**PhD student**

**E-mail: **

## Short bio

Paolo Scarabaggio received the B.Sc. degree in Mechanical Engineering and the M.Sc. degree in Management Engineering from the Politecnico di Bari, Bari, Italy.

He is currently working toward the Ph.D. degree in the Department of Electrical and Information Engineering of the same university under the supervision of Prof. Engr. Mariagrazia Dotoli.

His research interests include modeling, optimization, and control of energy distribution systems, distributed control systems, and model predictive control.

### Research interests

- energy storage systems;
- stochastic optimization;
- game theory
- smart grids

## Publications

### 2021

- 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}, }`

- 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

- 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}, }`

- 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}, references={Lawrenz, L., Xiong, B., Lorenz, L., Krumm, A., Hosenfeld, H., Burandt, T., Löffler, K., Von Hirschhausen, C., Exploring energy pathways for the low-carbon transformation in India-A model-based analysis (2018) Energies, 11, p. 3001; Oureilidis, K., Malamaki, K.N., Gallos, K., Tsitsimelis, A., Dikaiakos, C., Gkavanoudis, S., Cvetkovic, M., Ramos, J.L.M., Ancillary Services Market Design in Distribution Networks: Review and Identification of Barriers (2020) Energies, 13, p. 917; Eto, J.H., Berkeley, L., Undrill, J., Mackin, P., Daschmans, R., Williams, B., Haney, B., Illian, H., (2010) Use of Frequency Response Metrics to Assess the Planning and Operating Requirements for Reliable Integration of Variable Renewable Generation, , Lawrence Berkeley National Laboratory (LBNL): Berkeley, CA, USA; Peng, C., Zou, J., Lian, L., Dispatching strategies of electric vehicles participating in frequency regulation on power grid: A review (2017) Renew. 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- 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}, source={Scopus}, }`

