Can digital twin technology revolutionize wildfire management and energy resilience in Los Angeles?

Abstract

Wildfires increasingly threaten energy and water infrastructure in regions such as Los Angeles (LA), requiring real-time, resilient coordination strategies. This study introduces an integrated framework that combines digital twin technology with distributionally robust optimization (DRO) to manage uncertainty and improve operational resilience. The system dynamically models wildfire spread using terrain and wind data, and jointly simulates energy and water systems under real-time disruption scenarios. Validation is performed using real-world data from the Palisades Fire, benchmarked against deterministic dispatch models without robust adaptation. Simulation results show that the DRO-based method reduces average PV efficiency loss by 22.4% through real-time reconfiguration and increases battery discharge support during evacuation surges by 18.7%. Compared to baseline strategies, the proposed framework shortens average critical load outage duration by 35%, improves firefighting water delivery reliability by 21.8%, and lowers total daily water consumption by 12.5 million gallons. Fire spread prediction achieves a 24-hour localization error below 310 meters, ensuring precise hazard mapping. These outcomes confirm the framework’s ability to enhance system resilience, minimize resource waste, and support post-disaster recovery. The presented approach offers a scalable and adaptive tool for next-generation wildfire response planning under complex, uncertain conditions.