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Machine Learning for Solving Optimal Power Flow Problems

This page contains a list of papers on developing machine learning schemes for solving optimal power flow problems, organized in sections by the algorithmic structure.

Anyone can submit an edit (indeed, very welcome to do so!), which will then be reviewed and published.

Survey and Overview Papers

  1. G. Huang, G, F. Wu and C. Guo, "Smart grid dispatch powered by deep learning: a survey", in Frontiers of Information Technology & Electronic Engineering, pp. 1 - 14, 2022.
  2. L. Xie, X. Zheng, Y. Sun, T. Huang and T. Bruton, "Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI", arXiv preprint arXiv:2205.05180, 2022.
  3. B. Amos, "Tutorial on amortized optimization for learning to optimize over continuous domains", arXiv preprint arXiv:2202.00665.
  4. P. V. Hentenryck, "Machine Learning for Optimal Power Flows", in Tutorials in Operations Research: Emerging Optimization Methods and Modeling Techniques with Applications, 62 - 82, Oct. 2021.
  5. P. L. Donti and J. Z. Kolter, "Machine Learning for Sustainable Energy Systems", in Annual Review of Environment and Resources 2021, vol: 46, 2021.
  6. M. Massaoudi, H. Abu-Rub, S. S. Refaat, I. Chihi and F. S. Oueslati, "Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects", in IEEE Access, vol. 9, pp. 54558-54578, Apr. 2021.
  7. J. Kotary, F. Fioretto and P. V. Hentenryck, "End-to-End Constrained Optimization Learning: A Survey", arXiv preprint arXiv:2103.16378, 2021.
  8. G. Ruan, H. Zhong, G. Zhang, Y. He, X. Wang and T. Pu, "Review of Learning-Assisted Power System Optimization", in CSEE Journal of Power and Energy Systems, vol. 7, no. 2, pp. 221 - 231, Mar. 2021.
  9. L. Duchesne, E. Karangelos and L. Wehenkel, "Recent Developments in Machine Learning for Energy Systems Reliability Management", in Proceedings of IEEE, vol. 108, no. 9, pp. 1656-1676, Oct. 2020.
  10. F. Hasan, A. Kargarian and A. Mohammadi, "A Survey on Applications of Machine Learning for Optimal Power Flow", in Proceedings of 2020 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA, Feb. 6 - 7, 2020.
  11. L. Yin, Q. Gao, L. Zhao, B. Zhang, T. Wang, S. Li and H. Liu, "A review of machine learning for new generation smart dispatch in power systems", Engineering Applications of Artificial Intelligence, vol. 88, 103372, 2020.

The Learning-based End-to-end Framework

End2end.jpg

The idea behind the end-to-end framework is to train the ML model to output solutions directly from the input instance.

