  The design of new strategies that exploit methods from Machine Learning to
facilitate the resolution of challenging and large-scale mathematical
optimization problems has recently become an avenue of prolific and promising
research. In this paper, we propose a novel learning procedure to assist in the
solution of a well-known computationally difficult optimization problem in
power systems: The Direct Current Optimal Transmission Switching (DC-OTS)
problem. The DC-OTS problem consists in finding the configuration of the power
network that results in the cheapest dispatch of the power generating units.
With the increasing variability in the operating conditions of power grids, the
DC-OTS problem has lately sparked renewed interest, because operational
strategies that include topological network changes have proved to be effective
and efficient in helping maintain the balance between generation and demand.
The DC-OTS problem includes a set of binaries that determine the on/off status
of the switchable transmission lines. Therefore, it takes the form of a
mixed-integer program, which is NP-hard in general. In this paper, we propose
an approach to tackle the DC-OTS problem that leverages known solutions to past
instances of the problem to speed up the mixed-integer optimization of a new
unseen model. Although our approach does not offer optimality guarantees, a
series of numerical experiments run on a real-life power system dataset show
that it features a very high success rate in identifying the optimal grid
topology (especially when compared to alternative competing heuristics), while
rendering remarkable speed-up factors.
