AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic  operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to find feasible solutions with high computational efficiency. It predicts voltages of all buses and then uses them to obtain all remaining variables . A fast post-processing method is developed to enforce generation constraints. The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems . Compared with existing approaches , DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
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.
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power system
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find feasible solutions
solve AC-OPF problems
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It
Its unique design
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obtain all remaining variables
reconstruct the remaining variables without solving non-linear AC power flow equations
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method
process
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generation
the box
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case studies of several IEEE test systems
simulations on IEEE 118/300-bus systems and a 2000-bus test system
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approaches
studies
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a state-of-art
decent
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three
four
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has better performance in
comparable performance in optimality gap and