United States
Environmental Protection
Agency
MARKAL Scenario
Analyses of Technology
Options for the Electric Sector

The Impact on Air Quality

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                                                          EPA/600/R-06/114
                                                            September 2006
MARKAL Scenario Analyses of Technology Options for the
          Electric Sector: The Impact on Air Quality
                                  By
                   Timothy L. Johnson, Joseph F. DeCarolis
       Carol L. Shay, Daniel H. Loughlin, Cynthia L. Gage, and Samudra Vijay
                National Risk Management Research Laboratory
                 Air Pollution Prevention and Control Division
                 Research Triangle Park, North Carolina 27711
                 United States Environmental Protection Agency
                     Office of Research and Development
                          Washington DC 20460

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                                        Notice
The U.S. Environmental Protection Agency through its Office of Research and Development
funded and managed the research described here. It has been subjected to the Agency's review
and has been approved for publication as an EPA document. Mention of trade names, products,
or services does not convey, and should not be interpreted as conveying, official EPA approval,
endorsement or recommendation.

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                                       Foreword
The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting the
Nation's land, air, and water resources. Under a mandate of national environmental laws, the
Agency strives to formulate and implement actions leading to a compatible balance between
human activities and the ability of natural systems to support and nurture life. To meet this
mandate, EPA's research program is providing data and technical support for solving
environmental problems today and building a science knowledge base necessary to manage our
ecological resources wisely, understand how pollutants affect our health, and prevent or reduce
environmental risks in the future.

The National Risk Management Research Laboratory (NRMRL) is the Agency's center for
investigation of technological and management approaches for preventing and reducing risks
from pollution that threaten human health and the environment. The focus of the Laboratory's
research program is on methods and their cost-effectiveness for prevention and control of
pollution to air,  land, water, and subsurface resources; protection of water quality in public water
systems; remediation of contaminated sites, sediments and ground water; prevention and control
of indoor air pollution; and restoration of ecosystems. NRMRL collaborates with both public and
private sector partners to foster technologies that reduce the cost of compliance and to anticipate
emerging problems. NRMRL's research provides solutions to environmental problems by:
developing and  promoting technologies that protect and improve the environment; advancing
scientific and engineering information to support regulatory and policy decisions; and providing
the technical support and information transfer to ensure implementation of environmental
regulations and  strategies at the national, state, and community levels.

This publication has been produced as part of the Laboratory's strategic long-term research plan.
It is published and made available by EPA's Office of Research and Development to assist the
user community and to link researchers with their clients.
Sally Gutierrez, Director
National Risk Management Research Laboratory
                                                                                      in

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                                   Table of Contents
List of Figures	v
List of Tables	vii
Acronyms and Abbreviations	 viii
Executive Summary	x
Section 1: Electricity Generation and Air Quality	1
  1.1 Electricity Production and Demand	1
  1.2 Electricity and Air Quality	4
  1.3 Technology Assessment,  Scenario Analysis, and the Use of MARKAL	5
  1.4 Overview of the Electric Sector Technology Assessment	7
Section 2: EPA National Model Baseline	8
  2.1 Technology Characterization	8
  2.2 Model Constraints	10
  2.3 Baseline Results	15
Section 3: Sensitivity Analysis of the EPANM Database	21
  3.1 Background- Sensitivity Analysis	21
  3.2 Methodology	22
  3.3 Selecting Inputs and Outputs	23
  3.4 Global Sensitivity Results - Correlation Analysis	25
  3.5 Global Sensitivity Results -Normalized Linear Regression	29
  3.6 Parametric Sensitivity Analysis	38
  3.7 Summary of Observations from Sensitivity Analysis	45
Section 4: The Future Role of Nuclear Energy in the U.S	47
  4.1 Introduction	47
  4.2 Nuclear Technology Representation in MARKAL	47
  4.3 MARKAL Analysis	51
  4.4 Conclusions	60
Section 5: The Air Quality Implications of Carbon Capture and Sequestration in U.S. Electric
Markets	62
  5.1 CCS Technology  Background	63
  5.2 Implementation of CCS in the U.S. EPA National MARKAL Model	68
  5.3 CCS Results and Analysis	70
  5.4 Conclusions	80
Section 6: Nuclear Power, Carbon Capture and Sequestration, and Wind Generation: The
Competition Among Technology Alternatives	81
Section 7: Limitations, Future Model Development, and Analysis Directions	89
Section 8: References	92
                                                                                     IV

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                                    List of Figures
Figure 1.1: Energy Intensity and Growth Rate Trends for U.S. GDP and Electricity Use	2
Figure 1.2: Fuel Sources for U.S. Electricity Generation	3
Figure 1.3: U.S. Distribution of Electricity Usage in 2004	4
Figure 2.1: Generic Electric Sector RES	8
Figure 2.2: Baseline Electric Generation	16
Figure 2.3: Electricity Use by Demand Sector	16
Figure 2.4: Current Capacity and Capacity Additions	17
Figure 2.5: Electric Generation by Fuel Type	18
Figure 2.6: SO2 Retrofits	19
Figure 2.7: NOX Retrofits	19
Figure 2.8: Carbon Emissions in the Electric Sector	20
Figure 2.9: NOx and SC>2 Emissions in the Electric Sector	20
Figure 3.1: Factors affecting use of coal in electricity generation	30
Figure 3.2: Factors affecting use of natural gas in electricity generation	31
Figure 3.3: Factors affecting use of oil in electricity generation	32
Figure 3.4: Factors affecting CO2 emissions from electricity generation	33
Figure 3.5: Factors affecting use of NOx controls on coal-fired power plants	34
Figure 3.6: Factors affecting system-wide CO2 emissions	35
Figure 3.7: Factors affecting system-wide SO2 emissions	36
Figure 3.8: Factors affecting system-wide NOx emissions	37
Figure 3.9: Factors affecting fossil  fuel imports	38
Figure 3.10: Impact of natural gas cost on electricity generation by fuel type	39
Figure 3.11: Impact of oil cost on electricity generation by fuel type	40
Figure 3.12: Impact of nuclear capacity on electricity generation by fuel type	41
Figure 3.13: Impact of the new electricity generation technology hurdle rate on electricity
generation by fuel type	42
Figure 3.14: Changes in CO2 emissions from the electric sector in response to changes in inputs
	43
Figure 3.15: Changes in system-wide CO2 emissions in response to changes in inputs	44
Figure 3.16: Changes in net imports in response to changes in inputs	45
Figure 4.1: Schematic diagram of the light water reactor fuel cycle in the EPANMD	49
Figure 4.2: Schematic diagram of the high temperature gas-cooled reactor fuel cycle in the
EPANMD	50
Figure 4.3: Electricity generation in a baseline run of EPANMD with both conventional and
advanced nuclear technologies available	52
Figure 4.4: Change in the 2030 nuclear share of total electricity production  as coal price is
increased parametrically	53
Figure 4.5: Change in the 2030 nuclear share of total electricity production  as natural gas price is
increased parametrically	53
Figure 4.6: System-wide and electric sector  CO2 emissions relative to baseline scenario	55
Figure 4.7: Electricity generation by technology from non-BAU carbon trajectories and both
conventional and  advanced nuclear technologies are available	57
Figure 4.8: Conventional and advanced nuclear capacity over time, compared with the maximum
capacity allowed by the growth rate constraints	58

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Figure 4.9: SO2 and NOx emissions relative to the baseline scenario under the low carbon
scenarios presented in Figure 4.7	59
Figure 4.10: Electricity generation by generation source with a carbon trajectory reflecting 80
percent of 1995 levels from 2015 to 2030	60
Figure 5.1: Schematic of the three generic routes to carbon capture and sequestration	65
Figure 5.2: Electricity generation per-period by technology class for the baseline CCS scenario
	72
Figure 5.3: Electric sector CO2, SO2, and NOX and economy-wide CO2 emissions as a function
of time for the baseline CCS scenario	73
Figure 5.4: Electricity generation per-period by technology class for the 80 percent emissions
scenari	75
Figure 5.5: Electricity generation per-period by technology class for the 50 percent emissions
scenario	75
Figure 5.6: Electric sector CO2, SO2, and NOx and economy-wide CO2 emissions as a function
of time	76
Figure 5.7: Share of electricity generation for conventional gas and all CCS technologies under
three gas-price scenarios	79
Figure 6.1: Electricity generation per period by technology class for the baseline CCS scenario
	82
Figure 6.2: Electricity generation per-period by technology class for the 50 percent emissions
scenario	83
Figure 6.3: Electric sector SO2, and NOx emissions as a function of time for the 50 percent
emissions scenario	83
Figure 6.4: Fraction of 2030 electric power generation by technology class for five scenarios.. 84
Figure 6.5: Fraction of 2030 electric power generation by technology class for four scenarios
with the relaxed wind growth constraints	86
Figure 6.6: Electricity generation per-period by technology class for the 50 percent emissions
scenario	87
                                                                                        VI

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                                    List of Tables

Table 1.1: Percent of Total Emissions from U.S. Electric Generation Technologies	   4
Table 2.1: Existing Electric Generation Technologies	   9
Table 2.2: Electricity Generation Technologies and Associated Cost Parameters	   11
Table 2.3: Lifetime and Availability Parameters for Electricity Generation Technologies	  12
Table 2.4: Electricity Transmission and Distribution  Costs	   13
Table 2.5: Total Allowed Generating Capacity of Renewable Technologies in GW	   13
Table 2.6 Electric Sector NOx and SC>2 Emission Constraints in the EPANMD	   13
Table 2.7: Emission Factors for CO2 and SO2 and NOX in the EPANMD	  14
Table 2.8: Emission Control Retrofit Data	   15
Table 3.1: Ranges for inputs used in the Monte Carlo simulation	  24
Table 3.2: Correlation coefficients between model inputs and electric sector outputs	  25
Table 3.3: Correlation coefficients between model inputs and system-wide outputs	  27
Table 3.4: Correlation coefficients between electric sector and all outputs	  28
Table 3.5: Correlation coefficients between system outputs and all outputs	   29
Table 4.1: Cost and performance estimates for LWRs used in EPANMD	   48
Table 4.2: Cost and performance estimates used in EPANMD for HTGRs	   51
Table 4.3: Annual maximum growth rates by model time period and incremental capacity
allowable over the growth rate	   51
Table 4.4: GW of advanced nuclear added  by model  time period	  60
Table 5.1: Estimated cost and performance ranges  from the literature for new CCS
technologies as summarized by the IPCC	   66
Table 5.2: CCS technology parameters as implemented in the EPANMD	  68
Table 5.3: Baseline CCS scenario electric power generation by technology class and time ....   71
Table 5.4: Baseline CCS scenario new capacity investment by technology class	  72
Table 5.5: Share of electricity generation in 2030 by  technology for the initial CCS scenario
and four parametric analyses of differences in CCS technology costs relative to baseline
values	  77
Table 5.6: Share of electricity generation in 2030 by  technology for the initial CCS scenario
and four parametric analyses of differences in CCS retrofit technology costs and efficiencies
relative to baseline values	   78
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  Term
                          Acronyms and Abbreviations
Definition
AEO         Annual Energy Outlook
AER         Annual Energy Review
ANOVA     analysis of variance
AQA         air quality assessment
BAU         business as usual
BTU         British thermal units
CAA         Clean Air Act
CAIR        Clean Air Interstate Rule
CCS         carbon capture and sequestration
CCSP        Climate Change Science Program
CO          carbon monoxide
CO2         carbon dioxide
Conv Nuc    scenario of relaxed wind constraints and nuclear limited to conventional
DOE         Department of Energy
EIA         Energy Information Administration
ELC         electric
EOR         enhanced oil recovery
EPA         Environmental Protection Agency
EPA9R       EPA 9 region MARKAL database
EPANMD    EPA national model MARKAL database
EPRI         Electric Power Research Institute
FGD         flue gas desulfurization
GDP         gross domestic product
GHG         greenhouse gas
GJ           gigajoules
Gt           giagtonnes
GT-MHR    gas turbine-module helium reactor
GW         gigawatts
H2           hydrogen
H2S         hydrogen sulfide
HTGR       high temperature gas-cooled reactor
IE A         International Energy Agency
IGCC        integrated gasification  combine cycle
IHM         initial heavy metal, i.e. enriched uranium
IPCC         International Panel on Climate Change
ISA-W       Integrated Systems Analysis Workgroup
kg           kilogram
kt           kilotonnes
kW          kilowatts
kWh         kilowatt hours
LNB         low NOX burner
LHV         lower heating value
LWR         light water nuclear reactor
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MARKAL    MARKet ALlocation energy-systems computer model
MOX        mixed oxide reactor
M           million (mega)
MtCO2      megatonnesofCO2
MWd        megawatt days
MWe        megawatt electricity
N2           nitrogen
NETL        National Energy Technology Laboratory
NGA        natural gas
NGA MU    scenario with natural gas price mark up
NGCC       natural gas combined cycle
NGL        natural gas liquids
NOx         nitrogen oxides
2x Nuc Inv   scenario with nuclear investment costs doubled
62           oxygen
O&M        operation and maintenance
ORD        Office of Research and Development
PBMR       pebble bed modular reactor
PC          pulverized coal
PJ           petajoules
PM          particulate matter
PMio        PM with aerodynamic diameter 10 |j,m or less
PM2.5        fine PM with aerodynamic diameter of 2.5 |j,m or less
PU          plutonium
PUREX      plutonium and uranium extraction
PV          photovoltaics
R2          sum of squared residuals
RES         reference energy system
SCR         selective catalytic reduction
SNCR        selective non-catalytic reduction
SC>2          sulfur dioxide
TAG        technology assessment guide
Th          thorium
TIHM        tons of initial heavy metal
U           uranium
VOC        volatile organic carbon
yr           year
                                                                                   IX

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                                 Executive Summary

The U.S. EPA contributes to the U.S. Climate Change Science Program (CCSP) by working to
develop an understanding of the potential human health, ecosystem, and socioeconomic impacts
of global change. A central part of this work builds on traditional EPA expertise by examining
the connection between climate and air quality. Climate variability will likely result in changes
in regional meteorology.  These meteorological changes,  in turn, may affect air pollution levels
by  altering atmospheric  chemical fate and transport  processes  as  well  as biogenic and
anthropogenic emissions.   To characterize these changes, EPA's  Office of Research and
Development (ORD) is working on a multiyear Global Change Air Quality Assessment (AQA)
to explore the potential consequences  of global change on criteria pollutant emissions through
midcentury. The research  has advanced through a series of projects geared towards building the
ability to analyze the relationship between global  change and air quality and involves research
teams with expertise in  meteorological,  emissions,  and  air quality  modeling, as  well  as
technology assessment.   The  research  also takes into account demographic, economic, and
technological changes that would be expected to occur independent of global change.

EPA's Integrated Systems Analysis Workgroup (ISA-W) contributes to the AQA by crafting
scenarios of plausible technology  change  and assessing how this  evolution might impact air
pollutant emissions.  These activities focus on the two economic sectors that impact air quality
the most: transportation and energy (other sectors are considered to capture important system
effects).  Combined, the sectors account for roughly two-thirds of the pollutants that impact air
quality and are areas where significant technological changes are expected to occur over the next
several decades.  EPA researchers  will use the results of ISA-W s technology  assessments and
corresponding emission growth rates from the scenario analyses to calculate the future emission
profiles needed as input to the EPA's air quality  models.  In 2004, ISA-W produced a report
which outlined its  approach for technology assessments and provided initial  results for the
transportation sector.  The present technology assessment provides  a companion piece focused
on electricity generation.

The  future of air  quality will  be controlled by two  primary factors:  pollutant  emissions
(influenced by technology use and adoption) and ambient temperature (influenced by climate
change). This report presents a  series of scenario-driven technology assessments focused the first
of these drivers by examining  different  "classes" of power generation  technologies.   The
assessment investigates the barriers and potential  limits to the market penetration through 2030
of both conventional and alternative technologies, and examines the air emission impacts  from
their use.  Information from this assessment will  aid the selection  of scenarios for inclusion in
ORD's 2010 AQA synthesis report.

The report first provides a general  overview of EPA's national MARKAL database and energy
systems model (EPANMD) and presents results  for the business as usual  (BAU) baseline
scenario. Under baseline assumptions, total electricity use increases 1.3% annually from 13,378
PJ in  2000 to 19,622 PJ in 2030.  Annual growth in electricity demand varies between 1.0% in
the residential sector, to 2.1%  in the commercial  and 1.5% in the industrial sectors of the U.S.
economy.   A total of 293  GW of new electric generation capacity is added between 2000 and
2030 to meet this growth.  More than 76% of the  new capacity is natural gas technologies, with

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61% being natural gas combined cycle and 15% being natural gas combustion turbines. New
conventional  coal-fired power  plants are not added  until 2020, though  a small amount of
integrated gasification combined cycle generation comes on-line in 2015.  Renewables add 34
GW of capacity, with 61% coming from wind power generation, 15% from biomass combined
cycle, and 14% from geothermal.

Coal-fired power plants continue to generate the most electricity in the BAU scenario.  As the
emissions constraints in the electric generation sector tighten over time, much of the existing
capacity is retrofitted with NOx  and SO2 controls and 20 GW of new capacity is built. New coal
capacity  includes  FGD  for  SO2 and SCR  for  NOx and therefore  does not  need  retrofit
technologies for emissions controls.  Nuclear power capacity increases slightly, but this is due to
capacity upgrades at existing facilities; no new nuclear plants are built in the business-as-usual
case.   Overall, coal electric generation grows 0.5%  annually, natural gas grows 3.8%,  and
renewables grow 2.7%.

The model constrains NOX and SO2 starting in 2010 and  2015, respectively, to  conform with
DOE projections of the  impacts from the Clean Air Act regulations.  These constraints are
binding over the  applicable time periods and emissions do not drop below the upper limits.  CO2
emissions in the electric sector grow 1% annually from 689,296 kt in 2000 to 927,302 kt in 2030.

The EPANMD representation of coal plant retrofits is simplified in that it averages emissions
over the whole country and ignores regional issues related to air quality. As a result, the retrofit
coal capacity is smaller than it actually is (or will be), and when retrofits are  selected they tend to
be  lower cost technologies with lower removal  efficiencies.   Retrofits for SO2  control  are
currently installed on  4.8%  of existing coal fired power plants.   Tightening  of emissions
constraints leads to FGD units being installed on over 14%  of plants. Low NOx Burners (LNB)
for NOx control are currently on 80% of existing plants.  Over time, SNCR units are added to the
existing LNB units for increased emissions reductions.  In later years, SCR units play a larger
role (SCR would be favored from the start if the EPANMD captured emissions trading).

Sensitivity and uncertainty analyses  show that much of the EPANMD electric sector's behavior
appears to be  influenced by  whether specific technologies and fuels  meet base  or peak load
electricity demands. The predominant base load technologies are coal-fired power plants  and
nuclear power plants.  Coal is the most competitive, and has the largest market share.  Natural
gas- and oil-fueled technologies  are used to meet peak electricity demands. Both fuels experience
some cross-sector interactions with the transportation sector because natural gas and refined oil
products  can  be used within  vehicles.  While  there  was  some  evidence of cross-sector
interactions, these were  largely of secondary importance to  in-sector technology  and fuel
competition.

The electric sector emissions constraints have interesting effects on the generation technology
mix.   When  natural  gas becomes  more  expensive  and  natural gas technology  utilization
decreases, for instance, use of coal also  decreases.  This  behavior, which may  at first seem
counter-intuitive, is explained by the  system's response to the  electric sector NOx constraint.
Since coal technologies had higher NOx emissions than natural gas technologies, and since NOx
constraints  on electricity  generation were binding, the model opted to replace natural gas with
                                                                                      XI

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oil. Oil combustion also leads to greater NOx emissions than that from natural gas, thought it is
less than coal. Oil therefore displaces some coal. This  fuel-switching and related technology
change had implications on CO2 emissions as well.

The  electric  sector NOx constraint  also  affects the model's response  to  increased  nuclear
capacity. By introducing electricity generation capacity that does not have NOx emissions, coal-
fired power plants are less constrained in meeting the electric sector NOx constraint. In response,
the fraction of coal-fired plants projected to make use of NOX controls, such as SCR, decreases.
The same behavior can be expected from increased electricity generation from low- or zero-NOx
renewables, such as solar or hydropower.

The electricity generation investment hurdle rate has an additional impact  on future-year energy
sector technologies. Increasing the hurdle rate  effectively makes it more  difficult for new,
efficient  technologies  to  penetrate the electricity generation market.  This  has the effect of
increasing  the marginal  peak electricity  price,  increasing CO2  emissions  from electricity
generation, and decreasing the penetration  of renewables.  Thus,  addressing hesitancy to adopt
new technologies through  some  approaches for hedging risk  may  yield  a more  efficient
electricity generation system.

Imports of fossil fuels are correlated with the use of oil  in electricity generation, but inversely
correlated with natural gas use, reflecting the fact that  natural gas demand is largely met by
domestic supplies in the model.  Cross-sector fuel switching resulting from changes in natural
gas and  oil consumption in the transportation  sector may  also have an impact, but further
analysis is needed to characterize this behavior.

Finally, changes in system-wide CO2 emissions in response to variation  in model inputs were
minor, with decreases of less than 3% observed. This output is influenced by the inability of low-
CO2 emitting  technologies, and in particular, renewables, to achieve high market penetrations.
When these technologies do penetrate, the potential reductions in CO2 emissions are often offset
by increased use of coal and other fossil fuels, made possible via  the room under the NOx limit
created by the renewables.

Moving beyond its detailed sensitivity analysis of BAU results, the assessment focuses on the air
quality impacts of two advanced electric sector technologies: nuclear power and carbon capture
and sequestration  (CCS).  The analysis first treats these technologies separately in  order to
examine  their independent impacts on air quality relative to the BAU results; a final  series of
scenarios  allows  competition  between  these   advanced  technologies  and  a  rudimentary
representation of wind power.

The nuclear scenario results offer important insights into the role that this family of technologies
can play in the U.S. electricity system over the  next three decades.  Increased natural gas and
coal prices, for instance, do not have a significant impact on the penetration of new nuclear units.
As a result, nuclear power only  provides  a modestly effective hedge  against high fossil fuel
prices in the electric sector, at least over the fuel price ranges explored here.
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Though modest, these nuclear technology penetration levels contribute to meeting the electric
sector's CAA limits for SO2 and NOx.  Nuclear generation, however, has a limited  impact on
system-wide  carbon emissions. When the electric sector follows an arbitrary  reduced  carbon
emissions trajectory, nuclear capacity plays a more significant role and leads to SO2 and NOX
reductions below CAA constraints.

Although all nuclear  technology options are economical, the model  prefers advanced high
temperature gas-cooled reactors (HTGRs) to conventional designs such as light water and mixed-
oxide reactors.   The issues governing this preference include whether HTGR developers can
meet  the model's optimistic nth-of-a-kind cost  assumptions  and how rapidly  units can be
manufactured and deployed over the next few decades.

The assessment also approaches CCS from a combined technology adoption and air quality
perspective.  The EPANMD's energy  systems framework is important in this analysis as CCS
would compete with measures to reduce the carbon intensity of power production and efforts to
improve  end-use efficiency.  Driven by an arbitrary electric sector CO2 trajectory, for instance,
the model relies on coal-to-gas fuel-switching as much as it does on CCS.  The  model adopts
CCS mainly for baseload generation in the form of new IGCC  capture capacity.   CO2 capture
retrofits are not an economically attractive option as retrofit capacity incurs a significant energy
penalty.  Only when this penalty is reduced does retrofit capacity generate a  significant share of
the electric sector's output; model results are less sensitive to retrofit technology costs. Like the
nuclear power analysis, moderate  levels  of CCS adoption do not significantly affect electric
sector criteria pollutant emissions.  CCS  displaces new (conventional) baseload capacity when
the electric sector follows a non-BAU carbon trajectory, but this adoption does not significantly
impact the operation of existing coal-fired plants, which  contribute most of the sector's CAA
constrained SO2 and NOx emissions.

