Scalable Iterative Algorithm for Solving Optimal Transmission Switching with De-energization

TL;DR

This paper introduces a scalable iterative heuristic for the Optimal Transmission Switching with De-energization problem, effectively managing system security and connectivity loss in power grids, outperforming traditional solvers in speed.

Contribution

It presents a novel mixed-integer linear programming formulation and a fast heuristic algorithm for OTSD, addressing a gap in operational decision-making tools.

Findings

Heuristic finds solutions 100-1000x faster than Gurobi.

Formulation captures loss of connectivity without extra binary variables.

State-of-the-art solvers struggle with OTSD, especially on large instances.

Abstract

Transmission System Operators routinely use transmission switching as a tool to manage congestion and ensure system security. Motivated by sub-transmission operations at RTE, this paper considers the Optimal Transmission Switching with De-energization (OTSD), which captures potential loss of connectivity (and therefore localized blackout) following loss of transmission elements. While directly relevant to real-life operations, this problem has received very little attention in the literature. The paper proposes a new mixed-integer linear programming formulation for OTSD that represents post-contingency loss of connectivity without requiring additional binary variables. This new formulation provides the foundation for a fast, iterative heuristic algorithm. Computational experiments confirms that state-of-the-art optimization solvers struggle to solve the extensive formulation of OTSD, often failing to find even trivial solutions within reasonable time. In contrast, numerical results demonstrate the efficiency of the proposed heuristic, which finds high-quality feasible solutions 100-1000x faster than using Gurobi.