“20th PSCC 2018 papers submission and review platform

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A Coordinate-Descent Algorithm for Tracking Solutions in Time-Varying Optimal Power Flows

Tracking the solution of a convex optimisation problem, whose instances
vary continuously over time, has attracted much interest in power
systems applications, recently.
We propose to use a low-rank coordinate descent algorithm for solving
time-varying semidefinite programming (SDP) relaxations of
transmission-constrained problems.
On the example of the alternating-current optimal power flows (ACOPF),
we bound the difference between the current iterate generated by our algorithm
and the optimiser of the SDP relaxation using the most recent data from above
by a function of the properties of the instance, rate of the changes to the instance over time,
and the number of floating-point operations that can be performed per second.


Jie Liu    
Lehigh University
United States

Jakub Marecek    
IBM Research

Andrea Simonetto    
IBM Research

Martin Takac    
Lehigh University
United States


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