Randomized adaptive vehicle decomposition for large-scale power restoration

Authors:
Ben Simon;Carleton Coffrin;Pascal Van Hentenryck
Affiliations:
Brown University, Providence, RI;Brown University, Providence, RI;Optimization Research Group, NICTA & University of Melbourne, Australia
Venue:
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Year:
2012

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Abstract

This paper considers the joint repair and restoration of the electrical power system after significant disruptions caused by natural disasters. This problem is computationally challenging because, when the goal is to minimize the size of the blackout, it combines a routing and a power restoration component, both of which are difficult on their own. The joint repair/restoration problem has been successfully approached with a 3-stage decomposition, whose last step is a multiple-vehicle, pickup-and-delivery routing problem with precedence and capacity constraints whose goal is to minimize the sum of the delivery times (PDRPPCCDT). Experimental results have shown that the PDRPPCCDT is a bottleneck and this paper proposes a Randomized Adaptive Vehicle Decomposition (RAVD) to scale to very large power outages. The RAVD approach is shown to produce significant computational benefits and provide high-quality results for infrastructures with more than 24000 components and 1200 damaged items, giving rise to PDRPPCCDT with more than 2500 visits.