Several key results emerge from an integrated assessment  of nuclear, CCS, and wind power.
First,  the penetration  of advanced electric sector technologies does not  necessarily yield a
significant criteria pollutant benefit. Under all but the most radical departures from BAU electric
sector carbon trajectories, for instance, nuclear power and CCS merely replace the new coal and
gas capacity  that are needed  to meet increasing electricity demand.  The model maintains its
existing coal plants through their useful  lifetime, and criteria pollutant emissions remain near
their CAA limits as a result.  This picture changes when electric sector carbon emissions depart
furthest from their BAU levels and CO2 emissions from baseload plants declines.  Even in these
cases, however, the decrease in NOx emissions is more modest than that seen  in SO2.

Second, CCS is dominated by investment in new nuclear technologies as well as additional gas-
fired generation and wind power.  Only  when favorable  CCS cost and efficiency assumption
combine  with high nuclear investment  cost and  gas  price scenarios does CCS  approach its
modeled  growth constraints over the  full time horizon in  which it is available (2015-2030).
Furthermore, given that CO2  capture and geologic sequestration are both untested at even the
modest scales adopted here, the model results are probably optimistic.

Finally,  even with a  rudimentary conception of wind  resources, wind  generation  in the
EPANMD competes for a significant share of electric power output when the model incorporates
                                                                                     Xlll

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more favorable growth constraint assumptions for new turbine investment.  These growth limits
are modest compared to some predictions, though a more realistic representation of wind turbine
dispatch is needed to capture the technology's intermittency and evaluate its actual potential.

While  the model  results  provide  insight into future  energy  technology pathways, they  are
nonetheless based on a model, and all models have limitations that introduce caveats.  The use of
national rather than regional inputs to MARKAL is  perhaps  the most significant limitation
affecting the analysis. The EPANMD is a national database that does not contain region-specific
data. The  lack of regional  data manifests itself in three key limitations. First,  important regional
differences in resource supplies,  energy service demands, and technology availability cannot be
represented.  Second,  the EPANMD does  not include transportation  costs for coal or  other
resources.  Third,  air pollution regulations that have been implemented on a state  or regional
level must be modeled at a national level within EPANMD.  ISA-W is currently developing a 9-
region MARKAL database that will account for variations in these factors. Beyond its lack of
region-specific  detail, the EPANMD does not model unit dispatch, and contains a very generic
representation of the  existing fleet of U.S. coal-fired power  plants.  Equally important,  the
analysis excludes consideration of efficiency improvements as well as non-traditional generating
technologies which could supply a large share  of the nation's electricity in the coming decades.
Likewise,  the analysis does not consider the impact of radical technological innovation, such as
the emergence of "nano-bio-info" technologies, on future energy  demand.
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                                       Section 1

                      Electricity Generation and Air Quality
The U.S. EPA contributes to the U.S. Climate Change Science Program (CCSP) by working to
develop an understanding of the potential human health, ecosystem, and socioeconomic impacts
of global change. A central part of this work builds on traditional EPA expertise by examining
the connection between climate and air quality. Climate variability will likely result in changes
in regional meteorology.  These meteorological changes, in turn, may affect air pollution levels
by  altering atmospheric  chemical  fate and transport  processes  as  well  as biogenic and
anthropogenic emissions.   To characterize these changes, EPA's  Office of Research and
Development (ORD) is working on a multiyear Global Change Air Quality Assessment (AQA)
to explore the potential consequences  of global change on criteria pollutant emissions through
midcentury. The research  has advanced through a series of projects geared towards building the
ability to analyze the relationship between global  change and air quality and involves research
teams with expertise in  meteorological,  emissions,  and  air quality  modeling, as  well  as
technology assessment.   The  research  also takes into account demographic, economic, and
technological changes that would be expected to occur independent of global change.

EPA's Integrated Systems Analysis Workgroup (ISA-W) contributes to the AQA by crafting
scenarios of plausible technology change  and assessing how this evolution might impact air
pollutant emissions.  These activities focus on the two economic sectors that impact air quality
the most: transportation and energy (other sectors are considered to capture important system
effects).  Combined, the sectors account for roughly two-thirds of the pollutants that impact air
quality and are areas where significant technological changes are expected to occur over the next
several decades.  EPA researchers will use the results of ISA-W s technology  assessments and
corresponding emission growth rates from the scenario analyses to calculate the future emission
profiles needed as input to the EPA's air quality  models.  In 2004,  ISA-W produced a report
which outlined its  approach for technology assessments and provided initial results for the
transportation  sector (Gage et al.,  2004).  The  present  technology  assessment provides  a
companion piece focused on electricity generation.

1.1 Electricity Production  and Demand
Reliable access to electricity is fundamental to economic productivity,  quality of life, and the
comforts of daily existence.  Since its development in 1882 as a power source to operate 800 of
Thomas Edison's electric  light bulbs (Edison Electric Institute, 2006), the U.S. electric power
generating capacity has increased to over 1,051,000 megawatts.  Electricity is now the primary
power source for most commercial and residential building end-use services, including lighting,
space heating  and cooling, water heating,  ventilation, and refrigeration.  The technologies on
which the information economy rest  are also dependent on electricity, and  more traditional
manufacturing industries like chemical and paper production remain major consumers of electric
power.

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Demand for electricity has a strong historical linkage to the development of the U.S. economy.
Figure 1.1 illustrates the relationship between annual change in electricity use and annual growth
rate of Gross Domestic Product (GDP) for the past 55 years. Over this period, the energy used to
produce electricity  climbed from about 5,300 PJ in 1950 to almost 42,100 PJ  in 2005  (EIA,
2005b). As a result, the relative energy intensity of the economy doubled between  1950 and 1977
(see Figure 1.1).  While both electricity use and GDP have continued to grow, energy intensity
has declined by 33% since its mid-1970s peak  (BEA, 2006; EIA, 2005b). Improvements in end-
use energy efficiency and the shift from a manufacturing- to a service-based economy are largely
responsible for this decline.
       14%
       12%
	GDP
   Electricity Use
   Intensity
                                                                                1.6
                                                                                1.4
       -4%
         1950
               1955
                     1960
                           1965
                                 1970
                                        1975
                                              1980
                                                    1985
                                                          1990
                                                                 1995
                                                                       2000
                                                                             2005
  Figure 1.1: Energy Intensity and Growth Rate Trends for U.S. GDP and Electricity Use;
                            Sources: BEA (2006), EIA (2005b)
A variety of fuels serve as primary energy sources for U.S. electricity production, as Figure 1.2
illustrates (EIA, 2005b).   Coal, which is abundant  in  the  U.S.  has  dominated  electricity
production and currently accounts for 50% of U.S. generation. Oil was an important source until
the 1970's when U.S. production began  declining and the Arab oil embargo  caused  import
shortages. Oil now fuels 3% of electricity generation.  Natural gas use increased by nearly half
during the 1990s,  though more recent volatility  and  sustained high prices are discouraging
expanded use.

Fuel choice for electricity  generation  is also regionally dependent  (Edison Electric Institute,
2006b).  In both the West and East North Central census  regions, coal is the dominant  energy
fuel for electricity production, with generating shares of 77% and 70% respectively. In New
England, natural gas dominates at  38%.   Hydroelectric dominates  in the contiguous  Pacific

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region at 41%; and oil, at 52%, dominates in the noncontiguous Pacific. In the Middle Atlantic
region, nuclear and coal account for 35% and 36%, respectively.

Demand for electricity is governed  by the end-use energy services of the other sectors of the
economy:  residential,  commercial,  industrial,  and  transportation.   Figure  1.3  shows  the
distribution of electricity  use across these sectors for the U.S.  in  2004.   Residential and
commercial demand each  account for about one-third  of electricity use,  while industrial,
including  non-utility  direct  usage, accounts  for another  30%.    The  U.S.  transportation
infrastructure, in contrast, relies  on a negligible amount of electricity.   Demand  for end-use
services  (lighting,  cooling,  manufacturing, etc.)  will increase further  as the  economy and
population expand.  Projections show the U.S. GDP growing by 2.9 to 3.0% annually over the
next 25 years (EIA, 2006c) while the U.S. population expands by 29% (U.S. Census Bureau,
2006). This growth will impact electricity demand and potentially air quality.
        22,000
        20,000
        18,000
        16,000
        14,000
      ^ 12,000
      GO
      c

      1 10,000
        8,000
        6,000
        4,000
        2,000
                                                                              Coal
                                                                             Nuclear
           1950    1955    1960   1965
                                     1970
                                           1975   1980    1985    1990
                                                                    1995   2000    2005
       Figure 1.2: Fuel Sources for U.S. Electricity Generation; Source: EIA (2005b)

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                                  Non-Utility
                                  Direct Use  Transportation
                                           J  >0%
                                    4%
      Figure 1.3: U.S. Distribution of Electricity Usage in 2004; Source: EIA (2005b)

1.2 Electricity and Air Quality
Electricity  is produced  primarily from the  combustion of fossil fuels.  It is  this combustion
process which causes the production and release of a variety of atmospheric pollutants, including
SO2, NOX, particulates,  CO2, and mercury (U.S. EPA, 2005,  2006a, and 2006b). All of these
pollutants impact  human health  and the environment.  Table  1 shows the  percent of  total
pollutant emissions which are produced from electricity generating technologies.  SO2 and NOx
(which contribute to acid rain)  can also contribute to PM2.5 and  ozone formation, respectively,
further degrading ambient air quality.

  Table 1.1: Percent of Total Emissions from U.S. Electric Generation Technologies;  Sources:
                           U.S. EPA (2005), (2006a), and (2006b)
Impact
Pollutant
Emission %
Ambient Air Quality


PMio
16
PM2.5
3
Acid Rain
SO2
65
NOX
20
Toxic
Hg
43
Climate
Change
CO2
38
N2O
4
EPA has developed regulations for  several of these  pollutants  which have already produced
significant emission reductions.  The Acid Rain Program of the Clean Air Act (CAA) of 1990,

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for instance, regulates 862 and NOx from electric generation.  This program entered Phase II of
the reduction requirements in 2000.  By 2010, SC>2 emissions from power plants will be capped
at 8.95 million tons per year, a reduction of more than half from 1980 emission levels.  After
implementation of Phase II, NOX emissions from power plants have declined by a total of 2.1
million tons/year from  1980 levels.  In addition to the Acid  Rain Program, SC>2 and NOx
emissions are affected by the Clean Air Interstate Rule of 2005 which impacts 28  Eastern states
and the District of Columbia.  The Phase II caps for SO2 and NOX in 2015 will be 2.5 and 2.2
million tons, respectively.

How these caps will be met and maintained under growing demand for electricity is yet to be
determined.  Options include pre-  or  post-combustion  control  of emissions from  fossil fuel
sources, finding "cleaner"  technologies for  producing electricity, or implementing efficiency
improvements on both supply and demand.

1.3 Technology Assessment, Scenario Analysis, and the Use ofMARKAL
Technology assessment lies at the core of ISA-W's electric sector analysis.  Such assessments
are multidimensional in that they require a detailed characterization of individual technologies as
well as the economic and institutional factors that both drive and constrain their use. Resource
supply costs, demand estimates for  energy-related services, emission considerations, and other
drivers, for instance, combine to determine how existing and new technologies compete. Data
for these  assessments  comes primarily from  government agencies, academic  studies, other
published literature,  industry studies, and individual  consultations.   Mid-range  or  consensus
estimates  furnish the starting point for  inputs to the typical  assessment, though  sensitivity
analyses examine the effects of more divergent assumptions.

A systems perspective is needed to  capture the interaction of the diverse factors  required by a
complete  technology  assessment.   Such a perspective  goes beyond lifecycle  assessment's
"cradle-to-grave" analysis of resource needs  and environmental impacts for a given technology
to examine how multiple technologies compete with each other to meet  demand, and how
resource  constraints affect  this balance.   A  systems focus, therefore,  captures both direct
environmental impacts (e.g., the emissions  generated by  a particular technology) as well as
indirect effects (e.g., how reducing demand for a fuel in one economic sector might lower its cost
and consequently  increase  its  use—and associated  emissions—in  another).  These  indirect
feedbacks  can be counterintuitive, and a systems  perspective,  therefore,  helps  capture
unanticipated consequences.  In addition, real-world trade-offs in technological and economic
feasibility often emerge only at the  systems level, an example being that of a high capital cost
technology (e.g., a  coal-fired power plant) that only makes sense to build if its use is sufficient to
recover the initial investment.

Technology assessment  within a systems  framework  provides the  structure  for ISA-W's
research.  The use  of scenarios guides the actual analysis of technological futures and assists in
understanding how complex systems may evolve.  Scenarios are  internally consistent depictions
of how the  future may unfold, given  assumptions  about  economic, social,  political,  and
technological  developments as well as consumer preferences  (Schwartz,  1996).  Scenarios
explore  plausible  futures by using a  model  or models to generate an outcome  (or set of
alternative outcomes) consistent with  a set of motivating  assumptions, sometimes called a

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"storyline."   It is important to stress that  these consequences should not be interpreted as
predictions,  for  example, about  levels of  new technology market  penetration  or emission
trajectories.  Rather, the technology parameters and economic data used as inputs are best seen as
starting-point assumptions that reflect a range of reasonable estimates.

Scenario analysis aims to examine how changes in model  parameters (inputs) affect  outputs
across sets of related storylines, rather than focus on the results from a particular scenario. No
attempt is made to consider every possible future. These comparative analyses alternately look
forward ("What-if?")  to  examine how competing sets of input assumptions  drive technology
adoption and emissions, and backward ("How-could?") to identify the  energy technology
pathways  available to meet some future  environmental or technological  goal.   Scenarios,
therefore, facilitate assessment of the consequences of varying assumptions, the range of possible
futures, and  trade-offs and branch points that govern choices  among these futures.  Results from
a selected set of scenarios will serve as input to the ORD Air Quality Assessment.

In order to investigate scenarios of future electric generation technologies and their impact on
future air  pollutant  emissions,  ISA-W  adopted  the  MARKAL  energy-systems  modeling
framework  (see  Shay et al.  2006).   The Department of Energy's  Brookhaven National
Laboratory created MARKAL (short for MARket ALlocation)  in the late 1970s,  and a strong
international users group  has organized itself to support continuing applications and extensions.
MARKAL maps the energy economy from  primary  energy  sources through their refining and
transformation processes  to the point  at which a variety of technologies (e.g., classes of light-
duty personal vehicles, heat  pumps,  or gas furnaces) service  end-use energy demands (e.g.,
projected vehicle miles traveled, space heating).  All economic sectors—industrial, residential,
commercial,  and  transportation—are  covered.  A large linear  programming model, ISA-W's
MARKAL model, determines the least-cost pattern of technology  investment and utilization
required to  meet specified demands  and  model constraints and  then calculates  the resulting
criteria pollutant and greenhouse gas emissions.

MARKAL's strength lies in the fact that it is a systems model.  The energy system is complex
and interactions must be considered, but feedbacks are not always intuitive; reducing demand for
a fuel in one economic sector, for instance, might lower its cost and, therefore, increase its use in
another.  Such real-world trade-offs in technological  and economic feasibility often emerge only
at the systems level.   In addition, the readiness, costs,  and  performance of individual energy
technologies (e.g., wind turbines) must be considered in the full  socio-economic context of their
use.   MARKAL  quantifies  the  system-wide effects of changes  in resource supply and use,
technology availability, and environmental policy.

MARKAL is  a  data-driven energy-economic model.  The  user  specifies  the  energy system
structure, including resource supplies, energy conversion technologies, end-use energy service
demands, and the technologies needed to satisfy these demands.  The user must also provide data
to characterize individual technologies and resources, including their fixed and variable costs,
availability,  performance attributes, and  pollutant  emissions.   MARKAL is data-intensive.
Within  the  electric power   sector, for instance, ISA-W's  MARKAL database contains  24
generating technology options (both existing and future vintages).  The database is divided into

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five-year periods that stretch from 1995 to 2035. The time horizon will be extended to 2055 in
future work to meet AQA needs.

Finally, ISA-W has also developed a suite of analytical and visual tools for evaluating sensitivity
to uncertainties in model parameters and inputs and exploring the solution space.  Assessing the
sensitivity of results to assumptions is an important part of any scenario analysis.  Do the results,
for instance, depend on a narrow range of input parameters?  Or do several technology pathways
lead to equivalent outcomes?  Confidence in model results depends on answering such questions,
and these answers may also have important policy implications.

1.4 Overview of the Electric Sector Technology Assessment
The future  of air quality will  be controlled  by two primary  factors:  pollutant emissions
(influenced by technology  use and adoption) and ambient temperature (influenced by  climate
change). This report presents a series of technology assessments focused the first of these drivers
by examining different "classes" of power generation technologies.  The assessment  investigates
the barriers and potential limits to the market penetration through 2030 of both conventional and
alternative  technologies, and examines the air emission impacts from their use.  Information
from this  assessment will  aid the selection of scenarios for inclusion in ORD's  2010 AQA
synthesis report.

The following section provides  a general overview of EPA's MARKAL model and presents
results for the business as usual (BAU) baseline scenario. Section 3 examines the factors driving
BAU results through an extended sensitivity analysis.  The following two sections focus on
advanced  generation technologies by investigating  the  air  quality  impacts of  nuclear power
(Section 4)  and carbon capture  and  sequestration (Section  5).   The  analyses treat these  two
technologies separately in order to examine their independent impact on air quality relative to the
BAU results.  Section 6 then presents  a series  of scenarios which allow competition between
these advanced technologies and a rudimentary representation of wind power. Finally, Section 7
discusses the assessment's limitations and outlines future work.

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                                       Section 2

                           EPA National Model Baseline

The  Electric  Generation Sector in  the EPA National MARKAL  Database  (EPANMD)
characterizes existing and new technologies  available electricity generation.  Based on sector-
specific electricity demand (residential, commercial, industrial, and transportation), fuel prices,
technology costs, and the environmental and operational constraints incorporated in the model,
MARKAL determines the least cost  way to meet system electricity demand.   This  section
describes the EPANMD's representation of electric power generation technologies  and presents
the baseline, or business as usual (BAU), model results.   Shay et al. (2006) provides a complete
description of the EPANMD.

MARKAL is  structured  around a Reference Energy System (RES), a network diagram that
depicts an  energy  system from  resource supply to end-use consumption.  The RES divides  an
energy system up  into a series  of elements,  including primary energy resources plus process,
conversion, and demand technologies. These technologies feed into a final stage consisting of
end-use demands for useful energy services.  End-use demands include items such as residential
lighting,  commercial  space  conditioning, and  automobile passenger  miles traveled.  Energy
carriers interconnect the stages.   A large linear programming model, MARKAL determines the
least-cost means of meeting end-use demand  over the model's time horizon,  1995-2030.  (For a
detailed description of MARKAL, see ETSAP, 2004).

The electric generation sector specific RES consists of imported electricity resource  technologies
and conversion technologies (e.g., existing coal steam) which output electricity to the system.
"Dummy"  emissions  process technologies, which  have  no costs,  track emissions through the
system. Figure 2.1 provides a generic representation of the electric sector RES structure.
           energy carriers    Process Technologies    energy carriers    Conversion Technologies
                                                 Resource Technologies
                                                                        ELC
                         Figure 2.1: Generic Electric Sector RES
2.1 Technology Characterization
The EPANMD contains  twenty-four electric generation technologies,  sixteen of which have
existing  capacity  in  the U.S.   For  a  complete  description  of the  model's  technology
representation, see Shay  et  al.  (2006).  EPANMD is calibrated to the 2002 Annual Energy
Outlook (EIA, 2002a), except where noted.

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Available Technologies
Table 2.1 lists the existing electric generation technologies represented in the EPANMD with
their initial capacities.  Note that the EPANMD does not explicitly model the costs associated
with retiring these technologies.

Table 2.1: Existing Electric Generation Technologies
Technology
Coal Steam Plants
Natural Gas Steam Turbine
Conventional Nuclear LWR
Conventional Hydropower
Natural Gas Combustion Turbine
Diesel Combustion Turbine
Hydropower Pumped Storage
Residual Fuel Oil Steam
Natural Gas Combined Cycle
Biomass Combined Cycle
Diesel Internal Combustion Engine
Municipal Solid Waste
Geothermal
Wind Central Electric
Solar Central Thermal
Solar Photovoltaic
GW Existing Capacity (1995)
307.8
115.0
102.0
78.5
31.7
25.2
22.0
17.0
13.6
7.6
3.5
3.4
3.0
1.7
0.3
0.1
New Technologies
The new electric generation technologies available to the model in the base case include:
   •   Advanced Coal—Integrated Gasification Combined Cycle (IGCC)
   •   Advanced Coal—Pressurized Fluidized Bed
   •   Advanced Natural Gas Combined Cycle
   •   Advanced Natural Gas Combustion Turbine
   •   Distributive Generation—Baseload
   •   Distributive Generation—Peak

Technologies are characterized in the model by costs (investment, operation, and maintenance),
efficiencies, lifetimes, emissions, hurdle  rates, fractions in peak equations, and  availability.
Hurdle rates refer to discount rates applied to the investment costs of new technologies which are
meant to mimic hesitancy on the  part of the purchaser to invest in a newer technology over an
established technology.  The fraction in peak equations refers to the fraction of the technology's
total capacity in a specified period which can be counted on to be available to meet peak demand
and reserve margin  requirements.  Availability refers to  the  percentage  of  the  year  that a
technology is on-line and available accounting for forced and scheduled outages.
The  database draws  from five primary  data  sources, which are listed below in order of
precedence.

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    1. Annual Energy Outlook 2002 (EIA, 2002a)
    2. "Supporting Analysis for the Comprehensive Electricity Competition Act" (DOE, 1999)
    3. Technical Assessment Guide (EPRI, 1993)
    4. 1997 DOE MARKAL database (Tseng, 2001)
    5. National Energy Technology Laboratory (NETL) (Boilanger, 2002)

Tables 2.2 and 2.3 list the main parameters and their values for the available technologies.  All
values are in model base year (1995) dollars; post-2010 values remain constant.

In addition to the costs of each electric generation technology, all electricity generated by the
sector (except that from distributed generation) is subject to the transmission and distribution
costs shown in Table 2.4.

