Seyed Mohsen HOSSEINI

Post-Doc Research Fellow

E-mail: ti.ab1651262589ilop@1651262589inies1651262589soh.n1651262589eshom1651262589deyes1651262589


Short bio

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

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

Research interests

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

Publications

2021

  • Hosseini, S. M., Carli, R. & Dotoli, M. (2021) Robust Optimal Energy Management of a Residential Microgrid under Uncertainties on Demand and Renewable Power Generation. IN IEEE Transactions on Automation Science and Engineering, 18.618-637. doi:10.1109/TASE.2020.2986269
    [BibTeX] [Abstract] [Download PDF]
    Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users’ behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users’ demand uncertainties. To this aim, first, we formulate a min-max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min-max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints’ violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners – This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users’ payment and constraints’ violation rate considering the energy cost saving, the system technical limitations and the users’ comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand. © 2004-2012 IEEE.
    @ARTICLE{Hosseini2021618,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={Robust Optimal Energy Management of a Residential Microgrid under Uncertainties on Demand and Renewable Power Generation},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2021},
    volume={18},
    number={2},
    pages={618-637},
    doi={10.1109/TASE.2020.2986269},
    art_number={9093973},
    note={cited By 39},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100266630&doi=10.1109%2fTASE.2020.2986269&partnerID=40&md5=ab5c0a8a4eb48977e42d7186b3d4fc3b},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy},
    abstract={Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users' behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users' demand uncertainties. To this aim, first, we formulate a min-max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min-max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints' violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners - This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users' payment and constraints' violation rate considering the energy cost saving, the system technical limitations and the users' comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand. © 2004-2012 IEEE.},
    author_keywords={Demand-side management (DSM); microgrid; mixed-integer quadratic programming (MIQP); optimal energy scheduling; optimization; robust control},
    document_type={Article},
    source={Scopus},
    }

2020

  • Hosseini, S. M., Carli, R., Parisio, A. & Dotoli, M. (2020) Robust Decentralized Charge Control of Electric Vehicles under Uncertainty on Inelastic Demand and Energy Pricing IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 1834-1839. doi:10.1109/SMC42975.2020.9283440
    [BibTeX] [Abstract] [Download PDF]
    This paper proposes a novel robust decentralized charging strategy for large-scale EV fleets. The system incorporates multiple EVs as well as inelastic loads connected to the power grid under power flow limits. We aim at minimizing both the overall charging energy payment and the aggregated battery degradation cost of EVs while preserving the robustness of the solution against uncertainties in the price of the electricity purchased from the power grid and the demand of inelastic loads. The proposed approach relies on the so-called uncertainty set-based robust optimization. The resulting charge scheduling problem is formulated as a tractable quadratic programming problem where all the EVs’ decisions are coupled via the grid resource-sharing constraints and the robust counterpart supporting constraints. We adopt an extended Jacobi-Proximal Alternating Direction Method of Multipliers algorithm to solve effectively the formulated scheduling problem in a decentralized fashion, thus allowing the method applicability to large scale fleets. Simulations of a realistic case study show that the proposed approach not only reduces the costs of the EV fleet, but also maintains the robustness of the solution against perturbations in different uncertain parameters, which is beneficial for both EVs’ users and the power grid. © 2020 IEEE.
    @CONFERENCE{Hosseini20201834,
    author={Hosseini, S.M. and Carli, R. and Parisio, A. and Dotoli, M.},
    title={Robust Decentralized Charge Control of Electric Vehicles under Uncertainty on Inelastic Demand and Energy Pricing},
    journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
    year={2020},
    volume={2020-October},
    pages={1834-1839},
    doi={10.1109/SMC42975.2020.9283440},
    art_number={9283440},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098877625&doi=10.1109%2fSMC42975.2020.9283440&partnerID=40&md5=51febb550a1cfd7909fd0ff26527f5b0},
    affiliation={Polytechnic of Bari, Dept. of Electrical and Information Engineering, Bari, Italy; School of Electrical and Electronic Engineering, University of Manchester, Manchester, United Kingdom},
    abstract={This paper proposes a novel robust decentralized charging strategy for large-scale EV fleets. The system incorporates multiple EVs as well as inelastic loads connected to the power grid under power flow limits. We aim at minimizing both the overall charging energy payment and the aggregated battery degradation cost of EVs while preserving the robustness of the solution against uncertainties in the price of the electricity purchased from the power grid and the demand of inelastic loads. The proposed approach relies on the so-called uncertainty set-based robust optimization. The resulting charge scheduling problem is formulated as a tractable quadratic programming problem where all the EVs' decisions are coupled via the grid resource-sharing constraints and the robust counterpart supporting constraints. We adopt an extended Jacobi-Proximal Alternating Direction Method of Multipliers algorithm to solve effectively the formulated scheduling problem in a decentralized fashion, thus allowing the method applicability to large scale fleets. Simulations of a realistic case study show that the proposed approach not only reduces the costs of the EV fleet, but also maintains the robustness of the solution against perturbations in different uncertain parameters, which is beneficial for both EVs' users and the power grid. © 2020 IEEE.},
    author_keywords={ADMM; Charge scheduling; Decentralized control; Electric vehicles; Large-scale optimization; Robust optimization; Set-based uncertainty},
    document_type={Conference Paper},
    source={Scopus},
    }

