NewEnergyNews: TODAY’S STUDY: HOW RATEPAYERS BENEFIT WHEN UTILITIES BUY EFFICIENCY

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Gleanings from the web and the world, condensed for convenience, illustrated for enlightenment, arranged for impact...

The challenge now: To make every day Earth Day.

YESTERDAY

  • TODAY’S STUDY: A Way For New Energy To Meet Peak Demand
  • QUICK NEWS, December 5: Trial Of The Century Coming On Climate; The Wind-Solar Synergy; The Still Rising Sales Of Cars With Plugs
  • THE DAY BEFORE

  • Weekend Video: Trump Truth And Climate Change
  • Weekend Video: The Daily Show Talks Pipeline Politics
  • Weekend Video: Beyond Polar Bears – The Real Science Of Climate Change
  • THE DAY BEFORE THE DAY BEFORE

  • FRIDAY WORLD HEADLINE-Aussie Farmers Worrying About Climate Change
  • FRIDAY WORLD HEADLINE-The Climate Change Solution At Hand, Part 1
  • FRIDAY WORLD HEADLINE-The Climate Change Solution At Hand, Part 2
  • FRIDAY WORLD HEADLINE-New Energy And Historic Buildings In Europe
  • THE DAY BEFORE THAT

    THINGS-TO-THINK-ABOUT THURSDAY, December 1:

  • TTTA Thursday-First Daughter Ivanka May Fight For Climate
  • TTTA Thursday-Low Profile High Power Ocean Wind Energy
  • TTTA Thursday-A Visionary Solar Power Plant
  • TTTA Thursday-EVs Have A Growth Path
  • AND THE DAY BEFORE THAT

  • ORIGINAL REPORTING: How The Clean Power Plan Drove The Utility Power Mix Transition
  • ORIGINAL REPORTING: How Utilities Are Answering The Distributed Energy Resources Challenge
  • ORIGINAL REPORTING: Looking At New Rates To Unlock The Utility Of The Future
  • THE LAST DAY UP HERE

  • TODAY’S STUDY: The Power Potential Of Personal Wind
  • QUICK NEWS, November 29: Climate Change Forces Hard Choices In Alaska; New Energy To Utilities-“Can’t-Beat-Us-So-Join-Us”; Fact-Checking Trump Hot Air On Wind
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    Anne B. Butterfield of Daily Camera and Huffington Post, f is an occasional contributor to NewEnergyNews

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    Some of Anne's contributions:

  • Another Tipping Point: US Coal Supply Decline So Real Even West Virginia Concurs (REPORT), November 26, 2013
  • SOLAR FOR ME BUT NOT FOR THEE ~ Xcel's Push to Undermine Rooftop Solar, September 20, 2013
  • NEW BILLS AND NEW BIRDS in Colorado's recent session, May 20, 2013
  • Lies, damned lies and politicians (October 8, 2012)
  • Colorado's Elegant Solution to Fracking (April 23, 2012)
  • Shale Gas: From Geologic Bubble to Economic Bubble (March 15, 2012)
  • Taken for granted no more (February 5, 2012)
  • The Republican clown car circus (January 6, 2012)
  • Twenty-Somethings of Colorado With Skin in the Game (November 22, 2011)
  • Occupy, Xcel, and the Mother of All Cliffs (October 31, 2011)
  • Boulder Can Own Its Power With Distributed Generation (June 7, 2011)
  • The Plunging Cost of Renewables and Boulder's Energy Future (April 19, 2011)
  • Paddling Down the River Denial (January 12, 2011)
  • The Fox (News) That Jumped the Shark (December 16, 2010)
  • Click here for an archive of Butterfield columns

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    Some details about NewEnergyNews and the man behind the curtain: Herman K. Trabish, Agua Dulce, CA., Doctor with my hands, Writer with my head, Student of New Energy and Human Experience with my heart

    email: herman@NewEnergyNews.net

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      A tip of the NewEnergyNews cap to Phillip Garcia for crucial assistance in the design implementation of this site. Thanks, Phillip.

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    Pay a visit to the HARRY BOYKOFF page at Basketball Reference, sponsored by NewEnergyNews and Oil In Their Blood.

  • ---------------
  • TODAY AT NewEnergyNews, December 6:

  • TODAY’S STUDY: How To Balance Competing Solar Interests
  • QUICK NEWS, December 6: Sliver Of Hope? Al Gore In Climate Change Meet With Donald Trump; The Opportunity In New Energy; Google Seizing New Energy Opportunity

    Monday, March 31, 2014

    TODAY’S STUDY: HOW RATEPAYERS BENEFIT WHEN UTILITIES BUY EFFICIENCY

    The Program Administrator Cost of Saved Energy for Utility Customer-Funded Energy Efficiency Programs

    Megan A. Billingsley, Ian M. Hoffman, Elizabeth Stuart, Steven R. Schiller, Charles A. Goldman, Kristina LaCommare, March 2014 (Lawrence Berkeley National Laboratory)

    Executive Summary

    End-use energy efficiency is increasingly being relied upon as a resource for meeting electricity and natural gas utility system needs within the United States. There is a direct connection between the maturation of energy efficiency as a resource and the need for consistent, high-quality data and reporting of efficiency program costs and impacts. To support this effort, LBNL initiated the Cost of Saved Energy Project (CSE Project) and created a Demand-Side Management (DSM) Program Impacts Database to provide a resource for policy makers, regulators, and the efficiency industry as a whole.

