LAUSR

Abstract This paper proposes a peer-to-peer (P2P) energy trading framework, allowing distributed photovoltaic (PV) prosumers and consumers to participate in a community sharing market established by a stakeholder, i.e., an energy pawn (EP). The EP is responsible for installing, connecting, managing, and maintaining the specific P2P sharing network, and possesses a publicly accessible battery energy storage (ES) system that can be used to facilitate the energy sharing within the community. A hierarchical P2P sharing market infrastructure is considered, where the interactions among the EP, prosumers, and consumers are modeled by a leader–follower framework. The EP is responsible for i) optimizing the capacity scheduling of the ES system based on forecasting-based rolling-horizon decision-marking, and ii) determining the selling and buying prices within the market. Meanwhile, prosumers and consumers will adjust their energy consumption as response to different sharing prices for maximizing consumption satisfactions based on their utility functions. With the framework, both PV prosumers and consumers can trade with the EP to balance their excess solar generation or insufficient demand to reduce electricity bills. A dynamic pricing algorithm is proposed for EP to determine the internal buying and selling prices simultaneously, and Q-learning is employed to solve the proposed hierarchical decision-making problem. An energy sharing case with 10 agents is studied to validate the effectiveness in terms of the economic benefits and PV sharing enhancement, as well as the reduction of the negawatt fed back into the grid. This study serves to provide a promising win-win-win solution for the utility grid, EP, and P2P market agents.