# Optimal Transmission Switching: Improving Solver Performance Using Heuristics

arXiv: https://arxiv.org/abs/2106.12331 · HTML: https://mlsanity.com/paper/2106.12331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2021.

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning versus Optimization for Optimal Transmission Switching: A Comparative Study](https://mlsanity.com/paper/2607.10948/) (2026)

## Abstract (arXiv)

The optimal transmission switching problem (OTSP) is an established problem of changing a power grid's topology to obtain an improved operation by controlling the switching status of transmission lines. This problem was proven to be NP-hard. Proposed solution techniques based on mixed-integer formulations can guarantee globally optimal solutions but are potentially intractable in realistic power grids. Heuristics methods cannot guarantee global optimality but can provide tractable solution approaches. This paper proposes solving the OTSP using exact formulations alongside parallel heuristics that generate good candidate solutions to speed up conventional branch-and-bound algorithms. The innovative aspect of this work is a new asynchronous parallel algorithmic architecture. A solver instance solving the full OTSP formulation is run in parallel to another process that asynchronously generates solutions to be injected into the full OTSP solution procedure during run time. Our method is tested on 14 instances of the pglib-opf library: The largest problem consisting of 13659 buses and 20467 branches. Our results show a good performance for large problem instances, with consistent improvements over off-the-shelf solver performance. We find that the method scales well with an increase in parallel processors.