2.2 Model Constraints
Several constraints are active during the baseline run, including:
1.   A 30% reserve capacity (the amount by which the installed electricity generating capacity
    exceeds the average load of the season and time-of-day division of peak demand).
2.   A hurdle rate of 18% (applied to all new generation technologies).
3.   Nuclear capacity bound set at 2005 AEO levels (EIA, 2005a).
4.   IGCC investment in IGCC limited to the 2002 AEO projections (EIA, 2002a).
5.   Investment in renewables fixed at levels based on analysis by in-house researchers and 2005
    AEO (EIA, 2005a) estimates (see Table 2.5).
                                                                                     10

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Table 2.2: Electricity Generation Technologies and Associated Cost Parameters
Technology Name
Existing Coal Steam
Coal Steam - 2000
Coal Steam - 2005
Coal Steam -2010
Integrated Coal Gasif. Combined Cycle - 2000
Integrated Coal Gasif. Combined Cycle - 2005
Integrated Coal Gasif. Combined Cycle - 2010
Pressurized Fluidized Bed
Coal Gasification Molten Carb Fuel Cell
Distributed Generation-Base-2005
Distributed Generation-Base-201 0
Distributed Generation-Peak
Existing Natural Gas Combined Cycle
Natural Gas Combined Cycle-2000
Natural Gas Combined Cycle-2005
Natural Gas Combined Cycle-2010
Natural Gas Advanced Combined Cycle-2005
Natural Gas Advanced Combined Cycle-2010
Natural Gas Steam
Existing Natural Gas Combustion Turbine
Natural Gas Combustion Turbine-2000
Natural Gas Combustion Turbine-2005
Natural Gas Combustion Turbine-2010
Natural Gas Advanced Combustion Turbine-2005
Natural Gas Advanced Combustion Turbine-2010
Conventional Nuclear
Diesel internal combustion engine
Residual Fuel Oil Steam
Distillate Oil Combustion Turbine
Hydroelectric Pumped Storage
Biomass Gasification Combined Cycle
Geothermal Binary Cycle and Flashed Steam
Hydroelectric
Municipal Solid Waste-Landfill Gas
Solar Central Thermal
Central Photovoltaic
Photovoltaic-Residential
Local Wind Turbine
Wind Central Electric
Heat Rate
(BTU/kWh)
11990
9419
9253
9087
7969
7469
6968
9228
7575
10991
9210
10620
8030
7687
7343
7000
6639
6350
9500
11900
11467
11033
10600
8567
8000
10800
13648
9500
11900
10280
8911
32173
10280
13648
10280
10280
10280
10263
10280
Capital Costs
(1995 million
$/kW)
n/a
1119
1110
1083
1338
1315
1287
1570
2683
623
623
559
434
456
453
448
572
526
959
322
339
336
333
446
384
3445
376
959
322
1615
1725
1746
929
1429
2539
3830
7519
1246
982
Variable O&M
Costs
(1995 million
$/kW)
2.78
3.10
3.10
3.10
0.73
0.73
0.73
3.10
24.83
13.87
13.87
21.20
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.09
0.09
0.09
0.09
0.09
0.09
0.29
8.07
0.48
0.09
2.40
2.66
0.00
4.07
0.01
0.00
0.00
0.00
7.45
0.00
Fixed O&M
Costs
(1995 million
$/kW)
14.21
21.48
21.48
21.48
29.98
29.98
29.98
38.11
34.49
3.69
3.69
11.53
14.31
14.33
14.33
14.33
13.27
13.27
28.61
5.91
5.92
5.92
5.92
8.41
8.41
77.05
0.78
28.61
5.91
15.18
41.25
64.31
12.90
88.39
43.93
9.04
118.28
8.51
23.44
                                                                                       11

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Table 2.3: Lifetime and Availability Parameters for Electricity Generation Technologies
Technology Name
Existing Coal Steam
Coal Steam - 2000
Coal Steam - 2005
Coal Steam -2010
Integrated Coal Gasif. Combined Cycle - 2000
Integrated Coal Gasif. Combined Cycle - 2005
Integrated Coal Gasif. Combined Cycle - 2010
Pressurized Fluidized Bed
Coal Gasification Molten Carb Fuel Cell
Distributed Generation-Base-2005
Distributed Generation-Base-201 0
Distributed Generation-Peak
Existing Natural Gas Combined Cycle
Natural Gas Combined Cycle-2000
Natural Gas Combined Cycle-2005
Natural Gas Combined Cycle-2010
Natural Gas Advanced Combined Cycle-2005
Natural Gas Advanced Combined Cycle-2010
Natural Gas Steam
Existing Natural Gas Combustion Turbine
Natural Gas Combustion Turbine-2000
Natural Gas Combustion Turbine-2005
Natural Gas Combustion Turbine-2010
Natural Gas Advanced Combustion Turbine-2005
Natural Gas Advanced Combustion Turbine-2010
Conventional Nuclear
Diesel internal combustion engine
Residual Fuel Oil Steam
Distillate Oil Combustion Turbine
Hydroelectric Pumped Storage
Biomass Gasification Combined Cycle
Geothermal Binary Cycle and Flashed Steam
Hydroelectric
Municipal Solid Waste-Landfill Gas
Solar Central Thermal
Central Photovoltaic
Photovoltaic-Residential
Local Wind Turbine
Wind Central Electric
Technical
Lifetime
(years)
40
40
40
40
30
30
30
40
30
30
30
30
30
30
30
30
30
30
40
30
30
30
30
30
30
40
20
40
30
50
30
30
60
30
30
30
20
20
30
Availability
Fraction
0.85
0.85
0.85
0.85
0.85
0.85
0.85
0.85
0.87
0.84
0.84
0.84
0.91
0.91
0.91
0.91
0.91
0.91
0.85
0.92
0.92
0.92
0.92
0.92
0.92
0.80
0.84
0.85
0.92
varies by
timeslice
0.80
0.64
0.44
varies by
timeslice
varies by
timeslice
varies by
timeslice
varies by
timeslice
varies by
timeslice
varies by
timeslice
Fraction* of
unavailability
that is forced
0.37
0.37
0.37
0.37
0.37
0.37
0.37
0.50
0.80
0.63
0.63
0.63
0.57
0.57
0.57
0.57
0.57
0.57
0.37
0.47
0.47
0.47
0.47
0.47
0.47
0.42
0.63
0.37
0.47
n/a
0.80
1.00
0.10
n/a
n/a
n/a
n/a
n/a
n/a
Fraction of
Capacity
for Peak
and
Reserve
0.96
0.96
0.96
0.96
0.90
0.90
0.90
0.80
0.60
0.96
0.96
0.96
1.00
0.94
0.94
0.94
0.86
0.86
0.96
0.96
0.96
0.96
0.96
0.94
0.94
0.85
0.96
0.98
0.96
0.95
0.84
0.63
0.94
0.90
0.30
0.50
0.30
0.30
0.30
*This fraction is applied to (1- availability fraction).
                                                                                          12

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Table 2.4: Electricity Transmission and Distribution Costs
Transmission Investment Cost
Transmission O&M Cost
Distribution Investment Cost
Distribution O&M Cost
Transmission Efficiency
228.75
0.1
496
0.736
93.5
million U.S. $ perGW
million U.S. $ per PJ
million U.S. $ perGW
million U.S. $ per PJ
%
Table 2.5: Total Allowed Generating Capacity of Renewable Technologies in GW
Technology
Biomass Combined Cycle
Municipal Solid Waste
Geothermal
Hydropower
Solar Central Photovoltaic
Solar Photovoltaic - Residential
Wind Central Electric
2000
7.6
3.4
3.0
79.3
0.1
0.1
2.5
2005
8.3
3.8
3.5
79.3
0.2
0.2
9.1
2010
9.0
4.2
4.1
79.3
0.2
0.2
11.0
2015
9.8
4.6
4.8
79.3
0.4
0.4
13.2
2020
10.7
5.2
5.6
79.3
0.6
0.6
15.8
2025
11.7
5.7
6.6
79.3
0.8
0.8
19.0
2030
12.7
6.4
7.7
79.3
1.3
1.3
22.8
Emissions Constraints and the Coal Steam Retrofits
The Clean Air Act (CAA) imposes emissions constraints on SO2 and NOx emissions from the
electric power sector.  In order to keep model results in line with these emissions levels,
constraints on SO2 and NOx in the  electric sector were developed from DOE projections  and
applied in the model (Table 2.6).  These constraints reflect future emissions under the existing
trading schemes, though MARKAL does not model these trading dynamics internally. Note that
the current EPANMD does not represent mercury emissions; future database revisions will  add
the necessary emission coefficients and control technologies.
Table 2.6 Electric Sector NOX and SO2 Emission Constraints in the EPANMD
Emission (kt/PJ)
NOx emissions in the Electric Sector
SO2 emissions in the Electric Sector
2000
5875
11400
2005
5300
10500
2010
3500
9900
2015
3500
9000
2020
3500
9000
2025
3500
9000
2030
3500
9000
The technology-specific CO2 emission factors used in EPANMD (Table 2.7) were derived from
Table A-15 of the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2001. SO2 and
NOX factors were taken from the 1997 DOE MARKAL database with updates (EIA, 2004; OAR,
2002).
                                                                                     13

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Table 2.7: Emission Factors for CO2 and SO2 and NOX in the EPANMD; see Shay et al. (2006)
for details.
CfifctadcKDn
(KW^J)
Coal Steam Plants
Advanced Coal Plants
Natural Gas Steam Turbine
Natural Gas Combustion Turbine and Combined Cycle
Diesel Combustion Turbine
Residual Fuel Oil Steam
25.2
25.2
15.2
15.2
19.7
21.2
NOx (kT/PJ)
Existing Controls
0.0215
0.043
0.1075
0.043
0.1075
0.1075
Improved Controls
n/a
n/a
0.0108
0.0043
0.0108
0.0108

Coal Steam Plants
Advanced Coal Plants


Diesel Combustion Turbine
Residual Fuel Oil Steam
Bit
Lignite
Sub-Bit
Bit
Lignite
Sub-Bit




SO2 (kT/PJ)
High Sulfur
0.231
0.179
n/a
4.62
3.4
n/a

Existing Controls
0.024
0.492
Medium Sulfur
0.09
0.098
0.071
1.8
1.96
1.42

Improved Controls
n/a
0.024
Low Sulfur
0.046
0.04
0.033
0.92
0.8
0.66




 Bit = bituminous

Existing Steam Electric Retrofit Technologies
Coal plants account for roughly 41% of total installed U.S. electric generation capacity in 1995
and produce most of the electric sector's SO2 and NOx emissions.  A proper characterization of
the amount of coal capacity with pre-existing controls as well as the cost and removal efficiency
of new control retrofits is therefore important to the model's overall performance (Table 2.8).

The EPANMD  contains three coal types: bituminous, sub-bituminous, and lignite as well as
three  sulfur  levels: high, medium, and  low.  Flue gas desulfurization (FGD)  costs  and
efficiencies reflect the  fact that FGD performance varies  by both coal type and sulfur level.
Because NOx emissions are insensitive to sulfur level, the EPANMD includes three unique NOx
control processes (one for each coal type).  The following NOx control processes are available to
the model:
   •   LNB (Low NOX Burner)
   •   SCR (Selective Catalytic Reduction)
   •   SNCR (Selective Non-Catalytic Reduction)
   •   LNB-SCR combination
   •   LNB-SNCR combination
Note that SCR and SNCR can be installed by themselves, at the same time as LNB, or after LNB
controls are in place.
                                                                                    14

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Table 2.8: Emission Control Retrofit Data
SO2 Control Summary
Bituminous
2000 Residual Capacity (PJ/yr)
Equilibrium Emissions Rate (kT/PJ)
High Sulfur
FGD
3160
0.1186
No Retrofit
2.3723
Medium Sulfur
FGD
1313
0.0440
No Retrofit
1.0536
Low Sulfur
FGD
1093
0.0148
No Retrofit
0.2950
Sub-Bituminous
2000 Residual Capacity (PJ/yr)
Equilibrium Emissions Rate (kT/PJ)
High Sulfur
FGD
N/A
N/A
No Retrofit
N/A
Medium Sulfur
FGD
1684
0.0360
No Retrofit
0.7182
Low Sulfur
FGD
1718
0.0208
No Retrofit
0.4144
Lignite
2000 Residual Capacity (PJ/yr)
Equilibrium Emissions Rate (kT/PJ)
High Sulfur
FGD
149
0.0966
No Retrofit
1.9294
Medium Sulfur
FGD
1047
0.0666
No Retrofit
1.3356
NOX Control Summary
Bituminous
2000 Residual Capacity (PJ/yr)
Equilibrium Emissions Rate (kT/PJ)
Pass Through | LNB | SCR (after LNB) | SNCR (after LNB) | SCR only | SNCR | LNB-SCR | LNB-SNCR
15,600
0.3464 0.2300 0.0477 0.1194 0.0767 0.1916 0.0460 0.1150
Sub-Bituminous
2000 Residual Capacity (PJ/yr)
Equilibrium Emissions Rate (kT/PJ)
Pass Through | LNB | SCR (after LNB) | SNCR (after LNB) | SCR only | SNCR | LNB-SCR | LNB-SNCR
3600
0.3511 0.2080 0.0307 0.0766 0.0693 0.1733 0.0416 0.1040
Lignite
2000 Residual Capacity (PJ/yr)
Equilibrium Emissions Rate (kT/PJ)
Pass Through | LNB | SCR (after LNB) | SNCR (after LNB) | SCR only | SNCR | LNB-SCR | LNB-SNCR
470
0.2006 0.1284 0.0392 0.0981 0.0428 0.1070 0.0257 0.0642
For a complete description of the EPANMD retrofit technology methodology, see Shay et al.
(2006).

2.3 Baseline Results
The baseline (or BAU) results presented in this section are MARKAL results generated running
the baseline scenario of the EPANMD and cover 1995 (the base year) to 2030.

Electric Generation
Total electricity use increases 1.3% annually from 13,378 PJ in 2000 to  19,622  PJ in  2030
(Figure 2.2).  Annual growth in electricity demand varies between 1.0% in the residential sector,
to 2.1% in the commercial and 1.5% in the industrial sectors of the U.S. economy (Figure 2.3).
                                                                                      15

-------
   25000
LU
       0
        2000     2005     2010      2015     2020      2025     2030
                  Figure 2.2: Baseline Electric Generation
 o>
 to
u
"s_
"5
UJ
9000
8000
7000
6000
5000
4000
3000
2000
1000
    0
                                                      •Residential
                                                      •Commercial
                                                       Industrial
       2000
             2005
                         2010
2015
2020
2025
2030
                Figure 2.3: Electricity Use by Demand Sector
                                                                       16

-------
Capacity Additions
To meet this demand growth, the model adds a total of 293 GW  of new electric generation
capacity between 2000 and 2030.  More than 76% of the new capacity is in the form of natural
gas technologies,  with 61%  being  natural gas combined cycle and 15% being natural gas
combustion turbines (Figure 2.4). New conventional coal-fired power plants are not added until
2020  and  later, though a  small amount of integrated gasification combined  cycle generation
comes on-line in 2015. Renewables add 34 GW of capacity, with 61% coming from wind power
generation, 15% from biomass combined cycle, and 14% from geothermal.
      350 i
                                                          D existing capacity - 2000
                                                          • capacity additions 2005-2030
                                            ,     ^
                                                         '   /  /  ^
                                                            <7^     ^»
                  Figure 2.4: Current Capacity and Capacity Additions
Electricity Generation by Fuel
Coal-fired power plants continue to generate the most electricity (Figure 2.5). As electric sector
emissions constraints tighten over time, more of the original plants are retrofitted with NOX and
SC>2 controls, and 20 GW of new capacity is built. New coal capacity includes FGD for 862 and
SCR for NOx and therefore does not need retrofit technologies for emissions controls.  Nuclear
power capacity increases slightly, but this is due to capacity upgrades at existing facilities. No
new  nuclear plants are built in the BAU case.  Overall, coal electric generation grows  0.5%
annually, natural gas grows 3.8%, and renewables grow 2.7%.
                                                                                     17

-------
        25000
        20000
        15000
        10000
         5000
            2000
2005
2010
2015
2020
2025
2030
                      Figure 2.5: Electric Generation by Fuel Type
Use of Coal Retrofits
The EPANMD representation of retrofits is simplified in that it averages emissions over the
whole  country and ignores regional issues related to air quality.  As a result, the retrofit coal
capacity is smaller than it actually is (or will be), and when retrofits are selected they tend to be
lower cost technologies with lower removal efficiencies.

Retrofits for SC>2 control are currently installed on 4.8% of existing  coal fired power plants.
Tightening of emissions constraints leads to FGD units being installed on over 14% of plants
(Figure 2.6).  Low NOx Burners (LNB) for NOx control are currently on 80% of existing plants.
Over time, SNCR units are added to the existing LNB units for increased emissions reductions.
In later years, SCR units play a larger role  (Figure 2.7); SCR would  be favored earlier if the
EPANMD included emissions trading.
                                                                                      18

-------
  16%

  14%

  12%

  10%
£
o
•g  8%
a:
s?
   6%

   4%

   2%

   0%
                                              14.2%
                                                        14.1%
                           8.3%
                 4.5%
                                                                  13.7%
                                                                           13 4%
                 2000
                    2005
2010
2015
2020
2025
2030
                                Figure 2.6: SO2 Retrofits
          o%
                 2000
                    2005
2010
2015
 2020
 2025
 2030
                                I LNB a SNCR after LNB n SCR after LNB n LNB-SCR
                                Figure 2.7: NOX Retrofits

Emissions
CC>2 emissions in the electric sector grow 1% annually from 689,296 kt in 2000 to 927,302 kt in
2030 (Figure 2.8). The model constrains NOx and SC>2 starting in 2010 and 2015, respectively,
to conform with DOE projections of the impacts  from the  Clean Air Act regulations.   These
constraints are binding  over the applicable time periods and emissions do not  drop below the
upper limits (Figure 2.9).
                                                                                       19

-------
w
w
w
UJ
1200000




1000000




 800000




 600000




 400000




 200000
       2000
                    2005
  2010
  2015
  2020
 2025
 2030
           Figure 2.8: Carbon Emissions in the Electric Sector
12000
    0
    2000
                  2005
2010
2015
2020
2025
2030
         Figure 2.9: NOx and SOi Emissions in the Electric Sector
                                                                         20

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                                         Section 3

                    Sensitivity Analysis of the EPANM Database

The MARKAL Business As Usual (BAU) case presented in Section 2 provides a projection of
the evolution of the U.S. energy system from 1995 through 2030. The BAU case was generated
using best estimates for the values of model inputs, such as the characteristics of current and
future technologies, energy service demands,  and  regulations on  criteria  pollutant emissions.
Since the true values for many of these inputs are unknown, the BAU case represents only one of
many possible  outcomes. Further, it does not itself convey information regarding the sensitivity
of the energy system to alternative input assumptions.

This section describes the application of formal sensitivity analysis techniques to evaluate the
model's response to changes in input  assumptions.  The results aid  in characterizing and
communicating the drivers  that lead to such  outcomes  as: the penetration of particular
technologies, a decrease in pollutant emissions, or a reduction in fossil fuel imports.  Sensitivity
analysis also allows one to view the  BAU case in the context of the  range  of possible future
energy scenarios that may occur.

3.1 Background - Sensitivity Analysis
To evaluate sensitivities within  the EPANM database  and model, both  global and parametric
sensitivity techniques were  used.  Global sensitivity analysis techniques typically are applied in
practice when the goal is to  characterize the relationships among model inputs  and outputs over a
wide range  of input conditions. In  contrast, parametric  sensitivity  analysis, also known as local
sensitivity analysis, is used to evaluate the response to a change  in a  single input, holding all
other inputs constant.    This subsection provides  background  information about sensitivity
analysis techniques within these categories. For more information about sensitivity analysis, see
publications by Saltelli et al., (2000) and Cullen and Frey (1999).

Global  sensitivity  analysis involves  perturbing multiple  model  inputs  simultaneously and
evaluating the  effects of each input or of combinations of inputs on model outputs.  Inputs are
often perturbed via Monte Carlo simulation. In Monte Carlo simulation,  statistical or empirical
distributions are assumed for inputs of interest. A value is sampled from each distribution, and
the resulting set of values is fed into the model. The values of relevant outputs are recorded. The
combination of  a set  of  inputs  and the corresponding  outputs constitutes  one potential
"realization."   Typically fifty to several hundred realizations are  generated  in a Monte  Carlo
simulation.  These realizations are then evaluated using visualization and statistical techniques to
characterize the nature and strength in the relationships among inputs and outputs. *
1 Monte Carlo techniques are also used in uncertainty and risk analyses. In uncertainty analysis, the distributions of
model outputs are characterized to estimate the uncertainty associated with each. In risk analysis, the output values
are compared with a particular metric to determine the likelihood that the metric will or will not be exceeded. In
these techniques, much effort is typically put into developing the input distributions since these will have an impact
on the output distributions. In global sensitivity analysis, however, uniform distributions may suffice since the goal
is to characterize the range of potential combinations of inputs.
                                                                                         21

-------
A variety of global  sensitivity analysis techniques are available, with the selection of the best
technique for a particular application being a function of factors such as the degree of linearity in
the input-output  relationships, the number of realizations and their  coverage of the possible
outcomes, and the type of sensitivity information desired. One straightforward, readily applied
approach is  correlation analysis.   Correlation coefficients can  be calculated  between any
combination of inputs and outputs to the model.  The resulting coefficients indicate whether there
is a strong linear relationship between each pair  and whether this relationship  is positive  or
negative. Correlation coefficients cannot characterize the impact of combinations of inputs on  an
output and may provide misleading results if applied to nonlinear relationships.  In contrast,
scatterplots and various exploratory visualization techniques can be used to identify linear  or
nonlinear relationships. These techniques typically require human  expertise  to visually identify
relationships, and thus tend to be qualitative instead of quantitative.

Linear regression techniques also are used in global sensitivity analysis. Regression can evaluate
the impact of multiple inputs simultaneously.   Further,  if inputs are normalized along their
ranges, the resulting regression coefficients indicate  the relative  impact of each input on  an
output. Like correlation coefficients, linear regression may not be well suited for  characterizing
nonlinear relationships or those in which the input-output relationships involve discrete behavior.
Nonlinear regression techniques, regression trees, and statistical analysis of variance (ANOVA)
approaches may address these issues for some problems.

Parametric sensitivity analysis, in which one input is perturbed while others are held constant, is
very useful in characterizing incremental responses to changes in inputs from a base or reference
case.  These responses can be characterized quantitatively, such as with a sensitivity  metric  or
empirical derivative, or graphically. While  parametric techniques do not characterize  responses
over combinations of inputs, they often play an important role both in preliminary analyses, as a
cursory means to identify sensitivities  of interest, and in more detailed analyses of input-output
responses.

Global  and parametric  sensitivity techniques  can be used independently  or  together. This
analysis uses  two global sensitivity analysis techniques, correlation analysis and normalized
linear regression, to make broad  observations  regarding input-output relationships.  Global
sensitivity analysis was followed by a parametric analysis to provide more detailed information
about impacts of changes in specific inputs in the context of the BAU case.

3.2 Methodology
Steps taken to carry out the sensitivity analysis of MARKAL include:

    •   Key model outputs were identified, as were the input assumptions expected to impact
       those outputs;
    •   For each input, a range was estimated;
    •   Monte Carlo  simulation was carried out, with the inputs and  outputs of each of 1000
       realizations tracked;
    •   The Monte Carlo inputs and outputs were analyzed using correlation analysis to provide
       insight regarding correlations and tradeoffs;
                                                                                        22

-------
       Monte Carlo results were also evaluated with normalized multiple linear regression to
       estimate the relative impact of each input value on each output; and
       From the normalized regression analysis, the inputs with the highest influence on outputs
       were identified, and parametric sensitivity runs were used to evaluate model responses in
       more detail.
3.3 Selecting Inputs and Outputs
With  a focus  on the electric sector, key  outputs for the year 2030 were identified. These
included:  the market penetrations of various electricity generation  technologies;  fuel use  in
generating electricity; the summer  day peak marginal electricity  price;  NOx,  SC>2, and CC>2
emissions from the  electric generation sector; utilization  of existing  electricity  generation
capacity;  and use of NOx  controls on coal-fired boiler  emissions.  Since the electric sector
interacts with the rest of the energy system, system-wide fuel use,  emissions,  and fossil fuel
imports were also selected. In the category  of system-wide fuel use, the fuels that were tracked
include coal, natural  gas, oil, petroleum, uranium, and renewables, which include solar power,
wind power, hydropower, geothermal, and biomass. Oil and petroleum are  differentiated because
oil includes domestic and imported crude  oil,  both  of which are  refined domestically, while
petroleum includes imported petroleum products.

