Hybrid game model for electricity trading and pricing among multiple microgrids and consumers based on demand-side complex networks

Abstract

Local energy markets (LEMs) are pivotal for enhancing renewable energy consumption and facilitating green electricity by linking multiple microgrids (MMGs) and consumers. Existing studies often overlook the evolution of consumer purchasing strategies influenced by social spatial structures and social learning dynamics. This paper addresses this gap by introducing a hybrid game model for LEMs electricity trading and dynamic pricing, integrating demand-side complex network structures. This model encompasses dynamic interactions between MMGs and electricity and carbon markets, employing non-cooperative games among MMGs, complex network evolutionary game among consumers, and Stackelberg games between MMGs and consumers. It promotes efficient transactions and adaptive pricing through distributed algorithms optimizing equilibrium solutions. Key findings include: (1) Compared to Power-to-Grid and Peer-to-Peer models, our model increased MMGs' revenues by 44.10 % and 10.60 %, reduced consumers’ costs by 8.76 % and 1.15 %, and cut carbon emissions by 93.32 % and 77.30 %, significantly boosting economic and environmental benefits. (2) Complex network analysis underscores the importance of social learning in adjusting power consumption strategies and pricing mechanisms. (3) Multidimensional consumer decision-making enhances renewable energy valuation, fosters price competition among MMGs, and accelerates low-carbon transitions in LEMs, providing crucial theoretical support for LEM structure design and policy to enhance renewable energy use.