is still an open research need. Facing this issue, this paper proposes a novel metaheuristic approach, where the recent Harris Hawks optimization algorithm is used to efficiently obtain near-optimal configurations. The effectiveness of the proposed method is validated through numerical experiments on the IEEE 85-bus system, comparing the achieved performance with the results obtained by other related techniques. © 2021 IEEE.},
    author_keywords={Distribution Network Reconfiguration; Harris Hawks Optimization; Metaheuristic Optimization; Microgrids},
    keywords={Electric power transmission networks; NP-hard; Numerical methods, Distributed generation and storage; Meta-heuristic approach; Near-optimal solutions; Network re-configuration; Optimization algorithms; Reconfiguration of distribution networks; Reconfiguration problems; Voltage profile enhancements, Optimization},
    references={Hosseini, S.M., Carli, R., Dotoli, M., Robust optimal energy management of a residential microgrid under uncertainties on demand and renewable power generation (2021) IEEE Trans. Autom. Sci. Eng., 18 (2), pp. 618-637; Scarabaggio, P., Grammatico, S., Carli, R., Dotoli, M., Distributed demand side management with stochastic wind power forecasting (2021) IEEE Trans. Control Syst. Technol; Casalino, G., Castellano, G., Fanelli, A.M., Mencar, C., Enhancing the dissfcm algorithm for data stream classification (2018) International Workshop on Fuzzy Logic and Applications, pp. 109-122. , Springer, Cham September; Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., A novel optimization framework for the design of gilbert cell mixers (2017) IEEE Int. Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1418-1421; Dotoli, M., Epicoco, N., Falagario, M., Cavone, G., A timed petri nets model for intermodal freight transport terminals (2014) IFAC Proceedings Volumes, 47 (2), pp. 176-181; Cavone, G., Dotoli, M., Epicoco, N., Franceschelli, M., Seatzu, C., Hybrid petri nets to re-design low-automated production processes: The case study of a sardinian bakery (2018) IFAC-PapersOnLine, 51 (7), pp. 265-270; Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Minimizing and balancing ergonomic risk of workers of an assembly line by job rotation: A minlp model (2018) Int. J. Ind. Eng. Manag., 9 (3), pp. 129-138; Babaei, S., Jiang, R., Zhao, C., Distributionally robust distribution network configuration under random contingency (2020) IEEE Trans. Power Syst., 35 (5), pp. 3332-3341; Anderos, A., Koziel, S., Abdel-Fattah, M.F., Distribution network reconfiguration using feasibility-preserving evolutionary optimization (2019) J. Mod. Power Syst. Clean Energy, 7, pp. 589-598; Zhang, D., Fu, Z., Zhang, L., An improved ts algorithm for lossminimum reconfiguration in large-scale distribution systems (2007) ELECTR POW SYST RES, 77 (5-6), pp. 685-694; Samman, M.A., Mokhlis, H., Mansor, N.N., Mohamad, H., Suyono, H., Sapari, N.M., Fast optimal network reconfiguration with guided initialization based on a simplified network approach (2020) IEEE Access, 8, pp. 11948-11963; Mohamed, S., Shaaban, M.F., Ismail, M., Serpedin, E., Qaraqe, K.A., An efficient planning algorithm for hybrid remote microgrids (2019) IEEE Trans. Sustain. Energy, 10 (1), pp. 257-267; Merlin, A., Back, H., Search for a minimal loss operating spanning tree configuration in an urban power distribution system (1975) Proc. 5th Power System Computation Conf (PSCC), pp. 1-18; Civanlar, S., Grainger, J.J., Le, S.S.H., Distribution feeder reconfiguration for loss reduction (1988) IEEE Trans. Power Deliv., 3, pp. 1217-1223; Qiu, R., Lv, X., Chen, S., A survey on artificial intelligence algorithm for distribution network reconfiguration (2011) LECT NOTES CONTR INF, pp. 497-504. , Springer; Badran, O., Mekhilef, S., Mokhlis, H., Dahalan, W., Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies (2017) Renewable and Sustainable Energy Reviews, 73, pp. 854-867; Thakar, S., Vijay, A.S., Doolla, S., System reconfiguration in microgrids (2019) Sustainable Energy, Grids and Networks, 17, p. 100191; Jeon, Y., Kim, J., Kim, J., Shin, J., Lee, K.Y., An efficient simulated annealing algorithm for network reconfiguration in large-scale distribution systems (2002) IEEE Trans. Power Del., 17 (4), pp. 1070-1078; Tandon, A., Saxena, D., A comparative analysis of spso and bpso for power loss minimization in distribution system using network reconfiguration (2014) 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with Their Impact on Humanity (CIPECH), pp. 226-232; Nguyen, T.T., Truong, A.V., Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm (2015) INT J ELEC POWER, 68, pp. 233-242; Muhammad, M.A., Mokhlis, H., Naidu, K., Amin, A., Franco, J.F., Othman, M., Distribution network planning enhancement via network reconfiguration and dg integration using dataset approach and water cycle algorithm (2020) J MOD POWER SYST CLE, 8 (1), pp. 86-93; Prasad, K., Ranjan, R., Sahoo, N.C., Chaturvedi, A., Optimal configuration of radial distribution systems using a fuzzy mutated genetic algorithm (2005) IEEE Trans. Power Del., 20, pp. 1211-1213; Imran Md, A., Kowsalya, M., A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm (2014) Electr Power Syst Res, 62, pp. 312-322; Aman, M.M., Jamson, G.B., Aha, B., Mokhilis, H., Optimum network reconfiguration based on maximization of system load ability using continuation power flow theorem (2014) Electr Power Energy Syst, 54, pp. 123-133; Chen, T.H., Chen, M.S., Hwang, K.J., Kotas, P., Chebli, E.A., Distribution system power flow analysis-a rigid approach (1991) IEEE Trans. On Power Delivery, 6 (3), pp. 1146-1152; Lavorato, M., Franco, J.F., Rider, M.J., Romero, R., Imposing radiality constraints in distribution system optimization problems (2011) IEEE Trans. Power Syst., 27 (1), pp. 172-180; Garces, A., Uniqueness of the power flow solutions in low voltagedirect current grids (2017) Electric Power Systems Research, 151, pp. 149-153; Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H., Harris hawks optimization: Algorithm and applications (2019) Future Generation Computer Systems, 97, pp. 849-872; Yang, X.-S., Ting, T., Karamanoglu, M., Walks, R., Lévy flights, markov chains and metaheuristic optimization (2013) Future Information Communication Technology and Applications, pp. 1055-1064. , Springer; Zimmerman, R.D., Murillo-Sanchez, C.E., Thomas, R.J., Matpower: Steady-state operations, planning and analysis tools for power systems research and education (2011) IEEE Trans Power Syst, 26 (1), pp. 12-19},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Cavone, G., Epicoco, N., Carli, R., Del Zotti, A., Paulo Ribeiro Pereira, J. & Dotoli, M. (2021) Parcel delivery with drones: Multi-criteria analysis of trendy system architectures IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 693-698. doi:10.1109/MED51440.2021.9480332
    [BibTeX] [Abstract] [Download PDF]
    New technologies, such as Unmanned Aerial Vehicles (UAVs), are transforming facilities and vehicles into intelligent systems that will significantly modify logistic deliveries in any organization. With the appearance of automated vehicles, drones offer multiple new technological solutions that might trigger different delivery networks or boost new delivery services. Differently from the related works, where a single specific delivery system model is typically addressed, this paper deals with the use of UAVs for logistic deliveries focusing on a multi-criteria analysis of trendy drone-based system architectures. In particular, using the cross-efficiency Data Envelopment Analysis approach, a comparative analysis among three different delivery systems is performed: the classic system based on trucks only, the drone-only system using a fleet of drones, and the hybrid truck and drone system combining trucks and drones. The proposed technique constitutes an effective decision-making tool aimed at helping delivery companies in selecting the optimal delivery system architecture according to their specific needs. The effectiveness of the proposed methodology is shown by a simulation analysis based on a realistic data case study that pertains to the main logistic service providers. © 2021 IEEE.
    @CONFERENCE{Cavone2021693,
    author={Cavone, G. and Epicoco, N. and Carli, R. and Del Zotti, A. and Paulo Ribeiro Pereira, J. and Dotoli, M.},
    title={Parcel delivery with drones: Multi-criteria analysis of trendy system architectures},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={693-698},
    doi={10.1109/MED51440.2021.9480332},
    art_number={9480332},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113688309&doi=10.1109%2fMED51440.2021.9480332&partnerID=40&md5=b1147e916948e156664d7ff61520c1dd},
    abstract={New technologies, such as Unmanned Aerial Vehicles (UAVs), are transforming facilities and vehicles into intelligent systems that will significantly modify logistic deliveries in any organization. With the appearance of automated vehicles, drones offer multiple new technological solutions that might trigger different delivery networks or boost new delivery services. Differently from the related works, where a single specific delivery system model is typically addressed, this paper deals with the use of UAVs for logistic deliveries focusing on a multi-criteria analysis of trendy drone-based system architectures. In particular, using the cross-efficiency Data Envelopment Analysis approach, a comparative analysis among three different delivery systems is performed: the classic system based on trucks only, the drone-only system using a fleet of drones, and the hybrid truck and drone system combining trucks and drones. The proposed technique constitutes an effective decision-making tool aimed at helping delivery companies in selecting the optimal delivery system architecture according to their specific needs. The effectiveness of the proposed methodology is shown by a simulation analysis based on a realistic data case study that pertains to the main logistic service providers. © 2021 IEEE.},
    author_keywords={Data Envelopment Analysis; Drones; Multi-criteria decision making; Parcel delivery; UAVs},
    keywords={Antennas; Automation; Automobiles; Data envelopment analysis; Decision making; Drones; Fleet operations; Intelligent systems; Network architecture; Trucks, Automated vehicles; Comparative analysis; Decision making tool; Logistic services; Multi Criteria Analysis; Simulation analysis; System architectures; Technological solution, Computer architecture},
    references={Facchini, F., Digiesi, S., Mossa, G., Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making (2020) Int. J. Prod. Econ., 219, pp. 164-178; Bhatti, A., Akram, H., Basit, H., Khan, A., Mahwish, S., Naqvi, R., Bilal, M., E-commerce trends during COVID-19 pandemic (2020) Int. J. Future Gener. Commun. Netw., 13; Ranieri, L., Digiesi, S., Silvestri, B., Roccotelli, M., A review of last mile logistics innovations in an externalities cost reduction vision (2018) Sustainability, 10 (782); Amazon Testing Drones for Deliveries, , www.bbc.com, accessed: 2021-01-04; Jung, S., Kim, H., Analysis of amazon prime air UAV delivery service (2017) J. Knowl. Inf. Technol. Syst., 12, pp. 253-266; Guerrero, M.E., Mercado, D., Lozano, R., García, C., Passivity based control for a quadrotor UAV transporting a cable-suspended payload with minimum swing (2015) 54th IEEE Conf. Decision and Control. IEEE, pp. 6718-6723; Peterson, K., Dektas, M., (2017) Ups Tests Residential Delivery Via Drone Launched from Atop Package Car, , www.pressroom.ups.com; Carlsson, J.G., Song, S., Coordinated logistics with a truck and a drone (2018) Manage. Sci., 64 (9), pp. 4052-4069; Lee, T., Geometric control of quadrotor uavs transporting a cablesuspended rigid body (2017) IEEE Trans. Control Syst. Technol., 26 (1), pp. 255-264; Park, S., Zhang, L., Chakraborty, S., Battery assignment and scheduling for drone delivery businesses (2017) IEEE/ACM Int. Symp. Low Power Electronics and Design. IEEE, pp. 1-6; Dorling, K., Heinrichs, J., Messier, G.G., Magierowski, S., Vehicle routing problems for drone delivery (2016) IEEE Trans. Syst. Man Cybern. Syst., 47 (1), pp. 70-85; Gatteschi, V., Lamberti, F., Paravati, G., Sanna, A., Demartini, C., Lisanti, A., Venezia, G., New frontiers of delivery services using drones: A prototype system exploiting a quadcopter for autonomous drug shipments (2015) 39th IEEE Ann. Computer Software and Applications Conf., 2, pp. 920-927; Charnes, A., Cooper, W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444; Dotoli, M., Epicoco, N., Falagario, M., Multi-criteria decision making techniques for the management of public procurement tenders: A case study (2020) Appl. Soft Comput., 88. , 106064; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A crossefficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114; Sexton, T.R., Silkman, R.H., Hogan, A.J., Data envelopment analysis: Critique and extensions (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, pp. 73-105. , R. H. Silkman, Ed. San Francisco, CA: Jossey-Bass; Guo, R., Dong, Y., Meiqiang, W., Yongjun, L., Dea cross-efficiency evaluation method based on good relationship (2015) Int. J. Syst. Sci., 3 (1), pp. 14-24; Jeong, H.Y., Song, B.D., Lee, S., Truck-drone hybrid delivery routing: Payload-energy dependency and no-fly zones (2019) Int. J. Prod. Econ., 214, pp. 220-233; Linnik, I., (2018) How to Implement Drone Delivery Service for An Ecommerce Store, , www.onilab.com; Kesteloo, H., (2018) Drone Delivery Patent Issued to Workhorse for Their Horsefly Truck Launched Drone Package Delivery System, , www.dronedj.com; Murthy, J.G., Harshith, P.S., Joel, J.A., Rakesh, K., Sharath Kumar, A.J., Autonomous drone delivery system: A survey (2020) Int. Res. J. Eng. Technol., 7 (3), pp. 762-766; Ducato, , www.fiatprofessional.com, in Italian, accessed: 2021-01-04; www.planzer.ch, in Italian, accessed: 2021-01-04; Joshi, A., Kale, S., Chandel, S., Pal, D., Likert scale: Explored and explained (2015) Br. J. Appl. Sci. Technol., 7, pp. 396-403; Google Maps, , www.google.it/maps, accessed: 2021-01-04; Orlando, C., (2020) Prezzi Carburanti: Costo Benzina, Diesel, Gpl e Metano. Petrolio in Ribasso, , www.money.it, in Italian; I Prezzi Luce in Italia e all'Estero, , www.sorgenia.it, in Italian, accessed: 2021-01-04; EcoTransIT World, , www.ecotransit.org, accessed: 2021-01-04; Meet the Coolest Robots Working in Energy, , www.equinor.com, accessed: 2021-01-04; Vallecchi, L., (2019) Auto Elettriche e Diesel, un Confronto Su Emissioni di CO2 e Inquinanti, , www.qualenergia.it, in Italian},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Jantzen, J. & Dotoli, M. (2021) Stochastic model predictive control of community energy storage under high renewable penetration IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 973-978. doi:10.1109/MED51440.2021.9480353
    [BibTeX] [Abstract] [Download PDF]
    This paper focuses on the robust optimal on-line scheduling of a grid-connected energy community, where users are equipped with non-controllable (NCLs) and controllable loads (CLs) and share renewable energy sources (RESs) and a community energy storage system (CESS). Leveraging on the pricing signals gathered from the power grid and the predicted values for local production and demand, the energy activities inside the community are decided by a community energy manager. Differently from literature contributions commonly focused on deterministic optimal control schemes, to cope with the uncertainty that affects the forecast of the inflexible demand profile and the renewable production curve, we propose a Stochastic Model Predictive Control (MPC) approach aimed at minimizing the community energy costs. The effectiveness of the method is validated through numerical experiments on the marina of Ballen, Samso (Denmark). The comparison with a standard deterministic optimal control approach shows that the proposed stochastic MPC achieves higher performance in terms of minimized energy cost and maximized self-consumption of on-site production. © 2021 IEEE.
    @CONFERENCE{Scarabaggio2021973,
    author={Scarabaggio, P. and Carli, R. and Jantzen, J. and Dotoli, M.},
    title={Stochastic model predictive control of community energy storage under high renewable penetration},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={973-978},
    doi={10.1109/MED51440.2021.9480353},
    art_number={9480353},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113644274&doi=10.1109%2fMED51440.2021.9480353&partnerID=40&md5=6156faadbaf382124749771a715a60df},
    abstract={This paper focuses on the robust optimal on-line scheduling of a grid-connected energy community, where users are equipped with non-controllable (NCLs) and controllable loads (CLs) and share renewable energy sources (RESs) and a community energy storage system (CESS). Leveraging on the pricing signals gathered from the power grid and the predicted values for local production and demand, the energy activities inside the community are decided by a community energy manager. Differently from literature contributions commonly focused on deterministic optimal control schemes, to cope with the uncertainty that affects the forecast of the inflexible demand profile and the renewable production curve, we propose a Stochastic Model Predictive Control (MPC) approach aimed at minimizing the community energy costs. The effectiveness of the method is validated through numerical experiments on the marina of Ballen, Samso (Denmark). The comparison with a standard deterministic optimal control approach shows that the proposed stochastic MPC achieves higher performance in terms of minimized energy cost and maximized self-consumption of on-site production. © 2021 IEEE.},
    author_keywords={Community energy storage; Community renewables; Energy community; Energy management; On-line energy scheduling; Stochastic model predictive control},
    keywords={Electric power transmission networks; Energy storage; Model predictive control; Numerical methods; Optimization; Predictive control systems; Renewable energy resources; Stochastic control systems; Stochastic systems, Comparison with a standard; Controllable loads; Local production; Numerical experiments; On-site production; Online scheduling; Optimal control scheme; Renewable energy source, Stochastic models},
    references={Gjorgievski, V.Z., Cundeva, S., Georghiou, G.E., Social arrangements, technical designs and impacts of energy communities: A review (2021) Renewable Energy; Walker, G., Devine-Wright, P., Community renewable energy: What should it mean? (2008) Energy Policy, 36 (2), pp. 497-500; Hosseini, S.M., Carli, R., Dotoli, M., Robust optimal energy management of a residential microgrid under uncertainties on demand and renewable power generation (2021) IEEE Transactions on Automation Science and Engineering, 18 (2), pp. 618-637; Bartolini, A., Carducci, F., Muñoz, C.B., Comodi, G., Energy storage and multi energy systems in local energy communities with high renewable energy penetration (2020) Renewable Energy, 159, pp. 595-609; Dotoli, M., Epicoco, N., Falagario, M., Turchiano, B., Cavone, G., Convertini, A., A decision support system for real-time rescheduling of railways (2014) 2014 European Control Conference (ECC). IEEE, pp. 696-701; Boenzi, F., Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Greening activities in warehouses: Amodel for identifying sustainable strategies in material handling (2015) Annals of DAAAM & Proceedings, 26 (1); Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., A novel optimization framework for the design of gilbert cell mixers (2017) 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, pp. 1418-1421; Hossain, M.A., Pota, H.R., Squartini, S., Zaman, F., Guerrero, J.M., Energy scheduling of community microgrid with battery cost using particle swarm optimisation (2019) Applied Energy, 254, p. 113723; Pippia, T., Sijs, J., De Schutter, B., A single-level rule-based model predictive control approach for energy management of grid-connected microgrids (2019) IEEE Transactions on Control Systems Technology, 28 (6), pp. 2364-2376; Pezeshki, H., Wolfs, P., Ledwich, G., A model predictive approach for community battery energy storage system optimization (2014) IEEE PES General Meeting| Conference & Exposition. IEEE, pp. 1-5; Carli, R., Dotoli, M., Jantzen, J., Kristensen, M., Othman, S.B., Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the ballen marina in samsø (2020) Energy, 198, p. 117188; Lv, C., Yu, H., Li, P., Wang, C., Xu, X., Li, S., Wu, J., Model predictive control based robust scheduling of community integrated energy system with operational flexibility (2019) Applied Energy, 243, pp. 250-265; Scarabaggio, P., Grammatico, S., Carli, R., Dotoli, M., Distributed demand side management with stochastic wind power forecasting (2021) IEEE Trans. Control Syst. Technol., pp. 1-16; Parisio, A., Rikos, E., Glielmo, L., Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study (2016) Journal of Process Control, 43, pp. 24-37; Zhang, Y., Meng, F., Wang, R., Zhu, W., Zeng, X.-J., A stochastic MPC based approach to integrated energy management in microgrids (2018) Sustainable Cities and Society, 41, pp. 349-362; Zhang, Y., Fu, L., Zhu, W., Bao, X., Liu, C., Robust model predictive control for optimal energy management of island microgrids with uncertainties (2018) Energy, 164, pp. 1229-1241; Ramanathan, B., Vittal, V., A framework for evaluation of advanced direct load control with minimum disruption (2008) IEEE Transactions on Power Systems, 23 (4), pp. 1681-1688; Shapiro, A., Stochastic programming approach to optimization under uncertainty (2008) Mathematical Programming, 112 (1), pp. 183-220; Kim, S., Pasupathy, R., Henderson, S.G., A guide to sample average approximation (2015) Handbook of Simulation Optimization, pp. 207-243; Jantzen, J., Requirements specification: Deliverable d3. 4 [internet (2019) SMILE, , https://www.h2020smile.eu; Jantzen, J., Kristensen, M., (2019) The Ballen2016 Data Set, , http://arkiv.energiinstituttet.dk/643/, Accessed on: 20-01-2020},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N. & Dotoli, M. (2021) Modeling, estimation, and analysis of COVID-19 secondary waves: The Case of the Italian Country IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 794-800. doi:10.1109/MED51440.2021.9480319
    [BibTeX] [Abstract] [Download PDF]
    The recent trends of the COVID-19 research have been devoted to disease transmission modeling, with the aim of investigating the effects of different mitigation strategies mainly through scenario-based simulations. In this context we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 secondary waves. Specifically, this paper proposes an accurate SIRUCQTHE epidemiological model to get reliable predictions on the pandemic dynamics. Differently from the related literature, in the fitting phase, we make use of the google mobility reports to identify and predict the evolution of the infection rate. The effectiveness of the presented method is tested on the network of Italian regions. First, we describe the Italian epidemiological scenario in the COVID-19 second wave of contagions, showing the raw data available for the Italian scenario and discussing the main assumptions on the system parameters. Then, we present the different steps of the procedure used for the dynamical fitting of the SIRUCQTHE model. Finally, we compare the estimation results with the real data on the COVID-19 secondary waves in Italy. Provided the availability of reliable data to calibrate the model in heterogeneous scenarios, the proposed approach can be easily extended to cope with other scenarios. © 2021 IEEE.
    @CONFERENCE{Scarabaggio2021794,
    author={Scarabaggio, P. and Carli, R. and Cavone, G. and Epicoco, N. and Dotoli, M.},
    title={Modeling, estimation, and analysis of COVID-19 secondary waves: The Case of the Italian Country},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={794-800},
    doi={10.1109/MED51440.2021.9480319},
    art_number={9480319},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113602900&doi=10.1109%2fMED51440.2021.9480319&partnerID=40&md5=980feaa724975719c46c214ad1dcbfed},
    abstract={The recent trends of the COVID-19 research have been devoted to disease transmission modeling, with the aim of investigating the effects of different mitigation strategies mainly through scenario-based simulations. In this context we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 secondary waves. Specifically, this paper proposes an accurate SIRUCQTHE epidemiological model to get reliable predictions on the pandemic dynamics. Differently from the related literature, in the fitting phase, we make use of the google mobility reports to identify and predict the evolution of the infection rate. The effectiveness of the presented method is tested on the network of Italian regions. First, we describe the Italian epidemiological scenario in the COVID-19 second wave of contagions, showing the raw data available for the Italian scenario and discussing the main assumptions on the system parameters. Then, we present the different steps of the procedure used for the dynamical fitting of the SIRUCQTHE model. Finally, we compare the estimation results with the real data on the COVID-19 secondary waves in Italy. Provided the availability of reliable data to calibrate the model in heterogeneous scenarios, the proposed approach can be easily extended to cope with other scenarios. © 2021 IEEE.},
    author_keywords={COVID-19; Dynamical fitting; Estimation; Identification; Pandemic modeling},
    keywords={Forecasting, Disease transmission; Epidemiological modeling; Estimation results; Infection rates; Italian regions; Mitigation strategy; Predicting and analyzing; Scenario-based simulations, Shear waves},
    references={World Health Organization, , https://www.who.int/emergencies/diseases/novel-coronavirus-2019, Accessed: 2021-01-14; Mojur, M., Fattah, I.R., Alam, M.A., Islam, A.S., Ong, H.C., Rahman, S.A., Naja, G., Mahlia, T., Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic (2021) Sustainable Production and Consumption, 26, pp. 343-359; Auriemma, V., Iannaccone, C., COVID-19 pandemic: Socioeconomic consequences of social distancing measures in Italy (2020) Fron-tiers in Sociology, 5, p. 78; Paré, P.E., Beck, C.L., Başar, T., Modeling, estimation, and analysis of epidemics over networks: An overview (2020) Annu. Rev. Control, 50, pp. 345-360; Calaore, G.C., Novara, C., Possieri, C., A time-varying sird model for the COVID-19 contagion in Italy (2020) Annual Reviews in Control, 50, pp. 361-372; Ding, Y., Gao, L., An evaluation of COVID-19 in Italy: A data-driven modeling analysis (2020) Infectious Disease Modelling, 5, pp. 495-501; Loli Piccolomini, E., Zama, F., Monitoring Italian COVID-19 spread by a forced seird model (2020) PLoS ONE, 15 (8), p. 0237417; Della Rossa, F., Salzano, D., Di Meglio, A., De Lellis, F., A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic (2020) Nat. Commun., 11, p. 5106; Carli, R., Cavone, G., Epicoco, N., Scarabaggio, P., Dotoli, M., Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario (2020) Annu. Rev. Control, 50, pp. 373-393; Scharbarg, E., Moog, C.H., Mauduit, N., Califano, C., From the hospital scale to nationwide: Observability and identification of models for the COVID-19 epidemic waves (2020) Annu. Rev. Control, , https://doi.org/10.1016/j.arcontrol.2020.09.007, in press; Brugnano, L., Iavernaro, F., Zanzottera, P., A multiregional extension of the sir model, with application to the COVID-19 spread in Italy (2020) Math. Method. Appl. Sci.; Ansumali, S., Kaushal, S., Kumar, A., Prakash, M.K., Vidyasagar, M., Modelling a pandemic with asymptomatic patients, impact of lockdown and herd immunity, with applications to sars-cov-2 (2020) Annu. Rev. Control, , https://doi.org/10.1016/j.arcontrol.2020.10.003, in press; Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures (2020) Proc. Nat. Acad. Sci., 117 (19), pp. 10484-10491; Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N., Dotoli, M., (2021) Stochastic Non-pharmaceutical Optimal Control Strategies to Mitigate COVID-19, , https://doi.org/10.36227/techrxiv.14413259.v1, TechRxiv preprint; Google COVID-19 Community Mobility Reports, , www.google.com/covid19/mobility, Google LLC, Accessed: 2021-01-14; Bai, Y., Yao, L., Wei, T., Tian, F., Presumed asymptomatic carrier transmission of COVID-19 (2020) JAMA, 323 (14), pp. 1406-1407; The Civil Protection Department, , http://www.protezionecivile.gov.it/, Accessed: 2021-01-14; Hu, H., Nigmatulina, K., Eckhoff, P., The scaling of contact rates with population density for the infectious disease models (2013) Math. Biosci., 244 (2), pp. 125-134; Santamaria, C., Sermi, F., Spyratos, S., Iacus, S.M., Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data. A european regional analysis (2020) Saf. Sci., 132, p. 104925; Iacus, S.M., Santamaria, C., Sermi, F., Spyratos, S., Tarchi, D., Vespe, M., Human mobility and COVID-19 initial dynamics (2020) Nonlinear Dynamics, 101 (3), pp. 1901-1919; Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Clinical characteristics of coronavirus disease 2019 in China (2020) N. Engl. J. Med., 382 (18), pp. 1708-1720; Li, Q., Guan, X., Wu, P., Wang, X., Early transmission dynamics in Wuhan, China, of novel coronavirus. infected pneumonia (2020) N. Engl. J. Med.; Pedersen, M., Meneghini, M., (2020) Quantifying Undetected COVID-19 Cases and Effects of Containment Measures in Italy: Predicting Phase 2 Dynamics, , 03; Ehmann, K.Z., Drosten, C., Wendtner, C., Zange, M., Virological assessment of hospitalized cases of coronavirus disease 2019 (2020) Nature, 581, pp. 465-469; Liu, Y., Yan, L.-M., Wan, L., Xiang, T.-X., Viral dynamics in mild and severe cases of COVID-19 (2020) Lancet Inf. Dis.; Bertozzi, A.L., Franco, E., Mohler, G., Short, M.B., Sledge, D., The challenges of modeling and forecasting the spread of COVID-19 (2020) Proc. Nat. Acad. Sci., 117 (29), pp. 16732-16738; COVID-19: Indicazioni per la Durata e Il Termine della Quarantena, , http://www.salute.gov.it/portale/home.html, Accessed: 2021-01-14; Wei, C., Wang, Z., Liang, Z., Liu, Q., The focus and timing of COVID-19 pandemic control measures under healthcare resource constraints (2020) MedRxiv; Lemos-Paiao, A.P., Silva, C.J., Torres, D.F., A new compartmental epidemiological model for COVID-19 with a case study of Portugal (2020) Ecol. Complex., p. 100885; Giordano, G., Blanchini, F., Bruno, R., Colaneri, P., Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy (2020) Nat. Med., 26, pp. 855-860; Zhou, F., Yu, T., Du, R., Fan, G., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study (2020) Lancet; Aspelund, K., Droste, M., Stock, J.H., Walker, C.D., Identification and estimation of undetected COVID-19 cases using testing data from Iceland (2020) NBER Work. Pap. No. w27528; Decision & Control Laboratory, , http://dclab.poliba.it/covid-19, Accessed: 2021-04-27},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Hosseini, S. M., Carli, R. & Dotoli, M. (2021) Robust Optimal Energy Management of a Residential Microgrid under Uncertainties on Demand and Renewable Power Generation. IN IEEE Transactions on Automation Science and Engineering, 18.618-637. doi:10.1109/TASE.2020.2986269
    [BibTeX] [Abstract] [Download PDF]
    Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users’ behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users’ demand uncertainties. To this aim, first, we formulate a min-max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min-max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints’ violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners – This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users’ payment and constraints’ violation rate considering the energy cost saving, the system technical limitations and the users’ comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand. © 2004-2012 IEEE.
    @ARTICLE{Hosseini2021618,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={Robust Optimal Energy Management of a Residential Microgrid under Uncertainties on Demand and Renewable Power Generation},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2021},
    volume={18},
    number={2},
    pages={618-637},
    doi={10.1109/TASE.2020.2986269},
    art_number={9093973},
    note={cited By 27},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100266630&doi=10.1109%2fTASE.2020.2986269&partnerID=40&md5=ab5c0a8a4eb48977e42d7186b3d4fc3b},
    abstract={Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users' behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users' demand uncertainties. To this aim, first, we formulate a min-max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min-max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints' violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners - This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users' payment and constraints' violation rate considering the energy cost saving, the system technical limitations and the users' comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand. © 2004-2012 IEEE.},
    author_keywords={Demand-side management (DSM); microgrid; mixed-integer quadratic programming (MIQP); optimal energy scheduling; optimization; robust control},
    keywords={Budget control; Costs; Decision making; Electric utilities; Electromagnetic wave emission; Energy storage; Energy transfer; Housing; Integer programming; Microgrids; Multiobjective optimization; Plug-in electric vehicles; Quadratic programming; Renewable energy resources; Scheduling, Energy storage systems; Environmental benefits; Mixed integer quadratic programming; Peak to average ratios; Renewable energy source; Renewable power generation; Scheduling strategies; Technical limitations, Demand side management},
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Energy, 6 (3), pp. 924-933. , Jul; Kong, W., Dong, Z.Y., Hill, D.J., Luo, F., Xu, Y., Short-term residential load forecasting based on resident behaviour learning (2018) IEEE Trans. Power Syst., 33 (1), pp. 1087-1088. , Jan; Esther, B.P., Kumar, K.S., A survey on residential demand side management architecture, approaches, optimization models and methods (2016) Renew. Sustain. Energy Rev., 59, pp. 342-351. , Jun; Ramanathan, B., Vittal, V., A framework for evaluation of advanced direct load control with minimum disruption (2008) IEEE Trans. Power Syst., 23 (4), pp. 1681-1688. , Nov; Monteiro, V., Gonçalves, H., Ferreira, J.C., Afonso, J.L., Carmo, J.P., Ribeiro, J.E., Batteries charging systems for electric and plug-in hybrid electric vehicles (2012) New Advances in Vehicular Technology and Automotive Engineering, pp. 149-168. , Rijeka, Croatia: InTech; Parisio, A., Rikos, E., Glielmo, L., A model predictive control approach to microgrid operation optimization (2014) IEEE Trans. Control Syst. Technol., 22 (5), pp. 1813-1827. , Sep; Liu, Z., Zhang, C., Dong, M., Gu, B., Ji, Y., Tanaka, Y., Markovdecision- process-assisted consumer scheduling in a networked smart grid (2017) IEEE Access, 5, pp. 2448-2458; Bemporad, A., Morari, M., Control of systems integrating logic, dynamics, and constraints (1999) Automatica, 35 (3), pp. 407-427. , Mar; Wanka, G., Bot, R.-I., Multiobjective duality for convex-linear problems II (2001) Math. Methods Operations Res., 53 (3), pp. 419-433. , Jul; Rezzonico, S., Nowak, S., (1997) Buy-back Rates for Grid-connected Photovoltaic Power Systems, , Int. Energy Agency, Saint Ursen, Switzerland, Tech. Rep. IEA PVPS TI 1997 2, Nov; (2007) Electricity Prices for Household Consumers-Bi- Annual Data, , http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en, Accessed: Apr. 30, 2019. [Online]; Alberini, A., Prettico, G., Shen, C., Torriti, J., Hot weather and hourly electricity demand in Italy (2019) Energy, 177, pp. 44-56. , Jun; Tutkun, N., Can, O., San, E.S., Daily cost minimization for an offgrid renewable microhybrid system installed to a residential home (2015) Proc. Int. Conf. Renew. Energy Res. Appl. (ICRERA), pp. 750-754. , Palermo, Italy, Nov; Soyster, A.L., Technical note-convex programming with set-inclusive constraints and applications to inexact linear programming (1973) Oper. Res., 21 (5), pp. 1154-1157. , Oct; Hubert, T., Grijalva, S., Modeling for residential electricity optimization in dynamic pricing environments (2012) IEEE Trans. Smart Grid, 3 (4), pp. 2224-2231. , Dec},
    document_type={Article},
    source={Scopus},
    }
  • Helmi, A. M., Carli, R., Dotoli, M. & Ramadan, H. S. (2021) Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization. IN IEEE Transactions on Automation Science and Engineering, .. doi:10.1109/TASE.2021.3072862
    [BibTeX] [Abstract] [Download PDF]
    Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain–without guarantees–the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. IEEE
    @ARTICLE{Helmi2021,
    author={Helmi, A.M. and Carli, R. and Dotoli, M. and Ramadan, H.S.},
    title={Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2021},
    doi={10.1109/TASE.2021.3072862},
    note={cited By 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105879516&doi=10.1109%2fTASE.2021.3072862&partnerID=40&md5=1b9b5290b92a3a5ad8886f61ec903dc8},
    abstract={Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain--without guarantees--the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time. IEEE},
    author_keywords={Distribution network (DN) reconfiguration; Harris hawks optimization (HHO) algorithm; metaheuristic optimization; Microgrids; microgrids; Optimization; power losses reduction; Search problems; Sociology; Statistics; Sustainable development; Topology; voltage profile improvement.},
    keywords={Microgrids; NP-hard; Numerical methods; Optimal systems; Particle swarm optimization (PSO), Cuckoo search algorithms; Global optimal solutions; Meta heuristic algorithm; Meta-heuristic optimizations; Meta-heuristic techniques; Network re-configuration; Particle swarm optimization algorithm; Reconfiguration of distribution networks, Internet protocols},
    document_type={Article},
    source={Scopus},
    }
  • Scarabaggio, P., Grammatico, S., Carli, R. & Dotoli, M. (2021) Distributed Demand Side Management With Stochastic Wind Power Forecasting. IN IEEE Transactions on Control Systems Technology, .. doi:10.1109/TCST.2021.3056751
    [BibTeX] [Abstract] [Download PDF]
    In this article, we propose a distributed demand-side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this article is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation (SAA) technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real data set show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach. IEEE
    @ARTICLE{Scarabaggio2021,
    author={Scarabaggio, P. and Grammatico, S. and Carli, R. and Dotoli, M.},
    title={Distributed Demand Side Management With Stochastic Wind Power Forecasting},
    journal={IEEE Transactions on Control Systems Technology},
    year={2021},
    doi={10.1109/TCST.2021.3056751},
    note={cited By 18},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100918524&doi=10.1109%2fTCST.2021.3056751&partnerID=40&md5=4f491e205e325ad467291e71ce8dbff8},
    abstract={In this article, we propose a distributed demand-side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this article is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation (SAA) technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real data set show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach. IEEE},
    author_keywords={Demand-side management (DSM); model predictive control; Optimization; sample average approximation (SAA); smart grid; Smart grids; stochastic optimization.; Stochastic processes; Uncertainty; Wind forecasting; Wind power generation; Wind speed},
    keywords={Electric power generation; Electric power transmission networks; Electric utilities; Game theory; Probability density function; Smart power grids; Stochastic systems; Weather forecasting; Wind; Wind power, Grid optimization; Noncooperative game; Probability density function (pdf); Sample average approximation; Stochastic winds; Wind power availability; Wind power forecasting; Wind speed forecast, Demand side management},
    document_type={Article},
    source={Scopus},
    }

