DeepOPF-V: Solving AC-OPF Problems Efficiently

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...

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Bibliographic Details
Published inIEEE transactions on power systems Vol. 37; no. 1; pp. 800 - 803
Main Authors Huang, Wanjun, Pan, Xiang, Chen, Minghua, Low, Steven H.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
AC optimal power flow
Artificial neural networks
Constraints
deep neural network
Electric power systems
Flow equations
Load modeling
Mathematical models
Optimization
Power flow
Real-time systems
Simulation
Training
Urban areas
Voltage control
voltage prediction
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3114092