Supervised Learning-based Schemes

  1. K. Baker, "Emulating AC OPF Solvers With Neural Networks," accepted for publication in IEEE Transactions on Power Systems (early access), 2022
  2. X. Pan, W. Huang, M. Chen and S. H. Low, "DeepOPF-AL Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings", arXiv preprint arXiv:2206.03365, 2022.
  3. P. S. Torre and P. H-Gonzalez, "Decentralized Optimal Power Flow for Time-Varying Network Topologies Using Machine Learning", in Proceedings of Power Systems Computation Conference (PSCC), 27th June to 1st July, Porto, Portugal, 2022.
  4. A. Lotfi and Mehrdad Pirnia, "Constraint-Guided Deep Neural Network for Solving Optimal Power Flow", in Proceedings of Power Systems Computation Conference (PSCC), 27th June to 1st July, Porto, Portugal, 2022.
  5. 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.
  6. S. Liu, C. Wu, and H. Zhu, "Topology-aware Graph Neural Networks for Learning Feasible and Adaptive AC-OPF Solutions", arXiv preprint arXiv:2205.10129, 2022.
  7. Y. Jia, X. Bai, L. Zheng, Z. Weng and Y. Li, "ConvOPF-DOP: A Data-driven Method for solving AC-OPF based on CNN considering different operation patterns", accepted for publication in IEEE Transactions on Power Systems (early access), 2022.
  8. W. Huang, X. Pan, M. Chen and S. H. Low, "DeepOPF-V: Solving AC-OPF Problems Efficiently", IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 800 - 803, Jan. 2022. Also available on as technical report: arXiv preprint arXiv:2103.11793, 2021.
  9. 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.
  10. E. Lu, N. Wang, W. Zheng, X. Wang, X. Lei, Z. Zhu and Z. Gong, "Data-Driven Electricity Price Risk Assessment for Spot Market", International Transactions on Electrical Energy Systems, vol. 2022, 11 pages, 2022.
  11. 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.
  12. T. Zhao, X. Pan, M. Chen and S. H. Low, "Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems", arXiv preprint arXiv:2112.08091, 2021.
  13. M. Dolanyi, K. ESIM, K. Bruninx, J.F. Toubeau and E. Delaru, "Capturing Electricity Market Dynamics in the Optimal Trading of Strategic Agents using Neural Network Constrained Optimization", In Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS), virtual conference, Dec. 14, 2021.
  14. K. Yang, W. Gao and R. Fan, "Optimal Power Flow Estimation Using One-Dimensional Convolutional Neural Network", in Proceedings of 2021 North American Power Symposium (NAPS), College Station, TX, USA, Nov. 14 -16, 2021.
  15. G. Chen, H. Zhang, H. Hui, N. Dai and Y. Song, "Scheduling Thermostatically Controlled Loads to Provide Regulation Capacity Based on a Learning-Based Optimal Power Flow Model", in IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2459-2470, Oct. 2021.
  16. M. H. Dinh, F. Fioretto, Towards, M. Mohammadian and K. Baker, "Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions", arXiv preprint arXiv:2111.11168, 2021.
  17. R. Nellikkath and S. Chatzivasileiadis, "Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow", in Proceedings of the 11th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2021), in-Person and Virtual Conference, Aachen, Germany, Oct. 25 - 28, 2021.
  18. T. Falconer and L. Mones, "Leveraging power grid topology in machine learning assisted optimal power flow", in IEEE Transactions on Power Systems (early access), 2022.
  19. H. Zhen, Zhai H, W. Ma, L. Zhao, Y. Weng, Y. Xu, J. Shi, X. He, "Design and tests of reinforcement-learning-based optimal power flow solution generator", In Proceedings of 8th International Conference on Power and Energy Systems Engineering (CPESE), Fukuoka, Japan, Sept. 10 – 12 2021.
  20. 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.
  21. S. D. Jongh, S. Steinle, A. Hlawatsch, F. Mueller, M. Suriyah and T. Leibfried, "Neural Predictive Control for the Optimization of Smart Grid Flexibility Schedules", In Proceedings of the 56th International Universities Power Engineering Conference (UPEC), Middlesbrough, United Kingdom, Aug. 31 - Sept. 3, 2021.
  22. Y. Jia and X. Bai, "A CNN Approach for Optimal Power Flow Problem for Distribution Network", In Proceedings of Power System and Green Energy Conference (PSGEC), Shanghai, China, Aug. 20 - 22, 2021.
  23. G. Huang, L. Liao, L. Cheng and W. Hua, "Learning Optimal Power Flow with Infeasibility Awareness", In Proceedings of the 38th International Conference on Machine Learning Workshop, virtual conference, Jul. 23, 2021.
  24. 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.
  25. R. Nellikkath and S. Chatzivasileiadis, "Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow", arXiv preprint arXiv:2107.00465, 2021.
  26. S. Liu, C. Wu and H. Zhu, "Graph Neural Networks for Learning Real-Time Prices in Electricity Market", arXiv preprint arXiv:2106.10529, 2021.
  27. J. Kotary, F. Fioretto and P. V. Hentenryck, "Learning Hard Optimization Problems: A Data Generation Perspective", arXiv preprint arXiv:2106.02601.
  28. R. Sadnan and A. Dubey, "Learning Optimal Power Flow Solutions using Linearized Models in Power Distribution Systems", In Proceedings of IEEE 48th Photovoltaic Specialists Conference (PVSC), Fort Lauderdale, FL, USA, Jun. 20 - 25, 2021.
  29. X. Pan, T. Zhao, M. Chen and S. Zhang, "DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow", in IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 1725 - 1735, May. 2021.
  30. S. Gupta, V. Kekatos and M. Jin, "Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach", arXiv preprint arXiv:2105.00429, 2021.
  31. J. Rahman, C.Feng and J. Zhang, "A learning-augmented approach for AC optimal power flow", accepted for publication in International Journal of Electrical Power & Energy Systems, vol. 130, pp. 106908, Mar. (Publication time Sept) 2021.
