Multi-Source Collaborative Control Technology of Photovoltaic Power Generation Based on Differential Evolution-Gray Wolf Optimization Algorithm

Abstract

The existing photovoltaic power generation multi-source collaborative control technology cannot improve the control effect of microgrid, resulting in high peak load balancing cost of microgrid. Therefore, the photovoltaic power generation multi-source collaborative control technology based on differential evolution (DE)-gray wolf optimization (GWO) algorithm is adopted. The main components of the microgrid system, such as photovoltaic power generation equipment, hydroelectric power generation equipment, and battery pack, are modeled. With the goal of minimum peak cost and maximum transmission power, collaborative control of water, light and energy storage is carried out. The gray Wolf algorithm is used to solve the multi-objective problem of water and light storage in microgrid, and the gray Wolf algorithm is optimized by DE algorithm. Experiments show that the convergence speed of the optimized DEVOLU-Gray Wolf algorithm is obviously accelerated, and the problem of falling into the local optimal solution is completely avoided. After the application of this method, the photovoltaic, hydraulic, and battery of the microgrid run in order, ensure the smooth operation of the microgrid, and improve the economic benefit of the microgrid.