Inputs included in the analysis were:  the future costs of natural gas, coal, and oil; the hurdle rate
for new electric generation technologies; future nuclear capacity; growth bounds on renewables
(for biomass, landfill gas, hydropower,  geothermal, solar, and wind);  and  the availability factor
and maximum capacity of coal gasification  technologies.  Each of these inputs was expected to
have an impact  on the selected outputs.

The next step in the analysis was to characterize ranges for the inputs.  Each of the inputs, which
were assumed to be independent of each other, was represented with a uniform distribution. The
bounds of the distributions are shown in Table 3.1 and reflect modeler's judgment.  Fuel inputs
were allowed to range from approximately -20% to +100%  of their 2030 values in the BAU case.
The range was biased toward the high end to represent uncertainties related to political instability
and concerns about resource limitations. One half of the fuel cost  change was implemented in
2015, with the remaining half coming into effect in 2020.

The hurdle rate  for new electricity generation technologies ranged from 0.05, the current system-
wide discount rate, to 0.20.  Nuclear power capacity  was allowed to  range from 1995  levels to
125% of 1995 levels. One third of any increase in nuclear capacity was specified to come online
in 2015, with the remainder entering  in 2020.  In  our modeling, nuclear power capacity is
constrained  to  specific  levels instead  of allowing  nuclear  to  penetrate  via  economics. This
modeling approach was taken to  reflect the assumption that policy is the major driver that limits
or expands nuclear capacity.

Ranges for growth rates for renewables were selected to encompass the default values.  The
availability factor for coal gasification  ranged from 82.5% to 87.5%, and the overall  capacity
limit for the technology ranged from 0 to 1000 PJ.
                                                                                      23

-------
Table 3.1: Ranges for inputs used in the Monte Carlo simulation
Input
Natural gas cost
increase
Oil cost
increase
Coal cost
increase
New ELC
technology
hurdle rate
Nuclear power
capacity
multiplier
Biomass
growth rate
bound
Geothermal
growth rate
bound
Hydropower
growth rate
bound
Landfill gas
growth rate
bound
Solar PV
growth rate
bound
Solar thermal
growth rate
bound
Wind growth
rate bound
IGCC
availability
factor
IGCC
maximum
capacity bound
Units
$M/PJ
$M/PJ
$M/PJ
None
None
% growth
per 5-yr
period
% growth
per 5-yr
period
% growth
per 5-yr
period
% growth
per 5-yr
period
% growth
per 5-yr
period
% growth
per 5-yr
period
% growth
per 5-yr
period
None
Gigawatts
Default
0.0
0.0
0.0
0.18
1.0
9.0
17.0
0.0
11.0
50.0
3.0
21.0
0.846
16.0
Low
-0.896
-0.712
-0.5
0.05
1.0
0.0
0.0
0.0
0.0
2.5
0.0
0.0
0.825
0.0
High
8.86
7.12
1.5
0.20
1.25
25.0
20.0
10.0
25.0
50.0
10.0
50.0
0.875
1000.0
Description
Cost added to the natural gas and
liquid natural gas supply curves
Cost added to the supply curves
imported and domestic fuel oil,
as well as to imported petroleum
fuels
Cost added to the coal supply
curves
Technology-specific discount
rate applied to all electric sector
technologies except renewables
Multiplier used to increase future
nuclear capacity
Growth rate on biomass use in
electricity generation
Growth rate on geothermal
power for electricity generation
Growth rate on hydropower for
electricity generation
Growth rate for the combustion
of landfill gas from municipal
solid waste landfills for
electricity generation
Growth rate for solar
photovoltaics in electricity
generation
Growth rate for solar thermal
technologies in electricity
generation
Growth rate for wind turbines in
electricity generation
Fraction of time during which
IGCC is operational
Peak capacity for IGCC
technologies in 2030
                                                                                    24

-------
One thousand Monte Carlo realizations were performed and the inputs and outputs tabulated.
Correlation analysis and normalized multiple linear regression were then applied.

3.4 Global Sensitivity Results - Correlation Analysis
Correlation coefficients were calculated for each combination of model inputs and  outputs for
the year 2030.  The resulting coefficients  are provided in Tables 3.2 through 3.5. In each table,
correlation coefficients with absolute values of less than 0.1 are not  shown, while those with
absolute values greater than 0.7 are emphasized via bold type and light shading.
Table 3.2: Correlation coefficients between model inputs and electric sector outputs
                       Inputs











Outputs
Electricity Generated by
Various Fuels
Pulverized Coal
Gasified Coal
Oil
Natural Gas
Nuclear Power
Geothermal
Biofuels
Landfill Gas
Solar Power
Wind Power
Use of NOX Controls on Coal-
Fired Power Plants
Peak Electricity Price
Emissions from the Electric
Sector
C02
NOX
SO2







to
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3 ° To
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-0.42 -0.14

0.3 -0.67
-0.55 0.49


0.19

0.19
0.12

0.24
0.64


-0.44 -0.45


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Table 3.2 shows the correlation coefficients between each input and tracked electric sector
output.  Only five of the input-output pairs have correlations greater than 0.7.  The lack of a
larger number of strong linear relationships is indicative of the complexity  of the system, which
allows for discrete behavior associated with selecting technologies and fuel switching.
                                                                                       25

-------
Many of the correlations that are shown in the table verify anticipated behavior.  For example,
the new electricity generation technology hurdle rate and peak electricity price had a correlation
of 0.76, indicating that inhibiting uptake of new technologies for electricity generation resulted
in an increase in energy prices.  Similarly, although the correlation is only 0.56, there appeared to
be a relationship between the hurdle rate and the use of controls on NOx emissions from coal-
fired power plants: as it became more difficult for new, cleaner electric generation technologies
to be adopted, reliance on retrofits such as selective catalytic reduction increased.

While it is not a strong correlation, there does appear to be an inverse relationship between the
cost of natural gas and the use of coal.  This relationship can be attributed to the effect of the
limit on  electric sector  NOx emissions.  Increased natural gas costs led to  decreased use of
natural gas in electricity generation. Because natural gas-fueled technologies have lower NOX
emission rates than coal-fueled technologies, however, natural gas could only be substituted for
by coal if increased NOx controls were used on coal-fired boilers. The model instead, based upon
a comparison of the  relative costs, opted to fill this gap with oil-fueled technologies. While oil
has fewer NOX  emissions than coal, its emissions are greater than natural gas.  Reductions in
coal use were required to compensate.  These relationships are evident in the  results  of the
normalized multiple linear regression, shown in Figures 3.1 through 3.9.

Table 3.3 shows correlation coefficients between inputs and system-wide outputs. Only eight of
these coefficients exceeded 0.7. Many of the coefficients again confirmed expected behavior.
For example, natural gas cost increases resulted in decreased system-wide use of natural gas,
compensated for by increased use of renewables and petroleum.

An interesting result was that oil costs were much more highly correlated with NOx and SO2
emissions than natural gas or coal  costs were.  This is because the primary use of natural gas in
the model  is in electricity generation,  but the  representation of the Clean Air  Act emission
constraints places  limits  on the emissions of NOx and  SO2 from that sector. In contrast, the
transportation sector is the primary user of oil,  and this sector is not subject to NOx and SO2
emissions constraints.

The lack of strong correlations for some pairs was also insightful. For example, over the growth
rate constraints evaluated, market penetrations of various renewables did not appear to have had
a strong impact on system emissions or fuel use.
                                                                                       26

-------
Table 3.3: Correlation coefficients between model inputs and system-wide outputs
                    Inputs

















Outputs
System- Wide Emissions
CO2
NOX
S02
Fuel Inputs to System
Renewables
Coal
Petroleum
Oil
Natural Gas
Liquid Natural Gas
Uranium
Net Imports









to
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(/)

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13 c 5 —
ii w ra
ro = o
Z O 0

-0.29 -0.59
0.14 -0.82
0.3 -0.74

0.84 0.19
-0.5 0.28
0.87
-0.12 -0.79
-0.91
-0.82

0.68 -0.5
a>
to
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o
ra
CL
ro
0
ra
GJ
O
3
^

-0.29




-0.14




1





.22
0) ji <1) .S
to « to Q-
m ce ce ce .£ £
•^ *- .c r- ro ^
2 | 1 | | 1 JB
.c o 2 E £ (!) /S
"S 5 0 0 | ro _£.
O — ^ (/) i- C -i_i
»- (D OJ m fl E ^
0 E 3 5 ^ oi I
-^ oJ §. = CL K O
3 ^ 2 ~n *~ ^^ T3
•t; O -o c — — c
.2 <" >- m O O 3
03 CD X _J W W >

-0.17 -0.24

-0.11

0.14 0.14 0.18
-0.17 -0.12














±±
E
= 3r-


'ra Q.
< 0
O O
O O
o o













Table  3.4  shows  correlation coefficients between electricity  sector outputs  and all  tracked
outputs.  Very few of the values in this table show a strong degree of correlation (values of 1.0
typically correspond  to comparisons of the same output (e.g., pulverized coal vs.  pulverized
coal). These correlation coefficients plus those repeated elsewhere in the table  are shaded. The
coefficients in the table provide an indication that fuel-switching was occurring between natural
gas and oil in electricity generation.

System-wide NOx and SC>2 emissions showed a high degree of correlation with the use of oil in
electricity generation. Increased system-wide emissions were not a result of the electric sector,
however, since its NOx and SC>2 emissions are constrained. Instead, they were a result of fuel and
technology  switching within the transportation sector in response to drivers  of favorable oil
costs.

Net imports of fossil fuels  appeared to be correlated with oil use in electricity generation, but
inversely correlated with natural gas use.  These correlations indicate that increased demands for
oil resulted in increased fossil fuel imports. At the quantities modeled,  most of the natural gas
was supplied domestically, and thus had a negative correlation with imports.
                                                                                        27

-------
Table 3.4: Correlation coefficients between electric sector and all outputs
                             Electric Sector Outputs










All Tracked Outputs
Electricity Generated by Various Fuels





CO i_
O CD
0 en 3 - -
S J £ | | g
•g E S 1 « ? £
> 3 "o 'o 5 co ^
=! = CO=!CD.00g
Q-OZZOCOC/JS
Electricity Generated by Various Fuels
Pulverized Coal 1 1 .(X^M6 0.12-0.29 -0.24
Oil
Natural Gas
Nuclear Power
Geothermal
Biofuels
Solar Power
Wind Power
1.00 -0.65 -0.12 -0.21
1.00 -0.13
1.00
1.00
1.00
1.00 0.18
1.00
Use of NOX Controls on Coal-Fired
Power Plants
Peak Electricity Price
CO2 from Electric Sector

System- Wide Emissions
C02
NOX
S02
Fuel Inputs to System
Renewables
Coal
Petroleum
Oil
NGA
NGL
Uranium
Net Imports




0.41 0.51 -0.27 -0.29 -0.32 -0.42 -0.29
0.85 -0.60 -0.24
0.88 -0.57 -0.23 0.16

-0.51 -0.34 0.15 0.21 0.29 0.26
0.76 -0.62 0.36 -0.14 -0.19
-0.32 0.40 -0.54 0.12
0.65 -0.47 -0.15
0.32 -0.36 0.55 -0.35 -0.20
0.27 -0.29 0.55 -0.13
-0.29 -0.12 -0.13 1.00
-0.23 0.73 -0.74 -0.12

c-ffi o
0 £ °
(/) CO CD CD
0°- £ ^
'c CD °- •;=

00 o a
O ~& t3 r-
^ CD CD E
O _i_ _^ "*"
Si o CD O
3 O CL O

-0.38 0.55
0.19 0.38
-0.48
-0.33

-0.33
0.61 -0.35 -0.39
0.17 -0.17 -0.29

1.00 -0.38 -0.55
1.00
1.00


-0.57 0.20 0.96
-0.11 0.13 0.46
0.13 0.27

0.34 0.37 -0.67
-0.18 -0.22 0.33
0.17 0.64 -0.25
-0.13 -0.11 0.47
-0.47 -0.35 0.51
-0.19 -0.55 0.31
-0.33
0.53
                                                                                               28

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Table 3.5 shows correlation coefficients between combinations of system-wide outputs. Among
the notable relationships are the inverse correlations between system-wide natural gas use and
both renewables and petroleum use.
Table 3.5: Correlation coefficients between system outputs and all outputs
                      System-Wide Output

System-Wide Output
System- Wide Emissions
CO2
NOX
SO2
Fuel Inputs to System
Renewables
Coal
Petroleum
Oil
Natural Gas
Natural Gas Liquids
Uranium
Net Imports
Emissions

CM X CM
000
o z w

1 .00 0.63 0.40
1.00 0.89
1.00







Fuel Inputs to System
CO
;o
!^
^ ,- ra ra
.a £ O O
ro ^ £
3 ^ ra ra =!
CD _ O i= i= 'F
c ra i: 335
o> o a) = ro ro ii:
CC O CL O Z Z Z)

-0.56 0.16 -0.11 0.60 0.38 0.16 -0.29
-0.49 0.25 0.80 -0.20 -0.22
0.14 -0.63 0.40 0.63 -0.42 -0.30

1.00 -0.50 0.66 -0.22 -0.87 -0.66
1 .00 | -0.48 -0.22 0.51 0.37 -0.14
1.00^^3 -0.77 -0.91
1.00
1.00^(^5
1.00
1.00



CO
•c
o
CL
'<£
z

0.29
0.69
0.70

0.41
-0.55
0.82
0.45
-0.62
-0.83
1.00
3.5 Global Sensitivity Results - Normalized Linear Regression
To evaluate the relative magnitude of impact (as opposed to correlation) of each input on the
tracked outputs, normalized linear regressions were used. Normalization was carried out so that
the coefficients on each term would  be directly  comparable.   Regression results are depicted
graphically in the tornado diagrams provide in Figures 3.1 through 3.9. Tornado diagrams were
used to present the regression  results since these diagrams visually convey the sign and relative
magnitude of influence of each input.  R2 values are provided to give an indication of the quality
of the fit, indicating the ratio of explained variance to total variance.
                                                                                       29

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In Figure 3.1, the factor with the greatest influence on the quantity of coal used was natural gas
cost. As seen in Table 3.2, increased natural gas costs led to decreased coal use.  The correlation
between coal and nuclear power in electricity generation is much more straightforward since
nuclear power would offset coal in supplying base load electricity. In comparison, the
correlations between nuclear and natural gas and between nuclear and oil were much weaker.
          Natural Gas Cost I        -0.239
  Nuclear Capacity Multiplier I                       -0.169	


      Biofuels Growth Rate I                             „„,.,.[
            r,    ,       I                             -0.1 4D
            Bound       i                                  I
  Hydro Growth Rate Bound I                              -0.138^
  New Electricity Generation |                                 01271
   Technology Hurdle Rate  !                                      I
                 Oil Cost |                                               -0.059 |	


   Geothermal Growth Rate i                                                    nnicf
           _   .         i                                                    -U.Uob
           Bound         i                                                         I
   Wind Growth Rate Bound I           R2=0.67                                  -0.035
                       -0.3       -0.25        -0.2        -0.15       -0.1       -0.05
                                           Normalized Regression Coefficient


              Figure 3.1: Factors affecting use of coal in  electricity generation
                                                                                            30

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Figure 3.2 provides an indication of the relative sensitivities of natural gas use in electricity
generation to the Monte Carlo inputs. As might be expected, natural gas use decreased with
increases in natural gas costs, but increased with higher oil costs.  The impact of the hurdle rate,
which inhibited the introduction of new, more efficient natural gas technologies, was only about
a third as important as either of these two cost factors.
          Natural Gas Cost
                 Oil Cost
  New Electricity Generation
   Technology Hurdle Rate
  Nuclear Capacity Multiplier
       Biofuels Growth Rate
             Bound
   Hydro Growth Rate Bound
   Wind Growth Rate Bound
-0.231
                   -0.073
                      -0.053
                       -0.047
                        -0.036
                           -0.012 F
                                                            0.213
                        -0.3   -0.25   -0.2  -0.15   -0.1   -0.05    0    0.05    0.1    0.15   0.2    0.25
                                             Normalized Regression Coefficient
           Figure 3.2: Factors affecting use of natural gas in electricity generation

-------
Figure 3.3 shows the sensitivities of oil use in electricity generation to the various Monte Carlo
inputs.  Oil cost increases were the major factor affecting penetration. Natural gas cost increases
had approximately half the impact. The influence of nuclear capacity is less than one half that of
natural gas cost.
                Oil Cost
          Natural Gas Cost
  Nuclear Capacity Multiplier
   Wind Growth Rate Bound
  Hydro Growth Rate Bound
-0.555
                           -0.106
                                -0.040
                                                            -0.031
                                                           0.259
                       -0.6     -0.5     -0.4    -0.3    -0.2     -0.1      0     0.1
                                           Normalized Regression Coefficient
                                                     0.2
0.3
               Figure 3.3: Factors affecting use of oil in electricity generation
Figure 3.4 indicates that CC>2 emissions from the electric sector were impacted the greatest by
increases in  natural gas  and oil costs.   For natural  gas, this relationship represents the  same
behavior observed in Table 3.2: natural gas cost increases led to decreased penetration of natural
gas-fueled electricity generation technologies. These technologies had fewer NOX emissions than
pulverized coal plants, and, as a result,  the amount of coal  used in  electricity generation
decreased to meet NOx constraints. Because coal has higher CC>2 emissions than the other fuels,
this  decrease in coal use resulted in a decrease in CC>2 emissions as well.  Similar behavior
appeared to be occurring with increased oil  costs.

 The impact  of the hurdle rate was only slightly lower than the cost of natural gas or oil.  By
making  it more difficult  for new,  more  efficient  technologies to penetrate the electricity
generation market, the hurdle rate led to increases in CO2 emissions.
                                                                                          32

-------
          Natural Gas Cost


                 Oil Cost


   New Electricity Generation
    Technology Hurdle Rate


   Nuclear Capacity Multiplier


   Hydro Growth Rate Bound


       Biofuels Growth Rate
             Bound


    Wind Growth Rate Bound
    Geothermal Growth Rate
            Bound
-0.275
 -0.266
        -0.192
              -0.128
              -0.126
                     -0.051
                       -0.020
                                                      10.256
                                                R2=0.88
                       -0.4       -0.3       -0.2      -0.1       0        0.1
                                            Normalized Regression Coefficient
                                                0.2       0.3
           Figure 3.4: Factors affecting COi emissions from electricity generation
Another expected result was that increased nuclear capacity  led to decreased CC>2 emissions.
This influence was not as strong as that of natural gas costs, oil costs, and hurdle rate, however,
even though nuclear power has no CC>2 emissions. Again, the results were influenced by the NOx
limit  on electricity generation.  Increased use of nuclear power to meet base load electricity
generation effectively freed space under the NOx limit.  The result was that fewer NOx controls
were  placed on  coal-fired power  plant emissions and additional coal capacity was added, at the
expense of natural gas and oil technologies.

The new technology hurdle rate had the greatest impact on the  adoption of NOx retrofit controls,
such  as selective catalytic reduction, at coal-fired power plants  (Figure 3.5). Increases in the
hurdle rate effectively required that additional existing coal-fired  power plants remain online in
2030. To achieve electric sector NOx constraints with these plants, greater use of NOx controls
was required.
                                                                                          33

-------
  New Electricity Generation
   Technology Hurdle Rate
          Natural Gas Cost
                 Oil Cost
  Hydro Growth Rate Bound
         Nuclear Multiplier
    IGCC Availability Factor
-1.220
                                                                                       0.432
                                                   0.138
                                               ~~| 0.060
                                                 3.057
                                              GO,
0.044
                                                                                   R2=0.82
                       -1.4    -1.2    -1     -0.8    -0.6    -0.4    -0.2     0     0.2     0.4     0.6
                                            Normalized Regression Coefficient


        Figure 3.5: Factors affecting use of NOx controls on coal-fired power plants
Figure 3.6 illustrates that increasing oil costs had the effect of reducing system-wide CC>2
emissions. Contributing factors were fuel and technology switching in the transportation sector.
Additionally, fuel-switching within the electric generation sector may have played a role. By
favoring existing coal-fueled electricity generation technologies, the hurdle rate had the effect of
increasing system-wide carbon emissions.
                                                                                             34

-------
                 Oil Cost
  New Electricity Generation
   Technology Hurdle Rate
          Natural Gas Cost
  Nuclear Capacity Multiplier
  Hydro Growth Rate Bound
      Biofuels Growth Rate
            Bound
   Wind Growth Rate Bound
    Geothermal Growth Rate
           Bound
-0.359
         -0.181
          -0.167
               -0.106
               -0.103
                    -0.041
                      -0.017
                                                       0.311
                                                                                 R2=0.67
                       -0.4      -0.3     -0.2     -0.1      0       0.1      0.2      0.3      0.4
                                           Normalized Regression Coefficient


                  Figure 3.6: Factors affecting system-wide COi emissions
The regression coefficients shown in Figure 3.7 indicate that SC>2 emissions were most sensitive
to oil and natural gas costs.   While 862 emissions  from the electric sector were constrained,
changes in SC>2 emissions were occurring in other sectors, primarily in the transportation sector.
Decreased oil costs led to increased use of gasoline  and diesel within the transportation sector.
Diesel,  in particular, has the potential to increase  SO2 emissions, although this impact will be
reduced when  new regulations  on sulfur content  in diesel  fuels  come  into  effect. These
regulations are not currently represented in the MARKAL model.

The influence of increased natural gas costs was approximately half that of oil costs. One of the
reasons that increased natural gas costs resulted in increased 862 emissions was because of
switching from natural gas-fueled transportation technologies to oil-fueled technologies.
Changes in SC>2 emissions may also have been influenced by resulting technology changes
within the electricity sector, and resulting interactions with other sectors, although additional
analyses would need to be carried out to characterize these impacts.
                                                                                          35

-------
                Oil Cost
        Natural Gas Cost
New Electricity Generation
 Technology Hurdle Rate
        Nuclear Multiplier
Hydro Growth Rate Bound
-0.537
                           -0.119
                              -0.085
                                                               -0.0284
                      -0.6     -0.5     -0.4     -0.3     -0.2     -0.1      0       0.1
                                             Normalized Regression Coefficient
                                                              0.243
                                                         0.2      0.3
                  Figure 3.7: Factors affecting system-wide SOi emissions
                                                                                                 36

-------
Figure 3.8 presents the relative impacts of various inputs on system-wide NOx emissions. As
with  SC>2, inexpensive  oil inhibited the transition to more efficient vehicle technologies.  The
result was an increase in NOx emissions. The impact of other drivers was relatively small.
                 Oil Cost
          Natural Gas Cost
  Nuclear Capacity Multiplier
  New Electricity Generation
   Technology Hurdle Rate
                              -0.633
-0.067
                    0.13
                0.066
                       -0.7    -0.6     -0.5    -0.4     -0.3     -0.2     -0.1      0
                                            Normalized Regression Coefficient
                0.1
0.2
                  Figure 3.8: Factors affecting system-wide NOx emissions
                                                                                            37

-------
In MARKAL, reductions in fossil fuel imports can be achieved through either switching to more
efficient technologies or switching to fuels that are supplied domestically instead of imported.
Fuels that are largely supplied domestically in the model include coal, uranium, natural gas, and
renewables.  Oil and other petroleum products, in contrast, are largely imported fuels. Thus, in
Figure 3.9,  the two factors with the highest impact on imports  were natural gas costs and oil
costs, which had opposite effects on imports
          Natural Gas Cost
                Oil Cost
  New Electricity Generation
   Technology Hurdle Rate
  Nuclear Capacity Multiplier
   Wind Growth Rate Bound
  Hydro Growth Rate Bound
    Geothermal Growth Rate
           Bound
-0.446
                 -0.071
                    -0.023 f



                    -0.022 [
                                                    0.567
                               0.109
                           0.02
                      -0.6       -0.4       -0.2       0        0.2       0.4
                                           Normalized Regression Coefficient
                                                    0.6
0.8
                      Figure 3.9: Factors affecting fossil fuel imports
3.6 Parametric Sensitivity Analysis
From the global sensitivity  analysis, it was evident that many  of the key outputs were most
responsive to changes in natural gas and oil costs, nuclear capacity, and the hurdle rate for new
electricity generation technologies.  There were also cross-sector effects between the electricity
generation and transportation sectors that had an impact on technology adoption, fuel use, and
emissions.

