Scaling V2G: From Complexity to Real-World Deployment

Tuesday, November 17, 2026 at 11:00 AM to 12:00 PM · 1 hr. (America/Los_Angeles)
N261, North Hall, Level 2

Information

The scale of Vehicle-to-Grid (V2G) is inevitable, but the complexity is the hurdle. By 2035, the total battery capacity of EVs in the U.S. is projected to equal the coincident peak load of the entire national grid, roughly 700GW. This represents the most significant flexible load resource in history, yet unlocking it remains the most complex integration challenge modern energy technology has ever faced. This session will bridge the gap between the potential of V2G and the complexity of its execution. Panelists will move past the hype to examine the critical requirements for a scalable V2G ecosystem, from the evolving ISO 15118 standards to the reality of end-to-end interoperability. Attendees will gain a roadmap for navigating the technical, policy, and testing hurdles required to turn parked cars into reliable, grid-tied power. Key takeaways will include considerations for customer consent and data privacy, incentive structures, and system reliability. *Open to Full Conference passholders only.
Full Conference Pass Access
Full Conference EducationMonday Education
Education Tracks
Grid Integration & Load Growth
Pathways/Topics
Transmission & DistributionUtility Pathway
Technologies
Electric VehiclesEnergy Storage
Target Audience
Business DevelopmentPolicy Adviser/AnalystUtility
Learning Level
101 - Foundational
Learning Objective 1
Identify the core components of an end-to-end V2G system and the current state of standardization (such as ISO 15118-20) required for hardware-software interoperability.
SEPA Events and Tracks
Grid Modernization & Flexibility
Learning Objective 2
Formulate strategies to align grid services with consumer behavior so that V2G participation does not compromise vehicle reliability or user experience.
Learning Objective 3
Evaluate the technical, policy, and testing hurdles to scaling V2G, including customer consent and data privacy, incentive structures, and system reliability.
Presentation ID
3450749

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