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PNNL-SA-58293. Modeling EERE Deployment Programs. Donna Hostick Dave Belzer Pacific Northwest National Laboratory November 29, 2007. PNNL-SA-58293. Problem.

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Modeling eere deployment programs


Modeling EERE Deployment Programs

Donna Hostick

Dave Belzer

Pacific Northwest National Laboratory

November 29, 2007




  • EERE deployment programs contribute to overall program energy saving benefits, but are difficult to model in terms of the traditional cost and performance parameters

  • When programs are modeled within GPRA (PDS) framework, the approaches vary widely – estimates may be inconsistent from one program to another


Purpose of study


Purpose of Study

  • First phase of PAE effort to improve deployment modeling

    • Identify and characterize modeling of EERE deployment programs

    • Address possible improvements to modeling process

    • Note gaps in knowledge


Defining deployment


Addressing market barriers and consumer behavior

Currently available technologies

Preparing the market for future technologies

Demonstrations replicated as showcases

Does not include:



First-of-a-kind or scale-up demonstrations


Defining Deployment

“Activities that promote the adoption of advanced energy efficiency and renewable energy technologies and practices.”

EERE Deployment Inventory 2004


Rd3 activities over development timeline


RD3 Activities over Development Timeline


Fy08 request by primary focus


FY08 Request by Primary Focus


Fy08 eere deployment activities


FY08 EERE Deployment Activities


Fy08 eere deployment activities cont


FY08 EERE Deployment Activities, cont.


Eere deployment categories


EERE Deployment Categories

  • General information dissemination activities

  • Targeted training and workshops

  • Partnerships with others to solve technical and administrative issues

  • Recognition for key products and awards for products, institutions and/or individuals

  • Sponsoring and promoting competitions to solve specific deployment issues

  • Purchasing enabling technologies and programs

  • Developing and implementing standards and regulations

  • Providing technical assistance to “early adopters”

  • Providing privileges and incentives

  • Demonstrations of key technologies, systems, and designs.


Target audience and sector


Target Audience and Sector


Taxonomy of deployment


Taxonomy of Deployment

  • Stage-Avenue

    • Data Gathering/Market Research

    • Advanced Market Preparation and Infrastructure Development

    • Identifying Promising Technologies

    • Public Infrastructure and Policy, Regulation

    • Manufacturing and Business Infrastructure

    • Technology Adoption Supports

    • Marketing and Outreach

  • Each activity has a target sector

Part of R&D Process


How do deployment strategies save energy


How do Deployment Strategies Save Energy?

  • Reduce costs of energy-saving technologies and designs

    • Explicit costs

    • Implicit costs

  • Reduce risk associated with adopting new energy-saving or renewable technologies, designs, and strategies

  • Reduce time to market entry of technologies

  • Modify consumer behavior


Gpra pds modeling framework


GPRA (PDS) Modeling Framework

  • Modified versions of NEMS and MARKAL provide mid-term and long-term benefits estimates

    • Detailed technology representations of electricity markets, most residential and commercial end uses, and vehicle choice

    • Program cases represent adjustments to technology characterizations, cost, and performance parameters expected to result from program activities

  • Most deployment activities modeled “off-line”


Characterizing current eere deployment modeling


Characterizing Current EERE Deployment Modeling

  • Modeling R&D and Deployment Jointly

    • Hydrogen, Fuel Cells, and Infrastructure Technologies

    • Biomass Technologies

    • FreedomCAR and Vehicle Technologies

    • Solar Energy Technologies

    • Wind Technologies

  • Modeling Deployment Activities within the NEMS Framework

    • Building Technologies (Energy Star Appliances)

  • Off-Line (Non-Integrated) Modeling Approaches

    • Building Technologies

    • Industrial Technologies

    • Federal Energy Management Program (FEMP)

    • Weatherization and Intergovernmental Program


Modeling deployment activities within the nems framework


Modeling Deployment Activities within the NEMS Framework

  • General Approach

    • Alter parameters related to consumer or business decision making

    • Reduce “ancillary costs” associated with technology adoption

  • Consumer Decision Making

    • Modify discount rates or “time preference premiums” (residential and commercial modules)

