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Title:
Distributed Game-theoretical Control for Electric Vehicles-integrated Energy Communities |
Abstract:
Recently driven by the spread of energy communities (ECs), the paradigm shift from traditional to modern energy generation and distribution systems is indisputably bringing many advantages. First, end-users are capable of autonomous production, by means of renewable energy sources (RESs), thus allowing the spread of more economically and environmentally sustainable scenarios. Nevertheless, the intermittent nature of RESs makes it hard to consistently find the matching between demand and supply on a local basis. Moreover, the proliferation of electric vehicles (EVs) adds high variability to the demand side, given their necessity to be periodically recharged, usually by unilateral vehicle-to-grid (V1G) capability. This imbalance can be mitigated by using energy storage systems (ESSs), which are able to store energy during peak generation periods and release it when needed. Although convenient, the adoption of ESSs comes with considerable costs, related both to initial investment, space availability, and maintenance. However, on the one hand, EVs can be used for ancillary services, in order to suitably regulate the network dynamics when generation/load imbalances occur, or when RESs inject power into the main grid. On the other hand, when parked, EVs can be used to mimic the functionality of ESSs, by using their battery as a temporary energy buffer. In particular, a large fleet of long-parked EVs can closely act as a static group of ESSs. Since EVs' owners unpredictably unplug their vehicle from the grid, it is crucial, in practical applications, to consider the stochastic nature of parking time, in order to effectively employ the EVs' battery-based storage services.
In this context the talk presents a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with V1G and V2B capabilities. In particular, V2B services are provided by long-term parked EVs thus used as temporary storage systems by prosumers, who in turn offer the V1G service to EVs provisionally plugged to charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing to improve their energy allocation. 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 problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on duality based methods (e.g., the accelerated distributed augmented Lagrangian method - ADALM), showing that the convergence properties hold for the given formulation.The proposed model predictive control approach is validated through numerical simulations under realistic scenarios.
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Biography:
Raffaele Carli received the Laurea degree (Hons.) in electronic engineering and the Ph.D. degree in electrical and information engineering from the Polytechnic of Bari, Italy, in 2002 and 2016, respectively. From 2003 to 2004, he was a Reserve Officer with Italian Navy. From 2004 to 2012, he worked as a System and Control Engineer and the Technical Manager for a space and defense multinational company. He is currently a Senior Assistant Professor of Automatic Control at the Polytechnic of Bari. He is qualified for access to a position of full professor in control engineering since 2023.
He is the technical responsible for the Decision and Control Laboratory (coordinated by prof. Mariagrazia Dotoli) at the Department of Electric and Information Engineering (DEI) of the Polytechnic of Bari (http://dclab.poliba.it/).
He is an author of over 100 printed international publications. His area of expertise is the development of decision and control techniques for the modelling, optimization, management, and control of complex and large-scale systems. His research interests include the formalization, simulation, and implementation of decentralized, distributed, and hierarchical optimization and control algorithms, to be applied on distributed systems (cooperative and noncooperative), multi-agent systems, and networked systems in smart frameworks such as for example the industry and energy fields.
He was the Young Career Chair of the 2017 IEEE Conference on Automation Science and Engineering, the Pubblication Co-chair of the 2020 IEEE Conference on Automation Science and Engineering, the General Co-chair of 7th International Conference on Renewable Energy and Conservation (ICREC 2022), and the Tutorial Co-chair of the 2023 IEEE International Conference onSystems, Man, and Cybernetics (SMC 2023). He is the Finance Chair and Special Session Chair of 2024 IEEE International Conference on Automation Science and Engineering (2024 August 26-30 – Bari, Italy). He is an Associate Editor of the IEEE TRANSACTIONS ON AUTOMATION SCIENCE ANDENGINEERING and the and the IEEE TRANSACTIONS ON SYSTEMS,MAN, AND CYBERNETICS. He is a member of the conference editorial board for the IEEE Robotics and Automation Society (RAS) and IEEE Systems, Man, and Cybernetics Society (SMC), a member of the international program committee of several international conferences, and a guest editor for special issues on international journals.
For additional information, visit: http://dclab.poliba.it/people/raffaele-carli/
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