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

2020/12/18 by Liu, Haizhou, Yang, Lun, Shen, Xinwei +3
#FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2012.10125

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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