ML OPF wiki

Xpan — Sat, 03 Oct 2026 04:28:43 GMT

Add late-September 2026 ML-OPF papers and upgrade two PESGM records

← Older revision		Revision as of 04:28, 3 October 2026
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	==== AC-OPF: Advanced Neural Network Architectures ====		==== AC-OPF: Advanced Neural Network Architectures ====
			# L. Bhan, W. Yang, M. Capetz, and B. Zhang, "GridSFM: A Foundation Model for Solving AC Optimal Power Flow," ''arXiv preprint arXiv:2609.30173, 2026.''
			# Z. Liang, S. Liang, L. Xiong, B. Chen, D. Mo, and P. Li, "Deep Learning-Based Optimal Power Flow Algorithm with Grid Topology-Variation Perception," ''Energies'', vol. 19, no. 19, p. 4582, 2026, doi: 10.3390/en19194582.
			# L. Lyu and R. Bo, "Attention-Enhanced Graph Convolutional Network for Multi-Period Optimal Power Flow," in ''2026 IEEE Power & Energy Society General Meeting (PESGM)'', pp. 1-5, 2026, doi: 10.1109/PESGM58988.2026.11693614.
	# F. Bohigas-Daranas, H. Latif-Martínez, E. Prieto-Araujo, O. Gomis-Bellmunt, and P. Barlet-Ros, "Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation," ''arXiv preprint arXiv:2609.16738, 2026.''		# F. Bohigas-Daranas, H. Latif-Martínez, E. Prieto-Araujo, O. Gomis-Bellmunt, and P. Barlet-Ros, "Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation," ''arXiv preprint arXiv:2609.16738, 2026.''
	# K. Park, K. Song, Y. Lim, S. Park, K. Kim, and H. Kim, "UNION: A Unified AC-OPF Framework for Topology-Varying Real-Time Grid Operation," ''arXiv preprint arXiv:2608.25784, 2026.''		# K. Park, K. Song, Y. Lim, S. Park, K. Kim, and H. Kim, "UNION: A Unified AC-OPF Framework for Topology-Varying Real-Time Grid Operation," ''arXiv preprint arXiv:2608.25784, 2026.''
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	# J. Wang and P. Srikantha, "Data-driven Convex Relaxation for Sustainable Optimal Power Flow with Feasibility Guarantees," ''IEEE Transactions on Sustainable Computing'', vol. 11, no. 4, pp. 362-375, 2026.		# J. Wang and P. Srikantha, "Data-driven Convex Relaxation for Sustainable Optimal Power Flow with Feasibility Guarantees," ''IEEE Transactions on Sustainable Computing'', vol. 11, no. 4, pp. 362-375, 2026.
	# H. Jin, K. Song, Z. Memon, Y. Li, S. Fenu, H. Kim, L. Zhao, and K. Kim, "LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning," ''arXiv preprint arXiv:2605.02133, 2026.''		# H. Jin, K. Song, Z. Memon, Y. Li, S. Fenu, H. Kim, L. Zhao, and K. Kim, "LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning," ''arXiv preprint arXiv:2605.02133, 2026.''
	# X. Liu, X. He, and Y. Chen, "Scaling Laws of Machine Learning for Optimal Power Flow," ''~~arXiv preprint arXiv~~:~~2601~~.~~02706,~~ 2026.''		# X. Liu, X. He, and Y. Chen, "Scaling Laws of Machine Learning for Optimal Power Flow," in ''2026 IEEE Power & Energy Society General Meeting (PESGM)'', pp. 1-5, 2026, doi: 10.1109/PESGM58988.2026.11693637.
	# Y. C. Chu, A. Boukas, and M. Udell, "SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints," ''arXiv preprint arXiv:2602.09317, 2026.''		# Y. C. Chu, A. Boukas, and M. Udell, "SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints," ''arXiv preprint arXiv:2602.09317, 2026.''
	# B. N. Roy, R. Golder, and M. M. Hasan, "NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees," ''arXiv preprint arXiv:2605.00260, 2026.''		# B. N. Roy, R. Golder, and M. M. Hasan, "NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees," ''arXiv preprint arXiv:2605.00260, 2026.''
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	# A. Anrrango, A. Quisaguano, G. E. Constante-Flores, and C. Li, "Self-Supervised Learning of Parametric Approximation for Security-Constrained DC-OPF," ''arXiv preprint arXiv:2601.13486, 2026.''		# A. Anrrango, A. Quisaguano, G. E. Constante-Flores, and C. Li, "Self-Supervised Learning of Parametric Approximation for Security-Constrained DC-OPF," ''arXiv preprint arXiv:2601.13486, 2026.''
	# W. Huang, K. Gong, Z. Jiang, X. Zhang, and L. Zheng, "A Decentralized Unsupervised Learning Approach for AC-OPF Based on Multi-Branch Deep Neural Network," ''IEEE Access'', ''13'', pp. 206932-206947, 2025.		# W. Huang, K. Gong, Z. Jiang, X. Zhang, and L. Zheng, "A Decentralized Unsupervised Learning Approach for AC-OPF Based on Multi-Branch Deep Neural Network," ''IEEE Access'', ''13'', pp. 206932-206947, 2025.
	# Sl. Li, A. Tuor, D. Vrabie, L. Pileggi, and J. Drgona, "Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow," ''~~arXiv preprint arXiv~~:~~2511~~.~~11677, 2025~~.''		# S. Li, A. Tuor, D. Vrabie, L. Pileggi, and J. Drgona, "Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow," in ''2026 IEEE Power & Energy Society General Meeting (PESGM)'', pp. 1-5, 2026, doi: 10.1109/PESGM58988.2026.11693405.
