Edge Computing-Enabled Peer-to-Peer Energy Trading in a Local Energy Community

Local energy communities establish a platform for prosumers and consumers to share surplus generation via peerto-peer (P2P) energy trading. Traditional optimization techniques for energy management problems necessitate precise models and accurate predictions. In contrast, reinforcement learning has gained widespread attention as it can handle system uncertainties without relying on models. However, multi-agent reinforcement learning (MARL) approaches for P2P energy trading involve a large volume of communications and computations, hindering the practical deployment of decision-making algorithms on edge devices whose communication and computation resources are very limited. To this end, this paper proposes an efficient and privacy-preserving MARL approach for edge computing-enabled P2P energy trading. Specifically, we design an information interaction framework that shares representations rather than sensitive observations to protect the privacy of various agents. In addition, we develop an event-triggered communication approach that controls the frequency of interactions through a gating mechanism to reduce communication overhead among agents. To save the memory footprint of each agent, we investigate an evolutionary training method that updates the networks using perturbation without backward gradient computation. Case studies on a real-world dataset demonstrate that our proposed method yields significant efficiency improvements while maintaining high decision-making performance.