期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 36, 期 4, 页码 3715-3727出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.3043874
关键词
Planning; Meteorology; Indexes; Topology; Stochastic processes; Network topology; Generators; Resilience; distribution system; distributed generator; siting and sizing; stochastic scenario; progressive hedging
资金
- U.S. Department of Energy (DOE)
- DOE's Grid Modernization Laboratory Consortium (GMLC), Office of Electricity, and Building Technologies Office
- CURENT research center
- U.S. National Science Foundation (NSF)
- DOE under NSF award [EEC-1041877]
This study proposes a fuel-based distributed generator allocation strategy to enhance the distribution system resilience against extreme weather. It uses a two-stage stochastic mixed-integer programming model to make decisions under budget constraints and minimize operating costs in uncertain fault scenarios. The effectiveness of the algorithm is demonstrated through case studies on IEEE 33-bus and 123-bus test systems.
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.
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