	# X. Liu, "Neural Network Optimal Power Flow via Energy Gradient Flow and Unified Dynamics," ''arXiv preprint arXiv:2512.01219, 2025.''		# X. Liu, "Neural Network Optimal Power Flow via Energy Gradient Flow and Unified Dynamics," ''arXiv preprint arXiv:2512.01219, 2025.''
	# S. Ding, Y. Zhou, K. Hu, X. Yao, J. Yan, X. Tang, and Y. Shi, "Exploring the Boundary of Diffusion-Based Methods for Solving Constrained Optimization", arXiv preprint arXiv:2502.10330, 2025.		# S. Ding, Y. Zhou, K. Hu, X. Yao, J. Yan, X. Tang, and Y. Shi, "Exploring the Boundary of Diffusion-Based Methods for Solving Constrained Optimization", arXiv preprint arXiv:2502.10330, 2025.
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	=== Reinforcement Learning and Multi-Agent Learning ===		=== Reinforcement Learning and Multi-Agent Learning ===
			# C. Zhang, X. Liu, F. Huang, and J. Zhu, "A Data-Driven Robust Optimal Power Flow Method for An Islanded Microgrid with Droop Control," in ''2026 IEEE Power & Energy Society General Meeting (PESGM)'', pp. 1-5, 2026, doi: 10.1109/PESGM58988.2026.11693128.
	# H. Yan, S. Ding, J. Zhang, X. Yao, Y. Liu, H. Wang, C. Luo, and Y. Shi, "Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling," ''arXiv preprint arXiv:2608.03409, 2026.''		# H. Yan, S. Ding, J. Zhang, X. Yao, Y. Liu, H. Wang, C. Luo, and Y. Shi, "Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling," ''arXiv preprint arXiv:2608.03409, 2026.''
	# A. Sayed, K. Al Jaafari, X. Zhang, H. Zeineldin, A. Al-Durra, G. Wang, and E. Elsaadany, "Efficient optimal power flow learning: A deep reinforcement learning with physics-driven critic model," ''International Journal of Electrical Power & Energy Systems'', vol. 167, p. 110621, 2025.		# A. Sayed, K. Al Jaafari, X. Zhang, H. Zeineldin, A. Al-Durra, G. Wang, and E. Elsaadany, "Efficient optimal power flow learning: A deep reinforcement learning with physics-driven critic model," ''International Journal of Electrical Power & Energy Systems'', vol. 167, p. 110621, 2025.
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	=== Robustness and Worst-Case Performance ===		=== Robustness and Worst-Case Performance ===
			# A. M. Tumlin, B. Wooding, Z. Shao, D. Manzanas Lopez, T. Derr, and T. T. Johnson, "Reachability-Based Formal Verification of Graph Neural Networks with Node and Edge Features," in ''AI Verification: Third International Symposium, SAIV 2026, Proceedings'', pp. 271-298, 2026, doi: 10.1007/978-3-032-32357-6_13.
	# K. Zuo, C. Shen, P. Cheng, J. Song, and M. Sun, "Probabilistic Robustness Verified Data-Driven Transient Security-Constrained Optimal Power Flow," ''IEEE Transactions on Power Systems'', vol. 41, no. 2, pp. 914-926, 2026.		# K. Zuo, C. Shen, P. Cheng, J. Song, and M. Sun, "Probabilistic Robustness Verified Data-Driven Transient Security-Constrained Optimal Power Flow," ''IEEE Transactions on Power Systems'', vol. 41, no. 2, pp. 914-926, 2026.
	# Y. Zhang, P. M. Karve, and S. Mahadevan, "Operational risk quantification of power grids using graph neural network surrogates of the DC optimal power flow," ''Sustainable Energy, Grids and Networks'', vol. 43, p. 101748, 2025.		# Y. Zhang, P. M. Karve, and S. Mahadevan, "Operational risk quantification of power grids using graph neural network surrogates of the DC optimal power flow," ''Sustainable Energy, Grids and Networks'', vol. 43, p. 101748, 2025.
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	=== Data Generation, Sampling, and Benchmarks ===		=== Data Generation, Sampling, and Benchmarks ===
			# Z. Pan, X. Liu, Z. Cao, J. Wang, and D. Qiu, "PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning," ''arXiv preprint arXiv:2609.36052, 2026.''
	# Y. Li, E. Dey, S. Fenu, M. Lupo Pasini, T. Kuruganti, and K. Kim, "Scaling Laws for Physics-Aware ACOPF Surrogate Learning," ''arXiv preprint arXiv:2609.16282, 2026.''		# Y. Li, E. Dey, S. Fenu, M. Lupo Pasini, T. Kuruganti, and K. Kim, "Scaling Laws for Physics-Aware ACOPF Surrogate Learning," ''arXiv preprint arXiv:2609.16282, 2026.''
	# A. Ma, S. Ghamizi, A. Bojchevski, P. Zhang, and J. Cao, "SafePowerGraph: A safety-aware benchmarking framework for scalable graph neural networks in power flow and optimal power flow," ''Energy and AI'', vol. 25, p. 100819, 2026.		# A. Ma, S. Ghamizi, A. Bojchevski, P. Zhang, and J. Cao, "SafePowerGraph: A safety-aware benchmarking framework for scalable graph neural networks in power flow and optimal power flow," ''Energy and AI'', vol. 25, p. 100819, 2026.

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