ML OPF wiki - Revision history

Xpan: Deduplicate preprint and final publication entries

2026-06-01T15:16:36Z

Deduplicate preprint and final publication entries

Xpan: Reorganize papers by learning role in OPF workflow

2026-06-01T15:08:41Z

Reorganize papers by learning role in OPF workflow

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Xpan: Clean citation formatting

2026-06-01T15:01:09Z

Clean citation formatting

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Xpan: Add broader 2026 ML and LLM OPF papers

2026-06-01T14:55:13Z

Add broader 2026 ML and LLM OPF papers

← Older revision		Revision as of 14:55, 1 June 2026
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	# Y. Jia, Y. Wang, and Y. Zhou, "Scalable graph-based OPF learning," ''Sustainable Energy, Grids and Networks'', vol. 46, p.102169, 2026.		# Y. Jia, Y. Wang, and Y. Zhou, "Scalable graph-based OPF learning," ''Sustainable Energy, Grids and Networks'', vol. 46, p.102169, 2026.
	# J. Kim, S. Park, D. Kang, and H. Shin, "Spatio-Temporal Deep Learning-Assisted Multi-Period AC Optimal Power Flow," ''Electronics'', vol. 15, no. 4, p.761, 2026.		# J. Kim, S. Park, D. Kang, and H. Shin, "Spatio-Temporal Deep Learning-Assisted Multi-Period AC Optimal Power Flow," ''Electronics'', vol. 15, no. 4, p.761, 2026.
			# Y. Jia, Y. Wang, and Y. Zhou, "Multi-task learning for solving OPF in an evolving environment," ''Applied Energy'', vol. 404, p.127174, 2026.
			# M. Lupo Pasini, Y. Li, K. Kim, and T. Kuruganti, "Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids," ''arXiv preprint arXiv:2605.23194, 2026.''
			# Y. Li, Z. Memon, H. Jin, S. Fenu, K. Song, S. B. Sharma, P. Gasana, H. Kim, L. Zhao, and K. Kim, "LUMINA: Foundation Models for Topology Transferable ACOPF," ''arXiv preprint arXiv:2603.04300, 2026.''
	# M. Hoseinpour, and V. Dvorkin, "DiffOPF: Diffusion Solver for Optimal Power Flow," ''arXiv preprint arXiv:2510.14075, 2025.''		# 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.''
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	===== Distributed Optimal Power Flow =====		===== Distributed Optimal Power Flow =====
			# B. Dindar, C. B. Saner, H. K. Cakmak, and V. Hagenmeyer, "Privacy-preserving utilization of distribution system flexibility with multi-agent reinforcement learning for optimal power flow in AC/DC transmission systems," ''Applied Energy'', vol. 414, p.127848, 2026.
	# T. W. Mak and C. Minas, T. Mathieu and P. V. Hentenryck, "Learning Regionally Decentralized AC Optimal Power Flows with ADMM", ''arXiv preprint arXiv:2205.03787, 2022.''		# T. W. Mak and C. Minas, T. Mathieu and P. V. Hentenryck, "Learning Regionally Decentralized AC Optimal Power Flows with ADMM", ''arXiv preprint arXiv:2205.03787, 2022.''
	# D. S. Sarma, L. Cupelli, F. Ponci, and A. Monti, "Distributed Optimal Power Flow with Data-Driven Sensitivity Computation", In Proceedings of IEEE Madrid PowerTech, Madrid, Spain, 28 June - 2 July, 2021.		# D. S. Sarma, L. Cupelli, F. Ponci, and A. Monti, "Distributed Optimal Power Flow with Data-Driven Sensitivity Computation", In Proceedings of IEEE Madrid PowerTech, Madrid, Spain, 28 June - 2 July, 2021.
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	==== <big>Unsupervised Learning-based Schemes</big> ====		==== <big>Unsupervised Learning-based Schemes</big> ====
			# K. Chen, B. Knueven, and W. Jones, "A hard-constrained NN learning framework for rapidly restoring AC-OPF from DC-OPF," ''arXiv preprint arXiv:2602.06255, 2026.''
	# J. Zhang, H. Yan, H. Wang, Y. Shi, Z. Sheng, H. Yu, S. Ye, and Z. Yang, "Unsupervised Learning for AC Optimal Power Flow with Fast Physics-Aware Layer," ''arXiv preprint arXiv:2604.23548, 2026.''		# J. Zhang, H. Yan, H. Wang, Y. Shi, Z. Sheng, H. Yu, S. Ye, and Z. Yang, "Unsupervised Learning for AC Optimal Power Flow with Fast Physics-Aware Layer," ''arXiv preprint arXiv:2604.23548, 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.''		# 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.''
	# J. Zhang, H. Yan, H. Wang, Y. Shi, Y., Z. Sheng, H. Yu, S. Ye, and Z. Yang, "Unsupervised Learning for AC Optimal Power Flow with Fast Physics-Aware Layer," ''arXiv preprint arXiv:2604.23548, 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.''		# 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.''
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	==== Reinforcement Learning-based Schemes ====		==== Reinforcement Learning-based Schemes ====
			# X. Zhang, S. Ge, Y. Zhou, H. Liu, S. Zhang, and C. Jiang, "Graph-Based Safe Reinforcement Learning for Dynamic Optimal Power Flow," ''Journal of Modern Power Systems and Clean Energy'', vol. 14, no. 1, pp. 250-260, 2026.
	# Y. E. Jang, J. Ban, Y. J. Kim, and C. Chen, "RL-driven problem decomposition for computationally efficient AC optimal power flow," ''International Journal of Electrical Power & Energy Systems'', ''174'', p.111556, 2026.		# Y. E. Jang, J. Ban, Y. J. Kim, and C. Chen, "RL-driven problem decomposition for computationally efficient AC optimal power flow," ''International Journal of Electrical Power & Energy Systems'', ''174'', p.111556, 2026.
	# S. Li, Z. Liang, H. Yin, Y. Xiao, Z. Chen, and Z. Qi, "Graph reinforcement learning solver for alternating current optimal power flow considering topological contingencies," ''International Journal of Electrical Power & Energy Systems'', vol. 177, p.111798, 2026.		# S. Li, Z. Liang, H. Yin, Y. Xiao, Z. Chen, and Z. Qi, "Graph reinforcement learning solver for alternating current optimal power flow considering topological contingencies," ''International Journal of Electrical Power & Energy Systems'', vol. 177, p.111798, 2026.
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	==== Data Sampling ====		==== Data Sampling ====