- 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)}, references={Wang, Y., Yu, C., Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning (2017) International Journal of Information Management, 37 (3), pp. 179-189; Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., Guo, R., The independent cascade and linear threshold models (2015) Diffusion in Social Networks, pp. 35-48. , Springer; Lu, F., Zhang, W., Shao, L., Jiang, X., Xu, P., Jin, H., Scalable influence maximization under independent cascade model (2017) Journal of Network and Computer Applications, 86, pp. 15-23; Liben-Nowell, D., Kleinberg, J., Tracing information flow on a global scale using internet chain-letter data (2008) Proceedings of the National Academy of Sciences, 105 (12), pp. 4633-4638; Gomez-Rodriguez, M., Leskovec, J., Krause, A., Inferring networks of diffusion and influence (2012) Acm Transactions on Knowledge Discovery from Data (TKDD), 5 (4), p. 21; Domingos, P., Richardson, M., Mining the network value of customers (2001) Proceedings of the Seventh Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 57-66. , ACM; Richardson, M., Domingos, P., Mining knowledge-sharing sites for viral marketing (2002) Proceedings of the Eighth Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 61-70. , ACM; Kempe, D., Kleinberg, J., Tardos, E., Maximizing the spread of influence through a social network (2003) Proceedings of the Ninth Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 137-146. , ACM; Chen, W., Wang, C., Wang, Y., Scalable influence maximization for prevalent viral marketing in large-scale social networks (2010) Proceedings of the 16th Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 1029-1038; Kempe, D., Kleinberg, J., Tardos, E., Influential nodes in a diffusion model for social networks (2005) International Colloquium on Automata, Languages, and Programming, pp. 1127-1138. , Springer; Zhou, S., Cox, I.J., Hansen, L.K., Second-order assortative mixing in social networks (2017) International Workshop on Complex Networks, pp. 3-15. , Springer; Zhou, M.-Y., Xiong, W.-M., Wu, X.-Y., Zhang, Y.-X., Liao, H., Overlapping influence inspires the selection of multiple spreaders in complex networks (2018) Physica A: Statistical Mechanics and Its Applications, 508, pp. 76-83; Ma, L.-L., Ma, C., Zhang, H.-F., Wang, B.-H., Identifying influential spreaders in complex networks based on gravity formula (2016) Physica A: Statistical Mechanics and Its Applications, 451, pp. 205-212; Newman, M.E., Watts, D.J., Strogatz, S.H., Random graph models of social networks (2002) Proceedings of the National Academy of Sciences, 99, pp. 2566-2572; Rosa, D., Giua, A., On the spread of innovation in social networks (2013) Ifac Proceedings Volumes, 46 (27), pp. 322-327; Yang, W., Brenner, L., Giua, A., Influence maximization in independent cascade networks based on activation probability computation (2019) Ieee Access, 7, pp. 13745-13757; Yang, W., Brenner, L., Giua, A., Computation of activation probabilities in the independent cascade model (2018) 2018 5th International Conference on Control, Decision and Information Technologies, pp. 791-797. , IEEE; Aggarwal, C.C., Khan, A., Yan, X., On flow authority discovery in social networks (2011) Proceedings of the 2011 Siam International Conference on Data Mining. Siam, pp. 522-533; Yang, Y., Chen, E., Liu, Q., Xiang, B., Xu, T., Shad, S.A., On approximation of real-world influence spread (2012) Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 548-564. , Springer; Banerjee, S., Jenamani, M., Pratihar, D.K., (2018) A Survey on Influence Maximization in a Social Network, , arXiv preprint; Kunegis, J., Konect: The koblenz network collection (2013) Proceedings of the 22nd International Conference on World Wide Web, pp. 1343-1350}, document_type={Conference Paper}, 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}, references={Alleman, T., Torfs, E., Nopens, I., COVID-19: From model prediction to model predictive control (2020) https://biomath. ugent. be/sites/default/files/2020-04/Alleman_etal_v2. pdf, accessed April, 30, p. 2020; Bai, Y., Yao, L., Wei, T., Tian, F., Jin, D.-Y., Chen, L., Wang, M., Presumed asymptomatic carrier transmission of covid-19 (2020) Jama, 323 (14), pp. 1406-1407; Bertozzi, A.L., Franco, E., Mohler, G., Short, M.B., Sledge, D., The challenges of modeling and forecasting the spread of covid-19 (2020) arXiv preprint arXiv:2004.04741; Bin, M., Cheung, P., Crisostomi, E., Ferraro, P., Myant, C., Parisini, T., Shorten, R., On fast multi-shot epidemic interventions for post lock-down mitigation: Implications for simple covid-19 models (2020) arXiv preprint arXiv:2003.09930; Bin, M., Cheung, P., Crisostomi, E., Ferraro, P., Myant, C., Parisini, T., Shorten, R., On fast multi-shot epidemic interventions for post lock-down mitigation: Implications for simple covid-19 models (2020) arXiv preprint arXiv:2003.09930; Brugnano, L., Iavernaro, F., Zanzottera, P., A multiregional extension of the SIR model, with application to the COVID-19 spread in Italy (2020) Mathematical Methods in the Applied Sciences, , Wiley Online Library; Bussell, E.H., Dangerfield, C.E., Gilligan, C.A., Cunniffe, N.J., Applying optimal control theory to complex epidemiological models to inform real-world disease management (2019) Philosophical Transactions of the Royal Society B, 374 (1776), p. 20180284; Calafiore, G.C., Novara, C., Possieri, C., A modified SIR model for the COVID-19 contagion in Italy (2020) arXiv preprint arXiv:2003.14391; Carli, R., Cavone, G., Dotoli, M., Epicoco, N., Scarabaggio, P., Model predictive control for thermal comfort optimization in building energy management systems (2019) 2019 ieee international conference on systems, man and cybernetics (smc), pp. 2608-2613. , IEEE; Casella, F., Can the COVID-19 epidemic be controlled on the basis of daily test reports? (2021) IEEE Control Systems Letters, 5 (3), pp. 1079-1084; Chen, Z., Discrete-time vs. continuous-time epidemic models in networks (2019) IEEE Access, 7, pp. 127669-127677; Della Rossa, F., Salzano, D., Di Meglio, A., Intermittent yet coordinated regional strategies can alleviate the COVID-19 epidemic: A network model of the Italian case (2020) arXiv preprint arXiv:2005.07594; Di Domenico, L., Pullano, G., Coletti, P., Hens, N., Colizza, V., Expected impact of school closure and telework to mitigate COVID-19 epidemic in France (2020) Technical Report, , Report; Ferguson, N., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Cuomo-Dannenburg, G., Report 9: Impact of non-pharmaceutical interventions (npis) to reduce covid19 mortality and healthcare demand (2020) Imperial College London, 10, p. 77482; (2020), https://www.gazzettaufficiale.it/eli/id/2020/04/27/20A02352/sg, Gazzetta Ufficiale Repubblica Italiana, Decree of the President of the Council of Ministers april 26, 2020: urgent measures regarding the containment and management of the COVID-19 epidemiological emergency (in Italian) [Online; accessed 26. 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- 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. 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### 2019

- 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. 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