2020

  • Hosseini, S. M., Carli, R., Parisio, A. & Dotoli, M. (2020) Robust Decentralized Charge Control of Electric Vehicles under Uncertainty on Inelastic Demand and Energy Pricing. IN IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020-October.1834-1839. doi:10.1109/SMC42975.2020.9283440
    [BibTeX] [Abstract] [Download PDF]
    This paper proposes a novel robust decentralized charging strategy for large-scale EV fleets. The system incorporates multiple EVs as well as inelastic loads connected to the power grid under power flow limits. We aim at minimizing both the overall charging energy payment and the aggregated battery degradation cost of EVs while preserving the robustness of the solution against uncertainties in the price of the electricity purchased from the power grid and the demand of inelastic loads. The proposed approach relies on the so-called uncertainty set-based robust optimization. The resulting charge scheduling problem is formulated as a tractable quadratic programming problem where all the EVs’ decisions are coupled via the grid resource-sharing constraints and the robust counterpart supporting constraints. We adopt an extended Jacobi-Proximal Alternating Direction Method of Multipliers algorithm to solve effectively the formulated scheduling problem in a decentralized fashion, thus allowing the method applicability to large scale fleets. Simulations of a realistic case study show that the proposed approach not only reduces the costs of the EV fleet, but also maintains the robustness of the solution against perturbations in different uncertain parameters, which is beneficial for both EVs’ users and the power grid. © 2020 IEEE.
    @ARTICLE{Hosseini20201834,
    author={Hosseini, S.M. and Carli, R. and Parisio, A. and Dotoli, M.},
    title={Robust Decentralized Charge Control of Electric Vehicles under Uncertainty on Inelastic Demand and Energy Pricing},
    journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
    year={2020},
    volume={2020-October},
    pages={1834-1839},
    doi={10.1109/SMC42975.2020.9283440},
    art_number={9283440},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098877625&doi=10.1109%2fSMC42975.2020.9283440&partnerID=40&md5=51febb550a1cfd7909fd0ff26527f5b0},
    abstract={This paper proposes a novel robust decentralized charging strategy for large-scale EV fleets. The system incorporates multiple EVs as well as inelastic loads connected to the power grid under power flow limits. We aim at minimizing both the overall charging energy payment and the aggregated battery degradation cost of EVs while preserving the robustness of the solution against uncertainties in the price of the electricity purchased from the power grid and the demand of inelastic loads. The proposed approach relies on the so-called uncertainty set-based robust optimization. The resulting charge scheduling problem is formulated as a tractable quadratic programming problem where all the EVs' decisions are coupled via the grid resource-sharing constraints and the robust counterpart supporting constraints. We adopt an extended Jacobi-Proximal Alternating Direction Method of Multipliers algorithm to solve effectively the formulated scheduling problem in a decentralized fashion, thus allowing the method applicability to large scale fleets. Simulations of a realistic case study show that the proposed approach not only reduces the costs of the EV fleet, but also maintains the robustness of the solution against perturbations in different uncertain parameters, which is beneficial for both EVs' users and the power grid. © 2020 IEEE.},
    author_keywords={ADMM; Charge scheduling; Decentralized control; Electric vehicles; Large-scale optimization; Robust optimization; Set-based uncertainty},
    keywords={Charging (batteries); Costs; Electric load flow; Quadratic programming; Scheduling; Uncertainty analysis, Alternating direction method of multipliers; Battery degradation; Charging energies; Charging strategies; Quadratic programming problems; Robust optimization; Scheduling problem; Uncertain parameters, Electric power transmission networks},
    references={Xie, S., Qi, S., Lang, K., A data-driven power management strategy for plug-in hybrid electric vehicles including optimal battery depth of discharging (2020) IEEE Trans. Ind. Informat, 16 (5), pp. 3387-3396; Yilmaz, M., Krein, P.T., Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles (2013) IEEE Trans. Power Electron, 28 (5), pp. 2151-2169; Boenzi, F., Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Greening activities in warehouses: A model for identifying sustainable strategies in material handling (2015) Annals of DAAAM & Proceedings, 26 (1); Casalino, G., Del Buono, N., Mencar, C., Nonnegative matrix factorizations for intelligent data analysis (2016) Nonnegative Matrix Factorization Techniques. Signals and Communication Technology, , Naik G. (eds). Springer, Berlin, Heidelberg; D'Amato, G., Avitabile, G., Coviello, G., Talarico, C., Ddspll phase shifter architectures for phased arrays: Theory and techniques (2019) IEEE Access, 7, pp. 19461-19470; Turker, H., Bacha, S., Optimal minimization of plug-in electric vehicle charging cost with vehicle-to-home and vehicle-to-grid concepts (2018) IEEE Trans. Veh. Technol, 67 (11), pp. 10281-10292; Nafisi, H., Agah, S.M.M., Askarian Abyaneh, H., Abedi, M., Two-stage optimization method for energy loss minimization in microgrid based on smart power management scheme of phevs (2016) IEEE Trans. Smart Grid, 7 (3), pp. 1268-1276; Hosseini, S.M., Carli, R., Cavone, G., Dotoli, M., Distributed control of electric vehicle fleets considering grid congestion and battery degradation (2020) Internet Technology Letters, 2, p. e161; Liu, M., Phanivong, P.K., Shi, Y., Callaway, D.S., Decentralized charging control of electric vehicles in residential distribution networks (2019) IEEE Trans. Control Syst. Technol, 27 (1), pp. 266-281; Wang, P., Zou, S., Ma, Z., A partial augmented lagrangian method for decentralized electric vehicle charging in capacity-constrained distribution networks (2019) IEEE Access, 7, pp. 118229-118238; Carli, R., Dotoli, M., A decentralized control strategy for optimal charging of electric vehicle fleets with congestion management (2017) IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 63-67. , Bari; Zhang, L., Kekatos, V., Giannakis, G.B., Scalable electric vehicle charging protocols (2017) IEEE Trans. Power Syst, 32 (2), pp. 1451-1462; Liu, S., Etemadi, A.H., A dynamic stochastic optimization for recharging plug-in electric vehicles (2018) IEEE Trans. Smart Grid, 9 (5), pp. 4154-4161; Wang, R., Xiao, G., Wang, P., Hybrid centralized-decentralized (hcd) charging control of electric vehicles (2017) IEEE Trans. Veh. Technol, 66 (8), pp. 6728-6741; Bertsimas, D., Brown, D.B., Caramanis, C., Theory and applications of robust optimization (2011) SIAM Review, 53 (3), pp. 464-501; Bertsimas, D., Sim, M., The price of robustness (2004) Operational Research, 52 (1), pp. 35-53; Deng, W., Lai, M.J., Peng, Z., Yin, W., Parallel multi-block admm with o (1/k) convergence (2017) Journal of Scientific Computing, 71 (2), pp. 712-736; Wang, J., Bharati, G.R., Paudyal, S., Ceylan, O., Bhattarai, B.P., Myers, K.S., Coordinated electric vehicle charging with reactive power support to distribution grids (2018) IEEE Trans. Ind. Inform, 15 (1), pp. 54-63; ISO-NE, Locational Marginal Prices, , http://www.iso-ne.com, accessed on 19 February 2020; Vagropoulos, S.I., Bakirtzis, A.G., Optimal bidding strategy for electric vehicle aggregators in electricity markets (2013) IEEE Trans. Power Syst, 28 (4), pp. 4031-4041},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., La Scala, M., Carli, R. & Dotoli, M. (2020) Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: The Case of Northern Italy IN 12th AEIT International Annual Conference, AEIT 2020.. doi:10.23919/AEIT50178.2020.9241136
    [BibTeX] [Abstract] [Download PDF]
    The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people’s mobility. © 2020 AEIT.
    @CONFERENCE{Scarabaggio2020,
    author={Scarabaggio, P. and La Scala, M. and Carli, R. and Dotoli, M.},
    title={Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: The Case of Northern Italy},
    journal={12th AEIT International Annual Conference, AEIT 2020},
    year={2020},
    doi={10.23919/AEIT50178.2020.9241136},
    art_number={9241136},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097170993&doi=10.23919%2fAEIT50178.2020.9241136&partnerID=40&md5=a3abf95802bb66dc9a2d8715976cc126},
    abstract={The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people's mobility. © 2020 AEIT.},
    author_keywords={COVID-19; Lockdown; Machine learning; Neural networks; Power systems},
    keywords={Electric power utilization; Energy utilization; Feedforward neural networks, Economic trends; Forecasting modeling; Global economics; Ground truth data; Macrostructures; Mobility behavior; Multilayer feedforward neural networks; Northern Italy, Multilayer neural networks},
    references={Ferguson, N., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Cuomo-Dannenburg, G., (2020) Impact of Non-Pharmaceutical Interventions to Reduce Covid-19 Mortality and Healthcare Demand, , Preprint at Spiral; Chontanawat, J., Hunt, L.C., Pierse, R., Does energy consumption cause economic growth?: Evidence from a systematic study of over 100 countries (2008) Journal of Policy Modeling, 30 (2), pp. 209-220; (2020) Terna.it., , www.terna.it, [Online]; Baldwin, R., Weder, B., (2020) Economics in the Time of COVID-19, , CEPR Press; Carli, R., Dotoli, M., Pellegrino, R., Multi-criteria decision-making for sustainable metropolitan cities assessment (2018) Journal of Environmental Management, 226, pp. 46-61; Abbasi, S., Abdi, H., Bruno, S., La Scala, M., Transmission network expansion planning considering load correlation using unscented transformation," int (2018) Journal of Electrical Power & Energy Systems, 103, pp. 12-20; Cicala, S., (2020) Early Economic Impacts of Covid-19 in Europe: A View from the Grid, , Tech. rep. University of Chicago, Tech. Rep; Narajewski, M., Ziel, F., (2020) Changes in Electricity Demand Pattern in Europe Due to Covid-19 Shutdowns, , Preprint; Ziel, F., Liu, B., Lasso estimation for gefcom2014 probabilistic electric load forecasting," int (2016) Journal of Forecasting, 32 (3), pp. 1029-1037; Alpaydin, E., (2020) Introduction to Machine Learning, , MIT press; Blonbou, R., Very short-term wind power forecasting with neural networks and adaptive bayesian learning (2011) Renewable Energy, 36 (3), pp. 1118-1124; Ogcu, G., Demirel, O.F., Zaim, S., Forecasting electricity consumption with neural networks and support vector regression (2012) Procedia-Social and Behavioral Sciences, 58, pp. 1576-1585; Hamedmoghadam, H., Joorabloo, N., Jalili, M., (2018) Australia's Long-Term Electricity Demand Forecasting Using Deep Neural Networks, , Preprint; Kuo, P.-H., Huang, C.-J., A high precision artificial neural networks model for short-term energy load forecasting (2018) Energies, 11 (1), p. 213; (2020) World Bank Open Data., , data.worldbank.org, [Online]; Openweathermap, , www.openweathermap.org, [Online]; Jain, A., Nandakumar, K., Ross, A., Score normalization in mul-timodal biometric systems (2005) Pattern Recognition, 38 (12), pp. 2270-2285; Payal, A., Rai, C., Reddy, B., Comparative analysis of bayesian reg-ularization and levenberg-marquardt training algorithm for localization in wireless sensor network 2013 15th Int. Conference on Advanced Communications Technology, 2013, pp. 191-194; Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y., Short-term residential load forecasting based on lstm recurrent neural network (2017) Ieee Trans. On Smart Grid, 10 (1), pp. 841-851; Covid-19 Effects, , www.bruegel.org/publications/datasets/bruegel-electricity-tracker-of-covid-19-lockdown-effects, [Online]; (2020) Covid-19 Community Mobility Reports, , www.google.com/covid19/mobility, Google llc, [Online]; Vehtari, A., Gelman, A., Gabry, J., Practical bayesian model evaluation using leave-one-out cross-validation and waic (2017) Statistics and Computing, 27 (5), pp. 1413-1432},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2020) A dynamic programming approach for the decentralized control of discrete optimizers with quadratic utilities and shared constraint IN 2020 28th Mediterranean Conference on Control and Automation, MED 2020., 611-616. doi:10.1109/MED48518.2020.9183012
    [BibTeX] [Abstract] [Download PDF]
    This paper addresses the problem of controlling a large set of agents, each with a quadratic utility function depending on individual combinatorial choices, and all sharing an affine constraint on available resources. Such a problem is formulated as an integer mono-constrained bounded quadratic knapsack problem. Differently from the centralized approaches typically proposed in the related literature, we present a new decentralized algorithm to solve the problem approximately in polynomial time by decomposing it into a finite series of sub-problems. We assume a minimal communication structure through the presence of a central coordinator that ensures the information exchange between agents. The proposed solution relies on a decentralized control algorithm that combines discrete dynamic programming with additive decomposition and value functions approximation. The optimality and complexity of the presented strategy are discussed, highlighting that the algorithm constitutes a fully polynomial approximation scheme. Numerical experiments are presented to show the effectiveness of the approach in the optimal resolution of large-scale instances. © 2020 IEEE.
    @CONFERENCE{Carli2020611,
    author={Carli, R. and Dotoli, M.},
    title={A dynamic programming approach for the decentralized control of discrete optimizers with quadratic utilities and shared constraint},
    journal={2020 28th Mediterranean Conference on Control and Automation, MED 2020},
    year={2020},
    pages={611-616},
    doi={10.1109/MED48518.2020.9183012},
    art_number={9183012},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092158668&doi=10.1109%2fMED48518.2020.9183012&partnerID=40&md5=e3c2836d54651610b499a9686ac98fe7},
    abstract={This paper addresses the problem of controlling a large set of agents, each with a quadratic utility function depending on individual combinatorial choices, and all sharing an affine constraint on available resources. Such a problem is formulated as an integer mono-constrained bounded quadratic knapsack problem. Differently from the centralized approaches typically proposed in the related literature, we present a new decentralized algorithm to solve the problem approximately in polynomial time by decomposing it into a finite series of sub-problems. We assume a minimal communication structure through the presence of a central coordinator that ensures the information exchange between agents. The proposed solution relies on a decentralized control algorithm that combines discrete dynamic programming with additive decomposition and value functions approximation. The optimality and complexity of the presented strategy are discussed, highlighting that the algorithm constitutes a fully polynomial approximation scheme. Numerical experiments are presented to show the effectiveness of the approach in the optimal resolution of large-scale instances. © 2020 IEEE.},
    author_keywords={Decentralized optimization; Dynamic programming; Fully polynomial-time approximation scheme; Knapsack problem},
    keywords={Approximation algorithms; Combinatorial optimization; Dynamic programming; Polynomial approximation, Additive decomposition; Centralized approaches; Communication structures; Decentralized algorithms; Information exchanges; Numerical experiments; Optimal resolution; Quadratic knapsack problems, Decentralized control},
    references={Luh, P.B., Xiong, B., Chang, S.-C., Group elevator scheduling with advance information for normal and emergency modes (2008) Ieee Trans. Autom. Sci. Eng., 5 (2), pp. 245-258; Chu, C., Chu, F., Zhou, M., Chen, H., Shen, Q., A polynomial dynamic programming algorithm for crude oil transportation planning (2011) Ieee Trans. Autom. Sci. Eng., 9 (1), pp. 42-55; Cavone, G., Dotoli, M., Seatzu, C., Resource planning of intermodal terminals using timed petri nets (2016) 13th Intern. Workshop on Discrete Event Systems (WODES). Ieee, pp. 44-50; Vignali, R.M., Borghesan, F., Piroddi, L., Strelec, M., Prandini, M., Energy management of a building cooling system with thermal storage: An approximate dynamic programming solution (2017) Ieee Trans. Autom. Sci. Eng., 14 (2), pp. 619-633; Facchini, F., De Pascale, G., Faccilongo, N., Pallet picking strategy in food collecting center (2018) Applied Sciences, 8 (9), p. 1503; Bianchi, A., Pizzutilo, S., Vessio, G., An asm-based characterisation of starvation-free systems (2018) International Journal of Parallel, Emergent and Distributed Systems, 33 (1), pp. 35-51; Casalino, G., Castellano, G., Mencar, C., Data stream classification by dynamic incremental semi-supervised fuzzy clustering (2019) International Journal on Artificial Intelligence Tools, 28 (8), p. 1960009; Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., Real-time distance evaluation system for wireless localization (2020) Ieee Transactions on Circuits and Systems I: Regular Papers; Kellerer, H., Pferschy, U., Pisinger, D., (2003) Knapsack Problems. Springer, , Berlin; Sniedovich, M., (2010) Dynamic Programming: Foundations and Principles, , CRC press; Bertsimas, D., Demir, R., An approximate dynamic programming approach to multidimensional knapsack problems (2002) Management Science, 48 (4), pp. 550-565; Rader, D.J., Jr., Woeginger, G.J., The quadratic 0-1 knapsack problem with series-parallel support (2002) Operations Research Letters, 30 (3), pp. 159-166; Fomeni, F.D., Letchford, A.N., A dynamic programming heuristic for the quadratic knapsack problem (2014) Informs Journal on Computing, 26 (1), pp. 173-182; Kellerer, H., Strusevich, V.A., Fully polynomial approximation schemes for a symmetric quadratic knapsack problem and its scheduling applications (2010) Algorithmica, 57 (4), pp. 769-795; Kataoka, S., Yamada, T., Dp-based algorithm and fptas for the knapsack sharing and related problems (2019) Journal of the Operations Research Society of Japan, 62 (1), pp. 1-14; Carli, R., Dotoli, M., A decentralized control strategy for energy retrofit planning of large-scale street lighting systems using dynamic programming (2017) 2017 13th Ieee Conference on Automation Science and Engineering (CASE). Ieee, pp. 1196-1200; Pisinger, D., The quadratic knapsack problem-A survey (2007) Discrete Applied Mathematics, 155 (5), pp. 623-648; Boyd, S., Vandenberghe, L., (2004) Convex Optimization. Cambridge University Press; Bertsekas, D.P., (1995) Dynamic Programming and Optimal Control, 1 (2). , Athena scientific Belmont, MA; Powell, W.B., (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, 703. , John Wiley &Sons; https://www.ibm.com/support/knowledgecenter/en/SSSA5P12.6.2/ilog.odms.cplex.help/CPLEX/MATLAB/topics/gs.html, IBM, IBM ILOG CPLEX Optimization Studio Getting Started with CPLEX for MATLAB, accessed on 2019 Aug},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Cavone, G. & Dotoli, M. (2020) Smart control strategies for primary frequency regulation through electric vehicles: A battery degradation perspective. IN Energies, 13.. doi:10.3390/en13174586
    [BibTeX] [Abstract] [Download PDF]
    Nowadays, due to the decreasing use of traditional generators in favor of renewable energy sources, power grids are facing a reduction of system inertia and primary frequency regulation capability. Such an issue is exacerbated by the continuously increasing number of electric vehicles (EVs), which results in enforcing novel approaches in the grid operations management. However, from being an issue, the increase of EVs may turn to be a solution to several power system challenges. In this context, a crucial role is played by the so-called vehicle-to-grid (V2G) mode of operation, which has the potential to provide ancillary services to the power grid, such as peak clipping, load shifting, and frequency regulation. More in detail, EVs have recently started to be effectively used for one of the most traditional frequency regulation approaches: the so-called frequency droop control (FDC). This is a primary frequency regulation, currently obtained by adjusting the active power of generators in the main grid. Because to the decommissioning of traditional power plants, EVs are thus recognized as particularly valuable solutions since they can respond to frequency deviation signals by charging or discharging their batteries. Against this background, we address frequency regulation of a power grid model including loads, traditional generators, and several EVs. The latter independently participate in the grid optimization process providing the grid with ancillary services, namely the FDC. We propose two novel control strategies for the optimal control of the batteries of EVs during the frequency regulation service. On the one hand, the control strategies ensure re-balancing the power and stabilizing the frequency of the main grid. On the other hand, the approaches are able to satisfy different types of needs of EVs during the charging process. Differently from the related literature, where the EVs perspective is generally oriented to achieve the optimal charge level, the proposed approaches aim at minimizing the degradation of battery devices. Finally, the proposed strategies are compared with other state-of-the-art V2G control approaches. The results of numerical experiments using a realistic power grid model show the effectiveness of the proposed strategies under the actual operating conditions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
    @ARTICLE{Scarabaggio2020,
    author={Scarabaggio, P. and Carli, R. and Cavone, G. and Dotoli, M.},
    title={Smart control strategies for primary frequency regulation through electric vehicles: A battery degradation perspective},
    journal={Energies},
    year={2020},
    volume={13},
    number={17},
    doi={10.3390/en13174586},
    art_number={4586},
    note={cited By 6},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090919511&doi=10.3390%2fen13174586&partnerID=40&md5=d7f07f0a819d149b5f1c143b707e731d},
    abstract={Nowadays, due to the decreasing use of traditional generators in favor of renewable energy sources, power grids are facing a reduction of system inertia and primary frequency regulation capability. Such an issue is exacerbated by the continuously increasing number of electric vehicles (EVs), which results in enforcing novel approaches in the grid operations management. However, from being an issue, the increase of EVs may turn to be a solution to several power system challenges. In this context, a crucial role is played by the so-called vehicle-to-grid (V2G) mode of operation, which has the potential to provide ancillary services to the power grid, such as peak clipping, load shifting, and frequency regulation. More in detail, EVs have recently started to be effectively used for one of the most traditional frequency regulation approaches: the so-called frequency droop control (FDC). This is a primary frequency regulation, currently obtained by adjusting the active power of generators in the main grid. Because to the decommissioning of traditional power plants, EVs are thus recognized as particularly valuable solutions since they can respond to frequency deviation signals by charging or discharging their batteries. Against this background, we address frequency regulation of a power grid model including loads, traditional generators, and several EVs. The latter independently participate in the grid optimization process providing the grid with ancillary services, namely the FDC. We propose two novel control strategies for the optimal control of the batteries of EVs during the frequency regulation service. On the one hand, the control strategies ensure re-balancing the power and stabilizing the frequency of the main grid. On the other hand, the approaches are able to satisfy different types of needs of EVs during the charging process. Differently from the related literature, where the EVs perspective is generally oriented to achieve the optimal charge level, the proposed approaches aim at minimizing the degradation of battery devices. Finally, the proposed strategies are compared with other state-of-the-art V2G control approaches. The results of numerical experiments using a realistic power grid model show the effectiveness of the proposed strategies under the actual operating conditions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).},
    author_keywords={Electric vehicle batteries (EVBs); Electric vehicles (EVs); Frequency droop control (FDC); Vehicle-to-grid (V2G)},
    keywords={Automotive batteries; Charging (batteries); Electric control equipment; Electric generators; Electric network topology; Electric power transmission networks; Electric vehicles; Microgrids; Renewable energy resources; Vehicle-to-grid, Electric Vehicles (EVs); Frequency regulation services; Frequency regulations; Numerical experiments; Primary frequency regulation; Renewable energy source; Smart control strategies; Vehicle to Grid (V2G), Electric power system control},
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    document_type={Article},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R. & Dotoli, M. (2020) A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks IN IEEE International Conference on Automation Science and Engineering., 1281-1286. doi:10.1109/CASE48305.2020.9216800
    [BibTeX] [Abstract] [Download PDF]
    Traditionally, the management of power distribution networks relies on the centralized implementation of the optimal power flow and, in particular, the minimization of the generation cost and transmission losses. Nevertheless, the increasing penetration of both renewable energy sources and independent players such as ancillary service providers in modern networks have made this centralized framework inadequate. Against this background, we propose a noncooperative game-theoretic framework for optimally controlling energy storage systems (ESSs) in power distribution networks. Specifically, in this paper we address a power grid model that comprehends traditional loads, distributed generation sources and several independent energy storage providers, each owning an individual ESS. Through a rolling-horizon approach, the latter participate in the grid optimization process, aiming both at increasing the penetration of distributed generation and leveling the power injection from the transmission grid. Our framework incorporates not only economic factors but also grid stability aspects, including the power flow constraints. The paper fully describes the distribution grid model as well as the underlying market hypotheses and policies needed to force the energy storage providers to find a feasible equilibrium for the network. Numerical experiments based on the IEEE 33-bus system confirm the effectiveness and resiliency of the proposed framework. © 2020 IEEE.
    @CONFERENCE{Scarabaggio20201281,
    author={Scarabaggio, P. and Carli, R. and Dotoli, M.},
    title={A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2020},
    volume={2020-August},
    pages={1281-1286},
    doi={10.1109/CASE48305.2020.9216800},
    art_number={9216800},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094128798&doi=10.1109%2fCASE48305.2020.9216800&partnerID=40&md5=e4802c482f96ac90afd6c7a2c7f2b199},
    abstract={Traditionally, the management of power distribution networks relies on the centralized implementation of the optimal power flow and, in particular, the minimization of the generation cost and transmission losses. Nevertheless, the increasing penetration of both renewable energy sources and independent players such as ancillary service providers in modern networks have made this centralized framework inadequate. Against this background, we propose a noncooperative game-theoretic framework for optimally controlling energy storage systems (ESSs) in power distribution networks. Specifically, in this paper we address a power grid model that comprehends traditional loads, distributed generation sources and several independent energy storage providers, each owning an individual ESS. Through a rolling-horizon approach, the latter participate in the grid optimization process, aiming both at increasing the penetration of distributed generation and leveling the power injection from the transmission grid. Our framework incorporates not only economic factors but also grid stability aspects, including the power flow constraints. The paper fully describes the distribution grid model as well as the underlying market hypotheses and policies needed to force the energy storage providers to find a feasible equilibrium for the network. Numerical experiments based on the IEEE 33-bus system confirm the effectiveness and resiliency of the proposed framework. © 2020 IEEE.},
    keywords={Data storage equipment; Distributed power generation; Electric load flow; Electric network analysis; Electric power system economics; Electric power transmission; Energy storage; Game theory; Renewable energy resources; Storage as a service (STaaS), Distributed generation source; Energy Storage Systems (ESSs); Noncooperative game; Numerical experiments; Optimal power flows; Power distribution network; Renewable energy source; Transmission grids, Electric power transmission networks},
    references={Fang, X., Misra, S., Xue, G., Yang, D., Smart grid-the new and improved power grid: A survey (2011) Ieee Communications Surveys & Tutorials, 14 (4), pp. 944-980; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange (2019) IEEE/CAA Journal of Automatica Sinica, 6 (3), pp. 641-656; Rodrigues, E., Godina, R., Santos, S.F., Bizuayehu, A.W., Contreras, J., Catalão, J., Energy storage systems supporting increased penetration of renewables in islanded systems (2014) Energy, 75, pp. 265-280; Bahrami, S., Amini, M.H., Shafie-Khah, M., Catalao, J.P., A decentralized renewable generation management and demand response in power distribution networks (2018) Ieee Trans. Sustain. Energy, 9 (4), pp. 1783-1797; Dörfler, F., Bolognani, S., Simpson-Porco, J.W., Grammatico, S., Distributed control and optimization for autonomous power grids (2019) 2019 18th European Control Conference (ECC), pp. 2436-2453; Hübner, N., Rink, Y., Suriyah, M., Leibfried, T., (2019) Distributed Ac-Dc Optimal Power Flow in the European Transmission Grid with Admm, , arXiv preprint; Yang, L., Luo, J., Xu, Y., Zhang, Z., Dong, Z., A distributed dual consensus admm based on partition for dc-dopf with carbon emission trading (2019) Ieee Trans. Ind. Informat; Atzeni, I., Ordóñez, L.G., Scutari, G., Palomar, D.P., Fonol-Losa, J.R., Demand-side management via distributed energy generation and storage optimization (2012) Ieee Trans. Smart Grid, 4 (2), pp. 866-876; Chen, J., Zhu, Q., A game-theoretic framework for resilient and distributed generation control of renewable energies in microgrids (2016) Ieee Trans. Smart Grid, 8 (1), pp. 285-295; Carli, R., Dotoli, M., Palmisano, V., A distributed control approach based on game theory for the optimal energy scheduling of a residential microgrid with shared generation and storage (2019) 2019 Ieee 15th International Conference on Automation Science and Engineering (CASE), pp. 960-965; Wu, D., Yang, T., Stoorvogel, A.A., Stoustrup, J., Distributed optimal coordination for distributed energy resources in power systems (2016) Ieee Trans. Autom. Sci. Eng, 14 (2), pp. 414-424; Kargarian, A., Mohammadi, J., Guo, J., Chakrabarti, S., Barati, M., Hug, G., Kar, S., Baldick, R., Toward distributed/decentralized dc optimal power flow implementation in future electric power systems (2016) Ieee Trans. Smart Grid, 9 (4), pp. 2574-2594; Kisacikoglu, M.C., Kesler, M., Tolbert, L.M., Single-phase onboard bidirectional pev charger for v2g reactive power operation (2014) Ieee Trans. Smart Grid, 6 (2), pp. 767-775; Maskar, M.B., Thorat, A., Korachgaon, I., A review on optimal power flow problem and solution methodologies (2017) 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 64-70; Scutari, G., Palomar, D.P., Facchinei, F., Pang, J.-S., Convex optimization, game theory, and variational inequality theory (2010) Ieee Signal Process. Mag., 27 (3), pp. 35-49; Belgioioso, G., Grammatico, S., Projected-gradient algorithms for generalized equilibrium seeking in aggregative games arepre-conditioned forward-backward methods (2018) 2018 European Control Conference (ECC), pp. 2188-2193; Scarabaggio, P., Carli, R., Dotoli, M., (2020) Experiments Dataset, , https://sites.google.com/view/storagecontrol, [Online]; Vinkovic, A., Mihalic, R., A current-based model of an ipfc for newton-raphson power flow (2009) Electr. Pow. Syst. Res., 79 (8), pp. 1247-1254},
    document_type={Conference Paper},
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    }
  • Carli, R., Cavone, G., Pippia, T., Schutter, B. D. & Dotoli, M. (2020) A Robust MPC Energy Scheduling Strategy for Multi-Carrier Microgrids IN IEEE International Conference on Automation Science and Engineering., 152-158. doi:10.1109/CASE48305.2020.9216875
    [BibTeX] [Abstract] [Download PDF]
    We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers. © 2020 IEEE.
    @CONFERENCE{Carli2020152,
    author={Carli, R. and Cavone, G. and Pippia, T. and Schutter, B.D. and Dotoli, M.},
    title={A Robust MPC Energy Scheduling Strategy for Multi-Carrier Microgrids},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2020},
    volume={2020-August},
    pages={152-158},
    doi={10.1109/CASE48305.2020.9216875},
    art_number={9216875},
    note={cited By 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094114652&doi=10.1109%2fCASE48305.2020.9216875&partnerID=40&md5=92ef257518791ef22242636d1c989285},
    abstract={We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers. © 2020 IEEE.},
    author_keywords={Energy and Environment-Aware Automation; Microgrid; Optimization and Optimal Control; Robust Model Predictive Control; Set-based Uncertainty},
    keywords={Cogeneration plants; Electric energy storage; Electric loads; Microgrids; Model predictive control; Optimization; Predictive control systems; Renewable energy resources; Robust control; Uncertainty analysis, Deterministic modeling; Energy storage systems; External disturbances; Renewable energy generation; Renewable energy source; Robust model predictive controls (RMPC); Scheduling strategies; Uncertain parameters, Controllers},
    references={Fang, X., Misra, S., Xue, G., Yang, D., Smart grid-the new and improved power grid: A survey (2012) Ieee Commun. Surveys Tuts, 14 (4), pp. 944-980; Hirsch, A., Parag, Y., Guerrero, J., Microgrids: A review of technologies key drivers, and outstanding issues (2018) Renew. Sust. Energ. Rev., 90, pp. 402-411; Carli, R., Dotoli, M., Jantzen, J., Kristensen, M., Ben Othman, S., Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the ballen marina in samsø (2020) Energy, 198, p. 117188; Camacho, E.F., Alba, C.B., Model predictive control (2013) Advanced Textbooks in Control and Signal Processing, , Springer London; Mayne, D.Q., Model predictive control: Recent developments and future promise (2014) Automatica, 50 (12), pp. 2967-2986; Bittel, H., Jones, C.N., Parisio, A., Use of model predictive control for short-term operating reserve using commercial buildings in the united kingdom context (2018) 2018 Ieee Decis. Contr., pp. 7308-7313; Parisio, A., Rikos, E., Glielmo, L., A model predictive control approach to microgrid operation optimization (2014) Ieee Trans. Control Syst. Technol., 22 (5), pp. 1813-1827; Verrilli, F., Srinivasan, S., Gambino, G., Canelli, M., Himanka, M., Del Vecchio, C., Sasso, M., Glielmo, L., Model predictive control-based optimal operations of district heating system with thermal energy storage and flexible loads (2017) Ieee Trans. Autom. Sci. Eng., 14 (2), pp. 547-557; Parisio, A., Rikos, E., Glielmo, L., Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study (2016) J. Process Contr., 43, pp. 24-37; Liberati, F., Di Giorgio, A., Giuseppi, A., Pietrabissa, A., Habib, E., Martirano, L., Joint model predictive control of electric and heating resources in a smart building (2019) Ieee Trans. Ind Appl., 55 (6), pp. 7015-7027; Alavi, F., Park Lee, E., Wouw De, N.Van, De Schutter, B., Lukszo, Z., Fuel cell cars in a microgrid for synergies between hydrogen and electricity networks (2017) Appl. Energ., 192, pp. 296-304; Pereira, M., Peña De La, D.Muñoz, Limon, D., Robust economic model predictive control of a community micro-grid (2017) Renew. Energ., 100, pp. 3-17. , Special Issue: Control and Optimization of Renewable Energy Systems; Zhang, Y., Fu, L., Zhu, W., Bao, X., Liu, C., Robust model predictive control for optimal energy management of island microgrids with uncertainties (2018) Energy, 164, pp. 1229-1241; Zhai, M., Liu, Y., Zhang, T., Zhang, Y., Robust model predictive control for energy management of isolated microgrids (2017) 2017 Ieee In. C. Ind. Eng. Eng. Man., pp. 2049-2053; Bertsimas, D., Sim, M., The price of robustness (2004) Oper. Res., 52 (1), pp. 35-53; Bertsimas, D., Brown, D.B., Caramanis, C., Theory and applications of robust optimization (2011) Siam Review, 53 (3), pp. 464-501; Soyster, A.L., Convex programming with set-inclusive constraints and applications to inexact linear programming (1973) Oper. Res., 21 (5), pp. 1154-1157; Neon Neue Energieökonomik, , https://data.open-power-system-data.org, Technical University of Berlin, eth Zürich, and diw Berlin. Open-power-system-data; Emhires Dataset Solarpower, , https://setis.ec.europa.eu/publications/relevant-reports/emhires-dataset-part-ii-solarpower-generation, European Commission setis; Mekh & vaasaett energie-control Energy Price Index, , https://www.energypriceindex.com/latest-update; (2016) Gurobi Optimization, , https://www.gurobi.com, Inc. Gurobi optimizer reference manual},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2020) A Dynamic Programming Approach for the Decentralized Control of Energy Retrofit in Large-Scale Street Lighting Systems. IN IEEE Transactions on Automation Science and Engineering, 17.1140-1157. doi:10.1109/TASE.2020.2966738
    [BibTeX] [Abstract] [Download PDF]
    This article proposes a decision-making procedure that supports the city energy manager in determining the optimal energy retrofit plan of an existing public street lighting system throughout a wide urban area. The proposed decision model aims at simultaneously maximizing the energy consumption reduction and achieving an optimal allocation of the retrofit actions among the street lighting subsystems, while efficiently using the available budget. The resulting optimization problem is formulated as a quadratic knapsack problem. The proposed solution relies on a decentralized control algorithm that combines discrete dynamic programming with additive decomposition and value functions approximation. The optimality and complexity of the presented strategy are investigated, demonstrating that the proposed algorithm constitutes a fully polynomial approximation scheme. Simulation results related to a real street lighting system in the city of Bari (Italy) are presented to show the effectiveness of the approach in the optimal energy management of large-scale street lighting systems. Note to Practitioners-This article addresses the emerging need for decision support tools for the energy management of urban street lighting systems. The proposed decision-making strategy allows city energy managers and local policy makers taking retrofit decisions on an existing public street lighting system throughout a wide urban area. The presented strategy can be implemented in any engineering software, providing decision makers with a low-complexity and scalable Information and Communication Technology (ICT) tool for the optimization of the energy efficiency and environmental sustainability of street lighting systems. © 2004-2012 IEEE.
    @ARTICLE{Carli20201140,
    author={Carli, R. and Dotoli, M.},
    title={A Dynamic Programming Approach for the Decentralized Control of Energy Retrofit in Large-Scale Street Lighting Systems},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2020},
    volume={17},
    number={3},
    pages={1140-1157},
    doi={10.1109/TASE.2020.2966738},
    art_number={9007032},
    note={cited By 11},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087543588&doi=10.1109%2fTASE.2020.2966738&partnerID=40&md5=b2d2ba1ad644c81f451c0936fe963e18},
    abstract={This article proposes a decision-making procedure that supports the city energy manager in determining the optimal energy retrofit plan of an existing public street lighting system throughout a wide urban area. The proposed decision model aims at simultaneously maximizing the energy consumption reduction and achieving an optimal allocation of the retrofit actions among the street lighting subsystems, while efficiently using the available budget. The resulting optimization problem is formulated as a quadratic knapsack problem. The proposed solution relies on a decentralized control algorithm that combines discrete dynamic programming with additive decomposition and value functions approximation. The optimality and complexity of the presented strategy are investigated, demonstrating that the proposed algorithm constitutes a fully polynomial approximation scheme. Simulation results related to a real street lighting system in the city of Bari (Italy) are presented to show the effectiveness of the approach in the optimal energy management of large-scale street lighting systems. Note to Practitioners-This article addresses the emerging need for decision support tools for the energy management of urban street lighting systems. The proposed decision-making strategy allows city energy managers and local policy makers taking retrofit decisions on an existing public street lighting system throughout a wide urban area. The presented strategy can be implemented in any engineering software, providing decision makers with a low-complexity and scalable Information and Communication Technology (ICT) tool for the optimization of the energy efficiency and environmental sustainability of street lighting systems. © 2004-2012 IEEE.},
    author_keywords={Decision-making; dynamic programming; energy management; fully polynomial approximation scheme; optimization; urban street lighting},
    keywords={Approximation algorithms; Budget control; Combinatorial optimization; Decentralized control; Decision making; Decision support systems; Energy efficiency; Energy management; Energy management systems; Energy utilization; Green computing; Lighting fixtures; Managers; Polynomial approximation; Retrofitting; Street lighting; Sustainable development, Additive decomposition; Decision making procedure; Decision support tools; Decision-making strategies; Environmental sustainability; Information and Communication Technologies; Quadratic knapsack problems; Street lighting system, Dynamic programming},
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    document_type={Article},
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    }
  • Scarabaggio, P., Carli, R. & Dotoli, M. (2020) A fast and effective algorithm for influence maximization in large-scale independent cascade networks IN 7th International Conference on Control, Decision and Information Technologies, CoDIT 2020., 639-644. doi:10.1109/CoDIT49905.2020.9263914
    [BibTeX] [Abstract] [Download PDF]
