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A stochastic investment decision making method for distribution system resilience enhancement considering automation, hardening and distributed energy resources

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  • Ghosh, Puspendu
  • De, Mala

Abstract

Extreme weather events have always posed as a serious threat to the power system. In recent years the frequent occurrences of extreme events highlight the requirement for a resilient distribution system capable of maximizing load restoration under the most adverse situations. Because the requirement to reinforce the distribution system appears to be an expensive approach, it is crucial to make the proper investment decisions. In this regard, this article introduces a single-stage stochastic multi-criteria optimization method for maximizing network resilience over a series of investments while minimizing overall costs. This method conducts different categories of investments including the installation of sectionalizing switches, distributed energy resources, and hardening of overhead distribution lines. The optimization method is based on a genetic algorithm and can be used to address a large-scale network. Several numerical studies are tested on the IEEE 69-bus system to verify the effectiveness of the proposed method. The results of the investment decision-making method show the significance of combining planning and operational preparedness in distribution systems to respond to extreme weather events.

Suggested Citation

  • Ghosh, Puspendu & De, Mala, 2023. "A stochastic investment decision making method for distribution system resilience enhancement considering automation, hardening and distributed energy resources," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023003095
    DOI: 10.1016/j.ress.2023.109395
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    7. Ren, Chao & Wei, Zhinong & Zhou, Yizhou & Chen, Sheng & Han, Haiteng & Sun, Guoqiang & Zang, Haixiang & Ji, Wenlu, 2024. "Distributionally robust CVaR optimization for resilient distribution system planning with consideration for long-term and short-term uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
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    9. Wang, Wei & He, Yufei & Wang, Hongbin & Chen, Hongzhou & Xiong, Xiaofu, 2024. "Improving interdependent urban power and gas distribution systems resilience through optimal scheduling of mobile emergency supply and repair resources," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    10. Liu, Xin & Tian, Meng & Dong, Zhengcheng & Guo, Linhai & Zhou, Yufeng & Wang, Yu, 2026. "Multi-scenario robust stochastic programming based distributed energy resources allocation in distribution networks: Balancing economic efficiency and resilience," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
    11. Wang, Shuliang & Li, Weisha & Lin, Zhifang, 2026. "Resilience enhancement study of renewable energy power networks by integrating subnet division and priority enhanced Q-Learning," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
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    13. Tang, Hao & Tang, Daogui & Diaz-Londono, Cesar & Agundis Tinajero, Gibran David & Zio, Enrico, 2026. "Coordinated multi-energy and vehicle-to-grid strategies for enhancing port operational resilience," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).

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