IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7593-d942393.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7593/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/20/7593/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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).
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammed M. Alhaider & Ziad M. Ali & Mostafa H. Mostafa & Shady H. E. Abdel Aleem, 2023. "Economic Viability of NaS Batteries for Optimal Microgrid Operation and Hosting Capacity Enhancement under Uncertain Conditions," Sustainability, MDPI, vol. 15(20), pages 1-24, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun, 2021. "Hybrid time-scale energy optimal scheduling strategy for integrated energy system with bilateral interaction with supply and demand," Applied Energy, Elsevier, vol. 285(C).
    2. Eid Gul & Giorgio Baldinelli & Pietro Bartocci, 2022. "Energy Transition: Renewable Energy-Based Combined Heat and Power Optimization Model for Distributed Communities," Energies, MDPI, vol. 15(18), pages 1-18, September.
    3. Xiang, Yue & Guo, Yongtao & Wu, Gang & Liu, Junyong & Sun, Wei & Lei, Yutian & Zeng, Pingliang, 2022. "Low-carbon economic planning of integrated electricity-gas energy systems," Energy, Elsevier, vol. 249(C).
    4. Mengzhu Xiao & Manuel Wetzel & Thomas Pregger & Sonja Simon & Yvonne Scholz, 2020. "Modeling the Supply of Renewable Electricity to Metropolitan Regions in China," Energies, MDPI, vol. 13(12), pages 1-31, June.
    5. Ma, Chenshuo & Zhang, Yifei & Ma, Keni & Li, Chanyun, 2023. "Study on the relationship between service scale and investment cost of energy service stations," Energy, Elsevier, vol. 269(C).
    6. Feng Dong & Yuling Pan, 2020. "Evolution of Renewable Energy in BRI Countries: A Combined Econometric and Decomposition Approach," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    7. Younessi, Hiva Seyed & Bahramara, Salah & Adabi, Farid & Golpîra, Hêmin, 2023. "Modeling the optimal sizing problem of the biogas-based electrical generator in a livestock farm considering a gas storage tank and the anaerobic digester process under the uncertainty of cow dung," Energy, Elsevier, vol. 270(C).
    8. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    9. Marcus Brennenstuhl & Daniel Lust & Dirk Pietruschka & Dietrich Schneider, 2021. "Demand Side Management Based Power-to-Heat and Power-to-Gas Optimization Strategies for PV and Wind Self-Consumption in a Residential Building Cluster," Energies, MDPI, vol. 14(20), pages 1-29, October.
    10. Tang, Bao-Jun & Cao, Xi-Lin & Li, Ru & Xiang, Zhi-Bo & Zhang, Sen, 2024. "Economic and low-carbon planning for interconnected integrated energy systems considering emerging technologies and future development trends," Energy, Elsevier, vol. 302(C).
    11. Jieyu Xie & Xingying Chen & Kun Yu & Lei Gan & Haochen Hua & Bo Wang & Yuelong Qu, 2024. "Research on the Configuration of a 100% Green Electricity Supplied Zero-Carbon Integrated Energy Station," Energies, MDPI, vol. 17(16), pages 1-22, August.
    12. Azimian, Mahdi & Amir, Vahid & Javadi, Saeid, 2020. "Economic and Environmental Policy Analysis for Emission-Neutral Multi-Carrier Microgrid Deployment," Applied Energy, Elsevier, vol. 277(C).
    13. Wakui, Tetsuya & Akai, Kazuki & Yokoyama, Ryohei, 2022. "Shrinking and receding horizon approaches for long-term operational planning of energy storage and supply systems," Energy, Elsevier, vol. 239(PD).
    14. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).
    15. Pu, Yuchen & Li, Qi & Zou, Xueli & Li, Ruirui & Li, Luoyi & Chen, Weirong & Liu, Hong, 2021. "Optimal sizing for an integrated energy system considering degradation and seasonal hydrogen storage," Applied Energy, Elsevier, vol. 302(C).
    16. Pizzuti, Andrea & Jin, Lingkang & Rossi, Mosè & Marinelli, Fabrizio & Comodi, Gabriele, 2024. "A novel approach for multi-stage investment decisions and dynamic variations in medium-term energy planning for multi-energy carriers community," Applied Energy, Elsevier, vol. 353(PB).
    17. Dong, Haoxin & Shan, Zijing & Zhou, Jianli & Xu, Chuanbo & Chen, Wenjun, 2023. "Refined modeling and co-optimization of electric-hydrogen-thermal-gas integrated energy system with hybrid energy storage," Applied Energy, Elsevier, vol. 351(C).
    18. Qin, Yuxiao & Liu, Pei & Li, Zheng, 2022. "Multi-timescale hierarchical scheduling of an integrated energy system considering system inertia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    19. Anam Nadeem & Mosè Rossi & Erica Corradi & Lingkang Jin & Gabriele Comodi & Nadeem Ahmed Sheikh, 2022. "Energy-Environmental Planning of Electric Vehicles (EVs): A Case Study of the National Energy System of Pakistan," Energies, MDPI, vol. 15(9), pages 1-19, April.
    20. Bartolini, Andrea & Mazzoni, Stefano & Comodi, Gabriele & Romagnoli, Alessandro, 2021. "Impact of carbon pricing on distributed energy systems planning," Applied Energy, Elsevier, vol. 301(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7593-:d:942393. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.