2019

  • Hosseini, S. M., Carli, R. & Dotoli, M. (2019) A residential demand-side management strategy under nonlinear pricing based on robust model predictive control IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 3243-3248. doi:10.1109/SMC.2019.8913892
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a real-time demand side management framework based on robust model predictive control (RMPC) for residential smart grids. The system incorporates a number of interconnected smart homes, each equipped with controllable and non-controllable loads, as well as a shared energy storage system (ESS). We aim at minimizing the users’ energy payment and limiting the peak-to-average ratio (PAR) of the energy consumption while taking into account all device/comfort/contractual constraints, specifically the feasibility constraints on energy transferred between users and the power grid in presence of load demand uncertainty. We consider a quadratic cost function for energy bought from the grid. Firstly, the energy price and related constraints of the system are modeled. Then, a min-max robust problem is established to optimally schedule energy under an interval-based uncertainty set. We finally adopt model predictive control (MPC) to solve the resulting robust optimization problem iteratively over a finite-horizon time window based on the receding horizon concept. Moreover, the robustness of the proposed real-time approach against the level of conservativeness of the solution is addressed. The effectiveness of the method is validated through a simulated case study. © 2019 IEEE.
    @CONFERENCE{Hosseini20193243,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={A residential demand-side management strategy under nonlinear pricing based on robust model predictive control},
    journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
    year={2019},
    volume={2019-October},
    pages={3243-3248},
    doi={10.1109/SMC.2019.8913892},
    art_number={8913892},
    note={cited By 22},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076744329&doi=10.1109%2fSMC.2019.8913892&partnerID=40&md5=b8658f66ca7439fa6e7d9b02705e1b91},
    affiliation={Engineering of the Polytechnic of Bari, Department of Electrical and Information, Bari, 70125, Italy},
    abstract={This paper presents a real-time demand side management framework based on robust model predictive control (RMPC) for residential smart grids. The system incorporates a number of interconnected smart homes, each equipped with controllable and non-controllable loads, as well as a shared energy storage system (ESS). We aim at minimizing the users' energy payment and limiting the peak-to-average ratio (PAR) of the energy consumption while taking into account all device/comfort/contractual constraints, specifically the feasibility constraints on energy transferred between users and the power grid in presence of load demand uncertainty. We consider a quadratic cost function for energy bought from the grid. Firstly, the energy price and related constraints of the system are modeled. Then, a min-max robust problem is established to optimally schedule energy under an interval-based uncertainty set. We finally adopt model predictive control (MPC) to solve the resulting robust optimization problem iteratively over a finite-horizon time window based on the receding horizon concept. Moreover, the robustness of the proposed real-time approach against the level of conservativeness of the solution is addressed. The effectiveness of the method is validated through a simulated case study. © 2019 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Robust energy scheduling of interconnected smart homes with shared energy storage under quadratic pricing IN IEEE International Conference on Automation Science and Engineering., 966-971. doi:10.1109/COASE.2019.8843230
    [BibTeX] [Abstract] [Download PDF]
    In this paper, we propose a novel robust framework for day-ahead energy scheduling of interconnected smart homes with shared energy storage system (ESS), taking into account users’ behavior uncertainty. The objective is minimizing the total energy payment for each user while satisfying the constraint on the feasibility of energy transactions between users and the power grid in presence of data uncertainty. Unlike most existing robust scheduling frameworks that assume a linear cost function for energy purchased from the grid, our design presents a tractable robust optimization scheme to solve the energy scheduling problem with a more realistic quadratic cost function. We model device/comfort constraints as well as contractual obligations imposed by the power grid restricting the users’ energy consumption to a maximum level at each time slot. Thus, in our problem, uncertainty affects both the quadratic objective function and linear contractual constraints. To solve the resulting problem, we first formulate a deterministic model of the scheduling problem, then establish a min-max robust counterpart, and finally apply some mathematical transformations to solve the equivalent problem. We also deal with the conservatism of the robust control algorithm and flexibility of the method for application to different settings. The validity and effectiveness of the proposed approach is verified by simulation results. © 2019 IEEE.