    A characteristic of social networks is the ability to quickly spread information between a large group of people. The widespread use of online social networks (e.g., Facebook) increases the interest of researchers on how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network). Due to its practical importance in various applications (e.g., viral marketing), such a problem has been studied in several variants. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. In this context, based on independent cascade modeling of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. The application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, the comparison with the state of the art algorithms demonstrates that in large scale scenarios the proposed approach shows higher performance in terms of influence spread and running time. © 2020 IEEE.
    @CONFERENCE{Scarabaggio2020639,
    author={Scarabaggio, P. and Carli, R. and Dotoli, M.},
    title={A fast and effective algorithm for influence maximization in large-scale independent cascade networks},
    journal={7th International Conference on Control, Decision and Information Technologies, CoDIT 2020},
    year={2020},
    pages={639-644},
    doi={10.1109/CoDIT49905.2020.9263914},
    art_number={9263914},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098236199&doi=10.1109%2fCoDIT49905.2020.9263914&partnerID=40&md5=003f7d44f921ecd0bf436dacb2d3e136},
    abstract={A characteristic of social networks is the ability to quickly spread information between a large group of people. The widespread use of online social networks (e.g., Facebook) increases the interest of researchers on how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network). Due to its practical importance in various applications (e.g., viral marketing), such a problem has been studied in several variants. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. In this context, based on independent cascade modeling of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. The application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, the comparison with the state of the art algorithms demonstrates that in large scale scenarios the proposed approach shows higher performance in terms of influence spread and running time. © 2020 IEEE.},
    keywords={Economic and social effects; Iterative methods, Computationally efficient; Influence maximizations; Most influential users; Near-optimal solutions; Numerical experiments; On-line social networks; Practical importance; State-of-the-art algorithms, Social networking (online)},
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    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Dotoli, M., Jantzen, J., Kristensen, M. & Ben Othman, S. (2020) Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø. IN Energy, 198.. doi:10.1016/j.energy.2020.117188
    [BibTeX] [Abstract] [Download PDF]
    This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under time-varying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samsø (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master’s office equipped with a heat pump), PV production (60kWp), and the BESS (237 kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 h resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naïve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naïve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the naïve approach. © 2020 Elsevier Ltd
    @ARTICLE{Carli2020,
    author={Carli, R. and Dotoli, M. and Jantzen, J. and Kristensen, M. and Ben Othman, S.},
    title={Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø},
    journal={Energy},
    year={2020},
    volume={198},
    doi={10.1016/j.energy.2020.117188},
    art_number={117188},
    note={cited By 33},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082001819&doi=10.1016%2fj.energy.2020.117188&partnerID=40&md5=6477046a8e48af719b8ab95085e45579},
    abstract={This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under time-varying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samsø (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master's office equipped with a heat pump), PV production (60kWp), and the BESS (237 kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 h resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naïve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naïve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the naïve approach. © 2020 Elsevier Ltd},
    author_keywords={Demand side management; Energy management; Energy storage; Microgrid; Model predictive control; On-line scheduling; Optimization algorithm; Renewable energy},
    keywords={Costs; Demand side management; Economics; Electric utilities; Energy management; Energy storage; Energy utilization; Integer programming; Iterative methods; Marinas; Microgrids; Model predictive control; Photovoltaic cells; Predictive control systems; Pumping plants; Scheduling; Scheduling algorithms; Solar energy, Battery energy storage systems; European research project; Micro grid; Mixed integer linear programming; Online scheduling; Optimization algorithms; Renewable energies; Wastewater pumping station, Electric power system control, alternative energy; demand-side management; energy storage; exploitation; linear programing; optimization; photovoltaic system; pumping; smart grid; time series; wastewater, Denmark},
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    document_type={Article},
    source={Scopus},
    }
  • Carli, R., Dotoli, M., Digiesi, S., Facchini, F. & Mossa, G. (2020) Sustainable scheduling of material handling activities in labor-intensive warehouses: A decision and control model. IN Sustainability (Switzerland), 12.. doi:10.3390/SU12083111
    [BibTeX] [Abstract] [Download PDF]
    In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses’ energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously. © 2020 by the authors.
    @ARTICLE{Carli2020,
    author={Carli, R. and Dotoli, M. and Digiesi, S. and Facchini, F. and Mossa, G.},
    title={Sustainable scheduling of material handling activities in labor-intensive warehouses: A decision and control model},
    journal={Sustainability (Switzerland)},
    year={2020},
    volume={12},
    number={8},
    doi={10.3390/SU12083111},
    art_number={3111},
    note={cited By 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084607073&doi=10.3390%2fSU12083111&partnerID=40&md5=225791095c6b1ea49a9be39370e2fff5},
    abstract={In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses' energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously. © 2020 by the authors.},
    author_keywords={Battery charging; Decision and control; Demand-side management; Green warehouse; Material handling activity; Mobile material handling equipment; Optimization; Sustainable scheduling; Warehouse energy management},
    keywords={carbon dioxide; carbon emission; cooling; cost analysis; decision making; environmental economics; environmental impact; equipment; fuel consumption; greenhouse gas; heating; optimization; strategic approach; supply chain management; sustainability},
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    document_type={Article},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Othman, S. B. & Dotoli, M. (2020) IoT based architecture for model predictive control of HVAC systems in smart buildings. IN Sensors (Switzerland), 20.. doi:10.3390/s20030781
    [BibTeX] [Abstract] [Download PDF]
    The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
    @ARTICLE{Carli2020,
    author={Carli, R. and Cavone, G. and Othman, S.B. and Dotoli, M.},
    title={IoT based architecture for model predictive control of HVAC systems in smart buildings},
    journal={Sensors (Switzerland)},
    year={2020},
    volume={20},
    number={3},
    doi={10.3390/s20030781},
    art_number={781},
    note={cited By 27},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079071499&doi=10.3390%2fs20030781&partnerID=40&md5=b68287ad61a3091865fc7546425dce95},
    abstract={The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
    author_keywords={Heating ventilation and air conditioning system; Internet of things; Model predictive control; Predicted mean vote; Smart buildings},
    keywords={Air conditioning; Closed loop control systems; Energy conservation; Energy utilization; Heat pump systems; HVAC; Intelligent buildings; Model predictive control; Predictive control systems; Thermal comfort, Closed-loop control; Control architecture; Efficient managements; Heating ventilation and air conditioning; Indoor thermal comfort; Internet of thing (IOT); Predicted mean vote; Sensors and actuators, Internet of things},
    references={Ejaz, W., Naeem, M., Shahid, A., Anpalagan, A., Jo, M., Efficient energy management for the internet of things in smart cities (2017) IEEE Commun. Mag., 55, pp. 84-91; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Manag. Prod. Eng. Rev., 4, pp. 15-27; Facchini, F., Mummolo, G., Mossa, G., Digiesi, S., Boenzi, F., Verriello, R., Minimizing the carbon footprint of material handling equipment: Comparison of electric and LPG forklifts (2016) J. Ind. Eng. Manag., 9, pp. 1035-1046; Dean, B., Dulac, J., Petrichenko, K., Graham, P., (2016) Towards Zero-Emission Efficient and Resilient Buildings: Global Status Report, , https://backend.orbit.dtu.dk/ws/portalfiles/portal/127199228/GABC_Global_Status_Report_V09_november.pdf; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a buildings’ stock energy efficiency (2016) IEEE Trans. Syst. Man Cybern. Syst., 47, pp. 794-807; Jouhara, H., Yang, J., Energy efficient HVAC systems (2018) Energy Build, 179, pp. 83-85; Afram, A., Janabi-Sharifi, F., Theory and applications of HVAC control systems—A review of model predictive control (MPC) (2014) Build. Environ., 72, pp. 343-355; Talarico, C., D’Amato, G., Coviello, G., Avitabile, G., A high precision phase control unit for DDS-based PLLs for 2.4-GHz ISM band applications (2015) Proceeding of the 2015 IEEE 58Th International Midwest Symposium on Circuits and Systems (MWSCAS), Fort Collins, pp. 1-4. , CO, USA, 2–5 May; Casalino, G., Castellano, G., Mencar, C., Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering (2019) Int. J. Artif. Intell. Trans., 28; Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., Bemporad, A., Model Predictive Control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities (2018) Energies, 11, p. 631; Serra, J., Pubill, D., Antonopoulos, A., Verikoukis, C., Smart, H.V.A.C., Control in IoT: Energy consumption minimization with user comfort constraints. Sci (2014) World J, 2014; Atzori, L., Iera, A., Morabito, G., The internet of things: A survey (2010) Comput. Netw., 54, pp. 2787-2805; Wu, F., Rüdiger, C., Yuce, M., Real-time performance of a self-powered environmental IoT sensor network system (2017) Sensors, 17, p. 282; Hazyuk, I., Ghiaus, C., Penhouet, D., Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part II—Control algorithm (2012) Build. Environ., 51, pp. 388-394; Klaučo, M., Drgoňa, J., Kvasnica, M., Di Cairano, S., Building temperature control by simple mpc-like feedback laws learned from closed-loop data (2014) IFAC Proc, 47, pp. 581-586; Klaučo, M., Kvasnica, M., Explicit MPC approach to PMV-based thermal comfort control (2014) Proceeding of the 53Rd IEEE Conference on Decision and Control (CDC), pp. 4856-4861. , Los Angeles, CA, USA; (2005) ISO 7730: Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, p. 60. , ISO: Geneva, Switzerland; Fanger, P.O., (1970) Thermal Comfort: Analysis and Application in Environment Engineering, p. 244. , Danish Technical Press: Copenhagen, Denmark; Shaikh, P.H., Nor, N.B.M., Nallagownden, P., Elamvazuthi, I., Ibrahim, T., A review on optimized control systems for building energy and comfort management of smart sustainable buildings (2014) Renew. Sustain. Energy Rev., 34, pp. 409-429; Xu, Z., Hu, G., Spanos, C.J., Schiavon, S., PMV-based event-triggered mechanism for building energy management under uncertainties (2017) Energy Build, 152, pp. 73-85; Alamin, Y.I., Del Mar Castilla, M., Álvarez, J.D., Ruano, A., An economic model-based predictive control to manage the users’ thermal comfort in a building (2017) Energies, 10, p. 321; Cigler, J., Prívara, S., Váňa, Z., Žáčeková, E., Ferkl, L., Optimization of predicted mean vote index within model predictive control framework: Computationally tractable solution (2012) Energy Build, 52, pp. 39-49; Corbin, C.D., Henze, G.P., May-Ostendorp, P., A model predictive control optimization environment for real-time commercial building application (2013) J. Build. Perform. 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Handbook of Mechanical Engineering, , Laxmi Publication: New Delhi, India; Murray, F.W., (1966) On the Computation of Saturation Vapor Pressure, , Technical Report; Rand Corp.: Santa Monica, CA, USA; Pippia, T., Sijs, J., de Schutter, B., A Parametrized Model Predictive Control Approach for Microgrids (2018) Proceeding of the IEEE Conference on Decision and Control, pp. 3171-3176. , Miami, FL, USA; García, C.E., Prett, D.M., Morari, M., Model predictive control: Theory and practice-A survey (1989) Automatica, 25, pp. 335-348; (2019) Beeta Home Page, , https://www.beeta.it/en/; Singh, M., Rajan, M.A., Shivraj, V.L., Balamuralidhar, P., Secure MQTT for Internet of Things (IoT (2015) Proceeding of the 2015 5Th International Conference on Communication Systems and Network Technologies (CSNT), pp. 746-751. , Gwalior, India},
    document_type={Article},
    source={Scopus},
    }
  • Scarabaggio, P., Grammatico, S., Carli, R. & Dotoli, M. (2020) A distributed, rolling-horizon demand side management algorithm under wind power uncertainty IN IFAC-PapersOnLine., 12620-12625. doi:10.1016/j.ifacol.2020.12.1830
    [BibTeX] [Abstract] [Download PDF]
    In this paper, we consider a smart grid where users behave selfishly, aiming at minimizing cost in the presence of uncertain wind power availability. We adopt a demand side management (DSM) model, where active users (so-called prosumers) have both private generation and local storage availability. These prosumers participate to the DSM strategy by updating their energy schedule, seeking to minimize their local cost, given their local preferences and the global grid constraints. The energy price is defined as a function of the aggregate load and the wind power availability. We model the resulting problem as a non-cooperative Nash game and propose a semi-decentralized algorithm to compute an equilibrium. To cope with the uncertainty in the wind power, we adopt a rolling-horizon approach, and in addition we use a stochastic optimization technique. We generate several wind power production scenarios from a defined probability density function (PDF), determining an approximate stochastic cost function. Simulations results on a real dataset show that the proposed approach generates lower individual costs compared to a standard expected value approach. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license
    @CONFERENCE{Scarabaggio202012620,
    author={Scarabaggio, P. and Grammatico, S. and Carli, R. and Dotoli, M.},
    title={A distributed, rolling-horizon demand side management algorithm under wind power uncertainty},
    journal={IFAC-PapersOnLine},
    year={2020},
    volume={53},
    number={2},
    pages={12620-12625},
    doi={10.1016/j.ifacol.2020.12.1830},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105077171&doi=10.1016%2fj.ifacol.2020.12.1830&partnerID=40&md5=ee3d9dd2c0187078aed713ccc87a026b},
    abstract={In this paper, we consider a smart grid where users behave selfishly, aiming at minimizing cost in the presence of uncertain wind power availability. We adopt a demand side management (DSM) model, where active users (so-called prosumers) have both private generation and local storage availability. These prosumers participate to the DSM strategy by updating their energy schedule, seeking to minimize their local cost, given their local preferences and the global grid constraints. The energy price is defined as a function of the aggregate load and the wind power availability. We model the resulting problem as a non-cooperative Nash game and propose a semi-decentralized algorithm to compute an equilibrium. To cope with the uncertainty in the wind power, we adopt a rolling-horizon approach, and in addition we use a stochastic optimization technique. We generate several wind power production scenarios from a defined probability density function (PDF), determining an approximate stochastic cost function. Simulations results on a real dataset show that the proposed approach generates lower individual costs compared to a standard expected value approach. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license},
    author_keywords={Demand side management; Sample average approximation; Smart grid; Stochastic optimization},
    references={Afshar, K., Shokri Gazafroudi, A., Application of stochastic programming to determine operating reserves with considering wind and load uncertainties (2007) Journal of Operation and Automation in Power Engineering, 1 (2), pp. 96-109; Aghajani, G., Shayanfar, H., Shayeghi, H., Demand side management in a smart micro-grid in the presence of renewable generation and demand response (2017) Energy, 126, pp. 622-637; Atzeni, I., Ordóñez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R., Demand-side management via distributed energy generation and storage optimization (2012) IEEE Transactions on Smart Grid, 4 (2), pp. 866-876; Belgioioso, G., Grammatico, S., Projected-gradient algorithms for generalized equilibrium seeking in aggregative games are preconditioned forward-backward methods (2018) 2018 Eur. Control Conf. ECC 2018, pp. 2188-2193; Biswas, P.P., Suganthan, P., Amaratunga, G.A., Optimal power flow solutions incorporating stochastic wind and solar power (2017) Energy Conversion and Management, 148, pp. 1194-1207; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange (2019) IEEE/CAA Journal of Automatica Sinica, 6 (3), pp. 641-656; Estrella, R., Belgioioso, G., Grammatico, S., A shrinking-horizon, game-theoretic algorithm for distributed energy generation and storage in the smart grid with wind forecasting (2019) IFAC-PapersOnLine, 52 (3), pp. 126-131; Gao, B., Zhang, W., Tang, Y., Hu, M., Zhu, M., Zhan, H., Game-theoretic energy management for residential users with dischargeable plug-in electric vehicles (2014) Energies, 7 (11), pp. 7499-7518; Iizaka, T., Jintsugawa, T., Kondo, H., Nakanishi, Y., Fukuyama, Y., Mori, H., A wind power forecasting method and its confidence interval estimation (2014) Electrical Engineering in Japan, 186 (2), pp. 52-60; Kim, S., Pasupathy, R., Henderson, S.G., A guide to sample average approximation (2015) Handbook of Simulation Optimization, pp. 207-243. , Springer; Ko, W., Hur, D., Park, J.K., Correction of wind power forecasting by considering wind speed forecast error (2015) Journal of International Council on Electrical Engineering, 5 (1), pp. 47-50; Kun, Y., Zhang, K., Zheng, Y., Dawei, L., Ying, W., Zhenglin, Y., Irregular distribution of wind power prediction (2018) Journal of Modern Power Systems and Clean Energy, 6 (6), pp. 1172-1180; Pazouki, S., Haghifam, M.R., Moser, A., Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response (2014) International Journal of Electrical Power & Energy Systems, 61, pp. 335-345; Schinazi, R.B., Transformations of random variables and random vectors (2012) Probability with Statistical Applications, pp. 201-268. , Springer; Shapiro, A., (2008) Stochastic Programming Approach to Optimization under Uncertainty, 112. , Springer US},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Epicoco, N., Scarabaggio, P. & Dotoli, M. (2020) Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario. IN Annual Reviews in Control, 50.373-393. doi:10.1016/j.arcontrol.2020.09.005
    [BibTeX] [Abstract] [Download PDF]
    The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion. © 2020 Elsevier Ltd
    @ARTICLE{Carli2020373,
    author={Carli, R. and Cavone, G. and Epicoco, N. and Scarabaggio, P. and Dotoli, M.},
    title={Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario},
    journal={Annual Reviews in Control},
    year={2020},
    volume={50},
    pages={373-393},
    doi={10.1016/j.arcontrol.2020.09.005},
    note={cited By 19},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097753585&doi=10.1016%2fj.arcontrol.2020.09.005&partnerID=40&md5=adce49e71a999948867e93de3ae2e142},
    abstract={The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion. © 2020 Elsevier Ltd},
    author_keywords={COVID-19; Epidemic control; MPC; Multi-region SIR model; Pandemic modeling; Post-lockdown mitigation strategies; SIR model},
    keywords={Economics, Economic activities; Economic framework; Epidemiological modeling; Health-care system; Mitigation strategy; Non-pharmaceutical interventions; Nonlinear model predictive control; Optimal controls, Model predictive control},
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    document_type={Article},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Epicoco, N., Di Ferdinando, M., Scarabaggio, P. & Dotoli, M. (2020) Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles. IN Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12338 LNCS.84-99. doi:10.1007/978-3-030-61746-2_7
    [BibTeX] [Abstract] [Download PDF]
    Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms. © 2020, Springer Nature Switzerland AG.
    @ARTICLE{Carli202084,
    author={Carli, R. and Cavone, G. and Epicoco, N. and Di Ferdinando, M. and Scarabaggio, P. and Dotoli, M.},
    title={Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles},
    journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    year={2020},
    volume={12338 LNCS},
    pages={84-99},
    doi={10.1007/978-3-030-61746-2_7},
    note={cited By 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093852465&doi=10.1007%2f978-3-030-61746-2_7&partnerID=40&md5=1c7da6000e4015880227c1eafe608f20},
    abstract={Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms. © 2020, Springer Nature Switzerland AG.},
    author_keywords={Swarm intelligence; Trajectory control; Unmanned Aerial Vehicles},
    keywords={Aircraft detection; Antennas; Data fusion; Distributed parameter control systems; Military applications; Military vehicles; Remote sensing; Unmanned aerial vehicles (UAV), Control strategies; Discrete-time integrators; Distributed control strategy; Environmental surveillance; Local communications; Network communications; Onboard processors; Time-varying formations, Mobile ad hoc networks},
    references={Bandala, A., Dadios, E., Vicerra, R., Lim, L.G., Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors: Swarm behavior for aggregation, foraging, formation, and tracking (2014) J. Adv. Comput. Intell. Intell. Inform., 18 (5), pp. 745-751; Barabasi, A.L., Taming complexity (2005) Nat. Phys., 1 (2), pp. 68-70; Colorado, J., Barrientos, A., Martinez, A., Lafaverges, B., Valente, J., Mini-quadrotor attitude control based on Hybrid Backstepping & Frenet-Serret theory (2010) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA); Dong, X., Yu, B., Shi, Z., Zhong, Y., Time-varying formation control for unmanned aerial vehicles: Theories and applications (2015) IEEE Trans. Contr. Syst. Techn., 23 (1), pp. 340-348; Dong, X., Zhou, Y., Ren, Z., Zhong, Y., Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying (2016) IEEE Trans. Ind. Electron., 64 (6), pp. 5014-5024; Gazi, V., Passino, K., (2011) Swarm Stability and Optimization, , https://doi.org/10.1007/978-3-642-18041-5, Springer, Heidelberg; Gioioso, G., Franchi, A., Salvietti, G., Scheggi, S., Prattichizzo, D., The flying hand: A formation of UAVs for cooperative aerial telemanipulation (2014) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4335-4341; Gu, Z., Shi, P., Yue, D., Ding, Z., Decentralized adaptive event-triggered h∞ filtering for a class of networked nonlinear interconnected systems (2018) IEEE Trans. Cybern., 49 (5), pp. 1570-1579; Indu, C., Singh, R., Trajectory planning and optimization for UAV communication: A review (2020) J. Discret. Math. Sci. Cryptog., 23 (2), pp. 475-483; Kushleyev, A., Mellinger, D., Powers, C., Kumar, V., Towards a swarm of agile micro quadrotors (2013) Auton. Robots, 35 (4), pp. 287-300; Lee, H., Kim, H., Kim, H., Path planning and control of multiple aerial manipulators for a cooperative transportation (2015) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Low, C., A dynamic virtual structure formation control for fixedwing UAVs (2011) Proceedings of the 9Th IEEE IEEE International Conference on Control and Automation (ICCA), pp. 627-632. , pp; Magnussen, O., Ottestad, M., Hovland, G., Experimental validation of a quaternion-based attitude estimation with direct input to a quadcopter control system (2013) Proceedings of the International Conference on Unmanned Aircraft Systems Unmanned Aircraft Systems (ICUAS); Mellinger, D., Michael, N., Kumar, V., Trajectory generation and control for precise aggressive maneuvers with quadrotors (2014) Exp. Robot., 79, pp. 361-373; Monteiro, S., Bicho, E., A dynamical systems approach to behavior-based formation control (2002) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2606-2611. , pp; Mu, B., Shi, Y., Distributed LQR consensus control for heterogeneous multia-gent systems: Theory and experiments (2018) IEEE/ASME Trans. Mech., 23 (1), pp. 434-443; Nathan, P., Almurib, H., Kumar, T., A review of autonomous multi-agent quadrotor control techniques and applications (2011) Proceedings of 4Th International Conference on Mechatronics (ICOM); Pantelimon, G., Tepe, K., Carriveau, R., Ahmed, S., Survey of multi-agent communication strategies for information exchange and mission control of drone deployments (2019) J. Intell. Robot. Syst., 95, pp. 779-788; Ren, W., Beard, R., (2008) Distributed Consensus in Multi-Vehicle Cooperative Control-Theory and Applications, , https://doi.org/10.1007/978-1-84800-015-5, Springer, London; Roldo, V., Cunha, R., Cabecinhas, D., Silvestre, C., Oliveira, P., A leader-following trajectory generator with application to quadrotor formation flight (2014) Robot. Auton. 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Franklin Inst., 356 (2), pp. 998-1020; Toksoz, M., Oguz, S., Gazi, V., Decentralized formation control of a swarm of quadrotor helicopters (2019) Proceedings of the IEEE 15Th International Conference on Control and Automation (ICCA); Wang, C., Tnunay, H., Zuo, Z., Lennox, B., Ding, Z., Fixed-time formation control of multirobot systems: Design and experiments (2019) IEEE Trans. Ind. Electron., 66 (8), pp. 6292-6301; Wang, C., Zuo, Z., Qi, Z., Ding, Z., Predictor-based extended-state-observer design for consensus of MASs with delays and disturbances (2009) IEEE Trans. Cybern., 49 (4), pp. 1259-1269; Wang, J., Zhou, Z., Wang, C., Shan, J., Multiple quadrotors formation flying control design and experimental verification (2019) Unmanned Syst, 7 (1), pp. 47-54; Zhao, S., Affine formation maneuver control of multi-agent systems (2018) IEEE Trans. Autom. Contr., 63 (12), pp. 4140-4155; Zhao, S., Dimarogonas, D., Sun, Z., Bauso, D., A general approach to coordination control of mobile agents with motion constraints (2018) IEEE Trans. Automat. Contr., 63 (5), pp. 1509-1516},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Digiesi, S., Dotoli, M. & Facchini, F. (2020) A control strategy for smart energy charging of warehouse material handling equipment IN Procedia Manufacturing., 503-510. doi:10.1016/j.promfg.2020.02.041
    [BibTeX] [Abstract] [Download PDF]
    The common driver of the ‘green-warehouse’ strategy is based on the reduction of energy consumption. In warehouses with ‘picker-to-part’ operations the minimization of energy due to material handling activities can be achieved by means of different policies: by adopting smart automatic picking systems, by adopting energy-efficient material handling equipment (MHE) as well as by identifying flexible layouts. In most cases, these strategies require investments characterized by high pay-back times. In this context, management strategies focused on the adoption of available equipment allow to increase the warehouse productivity at negligible costs. With this purpose, an optimization model is proposed in order to identify an optimal control strategy for the battery charging of a fleet of electric mobile MHE (e.g., forklifts), allowing minimizing the economic and environmental impact of material handling activities in labor-intensive warehouses. The resulting scheduling problem is formalized as an integer programming (IP) problem aimed at minimizing the total cost, which is the sum of the penalty cost related to makespan over all the material handling activities and the total electricity cost for charging batteries of MHE. Numerical experiments are used to investigate and quantify the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing.
    @CONFERENCE{Carli2020503,
    author={Carli, R. and Digiesi, S. and Dotoli, M. and Facchini, F.},
    title={A control strategy for smart energy charging of warehouse material handling equipment},
    journal={Procedia Manufacturing},
    year={2020},
    volume={42},
    pages={503-510},
    doi={10.1016/j.promfg.2020.02.041},
    note={cited By 5},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084243764&doi=10.1016%2fj.promfg.2020.02.041&partnerID=40&md5=795bb61103f666156bed4ba693a6503f},
    abstract={The common driver of the 'green-warehouse' strategy is based on the reduction of energy consumption. In warehouses with 'picker-to-part' operations the minimization of energy due to material handling activities can be achieved by means of different policies: by adopting smart automatic picking systems, by adopting energy-efficient material handling equipment (MHE) as well as by identifying flexible layouts. In most cases, these strategies require investments characterized by high pay-back times. In this context, management strategies focused on the adoption of available equipment allow to increase the warehouse productivity at negligible costs. With this purpose, an optimization model is proposed in order to identify an optimal control strategy for the battery charging of a fleet of electric mobile MHE (e.g., forklifts), allowing minimizing the economic and environmental impact of material handling activities in labor-intensive warehouses. The resulting scheduling problem is formalized as an integer programming (IP) problem aimed at minimizing the total cost, which is the sum of the penalty cost related to makespan over all the material handling activities and the total electricity cost for charging batteries of MHE. Numerical experiments are used to investigate and quantify the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing.},
    author_keywords={Battery smart charging; Green warehouse; Industrial/manufacturing demand side management; Integer programming; Material handling activity; Optimization; Warehouse energy management},
    references={Bartolini, M., Bottani, E., Grosse, E.H., Green warehousing: Systematic literature review and bibliometric analysis (2019) Journal of Cleaner Production, 226, pp. 242-258; Dhooma, J., Baker, P., An exploratory framework for energy conservation in existing warehouses (2012) International Journal of Logistics Research and Applications, 15 (1), pp. 37-51; (2016) International Energy Outlook, , https://www.iea.org/, accessed 2 September 2019; Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., Analysis and modeling of a novel SDR-based high-precision positioning system (2018) 2015 Int. Conf. On Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), pp. 13-16; Piccinni, G., Avitabile, G., Coviello, G., A novel distance measurement technique for indoor positioning systems based on Zadoff-Chu Sequences (2017) 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS), pp. 337-340; Gattullo, M., Evangelista, A., Uva, A.E., Fiorentino, M., Boccaccio, A., Manghisi, V.M., Exploiting augmented reality to enhance piping and instrumentation diagrams for information retrieval tasks in industry 4.0 maintenance (2019) Proceedings of the International Conference on Virtual Reality and Augmented Reality, pp. 170-180. , Springer, Cham; Uva, A.E., Fiorentino, M., Gattullo, M., Colaprico, M., de Ruvo, M.F., Marino, F., Trotta, G.F., Monno, G., Design of a projective AR workbench for manual working stations (2016) International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 358-367. , Springer, Cham; Freis, J., Vohlidka, P., Gunthner, W.A., Low-carbon warehousing: Examining impacts of building and intra-logistics design options on energy demand and the CO2 emissions of logistics centers (2016) Sustainability, 8, p. 448; Zhang, Q., Grossmann, I.E., Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016) Chemical Engineering Research and Design, 116, pp. 114-131; Moon, J.Y., Park, J., Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage (2014) International Journal of Production Research, 52 (13), pp. 3922-3939; Bortolini, M., Faccio, M., Ferrari, E., Gamberi, M., Pilati, F., Time and energy optimal unit-load assignment for automatic S/R warehouses (2017) International Journal of Production Economics, 190, pp. 133-145; Malaguti, E., Nannicini, G., Thomopulos, D., Optimizing allocation in a warehouse network (2018) Electronic Notes in Discrete Mathetatics, 64, pp. 195-204; Ghalehkhondabi, I., Masel, D.T., Storage allocation in a warehouse based on the forklifts fleet availability (2018) Journal of Algorithms & Computational Technology, 12 (2), pp. 127-135; Ene, S., Küçükoglu, I., Aksoy, A., Oztürk, N., A genetic algorithm for minimizing energy consumption in warehouses (2016) Energy, 114, pp. 973-980; Boysen, N., Fedtke, S., Weidinger, F., Optimizing automated sorting in warehouses: The minimum order spread sequencing problem (2018) European Journal of Operational Research, 270 (1), pp. 386-400; Boysen, N., Briskorn, D., Emde, S., Parts-to-picker based order processing in a rack-moving mobile robots environment (2017) European Journal of Operational Research, 262, pp. 550-562; Minav, T.A., Laurila, L.I.E., Immonen, P.A., Haapala, M.E., Pyrhonen, J.J., Electric energy recovery system efficiency in a hydraulic forklift (2009) Proceedings of IEEE EUROCON, pp. 758-765; Minav, T.A., Laurila, L.I.E., Pyrhonen, J.J., Analysis of electro-hydraulic lifting system's energy efficiency with direct electric drive pump control (2013) Automation in Construction, 30, pp. 144-150; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange IEEE/CAA (2019) Journal of Automatica Sinica, 6 (3), pp. 641-656; Finn, P., Fitzpatrick, C., Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing (2014) Applied Energy, 113, pp. 11-21; Gellings, C.W., The concept of demand-side management for electric utilities (1985) Proceedings of the IEEE, 73 (10), pp. 1468-1470; Paulus, M., Borggrefe, F., The potential of demand-side management in energy-intensive industries for electricity markets in Germany (2011) Applied Energy, 88 (2), pp. 432-441; Ramin, D., Spinelli, S., Brusaferri, A., Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process (2018) Applied Energy, 225, pp. 622-636; Zhao, S., Ochoa, M.P., Tang, L., Lotero, I., Gopalakrishnan, A., Grossmann, I.E., Novel formulation for optimal schedule with demand side management in multiproduct air separation processes (2019) Industrial & Engeneering Chemistry Research, 58 (8), pp. 3104-3117; Çelebi, E., Fuller, J.D., Time-of-use pricing in electricity markets under different market structures (2012) IEEE Transactions on Power Systems, 27 (3), pp. 1170-1181; Glover, F., Improved linear integer programming formulations of nonlinear integer problems (1975) Management Science, 22 (4), pp. 455-460; Achterberg, T., SCIP: Solving constraint integer programs (2009) Mathematical Programming Computation, 1, pp. 1-41},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2020) Distributed Alternating Direction Method of Multipliers for Linearly Constrained Optimization over a Network. IN IEEE Control Systems Letters, 4.247-252. doi:10.1109/LCSYS.2019.2923078
    [BibTeX] [Abstract] [Download PDF]
    In this letter we address the distributed optimization problem for a network of agents, which commonly occurs in several control engineering applications. Differently from the related literature, where only consensus constraints are typically addressed, we consider a challenging distributed optimization set-up where agents rely on local communication and computation to optimize a sum of local objective functions, each depending on individual variables subject to local constraints, while satisfying linear coupling constraints. Thanks to the distributed scheme, the resolution of the optimization problem turns into designing an iterative control procedure that steers the strategies of agents-whose dynamics is decoupled-not only to be convergent to the optimal value but also to satisfy the coupling constraints. Based on duality and consensus theory, we develop a proximal Jacobian alternating direction method of multipliers (ADMM) for solving such a kind of linearly constrained convex optimization problems over a network. Using the monotone operator and fixed point mapping, we analyze the optimality of the proposed algorithm and establish its o(1/t) convergence rate. Finally, through numerical simulations we show that the proposed algorithm offers higher computational performances than recent distributed ADMM variants. © 2019 IEEE.
    @ARTICLE{Carli2020247,
    author={Carli, R. and Dotoli, M.},
    title={Distributed Alternating Direction Method of Multipliers for Linearly Constrained Optimization over a Network},
    journal={IEEE Control Systems Letters},
    year={2020},
    volume={4},
    number={1},
    pages={247-252},
    doi={10.1109/LCSYS.2019.2923078},
    art_number={8736857},
    note={cited By 4},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068640431&doi=10.1109%2fLCSYS.2019.2923078&partnerID=40&md5=7f07a37d7f737276c9983542771cba08},
    abstract={In this letter we address the distributed optimization problem for a network of agents, which commonly occurs in several control engineering applications. Differently from the related literature, where only consensus constraints are typically addressed, we consider a challenging distributed optimization set-up where agents rely on local communication and computation to optimize a sum of local objective functions, each depending on individual variables subject to local constraints, while satisfying linear coupling constraints. Thanks to the distributed scheme, the resolution of the optimization problem turns into designing an iterative control procedure that steers the strategies of agents-whose dynamics is decoupled-not only to be convergent to the optimal value but also to satisfy the coupling constraints. Based on duality and consensus theory, we develop a proximal Jacobian alternating direction method of multipliers (ADMM) for solving such a kind of linearly constrained convex optimization problems over a network. Using the monotone operator and fixed point mapping, we analyze the optimality of the proposed algorithm and establish its o(1/t) convergence rate. Finally, through numerical simulations we show that the proposed algorithm offers higher computational performances than recent distributed ADMM variants. © 2019 IEEE.},
    author_keywords={Distributed control; distributed optimization; optimization algorithms},
    keywords={Computation theory; Convex optimization; Iterative methods, Alternating direction method of multipliers; Computational performance; Constrained convex optimizations; Distributed control; Distributed optimization; Engineering applications; Linearly constrained optimization; Optimization algorithms, Constrained optimization},
    references={Carli, R., Dotoli, M., A distributed control algorithm for waterfilling of networked control systems via consensus (2017) IEEE Control Syst. Lett., 1 (2), pp. 334-339. , Oct; Olfati-Saber, R., Fax, J.A., Murray, R.M., Consensus and cooperation in networked multi-agent systems (2007) Proc. IEEE, 95 (1), pp. 215-233. , Jan; Tsitsiklis, J.N., (1984) Problems in Decentralized Decision Making and Computation, , DTIC, LIDS, MIT, Cambridge, MA, USA, Rep. LIDS-TH-1424; Tsitsiklis, J.N., Bertsekas, D.P., Athans, M., Distributed asynchronous deterministic and stochastic gradient optimization algorithms (1986) IEEE Trans. Autom. Control, AC-31 (9), pp. 803-812. , Sep; Nedic, A., Ozdaglar, A., Distributed subgradient methods for multiagent optimization (2009) IEEE Trans. Autom. Control, 54 (1), p. 48; Nedic, A., Ozdaglar, A., Parrilo, P.A., Constrained consensus and optimization in multi-agent networks (2010) IEEE Trans. Autom. Control, 55 (4), pp. 922-938. , Apr; Chen, G., Yang, Q., Distributed constrained optimization for multiagent networks with nonsmooth objective functions (2019) Syst. Control Lett., 124, pp. 60-67. , Feb; Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., Distributed optimization and statistical learning via the alternating direction method of multipliers (2010) Found. Trends Mach. Learn., 3 (1), pp. 1-122; Makhdoumi, A., Ozdaglar, A., Convergence rate of distributed ADMM over networks (2017) IEEE Trans. Autom. Control, 62 (10), pp. 5082-5095. , Oct; Yu, Y., Açikmeşe, B., Mesbahi, M., Bregman parallel direction method of multipliers for distributed optimization via mirror averaging (2018) IEEE Control Syst. Lett., 2 (2), pp. 302-306. , Apr; Bastianello, N., Todescato, M., Carli, R., Schenato, L., Distributed optimization over lossy networks via relaxed Peaceman-Rachford splitting: A robust ADMM approach (2018) Proc. IEEE Eur. Control Conf., pp. 477-482; Mahey, P., Lenoir, A., A survey on operator splitting and decomposition of convex programs (2017) RAIRO Oper. Res. EDP Sci., 51 (1), pp. 14-41; Hosseini, S., Chapman, A., Mesbahi, M., Online distributed ADMM via dual averaging (2014) Proc. IEEE Int. Conf. Dec. Control, pp. 904-909. , Dec; Chang, T.-H., A proximal dual consensus ADMM method for multiagent constrained optimization (2016) IEEE Trans. Signal Process., 64 (14), pp. 3719-3734. , Jul; Gu, C., Wu, Z., Li, J., Guo, Y., (2018) Distributed Convex Optimization with Coupling Constraints over Time-varying Directed Graphs, , arXiv preprint; Notarnicola, I., Franceschelli, M., Notarstefano, G., A duality-based approach for distributed min-max optimization (2019) IEEE Trans. Autom. Control, 64 (6), pp. 2559-2566. , Jun; Notarnicola, I., Notarstefano, G., A duality-based approach for distributed optimization with coupling constraints (2017) Proc. IFAC World Congr., pp. 14891-14896; Grammatico, S., Dynamic control of agents playing aggregative games with coupling constraints (2017) IEEE Trans. Autom. Control., 62 (9), pp. 4537-4548. , Sep; Carli, R., Dotoli, M., Distributed control for waterfilling of networked control systems with coupling constraints (2018) Proc. IEEE Int. Conf. Dec. Control, pp. 3710-3715; Zhang, Y., Zavlanos, M.M., A consensus-based distributed augmented Lagrangian method (2018) Proc. IEEE Int. Conf. Dec. Control, pp. 1763-1768; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge, U.K.: Cambridge Univ. Press; Deng, W., Lai, M.-J., Peng, Z., Yin, W., Parallel multi-block ADMM with o(1/k) convergence (2017) J. Sci. Comput., 71 (2), pp. 712-736; Berinde, V., (2007) Iterative Approximation of Fixed Points, , Berlin, Germany: Springer; Bauschke, H.H., Combettes, P.L., (2011) Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 408. , New York, NY, USA: Springer; Rockafellar, R.T., Wets, R.J.B., (1998) Variational Analysis, , New York, NY, USA: Springer; Ryu, E.K., Boyd, S., Primer on monotone operator methods (2016) Appl. Comput. Math, 15 (1), pp. 3-43},
    document_type={Article},
    source={Scopus},
    }

