Power Systems

Follow this topic Ask about this topic

Power systems research examines how to generate, move, and use electricity reliably as grids incorporate renewables, storage, and flexible loads. It combines physical models and optimization with machine learning, including graph neural networks for grid structure, physics-informed models for power flow, and reinforcement learning for control and dispatch. Current work also uses forecasting and data-driven methods for fault detection, cybersecurity, outage response, and coordination between grids and large computing loads. A central challenge is making these tools generalize across changing networks and operating conditions while preserving physical feasibility, safety, and dependable performance in real-time deployment.

Latest papers

232 papers20 papers in the last four weeks, down 39% on the four weeks before. 0.1% of all new papers.

Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs
May 12, 2026Jose E. Aguilar Escamilla, Lingdong Zhou, Xiangqi Zhu +1RL Benchmarks Reinforcement Learning
Department of Electrical Engineering and Computer Science, Oregon State University, USA
Multi-market value-stacking: Battery control for combined imbalance participation and non-uniform FCR bidding
May 12, 2026Celle Hendrickx, Fabio Pavirani, Chris DevelderPower Systems
Gent University - imec, IDLab · Ghent, Belgium
ECTO: Exogenous-Conditioned Temporal Operator for Ultra-Short-Term Wind Power Forecasting
May 12, 2026Cao Yuan, Junjun WangTime Series Forecasting Non-Stationary Time Series Forecasting
Wuhan Polytechnic University, Wuhan, 430048, Hubei, China
Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
May 11, 2026Shourya Bose, Helgi Hilmarsson, Dhruv SuriRL Fine-Tuning Power Systems
Pravah
TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma
May 10, 2026JC Wu, Norton Lee, Kai Siang ChenTime Series Classification Critical Slowing Down
TaiScience Research Group, affiliated with Fu Jen Catholic University, Taiwan · Center for Geometry and Physics, Institute for Basic Science (IBS), South Korea · Fu Jen Catholic University, Taiwan
A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting
May 9, 2026Runyao Yu, Julia Lin, Derek W. Bunn +6Electricity Price Forecasting Time Series Forecasting
Delft University of Technology · Austrian Institute of Technology · London Business School +2
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
May 9, 2026Pavan Manjunath, Thomas PruferPower Systems Time Series Forecasting
Independent Research Scholar, Germany
Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention
May 7, 2026Tianxin Zhou, Xiang Li, Haibing LuGraph Attention Networks Graph Representation Learning
Department of Computer Science & Engineering, Santa Clara University, Santa Clara, USA · Dept. of Information Systems & Analytics, Santa Clara University, Santa Clara, USA
WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
May 7, 2026Dhruv Suri, Helgi Hilmarsson, Shourya BosePrimal-Dual Optimization Graph Neural Networks
Pravah
OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
May 6, 2026Jae-Won Chung, Zhirui Liang, Yanyong Mao +3Demand Response Power Systems
University of Michigan
Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning
May 5, 2026Job Groeneveld, Miguel Muñoz, Jan Viebahn +1Multi-Objective Evolutionary Optimization Combinatorial Optimization
TenneT TSO B.V., Arnhem, The Netherlands · Artelys, Paris, France · Department of Mathematics, Vrije Universiteit Amsterdam, The Netherlands
MARS-DA: A Hierarchical Reinforcement Learning Framework for Risk-Aware Multi-Agent Bidding in Power Grids
May 4, 2026Jiayi Chen, Xuan Zhang, Guiling WangHierarchical RL Risk-Sensitive RL
Department of Computer Science, New Jersey Institute of Technology
From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design
May 4, 2026Noman Bashir, Rob Sherwood, Le Xie +1Power Systems
Massachusetts Institute of Technology, USA · SwitchDin, Australia · Harvard University, USA
LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
May 4, 2026Hongwei Jin, Keunju Song, Zeeshan Memon +5Neural Surrogate Modeling Benchmark Design
Mathematics and Computer Science Division, Argonne National Laboratory · Department of Electronic Engineering, Sogang University · Department of Computer Science, Emory University +1
Towards Systematic Generalization for Power Grid Optimization Problems
May 3, 2026Zeeshan Memon, Yijiang Li, Hongwei Jin +2Optimal Power Flow Constrained Optimization
Emory University Atlanta, GA, USA · Argonne National Laboratory Lemont, IL, USA
SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting
Apr 30, 2026Arnaud ZinflouTime Series Imputation Incomplete Data Imputation
Hydro-Québec Research Institute Canada
Fairness for distribution network operations and planning
Apr 30, 2026Pedro F. C. de Carvalho, Zijie Liu, Md Umar Hashmi +1Fair Resource Allocation Algorithmic Fairness
KU Leuven, division Electa in Leuven · & EnergyVille in Genk, Belgium
GradMAP: Gradient-Based Multi-Agent Proximal Learning for Grid-Edge Flexibility
Apr 27, 2026Yihong Zhou, Hongtai Zeng, Thomas MorstynMulti-Agent Control Constrained RL
Department of Engineering Science, University of Oxford
An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids
Apr 26, 2026Ahmad Mohammad Saber, Ahmed Saber Refae, Davor Svetinovic +4Power Systems
Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada · Electric Power Engineering Department, Cairo University, Giza, Egypt · Department of Electrical Engineering, Khalifa University, Abu Dhabi, UAE +1
Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
Apr 26, 2026Md Abubakkar, Sajib Debnath, Md. Uzzal MiaHybrid CNN-Transformer Architectures Time Series Forecasting
Dept. of Computer Science, Midwestern State University, Dallas, TX, USA · O&M Analytics, AES Clean Energy, The AES Corporation, Louisville, CO, USA · Dept. of Information and Communication Engineering, Pabna University of Science and Technology, Bangladesh
Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards
Apr 25, 2026Taha Hammadia, Lucas Rea, Ahmad Mohammad Saber +2LLM Jailbreak Attacks LLM Safety Evaluation
ECE Department, University of Toronto, Toronto, ON Canada · CIISE, Concordia University, Montréal, QC, Canada
A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment
Apr 23, 2026Muhy Eddin Za'ter, Anna Van Boven, Bri-Mathias Hodge +1Mixed-Integer Linear Programming Power Systems
University of Colorado Boulder Boulder, CO, USA
A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs
Apr 22, 2026Patrick Wilk, Ethan Cantor, Yikui Liu +1Multi-Agent Reinforcement Learning Power Systems
aDepartment Electrical and Computer Engineering, Rowan University, 201 Mullica Hill Rd, Glassboro, New Jersey, 08028, United States · bSchool of Electrical Engineering, Sichuan University, 24 South Section 1, 1st Ring Road, Chengdu, 610065, China
Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids
Apr 22, 2026Burak Karabulut, Carlo Manna, Chris DevelderAnomaly Localization Graph Neural Networks
Ghent University – imec · VITO
A Network-Aware Evaluation of Distributed Energy Resource Control in Smart Distribution Systems
Apr 21, 2026Houchao GanDistributed Optimization Feedback Control
Department of Computer Science City University of New York
A Generalized Synthetic Control Method for Baseline Estimation in Demand Response Services
Apr 20, 2026Jonas Sievers, Mardavij RoozbehaniSynthetic Control Counterfactual Prediction
Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Boston, MA 02139, USA
Predicting Power-System Dynamic Trajectories with Foundation Models
Apr 16, 2026Haoran Li, Lihao Mai, Chenhan Xiao +2Neural Surrogate Modeling Zero-Shot Time Series Forecasting
School of Electrical, Computer and Energy Engineering at Arizona State University, Tempe, AZ 85281 USA · MOVEJ Analytics, Dayton, OH 45324 USA
Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
Apr 16, 2026Jan Niklas Lettner, Hadeer El Ashhab, Veit Hagenmeyer +1Electricity Price Forecasting Time Series Forecasting
Karlsruhe Institute of Technology · Karlsruhe, Germany
Hierarchical Reinforcement Learning with Runtime Safety Shielding for Power Grid Operation
Apr 15, 2026Gitesh MalikSafe RL Power Systems
Department of Electrical Engineering, Delhi Technological University (DTU), India
Thermodynamic Liquid Manifold Networks: Physics-Bounded Deep Learning for Solar Forecasting in Autonomous Off-Grid Microgrids
Apr 13, 2026Mohammed Ezzaldin Babiker AbdullahSolar Power Forecasting Physics-Informed ML
Department of Electrical and Electronic Engineering, Faculty of Engineering Sciences Omdurman Islamic University, Omdurman, Sudan

