.png){kind=icon}
返回
A topology-guided high-quality solution learning framework for security-constraint unit commitment based on graph convolutional network
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijepes.2024.110322.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Security-constrained unit commitment (SCUC) is of great importance for the economic and reliable operation of the power system. The computational hardness of SCUC remains a significant issue in the power system and electricity market operations, especially with the rapid expansion of the power system, leading to increased challenges of obtaining a high-quality solution in a fast way. In this sense, this paper proposes a topology-guided high-quality solution learning framework based on graph convolutional network (GCN) and neighborhood search (NS). Firstly, a GCN-based method is presented to learn the potential patterns between commitments and graph data associated with bus feature and power grid topology. Secondly, an adaptive threshold-based method is designed to fix binary variables to achieve model reduction. Thirdly, a customized prediction-based NS is developed to restore the feasibility of the predicted commitment. Case studies with different scales verify the effectiveness and efficiency of the proposed framework for SCUC. Compared with other methods, it demonstrates the superiority of learning based on power grid graph data. In the end, it can be concluded that the feasibility and high-quality of the solution can be guaranteed while reducing most of the computation time.
Keyword:
Security-constrained unit commitment
Graph convolutional network
Neighborhood search
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
5
论文数:
1.1W
被引数:
3.1W
机构
暂无机构信息
引用论文
Recovery of salinity gradient energy in desalination plants by reverse electrodialysis.png)
DesalinationOAAI
DesalinationIF0
OAAI分享收藏
分享收藏NIR light‐propelled bullet‐shaped carbon hollow nanomotors with controllable shell thickness for the enhanced dye removalExplorationOAAI
ExplorationIF0
OAAI分享收藏分享收藏
查看更多内容