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DeepOPF-V: Solving AC-OPF Problems Efficiently

Huang, Wanjun and Pan, Xiang and Chen, Minghua and Low, Steven H. (2021) DeepOPF-V: Solving AC-OPF Problems Efficiently. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210510-075001060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 operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to find feasible solutions with high computational efficiency. It predicts voltages of all buses and then uses them to obtain all remaining variables. A fast post-processing method is developed to enforce generation constraints. The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems. Compared with existing approaches, DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2103.11793arXivDiscussion Paper
ORCID:
AuthorORCID
Pan, Xiang0000-0002-6565-2339
Low, Steven H.0000-0001-6476-3048
Subject Keywords:AC optimal power flow, deep neural network, voltage prediction
Record Number:CaltechAUTHORS:20210510-075001060
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210510-075001060
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:10 May 2021 20:07

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