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Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

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  • Xiaodong Yu

    (Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Jinan 250061, China
    School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Xia Dong

    (School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Shaopeng Pang

    (School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Luanai Zhou

    (Qingdao Harbor Vocational and Technical College, Qingdao 266400, China)

  • Hongzhi Zang

    (Economic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250001, China)

Abstract

To better track the planned output (forecast output), energy storage systems (ESS) are used by wind farms to compensate the forecast error of wind power and reduce the uncertainty of wind power output. When the error compensation degree is the same, the compensation interval is not unique, different compensation intervals need different ESS sizing. This paper focused on finding the optimal compensation interval not only satisfied the error compensation degree but also obtained the max profit of the wind farm. First, a mathematical model was proposed as well as a corresponding optimization method aiming at maximizing the profit of the wind farm. Second, the effect of the influencing factors (compensation degree, electricity price, ESS cost, and wind penalty cost) on the optimal result was fully analyzed and deeply discussed. Through the analysis, the complex relationship between the factors and the optimal results was found. Finally, the comparison between the proposed and traditional method was given, and the simulation results showed that the proposed method can provide a powerful decision-making basis for ESS planning in current and future market.

Suggested Citation

  • Xiaodong Yu & Xia Dong & Shaopeng Pang & Luanai Zhou & Hongzhi Zang, 2019. "Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation," Energies, MDPI, vol. 12(24), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4755-:d:297536
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    References listed on IDEAS

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    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. Wei Wang & Chengxiong Mao & Jiming Lu & Dan Wang, 2013. "An Energy Storage System Sizing Method for Wind Power Integration," Energies, MDPI, vol. 6(7), pages 1-13, July.
    3. Jichun Liu & Zhengbo Chen & Yue Xiang, 2019. "Exploring Economic Criteria for Energy Storage System Sizing," Energies, MDPI, vol. 12(12), pages 1-17, June.
    4. Ping Liu & Zexiang Cai & Peng Xie & Xiaohua Li & Yongjun Zhang, 2019. "A Computationally Efficient Optimization Method for Battery Storage in Grid-connected Microgrids Based on a Power Exchanging Process," Energies, MDPI, vol. 12(8), pages 1-19, April.
    5. Diego Fernando Quintero Pulido & Gerwin Hoogsteen & Marnix V. Ten Kortenaar & Johann L. Hurink & Robert E. Hebner & Gerard J. M. Smit, 2018. "Characterization of Storage Sizing for an Off-Grid House in the US and the Netherlands," Energies, MDPI, vol. 11(2), pages 1-13, January.
    6. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    7. Nicolás Müller & Samir Kouro & Pericle Zanchetta & Patrick Wheeler & Gustavo Bittner & Francesco Girardi, 2019. "Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations," Energies, MDPI, vol. 12(9), pages 1-17, May.
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    Cited by:

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