You’re Getting Warmer: Impacts of New Approaches to Residential Demand Reduction

Ken Agnew and Miriam Goldberg, KEMA-XENERGY
Rob Rubin, San Diego Gas and Electric

Keywords

Abstract

San Diego Gas and Electric’s residential Smart Thermostat (ST) Program is a pilot mandated by the California Public Utilities Commission that tests a new combination of paging, Internet, and thermostat technology to raise air conditioner set points remotely. An important feature of the program is customer control of the thermostat both manually and through the Internet, even during re-set events.

The impact evaluation of this program finds that the program savings are lower than targeted by the program plans. Savings reported for the participants in the Statewide Pricing Pilot’s Critical Peak Pricing (CPP) program for the same re-set periods were higher than those for the participants in the ST program. Those in the CPP program had incentives to reduce usage during all peak periods, not just declared critical peaks, and also benefited from both airconditioning and non-air-conditioning usage reductions.

A key factor in the lower-than-targeted savings for the ST program is the timing of the reset events to coincide with statewide emergencies. As a result, the weather is often not hot in San Diego on these days, and many air conditioners are not being used. On the other hand, on warmer days, over-ride rates rise substantially. A stronger penalty structure could reduce the over-ride rate, but would also make program recruitment more challenging; hence, the net effect on program savings and costs is difficult to predict.

While the design constraints appear to limit the savings potential from this program, the impact analysis methods described can be applied to other re-set programs. These methods take advantage of the program’s re-set structure and advanced communications technology. The analysis utilizes end-use metering data for flip-flopping re-set and comparison groups, together with program operational data on signal non-receipt and over-ride rates. The methodology provides estimates that eliminate some key sources of bias while providing good statistical accuracy. The modeling also provides projected future savings as a function of weather and reset magnitude, taking into account both increased potential savings and increased over-ride rates at higher temperatures.