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Optimal Configuration Analysis Method of Energy Storage System Based on “Equal Area Criterion”

Author

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  • Yizheng Li

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
    State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China)

  • Yuan Zeng

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Zhidong Wang

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China)

  • Lang Zhao

    (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China)

  • Yao Wang

    (Economic and Technological Research Institute of State Grid Shanxi Electric Power Co., Ltd., Taiyuan 030021, China)

Abstract

In order to solve the problem of randomness and volatility caused by the rapid growth of renewable energy (RE), energy storage systems (ESSs)—as an important means of regulation—can effectively improve the flexible regulation capacity of power systems utilizing a high proportion of RE. Most of the current ESS capacity configuration procedures are carried out based on the typical scenario method or time series production simulation. This method tends to determine the size of the ESS configuration through multiple trial simulations. Uncertainty of simulation prediction data can result in the existence of an excess capacity or lack of configured capacity. In addition, this method reflects the ESS demand under specific targets, but it fails to fully utilize RE generation characteristics. The configuration process lacks the mathematical mechanism of RE consumption, and the calculation process is too complicated. In view of the shortcomings of traditional ESS optimal configuration methods, this paper examines the mathematical mechanism of RE consumption and proposes the ESS optimal configuration analysis method based on “equal area criterion”. First, the principle of RE consumption is analyzed and the “RE consumption characteristic curve” is proposed according to RE characteristics. In addition, a working principle diagram of RE consumption, including ESS, is constructed to visually show the consumption capacity of RE and the working position of ESS. Then, the ESS optimal configuration process, based on the “equal area criterion”, is proposed to achieve an accurate match between ESS capacity demand and RE consumption targets. Finally, the power grid of a region in China is taken as an example. We prove that the proposed method can save 1.41 × 10 3 MWh of ESS capacity and provide a more “mathematical” and “convenient” systematic solution for RE consumption and ESS optimization compared to the production simulation method.

Suggested Citation

  • Yizheng Li & Yuan Zeng & Zhidong Wang & Lang Zhao & Yao Wang, 2023. "Optimal Configuration Analysis Method of Energy Storage System Based on “Equal Area Criterion”," Energies, MDPI, vol. 16(24), pages 1-29, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:7940-:d:1295521
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    References listed on IDEAS

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    1. Feng, Cong & Zhang, Jie & Zhang, Wenqi & Hodge, Bri-Mathias, 2022. "Convolutional neural networks for intra-hour solar forecasting based on sky image sequences," Applied Energy, Elsevier, vol. 310(C).
    2. Takele Ferede Agajie & Ahmed Ali & Armand Fopah-Lele & Isaac Amoussou & Baseem Khan & Carmen Lilí Rodríguez Velasco & Emmanuel Tanyi, 2023. "A Comprehensive Review on Techno-Economic Analysis and Optimal Sizing of Hybrid Renewable Energy Sources with Energy Storage Systems," Energies, MDPI, vol. 16(2), pages 1-26, January.
    3. Mohammad Mehdi Davari & Hossein Ameli & Mohammad Taghi Ameli & Goran Strbac, 2022. "Impact of Local Emergency Demand Response Programs on the Operation of Electricity and Gas Systems," Energies, MDPI, vol. 15(6), pages 1-20, March.
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