Distributionally robust sequential load restoration of distribution system considering random contingencies

Abstract Natural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi‐time period and multi‐zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst‐case line contingency probability distribution. Case studies were carried out on the modified IEEE 123‐node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution.

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Publication Details

Journal
IET Generation Transmission & Distribution
Published
2024-08-20
DOI
https://doi.org/10.1049/gtd2.13155
Citations
2
Primary Topic
Optimal Power Flow Distribution
Type
article
Field-Weighted Citation Impact
0.42

Funders

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article

Distributionally robust sequential load restoration of distribution system considering random contingencies

Yunyun Xie, Heping Jin, Feifan Shen, Yangwu Shen et al.
2 citations
IET Generation Transmission & Distribution
Optimal Power Flow Distribution
0.42
article

Distributionally robust sequential load restoration of distribution system considering random contingencies

Yunyun Xie, Heping Jin, Feifan Shen, Yangwu Shen, Ziqian Li, Zhongchu Huang
article en
2 citations

Abstract

Abstract Natural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi‐time period and multi‐zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst‐case line contingency probability distribution. Case studies were carried out on the modified IEEE 123‐node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution.

IET Generation Transmission & DistributionVol. 18(18)
China Three Gorges Corporation (China) (CN), Hunan University (CN), State Grid Corporation of China (China) (CN), Nanjing University of Science and Technology (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 39%
Optimal Power Flow Distribution
0.42
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