Artificial intelligence insights address electric vehicle adoption challenges

Smart Electric Power Alliance (SEPA), in partnership with Bidgely, has released a new Insight Brief: AI for Transportation Electrification. SEPA’s insight brief, designed to educate utility stakeholders, highlights how artificial intelligence (AI) enables advanced grid impact analysis and facilitates better management of future electric vehicle (EV) demand. Case studies from leading utilities like Hydro One and NV Energy demonstrate how AI-powered tools enhance program evaluation and customer recruitment for load-shifting initiatives through EV detection and charging characterization.

“SEPA’s collaboration with Bidgely underscores the critical role of AI in addressing the complexities of transportation electrification,” says Sheri Givens, president and CEO of SEPA. “Together, we are providing utilities with practical strategies to advance EV adoption while ensuring grid resilience.”

With a focus on Bidgely’s disaggregation technology, SEPA outlines the key advantages utilities gain by disaggregating advanced meter infrastructure (AMI) data to gain insights into EVs and EV users in their service territories, including:

• Identify EV drivers more efficiently, as opposed to traditional self reports and surveys, for more accurate and personalized customer engagement.
• Access higher-quality EV charging characteristics, including differentiation of Level 1 and Level 2 chargers; hourly charging patterns (time of day, duration, and intensity); and amplitude of charges.
• Design better-targeted EV managed charging programs with the ability to measure variations in customer responses and improve future initiatives.
• Map transportation electrification trends to forecast EV load growth on individual grid assets and manage future infrastructure planning.

Detailing the innovative application of AI software by two North American utilities, the report explores how AI-powered data insights help solve unique challenges posed by EV adoption and integration.

For example, Hydro One, an electricity transmission and distribution utility serving the Canadian province of Ontario, identified 20,000 EVs charging on its grid via AMI data disaggregation—10x more than were self-reported through customer surveys. Hydro One further refined its customer engagement strategy using AI-powered consumption insights to personalize messages for enrollment in a pilot EV demand response program, resulting in 300 signups within 24 hours.

For NV Energy, a generation, transmission, and distribution utility serving northern and southern Nevada, AI-powered data disaggregation allowed the utility to gain a holistic understanding of how often EV drivers charge on-peak and how their behavior contributes to overall electricity demand. By using AI to identify certain customer use profiles and then engage only customers with high-value baseline charging behavior, NV Energy achieved a load-shift potential of 2kW to 4kW/vehicle per managed charging event as opposed to typical load shifts of 0.2kW – 0.8 kW/vehicle per event— 2.5x to 10x greater load-shift on average. Targeted load shifting initiatives like these can enhance utilities’ system resilience capabilities as EV charging increases, while yielding cost efficiencies for utilities and customers.

“AI is crucial for utilities to proactively address the grid challenges posed by the surge in electric vehicles,” says Abhay Gupta, CEO of Bidgely. “We support SEPA in creating a valuable resource that underscores the importance of developing sophisticated EV programs powered by AI-driven data analytics.”