Improved Proximal Policy Optimization Algorithm for Sequential Security-Constrained Optimal Power Flow Based on Expert Knowledge and Safety Layer

In recent years, reinforcement learning (RL) has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods. It has gradually been applied in the fields such as economic dispatch of power systems due to its strong self-learning and self-optimizing capabilities. However, existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration, which poses a risk of issuing instructions that threaten the safe operation of power system. Therefore, we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow (SCOPF) based on expert knowledge and safety layer to determine active power dispatch strategy, voltage optimization scheme of the units, and charging/discharging dispatch of energy storage systems. The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy. Additionally, to avoid line overload, we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem. Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.