Information

+39 080 596 3843
ti.ab1651259894ilop@1651259894oigga1651259894barac1651259894s.olo1651259894ap1651259894
Scopus Researcher Page
Curriculum Vitae (english)
Pubblicazioni

Paolo SCARABAGGIO

PhD student

Paolo Scarabaggio received the B.Sc. degree in Mechanical Engineering and the M.Sc. degree in Management Engineering from Politecnico di Bari, Italy, in 2017, and 2019 respectively. In 2019, he visited the Technical University of Delft, The Netherlands.

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, game theory, and control of complex multi-agent systems, with application in energy distribution systems, and social networks.


Pubblicazioni

2022

  • Scarabaggio, P., Grammatico, S., Carli, R. & Dotoli, M. (2022) Distributed Demand Side Management with Stochastic Wind Power Forecasting. IN IEEE Transactions on Control Systems Technology, 30.97-112. 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. © 1993-2012 IEEE.
    @ARTICLE{Scarabaggio202297,
    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={2022},
    volume={30},
    number={1},
    pages={97-112},
    doi={10.1109/TCST.2021.3056751},
    note={cited By 35},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100918524&doi=10.1109%2fTCST.2021.3056751&partnerID=40&md5=4f491e205e325ad467291e71ce8dbff8},
    affiliation={Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, 70125, Italy; Delft Center for Systems and Control, TU Delft, Delft, CD 2628, Netherlands},
    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. © 1993-2012 IEEE.},
    author_keywords={Demand-side management (DSM); model predictive control; sample average approximation (SAA); smart grid; stochastic optimization},
    document_type={Article},
    source={Scopus},
    }

