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Research to Inform Design of Residential Energy Use Behavior Change Pilot


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Presented by Ed Carroll and Mark Brown of Franklin Energy during Conservation Improvement Program (CIP) discussion hosted by the Minnesota Office of Energy Security on July 21st, 2009

Presented by Ed Carroll and Mark Brown of Franklin Energy during Conservation Improvement Program (CIP) discussion hosted by the Minnesota Office of Energy Security on July 21st, 2009

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  • 1. Research to Inform Design of Residential Energy Use Behavior Change Pilot Conservation Improvement Program Discussion Hosted by the Minnesota Office of Energy Security July 21st, 2009 Ed Carroll Mark Brown
  • 2. Flow of Discussion Why Consider Behavior Change? Project Overview and Research Approach Behavioral Change Interventions Overview Literature R i Lit t Review Utility Experiences with Behavior Change Pilot Programs Cost Effectiveness and Applicability to MN Key Lessons Learned – Utility Manager Perspectives Pilot Models to Consider Q&A
  • 3. Why Consider Behavior Change? Valuable tool to help you meet your CIP program goals Utility experiences show that interventions can lead to significant energy savings: 2% to 7% savings for program participants in the first year Traditional product-based prescriptive and custom incentive programs product based may be inadequate and provide diminishing returns: “We realized we would be unable to meet our energy savings targets for the residential sector with purely a product based product-based approach.” – Program Manager, Seattle City Light “The lighting program we have here is so successful that we are really going t run out of potential i th next f ll i to t f t ti l in the t few years. It is by f i b far the program that results in the biggest savings. It is sort of like the linchpin will be gone.” – Program Manager, SMUD Cost effectiveness as low as 3¢ per kWh in first-year savings
  • 4. Project Overview OBJECTIVE: to provide the Minnesota Office of Energy Security (OES) and utilities a solid plan for piloting residential energy use behavior change programs as part of their CIP efforts GOAL: to help Minnesota utilities better understand how to accelerate energy savings resulting from changes in residential energy-use behavior ACTIVITIES: (completed Nov. 2008 to Apr. 2009): Collected data and analysis from available published research Interviewed experienced program managers, consultants, and researchers Identified b t Id tifi d best practices and lessons learned f ti dl l d from studies and t di d pilots aimed at addressing consumer behavior Developed a report p p p providing recommendations for utility p g y pilot programs applicable to Minnesota’s residential market
  • 5. Information Resources Interview Respondents Published Research Resources Utilities/Administrators: ACEEE • Austin Utilities (K. Lady) EPRI • Baltimore Gas & Electric (R. Kiselewich) • BC Hydro (A. Korteland) Precourt Institute for Energy • Connexus Energy, MN (B. Sayler) Efficiency (Stanford Univ.) • City Utilities, MO (C. Shaefer) • Energy Trust of Oregon (K. Youngblood) Environmental Change Institute (UK) • Pacific Gas & Electric (J Medvitz) (J. BEHAVE Program (Europe) • Silicon Valley Power (L. Brown) • SMUD, Sacramento (A. Crawford) CIEE (California Institute for Energy and Environment) Consultants/Vendors: • Comverge/ComEd (K. Papadimitriu) Journal P bli ti J l Publications: • The Brattle Group (A. Faruqui) • Energy Efficiency • Paragon Consulting (B. Jackson) • Energy Policy • Positive Energy (A. Laskey) • Journal of Environmental Psychology • Van Denburgh Consulting (E. Van Denburgh) g g( g ) • Journal of Environmental Systems y Researchers: • Energy Center of Wisconsin (I. Bensch) • FSEC - Florida (D. Parker) • Org. for Energy and the Environment – the Netherlands (H. van Elburg)
  • 6. Interventions Overview
  • 7. Behavior Change Theory Behavior Change Impact of Feedback Decision Making • Identifies cost of Realize that there is a problem behavior or deviation from peers Habitual Behavior Realize relevance of behavior to problem • Indicates the impact of specific behavior Realize R li possibilities t ibiliti to changes influence problem • Turning on/off lights • Use of appliances Weigh motives: • S tti the thermostat Setting th th t t • Personal norms • Social norms • Can frame behavior in • Other motives (e.g., comfort) terms of cost ($) or Challenges impact on the Electricity: environment • E bli product Enabling d t Evaluate conflicting motive • Low-involvement • Intangible Take action • Repetitive prompts help to form new persistent • Dissatisfier habits • Low cost priority
  • 8. Monitors Categories of Interventions Reports We see three distinct behavioral change program categories: Rates In-home devices and displays providing feedback • Real-time feedback on energy use and costs • Devices interface with utility electric meter or through CT clips installed at electric panel • Examples: PowerCost Monitor, Kill-A-Watt, TED Customized, regular feedback delivered to consumers • Processed feedback via mailed reports or online interface • Opportunity to incorporate comparative data/feedback pp y p p • Examples: Positive Energy, BC Hydro’s Team Power Smart Dynamic pricing / rate designs (e.g., smart metering) D i i i t d i ( t t i ) • Protocols that allow for different rates to be charged based on time of use • Enabled by advanced metering infrastructure and two- two way communication between the utility and customer
