Research on Energy Storage Configuration Optimization Method for Wind Farm Substations Based on Wind Power Fluctuation Prediction Integrating Chaotic Features and Bidirectional Gated Recurrent Units

To address wind power fluctuations causing curtailment and high costs, this study proposes an integrated method combining wind power forecasting with substation optimization. An enhanced Bidirectional Gated Recurrent Unit (BiGRU) model is developed by incorporating chaotic features (maximum Lyapunov exponent) and sliding-window statistical features (mean, standard deviation), significantly improving short-term prediction accuracy. Based on these high-precision forecasts, a dynamic transformer switching optimization model is established to maximize the wind farm’s net profit. This model finely balances power generation revenue, wind curtailment penalties, and transformer losses (no-load and load) at a 15 min timescale. Experimental results from a wind farm in Xinjiang demonstrate that the proposed method effectively enhances the economic efficiency of wind farm operations. The study provides a valuable framework for optimizing energy storage configuration and improving profitability by leveraging accurate forecasting.