DEFEAT THE PEAK: BEHAVIORAL INSIGHTS FOR DEMAND RESPONSE PROGRAM DESIGN
Conference Year
January 2019
Abstract
Historically, utilities have used price signals to motivate changes in residential energy consumption. In the new energy system transition, however, consumers are being invited to participate in energy conservation programs based on their pro-social impulses to contribute meaningfully to their communities. The shifting tone of the relationship between consumers and energy providers will be explored in this talk to highlight the new possibilities for energy load malleability that non-price incentive structures can provide. In this study, a voluntary demand response program was analyzed using a difference-in-differences model. This analysis provides a useful measurement strategy to assess the overall impact of the program on the utility’s cost structure. This article should appeal to academics seeking to measure the impact of new energy behavior programming and to utilities seeking to better understand the dynamics between energy consumers and suppliers.
Primary Faculty Mentor Name
Jon Erickson
Status
Graduate
Student College
Rubenstein School of Environmental and Natural Resources
Program/Major
Natural Resources
Second Program/Major
Complex Systems
Primary Research Category
Food & Environment Studies
Secondary Research Category
Social Sciences
DEFEAT THE PEAK: BEHAVIORAL INSIGHTS FOR DEMAND RESPONSE PROGRAM DESIGN
Historically, utilities have used price signals to motivate changes in residential energy consumption. In the new energy system transition, however, consumers are being invited to participate in energy conservation programs based on their pro-social impulses to contribute meaningfully to their communities. The shifting tone of the relationship between consumers and energy providers will be explored in this talk to highlight the new possibilities for energy load malleability that non-price incentive structures can provide. In this study, a voluntary demand response program was analyzed using a difference-in-differences model. This analysis provides a useful measurement strategy to assess the overall impact of the program on the utility’s cost structure. This article should appeal to academics seeking to measure the impact of new energy behavior programming and to utilities seeking to better understand the dynamics between energy consumers and suppliers.