Modeling Renewable Electricity Generation: Issues, Technology Characteristics, and Resources

Presented to the NESCAUM NE-MARKAL Stakeholders’ Group

December 18, 2003

Boston, Massachusetts

Acknowledgements

• U.S. EPA State and Local Capability Building Branch, Art Diem

• U.S. DOE

• NREL Colleagues:– Walter Short– Liz Brady– Christy Herig

Overview

• Market Penetration Modeling of Renewable Electricity Generation Technologies

• Renewable Electricity Modeling Issues

• Renewable Electricity Technology Cost and Performance

• Renewable Energy Resources

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

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?

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

Why focus on modeling renewable electricity generation? • Environment

Emissions constraints would drive renewable energy use.

• EconomyMitigates fuel price risk; local economic benefit.

• Energy Security Domestic, renewable resource.

• Other Benefits– Distributed Generation

– Investment Risk

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.

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.

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?

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

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

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

Wind Energy Modeling Issues • Transmission

– Access– Cost

• Intermittency– Capacity Credit– Ancillary Services

• Resource– Available Windy Lands / Site Access– Temporal Profile of Resource

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

Wind Capital Costs

0

200

400

600

800

1000

1200

1400

1990 2000 2010 2020 2030 2040 2050 2060

Year

Ca

pit

al

Co

st

($/k

W)

EPRI/DOE TC

WINDS

AEO 2003

CEF

Wind Capacity Factors

0

10

20

30

40

50

60

1980 1990 2000 2010 2020 2030 2040 2050 2060

Year

Ca

pa

cit

y F

ac

tor

(Pe

rce

nt)

CEF

EPRI/DOE TC

TELLUS REPORT

TELLUS REPORT

TELLUS REPORT

WINDS

AEO 2003

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…)

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

Photovoltaics Capital Costs

0

2000

4000

6000

8000

10000

12000

14000

16000

1995 2000 2005 2010 2015 2020 2025 2030 2035

Year

Ca

pit

al

Co

st

($/k

W)

AEO 2003

CEF

EPRI/DOE Res

EPRI/DOE TC

SOLAR PROGRAM

SOLAR PROGRAM

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

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.

Biomass Capital Costs

0

500

1000

1500

2000

2500

3000

1995 2000 2005 2010 2015 2020 2025 2030 2035

Year

Ca

pit

al

Co

st

($/k

W)

EPRI/DOE TC

AEO 2003

CEF

Renewable Energy Resource Assumptions

• Wind

• Solar

• Biomass

• Ocean – not included here

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

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

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!)

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

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

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)?

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)

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.

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

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

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