Optimization of demand side management strategies for building clusters based on cooperative game theory

Abstract

Abstract In recent years, with the increasing difficulty of real-time supply-demand balance in the power system, the importance of system optimization scheduling has become increasingly prominent. By flexibly adjusting the elastic load on the power demand side, new ideas have been provided to alleviate the supply-demand contradiction in the power system. However, most existing demand-side management strategies use nonlinear optimization algorithms, which not only have complex optimization processes but also often overlook the ability of different energy flexibility systems to coordinate and cooperate when dealing with a large number of buildings connected to the grid. There is still significant room for improvement in the efficiency of control measures and the accuracy of results. This article proposes two optimization operation schemes for EFB clusters based on cooperative games, namely “system sharing” and “microgrid interconnection”. Among them, for the system sharing scheme, this article establishes an optimization control algorithm based on an alliance game, quantitatively analyzes the real-time contribution of each participant in the construction alliance, and determines the corresponding hourly control strategy based on this. For the microgrid interconnection scheme, this article adopts a bargaining game model to determine the control logic and related profit distribution principles of energy consumption interconnection within the building cluster. We simulate the game model by using MATLAB software, and the result shows that the system sharing mode can reduce the comprehensive electricity cost and peak valley energy consumption difference of the building cluster by 6.9%∼13.0% and 3.1%∼6.7%, respectively, which has a significant improvement effect on the problem of power grid supply-demand imbalance. Although the microgrid interconnection mode cannot change the peak and valley energy consumption of buildings, it can significantly improve the economic efficiency of building clusters and reduce the comprehensive electricity cost by 11.8% to 17.3%.