ML OPF wiki - Revision history

Xpan: /* d. Fully-connected Neural Networks */

2025-12-11T09:40:51Z

d. Fully-connected Neural Networks

← Older revision		Revision as of 09:40, 11 December 2025
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	====== d. Fully-connected Neural Networks ======		====== d. Fully-connected Neural Networks ======
			#D. T. Hien, K. Song, K. Kim, and H. Kim, "Alternative Learning Architecture for Solving AC-OPF via Supervised Relaxation and Cross Encoder. In ''NeurIPS Workshop on GPU-Accelerated and Scalable Optimization, 2025.''
	#A. Rosemberg, and M. Klamkin, "Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow," ''arXiv preprint arXiv:2504.01970, 2025.''		#A. Rosemberg, and M. Klamkin, "Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow," ''arXiv preprint arXiv:2504.01970, 2025.''
	#D. Owerko, A. Scaglione, and A. Ribeiro, "Learning Optimal Power Flow with Pointwise Constraints," ''arXiv preprint arXiv:2510.20777, 2025.''		#D. Owerko, A. Scaglione, and A. Ribeiro, "Learning Optimal Power Flow with Pointwise Constraints," ''arXiv preprint arXiv:2510.20777, 2025.''

Xpan: /* Unsupervised Learning-based Schemes */

2025-12-11T09:36:20Z

Unsupervised Learning-based Schemes

← Older revision		Revision as of 09:36, 11 December 2025
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	==== <big>Unsupervised Learning-based Schemes</big> ====		==== <big>Unsupervised Learning-based Schemes</big> ====
			# 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.''
	# 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.

Xpan: /* d. Fully-connected Neural Networks */

2025-12-11T09:30:44Z

d. Fully-connected Neural Networks

← Older revision		Revision as of 09:30, 11 December 2025
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	====== d. Fully-connected Neural Networks ======		====== d. Fully-connected Neural Networks ======
			#A. Rosemberg, and M. Klamkin, "Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow," ''arXiv preprint arXiv:2504.01970, 2025.''
	#D. Owerko, A. Scaglione, and A. Ribeiro, "Learning Optimal Power Flow with Pointwise Constraints," ''arXiv preprint arXiv:2510.20777, 2025.''		#D. Owerko, A. Scaglione, and A. Ribeiro, "Learning Optimal Power Flow with Pointwise Constraints," ''arXiv preprint arXiv:2510.20777, 2025.''
	#P. D. Grontas, A. Terpin, E. C. Balta, R. D'Andrea, and Lygeros, J., "Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers.", ''arXiv preprint arXiv:2508.10480. 2025.''		#P. D. Grontas, A. Terpin, E. C. Balta, R. D'Andrea, and Lygeros, J., "Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers.", ''arXiv preprint arXiv:2508.10480. 2025.''

Xpan: /* Unsupervised Learning-based Schemes */

2025-12-11T09:23:17Z

Unsupervised Learning-based Schemes

← Older revision		Revision as of 09:23, 11 December 2025
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	==== <big>Unsupervised Learning-based Schemes</big> ====		==== <big>Unsupervised Learning-based Schemes</big> ====
			# 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.
	# S. Park, and P. Van. Pascal, "Self-supervised learning for large-scale preventive security constrained DC optimal power flow," ''IEEE Transactions on Power Systems,'' ''(early access),'' (2024).		# S. Park, and P. Van. Pascal, "Self-supervised learning for large-scale preventive security constrained DC optimal power flow," ''IEEE Transactions on Power Systems,'' ''(early access),'' (2024).

Xpan: /* a. Physics-based Neural Networks */

2025-12-11T09:17:53Z

a. Physics-based Neural Networks

← Older revision		Revision as of 09:17, 11 December 2025
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	====== a. Physics-based Neural Networks ======		====== a. Physics-based Neural Networks ======
			# K. Xu, L. Qiu, X. Dai, Y. Ding, C. Ye, and Y. Fang, "Optimal power flow under varying topologies via physics-guided neural network with stack-learning," ''International Journal of Electrical Power & Energy Systems'', ''173'', p.111391, 2-25.
	# K. Chen, S. Bose, and Y. Zhang, "Physics-Informed Gradient Estimation for Accelerating Deep Learning-Based AC-OPF," ''IEEE Transactions on Industrial Informatics, 2025.''		# K. Chen, S. Bose, and Y. Zhang, "Physics-Informed Gradient Estimation for Accelerating Deep Learning-Based AC-OPF," ''IEEE Transactions on Industrial Informatics, 2025.''
	# H. Li, L. Liu, and Q. Wu, Q., 2025. Physics-guided Chebyshev graph convolution network for optimal power flow. ''Electric Power Systems Research'', ''245'', p.111651.		# H. Li, L. Liu, and Q. Wu, Q., 2025. Physics-guided Chebyshev graph convolution network for optimal power flow. ''Electric Power Systems Research'', ''245'', p.111651.

Xpan: /* d. Fully-connected Neural Networks */

2025-11-11T01:33:57Z

d. Fully-connected Neural Networks

← Older revision		Revision as of 01:33, 11 November 2025
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	====== d. Fully-connected Neural Networks ======		====== d. Fully-connected Neural Networks ======
			#D. Owerko, A. Scaglione, and A. Ribeiro, "Learning Optimal Power Flow with Pointwise Constraints," ''arXiv preprint arXiv:2510.20777, 2025.''
	#P. D. Grontas, A. Terpin, E. C. Balta, R. D'Andrea, and Lygeros, J., "Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers.", ''arXiv preprint arXiv:2508.10480. 2025.''		#P. D. Grontas, A. Terpin, E. C. Balta, R. D'Andrea, and Lygeros, J., "Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers.", ''arXiv preprint arXiv:2508.10480. 2025.''
	#H. T. Nguyen and P. L. Donti, "FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees", arXiv preprint arXiv:2506.00362, 2025.		#H. T. Nguyen and P. L. Donti, "FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees", arXiv preprint arXiv:2506.00362, 2025.

Xpan: /* Neural Network Robustness and Worst-case Performance */

2025-11-11T01:31:37Z

Neural Network Robustness and Worst-case Performance

← Older revision		Revision as of 01:31, 11 November 2025
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	==== Neural Network Robustness and Worst-case Performance ====		==== Neural Network Robustness and Worst-case Performance ====

			# B. Giraud, R. Nellikath, J. Vorwerk, M. Alowaifeer, and S. Chatzivasileiadis, "Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training," ''arXiv preprint arXiv:2510.23196, 2025.''
	# M. Gao, J. Yu, S. Kamel, and Z. Yang, "A Trustable Data-Driven Optimal Power Flow Computational Method With Robust Generalization Ability," IEEE Transactions on Neural Networks and Learning Systems*, 2024.		# M. Gao, J. Yu, S. Kamel, and Z. Yang, "A Trustable Data-Driven Optimal Power Flow Computational Method With Robust Generalization Ability," IEEE Transactions on Neural Networks and Learning Systems*, 2024.
	# A. W. Rosemberg, J. D. Garcia, R. Bent, and P. Van Hentenryck, "Sobolev Training of End-to-End Optimization Proxies," arXiv preprint arXiv:2505.11342, 2025.		# A. W. Rosemberg, J. D. Garcia, R. Bent, and P. Van Hentenryck, "Sobolev Training of End-to-End Optimization Proxies," arXiv preprint arXiv:2505.11342, 2025.

Xpan: /* Constraint simplification schemes */

2025-11-11T01:30:13Z

Constraint simplification schemes

← Older revision		Revision as of 01:30, 11 November 2025
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	''(This scheme use learning models to simplify the constraints in the optimal power flow problem, including linearizing or convexifying the power flow equations, and representing the chance-constraint or security constrained using learning models)''		''(This scheme use learning models to simplify the constraints in the optimal power flow problem, including linearizing or convexifying the power flow equations, and representing the chance-constraint or security constrained using learning models)''

			# A. B. Javadi, and A. Kargarian, "Explicit Ensemble Learning Surrogate for Joint Chance-Constrained Optimal Power Flow," ''arXiv preprint arXiv:2511.03515, 2025.''
	# J. Zhu, Y. Xu, N. Tai, Y. Xie, Q. Wen, and H. Sun, "Day-Ahead Joint Chance-Constrained Optimal Scheduling for Distribution Networks Using Partial Input Convex Neural Networks,' ''IEEE Transactions on Smart Grid, 2025.''		# J. Zhu, Y. Xu, N. Tai, Y. Xie, Q. Wen, and H. Sun, "Day-Ahead Joint Chance-Constrained Optimal Scheduling for Distribution Networks Using Partial Input Convex Neural Networks,' ''IEEE Transactions on Smart Grid, 2025.''
	# R. Cheng, Y. Yang, W. Liu, N. Liu, and Z. Wang, "Input Convex Neural Network-Assisted Optimal Power Flow in Distribution Networks: Modeling, Algorithm Design, and Applications", ''arXiv preprint arXiv:2407.20675''.		# R. Cheng, Y. Yang, W. Liu, N. Liu, and Z. Wang, "Input Convex Neural Network-Assisted Optimal Power Flow in Distribution Networks: Modeling, Algorithm Design, and Applications", ''arXiv preprint arXiv:2407.20675''.