  32. M. K. Singh, S. Gupta and V. Kekatos, "Machine Learning for Optimal Inverter Operation in Distribution Grids", in Proceedings of the 55th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, Mar. 24 - 26, 2021.
  33. M. K. Singh, V. Kekatos. Chen, and G. B. Giannakis, "Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks", accepted for publication in IEEE Transactions on Power Systems (early access). Also available on arXiv preprint arXiv:2103.14779, 2021.
  34. M. Chatzos, T. W. Mak and P. V. Hentenryck, "Spatial Network Decomposition for Fast and Scalable AC-OPF Learning", accepted for publication in IEEE Transactions on Power Systems (early access), 2021.
  35. F. Guo, B. Xu, W. -A. Zhang, C. Wen, D. Zhang and L. Yu, "Training Deep Neural Network for Optimal Power Allocation in Islanded Microgrid Systems: A Distributed Learning-Based Approach", accepted for publication in IEEE Transactions on Neural Networks and Learning Systems (early access), 2021.
  36. T. W. Mak, F. Fioretto and P. V. Hentenryck, "Load Embeddings for Scalable AC-OPF Learning", arXiv preprint arXiv:2101.03973, 2021.
  37. X. Lei, Z. Yang, J. Yu, J. Zhao, Q. Gao and H. Yu, "Data-Driven Optimal Power Flow: A Physics-Informed Machine Learning Approach", in IEEE Transactions on Power Systems, vol. 36, no. 1, pp. 346 - 354, Jan. 2021.
  38. P. Pareek and H.D. Nguyen, "Gaussian Process Learning-based Probabilistic Optimal Power Flow", in IEEE Transactions on Power Systems, vol. 36, no. 1, pp. 541 - 544, Jan. 2021.
  39. M. Giuntoli, V. Biagini and M. Chioua, "Artificial intelligence and optimization: a way to speed up the security constraint optimal power flow", in Automatisierungstechnik, vol. 68, issue 12, pp. 1035 - 1043, 2020.
  40. Y. Chen, S. Lakshminarayana, C. Maple and H. V. Poor, "A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations", arXiv preprint arXiv:2012.11524, 2020.
  41. K. Baker K, "Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions", arXiv preprint arXiv:2012.10031, 2020.
  42. 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.
  43. A. Zamzam and K. Baker, "Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow", 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.
  44. A. Venzke, G. Qu, S. Low and S. Chatzivasileiadis, "Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks", 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.
  45. M. K. Singh, S. Gupta , V. Kekatos, G. Cavraro and A. Bernstein, "Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks", 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.
  46. S. Gupta , V. Kekatos and M. Jin, "Deep Learning for Reactive Power Control of Smart Inverters under Communication Constraints", 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.
  47. L. Duchesne, E. Karangelos, A. Sutera and L. Wehenkel, "Machine Learning for Ranking Day-ahead Decisions in the Context of Short-term Operation Planning", Electric Power Systems Research, vol. 189, pp.106548, Dec. 2020.
  48. J. H. Woo, L. Wu, J-B. P and J. H. Roh, "Real-Time Optimal Power Flow Using Twin Delayed Deep Deterministic Policy Gradient Algorithm", in IEEE Access, vol. 8, pp. 213611 - 213618, Nov. 2020.
  49. J. Rahman, C. Feng and J. Zhang, "Machine Learning-Aided Security Constrained Optimal Power Flow", in Proceedings of 2020 IEEE Power & Energy Society General Meeting, Montreal, Canada, Aug. 2 - 6, 2020.
  50. 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.
  51. X. Pan, M. Chen, T. Zhao and S. H. Low, "DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems", arXiv preprint arXiv:2007.01002, 2020.
  52. M. Chatzos, F. Fioretto, T. W.K. Mak and P. V. Hentenryck, "High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow", arXiv preprint arXiv:2006.1635, 2020.
  53. M. Jalali, V. Kekatos, N. Gatsis and D. Deka, "Designing Reactive Power Control Rules for Smart Inverters Using Support Vector Machines", IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1759 - 1770, Mar. 2020.
  54. R. Dobbe, O. Sondermeijer, D. Fridovich-Keil, D. Arnold, D. Callaway and C. Tomlin, "Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning", in IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1296 - 1306, Mar. 2020.
  55. Y. Zhou, B. Zhang, C. Xu, T. Lan, R. Diao, D. Shi, Z. Wang and W. Lee, "Deriving Fast AC OPF Solutions via Proximal Policy Optimization for Secure and Economic Grid Operation", arXiv preprint arXiv:2003.12584, 2020.
  56. 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.
  57. 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.
  58. 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.
  59. D. Owerko, F. Gama and A. Ribeiro, "Optimal Power Flow Using Graph Neural Networks", in Proceedings of the 45th International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, May 4 - 8, 2020.
  60. 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.
  61. 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.
  62. 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.
  63. X. Pan, T. Zhao and M. Chen, "DeepOPF: Deep Neural Network for DC Optimal Power Flow", arXiv:1905.04479, May 11th, 2019.
  64. Y. Sun, X. Fan, Q. Huang, X. Li, R. Huang, T. Yin and G. Lin, "Local feature sufficiency exploration for predicting security-constrained generation dispatch in multi-area power systems", in Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando, FL, USA, Dec. 17 - 20, 2018.
  65. 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.
  66. Nellikkath, Rahul, and Spyros Chatzivasileiadis. "Physics-informed neural networks for ac optimal power flow." Electric Power Systems Research 212 (2022): 108412.

