Seyed Mohsen HOSSEINI

Post-Doc Research Fellow

E-mail:


Short bio

Seyed Mohsen Hosseini received the B.S. degree from the Shahed University, Tehran, Iran, in 2010, and the M.S. degree from the Semnan University, Semnan, Iran, in 2013, both in Electrical Engineering.  

He is currently working toward the Ph.D. degree in the Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy. From September 2019 to March 2020, he was a visiting Ph.D. student with the Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K. His research interests include modeling, optimization and control of energy systems, distributed control systems, robust control and model predictive control. 

Research interests

  • automation systems;
  • energy management and control of smart microgrids;
  • robust control;
  • model predictive control;
  • switching converters, inverters and rectifiers;
  • harmonic filters and PFC converters.

Publications

2021

  • 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},
    references={Tan, Y., Cao, Y., Li, Y., Lee, K.Y., Jiang, L., Li, S., Optimal day-ahead operation considering power quality for active distribution networks (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 425-436. , Apr; 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. , Sep; Fujimoto, Y., Distributed energy management for comprehensive utilization of residential photovoltaic outputs (2018) IEEE Trans. Smart Grid, 9 (2), pp. 1216-1227. , Mar; Yousefi, M., Hajizadeh, A., Soltani, M., Energy management strategies for smart home regarding uncertainties: State of the art, trends, and challenges (2018) Proc. IEEE Int. Conf. Ind. Technol. (ICIT), pp. 1219-1225. , Feb; Rahmani-Andebili, M., Shen, H., Energy scheduling for a smart home applying stochastic model predictive control (2016) Proc. 25th Int. Conf. Comput. Commun. Netw. (ICCCN), pp. 1-6. , Waikoloa, HI, USA, Aug; Yang, Y., Jia, Q.-S., Guan, X., Zhang, X., Qiu, Z., Deconinck, G., Decentralized EV-based charging optimization with building integrated wind energy (2019) IEEE Trans. Autom. Sci. Eng., 16 (3), pp. 1002-1017. , Jul; Teng, F., Strbac, G., Full stochastic scheduling for low-carbon electricity systems (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 461-470. , Apr; 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. , May; Yan, B., Luh, P.B., Warner, G., Zhang, P., Operation and design optimization of microgrids with renewables (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 573-585. , Apr; Verrilli, F., 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. , Apr; Ouammi, A., Achour, Y., Zejli, D., Dagdougui, H., Supervisory model predictive control for optimal energy management of networked smart greenhouses integrated microgrid (2020) IEEE Trans. Autom. Sci. Eng., 17 (1), pp. 117-128. , Jan; Carli, R., Dotoli, M., Energy scheduling of a smart home under nonlinear pricing (2014) Proc. 53rd IEEE Conf. Decis. Control, pp. 5648-5653. , Los Angeles, CA, USA, Dec; Carli, R., Dotoli, M., Decentralized control for residential energy management of a smart users microgrid with renewable energy exchange (2019) IEEE/CAA J. Automatica Sinica, 6 (3), pp. 641-656. , May; Wang, C., Zhou, Y., Wu, J., Wang, J., Zhang, Y., Wang, D., Robustindex method for household load scheduling considering uncertainties of customer behavior (2015) IEEE Trans. Smart Grid, 6 (4), pp. 1806-1818. , Jul; Kim, B.-G., Zhang, Y., Van-Der-Schaar, M., Lee, J.-W., Dynamic pricing and energy consumption scheduling with reinforcement learning (2016) IEEE Trans. Smart Grid, 7 (5), pp. 2187-2198. , Sep; 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. Innov. Smart Grid Technol. (ISGT), pp. 1-6. , Gothenburg, Sweden, Jan; 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 (2), p. 301. , Jan; Rajasekhar, B., Pindoriya, N., Tushar, W., Yuen, C., Collaborative energy management for a residential community: A non-cooperative and evolutionary approach (2019) IEEE Trans. Emerg. Topics Comput. Intell., 3 (3), pp. 177-192. , Jun; Samadi, P., Schober, R., Wong, V.W.S., Optimal energy consumption scheduling using mechanism design for the future smart grid (2011) Proc. IEEE Int. Conf. Smart Grid Commun. (SmartGridComm), pp. 369-374. , Brussels, Belgium, Oct; Tushar, M.H.K., Assi, C., Maier, M., Uddin, M.F., Smart microgrids: Optimal joint scheduling for electric vehicles and home appliances (2014) IEEE Trans. Smart Grid, 5 (1), pp. 239-250. , Jan; Yue, J., Hu, Z., Li, C., Vasquez, J.C., Guerrero, J.M., Economic power schedule and transactive energy through an