Power Dispatch

Power dispatch optimizes the allocation of electricity generation and distribution to meet demand while ensuring grid stability and reliability. Current research focuses on improving dispatch strategies using machine learning, particularly graph neural networks and reinforcement learning, to handle the increasing complexity and uncertainty introduced by renewable energy sources and evolving grid topologies. These advancements aim to enhance grid efficiency, reduce operational costs, and improve the integration of renewable energy, impacting both the scientific understanding of power systems and the practical operation of electricity grids. Benchmarking efforts are also underway to rigorously evaluate the performance of these new machine learning-based approaches.

Papers

August 23, 2024

Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation
Zhaoyang Qu, Zhenming Zhang, Nan Qu, Yuguang Zhou, Yang Li, Tao Jiang, Min Li, Chao Long
Process Extraction Image Encoder Aggregation Network Dynamic Scenario Energy Flexibility Power Dispatch

July 7, 2024

ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models
Xiyuan Zhou, Huan Zhao, Yuheng Cheng, Yuji Cao, Gaoqi Liang, Guolong Liu, Wenxuan Liu, Yan Xu, Junhua Zhao
Evaluation Benchmark Electricity Market Power Dispatch

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

January 19, 2024

Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning
Bairong Deng, Tao Yu, Zhenning Pan, Xuehan Zhang, Yufeng Wu, Qiaoyi Ding
Strong Generalization Adaptation Concern Contextual Markov Decision Process Power Dispatch

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 8, 2023

Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mapping
Meiyi Li, Javad Mohammadi
Optimization Process Neural Mapping Power Dispatch Large Scale Decision

October 29, 2023

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households
Gargya Gokhale, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya, Bert Claessens, Chris Develder
Power Grid Cluster Structure Energy Management Building Model Residential Household Power Dispatch Real World Implementation

February 4, 2022

Learning a Discrete Set of Optimal Allocation Rules in a Queueing System with Unknown Service Rate
Saghar Adler, Mehrdad Moharrami, Vijay Subramanian
Regret Bound Discrete Space Finite Time Regret Efficient Allocation Certainty Equivalent Power Dispatch Contextual Stochastic Block Model

Power Dispatch

Papers

Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation

ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models

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

Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning

Knowledge Graph Construction in Power Distribution Networks

Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mapping

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households

Learning a Discrete Set of Optimal Allocation Rules in a Queueing System with Unknown Service Rate