Optimal mileage-based PV array reconfiguration using swarm reinforcement learning

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 x 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).

Automated summary

This paper proposes a new optimal mileage-based PV array reconfiguration method to improve the power output of a PV power plant and reduce additional capacity and mileage costs in a performance-based frequency regulation market. By using swarm reinforcement learning and fast interior point method, the proposed method outperforms other methods in terms of total benefit under various partial shading conditions.