Two-stage Optimal Operation of Clustered Wind Farms and Shared Energy Storage Based on Distributionally Robust Pricing

Against the backdrop of the rapid development of the new-type power systems and their deep coupling with electricity market-oriented reforms, large-scale wind power is increasingly participating in market transactions, while independent shared energy storage is emerging as a key flexibility provider. To mitigate compliance risks caused by wind power uncertainty and the underutilization of fixed-price storage services, this study proposes a two-stage optimal operation strategy for clustered wind farms and shared energy storage within wind power bases. In the day-ahead stage, a distributionally robust leader-follower pricing model is established to determine service prices that balance both robustness and incentives. In the intraday stage, rolling optimization is applied to adjust wind farm leasing demand and charging–discharging strategies of the shared energy storage, thereby enhancing operational flexibility and economic efficiency. To ensure equitable cooperation among multiple wind farms, an improved Shapley value method that accounts for power regulation contributions is introduced to achieve equitable cost allocation. Case results demonstrate that the proposed strategy reduces compliance risks, enhances energy storage profitability, and offers a feasible pathway for the cooperative operation and commercialization of shared energy storage in wind power bases.