A scenario-based battery storage optimization method for diverse P2P energy trading

In P2P (peer-to-peer) energy trading that utilizes market mechanisms, there is often a need for Prosumers with battery storage to use data related to market trends for optimizing battery control. However, since trading participants use the same market trend data, there is a risk of bias in ordering timing, which could negatively affect the liquidity of P2P energy trading. This paper proposes a trading method using market supply scenarios to enable diversified tradings for each participant in the P2P energy trading market. By performing optimal battery control for Prosumers using the supply scenario, the proposal aims to promote the diversification of orders and activate tradings in the market. For the verification, simulations of P2P energy trading with 60 participants were conducted, and the reduction in P2P energy market surplus energy resulting from changes in Prosumer ordering due to the proposal was analyzed. The results showed that using the proposed scenario reduced surplus energy by approximately 7.5% compared to using a single prediction data for battery optimization.