Unsupervised Learning-based Schemes

  1. W. Huang and M. Chen, "DeepOPF-NGT: A Fast Unsupervised Learning Approach for Solving AC-OPF Problems without Ground Truth", In Proceedings of the 38th International Conference on Machine Learning Workshop, virtual conference, Jul. 23, 2021.
  2. P. L. Donti, D. Rolnick and J. Z. Kolter, "DC3: a learning method for optimization with hard constraints", in Proceedings of 9th International Conference on Learning Representations (ICLR), virtual conference, May 3 – 7, 2021.
  3. H. Lange, B. Chen, M. Berges, and S. Kar, "Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings", arXiv preprint arXiv:2012.09622, 2020.

Reinforcement Learning-based Schemes

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Y. Zhou, W. J. Lee, R. Diao, and D. Shi, "Deep Reinforcement Learning Based Real-Time AC Optimal Power Flow Considering Uncertainties", accepted for publication in Journal of Modern Power Systems and Clean Energy (early access), 2021.
  6. E. R. Sanseverino, M. L. Di Silvestre, L. Mineo, S. Favuzza, N. Q. Nguyen and Q. T. Tran, "A multi-agent system reinforcement learning based optimal power flow for islanded microgrids", in Proceedings of IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy, Jun. 7 - 10, 2016.

The Hybrid Framework

Assist.jpg

In the hybrid framework, the machine learning model is used to augment the conventional optimization solver with valuable pieces of information.

