农业光伏系统的控制策略：平衡发电量与农作物产量以实现可持续发展

Abstract Different control strategies have significant influence on the overall performance of agricultural photovoltaic (PV) system. In this study, an artificial neural network (ANN) algorithm was used to modify the solar irradiance prediction of a numerical weather prediction (NWP) model, and proposed the innovative control strategies for optimal yield and optimal electricity generation. Combined with an agricultural PV project in France, the electricity generation, crop yield and land equivalent ratio (LER) of agricultural PV system under different control strategies were investigated. The results show that the annual electricity generation of the optimal yield control strategy decreases by 23%, and the LER value decreases by 10% on average compared to the traditional solar tracking strategy. Compared with the traditional solar tracking control strategy, the optimal electricity generation control strategy can achieve a maximum daily electricity gain of 20.4% and an annual electricity generation increase of 15.6 MWh. Although the optimal electricity generation control strategy will lead to a decrease in agricultural yield, considering the comprehensive electricity and agricultural yield, the optimal electricity generation control strategy is slightly better than the traditional solar tracking strategy. The results of this study are helpful for realizing the efficient use of land resources, which is of great significance for promoting sustainable energy and agricultural development.