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Title: Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating

Abstract

In this paper, we propose a risk-based data-driven distributionally robust approach to investigating the optimal power flow with dynamic line rating. The risk terms, including penalties for load shedding, wind generation curtailment and line overload, are embedded into the objective function. To robustify the solution, we consider a distributional uncertainty set based on the second-order moment, that captures the correlation between wind generation outputs and line ratings, and also the Wasserstein distance, that hedges against data perturbations. We show that the proposed model can be reformulated as a convex conic program. Approximations of the proposed model are suggested, which leads to a significant reduction of the number of the constraints. For practical large-scale test systems, a distributionally robust optimal power flow model with Wasserstein-distance-based distributional uncertainty set and its convex reformulation are also provided. Simulation results on the 5-bus, the IEEE 118-bus and the Polish 2736-bus test systems validate the effectiveness of the proposed models.

Authors:
ORCiD logo; ORCiD logo; ORCiD logo; ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability; National Natural Science Foundation of China (NNSFC); Fundamental Research Funds for the Central Universities; National Science Foundation (NSF)
OSTI Identifier:
1487208
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 33; Journal Issue: 6; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
Dynamic line rating; Wasserstein distance; distributionally robust optimization; optimal power flow; risk

Citation Formats

Wang, Cheng, Gao, Rui, Qiu, Feng, Wang, Jianhui, and Xin, Linwei. Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating. United States: N. p., 2018. Web. doi:10.1109/TPWRS.2018.2844356.
Wang, Cheng, Gao, Rui, Qiu, Feng, Wang, Jianhui, & Xin, Linwei. Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating. United States. doi:10.1109/TPWRS.2018.2844356.
Wang, Cheng, Gao, Rui, Qiu, Feng, Wang, Jianhui, and Xin, Linwei. Thu . "Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating". United States. doi:10.1109/TPWRS.2018.2844356.
@article{osti_1487208,
title = {Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating},
author = {Wang, Cheng and Gao, Rui and Qiu, Feng and Wang, Jianhui and Xin, Linwei},
abstractNote = {In this paper, we propose a risk-based data-driven distributionally robust approach to investigating the optimal power flow with dynamic line rating. The risk terms, including penalties for load shedding, wind generation curtailment and line overload, are embedded into the objective function. To robustify the solution, we consider a distributional uncertainty set based on the second-order moment, that captures the correlation between wind generation outputs and line ratings, and also the Wasserstein distance, that hedges against data perturbations. We show that the proposed model can be reformulated as a convex conic program. Approximations of the proposed model are suggested, which leads to a significant reduction of the number of the constraints. For practical large-scale test systems, a distributionally robust optimal power flow model with Wasserstein-distance-based distributional uncertainty set and its convex reformulation are also provided. Simulation results on the 5-bus, the IEEE 118-bus and the Polish 2736-bus test systems validate the effectiveness of the proposed models.},
doi = {10.1109/TPWRS.2018.2844356},
journal = {IEEE Transactions on Power Systems},
issn = {0885-8950},
number = 6,
volume = 33,
place = {United States},
year = {2018},
month = {11}
}