“20th PSCC 2018 papers submission and review platform

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Tight Piecewise Convex Relaxations for Global Optimization of Optimal Power Flow

Since the alternating current optimal power flow (ACOPF) problem was introduced in 1962, developing efficient solution algorithms for the problem has been an active field of research. In recent years, there has been increasing interest in convex relaxations-based solution approaches that are often tight in practice. Based on these approaches, we develop tight piecewise convex relaxations with convex-hull representations, an adaptive, multivariate partitioning algorithm with bound tightening that progressively improves these relaxations and, given sufficient time, converges to the globally optimal solution. We illustrate the strengths of our algorithm using benchmark ACOPF test cases from the literature. Computational results show that our novel algorithm reduces the best-known optimality gaps for some hard ACOPF cases.

Author(s):

Mowen Lu    
Clemson University
United States

Harsha Nagarajan    
Los Alamos National Laboratory
United States

Russell Bent    
Los Alamos National Laboratory
United States

Sandra D. Eksioglu    
Clemson University
United States

Scott J. Mason    
Clemson University
United States

 

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