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Worst-Case Sensitivity of DC Optimal Power Flow Problems

Anderson, James and Zhou, Fengyu and Low, Steven H. (2020) Worst-Case Sensitivity of DC Optimal Power Flow Problems. In: 2020 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 3156-3163. ISBN 9781538682661. https://resolver.caltech.edu/CaltechAUTHORS:20200707-112527402

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Abstract

In this paper we consider the problem of analyzing the effect a change in the load vector can have on the optimal power generation in a DC power flow model. The methodology is based upon the recently introduced concept of the OPF operator. It is shown that for general network topologies computing the worst-case sensitivities is computationally intractable. However, we show that certain problems involving the OPF operator can be equivalently converted to a graphical discrete optimization problem. Using the discrete formulation, we provide a decomposition algorithm that reduces the computational cost of computing the worst-case sensitivity. A 27-bus numerical example is used to illustrate our results.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.23919/ACC45564.2020.9147770DOIArticle
https://arxiv.org/abs/2004.01572arXivDiscussion Paper
ORCID:
AuthorORCID
Anderson, James0000-0002-2832-8396
Zhou, Fengyu0000-0002-2639-6491
Low, Steven H.0000-0001-6476-3048
Additional Information:© 2020 AACC. This work is funded by NSF grants CCF 1637598, ECCS 1619352, CNS 1545096, ARPA-E through grant DE-AR0000699 and the GRID DATA program, and DTRA through grant HDTRA 1-15-1-0003.
Funders:
Funding AgencyGrant Number
NSFCCF-1637598
NSFECCS-1619352
NSFCNS-1545096
Advanced Research Projects Agency-Energy (ARPA-E)DE-AR0000699
Defense Threat Reduction Agency (DTRA)HDTRA 1-15-1-0003
Record Number:CaltechAUTHORS:20200707-112527402
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200707-112527402
Official Citation:J. Anderson, F. Zhou and S. H. Low, "Worst-Case Sensitivity of DC Optimal Power Flow Problems," 2020 American Control Conference (ACC), Denver, CO, USA, 2020, pp. 3156-3163, doi: 10.23919/ACC45564.2020.9147770
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104250
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Jul 2020 18:42
Last Modified:30 Oct 2020 22:17

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