			# S. Shekhar, A. Karn, K. Keshav, S. Bansal, and P. Pareek, "Fast Diffusion with Physics-Correction for ACOPF," ''arXiv preprint arXiv:2602.03020, 2026.''
	# L. Perbellini, L., M. Baù, and S. Grillo, "An Efficient and Scalable Algorithm for the Creation of Representative Synthetic AC-OPF Datasets," In ''2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)'' (pp. 653-658). IEEE.		# L. Perbellini, L., M. Baù, and S. Grillo, "An Efficient and Scalable Algorithm for the Creation of Representative Synthetic AC-OPF Datasets," In ''2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)'' (pp. 653-658). IEEE.
	# M. Gao, J. Yu, and Z. Yang, "Improving the Robustness of Data-driven OPF by Nonlinearity-Focused Sampling," ''IEEE Transactions on Power Systems, early access.''		# M. Gao, J. Yu, and Z. Yang, "Improving the Robustness of Data-driven OPF by Nonlinearity-Focused Sampling," ''IEEE Transactions on Power Systems, early access.''
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	(''This scheme'' use learning models to predict a wart-start point for iterative algorithms)		(''This scheme'' use learning models to predict a wart-start point for iterative algorithms)

			# D. Suri, H. Hilmarsson, and S. Bose, "WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers," ''arXiv preprint arXiv:2605.05728, 2026.''
	# R. Xie, L. Xu, C. Li, and X. Yu, "Neural-optimization integration for AC optimal power flow: A differentiable warm-start approach," ''Cyber-Physical Energy Systems, 2025.''		# R. Xie, L. Xu, C. Li, and X. Yu, "Neural-optimization integration for AC optimal power flow: A differentiable warm-start approach," ''Cyber-Physical Energy Systems, 2025.''
	# K. Xu, X. Zhang, and L. Qiu, "Explainable Warm-Start Point Learning for AC Optimal Power Flow Using a Novel Hybrid Stacked Ensemble Method", ''Sustainability'', vol. ''17, no.'' 2, p.438, 2025.		# K. Xu, X. Zhang, and L. Qiu, "Explainable Warm-Start Point Learning for AC Optimal Power Flow Using a Novel Hybrid Stacked Ensemble Method", ''Sustainability'', vol. ''17, no.'' 2, p.438, 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)''

