1. Resilience-Oriented DG Siting and Sizing Considering Stochastic Scenario Reduction.
- Author
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Shi, Qingxin, Li, Fangxing, Kuruganti, Teja, Olama, Mohammed M., Dong, Jin, Wang, Xiaofei, and Winstead, Chris
- Subjects
MONTE Carlo method ,FAULT location (Engineering) ,K-means clustering ,STOCHASTIC programming ,ALGORITHMS - Abstract
In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. The case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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