IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2411-d1086274.html
   My bibliography  Save this article

An Optimal Scheduling Method of Shared Energy Storage System Considering Distribution Network Operation Risk

Author

Listed:
  • Jiahao Chen

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

  • Bing Sun

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

  • Yuan Zeng

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

  • Ruipeng Jing

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

  • Shimeng Dong

    (State Grid Corporation of China, Beijing 100031, China)

  • Jingran Wang

    (State Grid Jibei Electric Power Company Limited, Beijing 065300, China)

Abstract

Shared energy storage systems (SESS) have been gradually developed and applied to distribution networks (DN). There are electrical connections between SESSs and multiple DN nodes; SESSs could significantly improve the power restoration potential and reduce the power interruption cost during fault periods. Currently, a major challenge exists in terms of how to consider both the efficiency of the operation and the reliability cost when formulating the SESS scheduling scheme. A SESS optimal scheduling method that considers the DN operation risk is proposed in this paper. First, a multi-objective day-ahead scheduling model for SESS is developed, where the user’s interruption cost is regarded as the reliability cost and it is the product of the occurrence probability of the expected accident and the loss of power outage. Then, an island partition model with SESS was established in order to accurately calculate the reliability cost. Via the maximum island partition and island optimal rectification, the SESS was carefully integrated into the power restoration system. Furthermore, in order to minimize the comprehensive operation cost, an improved genetic algorithm for the island partition was designed to solve the complex SESS optimal scheduling model. Finally, a case study on the improved PG&E 69 bus system was analyzed. Moreover, we found that the DN’s comprehensive operation cost decreased by 6.6% using the proposed method.