A parametric sensitivity analysis was carried out to evaluate the effect of incremental changes in
each of these inputs on the primary outputs  of interest: fuel use within the electricity generation
sector,  system-wide  emissions,  and  net  imports. By  evaluating changes  parametrically,
sensitivities  around the baseline  run were  characterized.  Results of the parametric  runs are
provided in Figures 3.10 through 3.16.
                                                                                         38

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Figure 3.10 shows the change in electricity generation by fuel type as natural gas cost changed in
relation to the baseline natural gas price in  2030.  In this figure, as well as in Figures 3.11
through 3.13, electricity generation categories with outputs of less than  500 PJ are not shown.
Decreases in natural gas costs led to an increase in natural gas use in electricity generation, at the
expense of coal and  oil. The range of decreased natural gas costs that was examined was not
sufficient to drive compressed natural gas vehicles to penetrate the light-duty vehicle market, so
there was limited interaction with the transportation sector.

Increasing  natural gas costs by 35% or more appeared to drive oil use in electricity generation
while at the same time resulting in decreased use  of coal. Natural gas use decreased with cost,
although the decrease was comparatively modest. These observations suggest that the increased
natural gas  cost may have triggered an emissions-related modeling tipping point that favors oil
use over coal.
                   9000
                   7000
                                                                  Pulverized Coal
£
01
  o
  01
  c
  Ol
  O
  o
  o
  m
                     ee-
                                                                   Natural Gas
                          o o
                   5000
                                                                  Nuclear Power
                 2000
                          O  0     I     0
                                                                         Oil
                                                                   Hydropower
   -40%     -20%
                       0%      20%      40%      60%     80%      100%     120%     140%
                                   Change in Natural Gas Cost
        Figure 3.10: Impact of natural gas cost on electricity generation by fuel type.
                                                                                         39

-------
Figure 3.11 shows that a decrease in the oil costs resulted in additional oil use in electricity
generation, accompanied by decreases in natural gas and coal use. Coal use decreased to offset
the loss of the low NOx-producing natural gas technologies. Increased oil prices led to decreases
in oil and coal use, with the corresponding demand met instead by natural gas. Coal decreases in
this case were likely adopted to offset the loss of low-NOx-producing oil technologies. Changes
in coal use corroborate the hypothesized effect of the emissions constraint suggested in the
discussion of Table 3.3 and Figure 3.4.
                       9000
CL^
Ol
  o
  13
  01
  O
                       7000
                       5000
                       2000
                      o10S° i
                                                            Pulverized Coal
                                                              Natural Gas
                                                             Nuclear Power
                                                               Hydropower
  -100%
               -50%
50%        100%
Change in Oil Cost
150%
 Oil
—^*
200%
250%
            Figure 3.11: Impact of oil cost on electricity generation by fuel type.
Figure 3.12 shows the response of the electric sector to increased nuclear capacity. The primary
fuel offset was coal, with only minor changes in natural gas use.  Interestingly, the total amount
of electricity generated, the sum across fuel types in the future, appears to increase with
additional nuclear capacity. An examination of residential energy demands suggests that
additional nuclear capacity resulted in fuel switching within that sector from natural gas and oil
to electricity (e.g., to meet demands for home heating). Similar fuel switching may have
occurred in the commercial and industrial sectors.
                                                                                        40

-------
    9000
                                    1.15       1.2       1.25       1.3       1.35       1.4
                                    Nuclear Capacity Multiplier
       Figure 3.12: Impact of nuclear capacity on electricity generation by fuel type.
Increasing the hurdle rate from 0.05 to 0.25 had the effect of decreasing the uptake of new
electricity generation technologies as shown in Figure 3.13. This decrease forces the cost of
generating electricity to increase. The net result was a decrease in the use of oil, coal, and natural
gas in electricity generation, as well as an overall decrease in the total amount of electricity
generated.  In response, MARKAL results suggested that the residential sector, commercial, and
industrial sectors would experience some degree of fuel switching from electricity to other fuels
in meeting demands such as space heating.
                                                                                         41

-------
    10000
     9000
  ^  8000
  Q_

  g;  7000
  01
  3
     6000
  o  5000
  S
  ai
  c
  ai
  O
  1
  m
4000


3000


2000


1000


   0
                       Pulverized Coal
                  Natural Gas
                       Nuclear Power
                                                             Hydropower
                                                                       Oil
                    0.05          0.1         0.15          0.2          0.25
                         Hurdle Rate for New Electricity Generation Technologies
                                                                            0.3
 Figure 3.13: Impact of the new electricity generation technology hurdle rate on electricity
                                 generation by fuel type.
Figure 3.14 shows percent changes in CC>2 emissions from the electric sector in response to each
of the four inputs.  This  is a different type  of parametric sensitivity graph that shows  the
responses to parametric changes in more than one input on an output.

The results suggest that nuclear power capacity had the most direct impact on CO2  emissions
from the electric sector. A 35% increase in nuclear capacity yielded less than a 6% reduction in
CO2, however.  Oil cost had a similar impact with respect to magnitude of CO2 response.

The overall magnitude of changes in electric sector CC>2 emissions is less  than  might be
expected. One  of the primary reasons  for the  moderate level  of these changes is that  the
renewable  technologies  with negligible CO2  emissions  are  not  sufficiently economically
competitive that they are able to offset large amounts  of fossil fuel usage. Instead, the primary
response of the electric generation system to changing inputs is fuel switching from  one fossil
fuel to another. This has some implications on CO2 emissions, as reflected in the small changes
observed.
                                                                                       42

-------
                   2.0%
  m -1CD%
   p
  O
  o
  _c
   01
   O)
   c
   ra
  ^
  O
100%    150%     200%     250%
                                    Natural Gas Cost
                                  - Oil Cost
                                    ELC Hurdle Rate
                                  "~ Nuclear Capacity
                  -6.0%
                              Change in Input
  Figure 3.14: Changes in COi emissions from the electric sector in response to changes in
                                         inputs.
In Figure 3.15, the system-wide impacts of parametric changes to each input on CC>2 emissions
are shown. The functions observed  are similar to those in Figure 3.14,  although  the overall
magnitude of CO2 changes is less, ranging from approximately +1% to -2.75%.

Sensitivity diagrams for electric sector NOx and SC>2 emissions are not shown here. The electric
sector NOx and SC>2 emissions limits were binding in all runs. System-wide impacts were
therefore largely dependent on the transportation sector.
                                                                                      43

-------
                   2.0%
   in
   O
   'in
   in
   c -1CD%
LJJ
CM
O
O
01
;c
3i
Ol
u
OT
   Ol
   O)
   ra
   .n
   O
                                    100%     150%     200%    250%
                                                                          Natural Gas Cost
                                                                         - Oil Cost
                                                                          ELC Hurdle Rate
                                                                         "~ Nuclear Capacity
                  -6.0%
                              Change in Input
   Figure 3.15: Changes in system-wide COi emissions in response to changes in inputs.
Figure 3.16 shows  the changes in  net imports.  Increased natural gas costs had the greatest
impact on increasing reliance on imports, with a 100% increase in natural gas costs resulting in
approximately  a 17% increase in imports.  The impact  of oil  costs  on net  imports  was
considerably less, reaching a maximum decrease in imports  of about  5%, corresponding to a
150% increase in oil costs.
                                                                                      44

-------
                  20.0%
  •c
  o
  Q.
  E
  in
  in
  O
  01
  O)
  c
  ra
  ^
  O
-1CO%
                                                                      Natural Gas Cost
                                                                     - Oil Cost
                                                                      ELC Hurdle Rate
                                                                      Nuclear Capacity
25|)%
                 -10.0%
                              Change in Input
           Figure 3.16: Changes in net imports in response to changes in inputs.
3.7 Summary of Observations from Sensitivity Analysis
The combination  of  correlation  coefficients,  normalized  multiple  linear  regression,  and
parametric analysis of the BAU case provides considerable insight into the inner-workings of the
MARKAL model and the response of the model to alternative input assumptions. In most cases,
the results of these analyses confirmed expected behavior in the model.  Additionally, the results
provided insight into the complicated response of the system to criteria pollutant emission limits.
In this subsection, we summarize many of the key observations from the sensitivity analysis.

Much of the EPANMD electric sector's behavior appears to be influenced by whether specific
technologies and fuels meet base or peak load electricity demands. The predominant base load
technologies are coal-fired power plants and nuclear power plants. Coal is the most competitive,
and has the largest market share.

Natural gas- and oil-fueled technologies  are used to meet peak electricity demands. Both fuels
experience some cross-sector interactions with the transportation sector because natural gas and
refined oil products can be used within vehicles. While there was some  evidence of cross-sector
                                                                                       45

-------
interactions,  these  were largely of secondary  importance to in-sector  technology and fuel
competition.

The electric sector emissions constraints have interesting effects on the electric generation mix.
For example, when natural gas became more expensive and gas technology utilization decreased,
use of coal also decreased. This behavior, which may at first seem counter-intuitive, is explained
by the response of the system to the electric sector NOx constraint. Since coal technologies had
higher NOX  emissions  than  natural gas technologies, and since NOX constraints on  electric
generation were binding, the model opted to replace natural gas with oil. Oil combustion also
leads to greater NOx emissions than that from natural gas, thought it is less than coal.  Some coal
was therefore  displaced  by  oil.  This  fuel-switching  and related  technology  change had
implications on CO2 emissions as well.

The electric  sector  NOx constraint  also affects  the model's response  to  increased  nuclear
capacity.  By introducing electricity generation capacity that does not have NOx emissions, coal-
fired power plant emissions are less constrained in meeting the electric sector NOX  constraint. In
response,  the fraction of coal-fired plants  projected to make use  of NOx controls, such  as
selective catalytic reduction, decreases.  The same behavior can be  expected from increased
electricity generation from low- or zero-NOx renewables, such as solar or hydropower.

The electricity generation hurdle rate has an additional  impact on  future-year energy sector
technologies. Increasing this rate effectively  makes  it more difficult for new, more efficient
technologies to penetrate the electricity generation market. This difficulty, in turn, has the effect
of increasing the  marginal  peak electricity  price,  increasing CO2  emissions from  electricity
generation, and decreasing the penetration of renewables.  Thus, addressing hesitancy to  adopt
new technologies through  some approaches for  hedging risk may  yield  a more efficient
electricity generation system.

Imports of fossil  fuels are correlated with the use  of oil in electricity generation,  but inversely
correlated with natural gas use, reflecting that much of the natural gas demand is being met by
domestic supplies in the model.  Cross-sector fuel  switching resulting from changes in natural
gas and oil consumption in the transportation sector may  also  have an impact, but further
analysis is needed to characterize this behavior.

Finally, changes in system-wide CO2  emissions in response to variation in model inputs were
minor, with decreases of less than 3% observed. This output is influenced by the inability of low-
CO2 emitting technologies, and in particular, renewables, to achieve high market  penetrations.
When these technologies do penetrate,  the potential  reductions in CO2 emissions are often offset
by increased  use of coal and other fossil  fuels, made possible via the room under the NOx limit
created by the renewables.

Sensitivity analysis  such as is  presented here has been a useful component of the MARKAL
model database development and quality assurance. By identifying key drivers and interactions,
sensitivity analysis facilitates an understanding of how the model responds to alternative input
assumptions which, in turn, aids model refinements.
                                                                                       46

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                                       Section 4

                  The Future Role of Nuclear Energy in the U.S.


4.1    Introduction
With  all nations facing  enormous challenges  related to energy  security,  sustainability  and
environmental quality, nuclear power will likely play an increasingly important role in the future.
In particular, the life-cycle emissions  of criteria pollutants and greenhouse gases from nuclear
power plants  are  significantly lower than from conventional  fossil-fueled plants,  renewing
interest in nuclear power as a low emissions source of electric power. In order for nuclear power
to emerge as a key future technology, four  basic challenges  must be met:  cost,  safely,
proliferation   prevention,   and  waste  management (Ansolabehere   et  al.,   2003).  While
acknowledging significant challenges related to the latter three items, this analysis focuses on an
engineering-economic assessment of future nuclear power in the U.S.

This section focuses  on the potential role of nuclear power in the U.S. electric sector over the
next 30 years by  analyzing results from the U.S. EPA National  MARKAL Database model
(EPANMD).  The  section  first describes the implementation of  conventional  and  advanced
nuclear technologies in the EPANMD. In the next subsection, modeling results are presented and
analyzed. Finally, implications of the penetration of nuclear technologies on the emissions from
the power sector are drawn.

4.2    Nuclear Technology Representation in MARKAL
In the EPANMD, all nuclear technologies draw  on a  single uranium supply curve. The uranium
supply curve  is based on estimates  of global  uranium reserves  and the cost of extraction
(OECD/IAEA, 2002). Because the energy density (energy per unit weight) of uranium is high,
transport costs were ignored.

The nuclear fuel cycles included in EPANMD were determined by careful consideration of the
nuclear technologies most  likely to be deployed in the U.S. over the next three decades.  The
analysis considers  the following technologies: light water reactors operating on a once-through
fuel  cycle,  mixed-oxide  reactors, heavy  water  reactors,  fast  breeder  reactors, and  high
temperature gas-cooled reactors. Although not a comprehensive list of nuclear technologies, they
represent the broad technical thrusts in the nuclear industry.

In an  increasingly  competitive world of deregulated electricity generation markets, other things
being equal, systems with  lower upfront costs and  shorter construction times are likely to be
preferred by investors over those with higher upfront costs and longer construction times. Light
water reactors (LWRs) operating on a once-through fuel cycle (no reprocessing) currently have
the lowest cost among commercially available  reactors. LWRs  have been widely adopted
globally and still currently serve roughly 20 percent of U.S. electricity demand.

Heavy water reactor technology typically calls for  larger plants with higher construction  and
capital costs, as  compared  to light water reactor plants. Moreover, the large amount of heavy
water  (deuterium) required  to  run  these  plants also  necessitates  significant  infrastructure
investments. A heavy water  reactor's key  advantage is its ability to use natural uranium, as
                                                                                      47

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compared to enriched uranium required by  light water reactors. However, the demand-supply
equilibrium is  such that  most analysts  expect enough enriched  uranium to be  available at
reasonable price for at  least the  next  half-century. As a result, heavy water reactors are not
included in the EPANMD.

Enough supplies of uranium exist to build and operate roughly 1000 reactors in a once-through
LWR nuclear fuel cycle.  If new uranium  resources do not become available and  existing
resources  are depleted at a rapid pace, then  breeder reactors may emerge as a viable option to
meet long-term energy supply goals. Breeder reactors not only fission uranium, but also convert
                            '7^8      	 O'lO                                    91Q       '711
fertile materials (primarily  U   and  Th  ) into fissile products  (primarily Pu   and  U  ).
Breeder reactors are designed to produce more fissile material than they fission. Breeder reactors
are not an economically attractive option in  the wake of prevailing enriched uranium prices, at
least in the short-term. Therefore, we did not  include breeder reactors in the EPANMD.

In the U.S., LWRs are likely to remain the  dominant nuclear technology because  there is
significant experience with design, construction and operation of these plants. However,  MOX
(mixed  oxide)  reactors  were also  included in the model,  since  it is at least plausible that
plutonium recycling would be considered in  the future, despite  the  high costs and  risks of
proliferation. MOX fuel  is created by first extracting the fissionable  uranium and plutonium from
the spent  LWR fuel in  a process known as PUREX (plutonium and uranium extraction). The
resultant fuel must be blended  with depleted uranium  (a byproduct of the uranium enrichment
process) to obtain the correct proportion of fissionable material in the fuel.

Costs and performance  characteristics  for LWRs were drawn from Ansolabehere et al. (2003)
and DOE  (2001). It should be noted that the  Ansolabehere et al. (2003) cost estimates for MOX
recycling are significantly higher than European estimates (Ansolabehere et al., 2003) and should
be interpreted as conservative.  Table 4.1 below shows the cost associated with the light water
reactor fuel cycle used in EPANMD.

Table 4.1: Cost and performance estimates for LWRs used in EPANMD.

Capital Cost* ($/kW)
O&M Cost ($/kWh)
Burnup (MWd/kg IHM)
Capacity Factor (%)
Efficiency (%)
Fuel Cost ($/kWh)
Lifetime (years)
Average Cost ($/kWh)
Data Source
Existing LWRs
N/A
0.0125
50
85
33
0.0051
40
0.018
NEI, 2003
LWRs in 2010
1440
0.005
50
90
36
0.0051
40
0.036
DOE, 2001
MOX Reactors
2000
0.0077
40
85
33
0.022
40
0.070
Ansolabehere et al., 2003
*The levelized costs for new plants are calculated with a 15% discount rate, representing a
private investor's expected rate of return.
                                                                                       48

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Figure 4.1  presents a  schematic diagram  of the LWR  fuel  cycle  in  the  EPANMD. The
enrichment step includes the cost for uranium ore purchase, conversion, enrichment, fabrication,
and storage and disposal  of nuclear waste.   Note that spent uranium can be reprocessed and
fissioned in a MOX (mixed oxide) reactor.
               Mined Uranium
                    Natural U
              Conversion, Enrichment
                 and Fabrication
                        Depleted U
                               Stockpile of
                               Depleted U
                 Enriched U
               Nuclear power plant
              Electricity
              to grid
                          Spent U
Hi PUR


	 1 Pu

PUREX
waste
Stockpile of
PUREX Waste

MOX Fabrication



MOX reactor
                                                     Electricity
                                                     to grid
                                                            MOX
                                                            waste
     Figure 4.1: Schematic diagram of the light water reactor (LWR) fuel cycle in the
                                       EPANMD.

LWRs have several technical drawbacks that have limited their deployment, particularly in the
U.S. Construction of the  reactor vessel and  associated fuel handling equipment is materials-
intensive and  requires large capital  outlays.  To defray the cost, LWRs exploit economies of
scale: LWR plants are typically 1 GW or larger and usually take 10 years to approve and build.
From a finance perspective, investors may be unwilling to bear the risk of ordering  a plant that
will not go online for many years, by which time others may have added new capacity in the
same region. Although large nuclear plants provide relatively low per kWh cost, they have the
drawback of exceeding demand growth in smaller-demand networks.

In addition, the low thermal efficiency  (approximately 33%)  and burnup (50 GWd/TIHM) of
LWRs create a significant amount of radioactive waste that requires storage and disposal. The
ultimate disposition of spent uranium from LWRs presents a serious technical challenge and a
public  policy concern. Finally, LWRs rely on active safety systems and human judgment and
intervention to prevent core meltdowns, which can fail, as evidenced by the accident at the Three
Mile Island nuclear facility outside of Harrisburg, Pennsylvania.

High temperature gas-cooled reactors (HTGRs) address many of the shortcomings of LWRs and
are therefore included in the EPANMD. HTGRs are built in modular units ranging from 100-300
MWe,  making nuclear a feasible  option in smaller markets. The smaller size and modular design
can also allay  investor concerns  about long construction times and the associated financial risk.
In addition, HTGRs  have higher burnup and thermal  efficiency,  resulting  in a proportional
reduction in spent fuel. Finally, HTGRs  incorporate a passive  safety design, such that heat
generated during fission can be  thermally conducted to the  ground without resulting in a core
meltdown.
                                                                                       49

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In order to dissipate heat passively through the ground, HTGRs must have a smaller core density
than LWRs, so multiple HTGR units are required to compete with a single LWR. At first glance,
it would seem that HTGRs would be  prohibitively expensive because the design does not take
advantage of the same economies  of scale as LWRs. HTGR  designers are depending on a
different economic scaling  law that will  make  HTGRs competitive  with LWRs:  factory
manufacturing of modules, shorter construction schedules, and sequential completion of units (1
year apart). Because  HTGRs are currently in the demonstration stage, this economic strategy
remains unproven.

There are two main competing HTGR  designs: the Pebble Bed Modular Reactor (PBMR) and the
Gas Turbine - Modular Helium Reactor (GT-MHR). A common feature of these two designs is
uranium oxide particles coated  with  pyrolytic  carbon to  contain the  fission products.  In the
PBMR design, the fuel particles are  embedded in carbon spheres roughly 2.5 inches in diameter,
which allows the continuous removal and reloading of fuel spheres without shutting down the
reactor.  Also,  both the PBMR  and  GT-MHR use helium as  a  coolant  and run modified
combustion turbines directly on the high temperature helium. A schematic diagram of the HTGR
fuel cycle without recycling is shown in Figure 4.2. Note that while in practice it is possible to
extract  enriched uranium and  plutonium  from the  spent fuel particles,  it is  prohibitively
expensive at present.
                      Mined Uranium
                            Natural U
                     Conversion, Enrichment
                        and Fabrication
                                           Depleted U
                          Uranium fuel pellets
PBMR or GT-MHR


Sf
                                    Spent pellet fuel
                     Electricity
                     to grid
  Figure 4.2: Schematic diagram of the high temperature gas-cooled reactor (HTGR) fuel
                                 cycle in the EPANMD.

Because  there has been no commercial development of HTGRs, the  cost estimates used in
EPANMD are speculative. The capital costs represent nth-of-a-kind estimates, which assume the
units are being mass produced. The speculative nature of the estimates and the nth-of-a-kind cost
assumptions make the characterizations of HTGRs in the EPANMD optimistic (see Table 4.2).
                                                                                    50

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Table 4.2: Cost and performance estimates used in EPANMD for HTGRs (advanced nuclear).

Capital Cost* ($/kW)
O&M Cost ($/kWh)
Burnup (MWd/kg IHM)
Capacity Factor (%)
Efficiency (%)
Fuel Cost ($/kWh)
Lifetime (years)
Average Cost ($/kWh)
Data Source
PBMR
1250
0.0025
80
95
40
0.005
60
0.03
DOE, 2001
GT-MHR
1122
0.0036
112
90
48
0.0077
60
0.033
DOE, 2001
* The levelized costs for new plants are calculated with a 15% discount rate, representing a
private investor's expected rate of return.

4.3    MARKAL Analysis
The EPANMD includes the following nuclear technologies: LWRs, MOX plants, PBMRs, and
GT-MHRs (the latter two being the high-temperature gas-cooled reactors). In the results that
follow, the LWR and MOX plants are grouped together and presented as "conventional nuclear"
and the PBMR and GT-MHR are grouped together and presented as "advanced nuclear".

