Machine learning (ML) is one of the emerging technologies for implementing the next generation smart grid. In recent years, the PES community has witnessed significant efforts to explore the potential of machine learning for solving complex power system problems. Applications cover almost every area within the interest of PES, including generation, transmission, distribution, microgrid and customers. Also, researchers have been exploring physics-informed, performance-guaranteed, or explainable ML techniques for power systems.
- The Working Group (WG) on Machine Learning for Power Systems (MLPS) is the professional home for researchers and engineers involved in the application of the latest machine learning techniques for the operation and planning of power systems. It is a repository of technical and educational materials such as technical papers, presentations, tutorials, and panel discussions. The WG is under the direction of the Technologies and Innovations (T&I) subcommittee of the IEEE PES Power Systems Operation, Planning and Economics (PSOPE) committee.
- Please visit the WG website and the PSOPE website, or contact the WG chair, Fangxing Fran Li, at .
- “2021 IEEE Workshop on Machine Learning for Power Systems”
Fangxing Fran Li, Jin Zhao, Junbo Zhao, Pengwei Du, Spyros Chatzivasileiadis, C.Y. Chung, Anurag K. Srivastava, Nov. 2021
- “Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification”
X. Zheng, B. Wang, D. Kalathil and L. Xie, IEEE Open Access Journal of Power and Energy, vol. 8, pp. 68-76, 2021
- “Safe Reinforcement Learning-Based Resilient Proactive Scheduling for a Commercial Building Considering Correlated Demand Response”
Zheming Liang, Can Huang, Wencong Su, Nan Duan, Vaibhav Donde, Bin Wang, and Xianbo Zhao, March 2021
- “Fully Decentralized Reinforcement Learning-Based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power”
R. El Helou, D. Kalathil and L. Xie, vol. 8, pp. 175-185, 2021
- “Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response”
Xiao Kou, Yan Du, Fangxing Li, Hector Pulgar-Painemal, Helia Zandi, Jin Dong, and Mohammed M. Olama, vol. 8, pp. 186-197, 2021
- “Novel Data-Driven Distributed Learning Framework for Solving AC Power Flow for Large Interconnected Systems”
Bharat Vyakaranam, Kaveri Mahapatra, Xinya Li, Heng Wang, Pavel Etingov, Zhangshuan Hou, Quan Nguyen, Tony Nguyen, Nader Samaan, Marcelo Elizondo, and Todd Hay, vol. 8, pp. 281-292, 2021
- “A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations”
Yexiang Chen, Subhash Lakshminarayana, Carsten Maple, and H. Vincent Poor, vol. 9, pp. 109-120, 2022
- “Human Mobility-Based Features to Analyze the Impact of COVID-19 on Power System Operation of Ireland”
Negin Zarbakhsh, M. Saeed Misaghian, and Gavin McArdle, vol. 9, pp. 213-225, 2022
- “Decomposition-Residuals Neural Networks: Hybrid System Identification Applied to Electricity Demand Forecasting”
Konstantinos Theodorakos, Oscar Mauricio Agudelo, Marcelo Espinoza, and Bart De Moor, vol. 9, pp. 241-253, 2022
- “Application of Big Data Analytics and Machine Learning to Large-Scale Synchrophasor Datasets: Evaluation of Dataset ‘Machine Learning-Readiness'”
Philip Hart, Lijun He, Tianyi Wang, Vijay S. Kumar, Kareem Aggour, Arun Subramanian, and Weizhong Yan, vol. 9, pp. 386-397, 2022
- “Deep Reinforcement Learning-Based Robust Protection in DER-Rich Distribution Grids”
Dongqi Wu, Dileep Kalathil, Miroslav Begovic, Kevin Q. Ding, Le Xie, 24 March 2022
- “Federated Learning for Short-term Residential Load Forecasting”
Christopher Briggs; Zhong Fan; Peter Andras, 12 September 2022
IEEE PES Electrification Magazine Articles:
“Data and Cyberphysical Systems”
IEEE Electrification Magazine, vol. 9, no. 1, March 2021
IEEE Power & Energy Magazine:
“Deep In Thought”
Tao Hong, Arkadiusz Jedrzejewski, Spyros Chatzivasileiadis, Yan Du, Andrés M. Alonso, Lingling Fan, vol. 20, no. 3, May 2022.
- Spanish edition of Deep In Thought
IEEE PES Transactions Articles:
IEEE Transactions on Power Systems
“Explicit Data-Driven Small-Signal Stability Constrained Optimal Power Flow”
J. Liu, Z. Yang, J. Zhao, J. Yu, B. Tan and W. Li, IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3726-3737, Sept. 2022.