			# F. Jiang, X. Li, and P. Van Hentenryck, "Deep Neural Network-Enhanced Frequency-Constrained Optimal Power Flow with Multi-Governor Dynamics," ''arXiv preprint arXiv:2602.11063, 2026.''
			# S. Panagi, C. Spanias, and P. Aristidou, "Enhanced Optimal Power Flow Using a Trained Neural Network Surrogate for Distribution Grid Constraints," ''arXiv preprint arXiv:2604.12422, 2026.''
	# A. B. Javadi, and A. Kargarian, "Explicit Ensemble Learning Surrogate for Joint Chance-Constrained Optimal Power Flow," ''arXiv preprint arXiv:2511.03515, 2025.''		# 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.''
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	# T. Tran, B. Nguyen, and T. X. Nghiem, "HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization," ''arXiv preprint arXiv:2604.13179, 2026.''		# T. Tran, B. Nguyen, and T. X. Nghiem, "HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization," ''arXiv preprint arXiv:2604.13179, 2026.''
	# M. Li, and J. Mohammadi, "Learning to Optimize Joint Chance-constrained Power Dispatch Problems", ''arXiv preprint arXiv:2501.12902''.		# M. Li, and J. Mohammadi, "Learning to Optimize Joint Chance-constrained Power Dispatch Problems", ''arXiv preprint arXiv:2501.12902''.
	# V. Di Vito, M. Mohammadian, K. Baker, and F. Fioretto, "Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF," ''~~arXiv preprint arXiv:2410~~.~~19157~~, ~~2024~~.''		# V. Di Vito, M. Mohammadian, K. Baker, and F. Fioretto, "Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF," ''Electric Power Systems Research'', vol. 254, p.112181, 2026.
	# F. Cengil, H. Nagarajan, R. Bent, S. Eksioglu, and B. Eksioglu, "Learning to Accelerate Tightening of Convex Relaxations of the AC Optimal Power Flow Problem," 2024.		# F. Cengil, H. Nagarajan, R. Bent, S. Eksioglu, and B. Eksioglu, "Learning to Accelerate Tightening of Convex Relaxations of the AC Optimal Power Flow Problem," 2024.
	# Y.E. Jang, J. Ban, Y.J. Kim, and C. Chen, "Multi-period Optimal Power Flow Using Decomposition and RL-Based Cutting Planes," Authorea Preprints, 2024.		# Y.E. Jang, J. Ban, Y.J. Kim, and C. Chen, "Multi-period Optimal Power Flow Using Decomposition and RL-Based Cutting Planes," Authorea Preprints, 2024.
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	# K. Baker, "A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow", arXiv preprint arXiv:2007.06074, 2020.		# K. Baker, "A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow", arXiv preprint arXiv:2007.06074, 2020.
	# D. Biagioni, P. Graf, X. Zhang, A.S. Zamzam, K. Baker and J. King, "Learning-Accelerated ADMM for Distributed Optimal Power Flow", arXiv preprint arXiv:1911.03019, 2019.		# D. Biagioni, P. Graf, X. Zhang, A.S. Zamzam, K. Baker and J. King, "Learning-Accelerated ADMM for Distributed Optimal Power Flow", arXiv preprint arXiv:1911.03019, 2019.


			==== Large Language Model-based Schemes ====
			''(This scheme uses large language models or language agents for OPF modeling, constrained reasoning, or OPF-related dispatch workflows)''

			# C. Shen, Z. Guo, X. Wan, Z. Yang, Y. Zhang, W. Huang, J. Song, Z. Zhang, and M. Sun, "ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling," ''arXiv preprint arXiv:2602.03070, 2026.''
			# F. Bernier, S. Ghamizi, P. Dogoulis, and M. Cordy, "Can Large Language Models Reason and Optimize Under Constraints?" ''arXiv preprint arXiv:2603.23004, 2026.''
			# J. Xu, L. Ma, K. Li, D. Li, L. Ge, and C. Jiang, "Semantic-probabilistic co-optimization framework for distribution system restoration in multi-fault scenarios using large language models," ''International Journal of Electrical Power & Energy Systems'', vol. 177, p.111774, 2026.

	==== Adversarial learning schemes ====		==== Adversarial learning schemes ====
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	= Machine Learning for Solving Power Flow Equations =		= Machine Learning for Solving Power Flow Equations =
			# J. Stiasny and J. Cremer, "Residual Power Flow for Neural Solvers," ''arXiv preprint arXiv:2601.09533, 2026.''
	# G. Prashal, P. Sumathi, and N. P. Padhy, "Matrix Completion-Based Learning of Probabilistic Power Flow Using a Meta-Graph Generative Autoencoder Network," ''IEEE Transactions on Industrial Informatics, 2026.''		# G. Prashal, P. Sumathi, and N. P. Padhy, "Matrix Completion-Based Learning of Probabilistic Power Flow Using a Meta-Graph Generative Autoencoder Network," ''IEEE Transactions on Industrial Informatics, 2026.''
	# S. Li, Z. Pan, H. Li, Y. Xiao, M. Liu, and X. Wang, “Convergence criterion of power flow calculation based on graph neural network,” J. Phys.: Conf. Ser., vol. 2703, no. 1, p. 012042, Feb. 2024.		# S. Li, Z. Pan, H. Li, Y. Xiao, M. Liu, and X. Wang, “Convergence criterion of power flow calculation based on graph neural network,” J. Phys.: Conf. Ser., vol. 2703, no. 1, p. 012042, Feb. 2024.

