System Frequency Control Method Driven by Deep Reinforcement Learning and Customer Satisfaction for Thermostatically Controlled Load

Abstract

The intermittence and fluctuation of renewable energy aggravate the power fluctuation of the power grid and pose a severe challenge to the frequency stability of the power system. Thermostatically controlled loads can participate in the frequency regulation of the power grid due to their flexibility. Aiming to solve the problem of the traditional control methods, which have limited adjustment ability, and to have a positive influence on customers, a deep reinforcement learning control strategy based on the framework of soft actor–critic is proposed, considering customer satisfaction. Firstly, the energy storage index and the discomfort index of different users are defined. Secondly, the fuzzy comprehensive evaluation method is applied to evaluate customer satisfaction. Then, the multi-agent models of thermostatically controlled loads are established based on the soft actor–critic algorithm. The models are trained by using the local information of thermostatically controlled loads, and the comprehensive evaluation index fed back by users and the frequency deviation. After training, each agent can realize the cooperative response of thermostatically controlled loads to the system frequency only by relying on the local information. The simulation results show that the proposed strategy can not only reduce the frequency fluctuation, but also improve customer satisfaction.