  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.
