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Pubblicazioni

2024

• Mignoni, N., Carli, R. & Dotoli, M. (2024) Optimal Decision Strategies for the Generalized Cuckoo Card Game. IN IEEE Transactions on Games, 16.185 – 194. doi:10.1109/TG.2023.3239795
  [BibTeX] [Abstract] [Download PDF]

  Cuckoo is a popular card game, which originated in France during the 15th century, and then, spread throughout Europe, where it is currently well-known under distinct names and with different variants. Cuckoo is an imperfect information game-of-chance, which makes the research regarding its optimal strategies determination interesting. The rules are simple: each player receives a covered card from the dealer; starting from the player at the dealer’s left, each player looks at its own card and decides whether to exchange it with the player to their left, or keep it; the dealer plays at last and, if it decides to exchange card, it draws a random one from the remaining deck; the player(s) with the lowest valued card lose(s) the round. We formulate the gameplay mathematically and provide an analysis of the optimal decision policies. Different card decks can be used for this game, e.g., the standard 52-card deck or the Italian 40-card deck. We generalize the decision model for an arbitrary number of decks’ cards, suites, and players. Finally, through numerical simulations, we compare the determined optimal decision strategy against different benchmarks, showing that the strategy outperforms the random and naive policies and approaches the performance of the ideal oracle. © 2018 IEEE.

  @ARTICLE{Mignoni2024185,
  author = {Mignoni, Nicola and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {Optimal Decision Strategies for the Generalized Cuckoo Card Game},
  year = {2024},
  journal = {IEEE Transactions on Games},
  volume = {16},
  number = {1},
  pages = {185 – 194},
  doi = {10.1109/TG.2023.3239795},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147283813&doi=10.1109%2fTG.2023.3239795&partnerID=40&md5=3913f77d7fa5cb1c10485170111b23d2},
  abstract = {Cuckoo is a popular card game, which originated in France during the 15th century, and then, spread throughout Europe, where it is currently well-known under distinct names and with different variants. Cuckoo is an imperfect information game-of-chance, which makes the research regarding its optimal strategies determination interesting. The rules are simple: each player receives a covered card from the dealer; starting from the player at the dealer's left, each player looks at its own card and decides whether to exchange it with the player to their left, or keep it; the dealer plays at last and, if it decides to exchange card, it draws a random one from the remaining deck; the player(s) with the lowest valued card lose(s) the round. We formulate the gameplay mathematically and provide an analysis of the optimal decision policies. Different card decks can be used for this game, e.g., the standard 52-card deck or the Italian 40-card deck. We generalize the decision model for an arbitrary number of decks' cards, suites, and players. Finally, through numerical simulations, we compare the determined optimal decision strategy against different benchmarks, showing that the strategy outperforms the random and naive policies and approaches the performance of the ideal oracle. © 2018 IEEE.},
  author_keywords = {Card games; games of chance; optimal decision strategy; optimization},
  keywords = {Artificial intelligence; Benchmarking; Emotion Recognition; 15th century; Card games; Computational modelling; Cultural difference; Emotion recognition; Europe; Game; Games of chance; Optimal decision strategy; Optimisations; Numerical models},
  type = {Article},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 1; All Open Access, Hybrid Gold Open Access}
  }

• Mignoni, N., Martinez-Piazuelo, J., Carli, R., Ocampo-Martinez, C., Quijano, N. & Dotoli, M. (2024) A Game-Theoretical Control Framework for Transactive Energy Trading in Energy Communities IN 2024 European Control Conference, ECC 2024., 786 – 791. doi:10.23919/ECC64448.2024.10591157
  [BibTeX] [Abstract] [Download PDF]

  Under the umbrella of non-cooperative game theory, we formulate a transactive energy framework to model and control energy communities comprised of heterogeneous agents including (yet not limited to) prosumers, energy storage systems, and energy retailers. The underlying control task is defined as a generalized Nash equilibrium problem (GNEP), which must be solved in a distributed fashion. To solve the GNEP, we formulate a Gauss-Seidel-type alternating direction method of multipliers algorithm, which is guaranteed to converge under strongly monotone pseudo-gradient mappings. As such, we provide sufficient conditions on the private cost and energy pricing functions of the community members, so that the strong monotonicity of the overall pseudo-gradient is ensured. Finally, the proposed framework and the effectiveness of the solution method are illustrated through a numerical simulation. © 2024 EUCA.