  • 9. Monitors (Direct Feedback) Pros: PowerCost Monitor • Users able to receive real-time feedback from their meter via a from Blue Line Innovations mobile monitor. • Real-time feedback allows users to experiment and see the impact of their behavior • Multiple utilities have demonstrated the savings achieved by customers using these devices • NSTAR: 3% annual energy savings in ongoing pilot • Hydro One: 6.5% annual savings in a 500-home pilot Click to Launch Video • Dominion: 6% saving in non-electric water heat homes Cons: • Opt in nature of programs (e.g., soliciting customers to install Opt-in (e g devices) leads to low adoption rates and limited scale The Energy Detective from Energy, Inc. • Low willingness to pay relative to device cost • Significant drop-out rates among participants as the novelty of the device wears off, monitors are put away, or batteries die • Questions about persistence of savings and cost effectiveness of the $130+ devices • Data capture reliability and resolution raises concerns Click to Launch Video #2 • Compatibility issues with meter/panel designs and interface
  • 10. Reports (Indirect Feedback) Pros: • Opt-out (vs. opt-in) nature allows utilities to design and conduct rigorous large-scale pilots and implementation for entire populations in desired segments • P id comparative f db k showing a customer’s performance Provide ti feedback, h i t ’ f relative to their neighbors; power of social norms • Customized reports based on housing, demographic, and psychographic factors to maximize appeal • Can operate with or without in-home devices and AMI • Cost effectiveness of savings achieved: • 3¢ per kilowatt hour in first year (Positive Energy at SMUD) Cons: • Will not match the real-time and (unless coupled with AMI-enabled technology) use-specific feedback that in-home devices provide gy) p p • Utilities must be careful in targeting and crafting their messaging in order to minimize potential negative effects: • Small minority of customers offended by comparative feedback • C t Customer may d id t i decide to increase th i energy consumption their ti
  • 11. Rates (Dynamic Pricing) Pros: • Dynamic pricing provides direct monetary incentives for consumers • Utilities are better able to match prices to energy production and/or purchase costs • Flexibility in rate design (e.g., time-of-use, real-time, critical-peak). • Solutions typically require in-home displays that provide feedback: • Real-time and cumulative cost/energy consumption info and associated energy savings impacts • Advantage of permanent installation/use Cons: • Costly infrastructure investment requiring substantial resources to install meters and develop integrated IT platforms • Programs costs are typically justified by returns from operational efficiency and capacity (i e peak load) management and savings (i.e., • Energy efficiency/conservation savings are typically secondary benefits and not primary drivers
  • 12. Literature Review
  • 13. Literature Review Major review studies: Key Findings Feedback leads to energy gy savings: • Direct: 5 to 15% • Indirect: 0 to 10% Characteristics of effective feedback: • Given frequently • Involves interaction • Involves appliance-specific information • Given over longer period • Presented in a user- friendly format Concern with experimental rigor of studies
  • 14. Findings from Literature Review (C. Fischer, 2008) Covers 5 review studies and 21 original studies across 10 countries Concludes that feedback stimulates energy savings – i l i ‘usual savings of 5% to 12%’ Characteristics of effective feedback: • Given frequently • Involves interaction • Involves appliance-specific pp p information • Given over longer period • Presented in an understandable and appealing way
  • 15. Findings from Literature Review (S. Darby, 2006) Savings from direct feedback – average from 5-15% Savings from indirect feedback (e.g., billing) - range from 0-10%. High energy users may respond more than low users to direct feedback Persistence of energy savings created from feedback when individuals develop new habits or invest in efficiency measures Useful display features include instantaneous usage, expenditure, and historic feedback Indirect feedback can be most helpful for evaluating heating load and the impact of investments in insulation/new major appliances Direct feedback is better for understanding the impact of smaller end- uses and the significance of moment to moment behavior
  • 16. Findings from Literature Review (Abrahamse et al, 2005) Reviews thirty-eight field studies from 1977 to 2004 aimed at thirty eight encouraging households to reduce energy consumption Identifies that much of the research on energy conservation interventions h l k d th appropriate experimental conditions ( i t ti has lacked the i t i t l diti (e.g., significant sample size, appropriate control groups) to validate findings and draw definitive conclusions The large majority of studies addressing feedback find it to be an effective means to generate energy savings, with more frequent feedback leading to greater effectiveness Rewards for energy conservation may influence behavior, but the effects are found to be short-lived Using inter entions in combination is fo nd to ha e an impro ed effect interventions found have improved