2019

  • Carli, R., Cavone, G., Dotoli, M., Epicoco, N., Manganiello, C. & Tricarico, L. (2019) ICT-based methodologies for sheet metal forming design: A survey on simulation approaches IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 128-133. doi:10.1109/SMC.2019.8914082
    [BibTeX] [Abstract] [Download PDF]
    Sheet metal forming processes are widely adopted in manufacturing industries and in the recent years there has been a growing demand for sheet metal items with different shapes and characteristics. However, the traditional process is unable to meet the modern industrial requirements, mainly due to the high costs of dies and the long manufacturing time cycles. On the contrary, developing products with high speed, low cost, and high quality is a key issue. Therefore, new methods and technologies to speed up the sheet metal forming process while keeping costs limited are needed. In particular, a key issue is the proper design of the forming process, which can benefit from the use of Information and Communications Technology (ICT) simulation techniques. This paper investigates the recent trends on ICT-based methodologies for sheet metal forming to identify the foremost research areas whose advancement will lead meeting the modern market’s needs. © 2019 IEEE.
    @CONFERENCE{Carli2019128,
    author={Carli, R. and Cavone, G. and Dotoli, M. and Epicoco, N. and Manganiello, C. and Tricarico, L.},
    title={ICT-based methodologies for sheet metal forming design: A survey on simulation approaches},
    journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
    year={2019},
    volume={2019-October},
    pages={128-133},
    doi={10.1109/SMC.2019.8914082},
    art_number={8914082},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076787727&doi=10.1109%2fSMC.2019.8914082&partnerID=40&md5=b067223578db03937c9be68febc5f920},
    abstract={Sheet metal forming processes are widely adopted in manufacturing industries and in the recent years there has been a growing demand for sheet metal items with different shapes and characteristics. However, the traditional process is unable to meet the modern industrial requirements, mainly due to the high costs of dies and the long manufacturing time cycles. On the contrary, developing products with high speed, low cost, and high quality is a key issue. Therefore, new methods and technologies to speed up the sheet metal forming process while keeping costs limited are needed. In particular, a key issue is the proper design of the forming process, which can benefit from the use of Information and Communications Technology (ICT) simulation techniques. This paper investigates the recent trends on ICT-based methodologies for sheet metal forming to identify the foremost research areas whose advancement will lead meeting the modern market's needs. © 2019 IEEE.},
    keywords={Costs; Information use; Metal forming; Metals, Developing product; Different shapes; Industrial requirements; Information and communications technology; Manufacturing industries; Manufacturing time; Simulation approach; Simulation technique, Sheet metal},
    references={Suchy, I., (2006) Handbook of Die Design, , McGraw-Hill; Vollertsen, F., Schmidt, F., Dry metal forming: Definition, chances and challenges (2014) Int. J. Precis. Eng. Manuf. Technol, 1 (1), pp. 59-62; Mulidrán, P., Spišák, E., Majerníková, J., Springback prediction in sheet metal forming via fea simulation (2017) Int. J. Eng. Sci, 6 (9), pp. 49-52; Geiger, M., Merklein, M., Kerausch, M., Finite element simulation of deep drawing of tailored heat treated blanks (2004) CIRP Ann, 53 (1), pp. 223-226; Ingarao, G., Di Lorenzo, R., Design of complex sheet metal forming processes: A new computer aided progressive approach (2010) Int. J. Mater. Form, 3 (1), pp. 21-24; (1998) Schuler GmbH., Metal Forming Handbook, , Springer Berlin Heidelberg; Ramezani, M., Ripin, Z.M., Forming of shallow parts using rubber tools (2012) Rubber-Pad Form. Process, pp. 65-102; Reddy, P., Reddy, G., Prasad, P., A review on finite element simulations in metal forming (2012) Int. J. Mod. Eng. Res, 2 (4), pp. 2326-2330; Zhou, D., Knowledge based cloud fe simulation of sheet metal forming processes (2016) J. Vis. Exp, 118; LS-DYNA, , www.ansys.com/en-gb/products/structures/ansys-ls-dyna, [Accessed: 18-Apr-2019]; AutoForm, , www.autoform.com/en/products/solution-overview, [Accessed: 18-Apr-2019]; Abaqus, , www.3ds.com/products-services/simulia/products/abaqus, [Accessed: 18-Apr-2019]; Pam-Stamp, , www.esi.com.au/software/pamstamp, [Accessed: 18-Apr-2019]; DeGarmo, E.P., Black, J.T., Kohser, R.A., (2017) Materials and Processes in Manufacturing, , Wiley; Ashby, M.F., Shercliff, H., Cebon, D., (2018) Materials: Engineering, Science, Processing and Design, , Elsevier; Iorio, L., Strano, M., Monno, M., Development of a die compensation algorithm for sheet metal stamping with deformable tools (2015) Proc. ASME 2015 International Manufacturing Science and Engineering Conference; Vrolijk, M., Ogawa, T., Camanho, A., Biasutti, M., Lorenz, D., A study with esi pam-stamp® on the influence of tool deformation on final part quality during a forming process (2018) AIP Conference Proceedings, 1960 (1); Cafolla, J., Hall, R.W., Norman, D.P., McGregor, J., Forming to crash' simulation in full vehicle models (2003) 4th European LS-DYNA Users Conference; Wang, W.R., Chen, G.L., Lin, Z.Q., Li, S.H., Determination of optimal blank holder force trajectories for segmented binders of step rectangle box using pid closed-loop fem simulation (2007) Int. J. Adv. Manuf. Technol, 32 (11-12), pp. 1074-1082; Wang, Y., Liu, D.-Z., Li, R., Numerical investigation for the flexible stretch-stamp forming process of sheet metal (2019) Adv. Mech. Eng, 11 (1), pp. 1-11; Slota, J., Jurišin, M., Springback prediction in sheet metal forming processes (2012) J. Technol. Plast, 37 (1), pp. 93-103; Mulidrán, P., Šiser, M., Slota, J., Spišák, E., Sleziak, T., Numerical prediction of forming car body parts with emphasis on springback (2018) Metals, 8 (6); Strano, M., Optimization under uncertainty of sheet-metal-forming processes by the finite element method (2006) Proc. Inst. Mech. Eng. Part B J. Eng. Manuf, 220 (8), pp. 1305-1315; Shaikh, A.M.G., Rao, T.B., Sheet metal forming simulations for heavy commercial vehicle parts by ls-dyna (2013) Glob. J. Res. Eng. Automot. Eng, 13 (1), pp. 35-40; Koreek, D., Solfronk, P., Sobotka, J., Kolnerová, M., Determination the influence of load-rate on strain and spring-back magnitude for titanium alloy by means of numerical simulation (2019) Manuf. Technol, 19 (1), pp. 82-88; Hochholdinger, B., Grass, H., Lipp, A., Hora, P., Determination of flow curves by stack compression tests and inverse analysis for the simulation of hot forming (2009) 7th European LS-DYNA Conference; Papadakis, L., Schober, A., Zaeh, M.F., Considering manufacturing effects in automotive structural crashworthiness: A simulation chaining approach (2013) Int. J. Crashworthiness, 18 (3), pp. 276-287; Jadhav, S., Schoiswohl, M., Buchmayr, B., Applications of finite element simulation in the development of advanced sheet metal forming processes (2018) BHM Berg-und Hüttenmännische Monatshefte, 163 (3), pp. 109-118; Mohamed, M., Norman, D., Petre, A., Melotti, F., Szegda, D., Advances in fem simulation of hfq® aa6082 tailor welded blanks for automotive applications (2018) IOP Conf. Ser. Mater. Sci. Eng, 418 (1), pp. 1-8; Karbasian, H., Tekkaya, A.E., A review on hot stamping (2010) J. Mater. Process. Technol, 210, pp. 2103-2118; Kuhn, H., Medlin, D., (2000) Mechanical Testing and Evaluation, , ASM International; Mosterman, P.J., Zander, J., Industry 4. 0 as a cyber-physical system study (2016) Softw. Syst. Model, 15 (1), pp. 17-29},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Dotoli, M., Epicoco, N. & Scarabaggio, P. (2019) Model predictive control for thermal comfort optimization in building energy management systems IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 2608-2613. doi:10.1109/SMC.2019.8914489
    [BibTeX] [Abstract] [Download PDF]
    Model Predictive Control (MPC) has recently gained special attention to efficiently regulate Heating, Ventilation and Air Conditioning (HVAC) systems of buildings, since it explicitly allows energy savings while maintaining thermal comfort criteria. In this paper we propose a MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings. We use Fanger’s Predicted Mean Vote (PMV) as thermal comfort index, while to predict the energy performance of the building, we adopt a simplified thermal model. This allows computing optimal control actions by defining and solving a tractable non-linear optimization problem that incorporates the PMV index into the MPC cost function in addition to a term accounting for energy saving. The proposed MPC approach is implemented on a building automation system deployed in an office building located at the Polytechnic of Bari (Italy). Several on-field tests are performed to assess the applicability and efficacy of the control algorithm in a real environment against classical thermal comfort control approach based on the use of thermostats. © 2019 IEEE.
    @CONFERENCE{Carli20192608,
    author={Carli, R. and Cavone, G. and Dotoli, M. and Epicoco, N. and Scarabaggio, P.},
    title={Model predictive control for thermal comfort optimization in building energy management systems},
    journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
    year={2019},
    volume={2019-October},
    pages={2608-2613},
    doi={10.1109/SMC.2019.8914489},
    art_number={8914489},
    note={cited By 7},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076778873&doi=10.1109%2fSMC.2019.8914489&partnerID=40&md5=3c982fb93adbcfb5202b48b60ad0f22d},
    abstract={Model Predictive Control (MPC) has recently gained special attention to efficiently regulate Heating, Ventilation and Air Conditioning (HVAC) systems of buildings, since it explicitly allows energy savings while maintaining thermal comfort criteria. In this paper we propose a MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings. We use Fanger's Predicted Mean Vote (PMV) as thermal comfort index, while to predict the energy performance of the building, we adopt a simplified thermal model. This allows computing optimal control actions by defining and solving a tractable non-linear optimization problem that incorporates the PMV index into the MPC cost function in addition to a term accounting for energy saving. The proposed MPC approach is implemented on a building automation system deployed in an office building located at the Polytechnic of Bari (Italy). Several on-field tests are performed to assess the applicability and efficacy of the control algorithm in a real environment against classical thermal comfort control approach based on the use of thermostats. © 2019 IEEE.},
    keywords={Air conditioning; Automation; Cost functions; Energy conservation; Energy management systems; Energy utilization; Intelligent buildings; Nonlinear programming; Office buildings; Predictive control systems; Thermal comfort, Building automation systems; Energy performance; Indoor thermal comfort; Non-linear optimization problems; Online optimization; Predicted mean vote; Thermal comfort control; Thermal comfort index, Model predictive control},
    references={Dean, B.P., Dulac, J., Petrichenko, K., Graham, Towards a zeroemission, efficient, and resilient buildings and construction sector (2016) Global Status Report; Ranieri, L., Mossa, G., Pellegrino, R., Digiesi, S., Energy recovery from the organic fraction of municipal solid waste: A real optionsbased facility assessment (2018) Sustain, 10 (2), p. 368. , Jan; Jouhara, H., Yang, J., Energy efficient hvac systems (2018) Energy and Buildings, 179, pp. 83-85; Piccinni, G., Avitabile, G., Coviello, G., Talarico, C., Distributed amplifier design for UWB positioning systems using the gm over id methodology (2016) 2016 13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2016; Shaikh, P.H., Nor, N.B.M., Nallagownden, P., Elamvazuthi, I., Ibrahim, T., A review on optimized control systems for building energy and comfort management of smart sustainable buildings (2014) Renewable and Sustainable Energy Reviews, 34, pp. 409-429; Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S., Haves, P., Model predictive control for the operation of building cooling systems (2012) IEEE Trans. Control Syst. Technol, 20 (3), pp. 796-803; Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., Bemporad, A., Model predictive control (mpc) for enhancing building and hvac system energy efficiency: Problem formulation, applications and opportunities (2018) Energies, 11 (3); Hazyuk, I., Ghiaus, C., Penhouet, D., Optimal temperature control of intermittently heated buildings using model predictive control: Part II-control algorithm (2012) Build. Environ, 51, pp. 388-394; Klauo, M., Drgoa, J., Kvasnica, M., Di Cairano, S., Building temperature control by simple mpc-like feedback laws learned from closed-loop data (2014) IFAC Proceedings Volumes (IFACPapersOnline), 19, pp. 581-586; Klauco, M., Kvasnica, M., Explicit mpc approach to pmv-based thermal comfort control (2014) Proceedings of the IEEE Conference on Decision and Control, pp. 4856-4861. , 2015-Febru February; (2005) ISO 7730: Ergonomics of the Thermal Environment-Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, p. 60. , International Organization for Standardization, ISO Stand; Fanger, P.O., (1970) Thermal Comfort: Analysis and Application in Environment Engineering; Afram, A., Janabi-Sharifi, F., Theory and applications of hvac control systems-a review of model predictive control (mpc) (2014) Building and Environment, 72; Xu, Z., Hu, G., Spanos, C.J., Schiavon, S., Pmv-based eventtriggered mechanism for building energy management under uncertainties (2017) Energy Build, 152, pp. 73-85; West, S.R., Ward, J.K., Wall, J., Trial results from a model predictive control and optimisation system for commercial building hvac (2014) Energy Build, 72, pp. 271-279; Alamin, Y.I., Del Mar Castilla, M., Álvarez, J.D., Ruano, A., An economic model-based predictive control to manage the users' thermal comfort in a building (2017) Energies, 10 (3); Cigler, J., Prívara, S., Váa, Z., Žáeková, E., Ferkl, L., Optimization of predicted mean vote index within model predictive control framework: Computationally tractable solution (2012) Energy Build, 52, pp. 39-49. , Sep; Corbin, C.D., Henze, G.P., May-Ostendorp, P., A model predictive control optimization environment for real-time commercial building application (2013) J. Build. Perform. Simul, 6 (3), pp. 159-174; Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., Vanoli, G.P., Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort (2016) Energy Build, 111, pp. 131-144; Pippia, T., Sijs, J., De Schutter, B., A parametrized model predictive control approach for microgrids (2019) Proceedings of the IEEE Conference on Decision and Control, pp. 3171-3176. , 2018-Decem; García, C.E., Prett, D.M., Morari, M., Model predictive control: Theory and practice-a survey (1989) Automatica, 25 (3), pp. 335-348; Farina, M., Betti, G., Scattolini, R., A solution to the tracking problem using distributed predictive control (2018) 2013 European Control Conference (ECC), pp. 4347-4352; Beeta, , https://www.beeta.it/en/, [Accessed: 24-Apr-2019]; Node-RED : User Guide, , https://nodered.org/docs/user-guide/, [Accessed: 24-Apr-2019]; Singh, M., Rajan, M.A., Shivraj, V.L., Balamuralidhar, P., Secure mqtt for internet of things (iot) (2015) Proceedings-2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015, pp. 746-751},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Hosseini, S. M., Carli, R. & Dotoli, M. (2019) A residential demand-side management strategy under nonlinear pricing based on robust model predictive control IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 3243-3248. doi:10.1109/SMC.2019.8913892
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a real-time demand side management framework based on robust model predictive control (RMPC) for residential smart grids. The system incorporates a number of interconnected smart homes, each equipped with controllable and non-controllable loads, as well as a shared energy storage system (ESS). We aim at minimizing the users’ energy payment and limiting the peak-to-average ratio (PAR) of the energy consumption while taking into account all device/comfort/contractual constraints, specifically the feasibility constraints on energy transferred between users and the power grid in presence of load demand uncertainty. We consider a quadratic cost function for energy bought from the grid. Firstly, the energy price and related constraints of the system are modeled. Then, a min-max robust problem is established to optimally schedule energy under an interval-based uncertainty set. We finally adopt model predictive control (MPC) to solve the resulting robust optimization problem iteratively over a finite-horizon time window based on the receding horizon concept. Moreover, the robustness of the proposed real-time approach against the level of conservativeness of the solution is addressed. The effectiveness of the method is validated through a simulated case study. © 2019 IEEE.
    @CONFERENCE{Hosseini20193243,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={A residential demand-side management strategy under nonlinear pricing based on robust model predictive control},
    journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
    year={2019},
    volume={2019-October},
    pages={3243-3248},
    doi={10.1109/SMC.2019.8913892},
    art_number={8913892},
    note={cited By 18},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076744329&doi=10.1109%2fSMC.2019.8913892&partnerID=40&md5=b8658f66ca7439fa6e7d9b02705e1b91},
    abstract={This paper presents a real-time demand side management framework based on robust model predictive control (RMPC) for residential smart grids. The system incorporates a number of interconnected smart homes, each equipped with controllable and non-controllable loads, as well as a shared energy storage system (ESS). We aim at minimizing the users' energy payment and limiting the peak-to-average ratio (PAR) of the energy consumption while taking into account all device/comfort/contractual constraints, specifically the feasibility constraints on energy transferred between users and the power grid in presence of load demand uncertainty. We consider a quadratic cost function for energy bought from the grid. Firstly, the energy price and related constraints of the system are modeled. Then, a min-max robust problem is established to optimally schedule energy under an interval-based uncertainty set. We finally adopt model predictive control (MPC) to solve the resulting robust optimization problem iteratively over a finite-horizon time window based on the receding horizon concept. Moreover, the robustness of the proposed real-time approach against the level of conservativeness of the solution is addressed. The effectiveness of the method is validated through a simulated case study. © 2019 IEEE.},
    keywords={Automation; Cost functions; Costs; Demand side management; Electric power transmission networks; Electric utilities; Energy utilization; Housing; Intelligent buildings; Iterative methods; Optimization; Predictive control systems; Robust control, Controllable loads; Energy storage systems; Non-linear pricing; Peak to average ratios; Quadratic cost functions; Robust model predictive control; Robust model predictive controls (RMPC); Robust optimization, Model predictive control},
    references={Ahmed, N., Levorato, M., Li, G.P., Residential consumer-centric demand side management (2018) IEEE Trans. Smart Grid, 9, pp. 4513-4524; Saleh, S.A., Pijnenburg, P., Castillo-Guerra, E., Load aggregation from generation-follows-load to load-follows-generation: Residential loads (2017) IEEE Trans. Ind. Appl, 53, pp. 833-842; Facchini, F., Mummolo, G., Mossa, G., Digiesi, S., Boenzi, F., Verriello, R., Minimizing the carbon footprint of material handling equipment: Comparison of electric and lpg forklifts (2016) Journal of Industrial Engineering and Management, 9 (5), pp. 1035-1046; D'Amato, G., Avitabile, G., Coviello, G., Talarico, C., Toward a novel architecture for beam steering of active phased-array antennas (2016) 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4. , October; Talarico, C., D'Amato, G., Coviello, G., Avitabile, G., A high precision phase control unit for dds-based plls for 2. 4-ghz ism band applications (2015) 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4. , August; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Management and Production Engineering Review, 4 (4), pp. 15-27; Kuzlu, M., Score-based intelligent home energy management (hem) algorithm for demand response applications and impact of hem operation on customer comfort (2015) IET Gener. Transm. Distrib, 9, pp. 627-635; Chen, C., Duan, S., Cai, T., Liu, B., Hu, G., Smart energy management system for optimal microgrid economic operation (2011) IET Renew. Power Gener, 5, pp. 258-267; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange (2019) IEEE/CAA Journal of Automatica Sinica, 6 (3), pp. 641-656. , May; Mohsenian-Rad, A., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) IEEE Innov. Smart Grid Tech; Yue, J., Hu, Z., Anvari-Moghaddam, A., Guerrero, J.M., A multimarket-driven approach to energy scheduling of smart microgrids in distribution networks (2019) Sustainability, 11, pp. 1-16; Kim, B., Zhang, Y., Schaar Der Van, M., Lee, J., Dynamic pricing and energy consumption scheduling with reinforcement learning (2016) IEEE Trans. Smart Grid, 7, pp. 2187-2198; Ma, J., Chen, H.H., Song, L., Li, Y., Residential load scheduling in smart grid: A cost efficiency perspective (2016) IEEE Trans. Smart Grid, 7, pp. 771-784; Carli, R., Dotoli, M., Epicoco, N., Cost-optimal energy scheduling of a smart home under uncertainty (2018) IEEE Conf. Control Technology and Applications; Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M., A stochastic framework for short-term operation of a distribution company (2013) IEEE Trans. Power Systems, 28, pp. 4712-4721; Ghasemi, A., Banejad, M., Rahimiyan, M., Integrated energy scheduling under uncertainty in a micro energy grid (2018) IET Gener. Transm. Distrib, 12, pp. 2887-2896; Chen, Z., Wu, L., Fu, Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization (2012) IEEE Trans. Smart Grid, 3, pp. 1822-1831; Zhang, Y., Fu, L., Zhu, W., Bao, X., Liu, C., Robust model predictive control for optimal energy management of island microgrids with uncertainties (2018) Energy, Elsevier, 164, pp. 1229-1241; Hosseini, S.M., Carli, R., Dotoli, M., Model predictive control for real-time residential energy scheduling under uncertainties (2018) IEEE Int. Conf. Systems, Man, and Cybernetics; Zhai, M., Liu, Y., Zhang, T., Zhang, Y., Robust model predictive control for energy management of isolated microgrids (2017) IEEE Int. Conf. Industrial Engineering and Engineering Management; Xiang, Y., Liu, J., Liu, Y., Robust energy management of microgrid with uncertain renewable generation and load (2016) IEEE Trans. Smart Grid, 7, pp. 1034-1043; Elghali, S.B., Outbib, R., Benbouzid, M., Selecting and optimal sizing of hybridized energy storage systems for tidal energy integration into power grid (2019) Journal of Modern Power Systems and Clean Energy, 7 (1), pp. 113-122; Bertsimas, D., Sim, M., The price of robustness (2014) Oper. Res, 52, pp. 35-53; Hosseini, S.M., Carli, R., Dotoli, M., Robust energy scheduling of interconnected smart homes with shared energy storage under quadratic pricing (2019) IEEE Conf. Automation Science and Engineering; Cavone, G., Blenkers, L., Boom Den Van, T., Dotoli, M., Seatzu, C., De Schutter, B., Railway disruption: A bi-level rescheduling algorithm (2019) International Conference on Control Decision and Information Technologies; Pippia, T., Sijs, J., De Schutter, B., A parametrized model predictive control approach for microgrids (2018) IEEE Conf. Dec. Contr, pp. 3171-3176},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Robust energy scheduling of interconnected smart homes with shared energy storage under quadratic pricing IN IEEE International Conference on Automation Science and Engineering., 966-971. doi:10.1109/COASE.2019.8843230
    [BibTeX] [Abstract] [Download PDF]
    In this paper, we propose a novel robust framework for day-ahead energy scheduling of interconnected smart homes with shared energy storage system (ESS), taking into account users’ behavior uncertainty. The objective is minimizing the total energy payment for each user while satisfying the constraint on the feasibility of energy transactions between users and the power grid in presence of data uncertainty. Unlike most existing robust scheduling frameworks that assume a linear cost function for energy purchased from the grid, our design presents a tractable robust optimization scheme to solve the energy scheduling problem with a more realistic quadratic cost function. We model device/comfort constraints as well as contractual obligations imposed by the power grid restricting the users’ energy consumption to a maximum level at each time slot. Thus, in our problem, uncertainty affects both the quadratic objective function and linear contractual constraints. To solve the resulting problem, we first formulate a deterministic model of the scheduling problem, then establish a min-max robust counterpart, and finally apply some mathematical transformations to solve the equivalent problem. We also deal with the conservatism of the robust control algorithm and flexibility of the method for application to different settings. The validity and effectiveness of the proposed approach is verified by simulation results. © 2019 IEEE.
    @CONFERENCE{Hosseini2019966,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={Robust energy scheduling of interconnected smart homes with shared energy storage under quadratic pricing},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2019},
    volume={2019-August},
    pages={966-971},
    doi={10.1109/COASE.2019.8843230},
    art_number={8843230},
    note={cited By 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072987292&doi=10.1109%2fCOASE.2019.8843230&partnerID=40&md5=6989d41f45c9a005b8a3dd9ebcf6bbfd},
    abstract={In this paper, we propose a novel robust framework for day-ahead energy scheduling of interconnected smart homes with shared energy storage system (ESS), taking into account users' behavior uncertainty. The objective is minimizing the total energy payment for each user while satisfying the constraint on the feasibility of energy transactions between users and the power grid in presence of data uncertainty. Unlike most existing robust scheduling frameworks that assume a linear cost function for energy purchased from the grid, our design presents a tractable robust optimization scheme to solve the energy scheduling problem with a more realistic quadratic cost function. We model device/comfort constraints as well as contractual obligations imposed by the power grid restricting the users' energy consumption to a maximum level at each time slot. Thus, in our problem, uncertainty affects both the quadratic objective function and linear contractual constraints. To solve the resulting problem, we first formulate a deterministic model of the scheduling problem, then establish a min-max robust counterpart, and finally apply some mathematical transformations to solve the equivalent problem. We also deal with the conservatism of the robust control algorithm and flexibility of the method for application to different settings. The validity and effectiveness of the proposed approach is verified by simulation results. © 2019 IEEE.},
    keywords={Automation; Cost functions; Digital storage; Electric power transmission networks; Energy storage; Energy utilization; Intelligent buildings; Mathematical transformations; Optimization; Robust control; Scheduling, Contractual obligations; Deterministic modeling; Energy storage systems; Linear cost functions; Quadratic cost functions; Quadratic objective functions; Robust optimization; Scheduling problem, Costs},
    references={Cao, X., Dai, X., Liu, J., Building energy-consumption status worldwide and the state-of-The-art technologies for zero-energy buildings during the past decade (2016) Energy AndBuildings, 128, pp. 198-213; Piccinni, G., Avitabile, G., Coviello, G., An improved technique based on Zadoff-Chu sequences for distance measurements (2016) IEEE Radio and Antenna Days of the Indian Ocean, pp. 1-2; Kim, B., Zhang, Y., Van Der Schaar, M., Lee, J., Dynamic pricing and energy consumption scheduling with reinforcement learning (2016) IEEE Trans. Smart Grid, 7 (5), pp. 2187-2198; Mohsenian-Rad, A., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) IEEEInnov. Smart Grid Tech.; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) IEEE Conf. Dec. Contr, pp. 5648-5653; Anvari-Moghaddam, A., Guerrero, J.M., Vasquez, J.C., Monsef, H., Rahimi-Kian, A., Efficient energy management for a grid-tied residential microgrid (2017) IETGener. Transm. Distrib, 11 (11), pp. 2752-2761; Pippia, T., Sijs, J., De Schutter, B., A parametrized model predictive control approach for microgrids (2018) IEEE Conf. Dec. Contr., pp. 3171-3176; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users' microgrid with renewable energy exchange (2019) IEEE/CAA Journal of Automatica Sinica, 6 (3), pp. 641-656; Facchini, F., Mummolo, G., Mossa, G., Digiesi, S., Boenzi, F., Verriello, R., Minimizing the carbon footprint of material handling equipment: Comparison of electric and lpg forklifts (2016) Journal OfIndustrialEngineering AndManagement, 9 (5), pp. 1035-1046; Hosseini, S.M., Carli, R., Dotoli, M., Model predictive control for realtime residential energy scheduling under uncertainties (2018) Proc. IEEE Int. Conf. Syst. Man. Cyb, pp. 1386-1391; Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M., A stochastic framework for short-term operation of a distribution company (2013) IEEE Trans. Power Systems, 28 (4), pp. 4712-4721; Ghasemi, A., Banejad, M., Rahimiyan, M., Integrated energy scheduling under uncertainty in a micro energy grid (2018) IET Gener. Transm. Distrib, 12 (12), pp. 2887-2896; Chen, Z., Wu, L., Fu, Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization (2012) IEEE Trans. Smart Grid, 3, pp. 1822-1831; Zhang, C., Xu, Y., Dong, Z.Y., Ma, J., Robust operation of microgrids via two-stage coordinated energy storage and direct load control (2017) IEEE Trans. Power Syst, 32 (4), pp. 2858-2868; Yi, W., Zhang, Y., Zhao, Z., Huang, Y., Multiobjective robust scheduling for smart distribution grids: Considering renewable energy and demand response uncertainty (2018) IEEEAccess, 6, pp. 45715-45724; Wang, C., Zhou, Y., Jiao, B., Wang, D., Robust optimization for load scheduling of a smart home with photovoltaic system (2015) Energy Conversion and Management, 102, pp. 247-257; Hussain, A., Bui, V., Kim, H., Robust optimal operation of ac/dc hybrid microgrids under market price uncertainties (2018) IEEE Access, 6, p. 26542667; Paridari, K., Parisio, A., Sandberg, H., Johansson, K.H., Robust scheduling of smart appliances in active apartments with user behavior uncertainty (2016) IEEETrans. Autom. Sci. Eng, 13 (1), pp. 247-259; Bertsimas, D., Sim, M., The price of robustness (2014) Oper. Res, 52 (1), pp. 35-53; Elghali, S.B., Outbib, R., Benbouzid, M., Selecting and optimal sizing of hybridized energy storage systems for tidal energy integration into power grid (2019) Journal of Modern Power Systems and Clean Energy, 7 (1), pp. 113-122; Samadi, P., Schober, R., Wong, V.W.S., Optimal energy consumption scheduling using mechanism design for the future smart grid (2011) IEEE Smart Grid Commun., pp. 369-374},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Dotoli, M. & Palmisano, V. (2019) A distributed control approach based on game theory for the optimal energy scheduling of a residential microgrid with shared generation and storage IN IEEE International Conference on Automation Science and Engineering., 960-965. doi:10.1109/COASE.2019.8843141
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a distributed control approach based on game theory for the energy scheduling of demand-side consumers sharing energy production and storage while purchasing further energy from the grid. The interaction between the controllers of consumers’ loads and the manager of shared energy resources is modeled as a two-level game. The competition among consumers is formulated as a noncooperative game, while the interaction between the consumers’ loads and the shared resources manager is formulated as a cooperative game. optimization problems are stated for each player to determine their own optimal strategies. The algorithms for loads controllers and shared resources’ manager are implemented through a distributed approach. Numerical experiments show the effectiveness of the proposed scheme. © 2019 IEEE.
    @CONFERENCE{Carli2019960,
    author={Carli, R. and Dotoli, M. and Palmisano, V.},
    title={A distributed control approach based on game theory for the optimal energy scheduling of a residential microgrid with shared generation and storage},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2019},
    volume={2019-August},
    pages={960-965},
    doi={10.1109/COASE.2019.8843141},
    art_number={8843141},
    note={cited By 4},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072980491&doi=10.1109%2fCOASE.2019.8843141&partnerID=40&md5=52d9b8791bf4c74bacd4b6c99e48e8b9},
    abstract={This paper presents a distributed control approach based on game theory for the energy scheduling of demand-side consumers sharing energy production and storage while purchasing further energy from the grid. The interaction between the controllers of consumers' loads and the manager of shared energy resources is modeled as a two-level game. The competition among consumers is formulated as a noncooperative game, while the interaction between the consumers' loads and the shared resources manager is formulated as a cooperative game. optimization problems are stated for each player to determine their own optimal strategies. The algorithms for loads controllers and shared resources' manager are implemented through a distributed approach. Numerical experiments show the effectiveness of the proposed scheme. © 2019 IEEE.},
    keywords={Controllers; Energy resources; Managers; Scheduling, Distributed approaches; Distributed control; Energy productions; Noncooperative game; Numerical experiments; Optimal strategies; Optimization problems; Residential microgrid, Game theory},
