Managing Extreme Wildfire Risk in California
Published:
Project Overview
This interdisciplinary research project addresses the critical challenge of extreme wildfire risk in California, developing innovative approaches for risk assessment, mitigation strategies, and power system decision-making framework.
Research Focus Areas
Wildfire Risk Modeling
- Advanced machine learning and AI techniques for wildfire prediction
- Integration of meteorological, topographical, and vegetation data
- Real-time risk assessment and deicision-making framework
Power System Resilience
- Optimization of Public Safety Power Shutoffs (PSPS)
- Grid hardening and infrastructure resilience strategies
- Distributed energy resource integration for enhanced reliability
Technical Innovation
- Data-Driven Approaches: Integration of weather data (both measured and simulated), and utility infrastructure information
- Optimization Algorithms: Advanced optimization and AI/ML models for decision support
- Stakeholder Engagement: Collaborative approach with utilities, agencies, and communities
Collaborative Network
- Academic Partners: UC Berkeley, UC Santa Barbara, Lawrence Livermore National Laboratory
- Research Institutions: Lawrence Berkeley National Laboratory
Funding Details
- Funding Agency: University of California Office of the President (UCOP)
- Award Amount: $500K over 3 years (LBNL portion)
- Co-Principal Investigators: Multi-million multi-campus collaboration across UC system
- Project Period: 2019-2024
Request a Demo
Interested in learning more about our wildfire risk management and power system resilience tools? Request a demo by contacting Dr. Bin Wang at wangbin@lbl.gov.