Research on master-slave game theory-based demand response strategies for renewable energy integration

As renewable energy sources are extensively incorporated into electrical grids, the necessity for enhanced flexibility and stability within the power system has significantly grown., Demand Response (DR) has attracted widespread attention as an effective load management tool. This study delves into the master-slave game theory-based demand response strategy integrated with renewable energy, aiming to optimize the interaction mechanism between grid operators and users participating in demand response through a game-theoretic framework, thereby enhancing the system’s economic efficiency and reliability. In this research, we first constructed a power system model that includes renewable energy sources such as wind and solar power. A master-slave game theory-based demand response strategy framework was proposed, where the energy suppliers act as the leader by setting demand response policies, while energy operators who act as followers decide their consumption behaviors to maximize their own interests. The strategy allows participants to adjust their strategies based on real-time market information and changes in renewable energy output so as to realize optimal scheduling of demand response resources. Through theoretical analysis and simulation experiments, the results illustrate that the demand response strategy affects the respective revenues of energy operators and energy suppliers by dispatching electricity purchases in four different modes. The effectiveness of the demand response strategy was verified in reducing operational costs of the grid, enhancing the system’s adaptability to fluctuations in renewable energy, and encouraging active user participation in demand response. In summary, the master-slave game theory-based demand response strategy for renewable energy integration proposed in this study not only promotes the economic and efficient operation of the power grid but also provides important theoretical support and technical references for the development of future smart grids.