RT Journal Article
T1 Learning‑assisted optimization for transmission switching
A1 Pineda-Morente, Salvador
A1 Morales-González, Juan Miguel
A1 Jiménez-Cordero, María Asunción
K1 Aprendizaje automático (Inteligencia artificial)
K1 Optimización matemática
AB The design of new strategies that exploit methods from machine learning to facilitatethe resolution of challenging and large-scale mathematical optimization problemshas recently become an avenue of prolific and promising research. In this paper, wepropose 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 infinding the configuration of the power network that results in the cheapest dispatchof 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 provedto be effective and efficient in helping maintain the balance between generation anddemand. The DC-OTS problem includes a set of binaries that determine the on/offstatus 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 approachto tackle the DC-OTS problem that leverages known solutions to past instances ofthe problem to speed up the mixed-integer optimization of a new unseen model.Although our approach does not offer optimality guarantees, a series of numericalexperiments run on a real-life power system dataset show that it features a very highsuccess rate in identifying the optimal grid topology (especially when compared toalternative competing heuristics), while rendering remarkable speed-up factors.
PB Springer
YR 2024
FD 2024-04
LK https://hdl.handle.net/10630/31030
UL https://hdl.handle.net/10630/31030
LA eng
NO Pineda, S., Morales, J.M. & Jiménez-Cordero, A. Learning-assisted optimization for transmission switching.  Vol. 32, nº 1 TOP (2024). https://doi.org/10.1007/s11750-024-00672-0
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026