Xpan: Add recent 2026 ML-OPF papers

2026-06-01T14:47:39Z

Add recent 2026 ML-OPF papers

Xpan: /* Machine Learning for Solving Power Flow Equations */

2026-05-16T13:07:58Z

Machine Learning for Solving Power Flow Equations

← Older revision		Revision as of 13:07, 16 May 2026
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	= Machine Learning for Solving Power Flow Equations =		= Machine Learning for Solving Power Flow Equations =
			# G. Prashal, P. Sumathi, and N. P. Padhy, "Matrix Completion-Based Learning of Probabilistic Power Flow Using a Meta-Graph Generative Autoencoder Network," ''IEEE Transactions on Industrial Informatics, 2026.''
	# S. Li, Z. Pan, H. Li, Y. Xiao, M. Liu, and X. Wang, “Convergence criterion of power flow calculation based on graph neural network,” J. Phys.: Conf. Ser., vol. 2703, no. 1, p. 012042, Feb. 2024.		# S. Li, Z. Pan, H. Li, Y. Xiao, M. Liu, and X. Wang, “Convergence criterion of power flow calculation based on graph neural network,” J. Phys.: Conf. Ser., vol. 2703, no. 1, p. 012042, Feb. 2024.
	# J. Jalving, M. Eydenberg, L. Blakely, A. Castillo, Z. Kilwein, J. K. Skolfield, F. Boukouvala, and C. Laird, "Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow", ''International Journal of Electrical Power & Energy Systems'', ''157'', p.109741, 2024.		# J. Jalving, M. Eydenberg, L. Blakely, A. Castillo, Z. Kilwein, J. K. Skolfield, F. Boukouvala, and C. Laird, "Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow", ''International Journal of Electrical Power & Energy Systems'', ''157'', p.109741, 2024.

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

2026-05-16T13:05:28Z

d. Fully-connected Neural Networks

← Older revision		Revision as of 13:05, 16 May 2026
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	====== d. Fully-connected Neural Networks ======		====== d. Fully-connected Neural Networks ======
			#J. Wang, and P. Srikantha, "Data-driven Convex Relaxation for Sustainable Optimal Power Flow with Feasibility Guarantees," ''IEEE Transactions on Sustainable Computing, 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," ''arXiv preprint arXiv:2601.02706, 2026.''

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

2026-05-16T13:04:35Z

d. Fully-connected Neural Networks

← Older revision		Revision as of 13:04, 16 May 2026
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	====== d. Fully-connected Neural Networks ======		====== d. Fully-connected Neural Networks ======
			#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," ''arXiv preprint arXiv:2601.02706, 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.''		#Y. C. Chu, A. Boukas, and M. Udell, "SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints," ''arXiv preprint arXiv:2602.09317, 2026.''

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

2026-05-16T13:01:23Z

d. Fully-connected Neural Networks

← Older revision		Revision as of 13:01, 16 May 2026
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	====== d. Fully-connected Neural Networks ======		====== d. Fully-connected Neural Networks ======
			#X. Liu, X. He, and Y. Chen, "Scaling Laws of Machine Learning for Optimal Power Flow," ''arXiv preprint arXiv:2601.02706, 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.''		#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.''

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

2026-05-16T13:00:15Z

d. Fully-connected Neural Networks

← Older revision		Revision as of 13:00, 16 May 2026
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	====== d. Fully-connected Neural Networks ======		====== d. Fully-connected Neural Networks ======
			#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.''
	#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.''		#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.''