  • 17. Utility Experiences with Behavior Change Programs
  • 18. Illustrative Case Studies Direct feedback via display devices: • Hydro One (Ontario, Canada) • NSTAR (Massachusetts) • Recent Findings Update: • Dominion (Virginia) • Seattle City Light y g • Energy Trust of Oregon Indirect feedback • Positive Energy/SMUD • Update on savings validation (Summit Blue) • BC Hydro
  • 19. Case Study: Hydro One - PowerCost Monitor Pilot Study Findings 6.5% aggregate reduction in electricity (kWh) consumption y( ) p 8% reduction in non-electrically heated homes 5% reduction in non-electric heat/hot water homes Pilot Program Methodology 16% reduction in non-electric Study period >1 year heat homes w/ electric hot water 400+ participants 1% reduction in electrically heated Sample across wide variation of homes; load “completely overwhelms” 11% of homes have electric heat in area climate and geography “income and demographic factors income Impact measured based on historical had no impact on the comparison responsiveness to the monitor” PowerCost Monitor (Blue Line 60% of participants felt the monitor Innovations) used by participants made a difference in their homes Source: Summary: The Impact of Real-Time Feedback on Residential Electricity Consumption: The Hydro One Pilot, March 2006
  • 20. Case Study: NSTAR - PowerCost Monitor Pilot Study Findings 2.9% savings for customers who used the monitor (~$64/year) 66%-75% installation rate Pilot Program Methodology 33% of initial users stopped using Pilot began May 2008 the monitor during the study period 3,100+ units sold 63% of participants indicate behavior change Media coverage (TV, print) coincided with significant rise in sales 60% noticed savings in their bill Offering Unit Price Adoption Rate Direct install during energy audit Free 95% PCM Offering previous audit customers free PCM Free 14% Retail $9.99 6% Price: Direct Mail Solicitation/ ~$140 $29.99 5% Media Promotion $49.99 $49 99 0.3% 0 3% Source: 2008 BECC Conference Presentation: Power Cost Monitor Pilot, David MacLellan, NSTAR, November 2008
  • 21. Recent In-Home Display Pilots: Dominion Virginia Power Study Findings 6% kWh energy savings in homes Pilot Program Methodology g gy without electric hot water Free PowerCost Monitor, pre- 19% kWh savings in homes with programmed with rate electric hot water Enrolled 1 000 users from 4 600 1,000 4,600 Savings estimates based on weather- g solicitations normalized billing analysis comparing historical consumption 13-month study; began Nov. 2007 53% of respondents reported GoodCents used as vendor to technical difficulties execute pilot Battery life 30% response to mailed survey soliciation, with pre-paid return and Sensor water damage coupon i incentive f f ti for free multipack lti k Plan to structure full rollout with $25 of CFLs user payment for the meter; to In process of completing post-study achieve “skin in the game” survey Using Blue Line PCM monitors that have AMI compatibility Source: AESP Webinar Presentation: Managing an In-Home Energy Display Pilot Project, July 2009
  • 22. Recent In-Home Display Pilots: Seattle City Light Study Findings 2-3% average electricity savings in comparison with control group Pilot Program Methodology No significant variation in savings achieved across different monitors Goal of 33 home energy monitors evaluated installed Somewhat disappointed with S h t di i t d ith Randomly chosen participants results to date; Single family homes only Expected greater savings based 3 types of meters installed on manufacturer claims and previously published studies PowerCost Monitor Difficulty in convincing customers to The Energy Detective participate in the study Cent-a-Meter Survey company found 8-month test period themselves having to sell hard Logistics issues for electrical permits, installation scheduling for panel devices Source: AESP Webinar Presentation: Managing an In-Home Energy Display Pilot Project, July 2009
  • 23. Recent In-Home Display Pilots: Energy Trust of Oregon Study Findings Preliminary findings indicate “monitors did not have a Pilot P Pil t Program Methodology M th d l significant impact on energy use for either cohort” Over 350 monitors deployed: Six month response rates: 164 sold via Web site @ $29 99 $29.99 57% of Early Adopters to “Early Adopters” (EA) 55% of HER cohort 201 installed as part of a Home 66% of EA and 64% of HER using Energy Review (HER) device after 6 months Savings evaluation involved control Two thirds of non-users report monitor groups with random stratified sample no longer functional with adequate regional and home vintage representation g p Lighting, space heating, and clothes dryers most often attributed as Surveys conducted within 3 weeks savings source of installation and at 6 months after While never significant, point First installations in Jan of 2008 estimates of energy savings were gy g highest at 3 months, and declined at 6-9 months Source: AESP Webinar Presentation: Managing an In-Home Energy Display Pilot Project, July 2009
  • 24. Indirect Feedback Programs
