The Sweet Spot of Bound Tightening for Topology Optimization

S. Pineda, J. M. Morales González

Topology optimization is a powerful tool to improve the flexibility and efficiency of power system operation, but it poses significant computational challenges due to its combinatorial structure and reliance on big-M formulations. Optimization-based bound tightening (OBBT) helps enhance MILP performance by refining variable bounds. However, in topology optimization, existing OBBT methods typically relax all switching variables in the bounding subproblems, resulting in overly loose feasible regions and limited improvements. This work introduces a topology-aware bound tightening approach that leverages network structure to selectively decide which switching variables to relax. Computational experiments on the IEEE 118-bus system show that maintaining a small subset of binary switching variables while relaxing the rest achieves a balance between computational cost and bound quality, significantly improving overall performance.

Palabras clave: Topology optimization, Optimal transmission switching, Big-M constants, Mixed-integer optimization, Optimization-based bound tightening.

SI Optimización y Aprendizaje Estadístico en Energía
3 de septiembre de 2026  09:00
Aula 28

M. Fischetti

J. González Díaz, I. Gómez-Casares, P. Belotti, B. Ghaddar

Á. M. González Rueda

I. Repiso López, S. Pineda Morente, J. M. Morales González