DeepOPF-V: Solving AC-OPF Problems Efficiently
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 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 and preserving the feasibility of the solution.
Cite
@article{arxiv.2103.11793,
title = {DeepOPF-V: Solving AC-OPF Problems Efficiently},
author = {Wanjun Huang and Xiang Pan and Minghua Chen and Steven H. Low},
journal= {arXiv preprint arXiv:2103.11793},
year = {2021}
}
Comments
4 pages, 1 figure