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Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization

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Listed:
  • Hui Wang

    (School of Electrical Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong 250061, China)

  • Tengxin Wang

    (School of Electrical Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong 250061, China)

  • Xiaohan Xie

    (State Grid Jinan Power Supply Company, 238 Luoyuan Street Road, Jinan, Shandong 250012, China)

  • Zhixiang Ling

    (State Grid Jinan Power Supply Company, 238 Luoyuan Street Road, Jinan, Shandong 250012, China)

  • Guoliang Gao

    (State Grid Jinan Power Supply Company, 238 Luoyuan Street Road, Jinan, Shandong 250012, China)

  • Xu Dong

    (State Grid Jinan Power Supply Company, 238 Luoyuan Street Road, Jinan, Shandong 250012, China)

Abstract

The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply’s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS) composed of batteries and ultracapacitors in this paper. A quantum-behaved particle swarm optimization (QPSO) algorithm that optimizes the HESS capacity is used. Based on the respective power compensation capabilities of ultracapacitors and batteries, a rational energy scheduling strategy is proposed using the principle of a low-pass filter and can help to avoid frequent batteries charging and discharging. Considering the rated power of each energy storage type, the respective compensation power is corrected. By determining whether the charging state reaches the limit, the value is corrected again. Additionally, a mathematical model that minimizes the daily cost of the HESS is derived. This paper takes an isolated micrgrid in north China as an example to verify the effectiveness of this method. The comparison between QPSO and a traditional particle swarm algorithm shows that QPSO can find the optimal solution faster and the HESS has lower daily cost. Simulation results for an isolated microgrid verified the effectiveness of the HESS optimal capacity configuration method.

Suggested Citation

  • Hui Wang & Tengxin Wang & Xiaohan Xie & Zhixiang Ling & Guoliang Gao & Xu Dong, 2018. "Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization," Energies, MDPI, vol. 11(2), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:454-:d:132574
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    Citations

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    Cited by:

    1. Lucrezia Manservigi & Mattia Cattozzo & Pier Ruggero Spina & Mauro Venturini & Hilal Bahlawan, 2020. "Optimal Management of the Energy Flows of Interconnected Residential Users," Energies, MDPI, vol. 13(6), pages 1-21, March.
    2. Xin-gang, Zhao & Ze-qi, Zhang & Yi-min, Xie & Jin, Meng, 2020. "Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization," Energy, Elsevier, vol. 195(C).
    3. Zheng Wu & Laifu Li & Yubo Yuan & Xiaodong Yuan & Chenyu Zhang & Li Kong & Wei Pei & Wei Deng, 2020. "Research on Additional Control Technology Based on Energy Storage System for Improving Power Transfer Capacity of Multi-Terminal AC/DC System with Low Cost," Energies, MDPI, vol. 13(2), pages 1-20, January.
    4. Sergey Obukhov & Ahmed Ibrahim & Mohamed A. Tolba & Ali M. El-Rifaie, 2019. "Power Balance Management of an Autonomous Hybrid Energy System Based on the Dual-Energy Storage," Energies, MDPI, vol. 12(24), pages 1-15, December.
    5. Xiaohui Yang & Jiating Long & Peiyun Liu & Xiaolong Zhang & Xiaoping Liu, 2018. "Optimal Scheduling of Microgrid with Distributed Power Based on Water Cycle Algorithm," Energies, MDPI, vol. 11(9), pages 1-17, September.
    6. Yang, Xiyun & Liu, Siqu & Zhang, Le & Su, Jianzheng & Ye, Tianze, 2020. "Design and analysis of a renewable energy power system for shale oil exploitation using hierarchical optimization," Energy, Elsevier, vol. 206(C).
    7. Yuanli Liu & Minwu Chen & Shaofeng Lu & Yinyu Chen & Qunzhan Li, 2018. "Optimized Sizing and Scheduling of Hybrid Energy Storage Systems for High-Speed Railway Traction Substations," Energies, MDPI, vol. 11(9), pages 1-29, August.

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