Frontiers in Energy Research (Oct 2024)
Energy sharing trading among photovoltaic prosumers: a dynamic game considering social learning
Abstract
This paper proposes a dynamic price-based demand response (DR) energy sharing model for peer-to-peer (P2P) transactions of photovoltaic (PV) prosumers in microgrids. First, a multi-subject dynamic game model is constructed between a retail electricity provider (REP), an energy sharing provider (ESP), and multiple prosumers participating in energy sharing transactions. The cost model of the prosumers is designed to reflect the DR from the perspectives of economic cost and the satisfaction of prosumers with electricity consumption patterns. Further, the effect of social learning (SL) among prosumers on multi-subject decision-making behavior is considered. The model is solved using a deep reinforcement learning algorithm, and the results show that: (1) SL reduces the volatility of electricity prices and provides more stable price signals for market participants. (2) When prosumers are unwilling to change their electricity consumption pattern, ESP and REP will increase the purchase price and reduce the sale price, encouraging prosumers to increase electricity consumption to some extent. (3) As the number of prosumers increases, the benefits to price setters increase, but the costs to prosumers rise accordingly. This study provides a valuable reference for promoting the development of the PV industry and the diffusion of sustainable energy.
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