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Multi-Objective Optimization of Offshore Wind Farm Configuration for Energy Storage Based on NSGA-II

Author

Listed:
  • Xin Lin

    (Power Grid Planning Research Center, Guangxi Power Grid, Nanning 530023, China)

  • Wenchuan Meng

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Ming Yu

    (Power Grid Planning Research Center, Guangxi Power Grid, Nanning 530023, China)

  • Zaimin Yang

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China
    College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Qideng Luo

    (Power Grid Planning Research Center, Guangxi Power Grid, Nanning 530023, China)

  • Zhi Rao

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Jingkang Peng

    (School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yingquan Chen

    (School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

The configuration of energy storage systems in offshore wind farms can effectively suppress fluctuations in wind power and enhance the stability of the power grid. However, the economic balance between the cost of energy storage systems and the fluctuations in wind power remains an urgent challenge to be addressed, especially against the backdrop of widespread spot trading in the electricity market. How to achieve effective wind power stabilization at the lowest cost has become a key issue. This paper proposes three different energy storage configuration strategies and adopts the non-dominated sorting genetic algorithm (NSGA-II) to conduct multi-objective optimization of the system. NSGA-II performed stably in dual-objective scenarios and effectively balanced the relationship between the investment cost of the energy storage system and power fluctuations through the explicit elite strategy. Furthermore, this study analyzed the correlation between the rated power and rated capacity of the energy storage system and the battery life, and corrected the battery life of the Pareto frontier solution obtained by NSGA-II. The research results show that when only considering the investment cost of the energy storage, the optimal configuration was a rated power of 4 MW and a rated capacity of 28 MWh, which could better balance the investment economy and power fluctuation. When further considering the participation of energy storage systems in the electricity spot market, the economic efficiency of the energy storage systems could be significantly improved through the fixed-period electricity price arbitrage method. At this point, the optimal configuration was a rated power of 8 MW and a rated capacity of 37 MWh. The corresponding project investment cost was CNY 242.77 million, and the annual fluctuation rate of the wind power output decreased to 17.84%.

Suggested Citation

  • Xin Lin & Wenchuan Meng & Ming Yu & Zaimin Yang & Qideng Luo & Zhi Rao & Jingkang Peng & Yingquan Chen, 2025. "Multi-Objective Optimization of Offshore Wind Farm Configuration for Energy Storage Based on NSGA-II," Energies, MDPI, vol. 18(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3061-:d:1675504
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    References listed on IDEAS

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    2. Honglin Chen & Hao Yu & Xiaojuan Yang & Yong Lin & Suhua Lou & Sui Peng, 2022. "Joint Planning of Offshore Wind Power Storage and Transmission Considering Carbon Emission Reduction Benefits," Energies, MDPI, vol. 15(20), pages 1-15, October.
    3. Aktaş, Ahmet & Kırçiçek, Yağmur, 2020. "A novel optimal energy management strategy for offshore wind/marine current/battery/ultracapacitor hybrid renewable energy system," Energy, Elsevier, vol. 199(C).
    4. Esteban, M. Dolores & Diez, J. Javier & López, Jose S. & Negro, Vicente, 2011. "Why offshore wind energy?," Renewable Energy, Elsevier, vol. 36(2), pages 444-450.
    5. Jiang, Zihao & Liu, Zhiying, 2022. "Policies and exploitative and exploratory innovations of the wind power industry in China: The role of technological path dependence," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
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