    references={Vytelingum, P., Voice, T.D., Ramchurn, S.D., Rogers, A., Jennings, N.R., Agent-based micro-storage management for the smart grid (2010) Proc. Int. Conf. AAMAS, 1, pp. 39-46. , May; Talarico, C., D'Amato, G., Coviello, G., Avitabile, G., A high precision phase control unit for DDS-based PLLs for 2.4-GHz ISM band applications (2015) 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4. , , August; Atzeni, I., Ordonez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R., Demand-side management via distributed energy generation and storage optimization (2013) IEEE Trans. Smart Grid, 4 (2), pp. 866-876. , Jun; Barbato, A., Capone, A., Optimization models and methods for demand-side management of residential users: A survey (2014) Energies, 7 (9), pp. 5787-5824; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) IEEE Int. Conf. Dec. Contr, , Dec. 15-18; Carli, R., Dotoli, M., Cooperative distributed control for the energy scheduling of smart homes with shared energy storage and renewable energy source (2017) IFAC WC, 50 (1), pp. 8867-8872. , Jul. 9-14, IFAC-PapersOnLine; Paridari, K., Parisio, A., Sandberg, H., Johansson, K.H., Demand response for aggregated residential consumers with energy storage sharing (2015) Proc. IEEE Int. Conf. Dec. Contr, pp. 2024-2030; Mediwaththe, C.P., Stephens, E.R., Smith, D.B., Mahanti, A., A dynamic game for electricity load management in neighborhood area networks (2016) IEEE Trans. Smart Grid, 7 (3), p. 13291336; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Management and Production Engineering Review, 4 (4), pp. 15-27; Xu, B., Shi, Y., Kirschen, D.S., Zhang, B., Optimal regulation response of batteries under cycle aging mechanisms (2017) Proc. IEEE Int. Conf. Dec. Contr., pp. 751-756; Mohsenian-Rad, A.-H., Wong, V., Jatskevich, J., Schober, R., Leon-Garcia, A., Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid (2010) IEEE Trans. Smart Grid, 1 (3), pp. 320-331; Samadi, P., Mohsenian-Rad, A.H., Schober, R., Wong, V.W., Jatskevich, J., Optimal real-time pricing algorithm based on utility maximization for smart grid (2010) IEEE Int. Conf. On Smart Grid Communications, pp. 415-420; Brandenburger, A., Stuart, H., Biform games (2007) Management Science, 53 (4), pp. 537-549; Gilles, R.P., (2010) The Cooperative Game Theory of Networks and Hierarchies, 44. , Springer Science &Business Media; Basqr, T., Olsder, G.J., (1999) Dynamic Noncooperative Game Theory, Ser. SIAM Series in Classics in Applied Mathematics, , Philadelphia, PA: SIAM; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Facchinei, F., Pang, J.-S., (2003) Finite-Dimensional Variational Inequalities and Complementarity Problem, , New York, NY, USA: Springer-Verlag; Pilz, M., Al-Fagih, L., Pfluegel, E., Energy storage scheduling with an advanced battery model: A game-theoretic approach (2017) Inventions, 2 (4), p. 30; Chai, B., Chen, J., Yang, Z., Zhang, Y., Demand response management with multiple utility companies: A two-level game approach (2014) IEEE Trans. Smart Grid, 5 (2), pp. 722-731; Gao, B., Zhang, W., Tang, Y., Hu, M., Zhu, M., Zhan, H., Game-theoretic energy management for residential users with dischargeable plug-in electric vehicles (2014) Energies, 7 (11), pp. 7499-7518; Tushar, W., Zhang, J.A., Smith, D.B., Poor, H.V., Thiebaux, S., Prioritizing consumers in smart grid: A game theoretic approach (2014) IEEE Trans. Smart Grid, 5 (3), pp. 1429-1438},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Robust day-ahead energy scheduling of a smart residential user under uncertainty IN 2019 18th European Control Conference, ECC 2019., 935-940. doi:10.23919/ECC.2019.8796182
    [BibTeX] [Abstract] [Download PDF]
    This paper develops a robust optimization framework for the day-ahead energy scheduling of a grid-connected residential user. The system incorporates a renewable energy source (RES), a battery energy storage system (BESS) as well as elastic controllable and critical noncontrollable electrical appliances. The proposed approach copes with the fluctuation and intermittence of the RES generation and non-controllable load demand by a tractable robust optimization scheme requiring minimum information on the sources of uncertainty. The main objective is minimizing the total energy payment for the user considering operational/technical constraints and a contractual constraint penalizing the excessive use of energy. The presented framework allows the decision maker to define different robustness levels for uncertain variables, and to flexibly establish an equilibrium between user’s payment and price of robustness. To validate the effectiveness of the proposed framework under uncertainty, we simulate the dynamics of a residential user as a case study. A comparison between the proposed robust approach and the same method with deterministic RES and loads profiles is carried out and discussed. © 2019 EUCA.
    @CONFERENCE{Hosseini2019935,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={Robust day-ahead energy scheduling of a smart residential user under uncertainty},
    journal={2019 18th European Control Conference, ECC 2019},
    year={2019},
    pages={935-940},
    doi={10.23919/ECC.2019.8796182},
    art_number={8796182},
    note={cited By 19},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071523023&doi=10.23919%2fECC.2019.8796182&partnerID=40&md5=dbaf920bef232ca07b714d294ec35e28},
    abstract={This paper develops a robust optimization framework for the day-ahead energy scheduling of a grid-connected residential user. The system incorporates a renewable energy source (RES), a battery energy storage system (BESS) as well as elastic controllable and critical noncontrollable electrical appliances. The proposed approach copes with the fluctuation and intermittence of the RES generation and non-controllable load demand by a tractable robust optimization scheme requiring minimum information on the sources of uncertainty. The main objective is minimizing the total energy payment for the user considering operational/technical constraints and a contractual constraint penalizing the excessive use of energy. The presented framework allows the decision maker to define different robustness levels for uncertain variables, and to flexibly establish an equilibrium between user's payment and price of robustness. To validate the effectiveness of the proposed framework under uncertainty, we simulate the dynamics of a residential user as a case study. A comparison between the proposed robust approach and the same method with deterministic RES and loads profiles is carried out and discussed. © 2019 EUCA.},
    keywords={Decision making; Optimization; Renewable energy resources; Robustness (control systems); Scheduling, Battery energy storage systems; Controllable loads; Electrical appliances; Minimum information; Renewable energy source; Robust optimization; Sources of uncertainty; Uncertain variables, Housing},
    references={Samadi, P., Mohsenian-Rad, H., Wong, V.W.S., Schober, R., Tackling the load uncertainty challenges for energy consumption scheduling in smart grid (2013) IEEE Trans. Smart Grid, 4 (2), pp. 1007-1016; Cascella, D., Avitabile, G., Cannone, F., Coviello, G., A 2-GS/s 0.35m SiGe track-and-hold amplifier with 7-GHz analog bandwidth using a novel input buffer (2011) IEEE International Conference on Electronics, Circuits, and Systems, pp. 113-116; Aghajani, G.R., Shayanfar, H.A., Shayeghi, H., Demand side management in a smart micro-grid in the presence of renewable generation and demand response (2017) Energy, 126, pp. 622-637; Zhang, Y., Gatsis, N., Giannakis, G.B., Robust energy management for microgrids with high-penetration renewables (2013) IEEE Trans. Sust. Energ, 4 (4), pp. 944-953; Sun, Q., Ge, X., Liu, L., Xu, X., Zhang, Y., Niu, R., Review of smart grid comprehensive assessment systems (2011) Energy Procedia, 12, pp. 219-229; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) IEEE Conf. Decis. Control; Guo, L., Wu, H.C., Zhang, H., Xia, T., Mehraeen, S., Robust optimization for home-load scheduling under price uncertainty in smart grids (2015) Int. Conf. Comp., Net. Commun; Digiesi, S., Mossa, G., Mummolo, G., Supply lead time uncertainty in a sustainable order quantity inventory model (2013) Management and Production Engineering Review, 4 (4), pp. 15-27; Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M., A stochastic framework for short-term operation of a distribution company (2013) IEEE Trans. Power Syst, 28 (4), pp. 4712-4721; Ghasemi, A., Banejad, M., Rahimiyan, M., Integrated energy scheduling under uncertainty in a micro energy grid (2018) IET Gener. Transm. Distrib, 12 (12), pp. 2887-2896; Paridari, K., Parisio, A., Sandberg, H., Johansson, K.H., Robust scheduling of smart appliances in active apartments with user behavior uncertainty (2016) IEEE Trans. Autom. Sci. Eng, 13 (1), pp. 247-259; Pedrasa, M.A., Spooner, E.D., MacGill, I.F., Robust scheduling of residential distributed energy resources using a novel energy service decision-support tool (2011) ISGT, Anaheim; Zhang, C., Xu, Y., Dong, Z.Y., Ma, J., Robust operation of microgrids via two-stage coordinated energy storage and direct load control (2017) IEEE Trans. Power Systems, 32 (4), pp. 2858-2868; Wang, C., Zhou, Y., Jiao, B., Wang, D., Robust optimization for load scheduling of a smart home with photovoltaic system (2015) Energy Conversion and Management, 102, pp. 247-257; Xiang, Y., Liu, J., Liu, Y., Robust energy management of microgrid with uncertain renewable generation and load (2016) IEEE Trans. Smart Grid, 7 (2), pp. 1034-1043; Chen, Z., Wu, L., Fu, Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization (2012) IEEE Trans. Smart Grid, 3 (4), pp. 1822-1831; Esther, B.P., Kumar, K.S., A survey on residential demand side management architecture, approaches, optimization models and methods (2016) Renew. Sustain. Energy. Rev, 59, pp. 342-351; Darivianakis, G., Georghiou, A., Smith, R.S., Lygeros, J., The power of diversity: Data-driven robust predictive control for energy-efficient buildings and districts IEEE Trans. Control Syst. Technol, in Press; Zhao, H.X., Magoul, F., A review on the prediction of building energy consumption (2012) Renewable and Sustainable Energy Reviews, 16 (6), pp. 3586-3592; Liu, X., Economic load dispatch constrained by wind power availability: A wait-and-see approach (2010) IEEE Trans. Smart Grid, 1 (3), pp. 347-355; Soyster, A.L., Convex programming with set-inclusive constraints and applications to inexact linear programming (1973) Operations Research, 21, pp. 1154-1157; Bertsimas, D., Brown, D.B., Caramanis, C., Theory and applications of robust optimization (2011) SIAM Review, 53 (3); Bertsimas, D., Sim, M., The price of robustness (2004) Operations Research, 52 (1), pp. 35-53},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2019) Decentralized control for residential energy management of a smart users’ microgrid with renewable energy exchange. IN IEEE/CAA Journal of Automatica Sinica, 6.641-656. doi:10.1109/JAS.2019.1911462
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users x02BC controllable loads. We assume that each smart home can both buy x002F sell energy from x002F to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy x002F sell locally harvested renewable energy from x002F to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness. © 2014 Chinese Association of Automation.
    @ARTICLE{Carli2019641,
    author={Carli, R. and Dotoli, M.},
    title={Decentralized control for residential energy management of a smart users’ microgrid with renewable energy exchange},
    journal={IEEE/CAA Journal of Automatica Sinica},
    year={2019},
    volume={6},
    number={3},
    pages={641-656},
    doi={10.1109/JAS.2019.1911462},
    art_number={8707104},
    note={cited By 43},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065583902&doi=10.1109%2fJAS.2019.1911462&partnerID=40&md5=dfb517c8f1147f9d27b6d1082a326908},
    abstract={This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users x02BC controllable loads. We assume that each smart home can both buy x002F sell energy from x002F to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy x002F sell locally harvested renewable energy from x002F to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness. © 2014 Chinese Association of Automation.},
    keywords={Automation; Decentralized control; Energy resources; Heuristic methods; Intelligent buildings; Microgrids; Nonlinear programming; Scheduling, Alternating direction method of multipliers; Decision variables; Distributed Energy Resources; Nonlinear programming problem; Optimization problems; Renewable energies; Residential energy; Scheduling problem, Iterative methods},
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Smart Grid, 1 (3), pp. 320-331; Zhang, W., Xu, Y., Li, S., Zhou, M., Liu, W., Xu, Y., A distributed dynamic programming-based solution for load management in smart grids (2018) IEEE Systems Journal, 12 (1), pp. 402-413. , March; Xu, Y., Pan, F., Tong, L., Dynamic scheduling for charging electric vehicles: A priority rule (2016) IEEE Trans. Automat. Contr., 61 (12), pp. 4094-4099; Parise, F., Colombino, M., Grammatico, S., Lygeros, J., Mean field constrained charging policy for large populations of Plug-in Electric Vehicles (2014) Proc. IEEE Conference on Decision and Control, pp. 5101-5106; Le Floch, C., Belletti, F., Saxena, S., Bayen, A.M., Moura, S., Distributed optimal charging of electric vehicles for demand response and load shaping (2015) Proc. 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Tyrrell Rockafellar; Wang, Y., Yin, W., Zeng, J., (2015) Global Convergence of ADMM in Nonconvex Nonsmooth Optimization; Diamond, S., Takapoui, R., Boyd, S., A general system for heuristic minimization of convex functions over non-convex sets Optimization Methods and Software, 33 (1), pp. 165-193; Vazirani, V.V., (2013) Approximation Algorithms, , Springer Science & Business Media; Deng, W., Lai, M.J., Peng, Z., Yin, W., Parallel multi-block ADMM with o(1 / k) convergence (2017) J. Sci. Comput., 71 (2), pp. 712-736; Bertsekas, D., (1999) Nonlinear Programming.; Hans, C.A., Braun, P., Raisch, J., Grune, L., Reincke-Collon, C., Hierarchical distributed model predictive control of interconnected microgrids (2018) IEEE Transactions on Sustainable Energy},
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    }
  • Carli, R. & Dotoli, M. (2019) Distributed Control for Waterfilling of Networked Control Systems with Coupling Constraints IN Proceedings of the IEEE Conference on Decision and Control., 3710-3715. doi:10.1109/CDC.2018.8619425
    [BibTeX] [Abstract] [Download PDF]
    In this paper we present a distributed control approach for the multi-user multi-constrained waterfilling. This a specific category of distributed optimization for Networked Control Systems (NCSs), where agents aim at optimizing a non-separable global objective function while satisfying both local constraints and coupling constraints. Differently from the existing literature, in the considered setting we adopt a fully distributed mechanism where communication is allowed between neighbors only. First, we formulate a general multi-user waterfilling-structured optimization problem including coupling constraints, which may represent many engineering distributed control problems. Successively, we define a low-complexity iterative distributed algorithm based on duality, consensus and fixed point mapping theory. Finally, applying the technique to a simulated case referring to the electric vehicles optimal charging problem, we show its effectiveness. © 2018 IEEE.
    @CONFERENCE{Carli20193710,
    author={Carli, R. and Dotoli, M.},
    title={Distributed Control for Waterfilling of Networked Control Systems with Coupling Constraints},
    journal={Proceedings of the IEEE Conference on Decision and Control},
    year={2019},
    volume={2018-December},
    pages={3710-3715},
    doi={10.1109/CDC.2018.8619425},
    art_number={8619425},
    note={cited By 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062189943&doi=10.1109%2fCDC.2018.8619425&partnerID=40&md5=3dfd050026baf187539419a0a975db57},
    abstract={In this paper we present a distributed control approach for the multi-user multi-constrained waterfilling. This a specific category of distributed optimization for Networked Control Systems (NCSs), where agents aim at optimizing a non-separable global objective function while satisfying both local constraints and coupling constraints. Differently from the existing literature, in the considered setting we adopt a fully distributed mechanism where communication is allowed between neighbors only. First, we formulate a general multi-user waterfilling-structured optimization problem including coupling constraints, which may represent many engineering distributed control problems. Successively, we define a low-complexity iterative distributed algorithm based on duality, consensus and fixed point mapping theory. Finally, applying the technique to a simulated case referring to the electric vehicles optimal charging problem, we show its effectiveness. © 2018 IEEE.},
    keywords={Computational complexity; Distributed parameter control systems; Iterative methods, Coupling constraints; Distributed control; Distributed control problems; Distributed optimization; Global objective functions; Local constraints; Networked Control Systems (NCSs); Structured optimization problem, Networked control systems},
    references={Gupta, R.A., Chow, M.Y., Networked control systems: Overview and research trends (2010) IEEE Trans. Ind. Electron, 57 (7), pp. 2527-2535; Gallager, R.G., (1968) Information Theory and Reliable Communication, , New York: Wiley; Stavrou, P.A., Charalambous, T., Charalambous, C.D., Filtering with fidelity for time-varying Gauss-Markov processes (2016) Proc. IEEE CDC, pp. 5465-5470. , Dec; Fang, S., Ishii, H., Chen, J., Trade-offs in information-limited feedback systems: MIMO Bode-type integrals and power allocation (2015) Proc. IEEE CDC, pp. 6178-6183. , Dec; Tzortzis, C.D., Charalambous, T., Infinite horizon average cost dynamic programming subject to ambiguity on conditional distribution (2015) Proc. IEEE CDC, pp. 7171-7176; Zhao, H., Yan, A., Zhang, C., Wang, P., An optimizing method based on water-filling for case attribute weight (2012) Proc. 24th Chin. Control Decis. Conf, pp. 3455-3458. , China; Gkatzikis, L., Salonidis, T., Hegde, N., Massoulié, L., Electricity markets meet the home through demand response (2012) Proc. IEEE CDC, pp. 5846-5851. , Dec; Palomar, D.P., Fonollosa, J.R., Practical algorithms for a family of waterfilling solutions (2005) IEEE Trans. Signal Proces.s, 53 (2), pp. 686-695; Scutari, G., Palomar, D.P., Barbarossa, S., Optimal linear precoding strategies for wideband non-cooperative systems based on game theory-part II: Algorithms (2008) IEEE Trans. Signal Process, 56 (3), pp. 1250-1267. , March; Carli, R., Dotoli, M., A distributed control algorithm for waterfilling of networked control systems via consensus (2017) IEEE Control Systems Letters, 1 (2), pp. 334-339. , Oct; Gan, L., Topcu, U., Low, S., Optimal decentralized protocol for electric vehicle charging' (2013) IEEE Trans. Power Syst., 28(2), pp. 940-951; He, P., Li, M., Zhao, L., Venkatesh, B., Li, H., Water-filling exact solutions for load balancing of smart power grid systems (2018) IEEE Trans. Smart Grid, 9 (2), pp. 1397-1407. , March; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) IEEE Int. Conf. Dec. Contr, , Dec. 15-18; Grammatico, S., Aggregative control of large populations of noncooperative agents (2016) Proc. IEEE Int. Conf. Dec. Contr, pp. 4445-4450; Chong, E.K., Zak, S.H., (2013) An Introduction to Optimization, 76. , John Wiley &Sons; Rockafellar, R.T., Wets, R.J.B., (1998) Variational Analysis, , Springer; Berinde, V., (2007) Iterative Approximation of Fixed Points, , Springer; Palomar, D.P., Convex primal decomposition for multicarrier linear MIMO transceivers (2005) IEEE IEEE Trans. Signal Process, 53 (12), pp. 4661-4674; Falsone, A., Margellos, K., Garatti, S., Prandini, M., Distributed constrained convex optimization and consensus via dual decomposition and proximal minimization (2016) Proc. IEEE CDC, pp. 1889-1894; Carli, R., Dotoli, M., A distributed control algorithm for optimal charging of electric vehicle fleets with congestion management (2018) IFAC Symposium on Control in Transportation Systems (CTS) 2018, pp. 373-378. , IFAC PapersOnLine 51-9; Ren, W., Beard, R.W., Overview of consensus algorithms in cooperative control (2008) Distributed Consensus in Multi-vehicle Cooperative Control: Theory and Applications, pp. 3-22},
    document_type={Conference Paper},
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    }
  • Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Model Predictive Control for Real-Time Residential Energy Scheduling under Uncertainties IN Proceedings – 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 1386-1391. doi:10.1109/SMC.2018.00242
    [BibTeX] [Abstract] [Download PDF]
    This paper proposes a real-time strategy based on Model Predictive Control (MPC) for the energy scheduling of a grid-connected smart residential user equipped with deferrable and non-deferrable electrical appliances, a renewable energy source (RES), and an electrical energy storage system (EESS). The proposed control scheme relies on an iterative finite horizon on-line optimization, implementing a quadratic cost function to minimize the electricity bill of the user’s load demand and to limit the peak-to-average ratio (PAR) of the energy consumption profile whilst considering operational constraints. At each time step, the optimization problem is solved providing the cost-optimal energy consumption profile for the user’s deferrable loads and the optimal charging/discharging profile for the EESS, taking into account forecast uncertainties by using the most updated predicted values of local RES generation and non-deferrable loads consumption. The performance and effectiveness of the proposed framework are evaluated for a case study where the dynamics of the considered residential energy system is simulated under uncertainties both in the forecast of the RES generation and the non-deferrable loads energy consumption. © 2018 IEEE.
    @CONFERENCE{Hosseini20191386,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={Model Predictive Control for Real-Time Residential Energy Scheduling under Uncertainties},
    journal={Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018},
    year={2019},
    pages={1386-1391},
    doi={10.1109/SMC.2018.00242},
    art_number={8616238},
    note={cited By 25},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062230383&doi=10.1109%2fSMC.2018.00242&partnerID=40&md5=2f024e90b4a067ff3c318f9e0a035f00},
    abstract={This paper proposes a real-time strategy based on Model Predictive Control (MPC) for the energy scheduling of a grid-connected smart residential user equipped with deferrable and non-deferrable electrical appliances, a renewable energy source (RES), and an electrical energy storage system (EESS). The proposed control scheme relies on an iterative finite horizon on-line optimization, implementing a quadratic cost function to minimize the electricity bill of the user's load demand and to limit the peak-to-average ratio (PAR) of the energy consumption profile whilst considering operational constraints. At each time step, the optimization problem is solved providing the cost-optimal energy consumption profile for the user's deferrable loads and the optimal charging/discharging profile for the EESS, taking into account forecast uncertainties by using the most updated predicted values of local RES generation and non-deferrable loads consumption. The performance and effectiveness of the proposed framework are evaluated for a case study where the dynamics of the considered residential energy system is simulated under uncertainties both in the forecast of the RES generation and the non-deferrable loads energy consumption. © 2018 IEEE.},
    author_keywords={energy scheduling; model predictive control (MPC); residential energy management; uncertainties},
    keywords={Cost functions; Cybernetics; Electric energy storage; Energy utilization; Housing; Predictive control systems; Renewable energy resources; Scheduling, Electrical energy storage systems; Operational constraints; Quadratic cost functions; Renewable energy source; Residential energy; Residential energy systems; Scheduling under uncertainty; uncertainties, Model predictive control},
    references={Li, Q., Xu, Z., Oka, K., Yang, L., Recent advancements on the development of microgrids (2014) J. Mod. Power Syst. Cle., 2 (3), pp. 206-211; Hou, L., Wang, C., Market-based mechanisms for smart grid management: Necessity, applications and opportunities (2017) IEEE. Int. Conf. Systems, Man, and Cybernetics, , October; Carli, R., Dotoli, M., Cooperative distributed control for the energy scheduling of smart homes with shared energy storage and renewable energy source (2017) IFAC World Congress., 50 (1), pp. 8867-8872; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) IEEE Conf Decis Control; Ma, J., Chen, H.H., Song, L., Li, Y., Residential load scheduling in smart grid: A cost efficiency perspective (2016) IEEE. Trans. Smart Grid, 7 (2), pp. 771-784; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) IEEE. Conf Decis Control; Rahmani-Andebili, M., Scheduling deferrable appliances and energy resources of a smart home applying multi-Time scale stochastic model predictive control (2017) Sustain. Cities. Soc., 32, pp. 338-347; Rahmani-Andebili, M., Shen, H., Cooperative distributed energy scheduling for smart homes applying stochastic model predictive control (2017) IEEE Int. Conf. Communications, , May; Parisio, A., Wiezorek, C., Kyntaja, T., Elo, J., Johansson, K.H., An mpc-based energy management system for multiple residential microgrids (2015) IEEE CASE; Samadi, P., Mohsenian-Rad, H., Wong, V.W.S., Schober, R., Tackling the load uncertainty challenges for energy consumption scheduling in smart grid (2013) IEEE. Trans. Smart Grid, 4 (2), pp. 1007-1016; Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) Innovative Smart Grid Technologies, , January; Barbato, A., Capone, A., Optimization models and methods for demandside management of residential users: A survey (2014) Energies, 7 (9), pp. 5787-5824; Esther, B.P., Kumar, K.S., A survey on residential demand side management architecture, approaches, optimization models and methods (2016) Renew Sust Energ Rev, 59, pp. 342-351; Liu, X., Economic load dispatch constrained by wind power availablity: A wait-And-see approach (2010) IEEE Trans. Smart Grid, 1 (3), pp. 347-355; Zack, J., (2003) Overview of Wind Energy Generation Forecasting., , Draft Report for NY State Energy Research and Development Authority and for NY ISO, True Wind Solutions LLC, NY, USA; Wu, Y., Lau, V.K.N., Tsang, D.H.K., Qian, L.P., Meng, L., Optimal energy scheduling for residential smart grid with centralized renewable energy source (2014) IEEE Systems Journal, 8 (2), pp. 562-576; Zhao, H.X., Magoulès, F., A review on the prediction of building energy consumption (2012) Renewable and Sustainable Energy Reviews, 16 (6), pp. 3586-3592; Camacho, E.F., Alba, C.B., (2013) Model predictive Control., , Springer Science & Business Media; Cococcioni, M., D'Andrea, E., Lazzerini, B., 24-hour-Ahead forecasting of Energy production in solar PV Systems (2011) Intelligent Systems Design and Applications (ISDA). 2011 11th InterNational Conference on, pp. 1276-1281. , November. IEEE; Schlegel, S., Korn, N., Scheuermann, G., On the interpolation of data with normally distributed uncertainty for visualization (2012) IEEE Trans. Vis. Comput. Gr., 18 (12), pp. 2305-2314},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Dotoli, M. & Pellegrino, R. (2019) A multi-period approach for the optimal energy retrofit planning of street lighting systems. IN Applied Sciences (Switzerland), 9.. doi:10.3390/app9051025
    [BibTeX] [Abstract] [Download PDF]
    Investing in the optimal measures for improving the energy efficiency of urban street lighting systems has become strategic for the economic, technological and social development of cities. The decision-making process for the selection of the optimal set of interventions is not so straightforward. Several criticalities-such as difficulties getting access to credit for companies involved in street lighting systems refurbishment, budget constraints of municipalities, and unawareness of the actual energy and economic performance after a retrofitting intervention-require a decision-making approach that supports the city energy manager in selecting the optimal street lighting energy efficiency retrofitting solution while looking not only based on the available budget, but also based on the future savings in energy expenditures. In this context, the purpose of our research is to develop an effective decision-making model supporting the optimal multi-period planning of the street lighting energy efficiency retrofitting, which proves to be more effective and beneficial than the classical single-period approach and has never before been applied to the considered public lighting system context. The proposed methodology is applied to a real street lighting system in the city of Bari, Italy, showing the energy savings and financial benefit obtained through the proposed method. Numerical experiments are used to investigate and quantify the effects of using a multi-period planning approach instead of a single-period approach. © 2019 by the authors.
    @ARTICLE{Carli2019,
    author={Carli, R. and Dotoli, M. and Pellegrino, R.},
    title={A multi-period approach for the optimal energy retrofit planning of street lighting systems},
    journal={Applied Sciences (Switzerland)},
    year={2019},
    volume={9},
    number={5},
    doi={10.3390/app9051025},
    art_number={1025},
    note={cited By 5},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063660288&doi=10.3390%2fapp9051025&partnerID=40&md5=80ca57851d94e4757320cee529983b5f},
    abstract={Investing in the optimal measures for improving the energy efficiency of urban street lighting systems has become strategic for the economic, technological and social development of cities. The decision-making process for the selection of the optimal set of interventions is not so straightforward. Several criticalities-such as difficulties getting access to credit for companies involved in street lighting systems refurbishment, budget constraints of municipalities, and unawareness of the actual energy and economic performance after a retrofitting intervention-require a decision-making approach that supports the city energy manager in selecting the optimal street lighting energy efficiency retrofitting solution while looking not only based on the available budget, but also based on the future savings in energy expenditures. In this context, the purpose of our research is to develop an effective decision-making model supporting the optimal multi-period planning of the street lighting energy efficiency retrofitting, which proves to be more effective and beneficial than the classical single-period approach and has never before been applied to the considered public lighting system context. The proposed methodology is applied to a real street lighting system in the city of Bari, Italy, showing the energy savings and financial benefit obtained through the proposed method. Numerical experiments are used to investigate and quantify the effects of using a multi-period planning approach instead of a single-period approach. © 2019 by the authors.},
    author_keywords={Energy efficiency management; Multi-period planning; Optimization; Street lighting},
    references={Beccali, M., Bonomolo, M., Ciulla, G., Galatioto, A., Brano, V.L., Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans The case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Pizzuti, S., Annunziato, M., Moretti, F., Smart street lighting management (2013) Energy Effic, 6, pp. 607-616; Richards, M., Carter, D., Good lighting with less energy (2009) Light. Res. Technol, 41, p. 285; Annunziato, M., Honorato Consonni, C., De Lia, F., Fumagalli, S., Giuliani, G., Gozo, N., Scognamiglio, A., (2012) LINEE GUIDA: I Fondamentali per una Gestione Efficiente Degli Impianti di Pubblica Illuminazione, , ENEA: Roma, Italy; (2013), http://www.covenantofmayors.eu/index_en.html, (accessed on 21 December 2018); Popa, M., Cepişca, C., Energy consumption saving solutions based on intelligent street lighting control system (2011) UPB Sci. Bull. Ser. C, 73, pp. 297-308; Raciti, A., Rizzo, S.A., Susinni, G., Parametric PSpice Circuit of Energy Saving Lamp Emulating Current Waveform (2019) Appl. Sci, 9, p. 152; Carli, R., Dotoli, M., Pellegrino, R., A decision-making tool for energy efficiency optimization of street lighting (2018) Comput. Oper. Res, 96, pp. 223-235; Cristea, M., Tîrnovan, R.A., Cristea, C., Pica, C.S., Fagaras an, C., A multi-criteria decision making approach for public lighting system selection (2018) MATEC Web Conf, 184; Beccali, M., Bonomolo, M., Leccese, F., Lista, D., Salvadori, G., On the impact of safety requirements, energy prices and investment costs in street lighting refurbishment design (2018) Energy, 165, pp. 739-759; Stuart, E., Larsena, P.H., Goldmana, C.A., Gilliganc, D., A method to estimate the size and remaining market potential of the U.S (2014) ESCO (energy service company) industry. Energy, 77, pp. 362-371; Suhonena Okkonenb, L., The Energy Services Company (ESCo) as business model for heat entrepreneurship-A case study of North Karelia, Finland (2013) Energy Policy, 61, pp. 783-787; Carbonara, N., Pellegrino, R., Public-private partnerships for energy efficiency projects: A win-win model to choose the energy performance contracting structure (2018) J. Clean. Prod, 170, pp. 1064-1075; Yao, Q., Wang, H., Uttley, J., Zhuang, X., Illuminance Reconstruction of Road Lighting in Urban Areas for Efficient and Healthy Lighting Performance Evaluation (2018) Appl. Sci, 8, p. 1646; Tan, B., Yavuz, Y., Otay, E.N., Çamlibel, E., Optimal selection of energy efficiency measures for energy sustainability of existing buildings (2016) Comput. Oper. Res, 66, pp. 258-271; Lauro, F., Longobardi, L., Panzieri, S., An adaptive distributed predictive control strategy for temperature regulation in a multizone office building (2014) Proceedings of the 2014 IEEE International Workshop on Intelligent Energy Systems (IWIES), pp. 32-37. , San Diego, CA, USA, 8 October; Pellegrino, R., Costantino, N., Giustolisi, O., Flexible investment planning for water distribution networks (2018) J. Hydroinform, 20, pp. 18-33; Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., Verriello, R., A cyber-based DSS for a low carbon integrated waste management system in a smart city (2015) IFAC-PapersOnLine, 48, pp. 2356-2361; Dickinson, M.W., Thornton, A.C., Graves, S., Technology portfolio management: Optimizing interdependent projects over multiple time periods (2001) IEEE Trans. Eng. Manag, 48, pp. 518-527; Leccese, F., Salvadori, G., Rocca, M., Critical analysis of the energy performance indicators for road lighting systems in historical towns of central Italy (2017) Energy, 138, pp. 616-628; Martello, S., (1990) Knapsack Problems: Algorithms and Computer Implementations, , Wiley-Interscience Series in Discrete Mathematics and Optimization;Wiley: Hoboken, NJ, USA; Wang, B., Xia, X., Optimal maintenance planning for building energy efficiency retrofitting from optimization and control system perspectives (2015) Energy Build, 96, pp. 299-308; Carli, R., Dotoli, M., Pellegrino, R., A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities (2017) IEEE Trans. Autom. Sci. Eng, 14, pp. 505-523; Ranieri, L., Mossa, G., Pellegrino, R., Digiesi, S., Energy Recovery from the Organic Fraction of Municipal Solid Waste: A Real Options-Based Facility Assessment (2018) Sustainability, 10, p. 368; Bruno, S., D'Aloia, M., De Benedictis, M., Lamonaca, S., La Scala, M., Rotondo, G., Stecchi, U., (2011) Studio di Fattibilità per la Integrazione di un Modello di Pubblica Illuminazione ad Alta Efficienza in un Power Park Urbano (Quartiere Eco-Sostenibile): Analisi di un Caso Pilota, , http://www.enea.it/it/Ricerca_sviluppo/documenti/ricerca-di-sistema-elettrico/smart-city/rds-328.pdf, accessed on 21 December 2018). (In Italian; (2015) EN 13201e2. Light and Lighting. Road Lighting-Part 2: Performance Requirements, , European Committee for Standardization: Brussels, Belgium; (2015) EN 13201e2. Light and Lighting. Road Lighting-Part 3: Calculation of Performance, , European Committee for Standardization: Brussels, Belgium; (2015) EN 13201e4. Light and Lighting. Road Lighting-Part 4: Methods of Measuring Lighting Performance, , European Committee for Standardization: Brussels, Belgium; Rea, M.S., (2000) The IESNA Lighting Handbook, , Illuminating Engineering Society of North America: New York, NY, USA; Lagorse, J., Paire, D., Miraoui, A., Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery (2009) Renew. Energy, 34, pp. 683-691; http://www.lighting.philips.com/main/systems/systemareas/roads-and-streets, (accessed on 21 December 2018); http://www.regione.puglia.it/elencoprezzi-2017, (accessed on 21 December 2018). 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    document_type={Article},
    source={Scopus},
    }