  @CONFERENCE{Mignoni2024786,
  author = {Mignoni, Nicola and Martinez-Piazuelo, Juan and Carli, Raffaele and Ocampo-Martinez, Carlos and Quijano, Nicanor and Dotoli, Mariagrazia},
  title = {A Game-Theoretical Control Framework for Transactive Energy Trading in Energy Communities},
  year = {2024},
  journal = {2024 European Control Conference, ECC 2024},
  pages = {786 – 791},
  doi = {10.23919/ECC64448.2024.10591157},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200567031&doi=10.23919%2fECC64448.2024.10591157&partnerID=40&md5=940a89b8b7a2b8a90d241be8bd443e4b},
  abstract = {Under the umbrella of non-cooperative game theory, we formulate a transactive energy framework to model and control energy communities comprised of heterogeneous agents including (yet not limited to) prosumers, energy storage systems, and energy retailers. The underlying control task is defined as a generalized Nash equilibrium problem (GNEP), which must be solved in a distributed fashion. To solve the GNEP, we formulate a Gauss-Seidel-type alternating direction method of multipliers algorithm, which is guaranteed to converge under strongly monotone pseudo-gradient mappings. As such, we provide sufficient conditions on the private cost and energy pricing functions of the community members, so that the strong monotonicity of the overall pseudo-gradient is ensured. Finally, the proposed framework and the effectiveness of the solution method are illustrated through a numerical simulation. © 2024 EUCA.},
  keywords = {Game theory; Power markets; Control energy; Control framework; Energy; Energy trading; Generalized Nash equilibrium problems; Heterogeneous agents; Modelling and controls; Non-cooperative game theory; Pseudo gradients; Storage energy; Numerical methods},
  type = {Conference paper},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 0}
  }

2023

• Mignoni, N., Carli, R. & Dotoli, M. (2023) Layout Optimization for Photovoltaic Panels in Solar Power Plants via a MINLP Approach. IN IEEE Transactions on Automation Science and Engineering, .1–14. doi:10.1109/TASE.2023.3322786
  [BibTeX] [Abstract] [Download PDF]

  Photovoltaic (PV) technology is one of the most popular means of renewable generation, whose applications range from commercial and residential buildings to industrial facilities and grid infrastructures. The problem of determining a suitable layout for the PV arrays, on a given deployment region, is generally non-trivial and has a crucial importance in the planning phase of solar plants design and development. In this paper, we provide a mixed integer non-linear programming formulation of the PV arrays’ layout problem. First, we define the astronomical and geometrical models, considering crucial factors such as self-shadowing and irradiance variability, depending on the geographical position of the solar plant and yearly time window. Subsequently, we formalize the mathematical optimization problem, whose constraints’ set is characterized by non-convexities. In order to propose a computationally tractable approach, we provide a tight parametrized convex relaxation. The resulting optimization resolution procedure is tested numerically, using realistic data, and benchmarked against the traditional global resolution approach, showing that the proposed methodology yields near-optimal solutions in lower computational time. Note to Practitioners—The paper is motivated by the need for efficient algorithmic procedures which can yield near-optimal solutions to the PV arrays layout problem. Due to the strong non-convexity of even simple instances, the existing methods heavily rely on global or stochastic solvers, which are computationally demanding, both in terms of resources and run-time. Our approach acts as a baseline, from which practitioners can derive more elaborate instances, by suitably modifying both the objective function and/or the constraints. In fact, we focus on the minimum set of necessary geometrical (e.g., arrays position model), astronomical (e.g., irradiance variation), and operational (e.g., power requirements) constraints which make the overall problem hard. The Appendices provide a guideline for suitably choosing the optimization parameters. All data and simulation code are available on a public repository at: https://github.com/nicomignoni/pvlayout.git. Authors

