Deep reinforcement learning based active disturbance rejection load frequency control of multi-area interconnected power systems with renewable energy

Abstract

The use of renewable energy to replace traditional coal, oil and natural gas for power generation is of great significance to the alleviation of environmental pollution and resource depletion. As the core of automatic generation control (AGC), load frequency control (LFC) is of great significance to the stable operation of the power system. However, adding renewable energy sources such as wind turbines and solar energy will bring unstable factors to the frequency response, while frequency fluctuations will reduce the power quality. To minimize the adverse effects caused by renewable energy, the linear active disturbance rejection control (LADRC) based on the soft actor-critic (SAC) algorithm is designed. Through the ability of LADRC to eliminate unknown disturbances and the decision-making ability of the SAC algorithm, an intelligent LFC controller is applied to a two-area interconnected power system with a solar thermal power plant (STPP) and a four-area interconnected power system with wind turbine generator (WTG), photovoltaic (PV) cells and hydro turbine. The simulation results are compared with that of the PID, MPC, and conventional LADRC controllers, highlighting the effectiveness of the proposed method.