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 compu-
tationally difficult optimization problem in power systems: The Direct Current Opti-
mal 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 con-
ditions 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.
