“20th PSCC 2018 papers submission and review platform

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Scalable Decomposition Methods for Preventive Security-Constrained Optimal Power Flow

In power system analysis, the alternating current optimal power flow (ACOPF) problem is modeled to minimize the total cost of generation while ensuring balance in the electrical network and addressing security considerations in which scenarios of partial network failure may occur. The ACOPF can be formulated as a nonconvex quadratically constrained quadratic optimization problem, which is well known to be challenging because of nonlinearity and nonconvexity. We present a decomposable reformulation of the ACOPF, in which variables are copied and linear consensus constraints are added. This decomposability is realized once the consensus constraints are relaxed, thus leading to a particular Lagrangian dual of the ACOPF. We apply a recently developed scalable dual solution approach to the ACOPF based on an augmented Lagrangian method (ALM) that integrates the proximal bundle method with the simplicial decomposition method (SDM) and a Gauss-Seidel method, called SDM-ALM, which is used to solve a primal, convexified characterization of the Lagrangian dual of the ACOPF. We provide computational results demonstrating the scalability of our dual solution approach.

Author(s):

Brian Dandurand    
Argonne National Laboratory
United States

Kibaek Kim    
Argonne National Laboratory
United States

 

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