Papers

  1. A. Agarwal, L. Pileggi, P. Donti and Z. Kolter, "Employing Adversarial Robustness Techniques for Large-Scale Stochastic Optimal Power Flow", in Proceedings of Power Systems Computation Conference (PSCC), 27th June to 1st July, Porto, Portugal, 2022.
  2. Y. Chen, L. Zhang and B. Zhang, "Learning to Solve DCOPF: A Duality Approach", in Proceedings of Power Systems Computation Conference (PSCC), 27th June to 1st July, Porto, Portugal ,2022.
  3. F. Cengil, H. Nagarajan, R. Bent, S. Eksioglu and B. Eksioglu, "Learning to accelerate globally optimal solutions to the AC Optimal Power Flow problem", in Proceedings of Power Systems Computation Conference (PSCC), 27th June to 1st July, Porto, Portugal ,2022.
  4. T. Pham, T and X. Li, "Reduced Optimal Power Flow Using Graph Neural Network," arXiv preprint arXiv:2206.13591, 2022.
  5. Y. Zhang, J. Liu, F. Qiu, T. Hong and R. Yao, "Deep Active Learning for Solvability Prediction in Power Systems," accepted for publication in Journal of Modern Power Systems and Clean Energy (Early Access), 2022.
  6. A. Mohammadi and A. Kargarian, "Learning-Aided Asynchronous ADMM for Optimal Power Flow," in IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 1671 - 1681, May 2022.
  7. G. Chen, H. Zhang, H. Hui and Y. Song, "Deep-quantile-regression-based surrogate model for joint chance-constrained optimal power flow with renewable generation", arXiv preprint arXiv:2204.04919, 2022.
  8. J. Liu, Y. Liu, G. Qiu and X. Shao "Learning-Aided Optimal Power Flow Based Fast Total Transfer Capability Calculation", Energies, vol 15, issue 4, pp 1 - 15, 2022.
  9. F. Hasan and A. Kargarian, "Topology-aware Learning Assisted Branch and Ramp Constraints Screening for Dynamic Economic Dispatch", accepted for publication in IEEE Transactions on Power Systems (early access), 2022.
  10. O. Akdag, "A Improved Archimedes Optimization Algorithm for multi/single-objective Optimal Power Flow", Electric Power Systems Research, vol. 206, pp. 107796, Jan. 2022.
  11. P. Donti, A. Agarwal, N. V. Bedmutha, L. Pileggi and J. Z. Kolter, "Adversarially robust learning for security-constrained optimal power flow", In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS), virtual conference, poster paper, Dec. 7 - 10, 2021.
  12. S. A. Sadat and M. Sahraei-Ardakani, "Initializing Successive Linear Programming Solver for ACOPF using Machine Learning," in Proceedings of 2021 North American Power Symposium (NAPS), College Station, TX, USA, Nov. 14 -16, 2021.
  13. L. Zhang and B. Zhang, "Learning to Solve the AC Optimal Power Flow via a Lagrangian Approach", arXiv preprint arXiv:2110.01653.
  14. G. Chen, H. Zhang, H. Hui, N. Dai and Y. Song, "Scheduling thermostatically controlled loads to provide regulation capacity based on a learning-based optimal power flow model", in IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2459 - 2470, Oct. 2021.
  15. R. Hu, Q. Li and F. Qiu, "Ensemble Learning Based Convex Approximation of Three-Phase Power Flow," in IEEE Transactions on Power Systems, vol. 36, no. 5, pp. 4042-4051, Sept. 2021.
  16. Z. Kilwein, F. Boukouvala, C. Laird, A. Castillo, L. Blakely, M. Eydenberg, J. Jalving, and L. Batsch-Smith, "AC-Optimal Power Flow Solutions with Security Constraints from Deep Neural Network Models." In Computer Aided Chemical Engineering, vol. 50, pp. 919 - 925. Elsevier, July, 2021.
  17. S. Liu, Y. Guo, W. Tang, H. Sun and W. Huang, "Predicting Active Constraints Set in Security-Constrained Optimal Power Flow via Deep Neural Network", in Proceedings of 2021 IEEE Power & Energy Society General Meeting (PESGM), Washington, DC, USA, Jul. 26 - 29, 2021.
