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.
| Original language | English |
|---|---|
| Article number | 9298836 |
| Pages (from-to) | 3715-3727 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 36 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jul 2021 |
Funding
Manuscript received August 6, 2020; revised November 3, 2020; accepted December 1, 2020. Date of publication December 18, 2020; date of current version June 18, 2021. This work was supported in part by the U.S. Department of Energy (DOE), including DOE’s Grid Modernization Laboratory Consortium (GMLC), Office of Electricity, and Building Technologies Office, and in part by the CURENT research center which is an Engineering Research Center funded by the U.S. National Science Foundation (NSF) and DOE under NSF award EEC-1041877. Paper no. TPWRS-01331-2020. (Corresponding author: Fangxing Li.) Qingxin Shi, Fangxing Li, and Xiaofei Wang are with the Department of Electrical Engineering and Computer Science, the University of Tennessee, Knoxville, TN 37996 USA (e-mail: [email protected]; [email protected]; [email protected]). This work was supported in part by the U.S. Department of Energy (DOE), including DOE's Grid Modernization Laboratory Consortium (GMLC), Office of Electricity, and Building Technologies Office, and in part by the CURENT research center which is an Engineering Research Center funded by the U.S. National Science Foundation (NSF) and DOE under NSF award EEC-1041877
Keywords
- Distributed generator
- Distribution system
- Progressive hedging
- Resilience
- Siting and sizing
- Stochastic scenario