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arXiv · 2103.11793

DeepOPF-V: Solving AC-OPF Problems Efficiently

Wanjun Huang·Xiang Pan·Minghua Chen·Steven H. Low
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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 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.

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BibTeXRIS

Wanjun Huang, Xiang Pan, Minghua Chen, Steven H. Low. 2021-03-22. DeepOPF-V: Solving AC-OPF Problems Efficiently. https://arxiv.org/abs/2103.11793

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