IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p359-d1316765.html
   My bibliography  Save this article

An Optimization Method for the Distributed Collaborative Operation of Multilateral Entities Considering Dynamic Time-of-Use Electricity Price in Active Distribution Network

Author

Listed:
  • Gang Liang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
    Jinghai Power Supply Branch of State Grid Tianjin Electric Power Company, Tianjin 301600, China)

  • Yu Wang

    (Dongli Power Supply Branch of State Grid Tianjin Electric Power Company, Tianjin 300308, China)

  • Bing Sun

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Zheng Zhang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

More and more microgrids, energy storage systems, and other emerging entities are integrated into active distribution networks. However, a microgrid is characterized by autonomous operation and privacy protection. The rapid development of equipment such as shared energy storage brings strong uncertainty to a traditional dispatcher. The observability and controllability of the distribution system decrease, and traditional regulatory methods are no longer applicable. To deal with the above challenges, a distributed collaborative operation optimization method of multilateral participants is proposed. Guided by the dynamic time-of-use electricity price, the collaborative operation of multilateral participants can be realized. Firstly, the cooperative operation architecture is established considering the dynamic time-of-use electricity price. In this architecture, the residual capacity of shared energy storage is used for arbitrage by storing electricity at low electricity prices and generating electricity at high electricity prices. Then, the optimization operation models of a microgrid alliance, shared energy storage, and an active distribution network are established. The final operation scheme and the dynamic time-of-use price of the distribution network are formulated through the cyclic iteration among the three participants. Finally, a case study is carried out to analyze the optimization effect of each participant with the proposed method. It is found that the overall interests and the interests of each participant can be taken into account effectively and the consumption of renewable energy can be promoted by the method proposed in the paper. In addition, an oscillation phenomenon is found during the distributed collaborative operation, and the strategy to eliminate the oscillation phenomenon is given.