Xpan: /* c. Non-neural network methods */

2025-10-28T14:30:11Z

c. Non-neural network methods

← Older revision		Revision as of 14:30, 28 October 2025
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	# O. Sondermeijer, R. Dobbe, D. Arnold, C. Tomlin and T. Keviczky, "Regression-based inverter control for decentralized optimal power flow and voltage regulation", arXiv preprint arXiv:1902.08594, 2019.		# O. Sondermeijer, R. Dobbe, D. Arnold, C. Tomlin and T. Keviczky, "Regression-based inverter control for decentralized optimal power flow and voltage regulation", arXiv preprint arXiv:1902.08594, 2019.
	# S. Karagiannopoulos, P. Aristidou and G. Hug, "Data-driven Local Control Design for Active Distribution Grids using off-line Optimal Power Flow and Machine Learning Techniques", in IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6461 - 6471, Nov. 2019.		# S. Karagiannopoulos, P. Aristidou and G. Hug, "Data-driven Local Control Design for Active Distribution Grids using off-line Optimal Power Flow and Machine Learning Techniques", in IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6461 - 6471, Nov. 2019.
	# G. Neel, Z. Wang and A. Majumdar, "Machine Learning for AC Optimal Power Flow", In Proceedings of the 36th International Conference on Machine Learning Workshop, Long Beach, CA, USA, Jun. 10 - 15, 2019.

	====== d. Fully-connected Neural Networks ======		====== d. Fully-connected Neural Networks ======
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	# F. Fioretto, T. Mak and P. V. Hentenryck, "Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods", in Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, Feb. 7 - 12, 2020.		# F. Fioretto, T. Mak and P. V. Hentenryck, "Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods", in Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, Feb. 7 - 12, 2020.
	# F. Fioretto, P. V. Hentenryck, T. W. Mak, C. Tran, F. Baldo and M. Lombardi, "Lagrangian Duality for Constrained Deep Learning", arXiv preprint arXiv:2001.09394, 2020.		# F. Fioretto, P. V. Hentenryck, T. W. Mak, C. Tran, F. Baldo and M. Lombardi, "Lagrangian Duality for Constrained Deep Learning", arXiv preprint arXiv:2001.09394, 2020.
			# G. Neel, Z. Wang and A. Majumdar, "Machine Learning for AC Optimal Power Flow", In Proceedings of the 36th International Conference on Machine Learning Workshop, Long Beach, CA, USA, Jun. 10 - 15, 2019.

	===== Distributed Optimal Power Flow =====		===== Distributed Optimal Power Flow =====

Xpan: /* b. Advanced Neural Network Architectures */

2025-10-23T02:22:52Z

b. Advanced Neural Network Architectures

← Older revision		Revision as of 02:22, 23 October 2025
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	====== b. Advanced Neural Network Architectures ======		====== b. Advanced Neural Network Architectures ======

			# M. Hoseinpour, and V. Dvorkin, "DiffOPF: Diffusion Solver for Optimal Power Flow," ''arXiv preprint arXiv:2510.14075, 2025.''
	# H. Zhu, Z. Zhang, Z. Zhang, Y. Bai, S. Wen, H. Wang, D. Ergu, Y. Cai, and Y. Zhao, "Dynamic Domain Adaptation-Driven Physics-Informed Graph Representation Learning for AC-OPF," ''arXiv preprint arXiv:2506.00478, 2025.''		# H. Zhu, Z. Zhang, Z. Zhang, Y. Bai, S. Wen, H. Wang, D. Ergu, Y. Cai, and Y. Zhao, "Dynamic Domain Adaptation-Driven Physics-Informed Graph Representation Learning for AC-OPF," ''arXiv preprint arXiv:2506.00478, 2025.''
	# O. Arowolo, J. L. and Cremer, "Towards Generalization of Graph Neural Networks for AC Optimal Power Flow.," ''arXiv preprint arXiv:2510.06860, 2025.''		# O. Arowolo, J. L. and Cremer, "Towards Generalization of Graph Neural Networks for AC Optimal Power Flow.," ''arXiv preprint arXiv:2510.06860, 2025.''