Reinforcement Learning for Energy Community Management: A European-Scale Study

Giulia Palma (), Leonardo Guiducci (), Marta Stentati, Antonio Rizzo and Simone Paoletti
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Giulia Palma: Dipartimento di Scienze Sociali, Politiche e Cognitive, Università di Siena, 53100 Siena, Italy
Leonardo Guiducci: Dipartimento di Scienze Sociali, Politiche e Cognitive, Università di Siena, 53100 Siena, Italy
Marta Stentati: Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università di Siena, 53100 Siena, Italy
Antonio Rizzo: Dipartimento di Scienze Sociali, Politiche e Cognitive, Università di Siena, 53100 Siena, Italy
Simone Paoletti: Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università di Siena, 53100 Siena, Italy

Energies, 2024, vol. 17, issue 5, 1-19

Abstract: Efficient management of renewable energy resources is imperative for promoting environmental sustainability and optimizing the utilization of clean energy sources. This paper presents a pioneering European-scale study on energy management within renewable energy communities (RECs). With a primary focus on enhancing the social welfare of the community, we introduce a reinforcement learning (RL) controller designed to strategically manage Battery Energy Storage Systems (BESSs) and orchestrate energy flows. This research transcends geographical boundaries by conducting an extended analysis of various energy communities and diverse energy markets across Europe, encompassing different regions of Italy. Our methodology involves the implementation of an RL controller, leveraging optimal control theory for training and utilizing only real-time data available at the current time step during the test phase. Through simulations conducted in diverse contexts, we demonstrate the superior performance of our RL agent compared to a state-of-the-art rule-based controller. The agent exhibits remarkable adaptability to various scenarios, consistently surpassing existing rule-based controllers. Notably, we illustrate that our approach aligns with the intricate patterns observed in both Italian and European energy markets, achieving performance levels comparable to an optimal controller assuming perfect theoretical knowledge of future data.

Keywords: reinforcement learning; energy community; social welfare; energy management; online scheduling; mixed-integer optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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