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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

PNNL-SA-58293

Modeling EERE Deployment Programs

Donna Hostick

Dave Belzer

Pacific Northwest National Laboratory

November 29, 2007


Problem

PNNL-SA-58293

Problem

  • 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

2


Purpose of study

PNNL-SA-58293

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

3


Defining deployment

Includes:

Addressing market barriers and consumer behavior

Currently available technologies

Preparing the market for future technologies

Demonstrations replicated as showcases

Does not include:

Research

Development

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

PNNL-SA-58293

Defining Deployment

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

EERE Deployment Inventory 2004

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Rd3 activities over development timeline

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RD3 Activities over Development Timeline

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Fy08 request by primary focus

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FY08 Request by Primary Focus

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Fy08 eere deployment activities

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FY08 EERE Deployment Activities

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Fy08 eere deployment activities cont

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FY08 EERE Deployment Activities, cont.

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Eere deployment categories

PNNL-SA-58293

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.

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Target audience and sector

PNNL-SA-58293

Target Audience and Sector

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Taxonomy of deployment

PNNL-SA-58293

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

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How do deployment strategies save energy

PNNL-SA-58293

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

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Gpra pds modeling framework

PNNL-SA-58293

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”

13


Characterizing current eere deployment modeling

PNNL-SA-58293

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

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Modeling deployment activities within the nems framework

PNNL-SA-58293

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

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Example energy star appliances

PNNL-SA-58293

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

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Example biomass fuels

PNNL-SA-58293

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?

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Key elements of sebold fields dynamic adoption model

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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

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Newell anderson study of iac program

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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

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Ordered logit techniques for estimating mt interventions

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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

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Modeling choices of steam generation technologies in cims

PNNL-SA-58293

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)

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Key modeling aspects demand sectors

PNNL-SA-58293

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

22


Retrospective analysis cfl sales in the northwest

PNNL-SA-58293

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

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Modeling risks of renewable energy investments

PNNL-SA-58293

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

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Key issues questions re modeling

PNNL-SA-58293

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?

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