  @ARTICLE{Mignoni20231,
  author = {Mignoni, Nicola and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {Layout Optimization for Photovoltaic Panels in Solar Power Plants via a MINLP Approach},
  year = {2023},
  journal = {IEEE Transactions on Automation Science and Engineering},
  pages = {1–14},
  doi = {10.1109/TASE.2023.3322786},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181574361&doi=10.1109%2fTASE.2023.3322786&partnerID=40&md5=81a2752bb7efb922f17af8b437e4693b},
  abstract = {Photovoltaic (PV) technology is one of the most popular means of renewable generation, whose applications range from commercial and residential buildings to industrial facilities and grid infrastructures. The problem of determining a suitable layout for the PV arrays, on a given deployment region, is generally non-trivial and has a crucial importance in the planning phase of solar plants design and development. In this paper, we provide a mixed integer non-linear programming formulation of the PV arrays’ layout problem. First, we define the astronomical and geometrical models, considering crucial factors such as self-shadowing and irradiance variability, depending on the geographical position of the solar plant and yearly time window. Subsequently, we formalize the mathematical optimization problem, whose constraints’ set is characterized by non-convexities. In order to propose a computationally tractable approach, we provide a tight parametrized convex relaxation. The resulting optimization resolution procedure is tested numerically, using realistic data, and benchmarked against the traditional global resolution approach, showing that the proposed methodology yields near-optimal solutions in lower computational time. Note to Practitioners—The paper is motivated by the need for efficient algorithmic procedures which can yield near-optimal solutions to the PV arrays layout problem. Due to the strong non-convexity of even simple instances, the existing methods heavily rely on global or stochastic solvers, which are computationally demanding, both in terms of resources and run-time. Our approach acts as a baseline, from which practitioners can derive more elaborate instances, by suitably modifying both the objective function and/or the constraints. In fact, we focus on the minimum set of necessary geometrical (e.g., arrays position model), astronomical (e.g., irradiance variation), and operational (e.g., power requirements) constraints which make the overall problem hard. The Appendices provide a guideline for suitably choosing the optimization parameters. All data and simulation code are available on a public repository at: https://github.com/nicomignoni/pvlayout.git. Authors},
  author_keywords = {Azimuth; convexification; Layout; Mathematical models; mixed integer non-linear programming; non-convex optimization; Observers; Optimization; parametrization; Photovoltaic; Photovoltaic systems; solar array layout; solar power plants; Sun},
  keywords = {Convex optimization; Integer programming; Nonlinear programming; Optimal systems; Relaxation processes; Solar energy; Solar panels; Solar power generation; Stochastic systems; Array layout; Azimuth; Convexification; Layout; Mixed-integer nonlinear programming; Nonconvex optimization; Observer; Optimisations; Parametrizations; Photovoltaic systems; Photovoltaics; Solar array layout; Solar arrays; Solar power plants},
  type = {Article},
  publication_stage = {Article in press},
  source = {Scopus},
  note = {Cited by: 0; All Open Access, Green Open Access, Hybrid Gold Open Access}
  }

• Mignoni, N., Carli, R. & Dotoli, M. (2023) A Noncooperative Stochastic Rolling Horizon Control Framework for V1G and V2B Scheduling in Energy Communities IN 2023 European Control Conference, ECC 2023.. doi:10.23919/ECC57647.2023.10178202
  [BibTeX] [Abstract] [Download PDF]

  In this paper, we propose a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs) thus acting as temporary storage systems by prosumers, which in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve their energy allocation. Prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method (ADALM). The convergence and effectiveness of the proposed approach are validated through numerical simulations under realistic scenarios. © 2023 EUCA.

