DeepOPF-V: Solving AC-OPF Problems Efficiently

Wanjun Huang

doi:10.1109/TPWRS.2021.3114092

DeepOPF-V: Solving AC-OPF Problems Efficiently

2022-01-01

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DOI：10.1109/TPWRS.2021.3114092

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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.

Keywords:

Training

Mathematical models

Load modeling

Voltage control

Urban areas

Simulation

Real-time systems

AC optimal power flow

deep neural network

voltage prediction

DeepOPF-V: Solving AC-OPF Problems Efficiently

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