This paper constructs a new optimal mileage-based PV array reconfiguration (OMAR) in a PV power plant under partial shading conditions. It aims to maximize the power output of a PV power plant, and minimize the additional capacity and mileage payments resulting from the power fluctuation in a performance-based frequency regulation market. To reduce the optimization difficulty of OMAR, it is decomposed into two optimization sub-problems, including an upper-layer discrete optimization of PV array reconfiguration and a lower-layer continuous optimization of real-time generation scheduling. The upper-layer discrete optimization is addressed by the proposed swarm reinforcement learning (SRL), which can implement an efficient exploration and exploitation with multiple cooperative agents instead of a single learning agent. The rest lower-layer optimization is handled by the fast interior point method. The proposed method’s effectiveness is thoroughly evaluated on the 10 × 10 total-cross-tied PV arrays under various partial shading conditions. Simulation results demonstrate that the proposed SRL can obtain a larger total benefit than genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA), harris hawks optimizer (HHO), butterfly optimization algorithm (BOA), and Q-learning, in which the benefit increment can reach from 2.12% (against PSO) to 10.62% (against Q-learning).

本文构建了一种在部分阴影条件下的光伏电站中基于里程的最佳最优光伏阵列重新配置（OMAR）。它旨在最大程度地提高光伏电站的功率输出，并最小化基于性能的频率调节市场中因功率波动而导致的额外容量和里程支出。为了降低OMAR的优化难度，将其分解为两个优化子问题，包括PV阵列重构的上层离散优化和实时发电调度的下层连续优化。提议的群体强化学习（SRL）解决了上层离散优化问题，该算法可以使用多个协作代理而不是单个学习代理来实现有效的探索和开发。其余的下层优化由快速内部点方法处理。在各种局部遮光条件下，在10×10的全交叉光伏阵列上对所提出方法的有效性进行了全面评估。仿真结果表明，与遗传算法（GA），粒子群优化（PSO），蚱hopper优化算法（GOA），哈里斯·霍克斯优化器（HHO），蝶形优化算法（BOA）和Q -学习，其中收益增量可以从2.12％（相对于PSO）提高到10.62％（相对于Q学习）。