Growth Rates
The choice of a nuclear growth rate constraint is a key assumption, as it prevents MARKAL
from building an unrealistic amount of nuclear capacity. Ideally, the growth rate  constraint
would be based on the amount of capacity installed in the previous time period, but MARKAL
does not provide the capability to define a capacity-dependent growth rate constraint. Instead,
MARKAL allows the specification of growth rate constraints by time period only. The maximum
growth rate decreases in later time periods under the assumption that nuclear capacity will grow
at a slower pace as  the nuclear capacity base grows larger. In addition, another growth parameter
was used to allow a small increment of capacity to be added above the upper bound growth
constraint. For conventional LWRs, the incremental  capacity allowance is 4 GW-representing
roughly four new plants-and the capacity allowance is 3 GW for advanced nuclear-representing
roughly 15 new units. This incremental capacity provides an upper bound on the nuclear capacity
the first year it enters the model, and allows the 3 - 4 GW addition over and above the specified
growth rate in all subsequent periods. Growth constraint specifications are shown in  Table 4.3.
Conventional nuclear can first enter in 2010, and 4 GW max can be built. Note that these growth
rates apply to each individual technology; there are two advanced and two conventional nuclear
technologies.

Table 4.3:  Annual maximum growth rates (%) by model time period and incremental capacity
(GW) allowable over the growth rate.

                      Incremental GW   2015      2020      2025       2030
         Conventional 4                25        25        18         12
         Advanced    3                0          25        25         18
                                                                                    51

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While the choice of future  growth constraints is somewhat arbitrary,  the growth constraints
shown in Table 4.3 are plausible. From the inception of Eisenhower's Atoms for Peace Program
in the early  1950s to the mid-1980s when the last U.S. nuclear units came online, roughly 100
GW of nuclear capacity was  built (EIA, 2003).  This benchmark indicates the U.S. built 100 GW
over 30 years, or roughly  3  GW/yr assuming linear capacity additions. With the upper bound
growth  constraints  in Table  4.3,  the  maximum conventional nuclear capacity would be
approximately 130 GW by 2030. Likewise, the maximum advanced nuclear capacity would be
approximately  80  GW by 2030.  Though  this upper bound on  growth  allows  for the
unprecedented expansion of  nuclear, a mature nuclear industry and improved technology make
such growth feasible.

Model Results
Electricity production is shown in Figure 4.3 when EPANMD is run with both conventional and
advanced nuclear technologies. The  results include 16 GW of new conventional nuclear and 50
GW of new advanced nuclear.
    25000
                                                          2025
                                                                    2030
                                                                         • Advanced Nuclear
                                                                         ffl New Pulverized Coal
                                                                         D Other
                                                                         H Renewables
                                                                         D Natural Gas
                                                                         • Conventional Nuclear
                                                                         • Residual Coal
  Figure 4.3: Electricity generation in a baseline run of EPANMD with both conventional
                      and advanced nuclear technologies available.

The new nuclear capacity did not reach the upper bound growth constraints shown in Table 4.3.
In 2030, nuclear power accounts for 17.4% of the electricity generated, which maintains the
current amount of nuclear power on a relative  share basis. Much of the growth in electricity
demand over the model time horizon was met by new combined-cycle natural gas (248.8 GW).

Because fossil fuel prices are a key future determinant of electric sector technology, parametric
analysis of natural gas and coal prices was  performed to observe the impact  on  nuclear power
installations. Figures 4.4 and 4.5 show the change in the nuclear share as coal  and gas prices are
increased, respectively.  Doubling the coal price (+2 $/GJ) only increases the nuclear share by
approximately 1% (Figure 4.4).  While doubling the natural gas price (+5 $/GJ) increases the
share of nuclear by approximately 5% (Figure 4.5).
                                                                                     52

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£ 20% -
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24. 1% 24.5% 25.0% ^ 25.3% ^
















































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              Reference
  $0.50             $1.00
   Coal Price Increase ($/GJ)
$2.00
Figure 4.4: Change in the 2030 nuclear share of total electricity production as coal price is
                               increased parametrically.
c tu/o -
.g
5 35% -
0)
^ 30% -
| 25%-

-------
Figures 4.4 and 4.5 demonstrate that nuclear power can provide a limited hedge against high
fossil fuel prices in the electric sector.   An additional scenario was run that included both an 8
$/GJ markup on natural gas and 2 $/GJ markup on coal prices to examine the synergistic effect
on nuclear capacity. The result was a nuclear share of total electricity that was approximately
37% (approximately 3% more than an 8 $/GJ mark up on natural gas alone). The high natural gas
and coal prices together did not push conventional nuclear to its growth  limits, which would
allow nuclear to achieve a maximum share of  approximately  45% in 2030. Because EPANMD
supply curves for coal  and natural  gas  are based on EIA's Annual Energy Outlook, which
employs very conservative assumptions regarding fuel prices, the range of costs tested in the
sensitivity analysis above is plausible.

Surprisingly,  the availability  of  nuclear  has  no  effect on  SO2 and NOX  emissions. In all
scenarios, the Clean Air Act  emission  constraints on electric sector emissions are in effect.
Because air pollutant emissions are driven  largely by pre-existing coal plants, the emissions
constraints require much of this capacity to be retrofitted to  reduce  emissions. However, new
capacity is largely SO2 and NOX emissions free: nuclear has zero  operating emissions, new
pulverized coal capacity includes FGD for 862 control and SCR  for NOx control that eliminate
more than 90%  of  emissions, and natural gas turbines have  low emissions.  Because the
incremental cost to retrofit existing coal  plants  is low, the pre-existing coal plants are retrofitted
in all model scenarios and continue to run over the entire model time horizon. The availability of
new nuclear capacity only affects the construction of new plants, which has little effect on air
pollutant emissions.

Figure 4.6 shows that CO2 emissions are also  minimally impacted. The  "baseline" scenario
assumes no new nuclear capacity additions and the "advanced + conventional  nuclear" scenario
allows the addition of both LWRs and HTGRs.  Between 2000 and 2030, 66 GW of new nuclear
(conventional + advanced) is built. By 2030, new nuclear capacity results in only a 7% reduction
in electric sector carbon emissions  compared with a model scenario that  does not allow new
nuclear capacity to be built. This modest emissions benefit is partly explained by the fact that the
availability of nuclear results in 20 GW of total additional generating  capacity over the constant
nuclear case, presumably because some service  demands can be met more cost-effectively by
electricity when nuclear is a supply option. The nuclear capacity additions  have a negligible
effect on carbon  emissions in sectors other  than electric; the modest electric sector emissions
benefit is largely obscured in the system-wide carbon emissions.
                                                                                      54

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   0.6 -
   o.4-
             - Advanced + Conventional Nuclear
             - Baseline
§
80.6
           -Advanced + Conventional Nuclear
           - Baseline
    2000  2005  2010  2015   2020   2025   2030
                   Year
  2000   2005   2010   2015   2020   2025   2030
                 Year
 Figure 4.6: System-wide (left) and electric sector (right) COi emissions relative to baseline
                                         scenario.

In the analyses above, nuclear was unable to gain a significant market  share and, as a result,
emission reductions were minimal. This is a consequence of the CAA restrictions being tighter
than the beneficial role nuclear played in generating electricity.  Circumstances that depart even
further from BAU model  assumptions,  however, could provide stronger incentive for  new
nuclear investment.

Advanced nuclear generation technologies, for instance, may play a larger role when electric
sector CC>2 emission  trajectories depart significantly from BAU assumptions (see Section 2).
Alternate  (non-BAU) carbon trajectories, though  they  represent a  realistic stimulus for  new
nuclear investment, primarily offer a means to flex the  EPA MARKAL model  and facilitate a
more convincing examination of how this  investment might impact criteria pollutant emissions.
The trajectories used in this analysis include: (1) electric  sector carbon emissions  limited to 1995
levels from the BAU scenario (Section 2) starting  in 2015, (2)  electric sector carbon  emissions
limited to 80% of 1995  levels from 2015  on, and (3) electric sector carbon emissions  limited to
50%  of 1995  levels from 2015  on.  Note that these low carbon scenarios apply only to the
electric sector, not system-wide.

These carbon  trajectory  scenarios are deliberately arbitrary in  that they do not reflect known
projections, proposed policy, or a preferred carbon  emissions profile.  The non-BAU trajectories
merely serve  to  force  a signal—in this  case, the adoption of advanced  nuclear generating
technologies.  One, of  course,  could constrain the model to produce a certain fraction of its
electricity from  new nuclear capacity and examine the  effects on criteria pollutant emissions.
Doing so, however, would be equivalent to using  MARKAL as a calculator and would fail to
take advantage of its strength as an energy systems model.  The non-BAU carbon trajectories
provide an incentive not only for new nuclear investment, but also for the addition of competing
technologies like natural gas  and renewables.  It such this competition that will drive criteria
pollutant emissions in any scenario, and accounting for these emergent system effects provides a
more realistic picture of the corresponding  air quality implications.
                                                                                        55

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Figure 4.7 presents electric sector  technology penetrations for the three alternative carbon
trajectories.  In general, pre-existing coal capacity is forced off-line, and combined-cycle natural
gas turbines appear to be the most effective technological response  in carbon limited scenarios.
However, nuclear plays an increasingly important role  as the carbon trajectory departs from
BAU results. Even with high costs for the MOX fuel cycle, MOX plants play a role in the lowest
carbon scenario. Figure 4.8 illustrates how much  conventional and advanced nuclear capacity
was built compared with  the capacity limits  set by the growth  constraints.  The  low carbon
trajectories are the  same as shown in Figure 4.7. Note that the growth in advanced nuclear is
steeper because the capacity of both PBMR and GT-MHR have been added together.
                                                                                       56

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  25000
  20000
                                          Carbon Limited to 1995 Level
                                                                              • Advanced Nuclear
                                                                              H Other
                                                                              H Renewables
                                                                              D Natural Gas
                                                                              D Conventional Nuclear
                                                                              • Residual Coal
  25000
                                                             2025
                                                                        2030
                                                                              • Advanced Nuclear
                                                                              H Other
                                                                              H Renewables
                                                                              D Natural Gas
                                                                              Q Conventional Nuclear
                                                                              • Residual Coal
   25000
   5000
      2000
                  2005
                                                              2025
                                                                          2030
                                                                               • Advanced Nuclear
                                                                               nMOX Nuclear
                                                                               H Other
                                                                               H Renewables
                                                                               D Natural Gas
                                                                               Q Conventional Nuclear
                                                                               • Residual Coal
Figure 4.7: Electricity generation by technology from non-BAU carbon trajectories and
            both conventional and advanced nuclear technologies are available.
                                                                                                     57

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       140
       120 -
       100
     o
     o
       60 -
                                     Conventional Nuclear 2030 Maximum
                Advanced Nuclear
                -Conventional Nuclear
         2010  2012   2014   2016   2018   2020   2022   2024   2026   2028   2030
                                                                      Carbon 50% of 95 Level
                                                                      Carbon 80% of 95 Level
                                                                      All Scenarios
                                                                      Carbon Limited to 95
                                                                      Level
                                                                      Baseline
   Figure 4.8: Conventional and advanced nuclear capacity over time, compared with the
                maximum capacity allowed by the growth rate constraints.

Figure 4.8 demonstrates that advanced nuclear (HTGRs) are a compelling option for the future.
In all scenarios, the model builds the maximum allowable advanced nuclear capacity. While the
costs for HTGRs  in the model  are highly  speculative,  the  results can be  interpreted as
prescriptive: if developers can meet the currently projected nth-of-a-kind cost targets for HTGRs,
they are likely  to play  an important role  in the future U.S. electricity system.  Conventional
nuclear only  hits the  growth constraint limit in the scenario with the tightest carbon trajectory.
The higher capital costs for LWRs put them at an economic disadvantage relative to HTGRs.

An interesting result is how advanced technologies in the U.S. electricity sector do have ancillary
benefits for air quality.  Figure 4.9 presents SC>2 and NOx emissions under the three non-BAU
carbon trajectories shown in Figure 4.7.
                                                                                         58

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     1.2
  .=   1
   o  0.8
     0.6
   «  0.4
E
UJ

8
     0.2
                                           *-_	
—*—Carbon: Baseline
—o—Carbon: 95 Levels
- -*- - Carbon: 80% of 95 Levels
- - -x- - - Carbon: 50% of 95 Levels
      2000
                  2005
                              2010
                                          2015
                                          Year
                                                      2020
                                                                  2025
                                                                             2030
     1.2
   m
   .E   1 *
   o  0.8

   >
   «
   1  0.6
   «  0.4
   «

   UJ
   ><  0.2]
       —*—Carbon: Baseline
       —o^ Carbon: 95 Levels
       _ -a- _ Carbon: 80% of 95 Levels
       - - -x- - - Carbon: 50% of 95 Levels
      2000
                  2005
                              2010
                                          2015
                                          Year
                                                      2020
                                                                  2025
                                                                              2030
  Figure 4.9: SOi (top) and NOx (bottom) emissions relative to the baseline scenario under
                      the low carbon scenarios presented in Figure 4.7.

The alternative  CC>2 trajectories have a  significant impact  on 862 emissions,  with deep
reductions in emissions under the two scenarios that depart furthest from B AU assumptions. The
NOx emissions constraint,  however,  is binding except under the 80 and 50  percent trajectory,
when 87  percent of the  pre-existing coal-derived electricity is replaced by  technologies with
lower emissions. As a result, low carbon trajectories  in the electric sector will only reduce NOx
emissions when most of the pre-existing coal capacity is displaced

Suppose  there  is  no  limit to  the rate  at which  nuclear capacity can grow.  Figure 4.10
demonstrates the mix of generation sources (with nuclear growth rate constraints removed) under
a carbon  trajectory that is  20% below  1990 levels from 2015  to 2030. In this case, advanced
nuclear plays a central role  in electricity production after 2020.
                                                                                           59

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     25000
     20000
o
1

-------
Increased costs for natural gas and coal do not have a significant impact on the penetration of
nuclear. As a result, nuclear only provides a modestly effective hedge against high fossil fuel
prices in the electric sector, at least over the price range explored here. These modest penetration
levels contribute to meeting the electric sector's CAA limits for 862 and NOX.

Although both  conventional and advanced  nuclear  are economical  in the base  case, nuclear
generation only has a modest impact on system-wide carbon emissions. When the electric sector
follows non-BAU carbon trajectories, new nuclear capacity plays a more  significant role and
leads to SC>2 and NOX  reductions  below CAA constraints.  In all  cases, the model  prefers
advanced nuclear (the high  temperature gas-cooled reactors) to conventional nuclear (the light
water  and MOX  reactors).  The issues  governing this  preference  include  whether  HTGR
developers can meet the model's optimistic nth-of-a-kind cost assumptions and how rapidly units
can be manufactured and  deployed over the next few decades.
                                                                                      61

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                                      Section 5

  The Air Quality Implications of Carbon Capture and Sequestration in U.S.
                                  Electric Markets

With its abundant coal reserves, the U.S. has built an electric power infrastructure dominated by
coal-fired generation. Coal plants produce roughly half the nation's electricity, with natural gas
and nuclear plants  each contributing nearly  twenty  percent and renewables making  up the
difference (EIA, 2006a).  Given the long-lived nature of this infrastructure and the lack of an
economically-competitive substitute technology,  the nation's dependence on coal is unlikely to
cease.  The U.S. Department of Energy (DOE)  is therefore assessing technological options to
reduce the environmental  impacts of coal plants.  Technologies included in this assessment
include coal gasification and carbon capture and sequestration (CCS). By itself, coal gasification
would nearly eliminate  atmospheric  SC>2, NOX, and  Hg emissions; integrated with CCS, the
combined technology could also  make potentially significant reductions in electric sector CCh
emissions,  should the   need  to   do  so arise (NETL,  2005).   Integrated  gasification-CCS
technologies could also provide the technological underpinnings of a future hydrogen economy
(FutureGen Industrial Alliance, 2006).

This section examines the air quality benefits of CCS in an energy  systems context.  While
climate concerns would drive the adoption  of CCS, the technology could yield important air
quality benefits—benefits that would depend on the rate at which CCS units enter the market and
the technologies they displace. In addition, CCS would compete with an expanded use of natural
gas, nuclear power, and renewable energy sources.  A systems level  assessment is needed to
examine  how these routes to electric  sector CC>2 reduction interact, how this interaction affects
the economic  attractiveness  of CCS,  and  how these dynamics  collectively impact  criteria
pollutant emissions.

This section begins to meet the need for a comprehensive assessment.  The  first two subsections
define CCS as examined here, list its prominent  advantages and disadvantages, briefly describe
the current state of the technology, and discuss its implementation in the U.S. EPA National
MARKAL model.   The following subsection  presents the analysis by identifying scenarios in
which  CCS  technologies  enter, given business as  usual  assumptions  about competing
technologies.   Section  6 of this  report  broadens the assessment to look at  how CCS might
compete with other supply-side abatement alternatives  under more optimistic scenarios about the
latter. An evaluation of CCS relative to end-use efficiency improvements is  left for future work.

The assessment in this section focuses on CCS from an energy system perspective and seeks to
identify scenarios—ranges of CCS costs and performance factors, for  instance—that lead to its
adoption.  The  section  examines  generic classes of CCS power generation units and does not
evaluate  the merits of  particular  CCS technologies.   The analysis also concentrates on CC>2
capture and considers only the aggregate cost of sequestration—not the many other significant
issues surrounding underground injection of the  gas.  Finally, the  analysis  does not evaluate or
propose policies related to CC>2  control.  CCS technologies, of course, will not be adopted
without the need to reduce electric sector CC>2 emissions.  The analytical approach employed
here examines how CCS technologies fare relative to  other emission abatement options given a
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CC>2 emission trajectory expressed as some percentage of business as usual projections in order
to examine the impact on air quality.

5.1  CCS Technology Background
CCS provides a means of reducing electric sector CC>2 emissions. In this context, CC>2 capture
works with any power generation unit utilizing coal or natural gas as its primary fuel, and may
take place before or after combustion.  Sequestration occurs by injecting the captured CC>2 into a
suitable geological formation after transportation - most  likely  by  pipeline - from the plant.
Deep saline  aquifers,  as well as active and depleted  oil  and gas reservoirs are potential
sequestration sites.   CO2  may  also  be  captured from   industrial  processes,   and  "CO2
sequestration" is often used to refer to the uptake of atmospheric  CC>2 in biomass and soils.
Neither of these options is considered here.  Recent publications from the International Energy
Agency (IEA, 2004) and the Intergovernmental Panel on Climate Change (IPCC, 2005)  provide
up-to-date and comprehensive background information on CCS.

The value of CCS lies in its potential to ease the world's dependence on an energy infrastructure
dominated by fossil fuel consumption  (Johnson and Keith, 2004).  The long life and slow
turnover of this infrastructure and the current unavailability of an economic substitute  support
the  continued use of fossil energy—especially coal for power generation.  CCS is  compatible
with the electric sector as it exists today.  On the generation side, power plants with CC>2 capture
(new or retrofit) would  have the  same capacity as their conventional counterparts,  and follow
similar  dispatch rules.   Construction and management expertise at  the plant level would also
transfer. Beyond the plant, the electric power distribution network would remain the same and
electricity consumers would experience no change in how  they use energy (though it would
likely cost more).   Finally,  the  utilities and other energy  companies that currently  supply
electricity would continue to do so, and the ability to exploit niche markets and take advantage of
synergies (e.g.,  selling captured CC>2 for enhanced oil recovery) might increase their competitive
advantage.

CCS, of course, is not without significant drawbacks. Although a potentially valuable transition
technology, CCS is an "end-of-pipe" solution that does not address the more fundamental need
to move away from an energy system reliant on carbon-intensive—and, in the case of petroleum
and natural gas, ultimately limited—fossil energy resources.  In addition, successful adoption of
CCS could ease the pressure to  develop more sustainable  energy  alternatives, and resources
invested in CCS  technologies are resources not  invested  in  renewable options (i.e.,  an
opportunity cost).  CC>2 capture also requires considerable energy, which increases both the cost
of generating electricity  and the amount of CC>2 produced per kWh.  As a result, while a sudden
release of sequestered CC>2 near a low-lying inhabited area poses the vivid risk of asphyxiation, a
more general failure to contain  CC>2 could—in  the extreme—result in higher atmospheric
concentrations  of the greenhouse gas  than would  have  been the case if CCS had not been
pursued.  These disadvantages, along with technological uncertainties and the need to  address
important  issues related  to regulation, liability, long-term monitoring, and public  acceptance,
pose obstacles to the adoption of CCS (Palmgren, et al., 2004; Wilson, 2004).

In terms  of promise, however,  the potential  benefits  of  CCS   are  seen  as offsetting  its
disadvantages,  and  development of  the  technology  has   consequently  progressed   to  the
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demonstration phase.  The DOE's FutureGen Initiative, for instance,  is the most visible U.S.
attempt to develop a large-scale power production facility with CC>2 capture and sequestration—
one that will  also produce H2 and serve as a joint industry-government sponsored research
testbed within the coming decade (FutureGen Industrial  Alliance, 2006).  Utilities are also
beginning to  evaluate the merits of designing new,  retrofit  compatible coal-fired  or coal
gasification power generation units should they eventually decide to pursue CC>2 capture (EPRI,
2005).  On the sequestration side, several  ventures are  operational, and three of these—in
Norway, Canada (with CO2 captured in the  U.S.),  and Algeria—have achieved injection rates
close to 1 MtCC>2 per year (IPCC, 2005).  These examples provide an indication of the resources
being devoted to  CCS-related projects,  and are only a  reflection of  the  growing level  of
international research activities (see IPCC, 2005, for a review).

The initial success of CCS will depend on the integration of disparate,  but largely mature,
component technologies, and scaling the resulting system up to a  level able to handle the amount
of CC>2 generated annually by a typical power plant (roughly 1 MtCCVyear  for a 500 MW unit).
Niche applications—in which an electric power plant with capture would  provide a supply of
CC>2 for enhanced  oil  recovery (EOR), for instance—might help lower initial costs and lead to
learning-related cost reductions and performance improvements.  Long-term success will rest on
improvements in capture technology, development of a legal framework to govern sequestration,
and public acceptance. This  section focuses on the first of these three requirements (capture),
though the other two are likely to be equally significant.

CC>2 capture as currently envisioned may take place along one  of three  general routes (Figure
5.1).  The first of these is equivalent to traditional "smoke stack" controls for SO2 and NOX and
involves post-combustion separation of CC>2  from the remaining  flue gases.  Applicable to both
coal-steam and natural gas combustion turbines, this  approach  would likely be the preferred
means of retrofitting  existing power generation units (short of a complete repowering,  as
discussed below). Existing post-combustion capture technology relies on  chemical absorption of
CC>2 using a monoethanolamine solvent—a mature industrial process that has provided CC>2 for
use in food, beverage, and chemical production  since the 1950s.   Amine  separation can remove
up to  95% of the CC>2 from a gas stream, though removal  efficiencies  in the 80% range would
likely be more common in practice. Table 5.1 (below) summarizes cost  and performance data.

