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Stochastic pre-event preparation for enhancing resilience of distribution systems

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  • Zhang, Qianzhi
  • Wang, Zhaoyu
  • Ma, Shanshan
  • Arif, Anmar

Abstract

Extreme weather events are the common causes for power supply interruptions and power outages in electrical distribution systems. Improving the distribution system and enhancing its resilience is becoming crucial due to the increased frequency of extreme weather events. Preparation and allocation of multiple flexible resources, such as mobile resources, fuel resources, and labor resources before extreme weather events can mitigate the effects of extreme weather events and enhance the resilience of power distribution systems. In this paper, a two-stage stochastic mixed-integer linear programming (SMILP) is proposed to optimize the preparation and resource allocation process for upcoming extreme weather events, which leads to faster and more efficient post-event restoration. The objective of the proposed two-stage SMILP is to maximize the served load and minimize the operating cost of flexible resources. The first stage in the optimization problem selects the amounts and locations of different resources. The second stage considers the operational constraints of the distribution system and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, three types of PV systems are considered during a power outage and the resilience improvements with different PV penetration levels are compared. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the effectiveness and scalability of the proposed method.

Suggested Citation

  • Zhang, Qianzhi & Wang, Zhaoyu & Ma, Shanshan & Arif, Anmar, 2021. "Stochastic pre-event preparation for enhancing resilience of distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:rensus:v:152:y:2021:i:c:s1364032121009114
    DOI: 10.1016/j.rser.2021.111636
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    Cited by:

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    4. Liu, Hanchen & Wang, Chong & Ju, Ping & Li, Hongyu, 2022. "A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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