  • 25. Case Study: Positive Energy @ SMUD Study Findings (Ongoing) 2% savings achieved on average for treatment group (~250kWh p.a.) Pilot Program Methodology g gy 3¢ per kWh savings cost average Program launched April 2008 Significantly higher savings among: 35,000 customer treatment group • Higher energy consumers (non-targeted) • Greenergy (renewable energy) • 25,000 homes receiving monthly customers • 10,000 homes receiving quarterly • Monthly vs. quarterly recipients 55,000 55 000 customer control group t t l Indication of correlation of higher Random sampling to create savings for lower income population representative population 800 of 35,000 decided to opt out Reports provide a ‘h ’ h R t id ‘here’s how you <1% of 35,000 opted to set personal compare to your neighbors’ goal message customized to the home (type, size, location) Positive customer feedback Customized energy savings tips Few very negative reactions provided along with report Source: Interviews with President of Positive Energy and Program Manager at SMUD
  • 26. Verification Analysis for Impact of Positive Energy at SMUD Summit Blue Consulting - May 2009 “The estimate of annual savings from each of the three methods ranged from 2.1% to 2.2% showing strong robustness of results. The range around each of these estimates is tight, providing good reliability and precision…The strength of these estimates rests on the clean design of the experiment and the very large sample sizes that were used. It is often difficult to accurately assess a program savings of 2% from billing analysis because of the wide range of variability in customer bills, b t th l t bill but the large scale of thi experiment allowed l f this i t ll d for accurate assessment of savings from this program.” Source: Impact Evaluation of Positive Energy SMUD Pilot Study, May 26, 2009
  • 27. Behavioral Science Research – Normative Feedback Seminal research published in 2007 by Nolan J. M.. Schultz, P. W., Nolan, J M Schultz P W Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. Used doorhanger messages to test response to four conservation messages among California residents: (1) they could save money by conserving energy (2) they could save the earth’s resources by conserving energy (3) they could be socially responsible citizens by conserving energy (4) the majority of their neighbors tried regularly to conserve energy Only the social norming message produced significant savings Source: "Normative Social Influence is Underdetected," J.M. Nolan, P.W. Schultz, R. B. Cialdini, N.J. Goldstein, and V. Griskevicius, Personality and Social Psychology Bulletin (July 2008).
  • 28. Positive Energy – Home Electricity Report Example Source: Positive Energy
  • 29. Positive Energy – Home Electricity Report Example Source: Positive Energy
  • 30. Positive Energy – Home Electricity Report Example Source: Positive Energy
  • 31. Positive Energy – Home Electricity Report Example Customized Tips Driven By: Housing • Si Size • Age • Fuel type • Pool etc Pool, etc. Consumption • Amount • Pattern Demographics • Income •A Age • Length of residence • DIY • Green Source: Positive Energy
  • 32. Positive Energy – Home Electricity Report Example Source: Positive Energy
  • 33. Case Study: BC Hydro Behavior Change Market Test Study Findings Reduction target had significant impact on recruitment success Pilot Program Methodology 5% target h d significant had i ifi 1-Year pilot launched early 2007 freeridership problem 10% goal found to be optimal Recruited employees of BC Hydro’s largest customer l t t Cash rewards more appealing than prize draw rewards Employees encouraged to eNewsletter drove online visits participate: More frequent visitors to online • Commit to a given electricity tool hi t l achieved higher electricity d hi h l t i it reduction target savings • Use online tool to track/compare Reported behavior changes consumption • Turning o lights u g off g ts • Participants received cash rebate for • Changing laundry habits achieving target (e.g. 5% electricity rebate for achieving • Shorter showers • Unplugging chargers 4 Different incentive rewards tested • Turning down the thermostat Source: BC Hydro
  • 34. BC Hydro – Team Power Smart Online tools allow anyone in BC to enroll by committing to use 10% less energy over one year Track consumption Compare consumption to similar households Visibility to community rivalry and promotion of “Pride of Province” Members benefits i l d special offers and opportunities t win M b b fit include i l ff d t iti to i prizes in drawings and contests Program supported by a roster of Team Power Smart Leaders including celebrity athletes and community leaders Expected results among participants (~4% to 5% total savings): 17% become Achievers – average savings of 21% 24% become Savers – average savings of 4% 59% become Non-Achievers – no savings on average Currently 74,000 members (4% of customers) enrolled toward goal of 210,000 by 2010 Source: BC Hydro
  • 35. BC Hydro – Team Power Smart Source: BC Hydro
  • 36. Psychographic Segmentation Targeting “Stumbling Stumbling Proponents” with Team Power Smart Cross references utility-focused categories (e.g., home heating, appliances, and lighting) with emotive categories: • Health+Wellness • Food+Drink • Life+Leisure • Family+Friends • Home+Garden G • Gadgets+Tech. Uses survey data and demographic/housing parameters to target customized messages most likely to be received positively to a given audience audience. Source: BC Hydro
  • 37. Cost Effectiveness and Applicability to MN