Several technical issues,  however, create disincentives to  the use of  post-combustion capture
processes relying on amine separation.  Solvent regeneration, steam requirements, and the need
to compress the captured CC>2 (which constitutes only 3-15% of power plant  flue gas by volume),
for instance, impose an energy penalty on the order of 10-40% of the unit's output. In addition,
the amine separation  process  requires the use  of scrubbers to  remove  SC>2, NOx, particulate
matter, and other flue gas impurities prior  to CC>2 capture.  Scale is yet another issue, with
contemporary  commercial amine capture systems more than an order of magnitude smaller than
that required for a  typical coal-fired power plant. Amine-based  capture is therefore unlikely to
be  economically  competitive  relative   to  other  electric  sector CC>2 emission abatement
alternatives. Post-combustion CC>2 capture from air-fired power plants may be viable if an amine
substitute becomes available.  Research is currently focusing on absorption using novel solvents
(both  liquid and solid), the  development of adsorption processes,  and the use of membranes
                                                                                      64

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(IPCC, 2005). The advantages of finding a "cheap" retrofit option for existing coal-fired power
plants provide incentives.
       Post combustson
                        Doma^i
AiiVQ,
Ceal I Stean
BiOITBSS f X \

hTB COmDUSIIOn ijjmiwson
Qas.Oil — ^

DOil
Oxyfuel . L>as •


Air
Coal

Industrial processes G« ^«
Biarrtas?
CO,
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-X I
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\x


con
/A Dal:ydialioii
— JT

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                                  Riw rraUriil
                                                Gas, Ammonia, Steel
   Figure 5.1: Schematic of the three generic routes to carbon capture and sequestration;
    industrial process are shown, but not included in this analysis; Source: IPCC (2005)

Amine-based CC>2 capture is conceptually similar to post-combustion controls for SC>2 and NOx;
except for the energy penalty, operation of a power plant would not "look" different with flue
gas CC>2 capture.  So-called  oxy-fuel processes offer a second post-combustion route to CC>2
capture, but one that would require more significant changes in power plant design.  Rather than
combusting coal or natural  gas in air, an  oxy-fuel system would use a mix  of pure O2 and
recycled flue gas CC>2.  As  combustion in  62 produces mainly CC>2 (at least 80% of flue gas
volume) and water vapor, capture would  require  little more  than drying the flue  gas and
compressing the CC>2.  An oxy-fuel  system would  therefore  capture nearly all CC>2  produced
(again, see Table 5.1 for cost and performance data).  Small-scale oxy-fuel combustion processes
have found industrial applications, but do not exist as integrated systems at the scale needed for
power generation.   Like amine separation,  an oxy-fuel power plant would operate at a lower
efficiency  than its  conventional  counterpart,  with  the  C>2 production  process (a  mature
technology) responsible for much of the added energy requirement.  Applied to existing power
plants, oxy-fuel conversion is considered to  be a repowering option (i.e., replacing a plant's core
energy generating technologies), rather than  a retrofit modification.

The last generic route to electric sector CO2 capture differs from the first two by separating the
carbon from the fuel  stream prior to  combustion.  The approach to separation most likely to
shape the design  of new power plants with CC>2 control,  pre-combustion capture  is a mature
process that the fk, synthetic fuel, and chemical  industries use routinely.  The process begins
with either steam reforming or partial oxidation of natural gas,  or gasification of coal, to produce
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H2 and CO (other byproducts—such as H2S from coal gasification—need to be removed).  A
water gas shift reaction then produces additional H2 while converting CO into a high pressure
CO2 stream.    The higher pressure  simplifies the  CO2 capture process (which is typically
accomplished via physical absorption) and reduces its energy requirements,  improving overall
system efficiency.  The H2 is  available  for use in a combined turbine and steam  cycle power
generation unit, though it could  also be  used in a fuel cell to produce power or sold as a
transportation fuel or industrial feedstock.  Coal-based IGCC (integrated gasification combined-
cycle)  plants  with CO2 capture  are the most frequently mentioned  pre-combustion  CCS
technology in the literature, both for new plant construction and existing unit repowering. IGCC
technologies (with or without capture), however, are currently limited to high-ranked coals.  Like
oxy-fuel conversion, pre-combustion CO2 capture applied to existing power plants would involve
significant repowering rather than  a retrofit add-on.

Table 5.1  compares cost and performance  estimates for new technologies representing the  three
generic  routes to CO2 capture  described above,  and contrasts these  with  their  non-CCS
counterparts.  With the benefit of research and learning-by-doing, newly-built plants with CO2
capture  in 2020  are expected to achieve costs and efficiencies similar to  their non-capture
equivalents today (IPCC, 2005).
Table 5.1: Estimated cost and performance ranges from the literature for new CCS technologies
as summarized by the IPCC (2005).
Technology
Post-Combustion
PC*
PC + CCS
Pre-Combustion
IGCC
IGCC + CCS
NGCC**
NGCC + CCS
Oxyfuel
Oxyfuel
Oxyfuel + CCS
Capital Cost
($/kW)

1161-1486
1894-2578

1169-1565
1414-2270
515-724
909-1261

1260-1500
1857-2853
Cost of
Electricity
($/MWh)

43-52
62-86

41-61
54-79
31-50
43-72

44-45
58-83
Thermal
Efficiency (%
LHV)

41-45
30-35

38-47
31-40
55-58
47-50

37-44
25-35
CO2 Capture
Efficiency

n/a
85-90

n/a
85-91
n/a
85-90

n/a
Insufficient data
*PC = pulverized coal
**NGCC= natural gas combined-cycle

The costs of retrofitting existing plants, in contrast, are highly uncertain and are more likely to be
site-specific.   The IPCC, for instance, notes that "[tjhere has  not yet  been any  systematic
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comparison of the feasibility and cost of alternative retrofit and repowering options for existing
plants" (IPCC, 2005, p. 344). The energy penalty associated with amine-based post-combustion
retrofits renders this option economically unattractive; oxy-fuel or pre-combustion repowering of
coal units, however, may be  worthwhile as the end  result is  essentially a new—and  likely
larger—plant without the obstacles  associated with developing a  greenfield site.   A further
distinction lies between retrofitting or repowering plants that exist today, and designing new non-
capture power plants for future conversion.  The cost of the latter, of course, will be cheaper and
more predictable.

The analysis that follows focuses on  the penetration of electric sector CC>2 capture technologies.
The sequestration component of CCS enters the assessment only as a cost, which consists of CC>2
transport,  injection,  and  monitoring components.   The first two elements involve mature
technologies and processes with which the oil and gas industries have considerable experience.
Land-based pipeline transport as currently practiced, for instance, costs 1-5  $/tCC>2 per  100 km.
Applied to CCS, future injection cost estimates range more widely—from 0.5 to 8.0 $/tCC>2,
depending on the site—with the  need to monitor CO2 containment adding 0.1 to 0.3 $/tCO2
(IPCC, 2005).  Note that sequestration capacity is  not likely to pose a problem.  Deep  saline
aquifers (i.e., below 800 m) alone may hold  1000 to  10,000 GtCO2—several  orders of magnitude
greater than yearly global CC>2 emissions (which are on the order of 25 GtCCVyear; IEA, 2004).
Sequestration in depleted oil and gas  reservoirs provides another option and, as noted,  the ability
to sell CC>2 for enhanced oil recovery (EOR) might provide a profitable and early niche market—
and a route to achieving learning-by-doing technology improvements.   EOR operations  in the
U.S., for instance, have historically paid 10-16 $/tCC>2, depending on oil and gas prices  (LEA,
2004).

While the cost of CC>2 sequestration is likely to be significantly less than that of capture, the
institutional uncertainties associated with injection and storage are greater.  The necessary legal
frameworks (both domestic and international) are not in place, risk management trade-offs are in
need of resolution, and public acceptance  of large-scale underground CC>2 storage  remains
untested.   In addition,  site-specific factors will  play  a  much greater  role  in determining
sequestration (and, hence, emission abatement) potential  in a given  region.  The future of CCS
rests as much on resolving  institutional issues such as these as its does on solving technical
problems (Wilson, Johnson, and Keith, 2003).

Integrated assessments, however,  suggest that CC>2  capture technologies could sequester nearly
50% of projected global CC>2  emissions by mid-century at a cost ranging  between 25 and 50
$/tCO2 (IEA, 2004;  IPCC, 2005).   While  CO2  capture  would  add at least 2-3  cents/kWh to
electricity prices today, this premium would likely drop by half over a few decades. CCS could
therefore be an important element in a portfolio of electric sector emission abatement  options.
The remainder of this section  examines the place of CCS in this portfolio from  an air quality
standpoint.
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5.2 Implementation of CCS in the U.S. EPA National MARKAL Model
The analytical approach adopted in the remainder of this section examines generic classes of
electric  sector CCS technologies (e.g., IGCC with CO2 capture) rather than specific proposed
designs  (e.g., a Texaco oxygen-blown, quench-based gasifier).  Reference values from the range
of studies surveyed by the IPCC (2005) and summarized in Table 5.1 provide baseline cost and
performance  data for these technology representations—a starting  point to more  informative
uncertainty analyses. Anderson and Newell (2004), IEA (2004), Johnson and Keith (2004), and
EIA (2006b) provide additional data. Table 5.2 lists the CCS-related technologies included in
the U.S.  EPA  National  MARKAL  model  database,  and provides   details  about  their
specifications. EPA (Shay,et al., 2006) provides documentation on the base MARKAL model.
All CCS technologies become available in the 2015 model period, have a 40 year useful life, and
share an 18 percent investment hurdle rate.
Table 5.2: CCS technology parameters as implemented in the EPANMD. All figures were
converted into common units from their actual MARKAL values.
Technology
Retrofits
Existing Coal
Retrofit
New PC Retrofit
IGCC Retrofit
NGCC Retrofit
New Integrated
Technologies
IGCC+CCS
NGCC+CCS
Capital Cost
($/kW)

1414
1345
966
763

1873
1021
Variable
Operating
Cost ($/kWh)

0.0093
0.0085
0.0045
0.0022

0.0040
0.0027
Fixed
Operating
Cost ($/kW)

26.24
21.84
8.99
5.77

41.44
18.12
Thermal
Efficiency
(%)

65
70
80
80

40.0
42.9
CO2 Capture
Efficiency
(%)

85
85
90
85

90
90
The retrofit parameterization requires a few words of explanation.  First, the incremental retrofit
costs  are higher than published estimates for amine-based  systems  (see, e.g., Simbeck and
McDonald, 2001; IPCC, 2005) to account for the fact that the MARKAL  model employs base
plant  (i.e., conventional PC, IGCC,  and NGCC capacity) cost figures that are more optimistic
than the retrofit studies typically assume.  To prevent MARKAL from immediately retrofitting
newly-built conventional capacity instead of building a new integrated capture plant, the model
adopts retrofit cost parameters that ensure that the combined investment and operating costs of
retrofit capacity (base plant costs  + retrofit costs)  are at  least as great as those of the
corresponding integrated capture plant.   Second,  the  retrofit "efficiency" is  perhaps better
interpreted as an energy penalty (i.e., a base  plant output derating). The inverse of the retrofit
efficiency (expressed as a decimal fraction) is the increase in input energy required per unit of
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retrofit energy output.  The corresponding energy penalty is then this increase divided by 1 plus
itself [energy penalty = increase/(l+increase)].  Finally, since all retrofits sit in the fuel chain
leading into the base plant, the MARKAL database adjusts the retrofit parameters to account for
the base plant efficiency.

Note that the model does not include new integrated PC  capture plants or an  oxyfuel option.
These technologies are  not sufficiently  different  from new  IGCC  capture units  to  be
meaningfully distinct in MARKAL.  IGCC is generally seen as the least-cost coal-fired capture
alternative and the model therefore adopts the label for its new coal CCS technology; investment
in IGCC  capture technologies could therefore represent one of several technologies.

In keeping with this aggregate technology representation and the analytical focus on supply-side
CC>2 abatement options, the augmented model uses a single figure (28 $/tC, or approximately 7.5
$/tCC>2) to represent the cost of CC>2 transport, injection, and long-term monitoring.  This value
reflects the  upper end of published estimates, and assumes that  geological injection sites are
located within 300 km of all central station power generating units (IPCC, 2005).

This  generic  representation  of  CCS  technologies  is  compatible with the  level of  detail
characterizing other  electric sector  power  generation technologies in the  EPA  National
MARKAL database (e.g., three model plants represent all  existing U.S.  coal-fired units).  This
level of  detail is also compatible with both the uncertainty inherent in  CCS technologies, the
nature of a linear  programming optimization model  like MARKAL, and the  scenario-based
analytical strategy that guides the following assessment.

Choices made in modeling carbon capture retrofits provide a useful illustration of this point. As
discussed, CC>2 capture using solvent absorption is a mature industrial process.   The costs and
performance issues  associated with retrofitting  existing coal-fired power plants  at the  scale
necessary to make  a significant reduction in electric sector emissions, however, are uncertain.
Contemporary amine-based technologies would impose such a significant energy  penalty that
alternative technologies  (e.g., membranes) would likely  be needed  for  the post-combustion
retrofit option to become economically feasible.  All retrofit technologies would be subject to
uncertain learning, and site-specific details such as the space available for the capture equipment
would drive installation costs.  Differences in the specifications of proposed retrofit technologies
easily fall within the range of this uncertainty, but an optimization model like  MARKAL will
always pick the technological  option with the  lowest overall  costs,  inflating the  apparent
significance of that technology and possibly leading to rapid and unstable period-to-period shifts
in favored options.  Modeling  identifiable but marginally different retrofit schemes  would
therefore produce meaningless and potentially misleading results. (Note that these schemes may
have significantly different technological potential; unless they also have substantially different
costs and efficiencies, however, they essentially look the same within MARKAL. The most one
could do is lower a technology's  availability to reflect decreased reliability. Radically different
retrofit designs, of course, should be modeled independently.)

Given that small differences in parameter values often lack practical significance in a modeling
context like this, a reasonable alternative would look only  at broad technology classes and then
use sensitivity analysis around their reference parameter values to identify ranges of these values
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that influence results.  MARKAL characterizes CCS retrofits, for instance, by their costs and
energy penalties.  One could use parametric sensitivity analysis to examine how variations in
model  retrofit specifications affect retrofit  adoption, and  then  trace "successful"  parameter
combinations back to specific retrofit designs.  The difference in approach between modeling a
suite of technologies and using sensitivity analysis in conjunction with a representative example
may seem subtle, but the generic strategy—for reasons described above—is more defensible for
this type of analysis and is consequently employed below.

5.3 CCS Results and Analysis
The remainder of this  section does  for CCS what the previous section did for nuclear power
generating technologies.  Building on the business as usual  (BAU) scenario results (Section 2),
the analysis examines the extent to which CCS technologies contribute to meeting electricity
demand and  its impact on  air quality under the same  set of alternative electric sector CO2
trajectories that framed the nuclear assessment.   It is  worth repeating  that these  non-BAU
trajectories are arbitrary and merely a modeling device used to stimulate CCS investment, and do
not represent policy or endorse a particular carbon emissions  profile.

Outcomes of interest from this analysis include: patterns of investment in CCS technologies and
their electricity output;  the balance  between new,  integrated  CCS units and existing capacity
retrofits;  the share of coal- and natural gas-fueled CCS technologies; the competition between
coal-to-gas fuel switching and CCS; and ultimately the impact of CCS penetration on electric
sector criteria pollutant emissions. Scenario drivers within the CO2 trajectory framework include
variations in  CCS  parameters  (especially  costs and  efficiencies), natural  gas prices,  and
sequestration costs.  This analysis assumes  BAU nuclear and renewable generating capacity.
Section 6 allows  new  nuclear and  renewable  technologies into the model and concludes the
report  with  a look  at  the competition between these alternatives  to  fossil-fuel  based power
generation and CCS.

Tables 5.3 and 5.4  summarize  electricity generation by technology class and new capacity
investment, respectively, for a model  run with  CCS technologies (as described above)  and an
electric sector CO2  trajectory that  holds emissions constant at  1995  levels from  2015 on
(approximately 2.4  Gt CO2  per  five-year  period).    Figure  5.2  illustrates how  different
technologies contribute to meeting increasing power demand over time, and Figure 5.3 shows the
impact of CCS technology penetration on electric sector CO2,  SO2, and NOx emissions, as well
as economy-wide CO2  output.  Compared to the non-CCS BAU analysis (Section 2), coal-fired
generation  actually  increases slightly  at the  expense of gas, with new integrated IGCC+CCS
capacity providing the CO2  emissions reduction.  Neither NGCC units with CO2 capture nor
retrofits of any types enter. Note that the level of investment in conventional gas technologies is
not significantly different from the non-CCS base case.
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Table 5.3: Baseline CCS scenario electric power generation (in PJ/period) by technology class
and time.
Technology
Nuclear
Existing Coal
New PC
IGCC
Existing Coal
Retrofit
New PC
Retrofit
IGCC Retrofit
IGCC+CCS
Gas
NGCC Retrofit
NGCC+CCS
Renewables
Other*
Electricity Generation (PJ/period)
2000 2005 2010 2015 2020 2025 2030
2734
7485
0
0
0
0
0
0
1424
0
0
702
550
2788
8095
64
0
0
0
0
0
1661
0
0
781
481
2831
7900
64
0
0
0
0
0
2676
0
0
827
439
2874
6452
64
0
0
0
0
674
3767
0
0
1369
132
2916
6225
64
0
0
0
0
2012
4402
0
0
1445
146
2916
5972
64
0
0
0
0
2250
5003
0
0
1583
143
2916
5558
64
0
0
0
0
3201
5713
0
0
1685
220
*Other includes natural gas-fired microturbines (distributed generation), diesel combustion
engines, and generation from municipal solid waste and landfill gas.

Understanding  the results involves analyzing the relationship  between the  ways  in which
different technologies meet electricity demand, air quality goals, and the need to recover capital
investment.   In  general, coal-fired power plants have high capital  requirements,  but  low
operating costs relative to gas turbines (the latter due largely to differences in fuel costs).  This
cost difference,  combined with dissimilar operating characteristics  (i.e.,  coal plants are not
simply "turned on"), explain why coal units typically  supply baseload power, while natural gas
units are especially suited for load-following during peak demand hours.  Coal,  of course, is the
most carbon-intensive fossil fuel and baseload generation (to the extent that it  is not met by
nuclear plants) is therefore especially carbon intensive.  Hence, CCS  enters with coal  (here,
IGCC). This pattern also makes sense from a CCS investment standpoint. CC>2 capture is costly.
If a utility invests in CCS technology,  it will want to use that capacity to the limits of its
availability (reliability),  since the per kWh costs of the technology decline with increased power
production.  CCS therefore enters the  dispatch order to meet baseload demand, while peaking
units simply do not generate enough electricity to make CCS worthwhile (see Johnson and Keith,
2004).
                                                                                       71

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Table 5.4: Baseline CCS scenario new capacity investment (in GW/period) by technology class.
Technology
Nuclear
Existing Coal
New PC
IGCC
Existing Coal Retrofit
New PC Retrofit
IGCC Retrofit
IGCC+CCS
Gas
NGCC Retrofit
NGCC+CCS
Renewables
Other
New Capacity Investment (GW/period)
2000
0
0
0
0
0
0
0
0
38
0
0
0
4
2005
0
0
2
0
0
0
0
0
77
0
0
9
4
2010
0
0
0
0
0
0
0
0
0
0
0
1
4
2015
0
0
0
0
0
0
0
25
98
0
0
7
4
2020
0
0
0
0
0
0
0
50
43
0
0
4
4
2025
0
0
0
0
0
0
0
9
31
0
0
5
4
2030
0
0
0
0
0
0
0
35
32
0
0
6
4
        20
O *f.
o 15
o
      § 10
      £H
      

     O
      O


     '£
      o
2°000
                   Conventional

                   Retrofit CCS

                   New CCS
                                                               Renewable
                                                                    Gas
                                                                    Coal
                                                                    Nuclear
                           2010
                                        2020
2030
   Figure 5.2: Electricity generation per-period by technology class for the baseline CCS

 scenario (electric sector COi emissions held constant at 1995 levels from 2015 on, with no

                             CCS growth constraints).
                                                                              72

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The  emissions numbers (Figure 5.3) tell an interesting story.  Electric sector CC>2 emissions
decline  to nearly two-thirds of their BAU levels by  2025,  while system-wide carbon output
decreases by about 10 percent.   Electric  sector NOX  emissions, however, remain  essentially
unchanged at their constrained BAU levels (which account for Clean Air Act mandated limits),
while SC>2 declines roughly 20 percent for two periods before hitting its cap once again.   The
explanation  for this behavior lies with the deployment of existing coal plant criteria pollutant
control  retrofits  (FGD especially), which  are used approximately 20 percent less in the  CCS
scenario.  IGCC plants with CC>2 capture also reduce criteria pollutant emissions,  easing the
burden on coal plant retrofits to meet emission constraints.
               1.20
               0.60
                  2000
2005
2010
2015
2020
2025
2030
                           O - - System-wide CO2
                          -A—Electric Sector NOX
                          -Electric Sector CO2
                          -Electric Sector SO2
   Figure 5.3: Electric sector COi, SOi, and NOx and economy-wide COi emissions as a
                      function of time for the baseline CCS scenario.
One objection to these results is the rate at which CCS investment takes place, starting in 2015
(Table 5.4).  MARKAL, as a "perfect foresight" modeling framework, assumes the position of a
rational decision maker with complete information about the future. The model thus determines
the least-cost means of meeting demand for energy services over the entire time horizon, making
all investment and operating decisions simultaneously.  Actual investors do not have the luxury
of clairvoyance, or necessarily the long-lead time represented here (20 years from 1995 to 2015),
to pursue research and development and ready new technology (such as IGCC with CO2 capture)
for the market.  Results would likely look different in a myopic model that made investment
decisions  on a period-by-period  basis without  consideration of future operating constraints.
Adoption of novel technologies like IGCC would be more gradual, at least until reliability issues
were resolved, sufficient learning occurred, and economies of scale were achieved.

Picking a growth rate, however, is difficult for the non-BAU world represented here.  Achieving
an emissions trajectory similar to the CCS scenario's would induce technology change in an
                                                                                      73

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unpredictable manner.   With sufficient leadtime, levels of technology adoption as shown in
Table 5.4 would not necessarily  be unreasonable,  though one  might expect to see IGCC
appearing gradually from an earlier date (with CC>2 capture used when needed).  Furthermore, the
observed model behavior does not seem far out of line when one considers that sequestered CO2
represents 6 percent of the CCS  scenario's electric  sector total carbon emissions  in 2015 (5
percent of the BAU scenario's), and increases to only 30 percent (21 percent of the BAU level)
in 2030.  Finally,  as noted  earlier, the model  labels its integrated  coal-fired, CC>2 capture
technology "IGCC" as current thinking tends to assume that this combination would be cheaper
than new pulverized coal with capture.   Advanced  PC plants with capture, however, could
achieve costs and efficiencies similar to those used here to represent IGCC and would be nearly
indistinguishable in MARKAL.  The large per-period investment in what appears to be a single
technology could actually be a mix of advanced coal-based designs.  The  following set of
scenarios explore the growth rate issue in tandem with the other CC>2 trajectories. One must keep
in mind, however, that results should always be interpreted  as indicating what would be optimal
from a least-cost perspective  - a target, for instance,  for allocating research, development,  and
deployment resources.

The initial CCS scenario added a total of 119 GW of new IGCC units with capture through 2030,
starting  with 25 GW in 2015.  Restricting CCS  growth to an  initial 10 GW per  technology
(including retrofits) with a 10 percent annual growth rate limit reduces the  cumulative CCS
installation over the same timeframe to 106 GW, with 94 GW  IGCC (a binding constraint) plus
12 GW NGCC; 40 GW of new conventional NGCC, entering between 1995 and 2030, makes up
the  difference.  A 5 GW initial period limit with the same growth rate results in 89 GW,  split
nearly evenly between  new IGCC and NGCC with capture.  Both restrictions lead to a small
investment in existing coal plant retrofits (< 10 GW).  Note that a growth rate restriction similar
to that employed in the nuclear analysis (i.e., a 3  or 4 GW first-period limit and initial annual
growth rate of 25 percent) does not affect cumulative CCS installation.  The  remainder of the
section (except where noted) uses a 10 GW per technology initial investment limit with a 10
percent annual growth rate.  The more restrictive 5 GW initial limit may seem more realistic but,
as argued above, induced technology change is difficult to predict and the looser limit reduces
the  chance that modeling assumptions will arbitrarily drive results.