  @CONFERENCE{Mignoni2023,
  author = {Mignoni, Nicola and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {A Noncooperative Stochastic Rolling Horizon Control Framework for V1G and V2B Scheduling in Energy Communities},
  year = {2023},
  journal = {2023 European Control Conference, ECC 2023},
  doi = {10.23919/ECC57647.2023.10178202},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166479889&doi=10.23919%2fECC57647.2023.10178202&partnerID=40&md5=e34ca4551ecbe785858f9937e0c0a38c},
  abstract = {In this paper, we propose a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs) thus acting as temporary storage systems by prosumers, which in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve their energy allocation. Prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method (ADALM). The convergence and effectiveness of the proposed approach are validated through numerical simulations under realistic scenarios. © 2023 EUCA.},
  keywords = {Lagrange multipliers; Stochastic systems; Variational techniques; Vehicle-to-grid; Vehicles; Control framework; Control strategies; Energy; Horizon control; Optimal scheduling; Rolling horizon; Stochastics; Temporary storage; Unidirectional vehicles; Vehicle to grids; Constrained optimization},
  type = {Conference paper},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 0}
  }

• Mignoni, N., Carli, R. & Dotoli, M. (2023) Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities. IN IEEE Transactions on Control Systems Technology, 31.2159 – 2172. doi:10.1109/TCST.2023.3291549
  [BibTeX] [Abstract] [Download PDF]

  In this article, we propose a novel control strategy for the optimal scheduling of an energy community (EC) constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs), used as temporary storage systems by prosumers, who in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve the energy allocation process. Acting as selfish agents, prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium (NE) problem, addressed through the variational inequality (VI) theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method (ADALM), showing sufficient conditions for guaranteeing convergence. The proposed model predictive control (MPC) approach is validated through numerical simulations under realistic scenarios. © 1993-2012 IEEE.

  @ARTICLE{Mignoni20232159,
  author = {Mignoni, Nicola and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities},
  year = {2023},
  journal = {IEEE Transactions on Control Systems Technology},
  volume = {31},
  number = {5},
  pages = {2159 – 2172},
  doi = {10.1109/TCST.2023.3291549},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165268827&doi=10.1109%2fTCST.2023.3291549&partnerID=40&md5=a1747e82483205009749a103f96f0f12},
  abstract = {In this article, we propose a novel control strategy for the optimal scheduling of an energy community (EC) constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs), used as temporary storage systems by prosumers, who in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve the energy allocation process. Acting as selfish agents, prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium (NE) problem, addressed through the variational inequality (VI) theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method (ADALM), showing sufficient conditions for guaranteeing convergence. The proposed model predictive control (MPC) approach is validated through numerical simulations under realistic scenarios. © 1993-2012 IEEE.},
  author_keywords = {Distributed optimization; electric vehicles (EVs); energy communities (ECs); game theory; model predictive control (MPC); unilateral vehicle-to-grid (V1G); vehicle-to-building (V2B)},
  keywords = {Charging (batteries); Constrained optimization; Electric vehicles; Lagrange multipliers; Model predictive control; Predictive control systems; Random processes; Secondary batteries; Stochastic control systems; Stochastic models; Stochastic systems; Variational techniques; Vehicle-to-grid; Battery; Distributed optimization; Electric vehicle; Energy; Energy community; Game; Microgrid; Model predictive control; Model-predictive control; Symmetric matrices; Uncertainty; Unilateral vehicle-to-grid (V1G); Vehicle to grids; Vehicle-to-building (V2B); Game theory},
  type = {Article},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 13; All Open Access, Green Open Access, Hybrid Gold Open Access}
  }

• Mignoni, N., Scarabaggio, P., Carli, R. & Dotoli, M. (2023) Control frameworks for transactive energy storage services in energy communities. IN Control Engineering Practice, 130.. doi:10.1016/j.conengprac.2022.105364
  [BibTeX] [Abstract] [Download PDF]