    • Modify parameters associated with “riskiness” of new technologies (vehicle choice in response to FreedomCAR activities)

  • Business Decision Making (Renewable Energy Suppliers and Investors)

    • Modify risk premium component in cost of capital (Biomass, Wind)

    • Risk premium sometimes represented as “beta” coefficient in Capital Asset Pricing Model

  • Ancillary Costs

    • Interconnection costs

    • Environmental studies and permitting


Example energy star appliances


Example: Energy Star Appliances

  • Logit choice algorithms in NEMS residential model

    • Separate parameters on appliance cost and annual energy cost

    • Ratio of parameters is roughly equal to the (average) discount rate

  • Modeling Energy Star for GPRA

    • One of the logit parameters is adjusted to effectively lower discount rate – reflects informational aspect of Energy Star labels

    • Adjustment made to increase market penetration of Energy Star products to meet program goals (currently, part of baseline)

  • Key issue: NEMS framework is suitable for representing program activities, but not predicting outcomes

  • Energy Star has performed variety of assessment studies, but none indicate impact on parameters associated with consumer decision making


Example biomass fuels


Example: Biomass Fuels

  • Equity premium “beta” coefficients in NEMS Renewable Fuels Module can be used alter cost of capital for future cellulosic ethanol plants

    • The risk of an average investment (i.e., broad portfolio of common stocks) is multiplied by beta and then added to “risk-free” rate = cost of capital

    • Corn-based ethanol plants (beta = 1.5), cellulosic ethanol (beta = 1.75)

    • Unlikely the actual betas would be so similar

  • Recent work by NREL and On-Location for FY2009 GPRA

    • Biomass Scenario Model used to characterize risk premium for different classes of investors

    • Blended Risk Premium used in NEMS model

    • Risk premium declines on basis of increases in productive capacity – presumably based upon Biomass Scenario Model

  • Issue: What empirical basis is there to establish appropriate risk premium and how much should it adjust as new plants are built?


Key elements of sebold fields dynamic adoption model


Key Elements of Sebold-Fields Dynamic Adoption Model

  • A Framework for Planning and Assessing Publicly Funded Energy Efficiency Programs (California PGC, 2001)

  • Adoption Process Model

    • Market share = Awareness * Willingness * Availability

  • Awareness has dynamic elements:

    • Awareness = (a0 + a1 INT ) x (1 – Awareness[t-1])

      + (a3 + a4 INT ) x Awareness [t-1]

  • Willingness has similar dynamic function; alternatively, Willingness can be described as function of Payback

  • Payback is function of intervention:

    • Payback = c0 + c1 Payback [t-1] + c2 * INT


Newell anderson study of iac program


Newell-Anderson Study of IAC Program

  • 2002 study analyzed audit data from Industrial Assessment Center Program

    • 9,000 assessments from period 1981-2000

    • Measure cost and estimated energy savings in database

  • Newell and Anderson hold out promise that study can quantify impact of information on discount rates

  • Empirical results indicate very short payback periods required to undertake efficiency measures (typically 1.25 – 1.5 year payback)

  • Conclusion is that program did not appreciably affect discount rates

    • Discount rates generally in accordance with other studies

    • Provides useful distribution of discount rates

  • Key points:

    • Casts doubt on modeling approaches that significantly lower discount rates as response to information programs

    • Program success is measured by number of firms made aware of cost-effective conservation options ($100 million in annual energy savings)

    • Estimates of program influence on decision making would have required careful program design with control group and pre- and post-participation interviews


Ordered logit techniques for estimating mt interventions


Ordered Logit Techniques for Estimating MT Interventions

  • Econometric technique to estimate market shares

    • ACEEE paper in 2004 summer study – Skumatz, Weitzel

    • Works with stated preferences, not revealed preferences

  • Develop alternative option sets

    • Technical and cost (size, efficiency, system cost)

