Source–load matching and energy storage optimization strategies for regional wind–solar energy systems

Abstract. In response to the issue of limited new energy output leading to poor smoothing effects on grid-connected load fluctuations, this paper proposes a load-power smoothing method based on “one source with multiple loads”. The method comprehensively considers the proximity between the source and the load, as well as the correlation between their power fluctuations, using these factors as evaluation criteria for source-side and load-side matching in regional power grids. Initially, loads are clustered and divided based on power frequency division. The EEMD algorithm is then applied to obtain wind and solar energy outputs with greater complementarity and smoother fluctuations, leveraging their low-frequency correlation. Subsequently, a load-tracking coefficient is used to compare the matching degree between wind–solar power output and different loads, selecting the most compatible load and output for source–load matching and smoothing. Concurrently, a gray-wolf-optimization (GWO) algorithm based on Tent chaotic mapping is employed to optimize edge energy storage at different load sides, minimizing overall grid-connected load-power fluctuations. Numerical results demonstrate that the proposed method can fully utilize the stable output from the low-frequency correlation of wind and solar energy, combined with energy storage, to significantly reduce the fluctuation rate of regional grid-connected loads. This effectively promotes local absorption of source loads, thereby alleviating the pressure on the grid side caused by the randomness and volatility on both sides of the source load.