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DeepOPF-V: Solving AC-OPF Problems Efficiently

Huang, Wanjun and Pan, Xiang and Chen, Minghua and Low, Steven H. (2022) DeepOPF-V: Solving AC-OPF Problems Efficiently. IEEE Transactions on Power Systems, 37 (1). pp. 800-803. ISSN 0885-8950. doi:10.1109/TPWRS.2021.3114092. https://resolver.caltech.edu/CaltechAUTHORS:20210510-075001060

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Abstract

AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to solve AC-OPF problems with high computational efficiency. Its unique design predicts voltages of all buses and then uses them to reconstruct the remaining variables without solving non-linear AC power flow equations. A fast post-processing process is also developed to enforce the box constraints. The effectiveness of DeepOPF-V is validated by simulations on IEEE 118/300-bus systems and a 2000-bus test system. Compared with existing studies, DeepOPF-V achieves decent computation speedup up to four orders of magnitude and comparable performance in optimality gap, while preserving feasibility of the solution.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/TPWRS.2021.3114092DOIArticle
https://arxiv.org/abs/2103.11793arXivDiscussion Paper
ORCID:
AuthorORCID
Huang, Wanjun0000-0002-6851-3705
Pan, Xiang0000-0002-6565-2339
Chen, Minghua0000-0003-4763-0037
Low, Steven H.0000-0001-6476-3048
Additional Information:© 2021 IEEE. Manuscript receivedMarch 22, 2021; revised July 12, 2021; accepted September 7, 2021. Date of publication September 21, 2021; date of current version December 23, 2021. This work was supported in part by a Start-up Grant from the School of Data Science under Project 9380118, in part by the City University of Hong Kong, and in part by General Research Fund from Research Grants Council, Hong Kong, Project No. 11206821. Paper no. PESL-00064-2021.
Funders:
Funding AgencyGrant Number
City University of Hong Kong9380118
Research Grants Council of Hong Kong11206821
Subject Keywords:AC optimal power flow, deep neural network, voltage prediction
Issue or Number:1
DOI:10.1109/TPWRS.2021.3114092
Record Number:CaltechAUTHORS:20210510-075001060
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210510-075001060
Official Citation:W. Huang, X. Pan, M. Chen and S. H. Low, "DeepOPF-V: Solving AC-OPF Problems Efficiently," in IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 800-803, Jan. 2022, doi: 10.1109/TPWRS.2021.3114092
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109017
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:10 May 2021 20:07
Last Modified:04 Jan 2022 20:40

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