Stochastic pre-event preparation for enhancing resilience of distribution systems

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

doi:10.1016/j.rser.2021.111636

Stochastic pre-event preparation for enhancing resilience of distribution systems

Journal Article · Fri Sep 17 00:00:00 EDT 2021 · Renewable and Sustainable Energy Reviews

DOI:https://doi.org/10.1016/j.rser.2021.111636· OSTI ID:1977615

^[1]; Wang, Zhaoyu ^[2]; Ma, Shanshan ^[3]; ^[4]

Iowa State Univ., Ames, IA (United States); OSTI
Iowa State Univ., Ames, IA (United States)
Arizona State Univ., Tempe, AZ (United States)
King Saud Univ., Riyadh (Saudi Arabia)

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. Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Iowa State Univ., Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office

Grant/Contract Number:: EE0008956

OSTI ID:: 1977615

Alternate ID(s):: OSTI ID: 1868610

Journal Information:: Renewable and Sustainable Energy Reviews, Journal Name: Renewable and Sustainable Energy Reviews Journal Issue: C Vol. 152; ISSN 1364-0321

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
Extreme weather events
PV penetration
Pre-event preparation
Resilience
Resource allocation
Two-stage stochastic model

Stochastic pre-event preparation for enhancing resilience of distribution systems

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