Related papers: DeepOPF-V: Solving AC-OPF Problems Efficiently

DeepOPF-V: Solving AC-OPF Problems Efficiently

URL: http://arxiv.org/abs/2103.11793v1
Date: Mon, 22 Mar 2021 12:59:06 GMT
Title: DeepOPF-V: Solving AC-OPF Problems Efficiently
Authors: Wanjun Huang, Xiang Pan, Minghua Chen, and Steven H. Low
Abstract summary: Deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to find feasible solutions with high computational efficiency. DeepOPF-V achieves speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.
Score: 12.512036656559683
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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