Multi-party transaction optimization decision-making in microgrid electricity market based on blockchain Berge-NS equilibrium

The article presents a decision-making method for multi-party transactions in microgrid electricity markets, utilizing blockchain-based Berge-NS equilibrium. The primary goal is to safeguard user interests, minimize carbon emissions, and reduce electricity costs for consumers. The authors begin by enhancing the utility function of electricity users and integrating the impact of blockchain technology on the overall electricity utility of market entities.

Consequently, they establish a peer-to-peer (P2P) electricity trading architecture for microgrids, which includes a corresponding blockchain network and utility function for electricity users.

The research further incorporates Evolutionary Game Theory with bounded rationality decision-making to construct a Berge-NS game model on both supply and demand sides of the electricity market. The distributed iterative algorithm and step size control method are employed to determine the Nash equilibrium, and the strategy evolution of demand-side subjects is analyzed throughout the game process.

Subsequently, the authors conduct numerical simulations to investigate the trading strategies in bilateral contract markets, centralized trading markets, and dual-layer decision-making models for electricity sellers. The findings validate the models and algorithms' feasibility and effectiveness.

Experimental results indicate that the proposed multi-agent trading Berge-NS decision-making method for microgrid electricity markets demonstrates promising performance in reducing carbon emissions, lowering user electricity costs, and enhancing overall user satisfaction. It is important to note that this study was not supported by any project funds.

The research was conducted by Guangdong Power Grid Co., Ltd. and the Information Center of Guangdong Power Grid Co., Ltd., located in Guangzhou, 51000, China. The authors are Pingyan Mo, Kai Li, Xin Hu, Yanqian Lu, and You Wen. The article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits non-commercial use, sharing, distribution, and reproduction under certain conditions.