Optimization of droop control based on deep reinforcement learning

With the global energy transition and rapid development of distributed generation technologies, droop control methods in microgrids have played a crucial role in achieving voltage and frequency stability. Traditional droop control methods with fixed parameters fail to adapt to the dynamic changes in system load and generation power, resulting in performance degradation. This paper proposes an adaptive droop control method based on Deep Deterministic Policy Gradient (DDPG), which optimizes frequency and voltage droop coefficients online by designing a rational reward function and constructing a reinforcement learning environment. Simulation results demonstrate that this method significantly improves dynamic response performance and steady-state control accuracy, effectively shortens dynamic response time, reduces steady-state deviation, and optimizes the balance of reactive power distribution. Compared to traditional methods, the proposed strategy exhibits greater reliability and adaptability under complex conditions, providing an innovative solution for the optimized operation of islanded microgrids and offering valuable guidance for the future optimization of distributed renewable energy applications.