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DeepOPF: Deep Neural Networks for Optimal Power Flow
Note: We help to maintain
a list of related
papers on machine learning for optimal power flow.
This is the first work in the literature applying neural networks to directly solve the optimal power flow (OPF) problem. Previous learning-based solutions employ machine learning techniques as a module to facilitate conventional OPF solvers.
In the first paper listed below, we develop DeepOPF as a Deep Neural Network
(DNN) based approach for solving direct current optimal power flow (DC-OPF)
problems. DeepOPF is inspired by the observation that solving DC-OPF for a
given power network is equivalent to characterizing a high-dimensional
mapping between the load inputs and the dispatch and transmission decisions.
We first train a DNN to learn the mapping and predict the generations from
the load inputs. We then directly reconstruct the phase angles from the
generations and loads by using the power flow equations. Such a
predict-and-reconstruct approach reduces the dimension of the mapping to
learn, subsequently cutting down the size of the DNN and the amount of
training data needed. We further derive a condition for tuning the size of
the DNN according to the desired approximation accuracy of the
load-generation mapping. We develop a post-processing procedure based on
l1-projection to ensure the feasibility of the obtained solution, which can
be of independent interest. Simulation results for IEEE test cases show that
DeepOPF generates feasible solutions with less than 0.2% optimality loss,
while speeding up the computation time by up to two orders of magnitude as
compared to a state-of-the-art solver.
We have also extended the aobve approach to the non-convex AC-OPF settings
and the most recent results show that DeepOPF can achieve 15,000x speedup for
solving AC-OPF problems over a 2000-bus test case with minor optimality loss,
and the obtained solutions are feasibile with or without simple
postprocessing.
Recently, we developed a preventive-learning approach to provably gaurantee DNN solution feasibility for DC-OPF problems and general optimization problems with linear constraints.
We also studied another fundamental problem of employing DNNs to solve non-convex AC-OPF problems admitting multiple load-solution mappings. We proposed an augmented learning approach,, named DeepOPF-AL, to address the issue and learn one unique augmented mapping that embeds all the different load-solution mappings. The learned augmented mapping can be used to generate solutions from the load input.
Tutorial
M. Chen and S. H. Low, “Machine Learning for Solving Optimal Power Flow Problems”, tutorial at IEEE SmartGridComm, October 31 - November 3, Glasgow, Scotland, 2023. [PDF]
M. Chen and S. H. Low, “Machine Learning for Solving Optimal Power Flow Problems”, tutorial at ACM SIGMETRICS / IFIP Performance, Mumbai, India, June 6-10, 2022.
Publications
X. Pan, T. Zhao, and M. Chen, “DeepOPF: Deep Neural Network for DC Optimal Power Flow”, in Proceedings of the 10th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2019), Beijing, China, October 21 - 24, 2019. [PDF] Also available as technical report posted onto arXiv in May 2019. [PDF]
X. Pan, T. Zhao, M. Chen, and S. Zhang, “DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow”, IEEE Transactions on Power Systems, vol. 36, issue 3, pp. 1725 - 1735, May 2021. [PDF] Also available as technical report posted onto arXiv in Oct. 2019. [PDF]
X. Pan, M. Chen, T. Zhao, and S. H. Low “DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems”, IEEE Systems Journal, accepted, 2022. [PDF] Also available as a technical report on arXiv in July 2020. [PDF]
W. Huang, X. Pan, M. Chen, and S. H. Low, “DeepOPF-V: Solving AC-OPF Problems Efficiently”, IEEE Transactions on Power Systems, accepted, 2021. Technical report as arXiv preprint arXiv:2007.01002, 2021. [PDF]
M. Zhou, M. Chen, and S. H. Low, “DeepOPF-FT: One Deep Neural Network for Multiple AC-OPF Problems with Flexible Topology”, IEEE Transactions on Power Systems, accepted for publication. [PDF]
T. Zhao, X. Pan, M. Chen, A. Venzke, and S. H. Low, “DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility”, in Proceedings of the 11th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2020), virtual conference, Nov. 11 - 13, 2020. [PDF]
T. Zhao, X. Pan, M. Chen, and S. H. Low, “Ensuring DNN Solution Feasibility for Optimization Problems with Linear Constraints”, in Proceedings of 11th International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 1-5, 2023. (Oral - top 25% accepted) [PDF] Also available as a technical report. [PDF]
X. Pan, W. Huang, M. Chen, and S. H. Low, “DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with a Multi-Valued Load-Solution Mapping”, in Proceedings of 14th International Conference on Future Energy Systems (ACM e-Energy 2023), Orlando, Florida, June 16 - 23, 2022. (Note paper) [ final version to be available ]. Technical report available as an arXiv preprint arXiv:2206.03365, 2022. [PDF]
E. Liang, M. Chen, and S. H. Low, “Low Complexity Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Optimization over (Non-)Convex Set”, in Proceedings of 40th International Conference on Machine Learning (ICML), Honolulu, Hawaii, July 23 - 29, 2023. [PDF]
E. Liang and M. Chen, “Generative Learning for Solving Non-Convex Problem with Multi-Valued Input-Solution Mapping”, in Proceedings of 12th International Conference on Learning Representations (ICLR), Vienna, Austria, May 7-11, 2024. [PDF]
W. Huang, M. Chen, and S. H. Low, “Unsupervised Learning for Solving AC Optimal Power Flows: Design, Analysis, and Experiment”, IEEE Transactions on Power Systems, accepted for publication. [PDF]
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