PV Energy Storage Capacity Optimization for Receiving End Grid Based on Grey Wolf Algorithm

Remote rural areas have high load randomness and grid fluctuations but are rich in solar energy resources, and the receiving end grid is vigorously developing renewable energy. However, due to the weak power supply capacity in remote rural areas and the instability and discontinuity of renewable energy generation, the quality of power supply cannot be guaranteed. Therefore the capacity optimization of renewable energy is of great significance. In this paper, for the capacity optimization problem of photovoltaic energy storage, it is proposed to establish a capacity optimization model by taking the economic optimization and the minimization of the grid variation coefficient as the objective function. Considering the load and time-of-use electricity pricing, it is proposed to solve the PV energy storage optimization problem under the constraints of remote rural areas by using the gray wolf algorithm. Finally, the effectiveness of the optimization results is illustrated by an example analysis, and the grid volatility decreases up to 40.94%, which helps to improve the power supply quality in remote rural areas.