← Older revision		Revision as of 15:16, 1 June 2026
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	# B. Jiang, Q. Wang, S. Wu, Y. Wang, and G. Lu, "Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review," Energies, vol. 17, no. 6, p. 1381, 2024.		# B. Jiang, Q. Wang, S. Wu, Y. Wang, and G. Lu, "Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review," Energies, vol. 17, no. 6, p. 1381, 2024.
	# M. Zhao and M. Barati, "Synergizing Machine Learning with ACOPF: A Comprehensive Overview," arXiv preprint arXiv:2406.10428, 2024.		# M. Zhao and M. Barati, "Synergizing Machine Learning with ACOPF: A Comprehensive Overview," arXiv preprint arXiv:2406.10428, 2024.
	~~# H. Khaloie, M. Dolanyi, J. F. Toubeau and F. Vallée, "Review of Machine Learning Techniques for Optimal Power Flow", ''Available at SSRN 4681955''.~~
	# V. Kekatos and M. K. Singh, "Deep Learning Techniques for Solving Optimal Power Flow Problems".		# V. Kekatos and M. K. Singh, "Deep Learning Techniques for Solving Optimal Power Flow Problems".
	# 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.		# 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.
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	# L. Zhou, W. Pan, R. Chen, and X. Chen, "Fast security-constrained optimal power flow under topology variations via heterogeneous graph neural network learning," ''Sustainable Energy, Grids and Networks'', p. 102251, 2026.		# L. Zhou, W. Pan, R. Chen, and X. Chen, "Fast security-constrained optimal power flow under topology variations via heterogeneous graph neural network learning," ''Sustainable Energy, Grids and Networks'', p. 102251, 2026.
	# N. Popli, E. Davoodi, F. Capitanescu, and L. Wehenkel, "On the robustness of machine-learnt proxies for security constrained optimal power flow solvers", 2023.		# N. Popli, E. Davoodi, F. Capitanescu, and L. Wehenkel, "On the robustness of machine-learnt proxies for security constrained optimal power flow solvers", 2023.
	# Y. Yu, Y. Gao, Y. Li, and Y. Yan, "Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch", ''IET Renewable Power Generation, 2023.''
	~~# S. Park and P. Van Hentenryck, "Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow", ''arXiv preprint arXiv:2311.18072, 2023.''~~
	# W. Chen, S. Park, M. Tanneau and P. V. Hentenryck, "Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch", arXiv preprint arXiv:2112.13469, 2021.		# W. Chen, S. Park, M. Tanneau and P. V. Hentenryck, "Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch", arXiv preprint arXiv:2112.13469, 2021.
	# A. Velloso and P. V. Hentenryck, "Combining Deep Learning and Optimization for Preventive Security-Constrained DC Optimal Power Flow", in IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 3618 - 3628, Jul, 2021.		# A. Velloso and P. V. Hentenryck, "Combining Deep Learning and Optimization for Preventive Security-Constrained DC Optimal Power Flow", in IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 3618 - 3628, Jul, 2021.
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	# 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.		# 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.
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	~~# X. Pan, T. Zhao and M. Chen, "DeepOPF: Deep Neural Network for DC Optimal Power Flow", arXiv:1905.04479, May 11th, 2019.~~
	# 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, Oct. 21 - 24, 2019.		# 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, Oct. 21 - 24, 2019.
	# A. Garg, M. Jalali, V. Kekatos and N. Gatsis, "Kernel-based learning for smart inverter control", in Proceedings of the 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, Nov. 26 - 29, 2018.		# A. Garg, M. Jalali, V. Kekatos and N. Gatsis, "Kernel-based learning for smart inverter control", in Proceedings of the 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, Nov. 26 - 29, 2018.
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	# 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.
	# M. Saffari, M. Khodayar, and M. E. Khodayar, "Physics-Informed Graph Capsule Generative Autoencoder for Probabilistic AC Optimal Power Flow", ''IEEE Transactions on Emerging Topics in Computational Intelligence, early access.''
	# S. Ghamizi, A. Ma, J. Cao, and P. R. Cortes, "OPF-HGNN: Generalizable Heterogeneous Graph Neural Networks for AC Optimal Power Flow," In ''2024 IEEE Power & Energy Society General Meeting (PESGM)'' (pp. 1-5). IEEE.		# S. Ghamizi, A. Ma, J. Cao, and P. R. Cortes, "OPF-HGNN: Generalizable Heterogeneous Graph Neural Networks for AC Optimal Power Flow," In ''2024 IEEE Power & Energy Society General Meeting (PESGM)'' (pp. 1-5). IEEE.
	# A. Varbella, D. Briens, B. Gjorgiev, G. A. D'Inverno, and G. Sansavini, "Physics-Informed GNN for non-linear constrained optimization: PINCO a solver for the AC-optimal power flow," ''arXiv preprint arXiv:2410.04818, 2024.''		# A. Varbella, D. Briens, B. Gjorgiev, G. A. D'Inverno, and G. Sansavini, "Physics-Informed GNN for non-linear constrained optimization: PINCO a solver for the AC-optimal power flow," ''arXiv preprint arXiv:2410.04818, 2024.''
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	# Y. Li, C. Zhao and C. Liu, "Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow", arXiv preprint arXiv:2206.01864, 2022.		# Y. Li, C. Zhao and C. Liu, "Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow", arXiv preprint arXiv:2206.01864, 2022.