    @CONFERENCE{Hosseini2019966,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={Robust energy scheduling of interconnected smart homes with shared energy storage under quadratic pricing},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2019},
    volume={2019-August},
    pages={966-971},
    doi={10.1109/COASE.2019.8843230},
    art_number={8843230},
    note={cited By 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072987292&doi=10.1109%2fCOASE.2019.8843230&partnerID=40&md5=6989d41f45c9a005b8a3dd9ebcf6bbfd},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy},
    abstract={In this paper, we propose a novel robust framework for day-ahead energy scheduling of interconnected smart homes with shared energy storage system (ESS), taking into account users' behavior uncertainty. The objective is minimizing the total energy payment for each user while satisfying the constraint on the feasibility of energy transactions between users and the power grid in presence of data uncertainty. Unlike most existing robust scheduling frameworks that assume a linear cost function for energy purchased from the grid, our design presents a tractable robust optimization scheme to solve the energy scheduling problem with a more realistic quadratic cost function. We model device/comfort constraints as well as contractual obligations imposed by the power grid restricting the users' energy consumption to a maximum level at each time slot. Thus, in our problem, uncertainty affects both the quadratic objective function and linear contractual constraints. To solve the resulting problem, we first formulate a deterministic model of the scheduling problem, then establish a min-max robust counterpart, and finally apply some mathematical transformations to solve the equivalent problem. We also deal with the conservatism of the robust control algorithm and flexibility of the method for application to different settings. The validity and effectiveness of the proposed approach is verified by simulation results. © 2019 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Robust day-ahead energy scheduling of a smart residential user under uncertainty IN 2019 18th European Control Conference, ECC 2019., 935-940. doi:10.23919/ECC.2019.8796182
    [BibTeX] [Abstract] [Download PDF]
    This paper develops a robust optimization framework for the day-ahead energy scheduling of a grid-connected residential user. The system incorporates a renewable energy source (RES), a battery energy storage system (BESS) as well as elastic controllable and critical noncontrollable electrical appliances. The proposed approach copes with the fluctuation and intermittence of the RES generation and non-controllable load demand by a tractable robust optimization scheme requiring minimum information on the sources of uncertainty. The main objective is minimizing the total energy payment for the user considering operational/technical constraints and a contractual constraint penalizing the excessive use of energy. The presented framework allows the decision maker to define different robustness levels for uncertain variables, and to flexibly establish an equilibrium between user’s payment and price of robustness. To validate the effectiveness of the proposed framework under uncertainty, we simulate the dynamics of a residential user as a case study. A comparison between the proposed robust approach and the same method with deterministic RES and loads profiles is carried out and discussed. © 2019 EUCA.
    @CONFERENCE{Hosseini2019935,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={Robust day-ahead energy scheduling of a smart residential user under uncertainty},
    journal={2019 18th European Control Conference, ECC 2019},
    year={2019},
    pages={935-940},
    doi={10.23919/ECC.2019.8796182},
    art_number={8796182},
    note={cited By 23},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071523023&doi=10.23919%2fECC.2019.8796182&partnerID=40&md5=dbaf920bef232ca07b714d294ec35e28},
    affiliation={Department of Electrical and Information Engineering of the Polytechnic of Bari, Bari, 70125, Italy},
