A Fast and Scalable Transmission Switching Algorithm for Boosting Resilience of Electric Grids Impacted by Extreme Weather Events

Abstract

A fast and scalable heuristic transmission switching (TS) algorithm for boosting resilience by reducing the load shed in electricity networks impacted by extreme weather events (EWEs) is presented. Finding the best transmission line to switch within a suitable time is the main challenge for wider applicability of TS. Here, we propose an algorithm that: i) finds the optimal TS candidate faster than well-known algorithms; ii) is compatible with existing optimal power flow formulations; and iii) scales for larger, realistic systems. Proof-of-concept results on the IEEE 39-bus, IEEE 118-bus, and the large-scale Polish 2383-bus systems validate our claims. Further, a case study of TS for improving grid resilience in a real-life hurricane event (Harvey, 2017) using a synthetic version of Texas’ ERCOT electric grid is presented for practical application. Finally, a path forward to utilize our proposed TS algorithms for evolving EWEs is discussed.

Keywords

• Contingency analysis
• extreme weather events
• high-performance computing
• load shed reduction
• post contingency violations
• resilience