  • 38. Examining Program Cost Effectiveness Home Energy Reports – Positive Energy @ SMUD (N=35,000) 250 kWh (~2%) first-year savings in non-targeted households First-year First year cost of conserved energy (i e assumes no persistence): (i.e., 3¢ per kWh (<$8 per household per year variable cost) In-Home Direct Feedback – PowerCost Monitor Device Cost: ~$140 (without installation) Requires utility subsidy of ~$100+ to spur adoption (e.g. NSTAR) Likely savings potential of 3% (NSTAR) to 7% (Hydro One) Cost of conserved energy @ $100/household program cost: Assumed Savings Persistence Horizon 1 Year 2 Years 5 Years 10 Years 20 Years 'first-year' Savings scenario: (cost of conserved energy: $ per kWh) 3% - 330kWh $0.32 $0.16 $0.07 $0.04 $0.02 7% - 770 kWh $0.14 $0.07 $0.03 $0.02 $0.01 (Assumes: $100 program cost, 11,000 kWh average consumption, 5% discount rate)
  • 39. Benchmarking First-Year Costs of Energy Savings Source: Summit Blue Consulting, 2008
  • 40. Importance of Opt-In vs. Opt-Out NSTAR’s PCM pilot sought to evaluate customer willingness to pay Findings indicate the utility would have to subsidize nearly $100 of the device cost in order to reach a significant population (>1%) Limited participation in opt-in programs has significant implications to achievable program savings: Even if device programs could yield 10% savings (as per literature), if only 5% participate a utility would be limited to a 0 5% population impact participate, 0.5% Conversely, a program like Positive Energy, saving only 2% among participants (possibly all customers), could have 4X the population impact NSTAR Pil t Fi di Pilot Findings – C t Customer Willingness t P Willi to Pay Offering Unit Price Adoption Rate Direct install during energy audit Free 95% Offering previous audit customers free PCM Free 14% $9.99 6% Direct Mail Solicitation/ $29.99 5% Media Promotion $49.99 0.3% Source: NSTAR
  • 41. Regional Energy Intensity Intensity in West North Central States matches national average Factors that can influence regional differences: Climate – associated HVAC energy use Age distribution of the housing stock – associated appliance and weatherization efficiency Population’s attitude toward conservation Amount of resources going toward EE and conservation programs Source: Energy Information Administration
  • 42. Residential Electricity End Use Consumption Drivers Average % of Household kWh by Region 35% 30% Climate and fuel source differences reflect relative % of Electricity Consumption . 25% share of electricity consumption by region 20% 15% 10% 5% 0% Air Space HVAC Kitchen Water Home Laundry Other Other End Lighting Conditioning Heating Appliances Appliances Heating Electronics Appliances Equipment Uses U.S. Avg. 16.0% 10.1% 5.0% 26.7% 9.1% 8.8% 7.2% 6.7% 2.5% 7.7% West North Central 14.7% 8.2% 6.1% 29.2% 7.7% 8.6% 7.0% 8.1% 2.1% 8.3% New England 6.6% 6.6% 4.5% 32.6% 8.0% 13.2% 11.3% 8.6% 4.6% 4.1% South Atlantic 21.4% 10.4% 4.2% 23.2% 12.4% 6.8% 5.7% 6.1% 2.4% 7.3% Source: Energy Information Administration
  • 43. Residential Electricity End Use Consumption Drivers Average Household kWh by Region 4,000 3,500 3,000 Annual Kilowatt Hours 2,500 t 2,000 Uniformity exists in uses involving frequent behavioral 1,500 interaction A 1,000 500 - Air Space HVAC Kitchen Water Home Laundry Other Other End Lighting Conditioning Heating Appliances Appliances Heating Electronics Appliances Equipment Uses U.S. Avg. 1,837 1,159 574 3,065 1,045 1,010 827 769 287 884 West North Central 1,689 942 701 3,356 885 988 805 931 241 954 New England 491 491 334 2,423 595 981 840 639 342 305 South Atlantic 3,150 1,531 618 3,415 1,825 1,001 839 898 353 1,075 Source: Energy Information Administration
  • 44. Evaluation of Miscellaneous Electric Loads ( (MELs)) Source: Energy Information Administration
  • 45. Residential Electricity Consumption by End Use y Source: Energy Information Administration
  • 46. Average Household Consumption by MEL Source: Energy Information Administration
  • 47. MELs in the Context of Total Energy Use Source: Energy Information Administration
  • 48. Water Heater Fuel Source by Region 100% 90% Fuel F l Oil 80% Gas 70% Gas ds Gas % of Household 60% Gas 50% Gas 40% o Electric 30% 63% Electric 20% 39% Electric Electric 29% Electric 10% 26% 20% 0% U.S. US West W t Midwest Mid t South S th Northeast N th t Source: Energy Information Administration
  • 49. Water Heater Fuel Source by Population Density y p y 100% Other 90% Fuel Oil 80% Gas 70% Gas Gas ds % of Household 60% Gas 50% 40% o Electric 30% 63% 20% Electric Electric 35% 35% Electric 10% 26% 0% Cities Citi Town T Suburbs S b b Rural R l Source: Energy Information Administration
  • 50. Average Household Energy Spending usehold by End Use and Region - (All Fuel Sources) $3,000 Average Annual Energy Spending per Hou $2,388 $2 388 $2,500 $1,885 All Other $2,000 $1,823 $1,788 $739 $1,634 All Other All Other $1,500 All Other Refrig. $647 All Other $596 $635 Water Heat Refrig. $642 Refrig. Refrig. A/C $1,000 , Water Heat Refrig. Refrig Water Heat E Water H t W t Heat Water Heat A/C A/C $500 A/C Heating A/C Heating Heating Heating Heating $0 Total West Midwest South Northeast 10%: $190 $160 $180 $180 $240 5%: 5% $95 $80 $90 $90 $120 2%: $38 $33 $36 $36 $28 Source: Energy Information Administration
  • 51. Key Lessons Learned – Utility Manger Perspectives
