DeepOPF-V: Solving AC-OPF Problems Efficiently
DeepOPF-V: Solving AC-OPF Problems Efficiently
Wanjun Huang,Xiang Pan,Minghua Chen,S. Low
TLDR
A deep neural network-based voltage-constrained approach to solve AC optimal power flow problems with a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
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.