2018

  • Carli, R., Dotoli, M. & Pellegrino, R. (2018) Multi-criteria decision-making for sustainable metropolitan cities assessment. IN Journal of Environmental Management, 226.46-61. doi:10.1016/j.jenvman.2018.07.075
    [BibTeX] [Abstract] [Download PDF]
    The recent development of metropolitan cities, especially in Europe, requires an effective integrated management of city services, infrastructure, and communication networks at a metropolitan level. A preliminary step towards a proper organizational and management strategy of the metropolitan city is the analysis, benchmarking and optimization of the metropolitan areas through a set of indicators coherent with the overall sustainability objective of the metropolitan city. This paper proposes the use of the Analytic Hierarchy Process multi-criteria decision making technique for application in the smart metropolitan city context, with the aim of analysing the sustainable development of energy, water and environmental systems, through a set of objective performance indicators. Specifically, the 35 indicators defined for the Sustainable Development of Energy, Water and Environment Systems Index framework are used. The application of the approach to the real case study of four metropolitan areas (Bari, Bitonto, Mola, and Molfetta) in the city of Bari (Italy) shows its usefulness for the local government in benchmarking metropolitan areas and providing decision indications on how to formulate the sustainable development strategy of the metropolitan city. Based on the Analytic Hierarchy Process characteristics, the results highlight that although one specific area (Mola in the considered case) is globally ranked at the first place, it is only ranked first with respect to some dimensions. Such a result has strong implications for the metropolitan city’s manager who has the possibility to identify and implement targeted actions, which may be designed ad hoc to improve specific dimensions based on the current state of the city, thus maximizing the efficiency and effectiveness of the actions undertaken for the sustainable development of energy, water and environmental systems of the whole metropolitan city. © 2018 Elsevier Ltd
    @ARTICLE{Carli201846,
    author={Carli, R. and Dotoli, M. and Pellegrino, R.},
    title={Multi-criteria decision-making for sustainable metropolitan cities assessment},
    journal={Journal of Environmental Management},
    year={2018},
    volume={226},
    pages={46-61},
    doi={10.1016/j.jenvman.2018.07.075},
    note={cited By 45},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051362383&doi=10.1016%2fj.jenvman.2018.07.075&partnerID=40&md5=57606eb68995a362a44497e2b3089e36},
    abstract={The recent development of metropolitan cities, especially in Europe, requires an effective integrated management of city services, infrastructure, and communication networks at a metropolitan level. A preliminary step towards a proper organizational and management strategy of the metropolitan city is the analysis, benchmarking and optimization of the metropolitan areas through a set of indicators coherent with the overall sustainability objective of the metropolitan city. This paper proposes the use of the Analytic Hierarchy Process multi-criteria decision making technique for application in the smart metropolitan city context, with the aim of analysing the sustainable development of energy, water and environmental systems, through a set of objective performance indicators. Specifically, the 35 indicators defined for the Sustainable Development of Energy, Water and Environment Systems Index framework are used. The application of the approach to the real case study of four metropolitan areas (Bari, Bitonto, Mola, and Molfetta) in the city of Bari (Italy) shows its usefulness for the local government in benchmarking metropolitan areas and providing decision indications on how to formulate the sustainable development strategy of the metropolitan city. Based on the Analytic Hierarchy Process characteristics, the results highlight that although one specific area (Mola in the considered case) is globally ranked at the first place, it is only ranked first with respect to some dimensions. Such a result has strong implications for the metropolitan city's manager who has the possibility to identify and implement targeted actions, which may be designed ad hoc to improve specific dimensions based on the current state of the city, thus maximizing the efficiency and effectiveness of the actions undertaken for the sustainable development of energy, water and environmental systems of the whole metropolitan city. © 2018 Elsevier Ltd},
    author_keywords={Analytic hierarchy process; Multi-criteria decision making; Performance evaluation; Planning; Sustainable development},
    keywords={analytical hierarchy process; assessment method; benchmarking; decision making; development strategy; local government; metropolitan area; multicriteria analysis; optimization; performance assessment; sustainability; sustainable development; urban planning, Article; benchmarking; city; decision making; energy resource; environmental sustainability; government; Italy; sustainable development; water supply; city; environmental protection; Europe, Bari [Bari (ADS)]; Bari [Puglia]; Italy; Puglia, Cities; Conservation of Natural Resources; Decision Making; Europe; Italy},
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    document_type={Article},
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    }
  • Carli, R., Dotoli, M. & Epicoco, N. (2018) Cost-Optimal Energy Scheduling of a Smart Home under Uncertainty IN 2018 IEEE Conference on Control Technology and Applications, CCTA 2018., 1668-1673. doi:10.1109/CCTA.2018.8511345
    [BibTeX] [Abstract] [Download PDF]
    We present a novel energy scheduling approach under uncertain data for smart homes taking into account the presence of controllable electrical loads, renewable energy sources, dispatchable energy generators, and energy storage systems. The problem is stated as a fuzzy linear programming and is aimed at minimizing energy costs. The proposed approach allows managing the use of electrical devices, plan the energy production and supplying, and program the storage charging and discharging profiles under uncertain data. The method is validated through a literature case study showing its effectiveness in exploiting the potential of local energy generation and storage and in reducing the energy consumption costs, while limiting the peak average ratio of the energy profiles and complying with the user’s energy needs. © 2018 IEEE.
    @CONFERENCE{Carli20181668,
    author={Carli, R. and Dotoli, M. and Epicoco, N.},
    title={Cost-Optimal Energy Scheduling of a Smart Home under Uncertainty},
    journal={2018 IEEE Conference on Control Technology and Applications, CCTA 2018},
    year={2018},
    pages={1668-1673},
    doi={10.1109/CCTA.2018.8511345},
    art_number={8511345},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056899368&doi=10.1109%2fCCTA.2018.8511345&partnerID=40&md5=df2f65f9300879823bcc53a5aada2f14},
    abstract={We present a novel energy scheduling approach under uncertain data for smart homes taking into account the presence of controllable electrical loads, renewable energy sources, dispatchable energy generators, and energy storage systems. The problem is stated as a fuzzy linear programming and is aimed at minimizing energy costs. The proposed approach allows managing the use of electrical devices, plan the energy production and supplying, and program the storage charging and discharging profiles under uncertain data. The method is validated through a literature case study showing its effectiveness in exploiting the potential of local energy generation and storage and in reducing the energy consumption costs, while limiting the peak average ratio of the energy profiles and complying with the user's energy needs. © 2018 IEEE.},
    keywords={Automation; Energy utilization; Intelligent buildings; Linear programming; Renewable energy resources; Scheduling, Dispatchable energies; Electrical devices; Electrical load; Energy productions; Energy storage systems; Fuzzy linear programming; Minimizing energy; Renewable energy source, Digital storage},
    references={Hubert, T., Grijalva, S., Realizing smart grid benefits requires energy optimization algorithms at residential level (2011) Proc. Innovative Smart Grid Technologies, 8; Schiefer, M., Smart home definition and security threats (2015) Proc. 9th Int. Conf. IT Security Incident Management & IT Forensics, pp. 114-118; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., Costantino, N., A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market (2014) Comput. Ind., 65 (4), pp. 774-785; Kailas, A., Cecchi, V., Mukherjee, A., A survey of contemporary technologies for smart home energy management (2013) Green Information and Communication Systems Handbook, , Academic Press; Liu, Y., Qiu, B., Fan, X., Zhu, H., Han, B., Review of smart home Energy Management Systems (2016) Energy Procedia, 104, pp. 504-508; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) 53rd IEEE Ann. Conf. Decision and Control (CDC); Mohsenian-Rad, A.-H., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) Proc. Innovative Smart Grid Technologies, p. 8; Theo, W.L., Lim, J.S., Ho, W.S., Hashim, H., Lee, C.T., Review of distributed generation (DG) system planning and optimization techniques: Comparison of numerical and mathematical modelling methods (2017) Renew. Sust. Energy Rev., 67, pp. 531-573; Sinha, S., Chandel, S.S., Review of recent trends in optimization techniques for solar photovoltaic-wind based hybrid energy systems (2015) Renew. Sust. Energy Rev., 50, pp. 755-769; Matho, T., Mukherjee, V., Energy storage systems for mitigating the variability of isolated hybrid power system (2015) Renew. Sust. Energy Rev., 51, pp. 1564-1577; Khan, I., Mahmood, A., Javaid, N., Razzaq, S., Khan, R.D., Ilahi, M., Home Energy Management Systems in future smart grids (2013) J. Basic. Appl. Sci. Res., 3 (3), pp. 1224-1231; Huang, Y., Wang, L., Guo, W., Kang, Q., Wu, Q., Chance constrained optimization in a home Energy Management System (2018) IEEE Trans. Smart Grid, 9 (1); Zeng, Y., Cai, Y., Huang, G., Dai, J., A review on optimization modeling of energy systems planning and GHG emission mitigation under uncertainty (2011) Energies, 4, pp. 1624-1656; Beaudin, M., Zareipour, H., Home energy management systems: A review of modelling and complexity (2015) Renew. Sust. Energy Rev., 45, pp. 318-335; Geramifar, H., Shahabi, M., Barforoshi, T., Coordination of energy storage systems and DR resources for optimal scheduling of microgrids uncer uncertainty (2017) IET Renew. Power Gen., 11, pp. 378-388; Nikmehr, N., Najafi-Ravadanegh, S., Khodaei, A., Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty (2017) Appl. Energy, 198, pp. 267-279; Chen, H., Zhang, R., Bai, L., Jiang, T., Li, G., Jia, H., Li, X., Stochastic scheduling of Integrated energy systems considering wind power and multi-energy loads uncertainties (2017) J. Energ. Eng., 143; Liang, R.H., Liao, J.H., A fuzzy-optimization approach for generation scheduling with wind and solar energy systems (2007) IEEE Trans. Pow. Sys., 22, pp. 1665-1674; Chaouachi, A., Kamel, R.M., Andoulsi, R., Nagasaka, K., Multiobjective intelligent energy management for a microgrid (2013) IEEE Trans. Ind. Electron., 60, pp. 1688-1699; Shukla, A.K., Nath, R., Muhuri, P.K., Energy efficient task scheduling with type-2 fuzzy uncertainty (2015) IEEE Int. Conf. Fuzzy Syst.; Suo, C., Li, Y.P., Wang, C.X., Yu, L., A type-2 fuzzy chance constrained programming method for planning Shangai's energy system (2017) Int. J. Electr. Power Energy Syst., 90, pp. 37-53; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A crossefficiency fuzzy Data Envelopment Analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput Ind Eng, 79, pp. 103-114; Zimmermann, H.J., Fuzzy set theory and its applications Kluwer Academic Publishers, Boston/Dordrecht/London, 2001. , 4th Ed},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2018) A Decentralized Control Strategy for the Energy Management of Smart Homes with Renewable Energy Exchange IN 2018 IEEE Conference on Control Technology and Applications, CCTA 2018., 1662-1667. doi:10.1109/CCTA.2018.8511617
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a decentralized control strategy for the scheduling of energy activities of interconnected smart homes that purchase energy from a supplier while exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is solved with a twofold design objective. First, the model aims at reducing the overall energy supply from the grid, by allowing users to borrow/lend some amount of renewable energy from/to other users. Second, the problem is formulated to optimally plan users’ controllable loads. We assume a time-varying quadratic pricing of the energy purchased from the distribution network. The proposed solution is based on a decentralized optimization algorithm combining parametric optimization with the proximal Jacobian Alternating Direction Method of Multipliers. The application of the proposed technique to a simulated case study under several scenarios shows its effectiveness. © 2018 IEEE.
    @CONFERENCE{Carli20181662,
    author={Carli, R. and Dotoli, M.},
    title={A Decentralized Control Strategy for the Energy Management of Smart Homes with Renewable Energy Exchange},
    journal={2018 IEEE Conference on Control Technology and Applications, CCTA 2018},
    year={2018},
    pages={1662-1667},
    doi={10.1109/CCTA.2018.8511617},
    art_number={8511617},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056821403&doi=10.1109%2fCCTA.2018.8511617&partnerID=40&md5=85a9dde2ca8fa544e4b4a5901580188f},
    abstract={This paper presents a decentralized control strategy for the scheduling of energy activities of interconnected smart homes that purchase energy from a supplier while exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is solved with a twofold design objective. First, the model aims at reducing the overall energy supply from the grid, by allowing users to borrow/lend some amount of renewable energy from/to other users. Second, the problem is formulated to optimally plan users' controllable loads. We assume a time-varying quadratic pricing of the energy purchased from the distribution network. The proposed solution is based on a decentralized optimization algorithm combining parametric optimization with the proximal Jacobian Alternating Direction Method of Multipliers. The application of the proposed technique to a simulated case study under several scenarios shows its effectiveness. © 2018 IEEE.},
    keywords={Automation; Energy resources; Intelligent buildings; Scheduling, Alternating direction method of multipliers; Controllable loads; Decentralized optimization; Design objectives; Distributed Energy Resources; Parametric optimization; Renewable energies; Scheduling problem, Decentralized control},
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    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Dotoli, M. & Pellegrino, R. (2018) A decision-making tool for energy efficiency optimization of street lighting. IN Computers and Operations Research, 96.223-235. doi:10.1016/j.cor.2017.11.016
    [BibTeX] [Abstract] [Download PDF]
    This paper develops a multi-criteria decision making tool to support the public decision maker in optimizing energy retrofit interventions on existing public street lighting systems. The related literature analysis clearly highlights that, to date, only a few number of studies deal with the definition of optimal decision strategies complying with multiple and conflicting objectives in the planning of street lighting refurbishment. To fill this gap, we propose a decision making tool that allows deciding, in an integrated way, the optimal energy retrofit plan in order to simultaneously reduce energy consumption, maintain comfort, protect the environment, and optimize the distribution of actions in subsystems, while ensuring an efficient use of public funds. The presented tool is applied to a real street lighting system of a wide urban area in Bari, Italy. The obtained results highlight that the approach effectively supports the city energy manager in the refurbishment of the street lighting systems. © 2017 Elsevier Ltd
    @ARTICLE{Carli2018223,
    author={Carli, R. and Dotoli, M. and Pellegrino, R.},
    title={A decision-making tool for energy efficiency optimization of street lighting},
    journal={Computers and Operations Research},
    year={2018},
    volume={96},
    pages={223-235},
    doi={10.1016/j.cor.2017.11.016},
    note={cited By 45},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044511260&doi=10.1016%2fj.cor.2017.11.016&partnerID=40&md5=f655fabb0054cefa3a05bcc0c63059f4},
    abstract={This paper develops a multi-criteria decision making tool to support the public decision maker in optimizing energy retrofit interventions on existing public street lighting systems. The related literature analysis clearly highlights that, to date, only a few number of studies deal with the definition of optimal decision strategies complying with multiple and conflicting objectives in the planning of street lighting refurbishment. To fill this gap, we propose a decision making tool that allows deciding, in an integrated way, the optimal energy retrofit plan in order to simultaneously reduce energy consumption, maintain comfort, protect the environment, and optimize the distribution of actions in subsystems, while ensuring an efficient use of public funds. The presented tool is applied to a real street lighting system of a wide urban area in Bari, Italy. The obtained results highlight that the approach effectively supports the city energy manager in the refurbishment of the street lighting systems. © 2017 Elsevier Ltd},
    author_keywords={Energy efficiency management; Multi-criteria optimization; Public street lighting},
    keywords={Decision making; Energy utilization; Lighting fixtures; Multiobjective optimization; Retrofitting; Street lighting; Urban planning, Conflicting objectives; Efficiency managements; Energy efficiency optimizations; Multi criteria decision making; Multicriteria optimization; Optimal decision strategy; Reduce energy consumption; Street lighting system, Energy efficiency},
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    document_type={Article},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2018) A Distributed Control Algorithm for Optimal Charging of Electric Vehicle Fleets with Congestion Management , 373-378. doi:10.1016/j.ifacol.2018.07.061
    [BibTeX] [Abstract] [Download PDF]
    This paper proposes a novel distributed control strategy for the optimal charging of a fleet of Electric Vehicles (EVs) in case of limited overall capacity of the electrical distribution network. The optimal charging is obtained as the solution of a scheduling problem aiming at a cost-optimal profile of the aggregated energy demand. The resulting optimization problem is formulated as a quadratic programming problem with a coupling of decision variables both in the objective function and in the constraint. We assume a minimal information structure, where users locally communicate only with their neighbors, without relying on a central decision maker. The solution approach relies on an iterative distributed algorithm based on duality, proximity, and consensus theory. A simulated case study demonstrates that the approach allows achieving the global optimum. © 2018
    @CONFERENCE{Carli2018373,
    author={Carli, R. and Dotoli, M.},
    title={A Distributed Control Algorithm for Optimal Charging of Electric Vehicle Fleets with Congestion Management},
    year={2018},
    volume={51},
    number={9},
    pages={373-378},
    doi={10.1016/j.ifacol.2018.07.061},
    note={cited By 11},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050093123&doi=10.1016%2fj.ifacol.2018.07.061&partnerID=40&md5=87f63aa0bd1f3aa33b573169b8a48e32},
    abstract={This paper proposes a novel distributed control strategy for the optimal charging of a fleet of Electric Vehicles (EVs) in case of limited overall capacity of the electrical distribution network. The optimal charging is obtained as the solution of a scheduling problem aiming at a cost-optimal profile of the aggregated energy demand. The resulting optimization problem is formulated as a quadratic programming problem with a coupling of decision variables both in the objective function and in the constraint. We assume a minimal information structure, where users locally communicate only with their neighbors, without relying on a central decision maker. The solution approach relies on an iterative distributed algorithm based on duality, proximity, and consensus theory. A simulated case study demonstrates that the approach allows achieving the global optimum. © 2018},
    author_keywords={Decentralized; Distributed Control; Electric Vehicles; Large scale optimization problems; Scheduling algorithms},
    keywords={Charging (batteries); Decision making; Electric vehicles; Fleet operations; Iterative methods; Quadratic programming; Scheduling algorithms, Decentralized; Distributed control; Distributed control algorithms; Distributed control strategy; Electric Vehicles (EVs); Electrical distribution networks; Large-scale optimization; Quadratic programming problems, Traffic congestion},
    references={Aravinthan, V., Jewell, W., Controlled electric vehicle charging for mitigating impacts on distribution assets (2015) IEEE Trans. Smart Grid, 6 (2), pp. 999-1009; Bertsekas, D.P., Tsitsiklis, J.N., (1989) Parallel and distributed computation: numerical methods, , Prentice hall Englewood Cliffs, NJ (Vol. 23); Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press UK 2004; Carli, R., Dotoli, M., (2014), pp. 5648-5653. , Energy Scheduling of a Smart Home under Nonlinear Pricing, Proc. IEEE CDC; Carli, R., Dotoli, M., Pellegrino, R., A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities (2017) IEEE Trans. Aut. Sci. Eng., 14 (2), pp. 505-523; Carli, R., Dotoli, M., A Distributed Control Algorithm for Waterfilling of Networked Control Systems via Consensus (2017) IEEE Control Systems Letters, 1 (2), pp. 334-339; Carli, R., Dotoli, M., (2017), Decentralized Optimal Charging Control of Electric Vehicle Fleets with Congestion Management Proc. IEEE SOLI, Bari, Italy; de Hoog, J., Alpcan, T., Brazil, M., Thomas, D.A., Mareels, I., Optimal charging of electric vehicles taking distribution network constraints into account (2015) IEEE Trans. Power Sys., 30 (1), pp. 365-375; Falsone, A., Margellos, K., Garatti, S., Prandini, M., Distributed constrained convex optimization and consensus via dual decomposition and proximal minimization (2016) Proc. IEEE CDC, pp. 1889-1894; Garin, F., Schenato, L., (2011), pp. 75-107. , Networked Control Systems. Springer, ch. A Survey on distributed estimation and control applications using linear consensus algorithms; Gan, L., Topcu, U., Low, S., „Optimal decentralized protocol for electric vehicle charging‟ (2013) IEEE Trans. Power Syst., 28 (2), pp. 940-951; Grammatico, S., Parise, F., Colombino, M., Lygeros, J., Decentralized convergence to Nash equilibria in constrained mean field control (2016) IEEE Trans. Aut. Contr, 61 (11), pp. 3315-3329; Grammatico, S., Dynamic Control of Agents Playing Aggregative Games With Coupling Constraints (2017) IEEE Trans. Aut. Contr, 62 (9), pp. 4537-4548; He, Y., Venkatesh, B., Guan, L., Optimal scheduling for charging and discharging of electric vehicles (2012) IEEE Trans. Smart Grid, 3 (3), pp. 1095-1105; Le Floch, C., Belletti, F., Moura, S., Optimal Charging of Electric Vehicles for Load Shaping: A Dual-Splitting Framework With Explicit Convergence Bounds (2016) IEEE Trans. Transp. Electr., 2 (2), pp. 190-199; Ma, Z., Callaway, D.S., Hiskens, I.A., Decentralized charging control of large populations of plug-in electric vehicles (2013) IEEE Trans. Control Syst. Technol., 21 (1), pp. 67-78; Ma, W.J., Gupta, V., Topcu, U., On distributed charging control of electric vehicles with power network capacity constraints (2014) Proc. IEEE ACC, pp. 4306-4311; Rivera, J., Wolfrum, P., Hirche, S., Goebel, C., Jacobsen, H.A., Alternating direction method of multipliers for decentralized electric vehicle charging control (2013) Proc. IEEE CDC, pp. 6960-6965; Shaaban, M.F., Atwa, Y.M., El-Saadany, E.F., PEVs modeling and impacts mitigation in distribution networks (2013) IEEE Trans. Power Syst., 28 (2), pp. 1122-1131; Sortomme, E., Hindi, M.M., MacPherson, S.D.J., Venkata, S.S., Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses (2011) IEEE Trans. Smart Grid, 2 (1), pp. 198-205; Stüdli, S., Crisostomi, E., Middleton, R., Shorten, R., A flexible distributed framework for realising electric and plug-in hybrid vehicle charging policies (2012) Int. J. Control, 85 (8), pp. 1130-1145; Su, W., Rahimi-Eichi, H., Zeng, W., Chow, M.Y., A survey on the electrification of transportation in a smart grid environment (2012) IEEE Trans. Ind. Inf., 8, pp. 1-10; Sun, B., Huang, Z., Tan, X., Tsang, D.H.K., Optimal Scheduling for Electric Vehicle Charging with Discrete Charging Levels in Distribution Grid (2016) IEEE Trans. Smart Grid, 99, p. 1; Wen, C.K., Chen, J.C., Teng, J.H., Ting, P., Decentralized plug-in electric vehicle charging selection algorithm in power systems (2012) IEEE Trans. on Smart Grid, 3 (4), pp. 1779-1789; Xu, Z., Hu, Z., Song, Y., Zhao, W., Zhang, Y., Coordination of PEVs charging across multiple aggregators (2014) Appl. Ener., 136, pp. 582-589},
    document_type={Conference Paper},
    source={Scopus},
    }