“Cost-Oriented Prediction Intervals: On Bridging the Gap Between Forecasting and Decision”
C. Zhao, C. Wan and Y. Song, IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 3048-3062, July 2022.
“Spatial Network Decomposition for Fast and Scalable AC-OPF Learning”
M. Chatzos, T. W. K. Mak and P. V. Hentenryck, IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 2601-2612, July 2022.
“Semi-Supervised Ensemble Learning Framework for Accelerating Power System Transient Stability Knowledge Base Generation”
L. Zhu, D. J. Hill and C. Lu, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 2441-2454, May 2022.
“Data-Driven Joint Voltage Stability Assessment Considering Load Uncertainty: A Variational Bayes Inference Integrated With Multi-CNNs”
M. Cui, F. Li, H. Cui, S. Bu and D. Shi, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 1904-1915, May 2022.
“Topology Identification of Distribution Networks Using a Split-EM Based Data-Driven Approach”
L. Ma, L. Wang and Z. Liu, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 2019-2031, May 2022.
“A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems”
S. Li, T. Ding, W. Jia, C. Huang, J. P. S. Catalão and F. Li, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 2259-2270, May 2022.
“Deep Learning Based Model-Free Robust Load Restoration to Enhance Bulk System Resilience With Wind Power Penetration””
J. Zhao, F. Li, X. Chen and Q. Wu, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 1969-1978, May 2022.
“Estimating Demand Flexibility Using Siamese LSTM Neural Networks”
G. Ruan, D. S. Kirschen, H. Zhong, Q. Xia and C. Kang, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 2360-2370, May 2022.
“Support Matrix Regression for Learning Power Flow in Distribution Grid With Unobservability”
J. Yuan and Y. Weng, IEEE Transactions on Power Systems, vol. 37, no. 2, pp. 1151-1161, March 2022.
“Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach”
T. Zhao, J. Wang, X. Lu and Y. Du, IEEE Transactions on Power Systems, vol. 37, no. 2, pp. 955-966, March 2022.
“Machine Learning-Driven Virtual Bidding With Electricity Market Efficiency Analysis”
Y. Li, N. Yu and W. Wang, IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 354-364, Jan. 2022.
IEEE Transactions on Smart Grid
“A Stacked Machine and Deep Learning-Based Approach for Analysing Electricity Theft in Smart Grids”
I. U. Khan, N. Javeid, C. J. Taylor, K. A. A. Gamage and X. Ma, IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1633-1644, March 2022.
“The Impact of PMU Data Precision and Accuracy on Event Classification in Distribution Systems”
F. L. Grando, A. E. Lazzaretti and M. Moreto, IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1372-1382, March 2022.
“Automated Event Region Identification and Its Data-Driven Applications in Behind-the-Meter Solar Farms Based on Micro-PMU Measurements”
P. Khaledian and H. Mohsenian-Rad, IEEE Transactions on Smart Grid, vol. 13, no. 3, pp. 2094-2106, May 2022.
“Grid-Forming Inverter Enabled Virtual Power Plants With Inertia Support Capability”
Q. Hu et al., IEEE Transactions on Smart Grid, vol. 13, no. 5, pp. 4134-4143, Sept. 2022.
“Detection and Localization of PMU Time Synchronization Attacks via Graph Signal Processing”
E. Shereen, R. Ramakrishna and G. Dán, IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 3241-3254, July 2022.
“Deep Reinforcement Learning-Based Model-Free On-Line Dynamic Multi-Microgrid Formation to Enhance Resilience”
J. Zhao, F. Li, S. Mukherjee and C. Sticht, IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 2557-2567, July 2022.
“A Neural Lyapunov Approach to Transient Stability Assessment of Power Electronics-Interfaced Networked Microgrids”
T. Huang, S. Gao and L. Xie, IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 106-118, Jan. 2022.
“Multifractal Characterization of Distribution Synchrophasors for Cybersecurity Defense of Smart Grids”
Y. Cui et al., IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1658-1661, March 2022.
“Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids Using Graph Neural Networks”
O. Boyaci, M. R. Narimani, K. R. Davis, M. Ismail, T. J. Overbye and E. Serpedin, IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 807-819, Jan. 2022.
“Reduced Order Model of Transactive Bidding Loads”
B. Liu, M. Akcakaya and T. E. McDermott, IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 667-677, Jan. 2022.
“Profile SR-GAN: A GAN Based Super-Resolution Method for Generating High-Resolution Load Profiles”
L. Song, Y. Li and N. Lu, IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 3278-3289, July 2022.
“A Novel Data-Driven Method for Behind-the-Meter Solar Generation Disaggregation With Cross-Iteration Refinement”
K. Pan et al., IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 354-364, Jan. 2022.
IEEE Transactions on Sustainable Energy
“Missing-Data Tolerant Hybrid Learning Method for Solar Power Forecasting”
