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The Role of CCS under a Clean Energy Standard. 30 th USAEE/IAEE Conference Oct 10, 2011 Washington, DC. Chris Nichols, Analyst Office of Strategic Energy Analysis, NETL. Overview. Background: Discussion of what we’re modeling and why we did it Approach Model used and scenarios run - PowerPoint PPT Presentation
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USAEE Conference 2011, CJN Oct 2011
The Role of CCS under a Clean Energy Standard
30th USAEE/IAEE ConferenceOct 10, 2011 Washington, DC
Chris Nichols, AnalystOffice of Strategic Energy Analysis, NETL
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Overview
• Background:– Discussion of what we’re modeling and why we did it
• Approach– Model used and scenarios run
• Results– Summary graphs of significant results
• Conclusions– What can we say about CCS in a CES under this
modeling regime
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Background• In his most recent State of the Union address,
President Obama proposed a Clean Energy Standard (CES) to require that 80 percent of the nation’s electricity come from clean energy technologies by 2035.– Other CO2 reduction strategies (taxes, cap and trade,
command and control) have been extensively modeled, while CES has not
– There is a need to understand the impacts of CO2 capture and storage (CCS) under a CES in terms of emissions reductions and electricity prices
– This analysis looks at the impact of R&D in CCS under a CES and comapres tax and cap & trade scenarios
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Modeling Approach
• We applied the Environmental Protection Agency’s Nine Region MARKAL Database (EPAUS9r) that was developed by EPA around the nine U.S. Census divisions. – Each of the nine (R1-R9) regions has its own
conventional Reference Energy System (RES) and these nine RESs are interconnected through trade technologies links.
• EPAUS9r contains both existing and future technologies, so even in a base case scenario, without additional constraints, a shift towards more cost-efficient technologies occurs.
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Regions Map and Definitions in EPAUS9r Database
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Scenario Definitions
• Base case: resource supply and end-use demands are taken from AEO 2010
• Base CES: 80% of electricity from “clean energy” by 2035– Renewables worth 1 credit, NG worth 0.5, coal with
CCS at 0.9• CES with Enhanced CCS: cost and performance of
CCS (both coal and NG) meet DOE goals• CO2 tax: $23/t CO2 emissions tax in 2020, increasing at
5.8 % annually• CO2 cap: CO2 reduction level from CES, via CO2 cap in
electricity generation
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MODEL RESULTSGraphs of electricity generation by technology typeRegional electricity pricesRelative CO2 reductions
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Base Case: National Electricity Generation, 2005-2055
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 -
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Elec
trici
ty g
ener
atio
n (T
hous
and
GW
h)
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Base CES: National Electricity Generation, 2005-2055
2010
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Electricity Production by Technology
Year
Elec
trici
ty g
ener
atio
n (T
hous
and
GW
h)
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CES w/ enhanced CCS: National Electricity Generation, 2005-2055
2010
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Elec
trici
ty g
ener
atio
n (T
hous
and
GW
h)
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CO2 Cap Scenario: National Electricity Generation, 2005-2055
2010
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Elec
trici
ty g
ener
atio
n (T
hous
and
GW
h)
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CO2 Tax Scenario: National Electricity Generation, 2005- 2055
2010
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Elec
trici
ty g
ener
atio
n (T
hous
and
GW
h)
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Base Case Scenario: Average Marginal Electricity Costs by Region, 2005-2055
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Base CES Scenario: Average Marginal Electricity Costs by Region, 2005-2055
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CES with enhanced CCS: Average Marginal Electricity Costs by Region, 2005-2055
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CO2 Cap Scenario: Average Marginal Electricity Costs by Region, 2005-2055
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CO2 Tax Scenario: Average Marginal Electricity Costs by Region, 2005-2055
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Conclusions
• In the modeling framework used for this analysis, R&D in CCS:– Limits the growth in electricity prices– Allows for the same levels of electricity generation as
the Base scenario– Provides more CO2 reductions in a CES, and similar
in scale to a tax and cap & trade • Natural gas is the “swing” resource
– Changes in the resource curve or requiring coupling with renewables change the results drastically
USAEE Conference 2011, CJN Oct 2011
DisclaimerThis report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference therein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed therein do not necessarily state or reflect those of the United States Government or any agency thereof.
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For more information…
Contact me via phone or email:Chris Nichols
[email protected] 285-4172
Thanks to my coauthors:Peter BalashNadja Victor
