A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow
Optimization and Control
2020-12-21 v1 Systems and Control
Systems and Control
Abstract
In this letter, we propose a data-driven warm start approach, empowered by artificial neural networks, 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, and optimality and feasibility of the solution can still be guaranteed.
Cite
@article{arxiv.2012.10125,
title = {A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow},
author = {Haizhou Liu and Lun Yang and Xinwei Shen and Qinglai Guo and Hongbin Sun and Mohammad Shahidehpour},
journal= {arXiv preprint arXiv:2012.10125},
year = {2020}
}
Comments
3 pages, 4 tables, submitted to IEEE PES Letter