BESS Reserve Optimisation in Energy Communities

Wolfram Rozas-Rodriguez (), Rafael Pastor-Vargas, Andrew D. Peacock, David Kane and José Carpio-Ibañez
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Wolfram Rozas-Rodriguez: ETS de Ingeniería Informática, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain
Rafael Pastor-Vargas: ETS de Ingeniería Informática, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain
Andrew D. Peacock: School of Energy, Geoscience, Infrastructure and Society (EGIS), Heriot-Watt University, Edinburg EH14 4AS, UK
David Kane: Trilemma Consulting Limited, Glasgow ML4 3NR, UK
José Carpio-Ibañez: ETS de Ingenieros Industriales, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain

Sustainability, 2024, vol. 16, issue 18, 1-18

Abstract: This paper investigates optimising battery energy storage systems (BESSs) to enhance the business models of Local Energy Markets (LEMs). LEMs are decentralised energy ecosystems facilitating peer-to-peer energy trading among consumers, producers, and prosumers. By incentivising local energy exchange and balancing supply and demand, LEMs contribute to grid resilience and sustainability. This study proposes a novel approach to BESS optimisation, utilising advanced artificial intelligence techniques, such as multilayer perceptron neural networks and extreme gradient boosting regressors. These models accurately forecast energy consumption and optimise BESS reserve allocation within the LEM framework. The findings demonstrate the potential of these AI-driven strategies to improve the BESS reserve capacity setting. This optimal setting will target meeting Energy Community site owners’ needs and avoiding fines from the distribution system operator for not meeting contract conditions.

Keywords: battery optimisation; solar energy; Local Energy Market; neural networks; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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