Personalized P2P energy trading framework based on attributed social network analysis and AC network constraints

Along with the widespread deployment of distributed energy resources, Peer-to-Peer (P2P) energy trading has become an active research topic. Driven by the fact that in real world, people's willingness on P2P energy trading would be affected by multi-fold factors (including both financial and non-financial factors), this paper proposes a personalized P2P energy trading system that facilitates energy trading among the participants by sufficiently considering their energy trading profit/cost, social relationships, and personal features. A Graph Convolutional Network (GCN)-based network analysis model is utilized to infer the matching degrees between two participants; based on this, an auction-based P2P energy market clearing model is proposed to maximize the participant population's social welfare. AC network constraints of the underlying grid are incorporated into the market clearing model to ensure the physical feasibility of the energy trading transactions and security of the grid; this makes the system applicable to different scales of P2P energy trading (e.g., citywide and local community scales). Numerical simulation is conducted based on the IEEE 33-bus distribution system to validate the effectiveness of the proposed system.