Suggested Citation

  • Jiahao Chen & Bing Sun & Yuan Zeng & Ruipeng Jing & Shimeng Dong & Jingran Wang, 2023. "An Optimal Scheduling Method of Shared Energy Storage System Considering Distribution Network Operation Risk," Energies, MDPI, vol. 16(5), pages 1-24, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2411-:d:1086274
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2411/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/5/2411/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Jiahao & Sun, Bing & Li, Yunfei & Jing, Ruipeng & Zeng, Yuan & Li, Minghao, 2022. "Credible capacity calculation method of distributed generation based on equal power supply reliability criterion," Renewable Energy, Elsevier, vol. 201(P1), pages 534-547.
    2. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    3. Zhao, Jinli & Zhang, Mengzhen & Yu, Hao & Ji, Haoran & Song, Guanyu & Li, Peng & Wang, Chengshan & Wu, Jianzhong, 2019. "An islanding partition method of active distribution networks based on chance-constrained programming," Applied Energy, Elsevier, vol. 242(C), pages 78-91.
    4. Chang, Hsiu-Chuan & Ghaddar, Bissan & Nathwani, Jatin, 2022. "Shared community energy storage allocation and optimization," Applied Energy, Elsevier, vol. 318(C).
    5. Zheng, Boshen & Wei, Wei & Chen, Yue & Wu, Qiuwei & Mei, Shengwei, 2022. "A peer-to-peer energy trading market embedded with residential shared energy storage units," Applied Energy, Elsevier, vol. 308(C).
    6. 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).
    7. Xiao, Jiang-Wen & Yang, Yan-Bing & Cui, Shichang & Liu, Xiao-Kang, 2022. "A new energy storage sharing framework with regard to both storage capacity and power capacity," Applied Energy, Elsevier, vol. 307(C).
    8. Walker, Awnalisa & Kwon, Soongeol, 2021. "Analysis on impact of shared energy storage in residential community: Individual versus shared energy storage," Applied Energy, Elsevier, vol. 282(PA).
    9. Walker, Awnalisa & Kwon, Soongeol, 2021. "Design of structured control policy for shared energy storage in residential community: A stochastic optimization approach," Applied Energy, Elsevier, vol. 298(C).
    10. Sun, Bing & Yu, Yixin & Qin, Chao, 2017. "Should China focus on the distributed development of wind and solar photovoltaic power generation? A comparative study," Applied Energy, Elsevier, vol. 185(P1), pages 421-439.
    11. Ji, Haoran & Wang, Chengshan & Li, Peng & Song, Guanyu & Wu, Jianzhong, 2018. "SOP-based islanding partition method of active distribution networks considering the characteristics of DG, energy storage system and load," Energy, Elsevier, vol. 155(C), pages 312-325.
    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. He, Ye & Wu, Hongbin & Wu, Andrew Y. & Li, Peng & Ding, Ming, 2024. "Optimized shared energy storage in a peer-to-peer energy trading market: Two-stage strategic model regards bargaining and evolutionary game theory," Renewable Energy, Elsevier, vol. 224(C).
    2. Yi Hao & Zhigang Huang & Shiqian Ma & Jiakai Huang & Jiahao Chen & Bing Sun, 2023. "Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation," Energies, MDPI, vol. 16(16), pages 1-17, August.
    3. 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).
    4. 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.
    5. Bian, Yifan & Xie, Lirong & Ye, Jiahao & Ma, Lan, 2024. "A new shared energy storage business model for data center clusters considering energy storage degradation," Renewable Energy, Elsevier, vol. 225(C).
    6. Jing Yu & Jicheng Liu & Yajing Wen & Xue Yu, 2023. "Economic Optimal Coordinated Dispatch of Power for Community Users Considering Shared Energy Storage and Demand Response under Blockchain," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
    7. Weiqiang Qiu & Sheng Zhou & Yang Yang & Xiaoying Lv & Ting Lv & Yuge Chen & Ying Huang & Kunming Zhang & Hongfei Yu & Yunchu Wang & Yuanqian Ma & Zhenzhi Lin, 2023. "Application Prospect, Development Status and Key Technologies of Shared Energy Storage toward Renewable Energy Accommodation Scenario in the Context of China," Energies, MDPI, vol. 16(2), pages 1-21, January.
    8. Cui, Shiting & Zhu, Ruijin & Wu, Jun, 2024. "A double layer energy cooperation framework for prosumer groups in high altitude areas," Renewable Energy, Elsevier, vol. 224(C).
    9. Shi, Mengshu & Huang, Yuansheng & Lin, Hongyu, 2023. "Research on power to hydrogen optimization and profit distribution of microgrid cluster considering shared hydrogen storage," Energy, Elsevier, vol. 264(C).
    10. Cui, Shiting & Wu, Jun & Gao, Yao & Zhu, Ruijin, 2023. "A high altitude prosumer energy cooperation framework considering composite energy storage sharing and electric‑oxygen‑hydrogen flexible supply," Applied Energy, Elsevier, vol. 349(C).
    11. Hu, Junjie & Wang, Yudong & Dong, Lei, 2024. "Low carbon-oriented planning of shared energy storage station for multiple integrated energy systems considering energy-carbon flow and carbon emission reduction," Energy, Elsevier, vol. 290(C).
    12. Du, Sipeng & Wu, Di & Dai, Zhong & Li, Guiqiang & Lahaxibai, Shala, 2023. "Regional collaborative planning equipped with shared energy storage under multi-time scale rolling optimisation method," Energy, Elsevier, vol. 277(C).
    13. Wei, Tianxi & Zhang, Yi & Zhang, Yuhang & Miao, Rui & Kang, Jian & Qi, He, 2024. "City-scale roof-top photovoltaic deployment planning," Applied Energy, Elsevier, vol. 368(C).
    14. Gao, Jianwei & Wang, Yaping & Huang, Ningbo & Wei, Lingli & Zhang, Zixuan, 2022. "Optimal site selection study of wind-photovoltaic-shared energy storage power stations based on GIS and multi-criteria decision making: A two-stage framework," Renewable Energy, Elsevier, vol. 201(P1), pages 1139-1162.
    15. 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).
    16. Li, Peng & Ji, Haoran & Yu, Hao & Zhao, Jinli & Wang, Chengshan & Song, Guanyu & Wu, Jianzhong, 2019. "Combined decentralized and local voltage control strategy of soft open points in active distribution networks," Applied Energy, Elsevier, vol. 241(C), pages 613-624.
    17. Hao Cai & Ling Liang & Jing Tang & Qianxian Wang & Lihong Wei & Jiaping Xie, 2019. "An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors," Sustainability, MDPI, vol. 11(23), pages 1-22, November.
    18. S M Mezbahul Amin & Abul Hasnat & Nazia Hossain, 2023. "Designing and Analysing a PV/Battery System via New Resilience Indicators," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    19. Lazzari, Florencia & Mor, Gerard & Cipriano, Jordi & Solsona, Francesc & Chemisana, Daniel & Guericke, Daniela, 2023. "Optimizing planning and operation of renewable energy communities with genetic algorithms," Applied Energy, Elsevier, vol. 338(C).
    20. Xu, Jiuping & Wang, Fengjuan & Lv, Chengwei & Huang, Qian & Xie, Heping, 2018. "Economic-environmental equilibrium based optimal scheduling strategy towards wind-solar-thermal power generation system under limited resources," Applied Energy, Elsevier, vol. 231(C), pages 355-371.

    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:16:y:2023:i:5:p:2411-:d:1086274. 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.