2017

  • Carli, R. & Dotoli, M. (2017) A decentralized control strategy for optimal charging of electric vehicle fleets with congestion management IN Proceedings – 2017 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2017., 63-67. doi:10.1109/SOLI.2017.8120971
    [BibTeX] [Abstract] [Download PDF]
    This paper proposes a novel decentralized control strategy for the optimal charging of a large-scale fleet of Electric Vehicles (EVs). The scheduling problem aims at ensuring a cost-optimal profile of the aggregated energy demand and at satisfying the resource constraints depending both on power grid components capacity and EV locations in the distribution network. The resulting optimization problem is formulated as a quadratic programming problem with a coupling of decision variables both in the objective function and in the inequality constraints. The solution approach relies on a decentralized optimization algorithm that is based on a variant of ADMM (Alternating Direction Method of Multipliers), adapted to take into account the inequality constraints and the non-separated objective function. A simulated case study demonstrates that the approach allows achieving both the overall fleet and individual EV goals, while complying with the power grid congestion limits. © 2017 IEEE.
    @CONFERENCE{Carli201763,
    author={Carli, R. and Dotoli, M.},
    title={A decentralized control strategy for optimal charging of electric vehicle fleets with congestion management},
    journal={Proceedings - 2017 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2017},
    year={2017},
    volume={2017-January},
    pages={63-67},
    doi={10.1109/SOLI.2017.8120971},
    note={cited By 9},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046260212&doi=10.1109%2fSOLI.2017.8120971&partnerID=40&md5=2ced08ace41770b457c6f07d8cbe98b5},
    abstract={This paper proposes a novel decentralized control strategy for the optimal charging of a large-scale fleet of Electric Vehicles (EVs). The scheduling problem aims at ensuring a cost-optimal profile of the aggregated energy demand and at satisfying the resource constraints depending both on power grid components capacity and EV locations in the distribution network. The resulting optimization problem is formulated as a quadratic programming problem with a coupling of decision variables both in the objective function and in the inequality constraints. The solution approach relies on a decentralized optimization algorithm that is based on a variant of ADMM (Alternating Direction Method of Multipliers), adapted to take into account the inequality constraints and the non-separated objective function. A simulated case study demonstrates that the approach allows achieving both the overall fleet and individual EV goals, while complying with the power grid congestion limits. © 2017 IEEE.},
    author_keywords={Alternating direction method of multipliers; Congestion management; Coupled objective function; Decentralized optimization; Electric vehicle charging; Sharing},
    keywords={Charging (batteries); Constraint satisfaction problems; Constraint theory; Decentralized control; Electric power transmission networks; Electric vehicles; Fleet operations; Quadratic programming; Traffic congestion, Alternating direction method of multipliers; Congestion management; Decentralized optimization; Electric vehicle charging; Objective functions; Sharing, Electric machine control},
    references={Su, W., Rahimi-Eichi, H., Zeng, W., Chow, M.-Y., A survey on the electrification of transportation in a smart grid environment (2012) IEEE Trans. Ind. Inf., 8, pp. 1-10; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy management of smart cities (2016) IEEE Trans. Aut. Sci. Eng.; Simpson, A., (2006) Cost-benefit Analysis of Plug-in Hybrid Electric Vehicle Technology, , Golden: National Renewable Energy Laboratory; Ma, Z., Callaway, D.S., Hiskens, I.A., Decentralized charging control of large populations of plug-in electric vehicles (2013) IEEE Transactions on Control Systems Technology, 21 (1), pp. 67-78; Gan, L., Topcu, U., Low, S., Optimal decentralized protocol for electric vehicle charging (2013) IEEE Trans. Power Syst., 28 (2), pp. 940-951; Sortomme, E., Hindi, M.M., MacPherson, S.D.J., Venkata, S.S., Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses (2011) IEEE Trans. Smart Grid, 2 (1), pp. 198-205; Carli, R., Dotoli, M., A distributed control algorithm for waterfilling of networked control systems via consensus IEEE Control Systems Letters, PP (99), p. 1; Rivera, J., Wolfrum, P., Hirche, S., Goebel, C., Jacobsen, H.A., Alternating direction method of multipliers for decentralized electric vehicle charging control (2013) Proc. 52nd IEEE Conf. Decision and Control, pp. 6960-6965; Le Floch, C., Belletti, F., Saxena, S., Bayen, A.M., Moura, S., Distributed optimal charging of electric vehicles for demand response and load shaping (2015) 2015 54th IEEE Conference on Decision and Control (CDC), pp. 6570-6576. , Osaka; Grammatico, S., Parise, F., Colombino, M., Lygeros, J., Decentralized convergence to Nash equilibria in constrained mean field control (2014) IEEE Trans. Aut. Contr., 61 (11), pp. 3315-3329. , Nov; Aravinthan, V., Jewell, W., Controlled electric vehicle charging for mitigating impacts on distribution assets (2015) IEEE Trans. Smart Grid, 6 (2), pp. 999-1009; De Hoog, J., Alpcan, T., Brazil, M., Thomas, D.A., Mareels, I., Optimal charging of electric vehicles taking distribution network constraints into account (2015) IEEE Trans. Power Sys., 30 (1), pp. 365-375; Xu, Z., Hu, Z., Song, Y., Zhao, W., Zhang, Y., Coordination of PEVs charging across multiple aggregators (2014) Appl. Ener., 136, pp. 582-589; Grammatico, S., (2016) Dynamic Control of Agents Playing Aggregative Games, , arXiv preprint; Ma, W.J., Gupta, V., Topcu, U., On distributed charging control of electric vehicles with power network capacity constraints (2014) 2014 American Control Conference, pp. 4306-4311. , June IEEE; Le Floch, C., Belletti, F., Moura, S., Optimal charging of electric vehicles for load shaping: A dual-splitting framework with explicit convergence bounds (2016) IEEE Trans. Transp. Electr., 2 (2), pp. 190-199; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) 53th IEEE Conference on Decision and Control (CDC), 6p; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., Distributed optimization and statistical learning via the alternating direction method of multipliers (2010) Found. Trends Mach. Learn., 3 (1), pp. 1-122; Deng, W., (2013) Parallel Multi-block ADMM with O (1/k) Convergence, , arXiv preprint},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2017) A distributed control algorithm for waterfilling of networked control systems via consensus. IN IEEE Control Systems Letters, 1.334-339. doi:10.1109/LCSYS.2017.2716190
    [BibTeX] [Abstract] [Download PDF]
    This letter presents a distributed waterfilling algorithm for networked control systems where users communicate with neighbors only. Waterfilling—a well-known optimization approach in communication systems—has inspired practical resolution methods for several control engineering and decision-making problems. This letter proposes a fully distributed solution for waterfilling of networked control systems. We consider multiple coupled waterlevels among users that locally communicate only with neighbors, without a central decision maker. We define two alternative versions (an exact one and an approximated one) of a novel distributed algorithm combining consensus, proximity, and the fixed point mapping theory, and show its convergence. We illustrate the technique by a case study on the charging of a fleet of electric vehicles. © 2017 IEEE.
    @ARTICLE{Carli2017334,
    author={Carli, R. and Dotoli, M.},
    title={A distributed control algorithm for waterfilling of networked control systems via consensus},
    journal={IEEE Control Systems Letters},
    year={2017},
    volume={1},
    number={2},
    pages={334-339},
    doi={10.1109/LCSYS.2017.2716190},
    note={cited By 14},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050079250&doi=10.1109%2fLCSYS.2017.2716190&partnerID=40&md5=595d4be8879ccf3a42f0de75793d9038},
    abstract={This letter presents a distributed waterfilling algorithm for networked control systems where users communicate with neighbors only. Waterfilling—a well-known optimization approach in communication systems—has inspired practical resolution methods for several control engineering and decision-making problems. This letter proposes a fully distributed solution for waterfilling of networked control systems. We consider multiple coupled waterlevels among users that locally communicate only with neighbors, without a central decision maker. We define two alternative versions (an exact one and an approximated one) of a novel distributed algorithm combining consensus, proximity, and the fixed point mapping theory, and show its convergence. We illustrate the technique by a case study on the charging of a fleet of electric vehicles. © 2017 IEEE.},
    author_keywords={Distributed control; Networked control systems; Optimization},
    keywords={Control theory; Decision making; Distributed parameter control systems; Fleet operations; Optimization, Decision makers; Decision-making problem; Distributed control; Distributed control algorithms; Distributed solutions; Optimization approach; Resolution methods; Water-filling algorithm, Networked control systems},
    references={Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge, U.K.: Cambridge Univ. Press; Stavrou, P.A., Charalambous, T., Charalambous, C.D., Filtering with fidelity for time-varying Gauss-Markov processes (2016) Proc. IEEE CDC, pp. 5465-5470. , Dec; Fang, S., Ishii, H., Chen, J., Trade-offs in information-limited feedback systems: MIMO Bode-type integrals and power allocation (2015) Proc. IEEE CDC, pp. 6178-6183. , Dec; Zhou, X.S., Rui, Y., Huang, T.S., Water-filling: A novel way for image structural feature extraction (1999) Proc. IEEE Int. Conf. Image Process. (ICIP), pp. 570-574. , Kobe, Japan; Palomar, D.P., Fonollosa, J.R., Practical algorithms for a family of waterfilling solutions (2005) IEEE Trans. Signal Process., 53 (2), pp. 686-695. , Feb; Scutari, G., Palomar, D.P., Barbarossa, S., Optimal linear precoding strategies for wideband non-cooperative systems based on game theory—Part II: Algorithms (2008) IEEE Trans. Signal Process., 56 (3), pp. 1250-1267. , Mar; Mou, Y., Xing, H., Lin, Z., Fu, M., Decentralized optimal demand-side management for PHEV charging in a smart grid (2015) IEEE Trans. Smart Grid, 6 (2), pp. 726-736. , Mar; He, P., Li, M., Zhao, L., Venkatesh, B., Li, H., Water-filling exact solutions for load balancing of smart power grid systems IEEE Trans. Smart Grid, , to be published; Berinde, V., (2007) Iterative Approximation of Fixed Points, , Heidelberg, Germany: Springer; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) Proc. IEEE CDC, pp. 5903-5908. , Dec; Parikh, N., Boyd, S., Proximal algorithms (2014) Found. Trends Optim., 1 (3), pp. 127-147; Facchinei, F., Pang, J.-S., (2003) Finite-Dimensional Variational Inequalities and Complementarity Problem, , New York, NY, USA: Springer-Verlag; Olfati-Saber, R., Fax, J.A., Murray, R.M., Consensus and cooperation in networked multi-agent systems (2007) Proc. IEEE, 95 (1), pp. 215-233. , Jan; Le Floch, C., Belletti, F., Saxena, S., Bayen, A.M., Moura, S., Distributed optimal charging of electric vehicles for demand response and load shaping (2015) Proc. IEEE CDC, pp. 6570-6576. , Osaka, Japan; Garin, F., Schenato, L., A survey on distributed estimation and control applications using linear consensus algorithms (2010) Networked Control Systems, pp. 75-107. , London, U.K.: Springer; Tsianos, K., Lawlor, S., Rabbat, M.G., Communication/computation tradeoffs in consensus-based distributed optimization (2012) Proc. Adv. Neural Inf. Process. Syst., pp. 1943-1951},
    document_type={Article},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2017) Bi-level programming for the energy retrofit planning of street lighting systems IN Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017., 543-548. doi:10.1109/ICNSC.2017.8000150
    [BibTeX] [Abstract] [Download PDF]
    This paper addresses strategic decision making issues for the energy management of urban street lighting. We propose a hierarchical decision procedure that supports the energy manager in determining the optimal energy retrofit plan of an existing public street lighting system throughout a wide urban area. A bi-level programming model integrates several decision making units, each focusing on the energy optimization of a specific limited zone lighting system, and a central decision unit, aiming at a fair distribution of actions among these various systems, while ensuring an efficient use of public funds. We apply the technique to the case study of the city of Bari (Italy), to demonstrate the applicability and efficiency of the proposed optimization model. © 2017 IEEE.
    @CONFERENCE{Carli2017543,
    author={Carli, R. and Dotoli, M.},
    title={Bi-level programming for the energy retrofit planning of street lighting systems},
    journal={Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017},
    year={2017},
    pages={543-548},
    doi={10.1109/ICNSC.2017.8000150},
    art_number={8000150},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028509849&doi=10.1109%2fICNSC.2017.8000150&partnerID=40&md5=7aa2cad3d9d4ab8f45ecac5cd40a808f},
    abstract={This paper addresses strategic decision making issues for the energy management of urban street lighting. We propose a hierarchical decision procedure that supports the energy manager in determining the optimal energy retrofit plan of an existing public street lighting system throughout a wide urban area. A bi-level programming model integrates several decision making units, each focusing on the energy optimization of a specific limited zone lighting system, and a central decision unit, aiming at a fair distribution of actions among these various systems, while ensuring an efficient use of public funds. We apply the technique to the case study of the city of Bari (Italy), to demonstrate the applicability and efficiency of the proposed optimization model. © 2017 IEEE.},
    author_keywords={Bilevel programming; Decision support system; Energy efficiency; Energy management; Hierarchical optimization; Street lighting},
    keywords={Artificial intelligence; Decision making; Decision support systems; Energy efficiency; Energy management; Lighting; Lighting fixtures; Optimization; Retrofitting; Street lighting; Urban planning, Bi-level programming; Bilevel programming models; Decision making unit; Hierarchical decisions; Hierarchical optimization; Optimization modeling; Strategic decision making; Street lighting system, Energy management systems},
    references={Adamo, F., Attivissimo, F., Cavone, F., Di Nisio, A., Spadavecchia, M., Channel characterization of an open source energy meter (2014) IEEE Trans. Instrum. Meas., 63 (5), pp. 1106-1115. , May; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy management of smart cities (2016) IEEE Trans. Autom. Sci. Eng; Jollands, N., Waide, P., Ellis, M., The 25 IEA energy efficiency policy recommendations to the G8 gleneagles plan of action (2010) Energy Policy, 38, pp. 6409-6418; De Paz, J.F., Bajo, J., Rodríguez, S., Villarrubia, G., Corchado, J.M., Intelligent system for lighting control in smart cities (2016) Information Sciences, 372, pp. 241-255; Hyari, K.H., Khelifi, A., Katkhuda, H., Multiobjective optimization of roadway lighting projects (2016) Journal of Transportation Engineering, p. 04016024; Rabaza, O., Gómez-Lorente, D., Pérez-Ocón, F., Peña-García, A., A simple and accurate model for the design of public lighting with energy efficiency functions based on regression analysis (2016) Energy, 107, pp. 831-842; Sdziwy, A., A new approach to street lighting design (2016) Leukos, 12 (3), pp. 151-162; Sdziwy, A., Kotulski, L., Towards highly energy-efficient roadway lighting (2016) Energies, 9 (4), p. 263; Liu, G., Sustainable feasibility of solar photovoltaic powered street lighting systems (2014) INT J ELEC POWER, 56, pp. 168-174; Lagorse, J., Paire, D., Miraoui, A., Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery (2009) Renewable Energy, 34 (3), pp. 683-691; Shahzad, G., Yang, H., Ahmad, A.W., Lee, C., Energy-efficient intelligent street lighting system using traffic-adaptive control (2016) IEEE Sensors Journal, 16 (13), pp. 5397-5405; Beccali, M., Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans. the case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Gutierrez-Escolar, A., Castillo-Martinez, A., Gomez-Pulido, J.M., Gutierrez-Martinez, J.M., González-Seco, E.P.D., Stapic, Z., A review of energy efficiency label of street lighting systems (2016) Energy Efficiency, pp. 1-18; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a building stock energy efficiency (2016) IEEE Trans. Syst., Man, Cybern., Syst; Carli, R., Dotoli, M., Andria, G., Lanzolla, A.M.L., Bi-level programming for the strategic energy management of a smart city (2016) Proc. IEEE EESMS 2016, pp. 1-6. , Bari; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) Proc. IEEE ETFA2015, , Luxembourg, September 8-11; Dempster, M.A.H., Fisher, M.L., Jansen, L., Lageweg, B.J., Lenstra, J.K., Kan, R., Rinnooy Kan, A.H.G., Analytical evaluation of hierarchical planning systems (1981) Operations Research, 29 (4), pp. 707-716; Kalashnikov, V.V., Dempe, S., Pérez-Valdés, G.A., Kalashnykova, N.I., Camacho-Vallejo, J.F., Bilevel programming and applications (2015) Mathematical Problems in Engineering; Sherali, H.D., Alameddine, A., A new reformulationlinearization technique for bilinear programming problems (1992) Journal of Global Optimization, 2 (4), pp. 379-410; Gümü, Z.H., Floudas, C.A., Global optimization of mixed-integer bilevel programming problems (2005) Computational Management Science, 2 (3), pp. 181-212},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2017) A decentralized control strategy for energy retrofit planning of large-scale street lighting systems using dynamic programming IN IEEE International Conference on Automation Science and Engineering., 1196-1200. doi:10.1109/COASE.2017.8256266
    [BibTeX] [Abstract] [Download PDF]
    This paper addresses the energy management of large-scale urban street lighting systems. We propose a multi-stage decision-making procedure that supports the energy manager in determining the optimal energy retrofit plan of an existing public street lighting system. The problem statement is based on a quadratic integer programming formulation and aims at simultaneously reducing the energy consumption, ensuring an optimal allocation of the retrofit actions, and efficiently using the available budget. The proposed solution relies on a decentralized optimization algorithm that is based on discrete dynamic programming. The methodology is applied to a real street lighting system in the city of Bari, Italy. © 2017 IEEE.
    @CONFERENCE{Carli20171196,
    author={Carli, R. and Dotoli, M.},
    title={A decentralized control strategy for energy retrofit planning of large-scale street lighting systems using dynamic programming},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2017},
    volume={2017-August},
    pages={1196-1200},
    doi={10.1109/COASE.2017.8256266},
    note={cited By 2},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044970276&doi=10.1109%2fCOASE.2017.8256266&partnerID=40&md5=7f8ba32dffa1946d990b4fa9bdfa7a59},
    abstract={This paper addresses the energy management of large-scale urban street lighting systems. We propose a multi-stage decision-making procedure that supports the energy manager in determining the optimal energy retrofit plan of an existing public street lighting system. The problem statement is based on a quadratic integer programming formulation and aims at simultaneously reducing the energy consumption, ensuring an optimal allocation of the retrofit actions, and efficiently using the available budget. The proposed solution relies on a decentralized optimization algorithm that is based on discrete dynamic programming. The methodology is applied to a real street lighting system in the city of Bari, Italy. © 2017 IEEE.},
    keywords={Budget control; Decision making; Energy management; Energy management systems; Energy utilization; Integer programming; Lighting fixtures; Retrofitting; Street lighting, Decentralized optimization; Decision making procedure; Energy managers; Optimal allocation; Problem statement; Quadratic integer programming; Retrofit actions; Street lighting system, Dynamic programming},
    references={Radulovic, D., Skok, S., Kirincic, V., Energy efficiency public lighting management in the cities (2011) Energy, 36 (4), pp. 1908-1915; Pizzuti, S., Annunziato, M., Moretti, F., Smart street lighting management (2013) Energy Efficiency, 6 (3), pp. 607-616; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy management of smart cities (2016) IEEE Trans. Aut. Sci. Eng; Gomez-Lorente, D., Rabaza, O., Espin, A., Pena-Garcia, A., Optimization of efficiency and energy saving in public lighting with multi-objective evolutionary algorithms (2013) Proc. ICREPQ, , March; Rabaza, O., Pena-Garcia, A., Perez-Ocon, F., Gomez-Lorente, D., A simple method for designing efficient public lighting, based on new parameter relationships (2013) ExpertSystAppl., 40 (18), pp. 7305-7315; Huang, S.C., Lee, L.L., Jeng, M.S., Hsieh, Y.C., Assessment of energy-efficient LED street lighting through large-scale demonstration (2012) Proc. Int. Conf. Renewable Energy Research and Applications, pp. 1-5; Burgos-Payan, M., Correa-Moreno, F., Riquelme-Santos, J., Improving the energy efficiency of street lighting. A case in the South of Spain (2012) Proc. Int. Conf. European Energy Market, pp. 1-8. , 10-12 May; Beccali, M., Improvement of energy efficiency and quality of street lighting in south Italy as an action of sustainable energy action plans. The case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) Proc. IEEEETFA, , Luxembourg, September; Carli, R., Dotoli, M., Cianci, E., An optimization tool for energy efficiency of street lighting systems in smart cities (2017) IFAC World Congress, , July 9-14, 2017, Toulouse, France; Pisinger, D., The quadratic knapsack problem-a survey (2007) Discrete Applied Mathematics, 155 (5), pp. 623-648; Sniedovich, M., (2010) Dynamic Programming: Foundations and Principles, , CRC press; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Bertsekas, D.P., (1995) Dynamic Programming and Optimal Control, 1 (2). , Belmont, MA: Athena Scientific; Achterberg, T., SCIP: Solving constraint integer programs (2009) Math. Progr. Comp., 1, pp. 1-41},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2017) Using the distributed proximal alternating direction method of multipliers for smart grid monitoring IN IEEE International Conference on Automation Science and Engineering., 418-423. doi:10.1109/COASE.2017.8256140
    [BibTeX] [Abstract] [Download PDF]
    Efficient and effective monitoring represents the starting point for a reliable and secure smart grid. Given the increasing size and complexity of power networks and the pressing concerns on privacy and robustness, the development of intelligent and flexible distributed monitoring systems represents a crucial issue in both structuring and operating future grids. In this context, this paper presents a distributed optimization framework for use in smart grid monitoring. We propose a distributed algorithm based on ADMM (Alternating Direction Method of Multipliers) for use in large scale optimization problems in smart grid monitoring. The proposed solution is based upon a local-based optimization process, where a limited amount of information is exchanged only between neighboring nodes in a locally broadcast fashion. Applying the approach to two illustrating examples demonstrates it allows exploiting the scalability and efficiency of distributed ADMM for distributed smart grid monitoring. © 2017 IEEE.
    @CONFERENCE{Carli2017418,
    author={Carli, R. and Dotoli, M.},
    title={Using the distributed proximal alternating direction method of multipliers for smart grid monitoring},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2017},
    volume={2017-August},
    pages={418-423},
    doi={10.1109/COASE.2017.8256140},
    note={cited By 2},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044966539&doi=10.1109%2fCOASE.2017.8256140&partnerID=40&md5=dbecf71fd06d5b7c380c0ef1f2547de4},
    abstract={Efficient and effective monitoring represents the starting point for a reliable and secure smart grid. Given the increasing size and complexity of power networks and the pressing concerns on privacy and robustness, the development of intelligent and flexible distributed monitoring systems represents a crucial issue in both structuring and operating future grids. In this context, this paper presents a distributed optimization framework for use in smart grid monitoring. We propose a distributed algorithm based on ADMM (Alternating Direction Method of Multipliers) for use in large scale optimization problems in smart grid monitoring. The proposed solution is based upon a local-based optimization process, where a limited amount of information is exchanged only between neighboring nodes in a locally broadcast fashion. Applying the approach to two illustrating examples demonstrates it allows exploiting the scalability and efficiency of distributed ADMM for distributed smart grid monitoring. © 2017 IEEE.},
    author_keywords={ADMM; distributed optimization; monitoring; sensors network; smart grid},
    keywords={Electric power transmission networks; Monitoring, ADMM; Alternating direction method of multipliers; Amount of information; Distributed monitoring systems; Distributed optimization; Large-scale optimization; Sensors network; Smart grid, Smart power grids},
    references={Zima, M., Larsson, M., Korba, P., Rehtanz, C., Andersson, G., Design aspects for wide-area monitoring and control systems (2005) Proceedings Ofthe IEEE, 93 (5), pp. 980-996; Adamo, F., Cavone, G., Di Nisio, A., Lanzolla, A., Spadavecchia, M., A proposal for an open source energy meter (2013) Proc. IEEE i2MTC, pp. 488-492. , May 6-9. Minneapolis, MN, USA; Carli, R., Dotoli, M., A distributed control algorithm for waterfilling of networked control systems via consensus IEEE Control Systems Letters, PP (99), pp. I-1; Chakrabarti, S., Kyriakides, E., Bi, T., Cai, D., Terzija, V., Measurements get together (2009) IEEE Power Energy Mag., 7 (1), pp. 41-49; Carli, R., Dotoli, M., Pellegrino, R., A hierarchical decision making strategy for the energy governance of smart cities (2016) IEEE Trans. Autom. Sci. Eng.; Kayastha, N., Niyato, D., Hossain, E., Han, Z., Smart grid sensor data collection, communication, and networking: A tutorial (2014) Wireless Communications and Mobile Computing, 14 (11), pp. 1055-1087; Santacana, E., Rackliffe, G., Tang, L., Feng, X., Getting smart (2010) IEEE Power Energy Mag., 8 (2), pp. 41-48; Gharavi, H., Ghafurian, R., (2011) Smart Grid: The Electric Energy System Ofthe Future, 99. , IEEE; Carli, R., Dotoli, M., Cooperative distributed control for the energy scheduling of smart homes with shared energy storage and renewable energy source (2017) IFAC 2017 WC, , Toulouse, France, July 914; Abur, A., Exposito, A.G., (2004) Power System State Estimation, Theory and Implementation, , Marcel Dekker; Liu, L., Han, Z., Multi-block ADMM for big data optimization in smart grid (2015) Proc. IEEE ICNC, p. 556561. , Garden Grove, CA; Di Bisceglie, M., Galdi, C., Vaccaro, A., Villacci, D., Cooperative sensor networks for voltage quality monitoring in smart grids (2009) PowerTech, 2009 IEEE Bucharest, 423, pp. 1-6. , June. IEEE; Wei, E., Ozdaglar, A., Distributed alternating direction method of multipliers (2012) Proc. IEEE CDC, pp. 5445-5450. , December; Formato, G., Loia, V., Paciello, V., Vaccaro, A., A decentralized and self organizing architecture for wide area synchronized monitoring of smart grids (2013) J HiGH SPEED NETW, 19 (3), pp. 165-179; Capriglione, D., Ferrigno, L., Paciello, V., Pietrosanto, A., Vaccaro, A., Experimental characterization of consensus protocol for decentralized smart grid metering (2016) Measurement, 77, pp. 292-306; Valverde, G., Terzja, V., PMU-based multi-area state estimation with low data exchange (2010) Proc. IEEE PES ISGT Europe, pp. 1-7. , October; Zhao, L., Abur, A., Multiarea state estimation using synchronized phasor measurements (2005) IEEE Trans. Power Syst., 20 (2), pp. 611-617. , May; Jiang, W., Vittal, V., Heydt, G.T., A distributed state estimator utilizing synchronized phasor measurements (2007) IEEE Trans. Power Syst., 22 (2), pp. 563-571. , May; Kekatos, V., Giannakis, G.B., Distributed robust power system state estimation (2013) IEEE Trans. Power Syst., 28 (2), pp. 1617-1626; Kar, S., Hug, G., Mohammadi, J., Moura, J.M.F., Distributed state estimation and energy management in smart grids: A consensus {+} innovations approach (2014) IEEE 1. Sel. Topics Signal Process., 8 (6), pp. 1022-1038. , Dec; Gomez-Exposito, A., De La Villa Jaen, A., Gomez-Quiles, C., Rousseaux, P., Van Cutsem, T., A taxonomy of multi-area state estimation methods (2011) ELECTR POW SYST RES, 81 (4), p. 10601069; Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., Distributed optimization and statistical learning via the alternating direction method of multipliers (2010) Foundation and Trends in Machine Learning, 3 (1), pp. 1-122. , Nov; Glowinski, R., Marocco, A., Sur I'approximation par elements finis d'ordre un, et la resolution part penalisation-dualite, d'une class de problems de Dirichlet non lineaires (1975) Rev. Francaise Automat, Informat. Recherche Ooperationalle Ser. Rouge, R2, pp. 41-76; Meng, D., Fazel, M., Mesbahi, M., Proximal alternating direction method of multipliers for distributed optimization on weighted graphs (2015) Proc. IEEE CDC, pp. 1396-1401. , December; Deng, W., Lai, M.J., Peng, Z., Yin, W., (2013) Parallel Multiblock ADMM with 0 (Ilk) Convergence, , arXiv preprint arXiv: 1312. 3040; Mateos, G., Schizas, I.D., Giannakis, G.B., Performance analysis of the consensus-based distributed LMS algorithm (2009) EURASIP J Adv Signal Process, 2009 (1), pp. 1-19; Bertsekas, D.P., Tseng, P., Partial proximal minimization algorithms for convex pprogramming (1994) SIAM J Optim, 4 (3), pp. 551-572},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Dotoli, M. & Cianci, E. (2017) An optimization tool for energy efficiency of street lighting systems in smart cities IN IFAC-PapersOnLine., 14460-14464. doi:10.1016/j.ifacol.2017.08.2292
    [BibTeX] [Abstract] [Download PDF]
    This paper develops a decision making tool to support the public decision maker in selecting the optimal energy retrofit interventions on an existing street lighting system. The problem statement is based on a quadratic integer programming formulation and deals with simultaneously reducing the energy consumption and ensuring an optimal allocation of the retrofit actions among the street lighting subsystems. The methodology is applied to a real street lighting system in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal energy management of the street lighting. © 2017
    @CONFERENCE{Carli201714460,
    author={Carli, R. and Dotoli, M. and Cianci, E.},
    title={An optimization tool for energy efficiency of street lighting systems in smart cities},
    journal={IFAC-PapersOnLine},
    year={2017},
    volume={50},
    number={1},
    pages={14460-14464},
    doi={10.1016/j.ifacol.2017.08.2292},
    note={cited By 26},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042493832&doi=10.1016%2fj.ifacol.2017.08.2292&partnerID=40&md5=b1ee03a53ee4a57450cc0e590002a9e6},
    abstract={This paper develops a decision making tool to support the public decision maker in selecting the optimal energy retrofit interventions on an existing street lighting system. The problem statement is based on a quadratic integer programming formulation and deals with simultaneously reducing the energy consumption and ensuring an optimal allocation of the retrofit actions among the street lighting subsystems. The methodology is applied to a real street lighting system in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal energy management of the street lighting. © 2017},
    author_keywords={Decision Making; Distribution Management Systems; Energy; Optimization; Smart Cities; Street Lighting},
    keywords={Energy efficiency; Energy utilization; Integer programming; Lighting fixtures; Optimization; Retrofitting; Smart city; Street lighting, Decision making tool; Distribution management systems; Energy; Optimal allocation; Optimization tools; Problem statement; Quadratic integer programming; Street lighting system, Decision making},
    references={Achterberg, T., SCIP: solving constraint integer programs (2009) Math. Progr. Comp., 1, pp. 1-41; Adamo, F., Cavone, G., Di, A., (2013), pp. 488-492. , Nisio, A. Lanzolla and M. Spadavecchia “A proposal for an open source energy meter,” Proc. IEEE I2MTC, Minneapolis, MN, USA, May 6-9; Beccali, M., Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans. The case study of Comiso (RG) (2015) Energy, 92, pp. 394-408; Burgos-Payan, M., Correa-Moreno, F., Riquelme-Santos, J., (2012), “Improving the energy efficiency of street lighting. A case in the South of Spain” Proc. IEEE EEM, pp.1,8, May; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., (2013), pp. 1288-1293. , “Measuring and Managing the Smartness of Cities: a Framework for Classifying Performance Indicators”, Proc. IEEE Conf. Sys. Man Cyber; Carli, R., Dotoli, M., Pellegrino, R., (2015), “ICT and optimization for the energy management of smart cities: The street lighting decision panel”, Proc. IEEE ETFA, Luxembourg, September 8-11; Carli, R., Dotoli, M., Pellegrino, R., (2016), ; Ranieri, L., “A decision making technique to optimize a building stock energy efficiency”, IEEE Trans. Sys. Man Cyb.: Sys. doi: 10.1109/TSMC.2016.2521836; Carli, R., Dotoli, M., Pellegrino, R., (2016), “A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities”, IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2016.2593101; Carli, R., Dotoli, M., Andria, G., Lanzolla, A.M.L., Bi-level programming for the strategic energy management of a smart city (2016) Proc. IEEE EESMS, Bari, pp. 1-6; Covitti, A., Delvecchio, G., Neri, F., Ripoli, A., Sylos Labini, M., Road Lighting Installation Design to Optimize Energy Use by Genetic Algorithms (2005) Proc. EUROCON, 2, pp. 1541-1544; Gallo, G., Hammer, P.L., Simeone, B., Quadratic knapsack problems (1980) Combinatorial Optimization, pp. 132-149. , Springer Berlin Heidelberg; Gargiulo, C., Natale, A., Russo, L., (2015), pp. 1-6. , “Smart community for the smart governance of the urban environment” Proc. IEEE ISC2, Guadalajara; Gómez-Lorente, D., Rabaza, O., Espín, A., Peña-García, A., (2013), “Optimization of efficiency and energy saving in public lighting with multi-objective evolutionary algorithms”, Proc. ICREPQ, 20-22 March; Huang, S.C., Lee, L.L., Jeng, M.S., Hsieh, Y.C., (2012), pp. 1-5. , “Assessment of energy-efficient LED street lighting through large-scale demonstration,” Proc. IEEE ICRERA; Kotulski, L., Towards Highly Energy-Efficient Roadway Lighting (2016) Energies, 9 (4); Novak, T., Pollhammer, K., Zeilinger, H., Schaat, S., Intelligent streetlight management in a smart city (2014) IEEE ETFA, 2014, pp. 1-8. , (September); Pizzuti, S., Annunziato, M., Moretti, F., Smart street lighting management (2013) Energy Efficiency, 6 (3), pp. 607-616; Rabaza, O., Palomares-Muñoz, Z.E., Peña-García, A., Gómez-Lorente, D., Arán-Carrión, J., Aznar-Dols, F., Espín-Estrella, A., (2014), “Multi-objective Optimization applied to Photovoltaic Street Lighting Systems”, Proc. IEEE ICREPQ, X, No.12, April; Radulovic, D., Skok, S., Kirincic, V., “Energy efficiency public lighting management in the cities” (2011) Energy, 36 (4), pp. 1908-1915; Ramadhani, F., Bakar, K.A., Shafer, M.G., (2013) Optimization of standalone street light system with consideration of lighting control, 588, pp. 9-11. , Proc. IEEE TAEECE, pp.583, May; Sittoni, A., Brunelli, D., Macii, D., Tosato, P., Petri, D., (2015), pp. 1-6. , “Street lighting in smart cities: A simulation tool for the design of systems based on narrowband PLC,” Proc. IEEE ISC2, Guadalajara; Wu, Y., Shi, C., Zhang, X., Yang, W., (2010) Design of new intelligent street light control system, 1427, pp. 9-11. , Proc. IEEE ICCA, pp.1423, June},
    document_type={Conference Paper},
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    }
  • Carli, R. & Dotoli, M. (2017) Cooperative Distributed Control for the Energy Scheduling of Smart Homes with Shared Energy Storage and Renewable Energy Source IN IFAC-PapersOnLine., 8867-8872. doi:10.1016/j.ifacol.2017.08.1544
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a distributed control technique for the energy scheduling of a group of interconnected smart city residential users. The proposed model aims at a simultaneous cost-optimal planning of users’ controllable appliances and of the shared storage system charge/discharge and renewable energy source. The distributed control algorithm is based on an iterative procedure combining parametric optimization with the block coordinate descent method. A realistic case study simulated in different scenarios demonstrates that the approach allows fully exploiting the potential of storage systems sharing to reduce individual users’ energy consumption costs and limit the peak average ratio of the energy profiles. © 2017
    @CONFERENCE{Carli20178867,
    author={Carli, R. and Dotoli, M.},
    title={Cooperative Distributed Control for the Energy Scheduling of Smart Homes with Shared Energy Storage and Renewable Energy Source},
    journal={IFAC-PapersOnLine},
    year={2017},
    volume={50},
    number={1},
    pages={8867-8872},
    doi={10.1016/j.ifacol.2017.08.1544},
    note={cited By 28},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031787804&doi=10.1016%2fj.ifacol.2017.08.1544&partnerID=40&md5=9d6a82e7d9f16756a8d263254e537f45},
    abstract={This paper presents a distributed control technique for the energy scheduling of a group of interconnected smart city residential users. The proposed model aims at a simultaneous cost-optimal planning of users’ controllable appliances and of the shared storage system charge/discharge and renewable energy source. The distributed control algorithm is based on an iterative procedure combining parametric optimization with the block coordinate descent method. A realistic case study simulated in different scenarios demonstrates that the approach allows fully exploiting the potential of storage systems sharing to reduce individual users’ energy consumption costs and limit the peak average ratio of the energy profiles. © 2017},
    author_keywords={Decentralized; Distributed Control; Distribution Management Systems; Energy; Energy Storage Operation; Large scale optimization problems; Planning; Smart Grids},
    keywords={Automation; Distributed parameter control systems; Energy storage; Energy utilization; Intelligent buildings; Iterative methods; Natural resources; Planning; Renewable energy resources; Scheduling, Decentralized; Distributed control; Distribution management systems; Energy; Large-scale optimization; Smart grid; Storage operations, Smart power grids},
    references={Adika, C.O., Wang, L., Non-Cooperative Decentralized Charging of Homogeneous Households’ Batteries in a Smart Grid (2014) IEEE Trans. Smart Grid, 5 (4), p. 1855. , pp. 1863, July; Arghandeh, R., Woyak, J., Onen, A., Jung, J., Broadwater, R.P., Economic optimal operation of Community Energy Storage systems in competitive energy markets (2014) Applied Energy, 135, pp. 71-80; Attivissimo, F., Di, A., Nisio, A.M., Lanzolla, L., Paul, M., Feasibility of a Photovoltaic–Thermoelectric Generator: Performance Analysis and Simulation Results (2015) IEEE Trans. Instrum. Meas, 64 (5), pp. 1158-1169; Atzeni, I., Ordonez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R., Demand-Side Management via Distributed Energy Generation and Storage Optimization (2013) IEEE Trans. Smart Grid, 4 (2), p. 866. , pp. 876; Bertsekas, D.P., (1999) Nonlinear programming, p. 780. , Athena scientific Belmont; Brusco, G., Burgio, A., Menniti, D., Pinnarelli, A., Sorrentino, N., Energy Management System for an Energy District With Demand Response Availability (2014) IEEE Trans. Smart Grid, 5 (5), pp. 2385-2393; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., (2013), pp. 1288-1293. , “Measuring and Managing the Smartness of Cities: a Framework for Classifying Performance Indicators” Proc. IEEE Conf. Sys. Man Cyber; Carli, R., Dotoli, M., (2014), pp. 5648-5653. , “Energy Scheduling of a Smart Home under Nonlinear Pricing” Proc. IEEE Int. Conf. Dec. Contr., Dec. 15-17; Carli, R., Dotoli, M., (2015), “A Decentralized Resource Allocation Approach For Sharing Renewable Energy among Interconnected Smart Homes,” IEEE Int. Conf. Dec. Contr, Dec. 15-18; Carli, R., Dotoli, M., Pellegrino, R., (2016), “A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities” IEEE Trans. Aut. Sci. Eng., 19 pp., doi:; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., (2016), “A Hierarchical Decision Making Technique to Optimize a Building Stock Energy Efficiency” IEEE Trans. Sys. Man Cyb.: Sys., 14 pp, doi:; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., Costantino, N., A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market (2014) Comput. Ind., 65 (4), pp. 774-785; Gkatzikis, L., Iosifidis, G., Koutsopoulos, I., Tassiulas, L., (2014), pp. 103-108. , (November). Collaborative placement and sharing of storage resources in the Smart Grid. IEEE SmartGridComm, Conf. 2014; Graves, F., Jenkins, T., Murphy, D., Opportunities for electricity storage in deregulating markets (1999) The Electricity J., 12 (8), pp. 46-56; Kefayati, M., Baldick, R., (2013), “On optimal operation of storage devices under stochastic market prices, Proc. IEEE Int. Conf. Dec. Contr; Harsha, P., Dahleh, M., (2011), “Optimal sizing of energy storage for efficient integration of renewable energy, Proc. IEEE Int. Conf. Dec. Contr; He, X., Delarue, E., D'haeseleer, W., Glachant, J.M., A novel business model for aggregating the values of electricity storage (2011) Energy Policy, 39 (3), pp. 1575-1585; Jesudasan, R.N., Andrew, L.L., Scheduling long term energy storage (2014) In INFOCOM WKSHPS, 2014, pp. 634-639. , (April); Mégel, O., Mathieu, J.L., Andersson, G., Scheduling distributed energy storage units to provide multiple services under forecast error (2015) INT J ELEC POWER, 72, pp. 48-57; Mohsenian-Rad, A.-H., Wong, V., Jatskevich, J., Schober, R., Leon-Garcia, A., Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid (2010) IEEE Trans. on Smart Grid, 1 (3), pp. 320-331; Sioshansi, R., Welfare impacts of electricity storage and the implications of ownership structure (2010) Energy J., 31 (2), p. 173; Stallings, W., (2015) Data and Computer Communications, , Pearson Education Limited; Vytelingum, P., Voice, T.D., Ramchurn, S.D., Rogers, A., Jennings, N.R., (2010), 1, pp. 39-46. , (May). Agent-based micro-storage management for the smart grid. Proc. Int. Conf. AAMAS:; Wang, Y., Lin, X., Pedram, M., Adaptive control for energy storage systems in households with photovoltaic modules (2014) IEEE Trans. Smart Grid, 5 (2), pp. 992-1001},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Dotoli, M., Pellegrino, R. & Ranieri, L. (2017) A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency. IN IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47.794-807. doi:10.1109/TSMC.2016.2521836
    [BibTeX] [Abstract] [Download PDF]
    This paper focuses on applying multicriteria decision making tools to determine an optimal energy retrofit plan for a portfolio of buildings. We present a two-step decision making technique employing a multiobjective optimization algorithm followed by a multiattribute ranking procedure. The method aims at deciding, in an integrated way, the optimal energy retrofit plan for a whole stock of buildings, optimizing efficiency, sustainability, and comfort, while effectively allocating the available financial resources to the buildings. The proposed methodology is applied to a real stock of public buildings in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal management of the buildings’ energy efficiency. © 2016 IEEE.
    @ARTICLE{Carli2017794,
    author={Carli, R. and Dotoli, M. and Pellegrino, R. and Ranieri, L.},
    title={A Decision Making Technique to Optimize a Buildings' Stock Energy Efficiency},
    journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
    year={2017},
    volume={47},
    number={5},
    pages={794-807},
    doi={10.1109/TSMC.2016.2521836},
    art_number={7407645},
    note={cited By 42},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018489196&doi=10.1109%2fTSMC.2016.2521836&partnerID=40&md5=db70236a891233a4576656e616023e08},
    abstract={This paper focuses on applying multicriteria decision making tools to determine an optimal energy retrofit plan for a portfolio of buildings. We present a two-step decision making technique employing a multiobjective optimization algorithm followed by a multiattribute ranking procedure. The method aims at deciding, in an integrated way, the optimal energy retrofit plan for a whole stock of buildings, optimizing efficiency, sustainability, and comfort, while effectively allocating the available financial resources to the buildings. The proposed methodology is applied to a real stock of public buildings in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal management of the buildings' energy efficiency. © 2016 IEEE.},
    author_keywords={Building management; energy efficiency; multiattribute analysis; multicriteria decision making; multiobjective optimization (MOO); optimization algorithms},
    keywords={Buildings; Energy efficiency; Multiobjective optimization; Optimization; Retrofitting, Building management; Financial resources; Multi criteria decision making; Multi-attribute analysis; Multiattribute rankings; Optimal management; Optimization algorithms; Public buildings, Decision making},
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  • Carli, R., Dotoli, M. & Pellegrino, R. (2017) A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities. IN IEEE Transactions on Automation Science and Engineering, 14.505-523. doi:10.1109/TASE.2016.2593101
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a hierarchical decision-making strategy for the energy management of a smart city. The proposed decision process supports the city energy manager and local policy makers in taking energy retrofit decisions on different urban sectors by an integrated, structured, and transparent management. To this aim, in the proposed decision strategy, a bilevel programming model integrates several local decision-making units, each focusing on the energy retrofit optimization of a specific urban subsystem, and a central decision unit. We solve the hierarchical decision problem by a game theoretic distributed algorithm. We apply the developed decision model to the case study of the city of Bari (Italy), where a smart city program has recently been launched. © 2015 IEEE.
    @ARTICLE{Carli2017505,
    author={Carli, R. and Dotoli, M. and Pellegrino, R.},
    title={A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2017},
    volume={14},
    number={2},
    pages={505-523},
    doi={10.1109/TASE.2016.2593101},
    note={cited By 44},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027724511&doi=10.1109%2fTASE.2016.2593101&partnerID=40&md5=b6d44e7085576476833b789dae66c70a},
    abstract={This paper presents a hierarchical decision-making strategy for the energy management of a smart city. The proposed decision process supports the city energy manager and local policy makers in taking energy retrofit decisions on different urban sectors by an integrated, structured, and transparent management. To this aim, in the proposed decision strategy, a bilevel programming model integrates several local decision-making units, each focusing on the energy retrofit optimization of a specific urban subsystem, and a central decision unit. We solve the hierarchical decision problem by a game theoretic distributed algorithm. We apply the developed decision model to the case study of the city of Bari (Italy), where a smart city program has recently been launched. © 2015 IEEE.},
    author_keywords={Bilevel programming; energy efficiency; energy management; game theory; hierarchical decision making; optimization; smart city},
    keywords={Decision theory; Energy efficiency; Energy management; Game theory; Optimization; Retrofitting; Smart city, Bi-level programming; Bilevel programming models; Decision modeling; Decision process; Decision strategy; Energy retrofit; Hierarchical decisions; Local decision-making, Decision making},
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Cities Soc., 15, pp. 105-119. , Jul; Suakanto, S., Supangkat, S.H., Suhardi, Saragih, R., Smart city dashboard for integrating various data of sensor networks (2013) Proc. IEEE Int. Conf. ICT Smart Soc. (ICISS), pp. 1-5. , Jun; Adepetu, A., Grogan, P., Alfaris, A., Svetinovic, D., De Weck, O., City. Net IES: A sustainability-oriented energy decision support system (2012) Proc. IEEE SysCon, pp. 1-7. , Mar; Yamagata, Y., Seya, H., Simulating a future smart city: An integrated land use-energy model (2013) Appl. Energy, 112, pp. 1466-1474. , Dec; Kim, S.A., Shin, D., Choe, Y., Seibert, T., Walz, S.P., Integrated energy monitoring and visualization system for smart green city development: Designing a spatial information integrated energy monitoring model in the context of massive data management on a Web based platform (2012) Autom. Construction, 22, pp. 51-59. , Mar; Phdungsilp, A., Integrated energy and carbon modeling with a decision support system: Policy scenarios for low-carbon city development in Bangkok (2010) Energy Policy, 38 (9), pp. 4808-4817; Juan, Y.-K., Wang, L., Wang, J., Leckie, J.O., Li, K.-M., A decisionsupport system for smarter city planning and management (2011) IBM J. Res. Develop., 55 (1-2), pp. 30-41; Gironès, V.C., Moret, S., Maréchal, F., Favrat, D., Strategic energy planning for large-scale energy systems: A modelling framework to aid decision-making (2015) Energy, 90, pp. 173-186. , Oct; Carli, R., Albino, V., Dotoli, M., Mummolo, G., Savino, M., A dashboard and decision support tool for the energy governance of smart cities (2015) Proc. IEEE EESM, pp. 23-28. , Jul; Kornai, J., Liptak, T., Two-level planning (1965) Econometrica, J. Econ. Soc., 33 (1), pp. 141-169; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Global Optim., 5 (3), pp. 291-306; Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G., Kalaitzakis, K., Decision support methodologies on the energy efficiency and energy management in buildings (2009) Adv. Building Energy Res., 3 (1), pp. 121-146; Martello, S., Toth, P., (1990) Knapsack Problems: Algorithms and Computer Implementations, , New York, NY, USA: Wiley; Bertsekas, D.P., (1999) Nonlinear Programming, p. 780. , Belmont, MA, USA: Athena Scientific; Dempe, S., (2002) Foundations of Bilevel Programming, , Dordrecht, The Netherlands: Kluwer; Gümüs, Z.H., Floudas, C.A., Global optimization of mixed-integer bilevel programming problems (2005) Comput. Manage. Sci., 2 (3), pp. 181-212; Vicente, L., Savard, G., Judice, J., Discrete linear bilevel programming problem (1996) J. Optim. Theory Appl., 89 (3), pp. 597-614; Colson, B., Marcotte, P., Savard, G., An overview of bilevel optimization (2007) Ann. Oper. 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B, Methodol., 18 (4-5), pp. 301-313; Bertsekas, D.P., Tsitsiklis, J.N., (1989) Parallel and Distributed Computation: Numerical Methods, 23. , Englewood Cliffs, NJ, USA: Prentice-Hall; Bhowmick, A., (2012) IBM Intelligent Operations Center for Smarter Cities Administration Guide, , IBM Redbooks; (2016) MATLAB Engine Documentation, , http://it.mathworks.com/help/matlab/matlab_external/introducing-matlab-engine.html, accessed on Mar. 18, [Online]; Achterberg, T., SCIP: Solving constraint integer programs (2009) Math. Program. Comput., 1 (1), pp. 1-41; (2016) OPTI: A Free MATLAB Toolbox for Optimization, , http://www.i2c2.aut.ac.nz/Wiki/OPTI/, accessed on Mar. 18, [Online]; (2016) Covenant Official Text, , http://www.covenantofmayors.eu/, accessed on Mar. 18, [Online]; Cassidy, R.G., Kirby, M.J.L., Raike, W.M., Efficient distribution of resources through three levels of government (1971) Manage. Sci., 17 (8), pp. B462-B473},
    document_type={Article},
    source={Scopus},
    }
  • Carli, R., Dotoli, M., Garramone, R., Andria, G. & Lanzolla, A. M. L. (2017) An average consensus approach for the optimal allocation of a shared renewable energy source IN 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 – Conference Proceedings., 270-275. doi:10.1109/SMC.2016.7844253
    [BibTeX] [Abstract] [Download PDF]
    This paper investigates the problem of optimally distributing the energy produced by a shared renewable energy source among users, without relying on a centralized decision maker. We assume that each user is only allowed to communicate with his neighbors and buys energy from a producer under non-linear pricing. We formulate a quadratic programming problem aimed at ensuring a social welfare-optimal allocation of the shared resource. We propose a low-complexity distributed algorithm that relies on average consensus. We show the convergence of the proposed algorithm to the unique optimal solution of the resource allocation problem. We also provide numerical simulations demonstrating that the approach allows exploiting the potential of renewable energy sources’ sharing to reduce users’ energy consumption costs. © 2016 IEEE.
    @CONFERENCE{Carli2017270,
    author={Carli, R. and Dotoli, M. and Garramone, R. and Andria, G. and Lanzolla, A.M.L.},
    title={An average consensus approach for the optimal allocation of a shared renewable energy source},
    journal={2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings},
    year={2017},
    pages={270-275},
    doi={10.1109/SMC.2016.7844253},
    art_number={7844253},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015803915&doi=10.1109%2fSMC.2016.7844253&partnerID=40&md5=8769614afb1c81c7bc718b4190b95117},
    abstract={This paper investigates the problem of optimally distributing the energy produced by a shared renewable energy source among users, without relying on a centralized decision maker. We assume that each user is only allowed to communicate with his neighbors and buys energy from a producer under non-linear pricing. We formulate a quadratic programming problem aimed at ensuring a social welfare-optimal allocation of the shared resource. We propose a low-complexity distributed algorithm that relies on average consensus. We show the convergence of the proposed algorithm to the unique optimal solution of the resource allocation problem. We also provide numerical simulations demonstrating that the approach allows exploiting the potential of renewable energy sources' sharing to reduce users' energy consumption costs. © 2016 IEEE.},
    author_keywords={Average consensus; Distributed optimization; Energy management; Multi-period resource allocation; Renewable energy sources},
    keywords={Computational complexity; Cybernetics; Decision making; Economics; Energy management; Energy utilization; Natural resources; Optimization; Quadratic programming; Resource allocation, Average consensus; Distributed optimization; Multi-period; Non-linear pricing; Optimal allocation; Quadratic programming problems; Renewable energy source; Resource allocation problem, Renewable energy resources},
    references={Adamo, F., Attivissimo, F., Di Nisio, A., Spadavecchia, M., Analysis of the uncertainty of the double-diode model of a photovoltaic panel (2011) Proc. IEEE Int. Instrumentation and Measurement Technology Conf, pp. 616-620. , Binjiang, China, May 10-12; Adamo, F., Attivissimo, F., Spadavecchia, M., A tool for Photovoltaic panels modeling and testing (2010) Proc. IEEE Int. Instrumentation and Measurement Technology Conf, pp. 1463-1466. , Austin, 3-6 May; Adamo, F., Cavone, G., Di Nisio, A., Lanzolla, A., Spadavecchia, M., A proposal for an open source energy meter (2013) Proc. IEEE Int. Instrumentation and Measurement Technology Conf, pp. 488-492. , Minneapolis, May 6-9; Attivissimo, F., Di Nisio, A., Lanzolla, A.M.L., Paul, M., Feasibility of a photovoltaic-thermoelectric generator: Performance analysis and simulation results (2015) IEEE Transactions on Instrumentation and Measurement, 64 (5), pp. 1158-1169. , May; Bertsekas, D.P., (1999) Nonlinear Programming, p. 780. , Belmont: Athena scientific; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5648-5653. , Dec. 15-17; Carli, R., Dotoli, M., A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes (2015) IEEE Int. Conf. Dec. Contr, , Dec. 15-18; Carli, R., Dotoli, M., Pellegrino R, R., A hierarchical decision making strategy for the energy management of smart cities (2016) IEEE Transactions on Automation Science and Engineering, , to appear; Cavraro, G., Carli, R., Zampieri, S., A distributed control algorithm for the minimization of the power generation cost in smart micro-grid (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5642-5647. , Dec. 15-17; Franceschelli, M., Giua, A., Seatzu, C., Distributed averaging in sensor networks based on broadcast gossip algorithms (2011) IEEE Sensors Journal, 3 (3), pp. 808-817; Garin, F., Schenato, L., Networked control systems Springer, 2011, Ch. A Survey on Distributed Estimation and Control Applications Using Linear Consensus Algorithms, pp. 75-107; Guérin, F., Lefebvre, D., Mboup, A.B., Parédé, J., Lemains, E., Ndiaye, P.A.S., Hybrid modeling for performance evaluation of multisource renewable energy systems (2011) IEEE Trans. Aut. Sci. Eng., 8 (3), pp. 570-580. , July; Huang, Z., Zhu, T., Gu, Y., Irwin, D., Mishra, A., Shenoy, P., Minimizing electricity costs by sharing energy in sustainable microgrids (2014) Proc. ACM Conf. Embed. Sys. Ener.-Effic. Build., pp. 120-129. , Nov; Katoh, N., Shioura, A., Ibaraki T, T., Resource allocation problems (2013) Handbook of Combinatorial Optimization, pp. 2897-2988; Loia, V., Terzija, V., Vaccaro, A., Wall, P., An affine-arithmetic-based consensus protocol for smart-grid computing in the presence of data uncertainties (2015) IEEE Transactions on Industrial Electronics, 62 (5), pp. 2973-2982. , May; Loia, V., Vaccaro, A., Decentralized economic dispatch in smart grids by self-organizing dynamic agents (2014) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44 (4), pp. 397-408. , April; Rodriguez-Amenedo, J.L., Arnalte, S., Burgos, J.C., Automatic generation control of a wind farm with variable speed wind turbines (2002) IEEE Trans. Energy Conversion, 17 (2), pp. 279-284. , Jun; Vaccaro, A., Loia, V., Formato, G., Wall, P., Terzija, V., A self-organizing architecture for decentralized smart microgrids synchronization, control, and monitoring (2015) IEEE Transactions on Industrial Informatics, 11 (1), pp. 289-298. , Feb; Wu, Y., Lau, V.K.N., Tsang, D.H.K., Qian, L.P., Meng, L., Optimal energy scheduling for residential smart grid with centralized renewable energy source (2014) Systems Journal, IEEE, 8 (2), pp. 562-576. , June; Xiao, L., Boyd, S., Fast linear iterations for distributed averaging (2004) Systems and Control Letters, 53 (1), pp. 65-78. , September; Zhong, W., Huang, Z., Zhu, T., Gu, Y., Zhang, Q., Yi, P., Jiang, D., Xiao, S., IDES: Incentive-driven distributed energy sharing in sustainable microgrids (2014) Green Computing Conference (IGCC) 2014, pp. 1-10. , 3-5 Nov; Zhu, T., Huang, Z., Sharma, A., Su, J., Irwin, D., Mishra, A., Shenoy, P., Sharing renewable energy in smart microgrids (2013) Proc. ACM/IEEE Int. Conf. Cyber-Phys. Sys., pp. 219-228. , April},
    document_type={Conference Paper},
    source={Scopus},
    }

2016

  • Carli, R., Dotoli, M., Andria, G. & Lanzolla, A. M. L. (2016) Bi-level programming for the strategic energy management of a smart city IN EESMS 2016 – 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, Proceedings.. doi:10.1109/EESMS.2016.7504820
    [BibTeX] [Abstract] [Download PDF]
    This paper addresses the emerging need for tools devoted to the strategic energy management of smart cities. We propose a novel decision model that supports the energy manager in governing the smart city while addressing different urban sectors with an integrated and structured energy retrofit planning. A bi-level programming model integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem, and a central decision unit. We solve the bi-level decision problem by a game theoretic distributed approach. We apply the developed decision model to the case study of the city of Bari (Italy), where a smart city program has recently been launched. © 2016 IEEE.
    @CONFERENCE{Carli2016,
    author={Carli, R. and Dotoli, M. and Andria, G. and Lanzolla, A.M.L.},
    title={Bi-level programming for the strategic energy management of a smart city},
    journal={EESMS 2016 - 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, Proceedings},
    year={2016},
    doi={10.1109/EESMS.2016.7504820},
    art_number={7504820},
    note={cited By 4},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980371756&doi=10.1109%2fEESMS.2016.7504820&partnerID=40&md5=645711d93f3fee6a0e376fe0decdd093},
    abstract={This paper addresses the emerging need for tools devoted to the strategic energy management of smart cities. We propose a novel decision model that supports the energy manager in governing the smart city while addressing different urban sectors with an integrated and structured energy retrofit planning. A bi-level programming model integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem, and a central decision unit. We solve the bi-level decision problem by a game theoretic distributed approach. We apply the developed decision model to the case study of the city of Bari (Italy), where a smart city program has recently been launched. © 2016 IEEE.},
    author_keywords={bi-level programming; decision support system; distributed optimization; energy efficiency; energy management; smart city},
    keywords={Artificial intelligence; Decision support systems; Distributed computer systems; Energy efficiency; Energy management; Game theory; Monitoring, Bi-level programming; Bilevel programming models; Decision making unit; Distributed approaches; Distributed optimization; Energy optimization; Smart cities; Strategic energy management, Decision making},
    references={Albino, V., Berardi, U., Dangelico, R.M., Smart cities: Definitions, dimensions, performance, and initiatives (2014) Journal of Urban Technology, (21); Başar, T., Olsder, G.J., Dynamic noncooperative game theory (1999) SIAM Series in Classics in Applied Mathematics, , Philadelphia, PA:SIAM; Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., Mahajan, A., Mixed-integer nonlinear optimization (2013) Acta Numerica, 22, pp. 1-131; Calvillo, C.F., Sánchez-Miralles, A., Villar, J., Energy management and planning in smart cities (2016) Renewable and Sustainable Energy Reviews, 55, pp. 273-287; Caponio, G., Massaro, V., Mossa, G., Mummolo, G., Strategic Energy Planning of Residential Buildings in a Smart City: A System Dynamics Approach (2015) International Journal of Engineering Business Management., 2015; Caragliu, A., Del Bo, C., Nijkamp, P., Smart cities in Europe (2009) Proc. 3rd Centr. Europ. Conf. Regional Science, , Oct; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. IEEE ETFA2014, pp. 1-7; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE SMC2013, pp. 1288-1293; Carli, R., Albino, V., Dotoli, M., Mummolo, G., Savino, M., A dashboard and decision support tool for the energy governance of smart cities (2015) Environmental, Energy and Structural Monitoring Systems (EESMS), 2015 IEEE Workshop on, pp. 23-28. , 9-10 July; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a building stock energy efficiency (2016) IEEE Trans. Sys., Man Cyb.: Systems; Gümüş, Z.H., Floudas, C.A., Global optimization of mixedinteger bilevel programming problems (2005) Computational Management Science, 2 (3), pp. 181-212; Kornai, J., Liptak, T., Two-level planning (1965) Econometrica: Journal of the Econometric Society, pp. 141-169; Lust, T., Teghem, J., The multiobjective multidimensional knapsack problem: A survey and a new approach (2012) International Transactions in Operational Research, 19 (4), pp. 495-520; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Glob. Optim., 5 (3), pp. 291-306; UN-HABITAT, (2011) Cities and Climate Change: Policy Directions, Global Report on Human Settlement},
    document_type={Conference Paper},
    source={Scopus},
    }

