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A Capacity Configuration Control Strategy to Alleviate Power Fluctuation of Hybrid Energy Storage System Based on Improved Particle Swarm Optimization

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  • Tiezhou Wu

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Xiao Shi

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Li Liao

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Chuanjian Zhou

    (Zhuhai Dongfan Technology Co., Ltd., Zhuhai 519000, China)

  • Hang Zhou

    (Department of Architecture and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, UK)

  • Yuehong Su

    (Department of Architecture and Built Environment, University of Nottingham, University Park, Nottingham NG7 2RD, UK)

Abstract

In view of optimizing the configuration of each unit’s capacity for energy storage in the microgrid system, in order to ensure that the planned energy storage capacity can meet the reasonable operation of the microgrid’s control strategy, the power fluctuations during the grid-connected operation of the microgrid are considered in the planning and The economic benefit of hybrid energy storage is quantified. A multi-objective function aiming at minimizing the power fluctuation on the DC bus in the microgrid and optimizing the capacity ratio of each energy storage system in the hybrid energy storage system (HESS) is established. The improved particle swarm algorithm (PSO) is used to solve the objective function, and the solution is applied to the microgrid experimental platform. By comparing the power fluctuations of the battery and the supercapacitor in the HESS, the power distribution is directly reflected. Comparing with the traditional mixed energy storage control strategy, it shows that the optimized hybrid energy storage control strategy can save 4.3% of the cost compared with the traditional hybrid energy storage control strategy, and the performance of the power fluctuation of the renewable energy is also improved. It proves that the proposed capacity configuration of the HESS has certain theoretical significance and practical application value.

Suggested Citation

  • Tiezhou Wu & Xiao Shi & Li Liao & Chuanjian Zhou & Hang Zhou & Yuehong Su, 2019. "A Capacity Configuration Control Strategy to Alleviate Power Fluctuation of Hybrid Energy Storage System Based on Improved Particle Swarm Optimization," Energies, MDPI, vol. 12(4), pages 1-11, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:642-:d:206621
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    References listed on IDEAS

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    2. Si, Yupeng & Wang, Rongjie & Zhang, Shiqi & Zhou, Wenting & Lin, Anhui & Zeng, Guangmiao, 2022. "Configuration optimization and energy management of hybrid energy system for marine using quantum computing," Energy, Elsevier, vol. 253(C).
    3. Zhong Guan & Hui Wang & Zhi Li & Xiaohu Luo & Xi Yang & Jugang Fang & Qiang Zhao, 2024. "Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm," Energies, MDPI, vol. 17(7), pages 1-20, April.
    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. Junxia Ma & Qiuling Fei & Fan Guo & Weili Xiong, 2019. "Variational Bayesian Iterative Estimation Algorithm for Linear Difference Equation Systems," Mathematics, MDPI, vol. 7(12), pages 1-16, November.
    6. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.
    7. Hao Ma & Jian Pan & Lei Lv & Guanghui Xu & Feng Ding & Ahmed Alsaedi & Tasawar Hayat, 2019. "Recursive Algorithms for Multivariable Output-Error-Like ARMA Systems," Mathematics, MDPI, vol. 7(6), pages 1-18, June.
    8. Shah Fahad & Shiyou Yang & Rehan Ali Khan & Shafiullah Khan & Shoaib Ahmed Khan, 2021. "A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems," Energies, MDPI, vol. 14(15), pages 1-11, July.
    9. Shuang Lei & Yu He & Jing Zhang & Kun Deng, 2023. "Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition," Energies, MDPI, vol. 16(11), pages 1-19, May.
    10. Lijuan Wan & Ximei Liu & Feng Ding & Chunping Chen, 2019. "Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems," Mathematics, MDPI, vol. 7(7), pages 1-20, July.
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