RFP 241155 EPIC 4.34 AI Enhanced Urban and Wildland Urban Interface Fire Conflagration Risk Model
PG&E seeks vendors to evaluate wildfire risk data products with AI-enhanced modeling capabilities for urban conflagration and probabilistic wildfire analysis across its 70,000-square-mile service territory; RFP responses due July 10, 2026.
Important Dates
Deadline
37 days remaining
AI Overview
Pacific Gas and Electric Company (PG&E) is soliciting proposals for the evaluation of wildfire risk data products from 2-4 wildfire risk data vendors through EPIC 4.34 project funding. The project aims to identify quality wildfire risk products leveraging emerging technologies that can supplement PG&E's internal planning and operational wildfire risk models. PG&E specifically seeks vendors capable of modeling urban conflagration, including structure-to-structure fire spread, probabilistic wildfire modeling, uncertainty assessment, ember-driven spread, suppression effects, evacuation and egress impacts, and wildfire consequences.
The scope of work involves obtaining wildfire risk data from selected vendors to enable comprehensive evaluation, along with providing limited technical support for participation in technical interviews and response to questions during the evaluation process. Solutions must provide data or modeled outputs relevant to evaluating wildfire spread, behavior, risk, and/or consequences resulting from user-specified ignition locations. This work will be conducted within PG&E's service territory, which spans approximately 70,000 square miles in northern and central California from Eureka to Bakersfield.
Key dates include: Interest registration deadline of June 3, 2026 at 5:00 PM (PST), RFP tentative release date of June 15, 2026 at 8:00 AM (PST), and formal RFP response deadline of July 10, 2026 at 3:00 PM (PST). All suppliers and their employees, subcontractors, and sub-suppliers must adhere to PG&E's Supplier Code of Conduct. The project will be subject to California Public Utilities Commission (CPUC) oversight as an EPIC-funded initiative.
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RFP 241155