Research on Demand Side Response Pricing Mechanism Based on Fuzzy Reinforcement Learning

The implementation of demand-side response needs to rely on a reasonable incentive mechanism to mobilize user participation through dynamic pricing or compensation strategies. On this basis, this paper proposes a dynamic pricing model for demand-side response based on Fuzzy Reinforcement Learning (FRL) to solve the problems of complexity of user behavior and response uncertainty in the power system. By combining fuzzy logic and reinforcement learning methods, the model dynamically optimizes the electricity pricing strategy on the basis of dealing with fuzzy variables such as user price sensitivity and load shedding willingness. The simulation results show that the FRL model outperforms the traditional fixed pricing mechanism in demand response, and at the same time, it can effectively reduce the operating cost of the power grid, enhance the economic benefits of users and their motivation to participate in demand response, and provide an intelligent solution for the efficient operation and economy of the power system.