Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a Mixed Integer Linear Programming (MILP) problem, and show that the problem is NPHard. Since the solution of such problem requires the knowledge of the user behavioral model, we propose an heuristic based on reinforcement learning with bounded regret to learn such model while optimizing the system performance. Comparison with the state-of-the-art approaches using realistic simulations based on real traces shows that our method outperforms existing schemes in several efficiency metrics. Besides, the results reveal that increasing the amount of produced energy improves the learning ability of the system even in a short period. It gives practical insights for implementation of energy sharing systems.

Agate V., Khamesi A.R., Silvestri S., & Gaglio S. (2020). Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning. In IEEE International Conference on Communications (pp. 1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICC40277.2020.9149337].

Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning

Agate V.
;
Gaglio S.
2020

Abstract

Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a Mixed Integer Linear Programming (MILP) problem, and show that the problem is NPHard. Since the solution of such problem requires the knowledge of the user behavioral model, we propose an heuristic based on reinforcement learning with bounded regret to learn such model while optimizing the system performance. Comparison with the state-of-the-art approaches using realistic simulations based on real traces shows that our method outperforms existing schemes in several efficiency metrics. Besides, the results reveal that increasing the amount of produced energy improves the learning ability of the system even in a short period. It gives practical insights for implementation of energy sharing systems.
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
978-1-7281-5089-5
Agate V., Khamesi A.R., Silvestri S., & Gaglio S. (2020). Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning. In IEEE International Conference on Communications (pp. 1-7). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICC40277.2020.9149337].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/515113
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