W. Liu, C. Ren and Y. Xu, IEEE Transactions on Sustainable Energy, vol. 13, no. 3, pp. 1843-1852, July 2022.
“DAFT-E: Feature-Based Multivariate and Multi-Step-Ahead Wind Power Forecasting”
F. De Caro, J. De Stefani, A. Vaccaro and G. Bontempi, IEEE Transactions on Sustainable Energy, vol. 13, no. 2, pp. 1199-1209, April 2022
“An Improved Mixture Density Network Via Wasserstein Distance Based Adversarial Learning for Probabilistic Wind Speed Predictions”
L. Yang, Z. Zheng and Z. Zhang, IEEE Transactions on Sustainable Energy, vol. 13, no. 2, pp. 755-766, April 2022.
“Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting”
J. Simeunović, B. Schubnel, P. -J. Alet and R. E. Carrillo, IEEE Transactions on Sustainable Energy, vol. 13, no. 2, pp. 1210-1220, April 2022.
“Identification of Important Locational, Physical and Economic Dimensions in Power System Transient Stability Margin Estimation”
R. I. Hamilton, P. N. Papadopoulos, W. Bukhsh and K. Bell, IEEE Transactions on Sustainable Energy, vol. 13, no. 2, pp. 1135-1146, April 2022.
“Maximum Power Tracking for a Wind Energy Conversion System Using Cascade-Forward Neural Networks”
M. Alzayed, H. Chaoui and Y. Farajpour, IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2367-2377, Oct. 2021.
“Multi-Source and Temporal Attention Network for Probabilistic Wind Power Prediction”
H. Zhang, J. Yan, Y. Liu, Y. Gao, S. Han and L. Li, IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2205-2218, Oct. 2021.
“A Combination Interval Prediction Model Based on Biased Convex Cost Function and Auto-Encoder in Solar Power Prediction”
H. Long, C. Zhang, R. Geng, Z. Wu and W. Gu, IEEE Transactions on Sustainable Energy, vol. 12, no. 3, pp. 1561-1570, July 2021.
“Deep Concatenated Residual Network With Bidirectional LSTM for One-Hour-Ahead Wind Power Forecasting”
M. -S. Ko, K. Lee, J. -K. Kim, C. W. Hong, Z. Y. Dong and K. Hur, IEEE Transactions on Sustainable Energy, vol. 12, no. 2, pp. 1321-1335, April 2021.
“SolarGAN: Multivariate Solar Data Imputation Using Generative Adversarial Network”
W. Zhang, Y. Luo, Y. Zhang and D. Srinivasan., IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 743-746, Jan. 2021.
“Operating a Commercial Building HVAC Load as a Virtual Battery Through Airflow Control”
J. Wang, S. Huang, D. Wu and N. Lu, IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 354-364, Jan. 2022.
“A Hybrid Ensemble Model for Interval Prediction of Solar Power Output in Ship Onboard Power Systems”
S. Wen, C. Zhang, H. Lan, Y. Xu, Y. Tang and Y. Huang, IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 14-24, Jan. 2021.
Physics-aware and Risk-aware Machine Learning for Power System Operations
IEEE-PES Webinar: Feb 2022
Speakers: Hao Zhu
Recent years have witnessed rapid transformations of contemporary advances in machine learning (ML) and data science to aid the transition of energy systems into a truly sustainable, resilient, and distributed infrastructure. A blind application of the latest-and-greatest ML algorithms to solve stylized grid operation problems, however, may fail to recognize the underlying physics models or safety constraint requirements. This talk will introduce three examples of bridging physics- and risk-aware ML advances into efficient and reliable grid operations. First, we develop a topology-aware approach using graph neural networks (GNNs) to predict the price and line congestion as the outputs of real-time optimal power flow problem. Building upon the underlying relation between prices and topology, this proposed solution significantly reduces the model complexity of existing end-to-end ML methods while efficiently adapting to varying grid topology. Second, we put forth a risk-aware ML method to ensure the safety guarantees of data-driven, scalable reactive power dispatch policies in distribution grids. The resultant policies can directly account for the statistical risks on prediction error to attain guaranteed voltage violation performance. Last, we consider a reinforcement learning framework for managing a large number of dynamical, flexible energy resources such as electrical vehicles, and demonstrate the need to simplify the system representation through physics-aware state/action aggregation.
Deep Learning for Power System Operation and Planning
IEEE-PES Webinar: July 2021
Speaker: Fangxing Li, University of Tennessee Knoxville, Di Shi, AINERGY LLC
Deep Learning (DL) and Artificial Intelligence (AI) is the emerging technology for realizing the next generation smart grid. In recent years, significant efforts have been devoted to exploring the potentials of DL and AI for solving the complex power system problems, from generations all the way down to the demand side. In this panel, the focus will be given to the application of DL in broad areas of power system operation and planning. Experts from academia and industry will share their original ideas and insights to this challenging and inspiring topic.