Power Network

Power network research focuses on optimizing the efficiency, reliability, and security of electricity grids, addressing challenges posed by increasing renewable energy integration and evolving demand patterns. Current research emphasizes the application of machine learning, particularly deep reinforcement learning and graph neural networks, to improve load forecasting, voltage control, and topology optimization, often incorporating techniques like transformer networks and proximal policy optimization for enhanced performance and interpretability. These advancements aim to enable more robust, efficient, and secure grid operation, impacting both the scientific understanding of complex power systems and the practical management of electricity distribution.

Papers

November 17, 2024

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents
Benjamin M. Peter, Mert Korkali
Reinforcement Learning Proximal Policy Optimization Reinforcement Learning Agent Power Network Hybrid Policy

November 9, 2024

Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting
Thuan Phamh, Xingpeng Li
Graph Neural Network Participation Constraint Optimal Power Flow Virtual Network Power Network Parameter Reduction

September 9, 2024

Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks
Maurizio Vassallo, Amina Benzerga, Alireza Bahmanyar, Damien Ernst
Reinforcement Learning Distribution System Photovoltaic System Power Network

July 30, 2024

July 2, 2024

Hierarchical Decoupling Capacitor Optimization for Power Distribution Network of 2.5D ICs with Co-Analysis of Frequency and Time Domains Based on Deep Reinforcement Learning
Yuanyuan Duan, Haiyang Feng, Zhiping Yu, Hanming Wu, Leilai Shao, Xiaolei Zhu
Deep Reinforcement Learning Power Network Temporal Domain

June 25, 2024

Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks
Feiyang Xu, Shunyu Liu, Yunpeng Qing, Yihe Zhou, Yuwen Wang, Mingli Song
Multi Agent Reinforcement Learning Transformer Network Power Network Voltage Control

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

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

January 31, 2024

Deterministic Computing Power Networking: Architecture, Technologies and Prospects
Qingmin Jia, Yujiao Hu, Xiaomao Zhou, Qianpiao Ma, Kai Guo, Huayu Zhang, Renchao Xie, Tao Huang, Yunjie Liu
Architecture Design Visionary ProSpect Technology Information Data Power Network Transmission Determinacy Network Paradigm

November 15, 2023

Knowledge Graph Construction in Power Distribution Networks
Xiang Li, Che Wang, Bing Li, Hao Chen, Sizhe Li
Knowledge Graph Knowledge Graph Construction Power Network Power Dispatch

November 6, 2023

PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks
Nan Lin, Stavros Orfanoudakis, Nathan Ordonez Cardenas, Juan S. Giraldo, Pedro P. Vergara
Power Grid Power System Message Passing Graph Neural Network Power Flow Power Network

September 22, 2023

DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks
Heng Liang, Changhong Zhao
Optimal Power Flow Power Network Multiple Network Unified Deep Neural Network

April 3, 2023

Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents
Malte Lehna, Jan Viebahn, Christoph Scholz, Antoine Marot, Sven Tomforde
Reinforcement Learning Comparative Study Agent Smith Action Space Power Grid Positive Reinforcement Power System Power Network

December 23, 2022

Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow
Ángela López-Cardona, Guillermo Bernárdez, Pere Barlet-Ros, Albert Cabellos-Aparicio
Graph Neural Network Proximal Policy Optimization Power Grid Optimal Power Flow Power Network

December 5, 2022

PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks
Anandsingh Chauhan, Mayank Baranwal, Ansuma Basumatary
Reinforcement Learning High Efficiency Power Grid Power Network Reinforcement Learning Framework

November 26, 2022

Quantitative Method for Security Situation of the Power Information Network Based on the Evolutionary Neural Network
Quande Yuan, Yuzhen Pi, Lei Kou, Fangfang Zhang, Bo Ye
Neural Network Quantitative Analysis Power Network Security Implication

June 8, 2022

Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer
Minrui Wang, Mingxiao Feng, Wengang Zhou, Houqiang Li
Transformer Based Multi Agent Reinforcement Learning Transformer Based Model Multi Agent Actor Critic Power Network Voltage Control

March 29, 2022

Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)
Hyunwook Park, Minsu Kim, Seongguk Kim, Keunwoo Kim, Haeyeon Kim, Taein Shin, Keeyoung Son, Boogyo Sim, Subin Kim, Seungtaek Jeong, Chulsoon Hwang, Joungho Kim
Reinforcement Learning Transformer Based Optimization Purpose Transformer Network Power Network High Bandwidth Memory Decap Optimization Policy

February 8, 2022

Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks
Alireza Sadeghi
Reinforcement Learning Native Robustness Robust Version Timely Communication Cyber Physical Renewable Generation Power Network Distributional Uncertainty

Power Network

Papers

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents

Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting

Fair Reinforcement Learning Algorithm for PV Active Control in LV Distribution Networks

Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow

Robust Load Prediction of Power Network Clusters Based on Cloud-Model-Improved Transformer

Hierarchical Decoupling Capacitor Optimization for Power Distribution Network of 2.5D ICs with Co-Analysis of Frequency and Time Domains Based on Deep Reinforcement Learning

Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks

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

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

Deterministic Computing Power Networking: Architecture, Technologies and Prospects

Knowledge Graph Construction in Power Distribution Networks

PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks

DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks

Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow

PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks

Quantitative Method for Security Situation of the Power Information Network Based on the Evolutionary Neural Network

Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer

Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)

Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks