1. Modeling Renewable Electricity Generation: Issues, Technology Characteristics, and Resources Presented to the NESCAUM NE- MARKAL Stakeholders’ Group December 18, 2003 Boston, Massachusetts

2. Acknowledgements U.S. EPA State and Local Capability Building Branch, Art Diem U.S. DOE NREL Colleagues: –Walter Short –Liz Brady –Christy Herig

3. Overview Market Penetration Modeling of Renewable Electricity Generation Technologies Renewable Electricity Modeling Issues Renewable Electricity Technology Cost and Performance Renewable Energy Resources

4. Market Penetration Modeling of Renewable Electricity Generation Technologies Limitations Recommendations: –Include priority resources and technologies –Assumptions and approaches should be carefully selected, well documented, and flexible –Keep limitations in mind when using results NREL research on renewables-specific models can help improve large, national market penetration models

5. Some Interesting Renewables Questions What are the costs of transmission access, intermittency, and site access for Wind? How well does PV availability match load? What is the distributed generation value of PV? What is the optimal allocation of biomass resource between electricity generation and other uses, such as transportation fuel?

6. Renewable Electricity Modeling Issues: Modeling Renewable Electricity is Different Capital intensive technologies New technologies (no business as usual) Some competitive; others under development Dispersed resources, intermittent resources Multiple uses for resources (biomass, solar) Target different electricity markets (wholesale, retail, green power) Policy incentives and disincentives uncertain and important

7. Why focus on modeling renewable electricity generation? Environment Emissions constraints would drive renewable energy use. Economy Mitigates fuel price risk; local economic benefit. Energy Security Domestic, renewable resource. Other Benefits –Distributed Generation –Investment Risk

8. General Assumptions Influence RE Results (1) How big is the demand for new electricity generation? Demand growth (depends on efficiency, economy, demographics) and electric generator retirement determine size of opportunity for renewables and other new generation technologies. What is the time frame? Opportunity for new technologies is greater in longer-term, and timing of market penetration varies by technology. What is the cost of capital? Renewable energy technologies are generally more capital- intensive than fossil fuel technologies.

9. General Assumptions Influence RE Results (2) What is the geographic structure of the model? Wind and solar resource are dispersed; aggregation necessary for modeling but limits analysis. How are electricity markets specified in terms of location, time (day, season), power quality, or other categories? Value of RE depends on specific electricity market. How are competitor technologies expected to perform? Major factors include fuel price, environmental compliance costs, and regulatory issues for other technologies.

10. Competitor Technology Cost and Performance Power Development: How does renewable electricity generation compare to other alternatives for new generation? New Natural Gas generally considered the primary competitor in wholesale market (also Coal, Nuclear) What are the competitors? Will Distributed electricity generation become a significant market segment?

11. Renewable Electricity Modeling Issues: Conclusions Modelers face substantial challenges in modeling renewable electricity General assumptions and assumptions about competitor technologies strongly influence renewables results Sensitivity analysis: necessary but insufficient Existing modeling frameworks and data limit ability to address some questions Two Challenges –Select reasonable assumptions and methods within imperfect frameworks –Improve modeling

12. Technology Cost and Performance Different assumptions have been developed, sometimes spanning large range of values. Differences arise from: –Different study objectives –Different perspectives on technology R&D risks –Different levels of detail Assumptions contribute, but are not alone in determining, model results for cost &/or amount of deployment

13. Technologies Wind Turbines in Wind Power Plants Photovoltaics on Buildings or Utility-Scale Installations Biomass Cofiring and Biomass Gasification Other Potentially Relevant Technologies: isolated wind turbines, biorefineries, hydropower, MSW, landfill gas, ocean

14. Wind Energy Modeling Issues Transmission –Access –Cost Intermittency –Capacity Credit –Ancillary Services Resource –Available Windy Lands / Site Access –Temporal Profile of Resource

15. Wind Energy Technology Cost and Performance Rapid deployment of wind energy technology in recent years Mature, commercial technology Policy incentives still very influential Remaining opportunities for improvement and learning continue to reduce costs Presents major modeling challenge because of large, demonstrated potential coupled with large uncertainties

