A Survey on Applications of Machine Learning for Optimal Power Flow

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works

Machine learning: Trends, perspectives, and prospects

Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works

Reinforcement Learning: A Survey

Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972 · see citing works

Reinforcement Learning: An Introduction

Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,821 · see citing works

Introduction to Reinforcement Learning

Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855 · see citing works

Semi-Supervised Learning

Chapelle, O., Schölkopf, B., & Zien, A. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.001.0001 · Cited by 4,560 · see citing works

Supervised Machine Learning: A Review of Classification Techniques

Kotsiantis, S. B. (2007) · Informatica · Cited by 4,192 · see citing works

Machine learning methods for solar radiation forecasting: A review

Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017) · Renewable Energy · doi:10.1016/j.renene.2016.12.095 · Cited by 1,836 · see citing works

Introduction to Machine Learning

Yu, F. R., & He, Y. (2019) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-030-10546-4_1 · Cited by 1,619 · see citing works

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Molzahn, D. K., Dörfler, F., Sandberg, H., Low, S. H., Chakrabarti, S., Baldick, R., & Lavaei, J. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2720471 · Cited by 1,278 · see citing works

Introduction to Machine Learning

Corso, I. A., Basili, R., & Motivazioni, W. (2010) · Kybernetes · doi:10.1108/k.2010.06739hae.001 · Cited by 1,196 · see citing works

Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]

Chapelle, O., Schölkopf, B., & Zien, E. A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2015974 · Cited by 1,102 · see citing works

Convex Relaxation of Optimal Power Flow—Part I: Formulations and Equivalence

Low, S. H. (2014) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2014.2309732 · Cited by 934 · see citing works

What Is Machine Learning?

Naqa, I. E., & Murphy, M. J. (2015) · Springer eBooks · doi:10.1007/978-3-319-18305-3_1 · Cited by 802 · see citing works

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

Chen, Y., Wang, Y., Kirschen, D. S., & Zhang, B. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2794541 · Cited by 679 · see citing works

A survey on smart metering and smart grid communication

Kabalcı, Y. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.114 · Cited by 646 · see citing works

A survey of the optimal power flow literature

Huneault, M., & Galiana, F. D. (1991) · IEEE Transactions on Power Systems · doi:10.1109/59.76723 · Cited by 610 · see citing works

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Alahakoon, D., & Yu, X. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2414355 · Cited by 597 · see citing works

Convex Relaxation of Optimal Power Flow -- Part I: Formulations and EquivalencePreprint

Low, S. H. (2014) · Cited by 570 · see citing works

Convex Relaxation of Optimal Power Flow—Part II: Exactness

Low, S. H. (2014) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2014.2323634 · Cited by 526 · see citing works

Power load forecasting using support vector machine and ant colony optimization

Niu, D., Wang, Y., & Wu, D. D. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.08.019 · Cited by 392 · see citing works

Contribution a l’etude du dispatching economique.

Carpentier, J. L. (1962) · Cited by 379 · see citing works

Power System Dynamics and Stability: With Synchrophasor Measurement and Power System Toolbox 2e

Sauer, P. W., Pai, M. A., & Chow, J. H. (2017) · Wiley eBooks · doi:10.1002/9781119355755 · Cited by 377 · see citing works

Convex Relaxation of Optimal Power Flow Part II: Exactness

Low, S. H. (2014) · Cited by 319 · see citing works

Optimal power dispatchߞA comprehensive survey

Happ, H. H. (1977) · IEEE Transactions on Power Apparatus and Systems · doi:10.1109/t-pas.1977.32397 · Cited by 309 · see citing works

A Survey of Relaxations and Approximations of the Power Flow Equations

Molzahn, D. K., & Hiskens, I. A. (2019) · Foundations and Trends® in Electric Energy Systems · doi:10.1561/3100000012 · Cited by 309 · see citing works

Integrating Variable Renewable Energy: Challenges and Solutions

Bird, L. A., USDOE Office of Energy Efficiency and Renewable Energy (EERE), Milligan, M. R., & Lew, D. J. (2013) · doi:10.2172/1097911 · Cited by 307 · see citing works

Toward Distributed/Decentralized DC Optimal Power Flow Implementation in Future Electric Power Systems

Kargarian, A., Mohammadi, J., Guo, J., Chakrabarti, S., Barati, M., Hug, G., Kar, S., & Baldick, R. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2614904 · Cited by 307 · see citing works

An Integrated IoT Architecture for Smart Metering

Lloret, J., Tomás, J., Cánovas, A., & Parra, L. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.1600647cm · Cited by 275 · see citing works

Optimal power dispatch: a comprehensive survey

Happ, H. L. (1977) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 270 · see citing works

A Survey of Relaxations and Approximations of the Power Flow Equations

Molzahn, D. K., & Hiskens, I. A. (2019) · now publishers, Inc. eBooks · doi:10.1561/9781680835410 · Cited by 207 · see citing works

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Xavier, Á. S., Qiu, F., & Ahmed, S. (2020) · INFORMS journal on computing · doi:10.1287/ijoc.2020.0976 · Cited by 192 · see citing works

Power System Dynamics and Stability: With Synchrophasor Measurement and Power System Toolbox

Sauer, P. W., Chow, J. H., & Pai, M. A. (2017) · CERN Document Server (European Organization for Nuclear Research) · Cited by 190 · see citing works

Unsupervised and Transfer Learning Challenge: a Deep Learning Approach

Mesnil, G., Dauphin, Y., Glorot, X., Rifai, S., Bengio, Y., Goodfellow, I., Lavoie, E., Muller, X., Desjardins, G., Warde-Farley, D., et al. (2011) · International Conference on Machine Learning · Cited by 176 · see citing works

Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques

Karagiannopoulos, S., Aristidou, P., & Hug, G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2905348 · Cited by 162 · see citing works

Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part I: Methodologies

Guo, Y., Baker, K., Dall’Anese, E., Hu, Z., & Summers, T. H. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2878385 · Cited by 151 · see citing works

Learning Warm-Start Points For Ac Optimal Power Flow

Baker, K. (2019) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2019.8918690 · Cited by 108 · see citing works

Learning for DC-OPF: Classifying active sets using neural nets

Deka, D., & Misra, S. (2019) · doi:10.1109/ptc.2019.8810819 · Cited by 101 · see citing works

Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets

Misra, S., Roald, L., & Ng, Y. (2021) · INFORMS journal on computing · doi:10.1287/ijoc.2020.1037 · Cited by 99 · see citing works

Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds Through Learning

Baker, K., & Bernstein, A. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2903767 · Cited by 94 · see citing works

Is Machine Learning in Power Systems Vulnerable?

Chen, Y., Tan, Y., & Deka, D. (2018) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm.2018.8587547 · Cited by 91 · see citing works

Optimal Power Flows: Uses, Methods and Developments

Carpentier, J. L. (1985) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)60410-5 · Cited by 88 · see citing works

An Unsupervised Deep Learning Approach for Scenario Forecasts

Chen, Y., Wang, X., & Zhang, B. (2018) · Power Systems Computation Conference · doi:10.23919/pscc.2018.8442500 · Cited by 83 · see citing works

Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part II: Case Studies

Guo, Y., Baker, K., Dall’Anese, E., Hu, Z., & Summers, T. H. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2878380 · Cited by 70 · see citing works

Statistical Learning for DC Optimal Power Flow

Ng, Y., Misra, S., Roald, L., & Backhaus, S. (2018) · Power Systems Computation Conference · doi:10.23919/pscc.2018.8442859 · Cited by 62 · see citing works

Stochastic optimal power flow based on convex approximations of chance constraints

Summers, T. H., Warrington, J., Morari, M., & Lygeros, J. (2014) · Power Systems Computation Conference · doi:10.1109/pscc.2014.7038376 · Cited by 46 · see citing works

