Power Grid

Power grids are critical infrastructure facing increasing complexity due to the energy transition and the rise of renewable energy sources. Current research focuses on improving grid operation and resilience through advanced modeling and control techniques, employing graph neural networks, reinforcement learning, and large language models to optimize power flow, predict and mitigate cascading failures, and enhance cybersecurity. These efforts aim to improve grid stability, efficiency, and security, impacting both the scientific understanding of complex systems and the practical operation of power grids worldwide.

Papers

June 25, 2024

Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline
Mengshuo Jia, Zeyu Cui, Gabriela Hug
Simulation Study Case Relevance Power Grid Power System Unseen Tool Power Grid Simulator

June 24, 2024

Fault Detection for agents on power grid topology optimization: A Comprehensive analysis
Malte Lehna, Mohamed Hassouna, Dmitry Degtyar, Sven Tomforde, Christoph Scholz
Deep Reinforcement Learning Agent Smith Fault Detection Power Grid Comprehensive Analysis Power Network

June 19, 2024

Utility Pole Fire Risk Inspection from 2D Street-Side Images
Rajanie Prabha, Kopal Nihar
Power Grid Power System Reliability Wildfire Risk

June 13, 2024

Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
Christian Nauck, Anna Büttner, Sebastian Liemann, Frank Hellmann, Michael Lindner
Machine Learning Power Grid

June 12, 2024

Predicting Cascading Failures with a Hyperparametric Diffusion Model
Bin Xiang, Bogdan Cautis, Xiaokui Xiao, Olga Mula, Dusit Niyato, Laks V. S. Lakshmanan
Diffusion Model Power Grid Information Diffusion Contagion Effect Cascading Failure Failure Transition

June 11, 2024

OPFData: Large-scale datasets for AC optimal power flow with topological perturbations
Sean Lovett, Miha Zgubic, Sofia Liguori, Sephora Madjiheurem, Hamish Tomlinson, Sophie Elster, Chris Apps, Sims Witherspoon, Luis Piloto
Power Grid Optimal Power Flow Additive Perturbation Large Scale Datasets OPTN Data

May 29, 2024

Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems
Xiaotong Cheng, Ioannis Tsetis, Setareh Maghsudi
Convex Optimization Power Grid Renewable Energy Energy Sharing Dynamical Network

May 25, 2024

Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements
Roel Bouman, Linda Schmeitz, Luco Buise, Jacco Heres, Yuliya Shapovalova, Tom Heskes
Anomaly Detection Power Grid Unlabeled Anomaly Change Point Detection Load Prediction Power System Measurement False in Season Anomaly Suppression

May 22, 2024

Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids
Hanyu Zeng, Pengfei Zhou, Xin Lou, Zhen Wei Ng, David K. Y. Yau, Marianne Winslett
Real Power Unlabeled Data Power Grid Smart Grid Cyber Attack Self Supervised Learning Framework Cyber Attack Detection

May 18, 2024

A Dual Power Grid Cascading Failure Model for the Vulnerability Analysis
Tianxin Zhou, Xiang Li, Haibing Lu
Power Grid Power Line Vulnerability Analysis Cascading Failure

May 16, 2024

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur Barnes, Russell Bent
Power Grid Heterogeneous Graph Neural Network Geomagnetic Storm

May 12, 2024

Graph neural networks for power grid operational risk assessment under evolving grid topology
Yadong Zhang, Pranav M Karve, Sankaran Mahadevan
Neural Network Graph Drawing Power Grid Optimal Power Flow Operational Risk Power Dispatch

May 9, 2024

May 1, 2024

HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
Malte Lehna, Clara Holzhüter, Sven Tomforde, Christoph Scholz
Deep Reinforcement Learning Power Grid Power Network Grid Search Goal Node Agent Topology

April 30, 2024

Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
Yi Li, Renyou Xie, Chaojie Li, Yi Wang, Zhaoyang Dong
Neural Network Graph Neural Network Power Grid Electric Vehicle Cyber Attack Demand Forecasting Federated Graph Learning Electric Vehicle Charging

April 11, 2024

Anomaly Detection in Power Grids via Context-Agnostic Learning
SangWoo Park, Amritanshu Pandey
Anomaly Detection Context Learning Power Grid Real Time Anomaly Detection Power Quality

April 4, 2024

Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage
Alvaro Carbonero, Shaowen Mao, Mohamed Mehana
New Machine Power Grid Data Perspective Sustainable Energy Promising Long Term Storage Solution

March 31, 2024

An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids
Mehdi Jabbari Zideh, Mohammad Reza Khalghani, Sarika Khushalani Solanki
Power Grid Adversarial AutoEncoder Cyber Attack Detection Distribution Grid Historical Load Unbalanced Power Distribution Grid

March 27, 2024

On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency
Georgios Tzolopoulos, Christos Korgialas, Constantine Kotropoulos
Power Grid Classifier Fusion One v All Classifier Network Frequency

Power Grid

Papers

Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline

Fault Detection for agents on power grid topology optimization: A Comprehensive analysis

Utility Pole Fire Risk Inspection from 2D Street-Side Images

Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning

Predicting Cascading Failures with a Hyperparametric Diffusion Model

OPFData: Large-scale datasets for AC optimal power flow with topological perturbations

Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems

Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements

Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids

A Dual Power Grid Cascading Failure Model for the Vulnerability Analysis

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement

Graph neural networks for power grid operational risk assessment under evolving grid topology

Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach

Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks

Anomaly Detection in Power Grids via Context-Agnostic Learning

Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage

An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids

On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency