1. Analyzing Social-Psychological Factors Affecting Acceptance of Demand Response (DR) Programs
Candace Pang1 and Elizabeth Price2; Mentors: Dr. Chien-fei Chen3, Dr. Xiaojing Xu3, Erica Davis3, and Jackson Lanier3
1 Oak Ridge High School 2 Bearden High School
3 The University of Tennessee, Knoxville
BACKGROUND
Energy use is an important topic of interest for engineers and social scientists alike. The high demand for energy, particularly during peak hours, has caused strain on electric utility companies, which can lead to blackouts and other difficulties for the power grid. The U.S. is the second leading country in energy consumption, where residents consume up to 50% of the total load during peak hours. Through conducting and analyzing a survey as shown below, certain factors have been identified with the acceptance of Demand Response (DR) programs.
DR Programs
Participants were asked if they would participate in several DR programs. The DR programs included an A/C automatic switcher, heat pump automatic switcher, automatic thermostat adjuster, one question for summer and one for winter, encouraging the participant to reduce their A/C during peak hours, encouraging the participant to reduce their heating during peak hours, and receiving s or texts during peak hours in the summer and winter. Each question was asked again under different circumstances for the participant that answered “maybe” or “no”. These include if they were given a $15.00 reward per month, $30.00 reward per summer or winter, what the minimum reward would be for the participant to participate, and/or the option to override the settings on certain DR programs.
Social-Psychological Factors and Habits
Various social-psychological factors were considered when creating the survey. Among them were energy concern, bill consciousness, trust of utility companies, privacy concern, comfort need during summer and winter, the norms or expectations from family to participate in DR programs, perceived behavioral control, attitudes, and need for control. Each variable had a series of questions that helped to determine how the participant should be classified. The survey also listed multiple habits with varying answer choices from “never” to “always”, as well as “does not apply”, to achieve a better understanding of the participant’s energy use.
METHODS
Participants
This survey questioned people from four different states, two each of similar climates and political affiliations. These included Tennessee, Virginia, Texas, and California. With 1600 participants, there was roughly an even number of people from each state, including 414 from TN, 412 from VA, 403 from TX, and 402 from CA. From this sample, 51% were females and 49% were males. The majority were Caucasian (55.8%), followed by other minorities. The relative majority of participants have an income of $35,000 to $99,999. These demographic ratios model census data and can be used to scale the larger U.S. population. Additionally, the average cooling setting of participants at home is 72 degrees, while not at home it is 74 degrees, while the efficiency setting is 76 degrees. The average heating setting at home is 72 degrees, while not at home it is 69 degrees, while the efficiency setting is 68 degrees.
Factor Analysis
To determine if the social-psychological questions for each variable (i.e. trust, privacy concern, etc.) were related, factor analysis was performed. This helped to confirm that the list of varying questions were in fact related and could accurately classify a participant as trusting, very concerned with privacy, etc. After confirming that each question related to its topic, the group of questions was combined to create one variable that could be tested using a regression model. This was done by averaging the answers together using the scale the participant had available to answer with, ranging from strongly disagree to strongly agree.
Regression
The variables obtained from factor analysis were used to perform regression models that could identify which variables have the highest impact on acceptance of DR programs. This is vital because being able to inform utility companies of exactly what kind of person will or will not take advantage of DR programs could help them improve upon the programs to make them more easily accepted.
Energy Use Info
Demographics
Social-Psychological
Average Monthly Bill
Age
Energy Concern
Stay Home (9am-5pm)
Gender
Bill Consciousness
Light Use
Income
Need for Comfort
Thermostat Settings
Education
Need for Convenience
Night Adjustments
Political Orientation
Need for Control
House Sqft
Trust
Household Size
Subjective Norm
Weather Region
Perceived Control
Independent Variable
Dependent Variable
B Value & Significance
Age
.489***
Teens
-1.275***
Energy Consumption: Cooling Temperature in Summer
Energy Concern
1.389*
.765***
Bill Consciousness
-.810***
Comfort Need
Independent Variable
B Value & Significance
Age
-.140*
Number in Home
.240*
Energy Concern
.264**
Bill Consciousness
.466***
Trust
.225*
Comfort Need
-.242*
Social Norms
.545***
Need for Control
-.399*
Independent Variable
B Value & Significance
Age
.122*
Number in Home
.250**
Energy Concern
.171*
Bill Consciousness
.260*
Comfort Need
-.659***
Social Norms
.498***
Perceived Behavioral Control
.224*
DR: Acceptance of Automatic Thermostat Device to Reduce Energy Consumption
DR: Acceptance of Reducing Energy Consumption by Encouragement
RESULTS
Acceptance of A/C Automatic Switcher: As acceptance grew, energy concern, bill consciousness, trust in utility companies, and norms grew while summer comfort need and need for control declined.
Acceptance of Automatic Thermostat Adjuster (summer): As acceptance grew, energy concern, bill consciousness, trust in utility companies, and norms grew while summer comfort and need for control declined.
Acceptance of encouraging the participant to reduce their A/C during peak hours: As acceptance grew, energy concern, bill consciousness, norms, and perceived behavioral control grew while summer comfort need declined.
Acceptance of s or texts during summer peak hours: As acceptance grew, energy concern, bill consciousness, trust, norms, and perceived behavioral control grew while summer comfort need and need for control declined.
Takeaway
Acceptance of Demand Response programs can be more accurately inferred through the participant’s social-psychological factors than their demographic variables. The survey asked a multitude of questions regarding demographics, while only age and number in home were found to be significant variables where acceptance of DR programs was concerned. Social-psychological factors, however, were very significant in relation to acceptance of the DR programs.
Citation:
This work was supported primarily by the ERC Program
of the National Science Foundation and DOE under NSF Award Number EEC