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Equilibrium allocation strategy of multiple ESSs considering the economics and restoration capability in DNs

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  • Wang, Zhaoqi
  • Zhang, Lu
  • Tang, Wei
  • Chen, Ying
  • Shen, Chen

Abstract

Distribution networks (DNs) face challenges in maintaining continuous power supply for critical loads under extreme natural disasters, such as typhoons. However, the economic benefits resulted from the investments of multiple energy storage systems (ESSs) is difficult to be evaluated and described in the resilience-oriented pre-typhoon allocations as a result of the huge differences lying in the impacts and probabilities of typhoons and normal states. This paper proposes an allocation strategy of multiple ESSs to seek for the equilibrium between the resilience and the economic benefits of DNs considering the advantages of fixed ESSs (FESSs) and mobile ESSs (MESSs). Firstly, the framework of equilibrium allocation strategy of multiple ESSs is captured, where indexes of resilience and economic benefits of DNs are formulated based on the evaluation of DN failure probabilities under typhoons using Gaussian mixture models (GMMs). The restoration states of outage loads and prioritized power requirement of loads are also considered based on FESSs and MESSs. Then, a bi-level allocation model of multiple ESSs in the whole planning period is proposed, in which the Nash equilibrium method is used to balance the resilience under typhoons and economic benefits under normal states. Finally, simulation tests of typhoon-prone DNs verify the superiority of the proposed bi-level equilibrium allocation model of multiple ESSs compared with other approaches.

Suggested Citation

  • Wang, Zhaoqi & Zhang, Lu & Tang, Wei & Chen, Ying & Shen, Chen, 2022. "Equilibrium allocation strategy of multiple ESSs considering the economics and restoration capability in DNs," Applied Energy, Elsevier, vol. 306(PA).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921013192
    DOI: 10.1016/j.apenergy.2021.118019
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    1. Dehghani, Nariman L. & Jeddi, Ashkan B. & Shafieezadeh, Abdollah, 2021. "Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning," Applied Energy, Elsevier, vol. 285(C).
    2. Matthias Ehrgott, 2005. "Multicriteria Optimization," Springer Books, Springer, edition 0, number 978-3-540-27659-3, December.
    3. Zhang, Shenxi & Cheng, Haozhong & Li, Ke & Tai, Nengling & Wang, Dan & Li, Furong, 2018. "Multi-objective distributed generation planning in distribution network considering correlations among uncertainties," Applied Energy, Elsevier, vol. 226(C), pages 743-755.
    4. Hussain, Akhtar & Bui, Van-Hai & Kim, Hak-Man, 2019. "Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience," Applied Energy, Elsevier, vol. 240(C), pages 56-72.
    5. Mishra, Sakshi & Anderson, Kate & Miller, Brian & Boyer, Kyle & Warren, Adam, 2020. "Microgrid resilience: A holistic approach for assessing threats, identifying vulnerabilities, and designing corresponding mitigation strategies," Applied Energy, Elsevier, vol. 264(C).
    6. Jeffrey A. Bennett & Claire N. Trevisan & Joseph F. DeCarolis & Cecilio Ortiz-García & Marla Pérez-Lugo & Bevin T. Etienne & Andres F. Clarens, 2021. "Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico," Nature Energy, Nature, vol. 6(3), pages 240-249, March.
    7. Jufri, Fauzan Hanif & Widiputra, Victor & Jung, Jaesung, 2019. "State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies," Applied Energy, Elsevier, vol. 239(C), pages 1049-1065.
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    Cited by:

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    2. Wang, Yijian & Cui, Yang & Li, Yang & Xu, Yang, 2023. "Collaborative optimization of multi-microgrids system with shared energy storage based on multi-agent stochastic game and reinforcement learning," Energy, Elsevier, vol. 280(C).
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    4. Chen, Lei & Jiang, Yuqi & Zheng, Shencong & Deng, Xinyi & Chen, Hongkun & Islam, Md. Rabiul, 2023. "A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks," Applied Energy, Elsevier, vol. 350(C).

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