DeepOPF-V: Solving AC-OPF Problems Efficiently
This work addresses the need for faster AC-OPF solutions in power systems, representing an incremental improvement with a novel method for a known computational bottleneck.
The paper tackles the challenge of solving AC optimal power flow (AC-OPF) problems more efficiently for stable and economic power system operation by proposing DeepOPF-V, a deep neural network-based voltage-constrained approach that achieves up to four orders of magnitude computation speedup with comparable optimality and feasibility on IEEE test systems.
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.