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AC optimal power flow (AC-OPF) problems need to be solved more frequently inthe future to maintain stable and economic power system operation. To tacklethis challenge, a deep neural network-based voltage-constrained approach(DeepOPF-V) is proposed to solve AC-OPF problems with high computationalefficiency. Its unique design predicts voltages of all buses and then uses themto reconstruct the remaining variables without solving non-linear AC power flowequations. A fast post-processing process is developed to enforce the boxconstraints. The effectiveness of DeepOPF-V is validated by simulations on IEEE118/300-bus systems and a 2000-bus test system. Compared with existing studies,DeepOPF-V achieves decent computation speedup up to four orders of magnitudeand comparable performance in optimality gap and preserving the feasibility ofthe solution.

DeepOPF-V: Solving AC-OPF Problems Efficiently