Improved adaptive gray wolf genetic algorithm for photovoltaic intelligent edge terminal optimal configuration

Photovoltaic (PV) intelligent edge terminals (IETs) integrate data acquisition, processing, storage and upload functions for operations of PV power stations. However, the cost installing a IET at one station is relatively high. In order to achieve goal multiple distributed stations sharing on premise ensuring reliability, paper proposes method optimal configuration IETs. First all, considering economy reliability optimizing IET, two-layer optimization model established. After that, solve nonlinearity proposed model, an improved adaptive genetic algorithm gray wolf (IAGA-GWO) proposed. Finally, through two application cases IETs, it proved that optimized in this can reduce under reliability.