Municipal Electric Utility · Energy & Utilities · Phoenix, AZ · 11 weeks
Smart meter data foundation built for 680K meters
A municipal electric utility installed 680,000 smart meters but only used the data for billing. The interval data sat unused. We developed an analytics system to process this data and launched the utility’s first demand response program. This directly cut capacity costs by $3.4 million in the first summer.
Challenge
The utility collected 15-minute data from 680,000 smart meters, but the data sat locked in a vendor’s system with no way to analyze it. Meanwhile, they spent $2.1 million each year on capacity reserves that a demand response program could reduce. The problem: they couldn’t pinpoint which customers to target or track how well the program worked.
Approach
We extracted interval data directly from the vendor system to create a solid smart meter data foundation. Then, we developed a load profile analytics layer to analyze customer energy use. Using load shape, peak contribution, and program eligibility, we identified the 42,000 customers with the highest demand response potential. Next, we built an analytics platform that tracks enrollment, dispatch, and curtailment performance in real time to monitor demand response program effectiveness.
Outcome
In its first year, the demand response program signed up 18,400 customers and cut capacity costs by $3.4 million. Building on smart meter data, we developed four more use cases: spotting non-technical losses, optimizing time-of-use rates, detecting outages, and targeting energy efficiency efforts. Early analysis of non-technical losses uncovered $680,000 in annual revenue leakage.
We dove into data everyone else ignored
So get this, the utility spent $180 million on smart meters and barely used them beyond sending out bills. Wild, huh? These meters collect a ton of data that can reveal how folks use power, catch non-technical losses, spot outages early, and even help with smarter pricing. We rolled up our sleeves, built some killer analytics tools to dig into that data, and shared the insights with multiple teams all at once. It was like unlocking a hidden goldmine and spreading the wealth across the whole company.
How We Found the Perfect Candidates for Demand Response Programs
We dove into load data from 680,000 customers to see who was driving peak demand and who might be able to shift their usage. Then, we gave each customer a score based on how much they affected demand and whether they qualified. From there, we zoomed in on 42,000 customers with the most flexibility. Focusing on this group helped us boost enrollment to 44 of every 100 in the first year.
Results
- $3.4M Avoided capacity costs in first summer
- 18,400 Demand response program enrollments in year one
- $680K Annual revenue leakage identified through loss detection
- 4 Additional analytics use cases enabled by the foundation
We spent $180 million on smart meters but were only using the data for billing. Thinklytics helped us create an analytics system that lets us do more with that data, like demand response, detecting non-technical losses, and a few other things. Just this past summer, we saved $3.4 million by avoiding capacity costs, which was better than we expected.