    abstract={This paper develops a robust optimization framework for the day-ahead energy scheduling of a grid-connected residential user. The system incorporates a renewable energy source (RES), a battery energy storage system (BESS) as well as elastic controllable and critical noncontrollable electrical appliances. The proposed approach copes with the fluctuation and intermittence of the RES generation and non-controllable load demand by a tractable robust optimization scheme requiring minimum information on the sources of uncertainty. The main objective is minimizing the total energy payment for the user considering operational/technical constraints and a contractual constraint penalizing the excessive use of energy. The presented framework allows the decision maker to define different robustness levels for uncertain variables, and to flexibly establish an equilibrium between user's payment and price of robustness. To validate the effectiveness of the proposed framework under uncertainty, we simulate the dynamics of a residential user as a case study. A comparison between the proposed robust approach and the same method with deterministic RES and loads profiles is carried out and discussed. © 2019 EUCA.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Hosseini, S. M., Carli, R. & Dotoli, M. (2019) Model Predictive Control for Real-Time Residential Energy Scheduling under Uncertainties IN Proceedings – 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 1386-1391. doi:10.1109/SMC.2018.00242
    [BibTeX] [Abstract] [Download PDF]
    This paper proposes a real-time strategy based on Model Predictive Control (MPC) for the energy scheduling of a grid-connected smart residential user equipped with deferrable and non-deferrable electrical appliances, a renewable energy source (RES), and an electrical energy storage system (EESS). The proposed control scheme relies on an iterative finite horizon on-line optimization, implementing a quadratic cost function to minimize the electricity bill of the user’s load demand and to limit the peak-to-average ratio (PAR) of the energy consumption profile whilst considering operational constraints. At each time step, the optimization problem is solved providing the cost-optimal energy consumption profile for the user’s deferrable loads and the optimal charging/discharging profile for the EESS, taking into account forecast uncertainties by using the most updated predicted values of local RES generation and non-deferrable loads consumption. The performance and effectiveness of the proposed framework are evaluated for a case study where the dynamics of the considered residential energy system is simulated under uncertainties both in the forecast of the RES generation and the non-deferrable loads energy consumption. © 2018 IEEE.
    @CONFERENCE{Hosseini20191386,
    author={Hosseini, S.M. and Carli, R. and Dotoli, M.},
    title={Model Predictive Control for Real-Time Residential Energy Scheduling under Uncertainties},
    journal={Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018},
    year={2019},
    pages={1386-1391},
    doi={10.1109/SMC.2018.00242},
    art_number={8616238},
    note={cited By 28},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062230383&doi=10.1109%2fSMC.2018.00242&partnerID=40&md5=2f024e90b4a067ff3c318f9e0a035f00},
    affiliation={Dept. of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy},
    abstract={This paper proposes a real-time strategy based on Model Predictive Control (MPC) for the energy scheduling of a grid-connected smart residential user equipped with deferrable and non-deferrable electrical appliances, a renewable energy source (RES), and an electrical energy storage system (EESS). The proposed control scheme relies on an iterative finite horizon on-line optimization, implementing a quadratic cost function to minimize the electricity bill of the user's load demand and to limit the peak-to-average ratio (PAR) of the energy consumption profile whilst considering operational constraints. At each time step, the optimization problem is solved providing the cost-optimal energy consumption profile for the user's deferrable loads and the optimal charging/discharging profile for the EESS, taking into account forecast uncertainties by using the most updated predicted values of local RES generation and non-deferrable loads consumption. The performance and effectiveness of the proposed framework are evaluated for a case study where the dynamics of the considered residential energy system is simulated under uncertainties both in the forecast of the RES generation and the non-deferrable loads energy consumption. © 2018 IEEE.},
    author_keywords={energy scheduling; model predictive control (MPC); residential energy management; uncertainties},
    document_type={Conference Paper},
    source={Scopus},
    }