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A Capacity Optimization Method for a Hybrid Energy Storage Microgrid System Based on an Augmented ε- Constraint Method

Author

Listed:
  • Xianjing Zhong

    (College of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China)

  • Xianbo Sun

    (College of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China)

  • Yuhan Wu

    (College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

In general, microgrids have a high renewable energy abandonment rate and high grid construction and operation costs. To improve the microgrid renewable energy utilization rate, the economic advantages, and environmental safety of power grid operation, we propose a hybrid energy storage capacity optimization method for a wind–solar–diesel grid-connected microgrid system, based on an augmented ε- constraint method. First, the battery is coupled with a seasonal hydrogen energy storage system to establish a hybrid energy storage model that avoids the shortcomings of traditional microgrid systems, such as a single energy storage mode and a small capacity. Second, by considering the comprehensive cost and carbon emissions of the power grid within the planning period as the objective function, the abandonment rate of renewable energy as the evaluation index, and the electric energy storage and seasonal hydrogen energy storage system operating conditions as the main constraints, the capacity allocation model of the microgrid can be constructed. Finally, an augmented ε- constraint method is implemented to optimize the model above; the entropy–TOPSIS method is used to select the configuration scheme. By comparative analysis, the results show that the optimization method can effectively improve the local absorption rate of wind and solar radiation, and significantly reduce the carbon emissions of microgrids.

Suggested Citation

  • Xianjing Zhong & Xianbo Sun & Yuhan Wu, 2022. "A Capacity Optimization Method for a Hybrid Energy Storage Microgrid System Based on an Augmented ε- Constraint Method," Energies, MDPI, vol. 15(20), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7593-:d:942393
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    References listed on IDEAS

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    1. Petkov, Ivalin & Gabrielli, Paolo, 2020. "Power-to-hydrogen as seasonal energy storage: an uncertainty analysis for optimal design of low-carbon multi-energy systems," Applied Energy, Elsevier, vol. 274(C).
    2. Yang, Xiaohui & Leng, Zhengyang & Xu, Shaoping & Yang, Chunsheng & Yang, Li & Liu, Kang & Song, Yaoren & Zhang, Liufang, 2021. "Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented ε-constraint method," Renewable Energy, Elsevier, vol. 172(C), pages 408-423.
    3. Cheng, Yaohua & Zhang, Ning & Kirschen, Daniel S. & Huang, Wujing & Kang, Chongqing, 2020. "Planning multiple energy systems for low-carbon districts with high penetration of renewable energy: An empirical study in China," Applied Energy, Elsevier, vol. 261(C).
    4. Ji, Ling & Liang, Xiaolin & Xie, Yulei & Huang, Guohe & Wang, Bing, 2021. "Optimal design and sensitivity analysis of the stand-alone hybrid energy system with PV and biomass-CHP for remote villages," Energy, Elsevier, vol. 225(C).
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    2. Binxin Zhu & Junliang Liu & Shusheng Wang & Zhe Li, 2025. "Enhanced Models for Wind, Solar Power Generation, and Battery Energy Storage Systems Considering Power Electronic Converter Precise Efficiency Behavior," Energies, MDPI, vol. 18(6), pages 1-21, March.

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