Peer-to-peer energy trading of solar and energy storage: A networked multiagent reinforcement learning approach

Chen Feng and Andrew L. Liu

Applied Energy, 2025, vol. 383, issue C, No S0306261925000133

Abstract: Utilizing distributed renewable energy resources, particularly solar and energy storage, in local distribution networks via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy systems’ resilience and sustainability. Consumers and prosumers (that is, those with solar PV and/or energy storage), however, do not have the expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose multi-agent reinforcement learning (MARL) frameworks to help automate consumers’ bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply–demand ratio. In addition, we show how the MARL frameworks can integrate physical network constraints, ensuring the physical feasibility of P2P energy trading and providing a possible pathway for practical deployment.

Keywords: Multi-agent reinforcement learning; Distributed energy resources; Peer-to-peer market (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125283

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