Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk
This addresses the problem of rapidly solving computationally challenging OPS problems for utilities to manage wildfire risks, though it is incremental as it extends existing ML-guided methods.
The paper tackles the Optimal Power Shutoff (OPS) problem, which optimizes power line de-energization to mitigate wildfire ignition risks while reducing load shedding, by developing a machine learning-guided framework that produces high-quality solutions faster than traditional methods on a large-scale realistic test system.
To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes line energization statuses to manage wildfire ignition risks through de-energizations while reducing load shedding. OPS problems are computationally challenging Mixed-Integer Linear Programs (MILPs) that must be solved rapidly and frequently in operational settings. For a particular power system, OPS instances share a common structure with varying parameters related to wildfire risks, loads, and renewable generation. This motivates the use of Machine Learning (ML) for solving OPS problems by exploiting shared patterns across instances. In this paper, we develop an ML-guided framework that quickly produces high-quality de-energization decisions by extending existing ML-guided MILP solution methods while integrating domain knowledge on the number of energized and de-energized lines. Results on a large-scale realistic California-based synthetic test system show that the proposed ML-guided method produces high-quality solutions faster than traditional optimization methods.