Suggested Citation

  • Gang Liang & Yu Wang & Bing Sun & Zheng Zhang, 2024. "An Optimization Method for the Distributed Collaborative Operation of Multilateral Entities Considering Dynamic Time-of-Use Electricity Price in Active Distribution Network," Energies, MDPI, vol. 17(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:359-:d:1316765
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/359/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/2/359/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Mingtao & Huang, Huijun & Song, Xiaoling & Peña-Mora, Feniosky & Zhang, Zhe & Chen, Jie, 2022. "Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach," Applied Energy, Elsevier, vol. 307(C).
    2. Wang, Chutong & Zhang, Xiaoyan & Wang, Yucui & Xiong, Houbo & Ding, Xi & Guo, Chuangxin, 2023. "Pricing method of electric-thermal heterogeneous shared energy storage service," Energy, Elsevier, vol. 281(C).
    3. Zhang, Tianhan & Qiu, Weiqiang & Zhang, Zhi & Lin, Zhenzhi & Ding, Yi & Wang, Yiting & Wang, Lianfang & Yang, Li, 2023. "Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets," Applied Energy, Elsevier, vol. 329(C).
    4. Cao, Wenzhi & Xiao, Jiang-Wen & Cui, Shi-Chang & Liu, Xiao-Kang, 2022. "An efficient and economical storage and energy sharing model for multiple multi-energy microgrids," Energy, Elsevier, vol. 244(PB).
    5. Guo, Tianyu & Guo, Qi & Huang, Libin & Guo, Haiping & Lu, Yuanhong & Tu, Liang, 2023. "Microgrid source-network-load-storage master-slave game optimization method considering the energy storage overcharge/overdischarge risk," Energy, Elsevier, vol. 282(C).
    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. Wang, Dongxue & Fan, Ruguo & Yang, Peiwen & Du, Kang & Xu, Xiaoxia & Chen, Rongkai, 2024. "Research on floating real-time pricing strategy for microgrid operator in local energy market considering shared energy storage leasing," Applied Energy, Elsevier, vol. 368(C).
    2. Bian, Yifan & Xie, Lirong & Ye, Jiahao & Ma, Lan & Cui, Chuanshi, 2024. "Peer-to-peer energy sharing model considering multi-objective optimal allocation of shared energy storage in a multi-microgrid system," Energy, Elsevier, vol. 288(C).
    3. Song, Xiaoling & Zhang, Huqing & Fan, Lurong & Zhang, Zhe & Peña-Mora, Feniosky, 2023. "Planning shared energy storage systems for the spatio-temporal coordination of multi-site renewable energy sources on the power generation side," Energy, Elsevier, vol. 282(C).
    4. Qiu, Rui & Zhang, Haoran & Wang, Guotao & Liang, Yongtu & Yan, Jinyue, 2023. "Green hydrogen-based energy storage service via power-to-gas technologies integrated with multi-energy microgrid," Applied Energy, Elsevier, vol. 350(C).
    5. Gao, Yang & Ai, Qian & He, Xing & Fan, Songli, 2023. "Coordination for regional integrated energy system through target cascade optimization," Energy, Elsevier, vol. 276(C).
    6. Alain Aoun & Mehdi Adda & Adrian Ilinca & Mazen Ghandour & Hussein Ibrahim, 2024. "Optimizing Virtual Power Plant Management: A Novel MILP Algorithm to Minimize Levelized Cost of Energy, Technical Losses, and Greenhouse Gas Emissions," Energies, MDPI, vol. 17(16), pages 1-23, August.
    7. Cui, Jingshi & Wu, Jiaman & Wu, Chenye & Moura, Scott, 2023. "Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies," Applied Energy, Elsevier, vol. 352(C).
    8. Han, Ouzhu & Ding, Tao & Zhang, Xiaosheng & Mu, Chenggang & He, Xinran & Zhang, Hongji & Jia, Wenhao & Ma, Zhoujun, 2023. "A shared energy storage business model for data center clusters considering renewable energy uncertainties," Renewable Energy, Elsevier, vol. 202(C), pages 1273-1290.
    9. Baohong Jin & Zhichao Liu & Yichuan Liao, 2023. "Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model," Energies, MDPI, vol. 16(6), pages 1-21, March.
    10. Zhaonian Ye & Yongzhen Wang & Kai Han & Changlu Zhao & Juntao Han & Yilin Zhu, 2023. "Bi-Objective Optimization and Emergy Analysis of Multi-Distributed Energy System Considering Shared Energy Storage," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    11. Tang, Qinghu & Guo, Hongye & Zheng, Kedi & Chen, Qixin, 2024. "Forecasting individual bids in real electricity markets through machine learning framework," Applied Energy, Elsevier, vol. 363(C).
    12. Hou, Langbo & Tong, Xi & Chen, Heng & Fan, Lanxin & Liu, Tao & Liu, Wenyi & Liu, Tong, 2024. "Optimized scheduling of smart community energy systems considering demand response and shared energy storage," Energy, Elsevier, vol. 295(C).
    13. 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).
    14. Luwen Pan & Jiajia Chen, 2024. "Optimal Energy Storage Configuration of Prosumers with Uncertain Photovoltaic in the Presence of Customized Pricing-Based Demand Response," Sustainability, MDPI, vol. 16(6), pages 1-18, March.
    15. Chen, Yujia & Pei, Wei & Ma, Tengfei & Xiao, Hao, 2023. "Asymmetric Nash bargaining model for peer-to-peer energy transactions combined with shared energy storage," Energy, Elsevier, vol. 278(PB).
    16. Wu, Chuantao & Zhou, Dezhi & Lin, Xiangning & Sui, Quan & Wei, Fanrong & Li, Zhengtian, 2022. "A novel energy cooperation framework for multi-island microgrids based on marine mobile energy storage systems," Energy, Elsevier, vol. 252(C).
    17. Xu, Jiazhu & Yi, Yuqin, 2023. "Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach," Energy, Elsevier, vol. 263(PB).
    18. Zhao, Xudong & Wang, Yibo & Liu, Chuang & Cai, Guowei & Ge, Weichun & Wang, Bowen & Wang, Dongzhe & Shang, Jingru & Zhao, Yiru, 2024. "Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition," Energy, Elsevier, vol. 302(C).
    19. Rabea Jamil Mahfoud & Nizar Faisal Alkayem & Emmanuel Fernandez-Rodriguez & Yuan Zheng & Yonghui Sun & Shida Zhang & Yuquan Zhang, 2024. "Evolutionary Approach for DISCO Profit Maximization by Optimal Planning of Distributed Generators and Energy Storage Systems in Active Distribution Networks," Mathematics, MDPI, vol. 12(2), pages 1-33, January.
    20. Qibo He & Changming Chen & Xin Fu & Shunjiang Yu & Long Wang & Zhenzhi Lin, 2024. "Joint Planning Method of Shared Energy Storage and Multi-Energy Microgrids Based on Dynamic Game with Perfect Information," Energies, MDPI, vol. 17(19), pages 1-20, September.

    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:2:p:359-:d:1316765. 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.