Incremental cost-consistent partitioning and power allocation strategy for wind-storage clusters in wind power fluctuation mitigation

To effectively mitigate wind power fluctuations and boost the economic performance of Distributed Wind Storage (DWS) systems, this paper proposes a strategy for wind-storage cluster partitioning and power optimization that ensures incremental cost consistency. First, an improved sliding average method is developed to address wind power volatility, incorporating adaptive sliding windows, weighted moving averages, and short-term forecasts to optimize grid integration commands. Next, a three-dimensional clustering metric reflecting the system’s volatility-smoothing characteristics is constructed, and a large-scale DWS clustering model is developed using a data-driven improved K-means algorithm. Finally, to ensure incremental cost consistency among individual DWS units, cluster output is optimized using the equal incremental rate criterion and a multi-agent consensus algorithm, achieving economically efficient power distribution. Simulation results show that the proposed method reduces power volatility and improves the operational economic efficiency of DWS systems.