IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i19p4883-d1488472.html
   My bibliography  Save this article

The Operation Strategy of a Multi-Microgrid Considering the Interaction of Different Subjects’ Interests

Author

Listed:
  • Siwen Wang

    (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Hui Chen

    (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Chunyang Gong

    (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Yanfei Shang

    (LITHOS NEW ENERGY GROUP COMPANY LIMITED, Shanghai 202156, China)

  • Zhixin Wang

    (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
    Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

As the share of renewable energy generation continues to increase, the new-type power system exhibits the characteristics of coordinated operation between the main grid, distribution networks, and microgrids. The microgrid is primarily concerned with achieving self-balancing between power sources, the network, loads, and storage. In decentralized multi-microgrid (MMG) access scenarios, the aggregation of distributed energy within a region enables the unified optimization of scheduling, which improves regional energy self-sufficiency while mitigating the impact and risks of distributed energy on grid operations. However, the cooperative operation of MMGs involves interactions among various stakeholders, and the absence of a reasonable operational mechanism can result in low energy utilization, uneven resource allocation, and other issues. Thus, designing an effective MMG operation strategy that balances the interests of all stakeholders has become a key area of focus in the industry. This paper examines the definition and structure of MMGs, analyzes their current operational challenges, compiles existing research methods and practical experiences, explores synergistic operational mechanisms and strategies for MMGs under different transaction models, and puts forward prospects for future research directions.

Suggested Citation

  • Siwen Wang & Hui Chen & Chunyang Gong & Yanfei Shang & Zhixin Wang, 2024. "The Operation Strategy of a Multi-Microgrid Considering the Interaction of Different Subjects’ Interests," Energies, MDPI, vol. 17(19), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4883-:d:1488472
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/19/4883/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/19/4883/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Bingying & Li, Qiqiang & Wang, Luhao & Feng, Wei, 2018. "Robust optimization for energy transactions in multi-microgrids under uncertainty," Applied Energy, Elsevier, vol. 217(C), pages 346-360.
    2. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    3. Michael Jong Kim & Andrew E.B. Lim, 2016. "Robust Multiarmed Bandit Problems," Management Science, INFORMS, vol. 62(1), pages 264-285, January.
    Full references (including those not matched with items on IDEAS)

    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. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
    2. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    3. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    4. Da Li & Shijie Zhang & Yunhan Xiao, 2020. "Interval Optimization-Based Optimal Design of Distributed Energy Resource Systems under Uncertainties," Energies, MDPI, vol. 13(13), pages 1-18, July.
    5. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Biskas, Pandelis N. & Keranidis, Stratos D. & Symeonidis, Polychronis A. & Voulgarakis, Dimitrios K., 2022. "A novel system for providing explicit demand response from domestic natural gas boilers," Applied Energy, Elsevier, vol. 317(C).
    6. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    7. Bao, Minglei & Hui, Hengyu & Ding, Yi & Sun, Xiaocong & Zheng, Chenghang & Gao, Xiang, 2023. "An efficient framework for exploiting operational flexibility of load energy hubs in risk management of integrated electricity-gas systems," Applied Energy, Elsevier, vol. 338(C).
    8. Ma, Huan & Sun, Qinghan & Chen, Qun & Zhao, Tian & He, Kelun, 2023. "Exergy-based flexibility cost indicator and spatio-temporal coordination principle of distributed multi-energy systems," Energy, Elsevier, vol. 267(C).
    9. 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).
    10. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
    11. Man Yiu Tsang & Tony Sit & Hoi Ying Wong, 2022. "Adaptive Robust Online Portfolio Selection," Papers 2206.01064, arXiv.org.
    12. Tie, Yingqi & Hu, Bo & Shao, Changzheng & Huang, Wei & Qi, Feng & Xie, Kaigui, 2023. "Integrated flexibility characterization and measurement of distributed multi-energy systems considering temporal coupling constraints," Energy, Elsevier, vol. 283(C).
    13. Jani, Ali & Karimi, Hamid & Jadid, Shahram, 2022. "Two-layer stochastic day-ahead and real-time energy management of networked microgrids considering integration of renewable energy resources," Applied Energy, Elsevier, vol. 323(C).
    14. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    15. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    16. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    17. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    18. Ponce de Leon Barido, Diego & Suffian, Stephen & Kammen, Daniel M. & Callaway, Duncan, 2018. "Opportunities for behavioral energy efficiency and flexible demand in data-limited low-carbon resource constrained environments," Applied Energy, Elsevier, vol. 228(C), pages 512-523.
    19. Chen, Yang & Park, Byungkwon & Kou, Xiao & Hu, Mengqi & Dong, Jin & Li, Fangxing & Amasyali, Kadir & Olama, Mohammed, 2020. "A comparison study on trading behavior and profit distribution in local energy transaction games," Applied Energy, Elsevier, vol. 280(C).
    20. Jun Dong & Yuanyuan Wang & Xihao Dou & Zhengpeng Chen & Yaoyu Zhang & Yao Liu, 2021. "Research on Decision Optimization Model of Microgrid Participating in Spot Market Transaction," Sustainability, MDPI, vol. 13(12), pages 1-26, June.

    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:17:y:2024:i:19:p:4883-:d:1488472. 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.