Transcript www.nescaum.org

Modeling Renewable Electricity
Generation: Issues, Technology
Characteristics, and Resources
Presented to the NESCAUM NEMARKAL 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
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•
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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.
• Economy
Mitigates 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 capitalintensive 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
1400
1200
Capital Cost ($/kW)
1000
800
EPRI/DOE TC
WINDS
AEO 2003
CEF
600
400
200
0
1990
2000
2010
2020
2030
Year
2040
2050
2060
Wind Capacity Factors
60
Capacity Factor (Percent)
50
40
CEF
EPRI/DOE TC
TELLUS REPORT
30
TELLUS REPORT
TELLUS REPORT
WINDS
AEO 2003
20
10
0
1980
1990
2000
2010
2020
Year
2030
2040
2050
2060
Photovoltaics Modeling Issues
• Highest value opportunities are in buildingintegrated applications – retail, not
wholesale electricity market
• Undeveloped markets for PV values:
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Distributed generation?
Load management?
Building material?
Reliability?
Risk? (demand, supply, fuel price, investment,
regulatory…)
Photovoltaics Technology Cost
and Performance
• Rapid declines in technology cost
• Rapid deployment growth rate in markets
with strong incentives
• Continued R&D to reduce costs
• Remaining opportunities for improvement
and learning continue to reduce costs, but
magnitude of effect on modeling results
generally small in near to mid term
Photovoltaics Capital Costs
16000
14000
Capital Cost ($/kW)
12000
10000
AEO 2003
CEF
EPRI/DOE Res
8000
EPRI/DOE TC
SOLAR PROGRAM
SOLAR PROGRAM
6000
4000
2000
0
1995
2000
2005
2010
2015
Year
2020
2025
2030
2035
Biopower Modeling Issues
• Multiple paths for resources: Ecosystem,
materials, agricultural products, energy
products, chemicals, waste.
• Range of technologies: Direct-fired, cofiring, 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
3000
2500
Capital Cost ($/kW)
2000
EPRI/DOE TC
AEO 2003
1500
CEF
1000
500
0
1995
2000
2005
2010
2015
Year
2020
2025
2030
2035
Renewable Energy Resource
Assumptions
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Wind
Solar
Biomass
Ocean – not included here
Choices in Wind Resource Inputs
for Modeling
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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 buildingintegrated 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
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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