2015

  • Carli, R., Dotoli, M. & Pellegrino, R. (2015) ICT and optimization for the energy management of smart cities: The street lighting decision panel IN IEEE International Conference on Emerging Technologies and Factory Automation, ETFA.. doi:10.1109/ETFA.2015.7301435
    [BibTeX] [Abstract] [Download PDF]
    The paper addresses the emerging need for tools devoted to the energy governance of smart cities. We propose a hierarchical decision process that supports the energy manager in governing the smart city while addressing different urban sectors with an integrated, structured, and transparent planning. Starting from the urban control center proposed in a previous contribution for the urban energy management, a hierarchical strategic decision structure is proposed. More in detail, a two-level decentralized programming model integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem. We focus on the presentation of the street lighting decision panel and on its application to the energy management of the public lighting of the city of Bari (Italy), where a smart city program has recently been launched. © 2015 IEEE.
    @CONFERENCE{Carli2015,
    author={Carli, R. and Dotoli, M. and Pellegrino, R.},
    title={ICT and optimization for the energy management of smart cities: The street lighting decision panel},
    journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA},
    year={2015},
    volume={2015-October},
    doi={10.1109/ETFA.2015.7301435},
    art_number={7301435},
    note={cited By 15},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952882893&doi=10.1109%2fETFA.2015.7301435&partnerID=40&md5=012ab885ea35346c5c280804295955c9},
    abstract={The paper addresses the emerging need for tools devoted to the energy governance of smart cities. We propose a hierarchical decision process that supports the energy manager in governing the smart city while addressing different urban sectors with an integrated, structured, and transparent planning. Starting from the urban control center proposed in a previous contribution for the urban energy management, a hierarchical strategic decision structure is proposed. More in detail, a two-level decentralized programming model integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem. We focus on the presentation of the street lighting decision panel and on its application to the energy management of the public lighting of the city of Bari (Italy), where a smart city program has recently been launched. © 2015 IEEE.},
    author_keywords={Cities and towns; Decision making; Lighting; Optimization; Smart cities},
    keywords={Application programs; Energy management; Factory automation; Lighting; Optimization; Street lighting, Cities and towns; Decision making unit; Energy optimization; Hierarchical decisions; ITS applications; Programming models; Smart cities; Strategic decisions, Decision making},
    references={Başar, T., Olsder, G.J., (1999) Dynamic Noncooperative Game Theory, , SIAM Series in Classics in Applied Mathematics. Philadelphia, PA:SIAM; Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., Portugali, Y., Smart cities of the future (2012) Eur. Phys. J. Spec. Top., 214 (1), pp. 481-518; Caragliu, A., Del Bo, C., Nijkamp, P., Smart cities in Europe (2009) Proc. 3rd Centr. Europ. Conf. Reg. Sci. (CERS), , Oct; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. IEEE ETFA2014, pp. 1-7; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE SMC2013, pp. 1288-1293; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Using multi-objective optimization for the integrated energy efficiency improvement of a smart city public buildings' portfolio (2015) IEEE CASE2015; Figueira, J., Greco, S., Ehrgott, M., (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, , Springer, Boston; Glover, F., Improved linear integer programming formulations of nonlinear integer problems (1975) Manag. Sci., 22 (4), pp. 455-460; Lagorse, J., Paire, D., Miraoui, A., Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery (2009) Renew. Ener., 34 (3), pp. 683-691; Lust, T., Teghem, J., The multiobjective multidimensional knapsack problem: A survey and a new approach (2012) Int. Trans. Op. Res., 19 (4), pp. 495-520; Marler, R.T., Arora, J.S., Survey of multi-objective optimization methods for engineering (2004) Structur. Multidisc. Optim., 26 (6), pp. 369-395; Nam, T.W., Pardo, T.A., Conceptualizing smart city with dimensions of technology, people, and institutions (2011) Proc. 12th Int. Digit. Governm. Res. Conf.; Narisada, K., Schreuder, D., (2004) Light Pollution Handbook, 322. , Springer Science & Business Media; Rea, M.S., (2000) The IESNA Lighting Handbook, , New York: Illuminating Engineering Society of North America; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Glob. Optim., 5 (3), pp. 291-306},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Dotoli, M., Pellegrino, R. & Ranieri, L. (2015) Using multi-objective optimization for the integrated energy efficiency improvement of a smart city public buildings’ portfolio IN IEEE International Conference on Automation Science and Engineering., 21-26. doi:10.1109/CoASE.2015.7294035
    [BibTeX] [Abstract] [Download PDF]
    The paper presents a multi-objective optimization algorithm to improve in an integrated and holistic way the building stock energy efficiency, sustainability, and comfort, while efficiently allocating the available budget to the buildings. The developed algorithm determines a set of optimal energy retrofit plans for a portfolio of public buildings in a smart city. An existing stock of public buildings located in the municipality of Bari, Italy is used as case study. The application results demonstrate that the developed algorithm is an effective support tool for the smart city governance in enhancing the energy efficiency performance of a stock of public buildings. © 2015 IEEE.
    @CONFERENCE{Carli201521,
    author={Carli, R. and Dotoli, M. and Pellegrino, R. and Ranieri, L.},
    title={Using multi-objective optimization for the integrated energy efficiency improvement of a smart city public buildings' portfolio},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2015},
    volume={2015-October},
    pages={21-26},
    doi={10.1109/CoASE.2015.7294035},
    art_number={7294035},
    note={cited By 25},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952780524&doi=10.1109%2fCoASE.2015.7294035&partnerID=40&md5=64470489d4914a6d949700900aa47d44},
    abstract={The paper presents a multi-objective optimization algorithm to improve in an integrated and holistic way the building stock energy efficiency, sustainability, and comfort, while efficiently allocating the available budget to the buildings. The developed algorithm determines a set of optimal energy retrofit plans for a portfolio of public buildings in a smart city. An existing stock of public buildings located in the municipality of Bari, Italy is used as case study. The application results demonstrate that the developed algorithm is an effective support tool for the smart city governance in enhancing the energy efficiency performance of a stock of public buildings. © 2015 IEEE.},
    keywords={Algorithms; Automation; Budget control; Buildings; Multiobjective optimization; Optimization, Building stocks; Energy efficiency improvements; Optimal energy; Public buildings; Smart cities; Support tool, Energy efficiency},
    references={Asadi, E., Gameiro Da Silva, M., Henggeler Antunes, C., Dias, L., Multi-objective optimization for building retrofit strategies: A model and an application (2012) Ener. Build, 44, pp. 81-87; Basuroy, S., Chuah, J.W., Jha, N.K., Making buildings energy-efficient through retrofits: A survey of available technologies (2013) Proc. IEEE Power and Energy Society Gen. Meet., pp. 1-5. , 21-25 July; Caccavelli, D., Gugerli, H., TOBUS- A European diagnosis and decision-making tool for office building upgrading (2002) Ener. Build, 34 (2), pp. 113-119; Carli, R., Albino, V., Dotoli, M., Mummolo, G., Savino, M., A dashboard and decision support tool for the energy governance of smart cities (2015) Proc. IEEE EESMS2015, 6. , July 9-10; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. IEEE ETFA2014, 6. , September 16-19; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE Conf. Systems, Man and Cybernetics (SMC 2013), pp. 1288-1293. , 13-16 Oct; Chuah, J.W., Raghunathan, A., Jha, N.K., ROBESim: A retrofitoriented building energy simulator based on EnergyPlus (2013) Energy and Buildings, 66; Dall'O, G., (2013) Green Energy Audit of Buildings-A Guide for A Sustainable Energy Audit of Buildings, , Springer: London, UK; Diakaki, C., Grigoroudis, E., Kabelis, N., Kolokotsa, D., Kalaitzakis, K., Stavrakakis, G., A multi-objective decision model for the improvement of energy efficiency in buildings (2010) Ener, 35 (12), pp. 5483-5496; Calcolo Semplificato Del Risparmio Annuo di Energia in Fonte Primaria Previsto Con un Intervento di Efficienza Energetica, , http://www.acs.enea.it/tecnici/calcolo_re.pdf, ENEA (in Italian); Directive 2002/91/ec of the european parliament and of the council on the energy performance of buildings (2003) L1/65, Off. J. Europ. Comm., , European Community; Towards a sustainable energy future (2008) IEA Programme of Work on Climate Change, Clean Energy and Sustainable Development, , http://ccs101.ca/assets/Documents/g8_towards_sustainable_future.pdf, International Energy Agency; Kaklauskas, A., Kazimieras Zavadskas, E., Raslanas, S., Multivariant design and multiple criteria analysis of building refurbishments (2005) Ener. Build, 37, pp. 361-372; Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G., Kalaitzakis, K., Decision support methodologies on the energy efficiency and energy management in buildings (2009) Adv. Build. Ener. Res, 3 (1), pp. 121-146; Liu, H., Zhao, Q.C., Huang, N.J., Zhao, X., A simulation-based tool for energy efficient building design for a class of manufacturing plants (2013) IEEE Trans. Aut. Sci. Eng, 10 (1), pp. 117-123; Ma, Z.J., Cooper, P., Daly, D., Ledo, L., Existing building retrofits: Methodology and state-of-the-art (2012) Ener. Build, 55, pp. 889-902; Marler, R.T., Arora, J.S., Survey of multi-objective optimization methods for engineering (2004) Structur. Multidisc. Optim, 26 (6), pp. 369-395; Pérez-Lombard, L., Ortiz, J., Pout, C., A review on buildings energy consumption information (2008) Ener. Build, 40, pp. 394-398; Roulet, C.A., Flourentzou, F., Labben, H.H., Santamouris, M., Koronaki, I., Dascalaki, E., Richalet, V., ORME: A multi-criteria rating methodology for buildings (2002) Build. Envir., 37 (6), pp. 579-586; Rysanek, A.M., Choudhary, R., Optimum building energy retrofits under technical and economic uncertainty (2013) Ener. Build, 57, pp. 324-337; Wulfinghoff, D.R., (1999) Energy Efficiency Manual, , Energy Institute Press, Wheaton, Maryland, US},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Dotoli, M., Epicoco, N., Angelico, B. & Vinciullo, A. (2015) Automated evaluation of urban traffic congestion using bus as a probe IN IEEE International Conference on Automation Science and Engineering., 967-972. doi:10.1109/CoASE.2015.7294224
    [BibTeX] [Abstract] [Download PDF]
    This paper presents an algorithm for the automated analysis and evaluation of vehicular traffic congestion in urban areas. The proposed approach is based on the concept of bus as a probe and makes use of GPS-generated data provided by a local transit bus tracking system. Archived GPS pulses are analyzed offline to extract valuable indices related to general urban traffic characteristics and aimed at generating a detailed view of the urban roads congestion. This information is useful both for policy makers, to effectively address the management of sustainable mobility in urban areas, and for citizens, to acquire awareness about congestion times and location zones. The presented algorithm is applied to a part of the urban road network of the municipality of Bari (Italy). © 2015 IEEE.
    @CONFERENCE{Carli2015967,
    author={Carli, R. and Dotoli, M. and Epicoco, N. and Angelico, B. and Vinciullo, A.},
    title={Automated evaluation of urban traffic congestion using bus as a probe},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2015},
    volume={2015-October},
    pages={967-972},
    doi={10.1109/CoASE.2015.7294224},
    art_number={7294224},
    note={cited By 22},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952762623&doi=10.1109%2fCoASE.2015.7294224&partnerID=40&md5=c2588eba274a644410a09da25d3699b5},
    abstract={This paper presents an algorithm for the automated analysis and evaluation of vehicular traffic congestion in urban areas. The proposed approach is based on the concept of bus as a probe and makes use of GPS-generated data provided by a local transit bus tracking system. Archived GPS pulses are analyzed offline to extract valuable indices related to general urban traffic characteristics and aimed at generating a detailed view of the urban roads congestion. This information is useful both for policy makers, to effectively address the management of sustainable mobility in urban areas, and for citizens, to acquire awareness about congestion times and location zones. The presented algorithm is applied to a part of the urban road network of the municipality of Bari (Italy). © 2015 IEEE.},
    keywords={Algorithms; Automation; Buses; Motor transportation; Probes, Automated analysis; Automated evaluation; Policy makers; Sustainable mobility; Tracking system; Urban road networks; Urban traffic; Urban traffic congestion, Traffic congestion},
    references={Bacon, J., Bejan, A.I., Beresford, A.R., Evans, D., Gibbens, R.J., Moody, K., Using real-time road traffic data to evaluate congestion (2011) Lect. Notes Comp. Sc., 6875, pp. 93-117; Bertini, R.L., You are the traffic jam: An examination of congestion measures (2006) Transp. Res. Board 85th Ann. Meet., , Washington DC; Bertini, R.L., Tantiyanugulchai, S., Transit buses as traffic probes: Use of geolocation data for empirical evaluation (2004) Transp. Res. Rec., 1870 (1), pp. 35-45; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) Proc. 19th IEEE Int. Conf. Emerging Technologies and Factory Automation, , Barcelona, Spain, September 16-19; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE Int. Conf. Systems, Man and Cybernetics, pp. 1288-1293. , October 13-16; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., A decision making technique to optimize a building stock energy efficiency (2015) IEEE Trans. Syst., Man, Cybern, Syst.; Casas, J., Torday, A., Perarnau, J., Breen, M., De Ruiz, V.A., Decision Support Systems (DSS) for traffic management assessment: Notes on current methodology and future requirements for the implementation of a DSS (2014) Proc. 5th Conf. Transp. Res. Arena: Transport Solutions from Research to Deployment, , Paris, France, April 14-17; Chabrol, M., Sarramia, D., Tchernev, N., Urban traffic systems modelling methodology (2006) Int. J. Prod. Econ., 99, pp. 156-176; Chakroborty, P., Kikuchi, S., Using bus travel time data to estimate travel times on urban corridors (2004) Transp. Res. Rec., 1870 (1), pp. 18-25; Chen, Y., Gao, L., Li, Z.-P., Liu, Y.-C., A new method for urban traffic state estimation based on vehicle tracking algorithm (2007) Proc. 2007 IEEE Intell. Transp. Sys. Conf., pp. 1097-1101. , Seattle, WA, USA, Sept. 30-Oct. 3; Chu, K.-C., Saitou, K., Optimization of probe vehicle deployment for traffic status estimation (2013) 2013 IEEE Int. Conf. on Automation Science and Engineering, pp. 880-885. , 17-20 Aug; Coifman, B., Kim, S., Using transit vehicles to measure freeway traffic conditions (2006) Proc. 9th Int. Conf. Applications of Advanced Technology in Transportation, , Chicago, Illinois, August, 13-16; Dotoli, M., Fanti, M.P., Iacobellis, G., An urban traffic network model by first order hybrid petri nets (2008) Proc. IEEE Int. Conf. Systems, Man and Cybernetics, pp. 1929-1934. , Singapore, 12-15 October; Dotoli, M., Fanti, M.P., Meloni, C., A signal timing plan formulation for urban traffic control (2006) Contr. Eng Pract, 14 (11), pp. 1297-1311. , November; Dotoli, M., Fanti, M.P., An urban traffic network model via coloured timed petri nets (2006) Contr. Eng Pract, 14 (10), pp. 1213-1229. , October; Dotoli, M., Hammadi, S., Jeribi, K., Russo, C., Zgaya, H., A multi-agent decision support system for optimization of co-modal transportation route planning services (2013) 52nd IEEE Conf. on Dec. and Contr., , Florence, Italy, December 10-13; Directive 2010/40/eu of the european parliament and of the council of 7 July 2010 on the framework for the deployment of intelligent transport systems in the field of road transport and for interfaces with other modes of transport (2010) Off. J. of the European Union, L 207, 53. , European Union, 6 August; Leduc, G., Road traffic data: Collection methods and applications (2008) Institute for Prospective Technological Studies, Joint Research Centre European Commission, p. 55; Litman, T., Developing indicators for comprehensive and sustainable transport planning (2007) Transp. Res. Rec., 2017 (1), pp. 10-15; Marchal, F., Hackney, J., Axhausen, K.W., Efficient map matching of large global positioning system data sets: Tests on speed-monitoring experiment in Zürich (2005) Transp. Res. Rec., 1935 (1), pp. 93-100; Pattara-Atikom, W., Pongpaibool, P., Thajchayapong, S., Estimating road traffic congestion using vehicle velocity (2006) Proc. 6th IEEE Int. Conf. ITS Telecom., pp. 1001-1004; Pu, W., Lin, J., Long, L., Real-time estimation of urban street segment travel time using buses as speed probes (2009) Transp. Res. Rec., 2129 (1), pp. 81-89; Taylor, M.A.P., Woolley, J.E., Zito, R., Integration of the global positioning system and geographical information systems for traffic congestion studies (2000) Transp. Res. C-Emer., 8 (1), pp. 257-285; (2000) Transportation Research Board, National Research Council, , Highway capacity manual. Washington DC; Uno, N., Kurauchi, F., Tamura, H., Iida, Y., Using bus probe data for analysis of travel time variability (2009) J. Intell. Transp. S., 13 (1), pp. 2-15; Zhu, T., Ma, F., Ma, T., Li, C., The prediction of bus arrival time using Global Positioning System data and dynamic traffic information (2011) Proc. IEEE Wireless and Mobile Networking Conf., pp. 1-5. , 2011 4th Joint IFIP},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Albino, V., Dotoli, M., Mummolo, G. & Savino, M. (2015) A dashboard and decision support tool for the energy governance of smart cities IN 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2015 – Proceedings., 23-28. doi:10.1109/EESMS.2015.7175846
    [BibTeX] [Abstract] [Download PDF]
    The paper addresses the findings of the research activities conducted in the framework of the RES NOVAE project for the design and development of the Urban Control Center (UCC), a control room of the smart city that allows the Public Administration to analyze the city dynamics and citizens to receive information on the performance of urban infrastructure and services. With a specific focus on energy efficiency and environmental sustainability, we present the architecture of an innovative dashboard and decision support tool for efficient urban governance. We investigate solutions to effectively measure the city energy performance and proficiently support the decision maker in determining the optimal action plan for implementing smartness strategies in the city energy governance. © 2015 IEEE.
    @CONFERENCE{Carli201523,
    author={Carli, R. and Albino, V. and Dotoli, M. and Mummolo, G. and Savino, M.},
    title={A dashboard and decision support tool for the energy governance of smart cities},
    journal={2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2015 - Proceedings},
    year={2015},
    pages={23-28},
    doi={10.1109/EESMS.2015.7175846},
    art_number={7175846},
    note={cited By 9},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950984321&doi=10.1109%2fEESMS.2015.7175846&partnerID=40&md5=675d5e25da8794e638be9ff52797dd47},
    abstract={The paper addresses the findings of the research activities conducted in the framework of the RES NOVAE project for the design and development of the Urban Control Center (UCC), a control room of the smart city that allows the Public Administration to analyze the city dynamics and citizens to receive information on the performance of urban infrastructure and services. With a specific focus on energy efficiency and environmental sustainability, we present the architecture of an innovative dashboard and decision support tool for efficient urban governance. We investigate solutions to effectively measure the city energy performance and proficiently support the decision maker in determining the optimal action plan for implementing smartness strategies in the city energy governance. © 2015 IEEE.},
    author_keywords={decision support system; energy efficiency; indicators dashboard; information and communication technologies; management; monitoring; multi-attribute analysis; multi-objective optimization; optimization; smart cities},
    keywords={Artificial intelligence; Decision making; Decision support systems; Information management; Management; Monitoring; Multiobjective optimization; Optimization; Public administration; Sustainable development, Decision support tools; Design and Development; Environmental sustainability; Information and Communication Technologies; Multi-attribute analysis; Research activities; Smart cities; Urban infrastructure, Energy efficiency},
    references={Albino, V., Berardi, U., Dangelico, R.M., Smart cities: Definitions, dimensions, performance, and initiatives (2015) Journal of Urban Technology, (21); Batty, M., Axhausen, K.W., Smart cities of the future (2012) Eur. Phys. J., 214 (1), pp. 481-518; Bhowmick, A., Francellino, E., (2012) IBM Intelligent Operations Center for Smarter Cities Administration Guide IBM Redbooks; Caponio, G., D'Alessandro, G., Digiesi, S., Mossa, G., Mummolo, G., Verriello, R., Minimizing carbon-footprint of municipal waste separate collection systems (2015) Enhancing Synergies in A Collaborative Environment, pp. 351-359. , Springer International Publishing; Caragliu, A., Del Bo, C., Nijkamp, P., Smart cities in Europe (2009) Proc. 3rd Centr. Europ. Conf. Regional Science, , Oct; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., An urban control center for the energy governance of a smart city (2014) 19th IEEE International Conference on Emerging Technologies and Factory Automation, , Barcelona, Spain, September 16-19; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE Conf. Systems, Man and Cybernetics, pp. 1288-1293. , 13-16 Oct; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) IEEE ETFA2015; Chourabi, H., Nam, T., Understanding smart cities: An integrative framework (2012) Proc. System Science (HICSS), 2012 45th Hawaii Int. Conf., pp. 2289-2297. , 4-7 Jan; Eagle, N., Pentland, A., Reality mining: Sensing complex social systems (2006) Personal Ubiquitous Computing, 10, pp. 255-268; EU Commission (2012) Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions, 'A European strategy for key enabling technologies-A bridge to growth and jobs'; Giffinger, R., Fertner, C., (2007) Smart Cities: Ranking of European Medium-Sized Cities, , http://www.smartcities.eu/download/smart_cities_final_report.pdf, Vienna, Austria: Centre of Regional Science (SRF), Vienna University of Technology; Harrison, C., Eckman, B., Foundations for smarter cities (2010) IBM Journal of Research and Development, 54 (4), pp. 1-16; Mitton, N., Papavassiliou, S., Puliafito, A., Trivedi, K.S., Combining Cloud and sensors in a smart city environment (2012) EURASIP Journal on Wireless Communications and Networking 2012, p. 247; Naphade, M., Banavar, Smarter cities and their innovation challenges (2011) Computer, 44 (6), pp. 32-39. , June; Savino, T., Albino, V., Dangelico, R.M., (2014) Environmental Management to Improve Quality of Life in Smart Cities, , IFKAD; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Glob. Optim., 5 (3), pp. 291-306; Washburn, D., Sindhu, (2010) Helping CIOs Understand "smart City" Initiatives, , Cambridge, MA: Forrester Research},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2015) A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes IN Proceedings of the IEEE Conference on Decision and Control., 5903-5908. doi:10.1109/CDC.2015.7403147
    [BibTeX] [Abstract] [Download PDF]
    The paper deals with the scheduling of energy activities of a group of interconnected users that buy energy from a producer and share a renewable energy source. The scheduling problem is stated and solved with a twofold goal. First, the model is formulated to ensure social welfare-optimal allocation of the energy produced from the shared renewable energy generator. Second, the model aims at cost-optimal planning of users’ controllable appliances taking into account a realistic time-varying quadratic pricing of the energy bought from the distribution network. The solution approach relies on a decentralized optimization algorithm that is composed by a two-level iterative procedure combining Gauss-Seidel decomposition with competitive game formulation. A case study simulated in different scenarios demonstrates that the approach allows exploiting the potential of renewable energy sources’ sharing to reduce individual users’ energy consumption costs, limiting the peak average ratio of energy profiles and complying with the customer’s energy needs. © 2015 IEEE.
    @CONFERENCE{Carli20155903,
    author={Carli, R. and Dotoli, M.},
    title={A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes},
    journal={Proceedings of the IEEE Conference on Decision and Control},
    year={2015},
    volume={54rd IEEE Conference on Decision and Control,CDC 2015},
    pages={5903-5908},
    doi={10.1109/CDC.2015.7403147},
    art_number={7403147},
    note={cited By 29},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962024908&doi=10.1109%2fCDC.2015.7403147&partnerID=40&md5=194027b41fd3ace430f8f40254ace4de},
    abstract={The paper deals with the scheduling of energy activities of a group of interconnected users that buy energy from a producer and share a renewable energy source. The scheduling problem is stated and solved with a twofold goal. First, the model is formulated to ensure social welfare-optimal allocation of the energy produced from the shared renewable energy generator. Second, the model aims at cost-optimal planning of users' controllable appliances taking into account a realistic time-varying quadratic pricing of the energy bought from the distribution network. The solution approach relies on a decentralized optimization algorithm that is composed by a two-level iterative procedure combining Gauss-Seidel decomposition with competitive game formulation. A case study simulated in different scenarios demonstrates that the approach allows exploiting the potential of renewable energy sources' sharing to reduce individual users' energy consumption costs, limiting the peak average ratio of energy profiles and complying with the customer's energy needs. © 2015 IEEE.},
    author_keywords={Energy consumption; Games; Home appliances; Optimization; Pricing; Renewable energy sources; Resource management},
    keywords={Algorithms; Automation; Costs; Domestic appliances; Economics; Energy utilization; Intelligent buildings; Iterative methods; Natural resources; Optimization; Resource allocation; Scheduling; Time varying networks, Decentralized optimization; Decentralized resource allocation; Games; Optimal allocation; Renewable energy generators; Renewable energy source; Resource management; Scheduling problem, Renewable energy resources},
    references={Adika, C.O., Wang, L.F., Non-cooperative decentralized charging of homogeneous households' batteries in a smart grid (2014) IEEE Trans. Smart Grids, 5 (4), pp. 1855-1863; Agarwal, T., Cui, S., Noncooperative Games for Autonomous Consumer Load Balancing over Smart Grid, , http://arxiv.org/abs/1104.3802; Atzeni, I., Ordonez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R., Demand-side management via distributed energy generation and storage optimization (2013) IEEE Trans. Smart Grids, 4 (2), pp. 866-876; Barbato, A., Capone, A., Carello, G., Delfanti, M., Merlo, M., Zaminga, A., House energy demand optimization in single and multiuser scenarios (2011) Proc. IEEE Int. Conf. Smart Grids Comm., pp. 345-350. , Oct. 17-20; Bertsekas, D.P., Tsitsiklis, J.N., (1989) Parallel and Distributed Computation: Numerical Methods, 23. , Englewood Cliffs, NJ: Prentice Hall; Boyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, UK; Carli, R., Deidda, P., Dotoli, M., Pellegrino, R., Ranieri, L., An urban control center for the energy governance of a smart city (2014) Proc. IEEE Int. Conf. Emerg. Techn. Fact. Autom., , Sept. 16-19; Carli, R., Dotoli, M., Pellegrino, R., ICT and optimization for the energy management of smart cities: The street lighting decision panel (2015) Proc. IEEE Int. Conf. Emerg. Techn. Fact. Autom., , Sept. 8-11; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5648-5653. , Dec. 15-17; Cavraro, G., Carli, R., Zampieri, S., A distributed control algorithm for the minimization of the power generation cost in smart micro-grid (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5642-5647. , Dec. 15-17; Deng, R., Yang, Z., Chen, J., Asr, N.R., Chow, M.Y., Residential energy consumption scheduling: A coupled-constraint game approach (2014) IEEE Trans. Smart Grids, 5 (3), pp. 1340-1350. , May; Huang, Z., Zhu, T., Gu, Y., Irwin, D., Mishra, A., Shenoy, P., Minimizing electricity costs by sharing energy in sustainable microgrids Proc. ACM Conf. Embed. Sys. Ener.-Effic. Build., pp. 120-129. , Nov. 2014; Johari, R., Tsitsiklis, J.N., A game theoretic view of efficiency loss in resource allocation (2005) Advances in Control, Communication Networks, and Transportation Systems, pp. 203-223. , Birkhäuser, Boston; Katoh, N., Shioura, A., Ibaraki T, T., Resource allocation problems (2013) Handbook of Combinatorial Optimization, pp. 2897-2988; Kelly, F., Charging and rate control for elastic traffic (1997) Eur. Trans. Telecom., 8 (1), pp. 33-37; Mohsenian-Rad, A.-H., Leon-Garcia, A., Optimal residential load control with price prediction in real-time electricity pricing environments (2010) IEEE Trans. Smart Grids, 1, pp. 120-133; Parise, F., Colombino, M., Grammatico, S., Lygeros, J., Mean field constrained charging policy for large populations of Plug-in Electric Vehicles (2014) Proc. IEEE Int. Conf. Dec. Contr., pp. 5101-5106. , Dec. 15-17, Dec; Rosen, J.B., Existence and uniqueness of equilibrium points for concave n-person games (1965) Econometr., 33, pp. 347-351; Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M., A distributed algorithm for managing residential demand response in smart grids (2014) IEEE Trans. Ind. Inf., 10 (4), pp. 2385-2393. , Nov; Sun, B., Luh, P.B., Jia, Q.-S., Jiang, Z., Wang, F., Song, C., Building energy management: Integrated control of active and passive heating, cooling, lighting, shading, and ventilation systems (2013) IEEE Trans. Aut. Sci. Eng., 10 (3), pp. 588-602; Zhu, T., Xiao, S., Ping, Y., Towsley, D., Gong, W., A secure energy routing mechanism for sharing renewable energy in smart microgrid (2011) Proc. IEEE Int. Conf. Smart Grids Comm., pp. 143-148. , Oct. 17-20; Zhu, T., Huang, Z., Sharma, A., Su, J., Irwin, D., Mishra, A., Shenoy, P., Sharing renewable energy in smart microgrids (2013) Proc. ACM/IEEE Int. Conf. Cyber-Phys. Sys., pp. 219-228. , April},
    document_type={Conference Paper},
    source={Scopus},
    }

2014

  • Carli, R., Deidda, P., Dotoli, M. & Pellegrino, R. (2014) An urban control center for the energy governance of a smart city IN 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014.. doi:10.1109/ETFA.2014.7005155
    [BibTeX] [Abstract] [Download PDF]
    The paper addresses the emerging need of providing urban managers with tools for energy governance of smart cities. We present the architecture of a decision support system called Urban Control Center (UCC). The UCC measures the city energy performance and supports the decision maker in determining the optimal action plan for implementing smartness strategies in the city energy governance. To this aim, the UCC relies on a two-level decentralized programming model that integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem. © 2014 IEEE.
    @CONFERENCE{Carli2014,
    author={Carli, R. and Deidda, P. and Dotoli, M. and Pellegrino, R.},
    title={An urban control center for the energy governance of a smart city},
    journal={19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014},
    year={2014},
    doi={10.1109/ETFA.2014.7005155},
    art_number={7005155},
    note={cited By 11},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946686880&doi=10.1109%2fETFA.2014.7005155&partnerID=40&md5=7ae375bfea52c5c6212722aca93bc04d},
    abstract={The paper addresses the emerging need of providing urban managers with tools for energy governance of smart cities. We present the architecture of a decision support system called Urban Control Center (UCC). The UCC measures the city energy performance and supports the decision maker in determining the optimal action plan for implementing smartness strategies in the city energy governance. To this aim, the UCC relies on a two-level decentralized programming model that integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem. © 2014 IEEE.},
    keywords={Artificial intelligence; Decision support systems; Factory automation, Control center; Decision makers; Decision making unit; Energy optimization; Energy performance; Optimal actions; Programming models; Smart cities, Decision making},
    references={Abdoullaev, A., A smart world: A development model for intelligent cities (2011) Proc. 11th IEEE Int. Conf. Comp. Inf. Tech.; Adckison, D., (2013) IBM Cognos Business Intelligence, , Packt Publishing; Adepetu, A., Grogan, P., Alfaris, A., Svetinovic, D., De Weck, O., City. Net IES: A sustainability-oriented energy decision support system (2012) Proc. IEEE SysCon2012, pp. 1-7; Belissent, J., (2011) The Core of A Smart City Must Be Smart Governance, , Cambridge: Forrester Research; Bhowmick, A., Francellino, E., Glehn, L., Loredo, R., Nesbitt, P., Yu, S.W., Mishr, S., (2012) IBM Intelligent Operations Center for Smarter Cities Administration Guide IBM Redbooks; Caputo, P., Costa, G., Ferrari, S., A supporting method for defining energy strategies in the building sector at urban scale (2013) Ener. Pol., 55, pp. 261-270; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE SMC2013, pp. 1288-1293; Coutinho-Rodrigues, J., Simãoa, A., Henggeler Antunes, C., A GIS-based multicriteria spatial decision support system for planning urban infrastructures (2011) Dec. Supp. Sys., 51 (3), pp. 720-726; Dall'O, G., Norese, M.F., Galante, A., Novello, C., A multi-criteria methodology to support public administration decision making concerning sustainable energy action plans (2013) Ener., 6 (8), pp. 4308-4330; Trends and Projections in Europe 2013-Tracking Progress Towards Europe's Climate and Energy Targets until, , http://www.eea.europa.eu/publications/trends-and-projections-2013, EEA-European Environment Agency; Fatta, D., Naoum, D., Loizidou, M., Integrated environmental monitoring and simulation system for use as a management decision support tool in urban areas (2002) J. Environm. Man., 64 (4), pp. 333-343; Figueira, J., Greco, S., Ehrgott, M., (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, , Springer, Boston; Fiorucci, P., Minciardi, R., Robba, M., Sacile, R., Solid waste management in urban areas: Development and application of a decision support system (2003) Resour. Conserv. Rec., 37 (4), pp. 301-328; Juan, Y.-K., Wang, L., Wang, J., Leckie, J.O., Li, K.-M., A decision-support system for smarter city planning and management (2011) IBM J. Res. Develop., 55 (1-2), pp. 31-12; Keirstead, J., Jennings, M., Sivakumar, A., A review of urban energy system models: Approaches, challenges and opportunities (2012) Ren. Sust. Ener. Rev., 16, pp. 3847-3866; Marler, R.T., Arora, J.S., Survey of multi-objective optimization methods for engineering (2004) Structur. Multidisc. Optim., 26 (6), pp. 369-395; McCormick, K., Abbott, D., Brown, M.S., Khabaza, T., Mutchler, S.R., (2013) IBM SPSS Modeler Cookbook, , Packt Publishing; Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., Morris, R., Smarter cities and their innovation challenges (2011) Computer, 44 (6), pp. 32-39; Pearson, L.J., Coggan, A., Proctor, W., Smith ., T.F., A sustainable decision support framework for urban water management (2010) Water Resour. Manag., 24, pp. 363-376; Quintero, A., Konaré, D., Pierre, S., Prototyping an intelligent decision support system for improving urban infrastructures management (2005) Eur. J. Op. Res., 162 (3), pp. 654-672; Stoilov, T., Stoilova, K., (1999) Noniterative Coordination in Multilevel Systems, , Kluwer; Vicente, L.N., Calamai, P.H., Bilevel and multilevel programming: A bibliography review (1994) J. Glob. Optim., 5 (3), pp. 291-306},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R. & Dotoli, M. (2014) Energy scheduling of a smart home under nonlinear pricing IN Proceedings of the IEEE Conference on Decision and Control., 5648-5653. doi:10.1109/CDC.2014.7040273
    [BibTeX] [Abstract] [Download PDF]
    The paper focuses on the scheduling of energy activities in smart homes equipped with controllable electrical appliances, renewable energy sources, dispatchable energy generators, and energy storage systems. We formulate a mixed integer quadratic programming energy scheduling algorithm for cost minimization under nonlinear pricing. The scheduling technique manages the use of electrical appliances, plans the energy production and supplying, and programs the storage systems charging/discharging. A case study simulated in different scenarios demonstrates that the approach allows full exploitation of the potential of local energy generation and storage to reduce the individual user energy consumption costs, while complying with the customer energy needs. © 2014 IEEE.
    @CONFERENCE{Carli20145648,
    author={Carli, R. and Dotoli, M.},
    title={Energy scheduling of a smart home under nonlinear pricing},
    journal={Proceedings of the IEEE Conference on Decision and Control},
    year={2014},
    volume={2015-February},
    number={February},
    pages={5648-5653},
    doi={10.1109/CDC.2014.7040273},
    art_number={7040273},
    note={cited By 34},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961994657&doi=10.1109%2fCDC.2014.7040273&partnerID=40&md5=55539508a30611826d5d2e0f0b11c853},
    abstract={The paper focuses on the scheduling of energy activities in smart homes equipped with controllable electrical appliances, renewable energy sources, dispatchable energy generators, and energy storage systems. We formulate a mixed integer quadratic programming energy scheduling algorithm for cost minimization under nonlinear pricing. The scheduling technique manages the use of electrical appliances, plans the energy production and supplying, and programs the storage systems charging/discharging. A case study simulated in different scenarios demonstrates that the approach allows full exploitation of the potential of local energy generation and storage to reduce the individual user energy consumption costs, while complying with the customer energy needs. © 2014 IEEE.},
    references={Achterberg, T., SCIP: Solving constraint integer programs (2009) Math. Progr. Comp., 1, pp. 1-41; Barbato, A., Capone, A., Carello, G., Delfanti, M., Merlo, M., Zaminga, A., Cooperative and non-cooperative house energy optimization in a smart grid perspective (2011) Proc. IEEE Int. Symp. World of Wireless, Mobile and Multimedia Networks, 6p; Carli, R., Dotoli, M., Pellegrino, R., Ranieri, L., Measuring and managing the smartness of cities: A framework for classifying performance indicators (2013) Proc. IEEE Conf. Systems, Man and Cybernetics (SMC 2013), pp. 1288-1293; Di Giorgio, A., Pimpinella, L., Liberati, F., A model predictive control approach to the load shifting problem in a household equipped with an energy storage unit (2012) Proc. MED 2012 Conf, pp. 1491-1498; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., Costantino, N., A nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity market (2014) Comp. Ind., 65 (4), pp. 774-785; Gatsis, N., Giannakis, G.B., Residential demand response with interruptible tasks: Duality and algorithms (2011) Proc. 50th IEEE CDC-IFAC ECC Int. Conf., pp. 1-6; Holland, S., Mansur, E., The short-run effects of time-varying prices in competitive electricity markets (2006) Ener. J., 27, pp. 127-155; Hubert, T., Grijalva, S., Realizing smart grid benefits requires energy optimization algorithms at residential level (2011) Proc. Innovative Smart Grid Technologies, 8p; Integration of demand side management, distributed generation, renewable energy sources and energy storages (2008) IEA Demand Side Man. Prog. Tech. Rep.; Ioakimidis, C.S., Eliasstam, H., Rycerski, P., Solar power forecasting of a residential location as part of a smart grid structure (2012) Proc. IEEE Int. Conf. Energytech, 6p; Kailas, A., Cecchi, V., Mukherjee, A., A survey of contemporary technologies for smart home energy management (2013) Green Information and Communication Systems Handbook, , Academic Press; Kok, K., Karnouskos, S., Nestle, D., Dimeas, A., Weidlich, A., Warmer, C., Strauss, P., Lioliou, V., Smart houses for a smart grid (2009) Proc. Int. Conf. on Electricity Distribution CIRED, 4p; Mohsenian-Rad, A.-H., Leon-Garcia, A., Optimal residential load control with price prediction in real-time electricity pricing environments (2010) IEEE Trans. Smart Grids, 1, pp. 120-133; Mohsenian-Rad, A.-H., Wong, V.W.S., Jatskevich, J., Schober, R., Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid (2010) Proc. Innovative Smart Grid Technologies, 8p; Molitor, C., Togawa, K., Bolte, S., Monti, A., Load models for home energy system and micro grid simulations (2012) Proc. Innovative Smart Grid Technologies, 6p; Sun, B., Luh, P.B., Jia, Q.-S., Jiang, Z., Wang, F., Song, C., Building energy management: Integrated control of active and passive heating, cooling, lighting, shading, and ventilation systems (2013) IEEE Trans. Aut. Sci. Eng., 10 (3), pp. 588-602; Tsui, K.M., Chan, S.C., Demand response optimi ation for smart home scheduling under real-time pricing (2012) IEEE Trans. Smart Grids, 3 (4), pp. 1812-1821; University of Auckland, OPTI - A Free MATLAB Toolbox for Optimization, , http://www.i2c2.aut.ac.nz/Wiki/OPTI/, Available at; Wacks, K.P., Utility load management using home automation (1991) IEEE Trans. Cons. Electr., 37, pp. 168-174; Walt, R.R., The BioMax/spl trade/new biopower option for distributed generation and CHP (2004) Proc. IEEE Power Engineering Soc. Gen. Meet., 2, pp. 1653-1656},
    document_type={Conference Paper},
    source={Scopus},
    }

2013

  • Carli, R., Dotoli, M., Pellegrino, R. & Ranieri, L. (2013) Measuring and managing the smartness of cities: A framework for classifying performance indicators IN Proceedings – 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013., 1288-1293. doi:10.1109/SMC.2013.223
    [BibTeX] [Abstract] [Download PDF]
    Due to the continuous increase of the world population living in cities, it is crucial to identify strategic plans and perform associated actions to make cities smarter, i.e., more operationally efficient, socially friendly, and environmentally sustainable, in a cost effective manner. To achieve these goals, emerging smart cities need to be optimally and intelligently measured, monitored, and managed. In this context the paper proposes the development of a framework for classifying performance indicators of a smart city. It is based on two dimensions: The degree of objectivity of observed variables and the level of technological advancement for data collection. The paper shows an application of the presented framework to the case of the Bari municipality (Italy). © 2013 IEEE.
    @CONFERENCE{Carli20131288,
    author={Carli, R. and Dotoli, M. and Pellegrino, R. and Ranieri, L.},
    title={Measuring and managing the smartness of cities: A framework for classifying performance indicators},
    journal={Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013},
    year={2013},
    pages={1288-1293},
    doi={10.1109/SMC.2013.223},
    art_number={6721976},
    note={cited By 62},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893629343&doi=10.1109%2fSMC.2013.223&partnerID=40&md5=2e7a1d0751ad2b7391780dc51c8b26b0},
    abstract={Due to the continuous increase of the world population living in cities, it is crucial to identify strategic plans and perform associated actions to make cities smarter, i.e., more operationally efficient, socially friendly, and environmentally sustainable, in a cost effective manner. To achieve these goals, emerging smart cities need to be optimally and intelligently measured, monitored, and managed. In this context the paper proposes the development of a framework for classifying performance indicators of a smart city. It is based on two dimensions: The degree of objectivity of observed variables and the level of technological advancement for data collection. The paper shows an application of the presented framework to the case of the Bari municipality (Italy). © 2013 IEEE.},
    author_keywords={Information and communication technologies; Management; Monitoring; Smart cities; Smartness indicators},
    keywords={Cost effective; Data collection; Information and Communication Technologies; Performance indicators; Smart cities; Strategic plan; Technological advancement; World population, Cybernetics; Electronic commerce; Information technology; Management; Monitoring, Benchmarking},
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    document_type={Conference Paper},
    source={Scopus},
    }