基于深度强化学习的海上风电并网系统备用容量优化配置方法OA

In response to the challenge of balancing economy and stability in traditional reserve capacity optimization methods in the context of large-scale offshore wind power integration,an improved deep reinforcement learning approach is proposed.Based on the deep deterministic policy gradient(DDPG)algorithm,a reward function incorporating transient stability constraints is designed,and a prioritized experience replay mechanism is introduced to optimize the efficiency of training sample utilization.A curriculum learning strategy is employed to implement progressive training to ensure model robustness.Simulation tests are conducted on a modified IEEE 39-bus system integrated with a 2 000 MW offshore wind farm cluster under typical daily and extreme fault scenarios.The results show that,under typical daily scenarios,the proposed method reduces the wind curtailment rate from 12.3%in traditional deterministic optimization to 4.2%,and increases the reserve adequacy rate to 97.5%.In extreme fault scenarios,the minimum frequency drops to 49.7 Hz,the minimum voltage is maintained at 0.88 p.u.,and the recovery time is shortened by more than 60%compared with traditional methods.Ablation experiments validate the effectiveness of the stability reward function and the prioritized experience replay mechanism,which enhance performance by 14.1%and improve training efficiency by 25%,respectively.This method effectively addresses the reserve capacity optimization problem under high offshore wind power penetration and provides a new technological approach for the safe and stable operation of power systems.