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A multi-period reactive power regulation method based on proximal policy optimization

The fluctuations of renewable energies and loads pose a great challenge to reactive power regulation. Considering the time-varying characteristics of new energies and loads, the multi-time reactive power regulation problem is constructed as a reinforcement learning problem. A constraint-objective partitioning and objective preconditioning approach is proposed to design the reward function, and a proximal policy optimization algorithm is used to solve the reinforcement learning problem and obtain the reactive power regulation policy. A case study is carried out with the improved IEEE39 system, and the results show that the proposed reward function can improve the convergence speed of the agent, and the reactive power regulation strategy solved based on reinforcement learning outperforms the traditional deterministic optimization algorithm in terms of decision effect and decision time.