May 12, 2026J/K move · Enter open · S save

Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs

Jose E. Aguilar Escamilla, Lingdong Zhou, Xiangqi Zhu, Huazheng Wang

Department of Electrical Engineering and Computer Science, Oregon State University, USA

RL Benchmarks Reinforcement Learning Demand Response Power Systems

Open paper AskPDF arXiv

Extreme weather and volatile wholesale electricity markets expose residential consumers to catastrophic financial risks, yet demand response at the distribution level remains an underutilized tool for grid flexibility and energy affordability. While a demand-response program can shield consumers by issuing financial credits during high-price periods, optimizing this sequential decision-making process presents a unique challenge for reinforcement learning despite the plentiful offline historical smart meter and wholesale pricing data available publicly. Offline historical data fails to capture the dynamic, interactive feedback loop between an electric utility's pricing signals and customer acceptance and adaptation to a demand-response program. To address this, we introduce DR-Gym, an open-source, online Gymnasium-compatible environment designed to train and evaluate demand-response from the electric utility's perspective. Unlike existing device-level energy simulators, our environment focuses on the market-level electric utility setting and provides a rich observational space relevant to the electric utility. The simulator additionally features a regime-switching wholesale price model calibrated to real-world extreme events, alongside physics-based building demand profiles. For our learning signal, we use a configurable, multi-objective reward function for specifying diverse learning objectives. We demonstrate through baseline strategies and data snapshots the capability of our simulator to create realistic and learnable environments.