Data-Driven Stochastic Game with Social Attributes for Peer-to-Peer Energy Sharing

Distributed energy resources create a prosumers era. Peer-to-peer (P2P) energy sharing is an effective way to conduct prosumers-based energy management. Integrating prosumers' social attributes in energy engineering substantially affects their decisions, which brings challenges to the energy scheduling under the cyber-physical-social environment. In this paper, a data-driven stochastic game model with prosumers' social attributes is proposed for P2P energy sharing. According to social psychology, the prosumers' social attributes are expressed as subjective probabilities, which are studied by the spatialoral graph convolutional networks. In the network, a double-layer feature graph is built to learn the social attributes based on the social survey data and load metering data. The P2P energy sharing incurred randomness comes from social attributes of interactive prosumers, which is formulated as a stochastic game model. In this game, a subjective utility model is proposed for prosumers, and the energy scheduling is conducted with the designed dynamic interval adjustment method. Numerical analysis reveals the results of the learning network and generalized Nash equilibrium. Through comparing with rational scenarios, the influence of prosumers' social attributes on P2P energy sharing is concluded.