  • 52. Program Manager Perspectives: Key Lessons Learned Motivation is the essential ingredient Upfront customer input is invaluable • “Don’t design a project within your own four walls.” Taking an iterative approach ensures consistency with goals and avoids technical issues • “Know your goals at the outset.” A cross functional pilot team helps to ensure success It is important to be sensitive to customer satisfaction impacts Leveraging peer utility experience improves likelihood of success
  • 53. Program Manager Perspectives: Key Lessons Learned Pre pilot surveys establish a baseline for analysis Pre-pilot Incorporate a control group Novelty of the feedback will wear off Meter interface can present barriers p IHDs can be hampered by low installation rates Solution S l ti must b well suited t th customer t be ll it d to the t population • “There probably isn t going to be a silver bullet ” There isn’t bullet. Tailoring messaging to specific segments can ensure messages resonate with your audience
  • 54. Program Models to Consider
  • 55. Program Models to Consider Model 2: Model 3: Model 1: Indirect/Comparative Hybrid Approach – Program Models In-Home Energy Use Feedback on Home Comparative and Direct Monitor Energy Use Feedback Participants receive regular p g Participants receive regular comparative feedback Participants receive a reports in the mail that will reports and energy tips. monitor that provides real- compare their energy use Participants will be time feedback on home Program Basics with neighbors in similar encouraged to make use of energy use in order to track homes. Targeted energy real-time power monitors a d e pe e t t t e and experiment with their saving ti will also b i tips ill l be that th t can be purchased or b h d energy use behavior communicated. borrowed for several months at a time. Customer Engagement Opt-out (reports) Opt-in Opt-out Method Opt-in (in-home device) 2% 2%+ Average in total customer Average in total customer 5% Targeted participant population; targeted population; targeted (mid of 3% to 7% range) household savings segments would have segments would have Valid among self-selected (as % of total kWh) significantly higher savings significantly higher savings participant population (e.g., in the 5% to 10% (e.g., in the 5% to 10% range) range) Real-time feedback for Cost effective approach with Hybrid approach maximizes Big Advantage participants broader reach savings potential Significantly Significantl higher cost per Requires Req ires integration with ith Greater comple it / complexity/ Big Disadvantage kWh saved system data resource requirements Source: Energy Information Administration
  • 56. Program Considerations: Model 3 Points of Emphasis • Give customers the ability to compare energy-use with their neighbors Program Objective • Provide opportunity for the utilization of in-home monitors, possibly on a temporary basis • Broad reach of the opt-out home energy report across geographic, housing, demographic strata Target Customer • Use data from indirect feedback program to identify customer segments with the greatest Market potential to benefit from direct feedback • Need internal IT system for report generation or contract third-party services • Detailed data on houses and homeowners may need to be obtained from third-party/proprietary Program Logistics sources • Consider subsidized purchase for feedback devices or model to provide on a temporary basis Customer • Utilize energy use reports as a platform for education about conservation ideas and promotion of Education the direct feedback program Enhancements • Raise awareness and promote associated devices to aid in customer behavior changes Trade Ally Plan • Evaluate need for technical/installation assistance for feedback devices Savings and Goals • Anticipated savings of 2% in indirect feedback population; additional savings from device group Assumptions • Ongoing measurement is necessary to establish baselines for long-term savings persistence • If a temporary device lending program is ruled out, subsidies for customer device purchases Marketing and would be necessary, promoted through the indirect feedback reports Incentive Strategy • Evaluate the incorporation of customer goal setting and commitments as a motivator Quality Control • Having adequate pilot scale, duration, and measurement systems will ensure accurate cost Plan effectiveness quantification Program Budget • Evaluate available internal resources, third-party service costs, and need for device subsidies Considerations Source: Energy Information Administration