Data-driven Security-Constrained AC-OPF for Operations and Markets

Halilbašić, L., Thams, F., Venzke, A., Chatzivasileiadis, S., & Pinson, P. (2018) · Power Systems Computation Conference · doi:10.23919/pscc.2018.8442786 · Cited by 44 · see citing works

Supervised learning for optimal power flow as a real-time proxy

Canyasse, R., Dalal, G., & Mannor, S. (2017) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt.2017.8086083 · Cited by 37 · see citing works

Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage RegulationPreprint

Sondermeijer, O., Dobbe, R., Arnold, D., Tomlin, C. J., & Keviczky, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08594 · Cited by 34 · see citing works

Optimized Local Control for Active Distribution Grids using Machine Learning Techniques

Bellizio, F., Karagiannopoulos, S., Aristidou, P., & Hug, G. (2018) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm.2018.8586079 · Cited by 28 · see citing works

KERNEL-BASED LEARNING FOR SMART INVERTER CONTROL

Garg, A., Jalali, M., Kekatos, V., & Gatsis, N. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646638 · Cited by 28 · see citing works

Unit Commitment Using Nearest Neighbor as a Short-Term Proxy

Dalal, G., Gilboa, E., Mannor, S., & Wehenkel, L. A. (2018) · Power Systems Computation Conference · doi:10.23919/pscc.2018.8442516 · Cited by 27 · see citing works

Prediction of Umbrella Constraints

Ardakani, A. J., & Bouffard, F. (2018) · Power Systems Computation Conference · doi:10.23919/pscc.2018.8450586 · Cited by 26 · see citing works

A data-driven analysis of lightning-initiated contingencies at a distribution grid with a PV farm using Micro-PMU data

Shahsavari, A., Farajollahi, M., Stewart, E., Roberts, C., & Mohsenian‐Rad, H. (2017) · North American Power Symposium (NAPS) · doi:10.1109/naps.2017.8107307 · Cited by 25 · see citing works

Data-Driven Security-Constrained OPF

Thams, F., Halilbašić, L., Pinson, P., Chatzivasileiadis, S., & Eriksson, R. (2017) · Cited by 23 · see citing works

Data-Driven Decentralized Optimal Power Flow.Preprint

Dobbe, R., Sondermeijer, O., Fridovich-Keil, D., Arnold, D., Callaway, D. S., & Tomlin, C. J. (2018) · arXiv (Cornell University) · Cited by 16 · see citing works

Local Feature Sufficiency Exploration for Predicting Security-Constrained Generation Dispatch in Multi-area Power Systems

Sun, Y., Fan, X., Huang, Q., Li, X., Huang, R., Yin, T., & Lin, G. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00208 · Cited by 14 · see citing works

Multi-contingency transient stability-constrained optimal power flow using multilayer feedforward neural networks

King, R. T. F. A., Tu, X., Dessaint, L., & Kamwa, I. (2016) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2016.7726774 · Cited by 10 · see citing works

JOINT CHANCE CONSTRAINTS REDUCTION THROUGH LEARNING IN ACTIVE DISTRIBUTION NETWORKS

Baker, K., & Bernstein, A. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646440 · Cited by 10 · see citing works

Machine Learning for the Grid

Deka, D., Backhaus, S., Chertkov, M., Lokhov, A. Y., Misra, S., Vuffray, M., & Dvijotham, K. (2016) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 2 · see citing works

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow

Pan, X., Zhao, T., Chen, M., & Zhang, S. (2020) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2020.3026379 · Cited by 257 · see citing works

Machine Learning for Sustainable Energy Systems

Donti, P. L., & Kolter, J. Z. (2021) · Annual Review of Environment and Resources · doi:10.1146/annurev-environ-020220-061831 · Cited by 123 · see citing works

A Physics-Guided Graph Convolution Neural Network for Optimal Power Flow

Gao, M., Yu, J., Yang, Z., & Zhao, J. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3238377 · Cited by 112 · see citing works

An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems

Dhibi, K., Mansouri, M. M., Bouzrara, K., Nounou, H. N., & Nounou, M. N. (2021) · IEEE Access · doi:10.1109/access.2021.3128749 · Cited by 81 · see citing works

