We all know—or should—that "conventional wisdom" often means hearsay repeated too often. Actual, bona fide research, of course, can cut through the fog of conventional wisdom, but only if the audience is receptive and willing to examine the facts.

Thus we offer some insights into customer behavior from a study conducted last summer by Karen Herter of Herter Energy Research Solutions, in conjunction with (and funded by) the Sacramento Municipal Utility District and the Lawrence Berkeley National Laboratory.

We referenced this study in Monday's column, titled "Customers Need Energy Data First."

I'll stick pretty close to Herter's language in her report, "SMUD's Residential Summer Solutions Study," because—although the premises were fairly straightforward—the results can get confusing without basic graphs and charts to keep it straight. I'd urge readers to read the original paper and pay close attention to the charts, which provide visual clarity to the variety of findings.

Herter worked with 265 residential customers in the SMUD service territory last summer in an effort to test responses to (and perceptions of) an integrated energy efficiency and demand response program with real-time energy information, a dynamic rate and thermostat automation.

The study's main purpose was to test the effect of three different "information treatments" on summer energy use, peak load and "event" loads for a groups of customers exposed to dynamic pricing, traditional direct load control, or both—all with automated thermostats.

Under an experimental, dynamic rate, participants in the study controlled their own automated response to events through a menu option in the thermostat. In contrast, participants on the direct load control program—the Automated Temperature Control option (ATC)—did not have thermostat menu options. (They could override only one event of 12 in a summer.)

Rather than run the risk of over-simplifying the study in a short column—the "short" report runs 50+ pages—I'll skip to the conclusions and a discussion of them.

Here are a few findings:

  • Customers on the dynamic rate lowered their summer energy use by 10 percent, reduced weekday peak loads by more than 20 percent, shed more than 50 percent of their load during events, and lowered their bills by about 15 percent overall.
  • Compared to those on the dynamic rate, customers on the direct load control option saved a similar amount of energy, but saved less energy during the weekday peak periods and events.
  • Real-time energy information showed modest benefits. Real-time information at the home level enhanced energy savings by about 4 percent. Real-time energy information at the appliance level enhanced daily peak savings by about 7 percent. Neither information type affected event savings.


As for the findings on the initial objective—how does real-time energy data affect electricity use?—Herter said it depended on which energy treatment was delivered.

"The answer for feedback at the aggregate home level was that it seems to reduce energy consumption by a few percent overall," Herter told me. "Participants saved slightly more energy during the waking hours than did the participants without real-time information.

"The folks with appliance-level data did not save more energy across the day," she continued, "they only saved more energy during the peak."

"One possible explanation for this pattern is that those [participants] with only home information, being less aware of individual contributions to total energy use, employed a more general strategy for keeping energy use low all the time," according to Herter's report. "In contrast, those with appliance energy data may have the precise knowledge required to be more strategic in their load management efforts."

 "That's what we found," Herter concluded, in a conversation with me. "It was one study, one summer, one group of customers. As far as I know that type of study has not been done anywhere else and one study is not enough to draw general conclusions. But it's a start."

Herter said she hoped to run the research again this summer to confirm her findings, which could give utilities several tools to apply, depending on their individual needs and goals. Reduce overall energy use? Reduce peak use? Curtail demand on "event" days? Find the customers who would like to participate and begin there. Build outward to a broader base of participants as you can show how they save energy and money. It's cheaper to provide smart thermostats and price signals than to build more power plants. That's the logic, anyway. 

What's just as important as the findings on customer behavior under the various treatments is that 90 percent of participants in the study chose to participate again this year.

Just thought we'd share some outcomes from a well-designed study that addresses many of the questions utilities and regulators are asking today.

Phil Carson
Intelligent Utility Daily