    • Factors influenced by deployment activities (i.e., rebates)

    • Reliability (warranty, experience in the field)

  • Construct sample of potential adopters, (HVAC installers, 200 50 in sample)

  • Respondents order option sets (on cards)

    • Option sets described by characteristics – not by name

    • In particular study, name of efficient technology was disclosed at end to reveal bias


Modeling choices of steam generation technologies in cims


Modeling Choices of Steam Generation Technologies in CIMS

  • CIMS Model of Canadian economy – Marc Jacquard (UBC)

  • Energy Journal – January 2005

  • Survey of nearly 600 industrial firms (260 in final sample)

  • Three types of steam generation:

    • Conventional boiler

    • High efficiency boiler

    • Cogeneration system

  • Stated preference approach yields flexibility for policy analysis

  • Multinomial logit model estimated from responses

    • Intangible cost (constant term) is a key output

    • l Intangible cost is highest for cogeneration, lowest for high efficiency

    • Interpretation is that cogeneration brings safety and reliability issues

  • Modeling information by segmenting market (“well-informed” or not)


Key modeling aspects demand sectors


Key Modeling Aspects – Demand Sectors

  • Basic question: what particular market barrier is being addressed

  • 1) Lack of awareness (i.e., not “well informed” ) (new technology)

    • Market segmentation in NEMS or MARKAL (Can use existing choice framework)

    • Ongoing surveys to track awareness – link to EERE activity

  • 2) Consumers not familiar with trade-off between first cost and operating costs

    • General information programs would affect average discount rate (or distribution of discount rates

    • Ongoing surveys to track consumer sensitivity – issue: specific link to EERE activity

  • 3) Risk perceptions, high costs of gathering information for specific technologies, ancillary implementation costs

    • Use terms in logit (or similar specifications) to adjust implicit or intangible cost (as is now done for vehicle choice)

    • Tracking impact of EERE deployment activities would require periodic studies to quantify these factors

    • Stated preference studies could disentangle effects from tangible and intangible costs


Retrospective analysis cfl sales in the northwest


Retrospective Analysis: CFL Sales in the Northwest

  • Addressing uncertainty in evaluation of MT (ACEEE 2004)

    • Stratus Consulting, Summit Blue Consulting

    • Energy Star program

    • Activities of NW Energy Efficiency Alliance

  • 2001 CFL sales increased by 7.7 million units in NW

    • 3.6 million from rebates, giveaways

    • Of remaining 4.1 million, how many due to Alliance?

  • Interviewed retailers, utility program managers (31)

    • Alliance totally responsible – set up infrastructure… OR

    • Alliance had minor influence – CFL sales up sharply elsewhere

  • Alternative scenarios

    • “High influence” (4.1 million), and “low influence” (2 million)

    • Used @Risk to characterize other uncertainty


Modeling risks of renewable energy investments


Modeling Risks of Renewable Energy Investments

  • A very few publicly available studies have characterized the risk premium

  • As with discount rates, no study has yet been found to directly link to governmental intervention

    • jor European study in 2004 suggests initial approach

    • EC-funded study surveyed 650 stakeholders – representatives from

      • Utilities

      • Project developers

      • Investors

      • Banks

      • Manufacturers

      • Government

    • Survey augmented by in-depth interviews

  • EC study developed ranges of risk premiums for different types of renewable projects

  • Other aspect is to assess deployment impact on ancillary (implementation) cost


Key issues questions re modeling


Key Issues/Questions Re: Modeling

  • Energy Models include technology cost and choice behavioral parameters

    • Few (no?) empirical studies as how interventions might change behavioral parameters – typically used to represent deployment

    • Program evaluation studies typically focus on market outcomes, rather than characterizing behavioral parameters

  • This study suggests some approaches for gathering empirical data to estimate deployment impacts

    • To what degree does a better understanding of past deployment activities serve to inform probable effects from future activity?

    • How should effort should be prioritized for analysis of activities that do not fit into NEMS framework?

    • Representation or Prediction?