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arXiv · 2012.10125

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

Haizhou Liu·Lun Yang·Xinwei Shen·Qinglai Guo·Hongbin Sun·Mohammad Shahidehpour
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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.

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Haizhou Liu, Lun Yang, Xinwei Shen, Qinglai Guo, Hongbin Sun, Mohammad Shahidehpour. 2020-12-18. A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow. https://arxiv.org/abs/2012.10125

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