DC3: A learning method for optimization with hard constraintsPreprint

Donti, P. L., Rolnick, D., & Kolter, J. Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.12225 · Cited by 68 · see citing works

ConvOPF-DOP: A Data-Driven Method for Solving AC-OPF Based on CNN Considering Different Operation Patterns

Jia, Y., Bai, X., Zheng, L., Weng, Z., & Li, Y. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3163381 · Cited by 62 · see citing works

Power market models for the clean energy transition: State of the art and future research needs

Haugen, M., Blaisdell-Pijuan, P., Botterud, A., Levin, T., Zhou, Z., Belsnes, M., Korpås, M., & Somani, A. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122495 · Cited by 60 · see citing works

Spatial Network Decomposition for Fast and Scalable AC-OPF Learning

Chatzos, M., Mak, T. W. K., & Hentenryck, P. V. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3124726 · Cited by 56 · see citing works

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman, Z., Tariq, N., Moqurrab, S. A., Yoo, J., & Srivastava, G. (2023) · Expert Systems · doi:10.1111/exsy.13467 · Cited by 63 · see citing works

Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time

Hasan, F., Kargarian, A., & Mohammadi, J. (2021) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2021.3053516 · Cited by 49 · see citing works

Solving optimal power flow problem for IEEE-30 bus system using a developed particle swarm optimization method: towards fuel cost minimization

Mohamed, S. A., Anwer, N., & Mahmoud, M. M. (2023) · International Journal of Modelling and Simulation · doi:10.1080/02286203.2023.2201043 · Cited by 48 · see citing works

Machine learning for modern power distribution systems: Progress and perspectives

Marković, M., Bossart, M., & Hodge, B. S. (2023) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0147592 · Cited by 49 · see citing works

Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives

Babiker, A., Ahmad, S. S., Ahmed, I., Khalid, M., Abido, M. A., & Al–Ismail, F. S. (2025) · IEEE Access · doi:10.1109/access.2025.3556168 · Cited by 48 · see citing works

Review of machine learning techniques for optimal power flow

Khaloie, H., Dolányi, M., Toubeau, J., & Vallée, F. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125637 · Cited by 47 · see citing works

Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids

Tiwari, D. R., Zideh, M. J., Talreja, V., Verma, V., Solanki, S. K., & Solanki, J. M. (2024) · IEEE Access · doi:10.1109/access.2024.3369068 · Cited by 45 · see citing works

High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power FlowPreprint

Chatzos, M., Fioretto, F., Mak, T. W. K., & Hentenryck, P. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16356 · Cited by 45 · see citing works

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Lin, Y., Li, B., Moiser, T. M., Griffel, L. M., Mahalik, M., Kwon, J., & Alam, S. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104123 · Cited by 36 · see citing works

Reactive power control in photovoltaic systems through (explainable) artificial intelligence

Utama, C., Meske, C., Schneider, J., & Ulbrich, C. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120004 · Cited by 34 · see citing works

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

Pesántez, G., Guamán, W., Córdova, J., Torres, M., & Benalcazar, P. (2024) · Energies · doi:10.3390/en17092167 · Cited by 26 · see citing works

Surrogate Modeling for Solving OPF: A Review

Mohammadi, S., Bui, V., Su, W., & Wang, B. (2024) · Sustainability · doi:10.3390/su16229851 · Cited by 25 · see citing works

Machine learning and its applications

Arora, V., Ng, E. Y. K., & Singh, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90789-7.00002-6 · Cited by 24 · see citing works

Approximating multi-purpose AC Optimal Power Flow with reinforcement trained Artificial Neural Network

Wang, Z., Menke, J., Schäfer, F., Braun, M., & Scheidler, A. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100133 · Cited by 22 · see citing works

Physics-Informed Neural Networks for Power Systems Warm-Start Optimization

Tudoras-Miravet, À., González-Iakl, E., Gomis‐Bellmunt, O., & Prieto‐Araujo, E. (2024) · IEEE Access · doi:10.1109/access.2024.3406471 · Cited by 20 · see citing works

Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow

Jalving, J., Eydenberg, M. S., Blakely, L., Castillo, A., Kilwein, Z., Skolfield, J. K., Boukouvala, F., & Laird, C. D. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109741 · Cited by 17 · see citing works

Smart grid dispatch powered by deep learning: a survey

Huang, G., Wu, F. Q., & Guo, C. (2022) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2000719 · Cited by 17 · see citing works

Optimal power flow solution via noise-resilient quantum interior-point methods

Amani, F., & Kargarian, A. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.111216 · Cited by 15 · see citing works

A Learning-boosted Quasi-Newton Method for AC Optimal Power FlowPreprint

Baker, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.06074 · Cited by 15 · see citing works

Learning Reactive Power Control Polices in Distribution Networks Using Conditional Value-at-Risk and Artificial Neural Networks

Ayyagari, K. S., Gonzalez, R. S., Jin, Y., Alamaniotis, M., Ahmed, S., & Gatsis, N. (2023) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2022.000477 · Cited by 14 · see citing works

Metaheuristic Algorithms in Optimal Power Flow Analysis: A Qualitative Systematic Review

Farag, M. M., Alhamad, R. A., & Nassif, A. B. (2023) · International Journal of Artificial Intelligence Tools · doi:10.1142/s021821302350032x · Cited by 14 · see citing works

GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network with High Renewables

Mahto, D. K., Bukya, M., Kumar, R., Mathur, A., & Saini, V. K. (2024) · IEEE Access · doi:10.1109/access.2024.3512993 · Cited by 14 · see citing works

A Physics-Informed Graph Convolution Network for AC Optimal Power Flow via Refining DC Solution

Liu, Y., Li, Y., He, S., Li, Y., Zhao, Y., & Zeng, Z. (2025) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2025.3589380 · Cited by 13 · see citing works

A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment

Zafar, R., Vu, B. H., & Chung, I. (2022) · IEEE Access · doi:10.1109/access.2022.3204803 · Cited by 13 · see citing works

Learning-Accelerated ADMM for Stochastic Power System Scheduling With Numerous Scenarios

Rajaei, A., Arowolo, O., & Cremer, J. L. (2025) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2025.3562640 · Cited by 13 · see citing works

Enhancing Power Grid Efficiency using Machine Learning Algorithms

Ahmed, S. R., Ali, L., Hussein, A. J., Al-Attar, B., Abubaker, B. A., Tawfeq, J. F., Hussain, A.-S. T., & Shah, P. (2024) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora61326.2024.10550851 · Cited by 12 · see citing works

Data Driven Approach for Optimal Power Flow in Distribution Network

Mahto, D. K., Saini, V. K., Mathur, A., Kumar, R., & Saxena, A. (2021) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon52037.2021.9702343 · Cited by 12 · see citing works

Large-Scale Grid Optimization: the Workhorse of Future Grid Computations

Pandey, A., Almassalkhi, M. R., & Chevalier, S. C. (2023) · Current Sustainable/Renewable Energy Reports · doi:10.1007/s40518-023-00213-6 · Cited by 11 · see citing works

HYBRID CHANCE-CONSTRAINED OPTIMAL POWER FLOW UNDER LOAD AND RENEWABLE GENERATION UNCERTAINTY USING ENHANCED MULTI-FIDELITY GRAPH NEURAL NETWORKS

Khayambashi, K., Hasnat, M. A., & Alemazkoor, N. (2024) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2024054885 · Cited by 11 · see citing works

An Efficient Primal-Dual Interior-Point Algorithm for Volt/VAR Optimization in Rectangular Voltage Coordinates

Mataifa, H., Krishnamurthy, S., & Kriger, C. (2023) · IEEE Access · doi:10.1109/access.2023.3266421 · Cited by 11 · see citing works