	# S. Park and P. V. Hentenryck, "Self-Supervised Primal-Dual Learning for Constrained Optimization," ''arXiv preprint arXiv:2208.09046, 2022.''		# S. Park and P. V. Hentenryck, "Self-Supervised Primal-Dual Learning for Constrained Optimization," ''arXiv preprint arXiv:2208.09046, 2022.''
	~~# K, Chen, S. Bose and Y. Zhang, "Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality", ''arXiv preprint arXiv:2212.03977, 2022''~~
	# J. Wang and P. Srikantha. Fast Optimal Power Flow With Guarantees Via an Unsupervised Generative Model. ''IEEE Transactions on Power Systems (early access), 2022.''		# J. Wang and P. Srikantha. Fast Optimal Power Flow With Guarantees Via an Unsupervised Generative Model. ''IEEE Transactions on Power Systems (early access), 2022.''
	# D. Owerko, F. Gama and A. Ribeiro, "Unsupervised Optimal Power Flow Using Graph Neural Networks", ''arXiv preprint arXiv:2210.09277, 2022.''		# D. Owerko, F. Gama and A. Ribeiro, "Unsupervised Optimal Power Flow Using Graph Neural Networks", ''arXiv preprint arXiv:2210.09277, 2022.''
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	# P. Wu, C. Chen, D. Lai, and J. Zhong, "A Safe DRL Method for Fast Solution of Real-Time Optimal Power Flow", ''arXiv preprint arXiv:2308.03420, 2023.''		# P. Wu, C. Chen, D. Lai, and J. Zhong, "A Safe DRL Method for Fast Solution of Real-Time Optimal Power Flow", ''arXiv preprint arXiv:2308.03420, 2023.''
	# A. R. Sayed, X. Zhang, G. Wang, C. Wang, and J. Qiu, "Optimal Operable Power Flow: Sample-efficient Holomorphic Embedding-based Reinforcement Learning", ''IEEE Transactions on Power Systems, (early access) 2023.''		# A. R. Sayed, X. Zhang, G. Wang, C. Wang, and J. Qiu, "Optimal Operable Power Flow: Sample-efficient Holomorphic Embedding-based Reinforcement Learning", ''IEEE Transactions on Power Systems, (early access) 2023.''
	# A. R. Sayed, C. Wang, H. Anis, and T. Bi, "Feasibility constrained online calculation for real-time optimal power flow: A convex constrained deep reinforcement learning approach", ''IEEE Transactions on Power Systems (ealy access) 2022.''		# A. R. Sayed, C. Wang, H. Anis, and T. Bi, "Feasibility Constrained Online Calculation for Real-Time Optimal Power Flow: A Convex Constrained Deep Reinforcement Learning Approach," ''IEEE Transactions on Power Systems'', early access, 2022.
	# Z. Wang, J. H. Menke, F. Schäfer, M. Braun and A. Scheidler, "Approximating multi-purpose AC optimal power flow with reinforcement trained artificial neural network.," ''Energy and AI'', ''7'', p. 100133., 2022.
	~~# A. R. Sayed, C. Wang, H. Anis~~ and T. Bi, "Feasibility Constrained Online Calculation for Real-Time Optimal Power Flow: A Convex Constrained Deep Reinforcement Learning Approach", ''IEEE Transactions on Power Systems (early access), 2022.''
	# L. Zeng, M. Sun, X. Wan, Z. Zhang, R. Deng and Y. Xu, "Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-based SCOPF," accepted for publication in IEEE Transactions on Power Systems (early access), 2022.		# L. Zeng, M. Sun, X. Wan, Z. Zhang, R. Deng and Y. Xu, "Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-based SCOPF," accepted for publication in IEEE Transactions on Power Systems (early access), 2022.
	# H. Zhen, H. Zhai, W. Ma, L. Zhao, Y. Weng, Y. Xu, J. Shi and X. He, "Design and tests of reinforcement-learning-based optimal power flow solution generator"~~, accepted for publication in~~ Energy Reports, 8, pp. 43-50~~, 2022.~~		# H. Zhen, H. Zhai, W. Ma, L. Zhao, Y. Weng, Y. Xu, J. Shi, and X. He, "Design and tests of reinforcement-learning-based optimal power flow solution generator," ''Energy Reports'', vol. 8, pp. 43-50, 2022.
	# Z. Wang, J-H. Menke, F. Schäfer, M. Braun, A. Scheidler, "Approximating multi-purpose AC Optimal Power Flow with reinforcement trained Artificial Neural Network," accepted for publication in Energy and AI, 100133, Vol. 7, 2022.
	# Z. Yan and Y. Xu, "A Hybrid Data-driven Method for Fast Solution of Security-Constrained Optimal Power Flow," accepted for publication in IEEE Transactions on Power Systems (early access),2022.		# Z. Yan and Y. Xu, "A Hybrid Data-driven Method for Fast Solution of Security-Constrained Optimal Power Flow," accepted for publication in IEEE Transactions on Power Systems (early access),2022.
	# Z. Wang, J.H. Menke, F. Schäfer, M. Braun and A. Scheidler, "Approximating multi-purpose AC optimal power flow with reinforcement trained artificial neural network", Energy and AI, vol. 7, p. 100133, 2022.		# Z. Wang, J. H. Menke, F. Schäfer, M. Braun, and A. Scheidler, "Approximating multi-purpose AC optimal power flow with reinforcement trained artificial neural network," ''Energy and AI'', vol. 7, p. 100133, 2022.
	# Z. Yan and Y. Xu, "Real-Time Optimal Power Flow: A Lagrangian based Deep Reinforcement Learning Approach", in IEEE Transactions on Power Systems, letter paper, vol 35, no. 4, pp. 3270 - 3273, Jul. 2020.		# Z. Yan and Y. Xu, "Real-Time Optimal Power Flow: A Lagrangian based Deep Reinforcement Learning Approach", in IEEE Transactions on Power Systems, letter paper, vol 35, no. 4, pp. 3270 - 3273, Jul. 2020.
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← Older revision		Revision as of 14:47, 1 June 2026
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	===== Security-constrained optimal power flow =====		===== Security-constrained optimal power flow =====

