Demand Response Management of Smart Grid Based on Bayesian Stackelberg Game Approach

LI Jun, WU Xiaotai, LI Tao

Journal of Systems Science & Complexity ›› 2025

PDF(401 KB)
PDF(401 KB)
Journal of Systems Science & Complexity ›› 2025

Demand Response Management of Smart Grid Based on Bayesian Stackelberg Game Approach

  • LI Jun1, WU Xiaotai1, LI Tao3
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Abstract

This article aims to establish a Bayesian Stackelberg game framework for analyzing the incomplete information demand response management with overlapping electricity sales areas, and further provide the corresponding equilibrium strategies. Considering that the satisfaction parameters of power users are private, a Bayesian game model is constructed among these power users, and a non-cooperative game model is established due to the price competition of microgrids. To ensure the sequential interactions of demand response, a Stackelberg game is developed by assuming that the microgrids are leaders and the power users are followers, and the Bayesian Nash equilibrium and Stackelberg equilibrium are proved to exist and are unique under some conditions. In addition, the Bayesian Nash equilibrium for power users is obtained using the fictitious play method in the symmetrical case, and an iterative algorithm is presented for determining the Stackelberg equilibrium. Finally, the numerical simulations are provided showing the effectiveness and convergence of the iterative algorithm, which indicates that our approach can enhance profits for microgrids while ensuring power supply and demand balance.

Key words

Demand response management / Bayesian game / Stackelberg game / smart grid / overlapping electricity sales areas / power supply and demand balance

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LI Jun , WU Xiaotai , LI Tao. Demand Response Management of Smart Grid Based on Bayesian Stackelberg Game Approach. Journal of Systems Science & Complexity, 2025

Funding

This work was funded by the National Natural Science Foundation of China under Grant No. 62273004 and No. 62261136550 and the Introduction of Talent Research Foundation of Anhui Polytechnic University under Grant No. 2023YQQ012.
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