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To address the need for smoothing offshore wind power output fluctuations, a method for optimizing energy storage configuration is proposed. This method utilizes wavelet packet decomposition to break down the offshore wind power output curve, obtaining the annual power response curve of the energy storage system. An improved scenario clustering algorithm, combining cloud model and fuzzy C-means clustering, is used to cluster the annual power response curve of the energy storage, generating typical scenarios for energy storage power response. An energy storage optimization configuration model is constructed with the objective of minimizing total economic investment over the planning period, and particle swarm optimization is employed to solve the model. Finally, the proposed method and model are validated through case simulations. The results demonstrate that the proposed model and method effectively consider the actual operating characteristics of offshore wind farm energy storage, providing effective guidance for energy storage configuration and construction planning in offshore wind farms.

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Compressed Air Energy Storage Capacity Allocation and Economic Analysis Based on Improved Scenario Clustering Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Shudong Wang,
  • Yong Fang,
  • Xiaoyan Li

摘要

To address the need for smoothing offshore wind power output fluctuations, a method for optimizing energy storage configuration is proposed. This method utilizes wavelet packet decomposition to break down the offshore wind power output curve, obtaining the annual power response curve of the energy storage system. An improved scenario clustering algorithm, combining cloud model and fuzzy C-means clustering, is used to cluster the annual power response curve of the energy storage, generating typical scenarios for energy storage power response. An energy storage optimization configuration model is constructed with the objective of minimizing total economic investment over the planning period, and particle swarm optimization is employed to solve the model. Finally, the proposed method and model are validated through case simulations. The results demonstrate that the proposed model and method effectively consider the actual operating characteristics of offshore wind farm energy storage, providing effective guidance for energy storage configuration and construction planning in offshore wind farms.