  Recently, the decreasing cost of storage technologies and the emergence of economy-driven mechanisms for energy exchange are contributing to the spread of energy communities. In this context, this paper aims at defining innovative transactive control frameworks for energy communities equipped with independent service-oriented energy storage systems. The addressed control problem consists in optimally scheduling the energy activities of a group of prosumers, characterized by their own demand and renewable generation, and a group of energy storage service providers, able to store the prosumers’ energy surplus and, subsequently, release it upon a fee payment. We propose two novel resolution algorithms based on a game theoretical control formulation, a coordinated and an uncoordinated one, which can be alternatively used depending on the underlying communication architecture of the grid. The two proposed approaches are validated through numerical simulations on realistic scenarios. Results show that the use of a particular framework does not alter fairness, at least at the community level, i.e., no participant in the groups of prosumers or providers can strongly benefit from changing its strategy while compromising others’ welfare. Lastly, the approaches are compared with a centralized control method showing better computational results. © 2022 Elsevier Ltd

  @ARTICLE{Mignoni2023,
  author = {Mignoni, Nicola and Scarabaggio, Paolo and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {Control frameworks for transactive energy storage services in energy communities},
  year = {2023},
  journal = {Control Engineering Practice},
  volume = {130},
  doi = {10.1016/j.conengprac.2022.105364},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140312671&doi=10.1016%2fj.conengprac.2022.105364&partnerID=40&md5=c2f86f25172d9c67e24d1f09c9b54689},
  abstract = {Recently, the decreasing cost of storage technologies and the emergence of economy-driven mechanisms for energy exchange are contributing to the spread of energy communities. In this context, this paper aims at defining innovative transactive control frameworks for energy communities equipped with independent service-oriented energy storage systems. The addressed control problem consists in optimally scheduling the energy activities of a group of prosumers, characterized by their own demand and renewable generation, and a group of energy storage service providers, able to store the prosumers’ energy surplus and, subsequently, release it upon a fee payment. We propose two novel resolution algorithms based on a game theoretical control formulation, a coordinated and an uncoordinated one, which can be alternatively used depending on the underlying communication architecture of the grid. The two proposed approaches are validated through numerical simulations on realistic scenarios. Results show that the use of a particular framework does not alter fairness, at least at the community level, i.e., no participant in the groups of prosumers or providers can strongly benefit from changing its strategy while compromising others’ welfare. Lastly, the approaches are compared with a centralized control method showing better computational results. © 2022 Elsevier Ltd},
  author_keywords = {Distributed control; Energy communities; Energy storage systems; Game theory; Smart grids; Transactive control; Transactive energy management},
  keywords = {Computation theory; Distributed parameter control systems; Electric power transmission networks; Energy storage; Smart power grids; Control framework; Distributed-control; Energy; Energy community; Energy storage system; Smart grid; Storage services; Storage systems; Transactive controls; Transactive energy management; Game theory},
  type = {Article},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 30; All Open Access, Green Open Access}
  }

• Mignoni, N., Carli, R. & Dotoli, M. (2023) An Optimization Tool for Displacing Photovoltaic Arrays in Polygonal Areas IN EUROCON 2023 – 20th International Conference on Smart Technologies, Proceedings., 573 – 578. doi:10.1109/EUROCON56442.2023.10198934
  [BibTeX] [Abstract] [Download PDF]

  In this paper, we discuss the problem of optimally designing the layout of a given number of photovoltaic arrays on a flat polygonal surface, in order to maximize a suitable objective function, e.g., the total generated energy. This means finding their optimal position, azimuth and tilt. The considered problem becomes non-trivial when considering effects such as irradiance variability and self-shadowing. We first provide a description of the system model and the associated optimization problem, showing how the resulting formulation presents non-convexities. Then, we provide a tight parametrized convex relaxation, which is computationally tractable and for which optimality conditions hold. We provide numerical simulations using realistic data, showing how the proposed methodology yields near-optimal solutions in lower computational time with respect to the traditional global resolution approach. © 2023 IEEE.

