Recursive Restoration Refinement: A Fast Heuristic for Near-Optimal Restoration Prioritization in Power Systems

TL;DR

This paper introduces the Recursive Restoration Refinement heuristic, a fast method that produces near-optimal power system restoration plans, significantly outperforming existing heuristics in speed while maintaining high quality.

Contribution

It presents a novel heuristic that balances solution quality and computational efficiency, enabling practical application in large-scale power restoration planning.

Findings

Produces near-optimal plans up to 1,000 times faster than existing methods

Effective on test cases with up to 500 buses and 700 damaged components

Available as open-source in PowerModelsRestoration package

Abstract

The prioritization of restoration actions after large power system outages plays a key role in how quickly power can be restored. It has been shown that fast and intuitive heuristics for restoration prioritization most often result in low-quality restoration plans. Meanwhile, mathematical optimization tools that find high-quality restoration plans are too slow to be applied to restoration planning problems of practical interest. This work makes a significant step in closing this quality vs compute time gap by proposing the Recursive Restoration Refinement heuristic for power system restoration. This heuristic is shown to produce near-optimal restoration plans up to 1,000 times faster than other state-of-the-art solution methods on a range of test cases with up to 500 buses and 700 damaged components. The potential impact of this new heuristic is demonstrated by a preliminary analysis of the key features of high-quality restoration plans. The recursive restoration refinement algorithm and other methods explored in this work have been made available as part of the open-source software package, PowerModelsRestoration, to support ongoing research in power restoration algorithms.