    This study is the first technical report of the LBNL CSE Project and provides an overview of the project scope, approach, and initial findings, including:

    • Providing a proof of concept that the program-level cost and savings data can be collected, organized, and analyzed in a systematic fashion;

    • Presenting initial program, sector, and portfolio level results for the program administrator CSE for a recent time period (2009-2011); and

    • Encouraging state and regional entities to establish common reporting definitions and formats that would make the collection and comparison of CSE data more reliable.

    The LBNL DSM Program Impacts Database includes the program results reported to state regulators by more than 100 program administrators in 31 states, primarily for the years 2009-2011. In total, we have compiled cost and energy savings data on more than 1,700 programs over one or more program-years for a total of more than 4,000 program-years’ worth of data, providing a rich dataset for analyses. We use the information to report costs-per-unit of electricity and natural gas savings for utility customer-funded, end-use energy efficiency programs. The program administrator CSE values are presented at national, state, and regional levels by market sector (e.g., commercial, industrial, residential) and by program type (e.g., residential whole home programs, commercial new construction, commercial/industrial custom rebate programs).

    In this report, the focus is on gross energy savings and the costs borne by the program administrator—including administration, payments to implementation contractors, marketing, incentives to program participants (end users) and both midstream and upstream trade allies, and evaluation costs. We collected data on net savings and costs incurred by program participants. However, there were insufficient data on participant cost contributions, and uncertainty and variability in the ways in which net savings were reported and defined across states (and program administrators). As a result, they were not used extensively in this report. It is also important to note that savings metrics reported by program administrators draw heavily from estimated values.

    Results

    The CSE values presented in this study are retrospective and may not necessarily reflect future CSE for specific programs, particularly given updated appliance and lighting standards. The CSE values are presented as either (a) the savings-weighted average values; (b) as an inter-quartile range with median3 values across the sample of programs; or (c) both.

    Table ES-1 provides an overall indication of national, savings-weighted average program administrator CSE values by sector using two indicators (e.g., levelized CSE 6% real discount rate and first-year CSE).4 Figure ES-1 indicates the savings-weighted averages, medians and inter-quartile ranges for levelized CSE values using a 6% discount rate.

    Our key national and regional findings are:

    • The U.S. average levelized CSE was slightly more than two cents per kilowatt-hour when gross savings and spending is aggregated at the national level and the CSE is weighted by savings.

    • Residential electricity efficiency programs had the lowest average levelized CSE at $0.018/kWh. Lighting rebate programs accounted for at least 44% of total residential lifetime savings with a savings-weighted average levelized CSE of $0.007/kWh. The residential CSE, when the lighting programs were removed, was $0.028/kWh. Low-income programs have an average levelized CSE at $0.070/kWh.

    • Commercial, industrial and agricultural (C&I) programs had an average levelized CSE of $0.021/kWh.

    • Not surprisingly, the levelized CSE varies widely, both among and within program types. We find that the median value is typically higher than the savings-weighted average for nearly all types of programs. One possible explanation is that our sample includes a number of very large programs and for any given program type, larger efficiency programs have lower CSE than smaller programs because administrative costs are spread over more projects (e.g., economies of scale).

    • In reviewing regional results, efficiency programs in the midwest had the lowest average levelized CSE ($0.014/kWh), while programs in northeast states had a higher average CSE value ($0.033/kWh). Programs in western states are at $0.023/kWh and for the southern states included in the database, the comparable program CSE was $0.028/kWh.

    • Natural gas efficiency programs had a national, program administrator savings-weighted average CSE of $0.38 per therm, with significant differences between the C&I and residential sectors (average values of $0.17 vs. $0.56 per therm, respectively).

    • The cost of saved energy may vary across program administrator portfolios for reasons that have little to do with programmatic efficiency. In some jurisdictions, a policy mandate of acquiring all reasonably available cost-effective energy efficiency can lead to a focus on more comprehensive programs which will tend to have a higher CSE because they are serving more diverse constituencies and technologies. In other jurisdictions, the focus may be on acquiring the cheapest savings possible.

    Program-level results

    We also examined the cost of saved energy by program type for both residential and C&I programs (see Chapter 3). Figure ES-2 shows an example for the C&I programs, including savings-weighted average (pale green bar) CSE values, the inter-quartile ranges (blue line) and median (red dotted line) CSE values. The median value and inter-quartile ranges for CSE are based on calculations for each individual program and gives equal weighting to programs irrespective of their relative size in terms of either savings or costs.

    The simplified C&I programs have median values for program administratorCSE that range from $0.01/kWh to $0.05/kWh. It is worth noting that the savings-weighted average CSE values for custom and prescriptive rebate program categories are $0.018/kWh and $0.015/kWh, respectively. Since these two program categories account for almost 70% of C&I sector savings, they tend to drive the overall CSE results for the C&I sector (less than $0.02/kWh).

