Open AccessThe L S B m a x algorithm for boosting resilience of electric grids post (N‐2) contingencies
Author(s) -
Hussain Tanveer,
Alam S M Shafiul,
Hansen Timothy M.,
Suryanarayanan Siddharth
Publication year - 2021
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/tje2.12081
Subject(s) - boosting (machine learning) , speedup , computer science , scalability , algorithm , computation , parallel computing , network topology , supercomputer , artificial intelligence , computer network , database
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 (LSBm a x ). The LSBm a xalgorithm 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 LSBm a xalgorithm 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.