Modeling EERE Deployment Programs

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

2. 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

3. 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

4. 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 4

5. PNNL-SA-58293 RD3 Activities over Development Timeline 5

6. PNNL-SA-58293 FY08 Request by Primary Focus 6

7. PNNL-SA-58293 FY08 EERE Deployment Activities 7

8. PNNL-SA-58293 FY08 EERE Deployment Activities, cont. 8

9. 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. 9

10. PNNL-SA-58293 Target Audience and Sector 10

11. 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 11

12. 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 12

13. 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

14. 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 14

15. 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 15

16. 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 16

17. 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? 17

18. PNNL-SA-58293 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 18

19. PNNL-SA-58293 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 19

20. PNNL-SA-58293 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 20

21. 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) 21

22. 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

23. 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 23

24. 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 24

25. 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? 25