Enhancing Power System Operational Resilience Against Wildfires

Abstract

Catastrophic impacts of wildfires on the performance of power grids have increased in the recent years. Though various methods have been applied to enhance power grid resilience against severe weather events, only a few have focused on wildfires. Most previous operational-based resilience enhancement methods have focused on corrective or restorative strategies during and after extreme events without proactively preparing the system for forecasted potential failures. Also, the propagation behavior of wildfires among system components induces further complexities resulting in a mathematically involved problem accompanied with many modeling challenges. During sequential failures, operators need to make decisions in a fast-paced manner to maintain reliable operation and avoid cascading failures and blackouts. Thereby, the complexity of decision processes increases dramatically during extreme weather events. This article proposes a probabilistic proactive generation redispatch strategy to enhance the operational resilience of power grids during wildfires. A Markov decision process is used to model system state transitions and to provide generation redispatch strategies for each possible system state given component failure probabilities, wildfire spatiotemporal properties, and load variation. For a realistic system representation, dynamic system constraints are considered including ramping rates and minimum up/down times of generating units, load demand profile, and transmission constraints. The IBM ILOG CPLEX optimization studio is utilized to solve the optimization problem. The IEEE 30-bus system is used to validate the proposed strategy under various impact scenarios. The results demonstrate the effectiveness of the proposed method in enhancing the resilience level of power grids during wildfires.