Optimization Model of Photovoltaic Module Cleaning Strategy Based on Particle Swarm Algorithm

Abstract

Photovoltaic (PV) power generation in desert areas has significant economic benefits. Accumulated dust on PV panels can significantly reduce their power output, so it is necessary to clean them after a certain period of operation. However, traditional periodic cleaning strategies often cannot maximize the economic benefits of PV power generation. Therefore, optimizing the cleaning strategy with equal intervals is essential. In this study, we establish a model to calculate PV power generation revenue and optimize the cleaning strategy based on environmental variables from some PV power stations in central Australia. We consider the cost of cleaning PV panels and the impact of environmental variables such as solar radiation intensity, relative humidity, and wind speed on dust accumulation. This model can calculate the dust accumulation on a specific day based on environmental data and then estimate the corresponding power generation revenue for that day. Meanwhile, a variant of the particle swarm algorithm is used to automatically adjust the cleaning interval under different environmental conditions, achieving the maximum power generation efficiency. After comparison and analysis with different datasets, it is verified that the optimization strategy generated by the proposed algorithm model can increase the economic benefits by more than 70 US dollars than Equal time interval cleaning strategy, which demonstrates its stability and reliability.