Linear Regression-Based Machine Learning Model for Dynamic Load Flow Analysis

Sharma, N. K., Kumar, R., Choube, S. C., & Arya, R. (2023) · IEEE International Conference on Renewable Energy and Sustainable E-Mobility (RESEM) · doi:10.1109/resem57584.2023.10236436 · Cited by 11 · see citing works

Data-Driven Multiperiod Optimal Power Flow for Power System Scheduling Considering Renewable Energy Integration

Zafar, R., & Chung, I. (2024) · IEEE Access · doi:10.1109/access.2024.3426052 · Cited by 10 · see citing works

PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systems

Ghamizi, S., Cao, J. H., Ma, A., & Rodríguez, P. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3465088 · Cited by 10 · see citing works

OPF-HGNN: Generalizable Heterogeneous Graph Neural Networks for AC Optimal Power Flow

Ghamizi, S., Ma, A., Cao, J. H., & Cortes, P. R. (2024) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm51994.2024.10688560 · Cited by 10 · see citing works

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Glover, D., Krishnamoorthy, G., Ren, H., Dubey, A., & Gebremedhin, A. H. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3599840 · Cited by 10 · see citing works

Review of Machine Learning Techniques for Optimal Power FlowPreprint

Khaloie, H., Dolányi, M., Toubeau, J., & Vallée, F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4681955 · Cited by 10 · see citing works

Machine learning-accelerated distributed optimisation methods for optimal power flow: A review

Steven, R. A., Victorovich, K. O., & Short, M. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116190 · Cited by 9 · see citing works

Varying Condition SCOPF Optimization Based on Deep Learning and Knowledge Graph

Liu, S., Guo, Y., Tang, W., Sun, H., Huang, W., & Hou, J. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3199238 · Cited by 9 · see citing works

Unsupervised Deep Lagrange Dual With Equation Embedding for AC Optimal Power Flow

Kim, M., & Kim, H. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3406437 · Cited by 8 · see citing works

Accelerating L-Shaped Two-Stage Stochastic SCUC With Learning Integrated Benders Decomposition

Hasan, F., & Kargarian, A. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3441646 · Cited by 8 · see citing works

MPGCN-OPF: A Message Passing Graph Convolution Approach for Optimal Power Flow for Distribution Network

Mahto, D. K., Saini, V. K., Mathur, A., Kumar, R., & Verma, S. (2022) · IEEE International Conference on Power Electronics, Drives and Energy Systems · doi:10.1109/pedes56012.2022.10080112 · Cited by 8 · see citing works

Machine learning models for the economic dispatch of islanded micro water-energy systems

Raza, N., Khazaei, J., & Moazeni, F. K. (2025) · Energy Conversion and Management · doi:10.1016/j.enconman.2025.119827 · Cited by 7 · see citing works

Operational risk quantification of power grids using graph neural network surrogates of the DC optimal power flow

Zhang, Y., Karve, P., & Mahadevan, S. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101748 · Cited by 7 · see citing works

Difference-of-Convex Approach to Chance-Constrained Optimal Power Flow Modelling the Dso Power Modulation Lever for Distribution Networks

Syrtseva, K., de Oliveira, W., Demassey, S., Morais, H. G. V., Javal, P., & Swaminathan, B. P. (2023) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2023.101168 · Cited by 7 · see citing works

An Analysis of the Reliability of AC Optimal Power Flow Deep Learning Proxies

Dinh, M. H., Fioretto, F., Mohammadian, M., & Baker, K. (2023) · IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) · doi:10.1109/isgt-la56058.2023.10328223 · Cited by 6 · see citing works

Data strategy for active distribution networks: a framework to quantify data granularity impact on cyber-physical planning and operation

Koziel, S., Ilić, M., Ferreira, D. M. V. P., Carvalho, P. M. S., & Hilber, P. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101763 · Cited by 5 · see citing works

Go competition challenge 3: problem, solvers, and solution analysis

Holzer, J., Elbert, S. T., Mittelmann, H. D., O’Neill, R. P., & Oh, H. (2025) · Energy Systems · doi:10.1007/s12667-024-00708-1 · Cited by 5 · see citing works

