As electric vehicles (EVs) surge toward the mainstream, utilities face unprecedented challenges in managing growing grid demand and integrating EV charging loads efficiently. The rapid adoption of EVs risks overloading local distribution assets and is driving up infrastructure costs, especially where it remains unmanaged. Utilities urgently need accurate, granular data on EV ownership and charging behaviors to inform planning, prevent transformer overloading, and unlock new opportunities for load flexibility and virtual power plants.
Artificial intelligence (AI) and machine learning (ML) are emerging as game-changing tools for utilities navigating this transformation. By leveraging advanced metering infrastructure (AMI) data and deep learning techniques, utilities can disaggregate EV charging loads with high accuracy, identify EV-driving customers more effectively than through traditional surveys, and design targeted managed charging programs.
Download SEPA’s latest report to learn how AI-driven insights are enabling utilities to engage customers with personalized recommendations, optimize load shifting, and map transportation electrification trends directly to grid assets for more adaptive grid management and customer service.
What You’ll Learn:
- How AI and ML can detect and characterize EV charging loads from AMI data, improving grid situational awareness and planning.
- Best practices for engaging EV-driving customers and recruiting them into managed charging and time-of-use (TOU) programs.
- Real-world case studies showcasing how utilities like Hydro One and NV Energy are using AI to accelerate EV adoption and grid integration.
- The importance of granular, data-driven insights for addressing local variability in charging patterns and optimizing grid upgrades.
- How utilities can turn the challenge of EV load growth into opportunities for load flexibility, cost savings, and enhanced customer partnerships.
As electric vehicles (EVs) surge toward the mainstream, utilities face unprecedented challenges in managing growing grid demand and integrating EV charging loads efficiently. The rapid adoption of EVs risks overloading local distribution assets and is driving up infrastructure costs, especially where it remains unmanaged. Utilities urgently need accurate, granular data on EV ownership and charging behaviors to inform planning, prevent transformer overloading, and unlock new opportunities for load flexibility and virtual power plants.
Artificial intelligence (AI) and machine learning (ML) are emerging as game-changing tools for utilities navigating this transformation. By leveraging advanced metering infrastructure (AMI) data and deep learning techniques, utilities can disaggregate EV charging loads with high accuracy, identify EV-driving customers more effectively than through traditional surveys, and design targeted managed charging programs.
Download SEPA’s latest report to learn how AI-driven insights are enabling utilities to engage customers with personalized recommendations, optimize load shifting, and map transportation electrification trends directly to grid assets for more adaptive grid management and customer service.
What You’ll Learn:
- How AI and ML can detect and characterize EV charging loads from AMI data, improving grid situational awareness and planning.
- Best practices for engaging EV-driving customers and recruiting them into managed charging and time-of-use (TOU) programs.
- Real-world case studies showcasing how utilities like Hydro One and NV Energy are using AI to accelerate EV adoption and grid integration.
- The importance of granular, data-driven insights for addressing local variability in charging patterns and optimizing grid upgrades.
- How utilities can turn the challenge of EV load growth into opportunities for load flexibility, cost savings, and enhanced customer partnerships.
