Optimal Configuration Method for Offshore Wind Power Energy Storage Based on Improved Typical Day Clustering Algorithm

To address the challenges of suppressing power fluctuation in grid-connected offshore wind farms and optimizing energy storage economic efficiency, this study proposes an energy storage optimization configuration method. Firstly, a cloud model-improved clustering algorithm was employed to aggregate annual power output curves of the wind farm, generating typical daily power scenarios. Subsequently, multi-threshold wavelet packet decomposition was applied to typical daily power curves to derive energy storage response profiles. An optimal energy storage configuration model was then constructed with the objective of minimizing annual comprehensive costs. The cheetah optimization algorithm was adopted to solve the configuration model. Empirical case studies on actual offshore wind farms demonstrate that the proposed model and methodology comprehensively consider the actual operational characteristics of energy storage systems on the wind farm side, providing effective guidance for energy storage configuration and construction planning in offshore wind power projects.