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Solutions of DC OPF are Never AC Feasible

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Kyri BakerAuthors Info & Claims

e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems

Pages 264 - 268

https://doi.org/10.1145/3447555.3464875

Published: 22 June 2021 Publication History

Abstract

In this paper, we analyze the relationship between generation dispatch solutions produced by the DC optimal power flow (DC OPF) problem by the AC optimal power flow (AC OPF) problem. While there has been much previous work in analyzing the approximation error of the DC assumption, the AC feasibility has not been fully explored, although difficulty achieving AC feasibility is known in practice. Here, under some light assumptions, we show that no solution to the DC OPF problem will satisfy the AC power flow constraints. Then, it is demonstrated that even with generation adjustments in DC OPF to account for losses, DC OPF solutions are still not AC feasible. Lastly, the computational benefits of DC OPF are analyzed in comparison with AC OPF.

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Han JWang WYang CNiu MYang CYan LLi Z(2024)FRMNet: A Feasibility Restoration Mapping Deep Neural Network for AC Optimal Power FlowIEEE Transactions on Power Systems10.1109/TPWRS.2024.335473339:5(6566-6577)Online publication date: Sep-2024
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Index Terms

Solutions of DC OPF are Never AC Feasible
1. Hardware
  1. Power and energy
    1. Energy distribution
      1. Power networks

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cover image ACM Other conferences

e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems

June 2021

528 pages

ISBN:9781450383332

DOI:10.1145/3447555

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2021

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e-Energy '21

e-Energy '21: The Twelfth ACM International Conference on Future Energy Systems

June 28 - July 2, 2021

Virtual Event, Italy

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Pourahmadi FKazempour J(2025)Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine ClassifierIEEE Transactions on Power Systems10.1109/TPWRS.2024.340040540:1(715-727)Online publication date: Jan-2025
https://doi.org/10.1109/TPWRS.2024.3400405
Han JWang WYang CNiu MYang CYan LLi Z(2024)FRMNet: A Feasibility Restoration Mapping Deep Neural Network for AC Optimal Power FlowIEEE Transactions on Power Systems10.1109/TPWRS.2024.335473339:5(6566-6577)Online publication date: Sep-2024
https://doi.org/10.1109/TPWRS.2024.3354733
Agarwal APandey APileggi L(2024)Continuous Switch Model and Heuristics for Mixed-Integer Nonlinear Problems in Power SystemsIEEE Transactions on Power Systems10.1109/TPWRS.2023.334011339:4(5780-5791)Online publication date: Jul-2024
https://doi.org/10.1109/TPWRS.2023.3340113
Le Franc AMagron VLasserre JRuiz MPanciatici P(2024)Minimal Sparsity for Second-Order Moment-SOS Relaxations of the AC-OPF ProblemIEEE Transactions on Power Systems10.1109/TPWRS.2023.333369139:3(5408-5415)Online publication date: May-2024
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Sayed AZhang XWang GWang CQiu J(2024)Optimal Operable Power Flow: Sample-Efficient Holomorphic Embedding-Based Reinforcement LearningIEEE Transactions on Power Systems10.1109/TPWRS.2023.326677339:1(1739-1751)Online publication date: Jan-2024
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Castelli ALara J(2024)Improved evaluation of large network matrices for linear power flow within optimization problems2024 IEEE Power & Energy Society General Meeting (PESGM)10.1109/PESGM51994.2024.10688992(1-5)Online publication date: 21-Jul-2024
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