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Dynamic graph learning: A solution for unit commitment problems with topology changes incorporating demand response

delete2026-07-13
delete0
PRE
AI
Y
Yujie Han
D
Dongru Han
夏
夏珉 (Min Xia) *
L
Liguo Weng
J
Jun Liu
DOI：10.1016/j.epsr.2026.113711delete
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摘要

摘要

En 中文
• 将随机单元维护编码到时序图结构中。• 提出DyGTCN以捕捉演变的时空网格模式。• 分层SC-DAM动态调整调度以确保可行性。• 实现高精度单元状态预测，同时保证计算效率。
Keyword:
Unit commitment
Topology change
Dynamic graph learning
Deep neural network

期刊

Electric Power Systems Research 封面图
Electric Power Systems Research
IF:
4.2
论文数:
1.2W
被引数:
2.2W

机构

C
china electric power research institute co., ltd
学者数:
7
论文数: 6
被引数: 0
N
Nanjing University of Information Science and Technology
学者数:
2.9K
论文数: 1.2K
被引数: 1.7W
引用论文

引用论文

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