A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow

被引：8

作者：

Liu, Haizhou ^{[1
]}

Yang, Lun ^{[1
]}

Shen, Xinwei ^{[1
]}

Guo, Qinglai ^{[2
]}

Sun, Hongbin ^{[2
]}

Shahidehpour, Mohammad ^{[3
]}

机构：

[1] Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Tsinghua Berkeley Shenzhen Inst, Shenzhen 518057, Peoples R China

[2] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

[3] IIT, Elect & Comp Engn Dept, Chicago, IL 60616 USA

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2021年 / 36卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Manganese; Pipelines; Compressors; Natural gas; Training; Steady-state; Mathematical model; Data-driven; convex relaxation; convex-concave procedure; optimal gas flow;

D O I：

10.1109/TPWRS.2021.3107201

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this letter, we propose a data-driven warm start approach, empowered by an artificial neural network, to boost the efficiency of convex relaxations in optimal gas flow. Case studies show that this approach significantly decreases the number of iterations for the convex-concave procedure algorithm, while optimality and feasibility of the solution can still be guaranteed. We also confirm the robustness of this algorithm, and show that this approach can be extended to the optimal dispatch of large-scale electricity-gas coupled integrated energy systems.

引用

页码：5948 / 5951

页数：4

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