			# L. Zhou, W. Pan, R. Chen, and X. Chen, "Fast security-constrained optimal power flow under topology variations via heterogeneous graph neural network learning," ''Sustainable Energy, Grids and Networks'', p.102251, 2026.
	# N. Popli, E. Davoodi, F. Capitanescu, and L. Wehenkel, "On the robustness of machine-learnt proxies for security constrained optimal power flow solvers", 2023.		# N. Popli, E. Davoodi, F. Capitanescu, and L. Wehenkel, "On the robustness of machine-learnt proxies for security constrained optimal power flow solvers", 2023.
	# Y. Yu, Y. Gao, Y. Li, and Y. Yan, "Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch", ''IET Renewable Power Generation, 2023.''		# Y. Yu, Y. Gao, Y. Li, and Y. Yan, "Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch", ''IET Renewable Power Generation, 2023.''
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	# A. Wen, B. Wen, J. Li, and J. Xu, "Heterogeneous Graph Neural Network with Local and Global Message Passing for AC-Optimal Power Flow Solutions," ''Applied System Innovation'', vol. ''9, no.'' 1, p.18, 2026.		# A. Wen, B. Wen, J. Li, and J. Xu, "Heterogeneous Graph Neural Network with Local and Global Message Passing for AC-Optimal Power Flow Solutions," ''Applied System Innovation'', vol. ''9, no.'' 1, p.18, 2026.
			# Y. Jia, Y. Wang, and Y. Zhou, "Scalable graph-based OPF learning," ''Sustainable Energy, Grids and Networks'', vol. 46, p.102169, 2026.
			# J. Kim, S. Park, D. Kang, and H. Shin, "Spatio-Temporal Deep Learning-Assisted Multi-Period AC Optimal Power Flow," ''Electronics'', vol. 15, no. 4, p.761, 2026.
	# M. Hoseinpour, and V. Dvorkin, "DiffOPF: Diffusion Solver for Optimal Power Flow," ''arXiv preprint arXiv:2510.14075, 2025.''		# 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.''
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	# D. K. Mahto, M. Bukya, R. Kumar, A. Mathur, and V. K. Saini, "GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network with High Renewables" ''IEEE Access''., early access.		# D. K. Mahto, M. Bukya, R. Kumar, A. Mathur, and V. K. Saini, "GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network with High Renewables" ''IEEE Access''., early access.
	# Y. Cao, Y. Chen, and Y. Xu, "A Differentially Private Quantum Neural Network for Probabilistic Optimal Power Flow," ''arXiv preprint arXiv:2411.16117, 2024.''		# Y. Cao, Y. Chen, and Y. Xu, "A Differentially Private Quantum Neural Network for Probabilistic Optimal Power Flow," ''arXiv preprint arXiv:2411.16117, 2024.''
	# Z. Hu, ~~and~~ Z. Zhu, "Advancing ~~Hybrid Quantum Neural Network~~ for ~~Alternative Current Optimal Power Flow~~," ''~~arXiv preprint arXiv:2410~~.~~20275~~, ~~2024~~.''		# Z. Hu, Z. Zhu, L. Zhu, X. Wei, S. Bu, and K. W. Chan, "Advancing hybrid quantum neural network for alternating current optimal power flow," ''Applied Energy'', vol. 417, p.127945, 2026.
	# Q. Tran, J. Mitra, and N. Nguyen," Learning model combining of convolutional deep neural network with a self-attention mechanism for AC optimal power flow," ''Electric Power Systems Research'', ''231'', p.110327, 2024.		# Q. Tran, J. Mitra, and N. Nguyen," Learning model combining of convolutional deep neural network with a self-attention mechanism for AC optimal power flow," ''Electric Power Systems Research'', ''231'', p.110327, 2024.
	# S. Zeng, Y. Kim, Y. Ren, and K. Kim, "QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints", ''arXiv preprint arXiv:2401.06820, 2024.''		# S. Zeng, Y. Kim, Y. Ren, and K. Kim, "QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints", ''arXiv preprint arXiv:2401.06820, 2024.''
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	==== Reinforcement Learning-based Schemes ====		==== Reinforcement Learning-based Schemes ====
	# Y. E. Jang, J. Ban, Y. J. Kim, and C. Chen, "RL-driven problem decomposition for computationally efficient AC optimal power flow," ''International Journal of Electrical Power & Energy Systems'', ''174'', p.111556, 2026.		# Y. E. Jang, J. Ban, Y. J. Kim, and C. Chen, "RL-driven problem decomposition for computationally efficient AC optimal power flow," ''International Journal of Electrical Power & Energy Systems'', ''174'', p.111556, 2026.
			# S. Li, Z. Liang, H. Yin, Y. Xiao, Z. Chen, and Z. Qi, "Graph reinforcement learning solver for alternating current optimal power flow considering topological contingencies," ''International Journal of Electrical Power & Energy Systems'', vol. 177, p.111798, 2026.
	# Y. Ge, H. Ye, X. Peng, Y. Zhai, and W. Mao, "Physics-Informed Multi-Agent Learning for Corrective Optimal Power Flow," ''IEEE Transactions on Power Systems, 2026.''		# Y. Ge, H. Ye, X. Peng, Y. Zhai, and W. Mao, "Physics-Informed Multi-Agent Learning for Corrective Optimal Power Flow," ''IEEE Transactions on Power Systems, 2026.''
	# Z. Wu, M. Zhang, S. Gao, Z. G. Wu, and X. Guan, "Physics-Informed Reinforcement Learning for Real-Time Optimal Power Flow with Renewable Energy Resources," ''IEEE Transactions on Sustainable Energy, early access.''		# Z. Wu, M. Zhang, S. Gao, Z. G. Wu, and X. Guan, "Physics-Informed Reinforcement Learning for Real-Time Optimal Power Flow with Renewable Energy Resources," ''IEEE Transactions on Sustainable Energy, early access.''
	# G. Tsaousoglou, P. Ellinas, J.S. Giraldo, and E. Varvarigos, "Distributed sequential optimal power flow under uncertainty in power distribution systems: A data-driven approach," Electric Power Systems Research, vol. 235, p.110816, 2024.		# G. Tsaousoglou, P. Ellinas, J.S. Giraldo, and E. Varvarigos, "Distributed sequential optimal power flow under uncertainty in power distribution systems: A data-driven approach," Electric Power Systems Research, vol. 235, p.110816, 2024.
	# T. Wolgast and A. Nieße, "Learning the Optimal Power Flow: Environment Design Matters," arXiv preprint arXiv:2403.17831, 2024.		# T. Wolgast and A. Nieße, "Learning the Optimal Power Flow: Environment Design Matters," arXiv preprint arXiv:2403.17831, 2024.
	# B. Feng, J. Zhao, G. Huang, Y. Hu, H. Xu, C. Guo, and Z. Chen, "Safe ~~deep reinforcement learning~~ for ~~real~~-time AC ~~optimal power flow~~: A ~~near~~-optimal ~~solution~~," CSEE Journal of Power and Energy Systems ''~~(early access)''~~, ~~2024~~.		# B. Feng, J. Zhao, G. Huang, Y. Hu, H. Xu, C. Guo, and Z. Chen, "Safe Deep Reinforcement Learning for Real-time AC Optimal Power Flow: A Near-optimal Solution," ''CSEE Journal of Power and Energy Systems'', vol. 12, no. 1, pp. 99-111, 2026.
	# P. Wu, C. Chen, D. Lai, J. Zhong, and Z. Bie, "Real-Time Optimal Power Flow Method via Safe Deep Reinforcement Learning Based on Primal-Dual and Prior Knowledge Guidance", ''IEEE Transactions on Power Systems (easrly access), 2024.''		# P. Wu, C. Chen, D. Lai, J. Zhong, and Z. Bie, "Real-Time Optimal Power Flow Method via Safe Deep Reinforcement Learning Based on Primal-Dual and Prior Knowledge Guidance", ''IEEE Transactions on Power Systems (easrly access), 2024.''
	# Z. Fan, W. Zhang, and W. Liu, "Data-Efficient Deep Reinforcement Learning-Based Optimal Generation Control in DC Microgrids", ''IEEE Systems Journal, vol. 18, no. 1, 2024.''		# Z. Fan, W. Zhang, and W. Liu, "Data-Efficient Deep Reinforcement Learning-Based Optimal Generation Control in DC Microgrids", ''IEEE Systems Journal, vol. 18, no. 1, 2024.''