Coordinated Operation Strategy for Wind Farms and Cloud Energy Storage Based on Stackelberg Game and Bi-Level Control

With the increasing proportion of wind power integrated into the grid, its high randomness and intermittency pose significant challenges to grid dispatch stability and market efficiency. To address the issues of high cost and low efficiency in self-owned energy storage for wind farms, this paper proposes a coordinated optimization method integrating Stackelberg game and bi-level model predictive control (MPC) between wind farms and cloud energy storage operators. In the day-ahead stage, a Stackelberg game model is established between wind farms and cloud storage operators to guide them in reaching a dynamic equilibrium in capacity leasing and pricing. In the real-time stage, a bi-level MPC framework is constructed: the upper level integrates day-ahead price signals and real-time forecast deviations to dynamically update scheduling strategies and generate instructive power compensation commands; the lower level optimizes the charging and discharging behavior of leased storage to ensure stable and controllable wind-storage output. Simulation results demonstrate that the proposed method significantly reduces deviation penalty costs and wind curtailment losses. Compared with scenarios without storage and with fixed cloud storage pricing, it achieves the best benefit-cost balance, verifying the effectiveness of the proposed strategy in enhancing system economic performance and grid stability.