intelligent centralized energy management system for a DC residential distribution system (2017) Energies, 10 (7), p. 916. , Jul; Kim, T.T., Poor, H.V., Scheduling power consumption with price uncertainty (2011) IEEE Trans. Smart Grid, 2 (3), pp. 519-527. , Sep; Wu, X., Hu, X., Yin, X., Moura, S.J., Stochastic optimal energy management of smart home with PEV energy storage (2018) IEEE Trans. Smart Grid, 9 (3), pp. 2065-2075. , May; Munkhammar, J., Widén, J., Rydén, J., On a probability distribution model combining household power consumption, electric vehicle homecharging and photovoltaic power production (2015) Appl. Energy, 142, pp. 135-143. , Mar; Kou, P., Liang, D., Gao, L., Stochastic energy scheduling in microgrids considering the uncertainties in both supply and demand (2018) IEEE Syst. J., 12 (3), pp. 2589-2600. , Sep; 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. , Dec; Guo, L., Wu, H.-C., Zhang, H., Xia, T., Mehraeen, S., Robust optimization for home-load scheduling under price uncertainty in smart grids (2015) Proc. Int. Conf. Comput., Netw. Commun. (ICNC), pp. 487-493. , Garden Grove, CA, USA, Feb; 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. , Jul; Yi, W., Zhang, Y., Zhao, Z., Huang, Y., Multiobjective robust scheduling for smart distribution grids: Considering renewable energy and demand response uncertainty (2018) IEEE Access, 6, pp. 45715-45724; Wang, C., Zhou, Y., Jiao, B., Wang, Y., Liu, W., Wang, D., Robust optimization for load scheduling of a smart home with photovoltaic system (2015) Energy Convers. Manage., 102, pp. 247-257. , Sep; Paul, S., Padhy, N.P., Resilient scheduling portfolio of residential devices and plug-in electric vehicle by minimizing conditional value at risk (2019) IEEE Trans. Ind. Informat., 15 (3), pp. 1566-1578. , Mar; Hussain, A., Bui, V.-H., Kim, H.-M., Robust optimal operation of AC/DC hybrid microgrids under market price uncertainties (2018) IEEE Access, 6, pp. 2654-2667; 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. , Jan; Doulabi, H.H., Jaillet, P., Pesant, G., Rousseau, L.M., Exploiting the structure of two-stage robust optimization models with exponential scenarios INFORMS J. Comput., , to be published; Zeng, B., Zhao, L., Solving two-stage robust optimization problems using a column-and-constraint generation method (2013) Oper. Res. Lett., 41 (5), pp. 457-461. , Sep; Ben-Tal, A., Goryashko, A., Guslitzer, E., Nemirovski, A., Adjustable robust solutions of uncertain linear programs (2004) Math. Program., 99 (2), pp. 351-376. , Mar; Aharon, B.-T., Boaz, G., Shimrit, S., Robust multi-echelon multiperiod inventory control (2009) Eur. J. Oper. Res., 199 (3), pp. 922-935. , Dec; Ouorou, A., Tractable approximations to a robust capacity assignment model in telecommunications under demand uncertainty (2013) Comput. Oper. Res., 40 (1), pp. 318-327. , Jan; Bertsimas, D., Brown, D., Caramanis, C., Theory and applications of robust optimization (2011) SIAM Rev., 53 (3), pp. 464-501; De Ruiter, F.J.C.T., Ben-Tal, A., Brekelmans, R.C.M., Hertog, D.D., Robust optimization of uncertain multistage inventory systems with inexact data in decision rules (2017) Comput. Manag. Sci., 14 (1), pp. 45-66; Bertsimas, D., Sim, M., The price of robustness (2004) Oper. Res., 52 (1), pp. 35-53. , Feb; Wu, X., Wang, Z., Du, J., Wu, G., Optimal operation of residential microgrids in the Harbin area (2018) IEEE Access, 6, pp. 30726-30736; Ahmadi, M., Rosenberger, J.M., Lee, W., Kulvanitchaiyanunt, A., Optimizing load control in a collaborative residential microgrid environment (2015) IEEE Trans. Smart Grid, 6 (3), pp. 1196-1207. , May; Yang, X., Zhang, Y., Wu, H., He, H., An event-driven ADR approach for residential energy resources in microgrids with uncertainties (2019) IEEE Trans. Ind. Electron., 66 (7), pp. 5275-5288. , Jul; Anvari-Moghaddam, A., Guerrero, J.M., Vasquez, J.C., Monsef, H., Rahimi-Kian, A., Efficient energy management for a grid-tied residential microgrid (2017) IET Gener., Transmiss. Distrib., 11 (11), pp. 2752-2761. , Aug; Orwig, K.D., Recent trends in variable generation forecasting and its value to the power system (2015) IEEE Trans. Sustain. 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},
    }

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},
    }

2019

  • 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},
    }
  • 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},
    }
  • 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},
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    }