Peer-to-peer energy trading with decentralized bidirectional matching of multipreference community prosumers

Peer-to-peer (P2P) energy trading of community prosumers is effective at coordinating grid-connected distributed energy resources. The existing methods fail to address the multiple preferences of prosumers in energy trading, and the information privacy and autonomy of prosumers are not respected. These issues hinder the effective and applicable P2P energy trading of community prosumers. To bridge this gap, this paper proposes a decentralized bidirectional matching method (DBMM) based on multiattribute decision making (MADM) to match the P2P energy trades between sellers and buyers. This method enables the bidirectional evaluation, selection, and matching of buyers/sellers, allowing prosumers to make decisions on energy trading autonomously without disclosing private information. The numerical analysis indicates that compared with prevalent trading methods, the proposed DBMM is more cost-effective and generalized for handling multiple preferences while preserving information privacy and autonomy. In the four-preference case, the total revenue of the proposed DBMM is 32.30 % greater than that of the other compared methods. Furthermore, the computational efficiency and scalability of this method are also validated.