A Fairness-Aware Framework for Value Sharing in Local Electricity Markets Using Graph and Sequence Learning with Multi-Agent Reinforcement

The evolution of Local Electricity Markets (LEMs) is being driven by the integration of distributed energy resources (DERs) and peer-to-peer (P2P) energy trading. This gives rise to the need for practical, equitable value allocation frameworks. Most traditional market approaches do not consider sequentially allocating revenues to prosumers and consumers, which causes suboptimal revenue distribution in the market. This study proposes a hybrid deep learning-based framework integrating Bidirectional Long Short-Term Memory (Bi-LSTM) for energy price forecasting, Graph Neural Networks (GNN) for transaction modelling, and Multi-Agent Deep Reinforcement Learning (MADRL) for dynamic pricing optimization. A self-attention-based Shapley Value computation is introduced to ensure fairness-aware revenue distribution among market participants.