  • 57. Example: Behavior Change Pilot Program Plan - Model 3 Critical Success Factors Process Step Inputs Actions Outputs (Application of Lessons Learned)  Identify required program pilot team with cross functional (operational, finance,  Project team  Available internal technical, customer service) capabilities to  Project plan Identify resources address all aspects of program execution and  Define pilot  A diverse pilot team helps to p p Team/  Potential business case assessment program outcome ensure success Objectives implementation  Define project timeline and specific pilot measures partners learning objectives (e.g., quantify savings  Pilot program potential and $/kWh for program) budget  Quantify resource and budget requirements  Review work of peer utilities; engage in  Determination of  Taking an iterative approach dialog di l program partner t to piloting solutions ensures  Engage program partners (if engagement consistency with goals  Identification of necessary/desired)  Identified Prepare for  Leveraging the experience potential program  Develop IT integration plan to enable challenges to Customer of peer utilities improves partners (e.g., generation of home energy use reports report generation Engagement chances of success Positive Energy)  Develop list of items on which to collect  Identified device  Validating the functionality of customer input preferences new technology can avoid  Obtain real-time feedback devices and test  Customer input headaches down the road internally objectives  Solicit customer engagement  Collect feedback from a focus group (or  Identified customer  Upfront customer input survey) concerns with provides invaluable  Collect feedback on key aspects of program reports guidance for successful  Small customer (e.g., marketing and execution:  Key themes to program design Collect focus group) o Receptivity to comparative feedback incorporate in  Ensure the solution is well Customer population o Desired report information elements, customer targeting suited to customer Input  Customer input format/graphics and messaging population objectives o Attitudes toward conservation  Identified barriers  Interfacing with meters for o Interest in real-time feedback devices to user acceptance p in-home devices can o Interest in device distribution/rental of device present barriers arrangements Source: Energy Information Administration
  • 58. Process Step Inputs Actions Outputs Applicable Lessons Learned  Establish desired customer segments on which to determine program i d t i impactt  Available data on  Calculate required program sample size (in each  Necessary program customer energy population) to allow for adequate treatment and  Incorporating a control group use and precision/confidence in program outcomes control group size that representative of the segmentation measurement*  Identified customer underlying population and Define parameters:  Establish a control group of (at least) similar size segment sufficiently large allows for Parameters o Level of for comparison that is representative of the representation the necessary precision and for C t f Customer energy use treatment group desired in pilot confidence to draw Comparison o Age  Develop customer education plans to maximize group conclusions about specific o Income awareness and satisfaction  Customer education sub-segments of the o Home  Determine means/parameters to group customer plan population size/type/age homes for energy use comparisons (e.g., 100  Program budget homes of similar size in neighborhood)  Determine program budget *Note: See Appendix 1 for discussion of sample size determination. Control and treatment groups should be defined to observe impact of indirect feedback. The selection bias of device user population requires historical data comparison to evaluate savings.  Develop energy use reports to communicate  Template for home  Motivation is the essential customer energy use in comparison to neighbors energy use report gy p ingredient g Develop and historical consumption  Customer  Means to determine  Look beyond traditional Energy  Develop/obtain comprehensive lists of energy segmentation customized savings customer segmentation Report savings measures to potentially recommend data tips to include (may models to find messages that Content  Establish means to select customized energy come from program resonate with particular savings tips for customers based on known partner) groups segmentation parameters  Id if plan f d i l di / Identify l for device lending/rental program l Develop  Device (e.g. distribution through mail, library checkout, Real-Time preferences  Real-time feedback gives etc.) Feedback  Identified barriers  Device lending users the opportunity to  Purchase adequate number of devices to support Device to user program resources experiment in finding energy pilot Distribution acceptance of saving behaviors  Develop necessary customer education materials Model device to facilitate device lending program Source: Energy Information Administration
  • 59. Process Step Inputs Actions Outputs Lessons Learned  Define survey to capture: o Home characteristics (e.g., appliances)  Baseline profile of o Demographics customer  Customer focus o E Energy use b h i / tt behaviors/patterns characteristics and h t i ti d group feedback o Attitudes toward conservation attitudes  Pre-pilot surveys can  Example surveys o History of participation in utility energy  Confirmation that establish baselines for Conduct Pre- from past efficiency programs (e.g., rebates, etc.) treatment and control analysis Pilot Survey programs and  Select pilot treatment and control groups (likely samples represent other utilities random/stratified sample) the underlying  Collect feedback from customers across treatment treatment, population control, and total customer populations  Selected  Distribute customer education materials describing treatment program/reports population  Regularly generate and distribute home energy use  Resources to reports to treatment group customers  Pilot program g support report o More frequent feedback has been shown to participation generation and lead to greater energy savings  Addressed