2021

  • Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N. & Dotoli, M. (2021) Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration IN IEEE International Conference on Automation Science and Engineering., 990-995. doi:10.1109/CASE49439.2021.9551418
    [BibTeX] [Abstract] [Download PDF]
    The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. 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 when partially and fully immune individuals are included in the population. Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses. We calibrate the model on the Israeli scenario using real data and we estimate the impact of the vaccine administration on the virus dynamics. Lastly, we assess the impact of the first dose of the Pfizer’s vaccine confirming the results of clinical tests. © 2021 IEEE.
    @CONFERENCE{Scarabaggio2021990,
    author={Scarabaggio, P. and Carli, R. and Cavone, G. and Epicoco, N. and Dotoli, M.},
    title={Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2021},
    volume={2021-August},
    pages={990-995},
    doi={10.1109/CASE49439.2021.9551418},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117035432&doi=10.1109%2fCASE49439.2021.9551418&partnerID=40&md5=489bcdcd710fb127ad522dbebe163265},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy; Ctr. of Excellence Dews (Des. Methodologies for Embedded Controllers Wireless Interconnect and Syst.-on-chip), University of l'Aquila, Department of Information Engineering, Computer Science and Mathematics (DISIM), L'Aquila, Italy},
    abstract={The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. 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 when partially and fully immune individuals are included in the population. Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses. We calibrate the model on the Israeli scenario using real data and we estimate the impact of the vaccine administration on the virus dynamics. Lastly, we assess the impact of the first dose of the Pfizer's vaccine confirming the results of clinical tests. © 2021 IEEE.},
    author_keywords={COVID-19; model predictive control; pandemic modeling; vaccine; vaccine distribution},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Jantzen, J. & Dotoli, M. (2021) Stochastic model predictive control of community energy storage under high renewable penetration IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 973-978. doi:10.1109/MED51440.2021.9480353
    [BibTeX] [Abstract] [Download PDF]
    This paper focuses on the robust optimal on-line scheduling of a grid-connected energy community, where users are equipped with non-controllable (NCLs) and controllable loads (CLs) and share renewable energy sources (RESs) and a community energy storage system (CESS). Leveraging on the pricing signals gathered from the power grid and the predicted values for local production and demand, the energy activities inside the community are decided by a community energy manager. Differently from literature contributions commonly focused on deterministic optimal control schemes, to cope with the uncertainty that affects the forecast of the inflexible demand profile and the renewable production curve, we propose a Stochastic Model Predictive Control (MPC) approach aimed at minimizing the community energy costs. The effectiveness of the method is validated through numerical experiments on the marina of Ballen, Samso (Denmark). The comparison with a standard deterministic optimal control approach shows that the proposed stochastic MPC achieves higher performance in terms of minimized energy cost and maximized self-consumption of on-site production. © 2021 IEEE.
    @CONFERENCE{Scarabaggio2021973,
    author={Scarabaggio, P. and Carli, R. and Jantzen, J. and Dotoli, M.},
    title={Stochastic model predictive control of community energy storage under high renewable penetration},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={973-978},
    doi={10.1109/MED51440.2021.9480353},
    art_number={9480353},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113644274&doi=10.1109%2fMED51440.2021.9480353&partnerID=40&md5=6156faadbaf382124749771a715a60df},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy; Samso Energy Academy, Samso, Denmark},
    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},
    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},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy; University of l'Aquila, Computer Science and Mathematics (DISIM), Center of Excellence DEWS (Design Methodologies for Embedded Controllers, Wireless Interconnect and System-on-chip), Department of Information Engineering, L'Aquila, Italy},
    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},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N. & Dotoli, M. (2021) Nonpharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread. IN IEEE Transactions on Automation Science and Engineering, .. doi:10.1109/TASE.2021.3111338
    [BibTeX] [Abstract] [Download PDF]
    This article proposes a stochastic nonlinear model predictive controller to support policymakers in determining robust optimal nonpharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying SIRCQTHE epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socioeconomic categories) to minimize the socioeconomic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified nonpharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries’ characteristics and different levels of the spatial scale. IEEE
    @ARTICLE{Scarabaggio2021,
    author={Scarabaggio, P. and Carli, R. and Cavone, G. and Epicoco, N. and Dotoli, M.},
    title={Nonpharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2021},
    doi={10.1109/TASE.2021.3111338},
    note={cited By 2},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115686920&doi=10.1109%2fTASE.2021.3111338&partnerID=40&md5=ef02a3b0f8f2eeff0ec2f0e0a861a0b8},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, 70126 Bari, Italy (e-mail: ti.ab1651259894ilop@1651259894oigga1651259894barac1651259894s.olo1651259894ap1651259894); Department of Electrical and Information Engineering, Polytechnic of Bari, 70126 Bari, Italy.; Department of Information Engineering, Computer Science and Mathematics (DISIM), University of L'Aquila, 67100 L'Aquila, Italy, and also with the Center of Excellence DEWS (Design methodologies for Embedded controllers, Wireless interconnect and System-on-chip), University of L'Aquila, 67100 L'Aquila, Italy.},
    abstract={This article proposes a stochastic nonlinear model predictive controller to support policymakers in determining robust optimal nonpharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying SIRCQTHE epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socioeconomic categories) to minimize the socioeconomic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified nonpharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries' characteristics and different levels of the spatial scale. IEEE},
    author_keywords={COVID-19; COVID-19; Data models; epidemic control; Medical services; mitigation strategies; pandemic modeling; Pandemics; Predictive models; stochastic model predictive control (MPC).; Stochastic processes; Uncertainty},
    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 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097170993&doi=10.23919%2fAEIT50178.2020.9241136&partnerID=40&md5=a3abf95802bb66dc9a2d8715976cc126},
    affiliation={Polytechnic of Bari, Dept. of Electrical and Information Engineering, Bari, Italy},
    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},
    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 7},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090919511&doi=10.3390%2fen13174586&partnerID=40&md5=d7f07f0a819d149b5f1c143b707e731d},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Via Orabona 4, Bari, 70125, Italy},
    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)},
    document_type={Article},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R. & Dotoli, M. (2020) A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks IN IEEE International Conference on Automation Science and Engineering., 1281-1286. doi:10.1109/CASE48305.2020.9216800
    [BibTeX] [Abstract] [Download PDF]
    Traditionally, the management of power distribution networks relies on the centralized implementation of the optimal power flow and, in particular, the minimization of the generation cost and transmission losses. Nevertheless, the increasing penetration of both renewable energy sources and independent players such as ancillary service providers in modern networks have made this centralized framework inadequate. Against this background, we propose a noncooperative game-theoretic framework for optimally controlling energy storage systems (ESSs) in power distribution networks. Specifically, in this paper we address a power grid model that comprehends traditional loads, distributed generation sources and several independent energy storage providers, each owning an individual ESS. Through a rolling-horizon approach, the latter participate in the grid optimization process, aiming both at increasing the penetration of distributed generation and leveling the power injection from the transmission grid. Our framework incorporates not only economic factors but also grid stability aspects, including the power flow constraints. The paper fully describes the distribution grid model as well as the underlying market hypotheses and policies needed to force the energy storage providers to find a feasible equilibrium for the network. Numerical experiments based on the IEEE 33-bus system confirm the effectiveness and resiliency of the proposed framework. © 2020 IEEE.
    @CONFERENCE{Scarabaggio20201281,
    author={Scarabaggio, P. and Carli, R. and Dotoli, M.},
    title={A game-theoretic control approach for the optimal energy storage under power flow constraints in distribution networks},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2020},
    volume={2020-August},
    pages={1281-1286},
    doi={10.1109/CASE48305.2020.9216800},
    art_number={9216800},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094128798&doi=10.1109%2fCASE48305.2020.9216800&partnerID=40&md5=e4802c482f96ac90afd6c7a2c7f2b199},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Bari, Italy},
    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.},
    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},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy},
    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.},
    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},
    affiliation={Department of Electrical and Information Engineering of the, Polytechnic of Bari, Italy; Delft Center for Systems and Control, TU Delft, Netherlands},
    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},
    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 29},
    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},
    affiliation={Dept. of Electrical and Information Engineering, Polytechnic of Bari via Orabona 4, Bari, 70125, Italy; Center of Excellence DEWS, Dept. of Information Engineering, Computer Science and Mathematics, University of L'Aquila via Vetoio (Coppito 1), L'Aquila, 67100, Italy},
    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},
    document_type={Article},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Epicoco, N., Di Ferdinando, M., Scarabaggio, P. & Dotoli, M. (2020) Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles. IN Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12338 LNCS.84-99. doi:10.1007/978-3-030-61746-2_7
    [BibTeX] [Abstract] [Download PDF]
    Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms. © 2020, Springer Nature Switzerland AG.
    @ARTICLE{Carli202084,
    author={Carli, R. and Cavone, G. and Epicoco, N. and Di Ferdinando, M. and Scarabaggio, P. and Dotoli, M.},
    title={Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles},
    journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    year={2020},
    volume={12338 LNCS},
    pages={84-99},
    doi={10.1007/978-3-030-61746-2_7},
    note={cited By 4},
    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},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; Center of Excellence DEWS, Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy},
    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},
    document_type={Conference Paper},
    source={Scopus},
    }

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 9},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076778873&doi=10.1109%2fSMC.2019.8914489&partnerID=40&md5=3c982fb93adbcfb5202b48b60ad0f22d},
    affiliation={Information Engineering of the Polytechnic of Bari, Department of Electrical, Bari, 70125, Italy},
    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.},
    document_type={Conference Paper},
    source={Scopus},
    }