    For the residential programs, several program categories have a relatively tight range of program CSE values (see Figure ES-3). For example, Consumer Product Rebate programs have an inter-quartile range of $0.01/kWh to $0.04/kWh and a low savings-weighted average (~$0.01/kWh). However, the residential prescriptive ($0.03/kWh to $0.11/kWh), new construction ($0.03/kWh to $0.11/kWh) and whole-home upgrade ($0.03/kWh to $0.21/kWh) program types have significantly larger ranges. There are several possible reasons for the range of CSE values in each of these program categories. The prescriptive simplified program category includes detailed program types that implement a wide variety of measures (e.g., HVAC, insulation, windows, pool pumps) as well as some generic “prescriptive” programs6 that often include measures also found in the consumer product rebate category. This broad measure mix, and the variation in costs and measure lifetimes associated with those measures, are possible drivers for the wide range of CSE values for the prescriptive category.

    For the Whole-Home Upgrade program category, the broad range of program designs and delivery mechanisms (this category includes audit, direct install, and retrofit/upgrade programs) may help explain the relatively wide range of CSE values. Overall, most C&I program categories have a relatively smaller inter-quartile range of CSE values compared to residential program categories.

    Total resource cost of saved energy

    Although we focus on program administrator costs in this report, it is important to note that these metrics do not reflect a total cost perspective since program administrators infrequently report participant costs. We were able to collect participant cost data from a handful of program administrators. However, given small sample size and uncertainty in how participant costs were derived, it is difficult to confidently assess the “all-in” or total resource cost of efficiency or analyze potential influences on the total cost of the efficiency resource. For these reasons, in Figure ES-4, we compare the program administrator’s levelized CSE vs. a total resource levelized CSE for illustrative purposes only. We calculate this total resource CSE for the simplified program categories where both program administrator and participant costs are available for more than 18 program years.

    For this small sample of programs, we found that the levelized total resource CSE values are typically double the program administrator CSE with the exception of the Residential Whole Home Upgrade program category (which has a savings-weighted total resource CSE about 25-30% higher than the program administrator CSE). Further data collection and analyses could better characterize the way in which the ratio of program administrator costs to participant costs varies as a function of sector, measure types, and market maturity; and how incentives and direct support might be optimized to pay no more than is necessary to meet a state’s efficiency policy objectives.

    Observations and Recommendations on Reporting

    In calculating the CSE, we utilized information on program administrator costs, annual energy savings, estimated lifetime of measures installed in a program, and an assumed discount rate. However, with respect to current program reporting practices, we observed several challenges to the collection of this data for the purposes of calculating the CSE:

    • Inconsistencies in the quality and quantity of the costs and savings data led LBNL to develop and attempt to apply consistent data definitions in reviewing and entering program data:

    o Program administrators in different states did not define savings metrics (e.g., varying definitions of net savings) and program costs consistently; and

    o Market sectors and program types were not characterized in a consistent fashion among program administrators.

    • Many program administrators did not provide the basic data needed to calculate CSE values at the program level (i.e., program administrator costs, lifetime savings or program-average measure lifetimes), which can introduce uncertainties into the calculation of CSE values (as we developed and utilized methods to impute missing values in some cases).

    As a practical matter, the quality and quantity of program data reported by program administrators is an important factor in assessing energy efficiency as a resource in the utility sector. Additional rigor, completeness, standard terms, and consensus on at least essential elements of reporting could pay significant dividends for program administrators and increase confidence in energy efficiency savings among policymakers and other stakeholders, particularly in situations where efficiency is treated as a resource in utility procurement decisions, ISO/RTO forward capacity markets or as an environmental compliance or mitigation option by state or federal environmental agencies.

    Of the 45 states currently running utility-customer funded efficiency programs (Barbose et. al. 2013), only 31 states provided reporting with sufficient transparency to complete a program-level CSE analysis, and almost all of the 31 states’ data required some interpretation for purposes of regional or national comparison. With more consistent and comprehensive reporting of program results, additional insights can quite possibly be obtained on trends in the costs of energy efficiency as a resource as program administrators scale up efforts, what saving energy costs among an array of strategies, and what and how cost efficiencies might be achieved.

    Therefore, we urge state regulators and program administrators to consider annually reporting certain essential data fields at a portfolio level and more comprehensive reporting of program-level data in order to facilitate the comparison of efficiency program results at state, regional and national levels. A diagram illustrating this reporting hierarchy approach can be found in Chapter 5, Figure 5-1.

    As part of the LBNL CSE Project, we intend to continue collecting energy efficiency program data and analyzing and reporting the CSE for efficiency actions funded by utility customers. We also plan to:

    • Work with state, regional and national stakeholders to encourage the collection of program cost and impact data using a common terminology and program typology as defined in this report and a companion policy brief (Hoffman et al. 2013). This is important for organizing program data into appropriate and consistent categories so that programmatic energy efficiency, as a regional and national resource, can be reliably assessed.

    • Annually compile data reported by program administrators and state agencies from across the United States.

    • Conduct additional analyses to help increase understanding of factors that influence EE program impacts, costs and the cost of saved energy.

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