customer distribution  Promote opportunities for participants to obtain real-  Ensure pilot execution Execute Pilot concerns  Device time feedback devices to aid in their efforts to save allows for measurement Study  Demand for real-time distribution/ energy of cost effectiveness feedback devices collection model  Facilitate distribution and collection of real-time  Motivated and  Resource to field feedback devices educated participants customer calls,  Assist/respond to customer questions/issues with questions, issues device installation/operation  Customer  Consider offerings customer the opportunity to communications establish an energy reduction goal  Develop survey instruments to evaluate: oPerceptions of home energy use reports/devices  Ability to adjust oImpact on motivation savings for oBehavior changes made concurrent efficiency Collect  Be sensitive to  Pilot program oInvestments made program participation Participant program’s impact on participation oParticipation in other utility energy efficiency  Survey data/feedback Feedback customer satisfaction programs (e.g., rebates/incentives) – Important on participant for savings adjustments/avoid double-counting experience and oConservation attitudes satisfaction  Collect feedback from pilot treatment/control groups Source: Energy Information Administration
  • 60. Process Step Inputs Actions Outputs Lessons Learned  Measurement of  Opt-out nature of participant energy program allows for  Energy savings results t be more lt to b consumption data  Obtain measures of actual consumption over  Determination of Evaluate reasonably extended to  Quantification of treatment period for treatment, control (if any), and program cost Program potential for savings in pilot program population (sample) effectiveness ($ per Results/Savin entire population costs  Compare to normalized historical consumption and kWh of savings) gs Cost  Specific customer  Data from control group data to determine impact of the feedback  Determination of Effectiveness segments (e.g., higher participant intervention on energy conservation differences across energy users) are likely feedback survey segments (e.g., to see different levels of savings for high savings energy users)  Data on device use Conduct  Execute customer surveys and data collection to  [Limited data exists on  Pilot program pattern ongoing determine persistence of energy savings and customer persistence of savings participation  Data on savings monitoring involvement from utility programs] persistence Source: Energy Information Administration
  • 61. Note on Sample Size Determination Opt-in device program inherently prohibit simple control group determination due to the self-selected nature of the treatment group Opt-out programs lend themselves to easier control group definition Avoids problems that can come from using historical consumption data beyond the need for weather normalization Economic conditions Media messaging Individual household factors: Tenant changes Occupancy Renovations Alternative approaches to evaluation of savings Confidence interval around the mean Confidence interval around the % change from prior period Linear regression and differenced linear fixed effects models g Source: Author’s calculations
  • 62. Note on Sample Size Determination The required sample size for a study aimed at verifying savings performance is a function of several parameters: Hypothesized magnitude of energy saving to detect (μ0-μ1) Standard deviation of energy consumption across households (σ) Desired confidence (1-α) and power (1-β): tolerance for making a wrong conclusion Sample size to test the difference in two population means Rule of thumb for 95% Confidence, 80% Power: 2z1 / 2  z1   2 16 n n   0  1  2    1  2  0          Hypothesized Annual Energy Savings (to Test) 1% 2% 5% 10% 100 kWh 100 kWh 200 kWh 200 kWh 500 kWh 500 kWh 1000 kWh 1000 kWh 1000 kWh 1,600 400 64 16 Std. Dev. of  2000 kWh 6,400 1,600 256 64 Annual  3000 kWh 14,400 3,600 576 144 Energy  Consumption Cons mption 4000 kWh 4000 kWh 25,600 6,400 1,024 256 5000 kWh 40,000 10,000 1,600 400 Source: Author’s calculations
  • 63. Thank you! y Questions? Ed Carroll: 608-310-6910 Mark Brown: 612-237-8268
  • 64. Behavior Change Through Rate Design 30 Studies have Average Customer shown that as 25 much as a 6% Cents / kWh Rate D energy savings can i 20 k be achieved from 15 inclining block Rate C rates that take 10 advantage of price Rate B Existing 5 elasticity in Flat Rate consumer demand. Rate A 0 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 1 000 1 200 1 400 1 600 1 800 2 000 kWh / Month Avg Percent Change in Usage Price Elasticity Rate A Rate B Rate C Rate D Short Run Mean -5.9% -2.2% -1.0% -0.5% Std Dev 2.0% 0.8% 0.3% 0.2% Long Run g Mean -18.4% -6.7% -3.1% -0.7% Std Dev 6.5% 2.4% 1.1% 0.4%
  • 65. Inclining Block Rate Bill Impacts I li i bl k rate would b d i Inclining block t ld be designed so th t only th hi h t users of d that l the highest f electricity would see billing increases. Simulated Distribution of Bill Impacts 30% Tier 1 Original Break- 20% Cutoff even Point 10% Change in Monthly Bill 0% -10% -20% n -30% Break-even Point w/Price Elasticity -40% -50% No Price Elasticity -60% With Price Elasticity -70% 100 200 300 400 500 600 700 800 900 1,000 1,100 1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 Customer Size (kWh/month)