Natural disasters have great destructive power and can potentially wipe out great lengths of power lines. A resilient grid can recover from adverse conditions and restore service quickly. Therefore, the present work proposes a novel methodology to reconfigure power grids through graph theory after an extreme event. The least-cost solution through a minimum spanning tree (MST) with a radial topology that connects all grid users is identified. To this end, the authors have developed an iterative minimum-path heuristic algorithm. The optimal location of transformers and maintenance holes in the grid is obtained with the modified Prim algorithm, and the Greedy algorithm complements the process. The span distance and capacity restrictions define the transformer’s number, where larger spans and capacities reduce the number of components in the grid. The performance of the procedure has been tested in the urban zone Quito Tenis of Ecuador, and the algorithm proved to be scalable. Grid reconfiguration is pushed through a powerful tool to model distribution systems such as CYMDIST, where the voltage drops were minor than 3.5%.
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