  @CONFERENCE{Mignoni2023573,
  author = {Mignoni, Nicola and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {An Optimization Tool for Displacing Photovoltaic Arrays in Polygonal Areas},
  year = {2023},
  journal = {EUROCON 2023 - 20th International Conference on Smart Technologies, Proceedings},
  pages = {573 – 578},
  doi = {10.1109/EUROCON56442.2023.10198934},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168705720&doi=10.1109%2fEUROCON56442.2023.10198934&partnerID=40&md5=918aa0710a8540e5ce9ea625b0a385d4},
  abstract = {In this paper, we discuss the problem of optimally designing the layout of a given number of photovoltaic arrays on a flat polygonal surface, in order to maximize a suitable objective function, e.g., the total generated energy. This means finding their optimal position, azimuth and tilt. The considered problem becomes non-trivial when considering effects such as irradiance variability and self-shadowing. We first provide a description of the system model and the associated optimization problem, showing how the resulting formulation presents non-convexities. Then, we provide a tight parametrized convex relaxation, which is computationally tractable and for which optimality conditions hold. We provide numerical simulations using realistic data, showing how the proposed methodology yields near-optimal solutions in lower computational time with respect to the traditional global resolution approach. © 2023 IEEE.},
  keywords = {Energy; Non-trivial; Nonconvexity; Objective functions; Optimal position; Optimization problems; Optimization tools; Photovoltaic arrays; Polygonal surface; System models; Relaxation processes},
  type = {Conference paper},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 0}
  }

2022

• Mignoni, N., Scarabaggio, P., Carli, R. & Dotoli, M. (2022) Game Theoretical Control Frameworks for Multiple Energy Storage Services in Energy Communities IN 2022 8th International Conference on Control, Decision and Information Technologies, CoDIT 2022., 1580 – 1585. doi:10.1109/CoDIT55151.2022.9804087
  [BibTeX] [Abstract] [Download PDF]

  In the last decade, distributed energy generation and storage have significantly contributed to the widespread of energy communities. In this context, we propose an energy community model constituted by prosumers, characterized by their own demand and renewable generation, and service-oriented energy storage providers, able to store energy surplus and release it upon a fee payment. We address the problem of optimally schedule the energy flows in the community, with the final goal of making the prosumers’ energy supply more efficient, while creating a sustainable and profitable business model for storage providers. The proposed resolution algorithms are based on decentralized and distributed game theoretical control schemes. These approaches are mathematically formulated and then effectively validated and compared with a centralized method through numerical simulations on realistic scenarios. © 2022 IEEE.

  @CONFERENCE{Mignoni20221580,
  author = {Mignoni, Nicola and Scarabaggio, Paolo and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {Game Theoretical Control Frameworks for Multiple Energy Storage Services in Energy Communities},
  year = {2022},
  journal = {2022 8th International Conference on Control, Decision and Information Technologies, CoDIT 2022},
  pages = {1580 – 1585},
  doi = {10.1109/CoDIT55151.2022.9804087},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134303591&doi=10.1109%2fCoDIT55151.2022.9804087&partnerID=40&md5=c5c686570c9998d9639b765af7b24245},
  abstract = {In the last decade, distributed energy generation and storage have significantly contributed to the widespread of energy communities. In this context, we propose an energy community model constituted by prosumers, characterized by their own demand and renewable generation, and service-oriented energy storage providers, able to store energy surplus and release it upon a fee payment. We address the problem of optimally schedule the energy flows in the community, with the final goal of making the prosumers' energy supply more efficient, while creating a sustainable and profitable business model for storage providers. The proposed resolution algorithms are based on decentralized and distributed game theoretical control schemes. These approaches are mathematically formulated and then effectively validated and compared with a centralized method through numerical simulations on realistic scenarios. © 2022 IEEE.},
  keywords = {Game theory; Numerical methods; Community model; Control framework; Demand generation; Distributed energy generation and storages; Energy; Energy flow; Energy supplies; Renewable generation; Service Oriented; Storage services; Energy storage},
  type = {Conference paper},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 1}
  }