Presentation on theme: "Michael Li, Senior Policy Advisor Office of Energy Efficiency and Renewable Energy U.S. Department of Energy."— Presentation transcript:
Michael Li, Senior Policy Advisor Office of Energy Efficiency and Renewable Energy U.S. Department of Energy
Source: What is Green Button? Common-sense idea that electricity customers should be able to download their own energy usage information in a consumer- and computer-friendly format.
Who is implementing green button? NSTAR PG&E Reliant SDG&E TXU Energy Utilities and electricity suppliers in 24 states across various regulatory regimes have committed to provide 30 million US homes and businesses Green Button data. Commitment to Green Button: American Electric Power Austin Energy Baltimore Gas & Electric CenterPoint Energy Chattanooga EPB Commonwealth Edison Glendale Water and Power National Grid Oncor PECO Pepco Holdings PPL Electric Utilities Pacific Power Rocky Mountain Power Southern California Edison Virginia Dominion Power Utilities & Electricity Suppliers with Green Button today: (almost 10 million homes) NSTAR PG&E Reliant SDG&E TXU Energy
Green Button Facts What it is: Energy usage information in a common XML format (NAESB ESPI data std) PG&E example: how much electricity (kWh) did one metered customer consume every hour for the last year Markets: Residential, commercial and industrial Type: initially electricity, but the data standard is extensible to gas and water data Timeliness: Not real time data, but 24 hour-old back office data. Time interval: Again the data standard is extensible and could include any interval of data. Most will provide 15 minute interval or hourly data. However, some will provide monthly data, too. Metering system: You don’t need to have AMI to participate. AMR works, too. Transfer of data: Green Button, Download My Data – goes directly from the utility to the customer; most will implement this version. Green Button Connect - automated data transfer from the utility to a third party with customer authorization is the 2 nd part of the data std; may be implemented as early as this year in 1-2 states.
Helping to spur new innovation and Green Button Apps Green Button Apps!
Apps for Energy Winners Best Overall App Grand Prize: LeafullyLeafully Location: Seattle, Washington Leafully, helps utility customers visualize their Green Button data, as a variety of units, such as the amount of trees needed to offset an individual’s energy usage. Best Overall App Second Prize: MelonMelon Location: Washington, DC The app uses Green Button to evaluate the energy performance of commercial buildings. Best Overall App Third Prize: VELObillVELObill Location: New York, NY VELObill app helps makes it easier for utility customers to view their energy usage, measure whether it is high or low, and compare it to that of their peers. Best Student App Grand Prize: wotz wotz Location: Irvine, CA Best Student App Second Prize: Budget It YourselfBudget It Yourself Location: Cleveland, OH
Working Group Leadership Susan Ackerman, OR PUC Vaughn Clark, OK SEO Todd Currier, WA SEO Jennifer Easler, IA Consumer Advocate Joshua Epel, CO PUC Jim Gallagher, NY ISO Bryan Garcia, CT Clean Energy Fund Frank Murray, NY SEO Pat Oshie, WA PUC Phyllis Reha, MN PUC Cheryl Roberto, OH PUC Janet Streff, MN SEO Keith Welks, PA Treasury Malcolm Woolf, MD SEO SEE Action Working Groups 7
Evaluation, Measurement, and Verification (EM&V) of Residential Behavior-Based Energy Efficiency Programs: Issues and Recommendations June 25, 2012 Michael Li US Department of Energy Annika Todd Lawrence Berkeley National Lab
This information was developed as a product of the State and Local Energy Efficiency Action Network (SEE Action), facilitated by the U.S. Department of Energy/U.S. Environmental Protection Agency. Content does not imply an endorsement by individuals or organizations that are part of SEE Action working groups, or reflect the views, policies, or otherwise of the federal government.
What is a behavior-based energy program? Why is evaluation of these programs hard? Why is designing a program as a “randomized controlled trial” (RCT) so important? Outline: Evaluation of Behavior-Based Programs 10
Programs that affect the way that consumers use energy without using traditional methods, such as prices and rebates Instead, use simple psychological levers or information to change behavior Example 1: Comparing your energy use with your neighbors Example 2: Providing real-time information about energy use Other examples: Competitions, rewards: Turning energy use into a game Education / Outreach: Information about energy behavior Display of feedback: Simplify / Framing What is a behavior-based energy program? 11
Potential Benefits In theory, potentially cheap to implement and result in significant energy savings cost effective As a result, increasingly being adopted nationwide Potential Concerns In reality, these programs are relatively new and evidence of energy saving effects is unclear Potential for unsubstantiated claims What are the potential benefits and concerns? 12
It is very important to measure effect of these programs Need to gain information about how well different types of programs work Are the estimates energy savings valid for utilities to claim savings? Why is evaluation crucially important? 13
Strong problems of “selection bias”: households who join (choice, screening) are fundamentally different Observed differences might be due to program; might difference between groups Selection bias can skew the results of the evaluation Why is evaluation of these programs hard? 14 Population Join Didn’t Join
Energy programs “selection bias”: households who opt-in may be more energy conscious Observed difference in energy use might be due to the program; but might be difference between groups Why is evaluation of these programs hard? 15 Opt-in Don’t opt-in
It may be more difficult to measure the impact of behavior-based programs correctly (in contrast to other programs such as appliance rebates) –Impacts vary significantly between households –Within a household, hard to disentangle changes in overall energy usage between program, other factors –Savings are relatively small: often 1-5%, so if an evaluation is biased, large implications Why is evaluation of these programs hard? 16
Bad evaluation could lead to bad policy decisions Why is evaluation of these programs hard? 17
“Evaluation, Measurement, and Verification (EM&V) of Residential Behavior-Based Energy Efficiency Programs” Provides guidelines and best practices for –Program design –Program analysis and evaluation given design –Provides rankings for different methods Target audiences: –Senior managers responsible for overseeing and reviewing efficiency program designs and evaluations –Practitioners, evaluation professionals, and staff responsible for reviewing efficiency program designs and evaluations SEE Action Report 18
Primary recommendation – a well designed, RCT program results in: –Transparent, straightforward analysis –Robust, accurate, valid program impact estimates –High degree of confidence in program effectiveness Why? –RCT means that households are assigned to the program randomly (as opposed to household choice or screening criteria) –Solves selection bias Why is designing a program as a randomized controlled trial (RCT) so important? 19
If RCTs are not feasible, recommendations for acceptable “quasi-experimental” methods –More opaque, difficult, complex analysis –Quasi-experimental methods try to correct for selection bias –Lower degree of confidence in validity of savings estimates Why is designing a program as a randomized controlled trial (RCT) so important? 20
Problem: Potential conflicts of interest –Recommendation: Third-party evaluator transparently defines and implements program evaluation, assignment to control and treatment groups, data selection Problem: The same savings may be claimed by two programs (e.g., a behavioral program & appliance rebate program both claim savings from appliances) –Recommendation: Estimate and account for this “double counted savings” overlap to the extent possible by comparing control to treatment groups Other Key Recommendations 21
The hope is that in the future, we will have conclusive evidence about the effectiveness of different types of behavior-based programs Move away from RCTs We are not yet at this point… Recommendations for the Future 22
Main point: evaluation of behavior-based programs is difficult, but if the program is designed in the right way (using a RCT) then we can be confident that the evaluation of the program’s energy savings is valid Many guidelines and technical recommendations in the report: –SEE Action website, –Lawrence Berkeley National Lab website: behavioranalytics.lbl.gov Mike Li: Annika Todd: Questions?