  18. F. Hasan, A. Kargarian and J. Mohammadi, "Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time," in IEEE Transactions on Industry Applications, vol. 57, no. 2, pp. 1325-1334, Apr. 2021.
  19. Q. Li, "Uncertainty-Aware Three-Phase Optimal Power Flow Based on Data-Driven Convexification," in IEEE Transactions on Power Systems, vol. 36, no. 2, pp. 1645-1648, Mar. 2021.
  20. Q. Hou, N. Zhang, D. S. Kirschen, E. Du, Y. Cheng and C. Kang, "Sparse Oblique Decision Tree for Power System Security Rules Extraction and Embedding", in IEEE Transactions on Power Systems, vol. 36, no. 2, pp. 1605 - 1615, Mar. 2021.
  21. A. Venzke and S. Chatzivasileiadis, "Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications", IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 383 - 397, Jan. 2021.
  22. W. Dong, Z. Xie , G. Kestor and L. Dong, "Smart-PGSim: using neural network to accelerate AC-OPF power grid simulation", in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20). IEEE Press, Article 63, 1 – 15, Nov. 2020.
  23. T. Liu, Y. Liu, J. Liu, J. Wang, L. Xu, G. Qiu and H. Cao, "A Bayesian Learning based Scheme for Online Dynamic Security Assessment and Preventive Control", in IEEE Transactions on Power Systems, vol 35, no. 5, pp. 4088 - 4099, Sep. 2020.
  24. L. Zhang, Y. Chen and B. Zhang, "A Convex Neural Network Solver for DCOPF with Generalization Guarantees", accepted for publication in IEEE Transactions on Control and Network Systems (early access), 2021; arXiv preprint arXiv:2009.09109, 2020.
  25. R. Hu, Q. Li, S. Lei, "Ensemble learning based linear power flow", in Proceedings of 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada, Aug. 2 - 6, 2020.
  26. K. Baker, "A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow", arXiv preprint arXiv:2007.06074, 2020.
  27. M. Jamei, L. Mones, A. Robson, L. White, J. Requeima and C. Ududec, "Meta-Optimization of Optimal Power Flow", in Proceedings of the 36th International Conference on Machine Learning Workshop, Long Beach, CA, USA, Jun. 10 - 15, 2019.
  28. A. Venzke, D. Viola, J. Mermet-Guyennet, G. Misyris and S. Chatzivasileiadis, "Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow to Mixed-Integer Linear Programs", arXiv preprint, arXiv:2003.07939, 2020.
  29. L. Chen and J.E. Tate, "Hot-Starting the AC Power Flow with Convolutional Neural Networks", arXiv preprint arXiv:2004.09342, 2020.
  30. Y. Chen and B. Zhang, "Learning to Solve Network Flow Problems via Neural Decoding", arXiv preprint arXiv:2002.04091, 2020.
  31. K. Baker and A. Bernstein, "Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds through Learning", in IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6376 - 6385, Nov. 2019.
  32. F. Diehl, "Warm-Starting AC Optimal Power Flow with Graph Neural Networks", in Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS) Workshop, Vancouver, BC, Canada, Dec. 8 - 14, 2019.
  33. A. Robson, M. Jamei, C. Ududec and L. Mones, "Learning an Optimally Reduced Formulation of OPF through Meta-optimization", arXiv preprint arXiv:1911.06784, 2019.
  34. 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.
  35. K. Baker, "Learning Warm-Start Points for AC Optimal Power Flow", in Proceedings of IEEE 29th Machine Learning for Signal Processing Conference, Pittsburgh, PA, USA, Oct. 13 - 16, 2019.
  36. D. Deka and S. Misra, "Learning for DC-OPF: Classifying Active Sets Using Neural Nets", Milan, Italy, Jun. 23 - 27, IEEE Milan PowerTech, 2019.
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Blog Posts, Talks and Other Materials

  1. Using Neural Networks for Predicting Solutions to Optimal Power Flow
  2. Using Meta-optimization for Predicting Solutions to Optimal Power Flow