An Analysis of Residential Demand Response Design Potential from Consumer Survey Data CURENT REU Seminar July 17 th 2014 Hayden Dahmm and Stanly Mathew

Background 2 Figure 1: Effect of Demand Response based AC Thermostat Change at 4pm An Occupant Based Dynamic Simulation Tool for Predicting Residential Power Demand and Quantifying the Impact of Residential Demand Response, Brandon Joshnson 2013 What is Demand Response? Price vs Incentive Based Demand Response Stability Implications Environmental implications

Tools of Residential Demand Response 3 Smart Meter programs Cycling on/off HVAC Systems Load reduction vs load shift Pricing Structures Critical Peak Pricing Real time Price Time of Use Peak time Rebate Figure 2: Electric Smart Meter from Jackson EMC

Review of Customer Segmentation and Demographics 4 Demographic Trends in Electricity Consumption, 2012 Segmentation by Consumption Patterns  Ireland, 2014  USA, 2013 Smart Meter Case Study, UK, 2012 The efficiency paradox

Price Elasticity Definition of elasticity Cold appliances such as refrigerators represent high price elastic potential Wet appliances such as dishwashers and washing machines represent low price elastic potential in the electricity market 5

Research Questions How can responsiveness to DR programs be categorized by demographic features? How can utilities segment customers to have the most effective demand response program? 6

Introduction to Survey Consumer Survey, May 2014  826 adults  12 DR programs Incentives given as percent of monthly bill 7 Figure 3: Qualtrics Survey Interface

Methods: Structuring of Incentive Levels Incentives grouped as low, medium and high 8 Figure 4: Division of Survey Population by Incentive Level for Temperature Change While at Home in Summer Table 1: Definition of Incentive Levels as Percent of Monthly Bill

Methods: Segmentation and Comparison Segment participants based on demographic traits Split incentive levels according to demographic Performed chi squared test against normal distribution  p< 0.05 used to determine statistical significance 9 P Value = [0.5 df/2 / Γ(df/2)]×(χ²) (df/2) -1 × e - χ²/2

Results: Segmentation Based on Income I Low income was defined as <$35k, Low/ middle defined as $35k- 50k, Middle/high defined as $50- $75k High defined as >$75k. Low income individuals represent a significant proportion of the low incentive level for temperature while at home in summer (p =.0060) 10 Figure 6: Breakdown of Survey Population According to Income Level Figure 7: Breakdown of Individuals Requiring Low Incentives for Temperature Change

Results: Segmentation Based on Income II Low income had greater representation in low incentives across all DR programs Pattern significant for five DR programs 11 Table 2: Chi square significant results

Results: Segmentation by Age and Income Population Group1: 18-40/<35k Group2: 18-40/35k-75k Group3: 18-40/>75k Group4: /<35k Group5: /35k- 75k Group6: 40 – 72/>75k

Results: Segmentation by Age and Income Summary 13 Young/low income:  non automated temperature changes and for 30 minute emergency situations.  Prefer medium incentives for shifting washer drier loads Older/low income:  High incentives for temperature changes during the winter and washer/drier and dishwasher load changes

Conclusions Survey answers suggest several significant patterns that should be considered in future DR design: Low income individuals, single individuals, young individuals and those with small house area - low incentives Larger households - medium incentives Older individuals - high incentives Ambivalence towards automated temperature change and 10 minute emergency situations Survey sample not highly representative of overall population 14

Future Work and Considerations Weight data or bootstrapping for under-sampling bias Study those who cannot be incentivized using proposed DR programs Develop structural equation models for predicting incentives Use occupancy models to predict price elasticity 15 Figure 8: Occupancy Behavior Model GUI

References Rajagopal, R., & Albert, A. (2013). Smart Meter Driven Segmentation: What Your Consumption Says About You. Power Systems. Vyas, C., & Gohn, B. (2012). EXECUTIVE SUMMARY: EXECUTIVE SUMMARY: Smart Grid Consumer Survey. Retrieved 06 05, 2014, from Pike Research. Pagliuca, Simone; Lampropoulos, Ioannis; Bonicolini, Matteo; Rawn, Barry; Gibescu, Madeleine; Kling, Wil L., "Capacity Assessment of Residential Demand Response Mechanisms," Universities' Power Engineering Conference (UPEC), Proceedings of th International, vol., no., pp.1,6, 5-8 Sept Pierce, J., Schiano, D. J., & Paulos, E. (2010). Home, Habits and Energy: Examining Domestic Interactions and Energy Consumption. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems,