# Presentation: 739163

## Modeling EERE Deployment Programs

- Donna Hostick
- Dave Belzer
- Pacific Northwest National Laboratory
- November 29, 2007

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## 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

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## 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

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## 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

- “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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## FY08 Request by Primary Focus

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## FY08 EERE Deployment Activities

| EERE Program  | Deployment Activity |
| --- | --- |
| Hydrogen | Hydrogen Education and Outreach ($3.9 million) |
|  | Codes and Standards Program ($16 million, primarily code research) |
|  | Technology Validation ($30 million) |
|  | Distributed Energy Fuel Cell Systems ($7.7 million) |
| Biomass | Integration of BioRefinery Technologies (supports Biofuels Initiative) ($92.103 million) |
|  | Biomass Products Development  ($10 million) |
|  | Bioconversion Platform R&D (R&D phase with marketing analysis) ($38.3 million total; only a small percentage is for deployment) |
|  | Thermochemical Platform R&D (R&D phase with marketing analysis) ($19.537 million total; only a small percentage is for deployment) |
|  | Regional Feedstock Partners Outreach, within Feedstock Infrastructure ($9.737 million total; only a small percentage is for deployment) |
|  | Education and Outreach (not called out separately within budget request) |
| Solar Energy | Solar America Initiative  (includes Technology Pathway Partnerships, Technology Acceptance, and Technology Evaluation) ($16.34 million is primarily deployment-focused) |
|  | Solar Decathlon (not called out separately within budget request) |
| Wind | Wind Resource Assessment (Systems Integration and Technology Acceptance) ($12.869 million is primarily deployment-focused) |
|  | Wind Powering America (not called out separately within budget request) |
| FreedomCAR/
Vehicles | Clean Cities (now Vehicle Technologies Deployment, $9.593 million) |
|  | Graduate Automotive Technology Education (GATE) ($0.5 million) |
|  | Advanced Vehicle Competitions ($1.3 million) |
|  | Legislative and Rulemaking activities ($1.8 million) |

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## FY08 EERE Deployment Activities, cont.

| EERE Program  | Deployment Activity |
| --- | --- |
| Buildings | Residential R&D:  Building America (some deployment components) and the National Builders Challenge Initiative ($19.7 million total; only a small percentage is for deployment) |
|  | Commercial Buildings R&D (some deployment components) ($7 million total; only a small percentage is for deployment) |
|  | Equipment Standards and Analysis ($13.361 million) |
|  | Energy Star ® ($6.776 million) |
|  | Rebuild America: Building Application Centers ($2.834 million total for Rebuild America) |
|  | Rebuild America: EnergySmart Schools and EnergySmart Hospitals ($2.834 million total for Rebuild America) |
|  | Building Energy Codes and Advanced Energy Codes Initiative ($3.751 million, primarily code deployment) |
|  | Rebuild America: Commercial Lighting Challenge ($2.834 million total for Rebuild America) |
| Industrial | Best Practices/Save Energy Now Program ($8.833 million) |
|  | Industrial Assessment Center ($4.035 million) |
| FEMP | Technical Guidance and Assistance ($6.519 million) |
|  | Project Financing (ESPC Support, UESC Support) ($7.935 million) |
|  | DOE Specific Investments (new initiative, not included in FY08 request) |
|  | Federal Fleet (part of FEMP’s $2.337 million Planning, Reporting and Evaluation activity) |
| WIP | Weatherization Assistance Program ($144 million) |
|  | State Energy Program ($45.501 million) |
|  | Tribal Energy Program ($2.957 million) |
|  | Asia Pacific Partnership ($7.5 million) |

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## 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.

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## Target Audience and Sector

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## 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?

- 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

- 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”

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## 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

- 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

- 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 

- 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

*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 

- 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

- 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

- 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

- 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

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## 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 

- 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 

- 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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