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Stochastic Optimization Method for Energy Storage System Configuration Considering Self-Regulation of the State of Charge

Author

Listed:
  • Delong Zhang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Yiyi Ma

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Jinxin Liu

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Siyu Jiang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Yongcong Chen

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Longze Wang

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Yan Zhang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing 102206, China)

  • Meicheng Li

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

Abstract

Photovoltaic (PV) power generation has developed rapidly in recent years. Owing to its volatility and intermittency, PV power generation has an impact on the power quality and operation of the power system. To mitigate the impact caused by the PV generation, an energy storage (ES) system is applied to the PV plants. The capacity configuration and control strategy based on the stochastic optimization method have become an important research topic. However, the accuracy of the probability distribution model is insufficient and a stochastic optimization method is rarely used in a control strategy. In this paper, a stochastic optimization method for the energy storage system (ESS) configuration considering the self-regulation of the battery state of charge (SoC) is proposed. Firstly, to reduce the sampling error when typical scenarios of PV power are generated, a time-divided probability distribution model of the ultra-short-term predicted error of PV power is established. On this basis, to solve the problem that SoC reaches the threshold frequently, a self-regulation model of the SoC based on multiple scenarios is established, which can regulate the SoC according to rolling PV power prediction. A stochastic optimization configuration model of the energy storage system is constructed, which can reduce the impact of PV uncertainty on the configuration result. Finally, the proposed stochastic optimization method is validated. The fitting error of the time-divided probability distribution model is 15.61% lower than that of the t-distribution. The expected revenue of the optimal configuration in this paper is 8.86% higher than the scheme with a fixed probability distribution model, and 16.87% higher than without considering the stochastic optimization method.

Suggested Citation

  • Delong Zhang & Yiyi Ma & Jinxin Liu & Siyu Jiang & Yongcong Chen & Longze Wang & Yan Zhang & Meicheng Li, 2022. "Stochastic Optimization Method for Energy Storage System Configuration Considering Self-Regulation of the State of Charge," Sustainability, MDPI, vol. 14(1), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:1:p:553-:d:718061
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    References listed on IDEAS

    as
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    5. Md. Sanwar Hossain & Abdullah G. Alharbi & Khondoker Ziaul Islam & Md. Rabiul Islam, 2021. "Techno-Economic Analysis of the Hybrid Solar PV/H/Fuel Cell Based Supply Scheme for Green Mobile Communication," Sustainability, MDPI, vol. 13(22), pages 1-29, November.
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    Cited by:

    1. Jie Ji & Fucheng Wang & Mengxiong Zhou & Renwei Guo & Rundong Ji & Hui Huang & Jiayu Zhang & Muhammad Shahzad Nazir & Tian Peng & Chu Zhang & Jiahui Huang & Yaodong Wang, 2022. "Evaluation Study on a Novel Structure CCHP System with a New Comprehensive Index Using Improved ALO Algorithm," Sustainability, MDPI, vol. 14(22), pages 1-20, November.

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