Resumen de Learning‑assisted optimization for transmission switching

Salvador Pineda Morente , Juan Miguel Morales Gonzalez , María Asunción Jiménez Cordero

• 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 prolifc and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difcult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in fnding the confguration 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 efective and efcient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/of status of the switchable transmission lines. Therefore, it takes the form of a mixedinteger 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 ofer 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.