Learning Optimal Power Flow Solutions using Linearized Models in Power Distribution Systems

Sadnan, R., & Dubey, A. (2021) · Conference record of the IEEE Photovoltaic Specialists Conference · doi:10.1109/pvsc43889.2021.9518472 · Cited by 5 · see citing works

Two-Stage Distributionally Robust Optimization for an Asymmetric Loss-Aversion Portfolio via Deep Learning

Zhang, X., Liu, S., & Pan, J. (2025) · Symmetry · doi:10.3390/sym17081236 · Cited by 4 · see citing works

Multi-objective optimal power flow based gray wolf optimization method

Mezhoud, N., Ayachi, B., & Amarouayache, M. (2022) · Electrical Engineering & Electromechanics · doi:10.20998/2074-272x.2022.4.08 · Cited by 4 · see citing works

Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF SolutionsPreprint

Dinh, M. H., Fioretto, F., Mohammadian, M., & Baker, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.11168 · Cited by 4 · see citing works

Energy Optimization

Bansal, A., Aggarwal, A. K., & Khosla, A. (2025) · doi:10.1002/9781394242139.ch14 · Cited by 4 · see citing works

Evaluation of XGBoost and ANN as Surrogates for Power Flow Predictions with Dynamic Energy Storage Scenarios

Yeptho, P., Saldaña-González, A. E., Aragüés‐Peñalba, M., & Barja-Martínez, S. (2025) · Energies · doi:10.3390/en18164416 · Cited by 3 · see citing works

GAT- DNet: High Fidelity Graph Attention Network for Distribution Optimal Power Flow Pursuit

Mahto, D. K., Saini, V. K., Mathur, A., Kumar, R., & Yadav, R. (2023) · IEEE India International Conference on Power Electronics · doi:10.1109/iicpe60303.2023.10474885 · Cited by 3 · see citing works

Herramienta de simulación para el análisis de flujo óptimo clásico utilizando multiplicadores de Lagrange

Anzola, D., Castro, J., & Giral, D. (2021) · Transactions on Energy Systems and Engineering Applications · doi:10.32397/10.32397/tesea.vol2.n1.1 · Cited by 3 · see citing works

Optimal Power Flow With Regression-Based Small-Signal Stability Constraints

Rossi, F., Prieto‐Araujo, E., Cheah-Mañé, M., & Gomis‐Bellmunt, O. (2024) · IEEE Access · doi:10.1109/access.2024.3495040 · Cited by 2 · see citing works

Prediction of critical condensation temperature for high voltage switchgear based on TimeGAN-augmented data

Wang, P., Chen, X., Li, J., Chen, D., Cai, W., & Lai, J. (2025) · International Conference on Energy System, Electricity, and Power (ESEP) · doi:10.1117/12.3060277 · Cited by 2 · see citing works

Optimal operation of grid-connected hydropower plants through voltage control methods

Melfald, E. G., & Øyvang, T. (2022) · Linköping electronic conference proceedings · doi:10.3384/ecp192015 · Cited by 2 · see citing works

Temporal Decomposition for Multi-Interval Optimization in Power Systems

Safdarian, F. (2020) · doi:10.31390/gradschool_dissertations.5259 · Cited by 2 · see citing works

Predictive modeling for power system state estimation

Knežević, S., Dobrić, G., & Žarković, M. (2024) · Electrical Engineering · doi:10.1007/s00202-024-02837-3 · Cited by 1 · see citing works

Machine learning-driven multi-agent-based AC optimal power flow with effective dataset creation for data privacy and interoperability

Dindar, B., Saner, C. B., Çakmak, H. K., & Hagenmeyer, V. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101672 · Cited by 1 · see citing works

Microblog Analysis with Machine Learning for Indic Languages: A Quick Survey

Roy, M. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-2937-2_33

Prediction of power supply interruption incidents using correlation matrices and SVM

Gomes, J. V. D. M. S., Leite, J. P., Batista, B. G., de Oliveira, E. M., Frinhani, R. D. M. D., Kuehne, B. T., Gonçalves, A. F. L., & Pagan, R. P. (2021) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias48185.2021.9677095

