A Deep Reinforcement Learning Based Framework for Power System Load Frequency Control

Abstract

With the increasing penetration of renewable energy and power electronic devices, the frequency fluctuation of the power system will increase sharply, the inertia will decrease significantly, the damping characteristics will be poor, and the frequency adjustment ability needs to be improved. In this paper, a DRL (deep reinforcement learning) based framework is propsed to solve the LFC (load frequency control) problem for power system. This paper first introduces the model of LFC and basic description of DRL. Then the flow of twin delayed deep deterministic policy gradient (TD3) algorithm and a general framework for load frequency control. Finally, the proposed method is verified through a single area LFC test system. The implementation demonstrates that the proposed method is effective in LFC problem.