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We present a computationally improved heuristic algorithm for transmission switching (TS) to recover load shed. Research from the past showed that changing power system topology may control power flows and remove line congestion. Hence, TS may reduce the required load shed. One of the main challenges is to find a potential TS candidate in a suitable time.Here, we propose a novel heuristic method that is capable of finding the potential TS candidate faster than existing algorithms in literature. The proposed method is compatible with both the AC and DC optimal power flows (OPF). Three metrics are used to compare the proposed algorithm with the state-of-the-art from literature to show the speedup and accuracy achieved. The proposed method is implemented on the IEEE 30-bus system, PEGASE 89-bus system, IEEE 118-bus system, and Polish 2383bus system. The results on the large-scale Polish 2383-bus system shows that the proposed algorithm is scalable to large real-world systems. Parallel computing is implemented to further improve the computational performance of the proposed algorithm.

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A computationally improved algorithm is presented to find the best transmission switching (TS) candidate for boosting resilience of electricity grids subject to (N‐2) contingencies. Here, resilience is computed as the reduction in load shed after the above‐mentioned (N‐2) contingencies. TS is a planned line outage, and past research shows that changing the transmission system's topology changes the power flow and removes post contingency violations. Finding the best TS candidate in a computationally suitable time for effectively boosting resilience is a challenge. The best TS candidate is found using a novel heuristic method by decreasing the search space based on proximity to the bus with the maximum load shedding (LSBmax). The LSBmax algorithm is faster than existing algorithms in the literature; and, it is compatible with both the AC and DC optimal power flow formulations. To validate the authors' claims of speedup and accuracy, two metrics are used to analyze the results from the IEEE 39‐bus and 118‐bus systems. Finally, the inherent parallelism of the LSBmax algorithm is leveraged on a high‐performance computing platform and applied to the large‐scale Polish 2383‐bus test system to validate scalability in both size and speedup in computation time.

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