Predicting Ac Optimal Power Flow Solutions Using Artificial Neural NetworkPreprint

Wubale, Z. A., & Yenealem, M. G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4816310

A Fast Learning-Based Unit Commitment Strategy with AC Optimal Power Flow for Large Grids with Direct Inclusion of Weather

Safdarian, F., Peeples, J., Richards, D., & Overbye, T. J. (2024) · IEEE Kansas Power and Energy Conference (KPEC) · doi:10.1109/kpec61529.2024.10676027

Optimal Allocation of DG in Power System: Leveraging Network Flow Optimization Techniques such as GA, FA and ALO

Rout, K. C., & Prasad, S. B. (2024) · IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE) · doi:10.1109/icspcre62303.2024.10675138

Optimal Power Flow in Modern Power System with Application of Osprey Optimization Algorithm

Kumari, N., Jha, B. M., Kumar, P., & Paul, K. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-9037-1_21

Analyzing Data Characteristics for Learning OPFs

Lugmania, H., Luna, J., Rodriguez-Veliz, X., & Córdova-García, J. (2024) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope62998.2024.10863796

Revenue Prediction for Integrated Renewable Energy Systems: ML Techniques

Zaidan, D. T., Mahmoud, M. Q., ALLayith, A. A., Faaq, A. S., Alsalami, Z. A., Sattar, Y., & Ghobash, A. (2024) · International Conference on IoT, Communication and Automation Technology (ICICAT) · doi:10.1109/icicat62666.2024.10922977

Empirical Assessment of Graph Neural Network Convolution Operators for AC-OPF Learning

Abelezele, S., Sung, W. J., Ramamurthy, A., Jin, H., & Mavris, D. N. (2025) · IEEE Workshop on Design Automation for CPS and IoT (DESTION) · doi:10.1145/3722573.3727828

AC Optimal Power Flow Based on Collaborative Neurodynamic Optimization

Zou, Y. H., Xu, M. R., & Chen, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-1233-1_27

ARTIFICIAL INTELLIGENCE AND ITS IMPLICATIONS FOR THE ECO-CLIMATE TRANSITION

MAIOR, D. A. (2025) · Curentul Juridic/Juridical Current · doi:10.62838/cjjc-2024-0052

Optimal Placement of Static VAR Compensators (SVCs) in Transmission Systems Using Deep Reinforcement Learning

Limprapassorn, S., Piamvilai, N., & Sirisumrannukul, S. (2025) · IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia) · doi:10.1109/gtdasia60461.2025.11313258

Pre-Trained Actor-Guided Proximal Policy Optimization for Feasible and Safe AC Optimal Power Flow in Large-Scale Power Systems

Zhou, L., Wu, P., Chen, R., & Chen, X. (2025) · IEEE Conference on Energy Internet and Energy System Integration (EI2) · doi:10.1109/ei268505.2025.11425389

Operational planning under joint uncertainties

Syrtseva, K. (2025) · doi:10.70675/453bb1e6z3cfcz41a6zb749z89e08780dc09

Review of machine learning techniques for optimal power flow

Khaloie, H., Dolányi, M., Toubeau, J., & Vallée, F. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125637 · Cited by 47 · see citing works

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Jiang, B., Wang, Q., Wu, S., Wang, Y., & Lu, G. (2024) · Energies · doi:10.3390/en17061381 · Cited by 17 · see citing works

Application of Machine Learning for Enhanced Optimal Power Flow in Power Systems: A ReviewPreprint

Xia, Z. (2024) · doi:10.36227/techrxiv.172418079.92152838/v1

ML-OPF-Bench: Benchmarking Machine Learning for Optimal Power FlowPreprint

Liu, X., He, X., Tsang, D. H. K., & Chen, Y. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2610.04307

Machine Learning for Optimal Power Flows

Hentenryck, P. V. (2021) · doi:10.1287/educ.2021.0234 · Cited by 26 · see citing works