Abstract
Wildfires pose a significant threat to urban regions, with cities like Los Angeles facing increasing challenges due to their vulnerability to frequent and severe wildfire events. This study proposes a novel framework for optimizing fire rescue vehicle scheduling and energy system operations during wildfire disasters. By integrating predictive wildfire modeling with microgrid-based energy systems, the framework dynamically allocates energy resources to critical demands such as emergency shelters, hospitals, and rescue operations when grid supply is disrupted. The wildfire model simulates fire growth, wind-driven spread, and infrastructure impact, ensuring that the framework adapts to real-time conditions. A case study focusing on Los Angeles demonstrates the practical application of the proposed methodology, showcasing improved emergency response, minimized infrastructure losses, and enhanced operational efficiency during wildfires. This research highlights the importance of combining energy systems and disaster management strategies to build resilience in wildfire-prone urban areas, offering valuable insights for emergency planners and policymakers.
| Original language | English |
|---|---|
| Article number | 20813 |
| Pages (from-to) | 1-20 |
| Number of pages | 20 |
| Journal | Scientific Reports |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jul 2025 |
Keywords
- Distributionally Robust Optimization
- Emergency rescue optimization
- Energy system resilience
- Fire modeling
- Los Angeles real-life applications
- Microgrids
- Wildfires
ASJC Scopus subject areas
- General