18. Photovoltaics Modeling Issues Highest value opportunities are in building- integrated applications – retail, not wholesale electricity market Undeveloped markets for PV values: –Distributed generation? –Load management? –Building material? –Reliability? –Risk? (demand, supply, fuel price, investment, regulatory…)

19. Photovoltaics Technology Cost and Performance Rapid declines in technology cost Rapid deployment growth rate in markets with strong incentives Continued R&D to reduce costs Remaining opportunities for improvement and learning continue to reduce costs, but magnitude of effect on modeling results generally small in near to mid term

21. Biopower Modeling Issues Multiple paths for resources: Ecosystem, materials, agricultural products, energy products, chemicals, waste. Range of technologies: Direct-fired, co- firing, gasification, anaerobic digestion, pyrolysis. Cogeneration / Combined Heat & Power opportunities

22. Biopower Technology Cost and Performance Varying maturity and cost reduction potential of technologies Electricity generation from biomass is best analyzed in an integrated framework that considers fuels, high-value products, and waste management Biomass is relatively more important in Northeast than in other regions.

24. Renewable Energy Resource Assumptions Wind Solar Biomass Ocean – not included here

25. Choices in Wind Resource Inputs for Modeling Wind resource data set Criteria for available windy lands Wind resource classes Geographic regions in model Time periods in model

26. Wind Resource Data Assumptions High resolution data available for all of Northeast through Wind Powering America (DOE) at www.eere.energy.gov/windpoweringamerica Includes Offshore Resource Wind resource estimates based on modeling validated with measurements Models use meteorological and topographic data Estimates at 50 meter height Validation seeks to achieve 80% of model results within 20% of measured value

27. Wind Resource Assumptions: Available Windy Lands Wind resource availability depends on land availability DOE Wind Powering America Program developed land exclusion assumptions Assumptions were developed for entire nation using mostly national data sets Different assumptions could be made based on: –Different exclusion criteria –Additional data (need state data sets and expertise!)

28. DOE Land Exclusion Criteria 100% exclusion of the following lands –Slope greater than 20% (not shown on resource maps) –Specially Designated Lands (Environmental Protection, Recreation, etc.) –Water; Wetlands; Urban areas; Airports/airfields –3km buffer (except water and slope) Isolated resources excluded (using density) 50% exclusions –Other Forest Service and DOD lands –Forest not on crest ridges –USGS GAP tier 2

32. Temporal Data NE-MARKAL input plan has 6 time bins: –Three Seasons (Summer, Winter, Intermediate) –Two Times of Day (Day, Night) Temporal data from high resolution maps is being evaluated and may be useable for this study

33. Solar Resource Assumptions Northeast U.S. resource lower quality than some but still substantial resource Part of “Resource” issue is quantification of good sites for building-integrated PV –Influence of site factors on opportunities for building- integrated PV not well characterized Is State or municipal buildings data available? Are there site-specific studies of distribution system (load and need for upgrades)?

35. Biomass Resource Assumptions Vast diversity of biomass resources –Wood residues –Agricultural residues –Dedicated energy crops –Municipal solid waste Variation in data quality and cost estimate availability across these resource types Variation in frequency of data update Geographic and temporal variability in cost data NREL is obtaining new data that includes cost per ton by county (not shown in maps)

40. Dedicated Energy Crops This resource could be considered, but would be relatively expensive. NREL is obtaining new data that will include dedicated energy crops. Cost data is available.

41. Biomass Resource Issues Summary Biomass resource data is very complex: –Diverse resources –Variability in the resource data quality –Limited time-series data available to evaluate year to year changes in resource amounts Resource amounts depend on economics and politics of multiple industries (agriculture, forestry, fuels, agrochemicals), and on weather Modeler must select which resources to include

42. Renewables Conclusions Renewable technology and resource assumptions will influence model results for renewables, as will other assumptions and methods Among the renewable energy assumptions, wind technology and resource assumptions will likely have the largest effect on results, followed by biomass and solar Renewables-specific models now being developed (WinDS, PV in Buildings) may help answer detailed renewables questions in future studies

43. Renewables Modeling Ideas Use regional wind supply curve from WinDS instead of non-generation costs and constraints Estimate plant-specific costs of biomass cofiring and use as inputs Analyze cost of PV under different rate structures and use as inputs