Intelligent Management of Renewable Energy Communities: An MLaaS Framework with RL-Based Decision Making

Rafael Gonçalves (), Diogo Gomes () and Mário Antunes
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Rafael Gonçalves: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Diogo Gomes: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Mário Antunes: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal

Energies, 2025, vol. 18, issue 13, 1-30

Abstract: Given the increasing energy demand and the environmental consequences of fossil fuel consumption, the shift toward sustainable energy sources has become a global priority. Renewable Energy Communities (RECs)—comprising citizens, businesses, and legal entities—are emerging to democratise access to renewable energy. These communities allow members to produce their own energy, sharing or selling any surplus, thus promoting sustainability and generating economic value. However, scaling RECs while ensuring profitability is challenging due to renewable energy intermittency, price volatility, and heterogeneous consumption patterns. To address these issues, this paper presents a Machine Learning as a Service (MLaaS) framework, where each REC microgrid has a customised Reinforcement Learning (RL) agent and electricity price forecasts are included to support decision-making. All the conducted experiments, using the open-source simulator Pymgrid, demonstrate that the proposed agents reduced operational costs by up to 96.41% compared to a robust baseline heuristic. Moreover, this study also introduces two cost-saving features: Peer-to-Peer (P2P) energy trading between communities and internal energy pools, allowing microgrids to draw local energy before using the main grid. Combined with the best-performing agents, these features achieved trading cost reductions of up to 45.58%. Finally, in terms of deployment, the system relies on an MLOps-compliant infrastructure that enables parallel training pipelines and an autoscalable inference service. Overall, this work provides significant contributions to energy management, fostering the development of more sustainable, efficient, and cost-effective solutions.

Keywords: renewable energy communities; energy trading; energy management; machine learning as a service; reinforcement learning; MLOps (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: 2025
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