Empowering utility customers with demand flexibility [partner content]

Episode Summary

Executive Summary: The episode examines how surging electricity demand, EVs, and complex rate plans are forcing utilities to become far more customer-centric. OGE and Bidgly show how AI can uncover actual customer behavior, avoid overbuilding, target personalized coaching, and improve affordability while strengthening trust, demand response, and long-term grid planning.

Main Topics: Rapid demand growth is changing utility planning (Priority: 5/5): Utilities are facing unprecedented load growth from data centers, factories, electrification, and new customer technologies, making old forecasting approaches insufficient. AI reveals real customer behavior and hidden grid risk (Priority: 5/5): Bidgly's analytics help utilities identify where demand is truly occurring, revealing that assumptions about EV and rate-plan impacts can be very different from actual usage patterns. Personalized customer engagement replaces generic messaging (Priority: 5/5): OGE found that customers respond better to tailored, transparent coaching than broad conservation messages, especially when bills and benefits are made clear. Demand response and flexible load become planning tools (Priority: 4/5): Utilities are using data to determine which customers can reliably shift load, improving the design of demand response, VPPs, and integrated resource plans. Affordability and fairness drive the business case (Priority: 4/5): The speakers argue that better data prevents overinvestment, keeps rates lower, and allocates grid costs more fairly across customers. Change management matters as much as technology (Priority: 4/5): Successful AI adoption depends on dedicated staff who learn the tools and translate analytics into action, not just purchasing software.

Key Arguments: Utilities can no longer rely on slow, predictable demand growth; the pace and complexity of load change have fundamentally altered the business. AI analytics help utilities avoid overbuilding infrastructure by distinguishing assumed problems from actual ones, protecting customer affordability. Customer behavior does not automatically change when customers enroll in a rate or program; utilities must identify who needs coaching and what messages will work. Personalized communication builds trust because customers expect utility experiences to be as tailored as other digital services. Understanding customer profiles allows utilities to match rebates, weatherization help, and rate options to the households that need them most. Demand-side resources are only valuable in planning if utilities can depend on them; analytics improve confidence in those resources. Consumers must be treated as partners in managing the grid during the next 5-10 years while utilities expand generation and transmission capacity. Technology adoption succeeds only when utilities invest in internal expertise and change management to turn insights into operational action.

Data Points: Expected EV-related shiftable load (initial assumption): 50 megawatts - OGE initially believed EV adoption would create this much shiftable load on its system. Measured EV-related impact after analytics: 3 megawatts - Bidgly analytics showed EV charging was far less disruptive than expected and not concentrated in peak hours. Customers identified in variable peak pricing program behaving opposite to intended behavior: around 900 customers - OGE found a group using more energy during the peak window instead of shifting usage away from it. Reduction in peak energy use after targeted messaging: 70% reduction - OGE reports major improvement after personalized visuals and nudges were sent to misbehaving customers. Peak energy swing achieved through coaching: around 9 megawatts - The shift in customer behavior translated into a substantial load reduction during the 2-7 p.m. peak period. Peak window referenced for time-based rate: 2 to 7 p.m. - OGE cited this as the critical period when some customers were increasing usage instead of reducing it. Change in bill from weatherization example: $300 out-of-pocket / $700 utility-paid - Bidgly described matching a likely low- or medium-income customer to a weatherization rebate opportunity. Payback/savings timeline for weatherization example: within 8 months - Illustrative estimate of when the customer could begin realizing meaningful savings. Expected transition buffer from customer-side flexibility: 5 to 10 years - Abe Gupta argued customers are needed to help manage the grid while capacity is built out.

Pivotal Quotes: "What we are going through, an era right now, is something that industry has never seen before." — Narrator: Describing the unprecedented pace of electricity demand growth and changing customer expectations. "They were able to see a shift from it was 70% reduction in that peak energy use, which was around nine megawatts of a swing." — Kirby Brindley: Explaining the results of targeted messaging for customers in the variable peak pricing program. "You don't just buy technology and it works. You need to actually have people who spend time on that." — Abe Gupta: Summarizing the importance of dedicated staff and change management for successful AI adoption.

Implications: Utilities will need AI, personalization, and customer trust to manage load growth affordably. Better analytics can lower rates, improve grid planning, and turn customers into active partners in flexibility programs.