Growth rate restrictions play a more important role  with  electric sector CC>2 trajectories that
depart  further from BAU  assumptions.   Figures 5.4 and 5.5  depict the least-cost mix of
generating technologies that attain emission trajectories corresponding to 80 and 50 percent of
1995 level, respectively, starting in 2015.  Both scenarios adopt a mix of IGCC  (94 GW in each)
and NGCC capture units (52 and 68 GW, respectively), with a minor retrofit investment.  As
before, the IGCC  limits are binding.  New NGCC units also play a larger role  in each scenario,
with nearly  500  GW  of new (conventional)  capacity installed in the most restrictive  CC>2
trajectory between  1995 and 2030.  Compared to the initial  CCS scenario (which held  CC>2
emissions at 1995 levels),  862 emissions decline  significantly below BAU output (Figure 5.6)
for both emission trajectories  examined here. Electric sector NOx emissions decline below their
constrained values only for the lowest carbon trajectory.
                                                                                     74

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                     Conventional
                     Retrofit CCS
                                      Renewable
                                                                   Gas
                                                                   Coal
                                                                   Nuclear
            2000
2010
2020
                                                   2030
   Figure 5.4: Electricity generation per-period by technology class for the 80 percent
emissions scenario (electric sector COi emissions held constant at 80 percent of 1995 levels
                 from 2015 on, with CCS growth constraints in place).
                     Conventional
                     Retrofit CCS
                     New CCS
                                      Renewable
                                                                   Gas
                                                                   Coal
                                                                   Nuclear
2000
                             2010
                 2020
                 2030
   Figure 5.5: Electricity generation per-period by technology class for the 50 percent
emissions scenario (electric sector COi emissions held constant at 50 percent of 1995 levels
                 from 2015 on, with CCS growth constraints in place).
                                                                                 75

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              1.20
              0.
                2000
2005
2010
2015
2020
2025
2030
                          O - • System-wide CO2
                         -A—Electric Sector NOX
                          -Electric Sector CO2
                          •Electric Sector SO2
                                                                               (a)
             0.00
                2000
2005
2010
2015
2020
2025
2030
                          D - - System-wide CO2
                         -A— Electric Sector NOX
                          -Electric Sector CO2
                          •Electric Sector SO2
                                                                               (b)
   Figure 5.6: Electric sector COi, SOi, and NOx and economy-wide COi emissions as a
 function of time for (a) the 80 percent emissions scenario and (b) the 50 percent emissions
                                        scenario.

The relative consistency of these results across emission scenarios points to the need to examine
how CCS technology specifications affect model  behavior.  Tables  5.5  and 5.6 show how
different generating technologies compete to meet demand for two sets  of parametric sensitivity
analyses under the original CC>2 trajectory.  To avoid conflating growth rate dynamics with the
effects of interest, both sets of analyses exclude these constraints.

The first set of analyses (Table 5.5) varies the costs and input fuel requirements (the inverse of
efficiency) of all CCS  technologies (new  and retrofit) by ± 20 percent  from their base line
                                                                                       76

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values.  Two trends stand out. First, when CCS technologies perform better and cost less than
baseline assumptions, new integrated NGCC units with CC>2 capture enter to meet part of the
shoulder generation (between base and peak load) previously supplied by  conventional NGCC
capacity. The overall share of gas technologies, however, remains constant (even though it is not
constrained) and existing coal units increase their output to meet part of the baseload generation
previously supplied by IGCC plants with capture.  The efficiency improvement in this scenario
reduces the NGCC capture unit operating (fuel) cost disadvantage relative to IGCC. Next, when
efficiencies are low, but costs high, existing coal plant retrofits  enter at the expense of IGCC
capture units.  These results correspond to intuition:  retrofits are at both a cost and efficiency
disadvantage to  new integrated CCS units.  When these  shortcomings  are  removed, retrofits
become somewhat  competitive.   The next set of sensitivity  analyses explores this  dynamic
further. Note that  the high CCS cost,  high fuel requirement (low efficiency) scenario looks
similar to the base CCS run; differences are due to rounding and the slightly reduced centralized
electric power production in the former scenario.
Table 5.5: Share of electricity generation in 2030 by technology for the initial CCS scenario
(CC>2 emissions held constant at 1995 level from 2015 on) and four parametric analyses of
differences in CCS technology costs (investment plus all operating) and efficiencies (expressed
here as change in input fuel requirements) relative to baseline values.
Share of Electricity Generation in 2030
Fuel Input
Cost
Nuclear
Existing Coal
New PC
IGCC
Existing Coal Retrofit
New PC Retrofit
IGCC Retrofit
IGCC+CCS
Gas
NGCC Retrofit
NGCC+CCS
Renewables
Other
-20%
-20%
0.15
0.36
0.00
0.00
0.00
0.00
0.00
0.10
0.14
0.00
0.15
0.09
0.01
-20%
20%
0.15
0.30
0.00
0.00
0.09
0.00
0.00
0.06
0.30
0.00
0.00
0.09
0.01
20%
-20%
0.15
0.26
0.00
0.00
0.00
0.00
0.00
0.20
0.30
0.00
0.00
0.09
0.01
20%
20%
0.15
0.27
0.00
0.00
0.00
0.00
0.00
0.18
0.30
0.00
0.00
0.09
0.01
Base CCS
Scenario
0.15
0.29
0.00
0.00
0.00
0.00
0.00
0.17
0.30
0.00
0.00
0.09
0.01
The second set of parametric sensitivity analyses attempts to tease out the main drivers of CCS
retrofit behavior  (Table 5.6).   Once again, two trends are noticeable.  First, energy penalty
improvements (decreases in input fuel requirements) help retrofits of existing pulverized coal
                                                                                      77

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plants and new IGCC capacity without CCS become economically competitive with integrated
IGCC capture units.  As efficiency improves further, coal plant retrofits loose market share to
NGCC capture conversions. This is the same pattern  seen above: the efficiency improvement
reduces the  operating cost  disadvantage gas plants experience as a result of their higher fuel
costs. New, integrated NGCC capture units  are not affected by this sensitivity analysis and do
not enter the mix of generating plants.  Equivalent improvements in retrofit costs do not have as
large an impact.  As investment and operating costs decline, retrofits of existing coal plant
increase, reducing the  share  of power  new  IGCC  capture units generate.   Simultaneous
improvement in both retrofit costs and efficiencies combines these  effects.   Note that new
pulverized coal plant retrofits never become a competitive investment option.
Table 5.6: Share of electricity generation in 2030 by technology for the initial CCS scenario
(CC>2 emissions held constant at 1995 BAU levels from 2015 on) and four parametric analyses of
differences in CCS retrofit technology costs (investment plus all operating) and efficiencies
(expressed here as change in input fuel requirements) relative to baseline values.
Share of Electricity Generation in 2030
Fuel Input
Cost
Nuclear
Existing Coal
New PC
IGCC
Existing Coal Retrofit
New PC Retrofit
IGCC Retrofit
IGCC+CCS
Gas
NGCC Retrofit
NGCC+CCS
Renewables
Other
-20%
100%
0.15
0.30
0.00
0.00
0.09
0.00
0.06
0.00
0.30
0.00
0.00
0.09
0.01
-40%
100%
0.15
0.37
0.00
0.02
0.04
0.00
0.02
0.00
0.12
0.18
0.00
0.09
0.01
100%
-20%
0.15
0.28
0.00
0.00
0.03
0.00
0.00
0.14
0.30
0.00
0.00
0.09
0.01
100%
-40%
0.15
0.28
0.00
0.00
0.06
0.00
0.00
0.12
0.30
0.00
0.00
0.09
0.01
-20%
-20%
0.15
0.34
0.00
0.00
0.06
0.00
0.05
0.00
0.20
0.10
0.00
0.09
0.01
Base CCS
Scenario
0.15
0.29
0.00
0.00
0.00
0.00
0.00
0.17
0.30
0.00
0.00
0.09
0.01
These CCS sensitivity analyses showed that gas units with CC>2 capture could be competitive
with their coal-fired counterparts when efficiency improvements reduced the impact of the price
difference between natural gas and coal.   More generally, fuel switching from coal to gas
provides an alternative to CCS.   Hence, the impact of  natural  gas prices on technology
penetration is worth exploring via its own sensitivity analysis. Figure 5.7 compares the share of
generation in 2030 from conventional gas versus CCS units for the baseline CCS  scenario plus
additional scenarios incorporating a 10 $/GJ markup in economy-wide natural gas prices as well
as a 3  $/GJ cut.  The markup decreases the share of gas generation by 3%,  and leaves CCS
                                                                                     78

-------
unchanged.  The gas price cut affects neither.  These results are partly an artifact of the
MARKAL model, which treats NGCC as non-baseload capacity and therefore prevents NGCC
capture units (new and retrofit) from substituting for their coal-fired counterparts.  Gas price
markups smaller than 10 $/GJ had no effect on results. Note that these scenarios do not include
the CCS growth constraints; equivalent runs with the constraints in place prevented investment
in additional coal-fired CCS capacity (the preferred option) and only increased the overall share
of  gas 4 percent by adding new NGCC plants with capture when gas  prices  were low
(conventional NGCC did not change).
           0.40
           0.30 -
           0.20-
           o.io -
           0.00
III
          • Gas
          DCCS
                  Base CCS
            10 $/GJ Markup

           Gas Price Scenario
1 $/GJ Cut
 Figure 5.1: Share of electricity generation for conventional gas and all CCS technologies
                     under three gas-price scenarios.
Finally, the analysis in this section has focused on the technological side of CC>2 capture.
Sequestration enters the analysis as a single 28 dollar cost per ton of carbon (approximately 7.5
S/tCCh) transported and injected, based on a moderate plant-to-well distance and a high-end
estimate for both costs derived from the IPCC synthesis report (2005).  Actual sequestration
costs will be site specific. Moreover, as noted, an average cost figure fails to capture important
drivers such as uncertainty over public acceptance, the regulatory environment, and the need for
long-term monitoring. Where CC>2 has economic value, such as for enhanced oil recovery, the
gas could be sold for profit.  Such opportunities could provide a niche market  for capture
technologies, fostering learning-by-doing and lowering its costs.  These issues require a more
detail analytical strategy and modeling  environment than  employed here.  As a check on
sensitivity to model assumptions, however, two additional scenarios explored the impact of
halving and doubling sequestration costs - the likely  range of  variation for power plants
operating in the U.S. Neither change had an appreciable effect on results for the baseline CCS
scenario, supporting the view that capture would dominate CCS from an economic perspective
(TEA, 2004; IPCC, 2005).
                                                               79

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5.4 Conclusions
This section has taken an  initial look at  how  carbon  capture and  sequestration technologies
function  in the U.S. EPA  National MARKAL energy  systems  model.   The  energy  systems
context is important as CCS would compete with measures to reduce the carbon intensity of
power production and efforts to improve  end-use efficiency.  Driven by an assumed electric
sector CC>2 trajectory, the model adopts CCS mainly for baseload  generation in the form of new
IGCC capture capacity.  The analysis does not consider demand-side measures, but the model is
seen to rely on coal-to-gas fuel-switching as much as it does on CCS under the  assumed carbon
trajectories.  The next section examines CCS relative to nuclear power and renewable energy
sources, two other prominent means of reducing the carbon intensity of power production. Like
the previous section's  nuclear  power  analysis,  moderate  levels of CCS  adoption do  not
significantly affect  electric sector criteria pollutant emissions.   CCS displaces new baseload
capacity  under these circumstances, but does not significantly impact the operation of existing
coal-fired plants, which contribute most of the sector's Clean Air Act constrained 862 and NOx.

Several limitations of this section's analysis  should be kept in mind.  First, MARKAL is not a
dispatch  model.  Consideration of how different generation technologies operate over the course
of a typical day would  help tease out how technology  use  affects investment patterns, as
discussed earlier (Johnson and Keith, 2004). Technology adoption—particularly the competition
between  CCS and coal-to-gas fuel switching—would also look  different in a model that treated
NGCC units as baseload capacity (a competitor to IGCC capture  units). Likewise, the analysis
would benefit from a more detailed representation of existing coal-fired power plants; the retrofit
option, for instance, might look better with greater model resolution.  Next, the model assumes
static cost and efficiency values for all CCS technologies.  Since, learning and economy-of-scale
improvements in both sets  of CCS parameters  would likely occur, this assessment of CCS is
probably conservative (though the course of technology  development is difficult to predict  and
all electric sector technologies would be subject to leaning).  Finally, CCS is not restricted to the
electric sector. IGCC capture plants, for instance, could produce electricity as needed during the
day, and use excess capacity during non-peak hours to produce H2 for use as  a transportation
fuel. Other industrial applications of CCS are possible (see, e.g., IPCC, 2005). Future work  will
explore these issues to see their full effect on air quality.
                                                                                      80

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                                      Section 6

  Nuclear Power, Carbon Capture and Sequestration, and Wind Generation:
                The Competition Among Technology Alternatives

The previous sections built on the business as usual (BAU) analysis to examine how nuclear
power or CC>2 capture and sequestration (CCS) technologies might contribute to meeting demand
in U.S. electric markets and, in turn, impact air quality.  Separate assessments enabled a higher-
resolution look at the factors that affect the adoption of these alternatives to conventional power
generation and the choice among the specific technologies that make up each class. This section
explores  how nuclear  and CCS compete in the EPA National MARKAL Model (EPANMD).
The analysis also loosens the constraints on wind generation to examine how these technologies
fare against  a prominent renewable  energy option.  Once again,  the focus  is on technology
investment and  how the resulting patterns  of activity  affect electric sector criteria pollutant
emissions.

Like the preceding sections, this analysis relies on the arbitrary non-BAU carbon trajectories as a
modeling device to  stimulate investment in nuclear and  CCS. Figure 6.1, for instance, shows
how CCS fares  against conventional nuclear  power (light water and mixed-oxide  reactors),
natural gas, and wind when electric sector CO2 emissions level off at its 1995 level in 2015. All
technologies incorporate the baseline costs, efficiencies,  and growth constraints assumed in the
previous  sections.  Nuclear power increases its contribution to baseload generation,  with 126
GW of new investment through 2030  (bumping up against its growth constraint), while a  more
modest 26 GW of new  IGCC capture capacity comes on-line.  Electric sector 862  and  NOx
emissions remain constant at the bounds representing CAA rules.   Offering advanced nuclear
technologies (gas turbine modular helium and  pebble  bed modular reactors) as investment
options increases nuclear power output slightly (from 33 to 34 percent of 2030 generation) at the
expense of CCS (which  decreases from 4 to 2 percent); the addition does  not affect gas output
(27 percent), wind (less than 10 percent), or criteria pollutant emissions.
                                                                                    81

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         20
      O  -.,-
      o  15
      o
      o
         2000
                    Conventional
                    Retrofit CCS
                    New CCS
                                            Renewable

                                            Gas
                                                                         Coal
                                                                         Nuclear
2010
2020
2030
   Figure 6.1: Electricity generation per period by technology class for the baseline CCS
 scenario (electric sector COi emissions held constant at 1995 levels from 2015 on, with no
 CCS growth constraints). Note that nuclear includes only conventional (LWR and MOX
                                 reactors) technology.
A further departure from the electric sector CC>2 trajectory alters this picture slightly.  Figure 6.2,
for instance, illustrates how different technologies contribute to meeting electricity demand for a
CO2 trajectory that holds steady at 50 percent of 1995 emissions from 2015  on.  When nuclear
investment is limited to conventional technologies, nuclear  power once again hits its growth
constraint.   CCS investment,  however, increases,  with integrated IGCC  CC>2  capture units
generating 7 percent of 2030's electrical output and NGCC with capture producing an additional
4 percent.  The model also adds CCS retrofits to a modest part of the existing  coal plant capacity.
Criteria pollutant emissions under this scenario decrease well below their upper  bounds,  as
generation in 2030 from the model's (nonretrofit)  existing  coal capacity drops from near 28
percent in the previous two scenarios to slightly more than 3 percent.  SCh emissions are reduced
by about 90% below CAA restrictions, and NOX emissions are reduced by half (Figure 6.3).

Expanding investment  options  to  include  advanced nuclear technologies increases nuclear
generation from 34 to 42 percent of total 2030 output, while the contribution from  both IGCC
and NGCC capture units drops by half.  Conventional  gas-fueled technologies in both  scenarios
hovers around 30 percent.
                                                                                    82

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                   Conventional
                   Retrofit CCS
                   New CCS
                                            Renewable
                                                                     Gas
                                                                     Coal
                                                                     Nuclear
         2000
  2010
             2020
                       2030
    Figure 6.2: Electricity generation per-period by technology class for the 50 percent
emissions scenario (electric sector COi emissions held constant at 50 percent of 1995 levels
                                  from 2015 on).
              1.20
                2000
2005
2010
2015
2020
2025
2030
                            •Electric SectorNOX
                       •Electric Sector SO2
Figure 6.3: Electric sector SOi, and NOx emissions as a function of time for the 50 percent
emissions scenario (electric sector COi emissions held constant at 50 percent of 1995 levels
                                  from 2015 on).
                                                                                83

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These results show that both nuclear and gas-fired generation dominate CCS, given baseline
technology assumptions. Figure 6.4 illustrates how generation by these technologies changes as
a function of assumptions about CCS costs and efficiencies, nuclear capital costs, and electric
sector natural gas prices.  (Except where noted, electric sector CO2 emissions level out at 1995
level in 2015, and only conventional nuclear technologies are available.)  Note that the BAU
wind constraints remain in place (and are preventing additional investment in wind turbines); this
assumption will be relaxed in the following set of scenarios.
      PH  0.00
                Better CCS   Better CCS +  Better CCS +   Better CCS +  Better CCS +
                               50%CO2     50%CO2+    2xNucInv   2xNucInv +
                                            NGA MU                  50% CO2 +
                                                                      NGAMU

         in Renewable E Coal (No CCS) S Gas (No CCS) 0 CCS B Nuclear

     Figure 6.4: Fraction of 2030 electric power generation by technology class for five
                                      scenarios.

The Figure 6.4 scenarios attempt to improve the competitiveness of CCS relative to conventional
nuclear power.  The first scenario  ("Better CCS") lowers all CCS  costs and  input  fuel
requirements  (the  inverse of  efficiency) by 20 percent.   Nuclear power's share  of 2030
generation falls from 33 to 26 percent, while that of CCS increases from 4 to 11 percent - a mix
of IGCC and NGCC units with CC>2 capture. CCS growth constraints bind for only two periods
(2015 and 2020).  Since the output from existing coal plants does not change, criteria pollutants
remain at their BAU levels (i.e., the limits of their regulatory constraints).

The  second  scenario ("Better CCS +  50%  CO2")  examines  the  same  CCS  technology
improvements under the 50 percent CC>2  trajectory.   CCS maintains  its higher share of output,
but nuclear returns to its previous (limited) 33 percent share at the expense of existing coal-fired
generation. The model also adds non-capture IGCC capacity, which contributes 12 percent of
2030 generation. The result is  a significant decrease in electric sector SC>2 and NOx emissions,
which decline to 18 and 34 percent, respectively, of their 2030 BAU values.   As this scenario
also  sees an increase  of (non-CCS) gas-fueled  generation from  21  to 26 percent, it  is worth
looking at the effects of a gas  price increase. The third scenario ("Better CCS + 50% CO2 +
                                                                                    84

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NGA MU") adds 10 $/GJ to electric sector gas prices.  The price increase lowers the output of
the model's gas capacity to its previous value; IGCC and NGCC units with CC>2 capture make up
the difference (nuclear power continues to hit its growth constraint).  Existing coal plants reduce
their output further and non-capture IGCC  increases its  output  to  19 percent,  yielding an 88
percent reduction in 2030 862 emissions and a 48 percent drop in NOx from the electric sector.

The  remaining two scenarios  in Figure 6.4  attempt to advantage  CCS further by increasing
nuclear investment costs.  Nuclear construction costs have been notoriously difficult to predict,
and total project costs of nearly twice that of early estimates, for instance, mark the historical
record (EIA,  1986).  The fourth scenario ("Better CCS + 2x Nuc Inv") is equivalent to the first,
with all (conventional) nuclear investment costs doubled.  The results are similar, except that
nuclear power's share of 2030  generation drops to  15  percent, while that  of CCS increased to
nearly 20 (with growth constraints binding through 2025);  since existing coal capacity does not
change, criteria pollutant emissions remain at BAU levels.  The last scenario ("Better CCS + 2x
Nuc  Inv +  50% CO2  + NGA MU") adds the natural gas price markup and looks at technology
penetration assuming the lower electric sector CO2 trajectory.   CCS maintains its higher output,
but nuclear generation increases to 24 percent; non-capture IGCC provides 17 percent of 2030
output and existing coal units  once again drop below 5 percent.   These dynamics result in the
lowest electric SC>2 emissions of any scenario (8 percent of BAU levels) and a NOx  reduction to
approximately two-thirds of the modeled CAA limits.

Finally, these scenarios pitting nuclear and CCS are somewhat unrealistic in that they maintain
tight growth constraints on renewables.  Wind generation is becoming economically competitive
with gas as natural gas prices  remain at historically high levels  and wind turbine technology
continues to improve.  Wind therefore deserves consideration in this analysis.  While nuclear and
CCS can be dispatched to supply baseload electricity,  wind is an intermittent source of power
that tends to  displace load-following units such as gas turbines.  The EPANMD also lacks the
regional specificity needed to capture important differences in the availability of wind resources
(i.e., regional wind classes, which characterize typical wind speeds). In the EPANMD, wind is
simply available anywhere, subject to the assumed growth and availability constraints.  A more
accurate representation would  include a resource supply curve to reflect the increasing cost of
deploying wind turbines in less-suitable regions of the country. The same  argument, of course,
holds for solar and other renewable technologies.

These caveats should be kept in mind while considering the final  set of scenarios, which look at
the competition between wind generation, nuclear power, and  CCS  under the relaxed wind
growth constraints (these are equivalent to the Section 4 nuclear growth constraints). Figure 6.5
summarizes 2030 generation by technology class; all scenarios adopt the 50 percent of BAU CC>2
trajectory to stimulate alternative technology penetration.
                                                                                      85

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   Better CCS
                  OH Wind d Coal (No CCS) & Gas (No CCS) 0 CCS • Nuclear

    Figure 6.5: Fraction of 2030 electric power generation by technology class for four
                   scenarios with the relaxed wind growth constraints.

The first relaxed wind scenario ("Conv Nuc") restricts nuclear investment to conventional
technologies.  Wind and nuclear each contribute around 30 percent of 2030 generation, with gas
responsible for an additional 25 percent and existing coal units making up the difference (Figure
6.6). Electric sector 862 emissions fall to 10 percent of their constrained values, while NOx
declines to 75 percent (the increase in gas-fired generation largely explains this pattern).  CCS
plays a trivial role. An increase in gas prices coupled with higher nuclear investment costs
("Conv Nuc + NGA MU + 2x Nuc Inv") increases wind's share of generation to 45 percent,
decreases nuclear output to 15  percent, and results in the growth of CCS to 12 percent by 2030.
Existing coal plants (18 percent) and gas (10 percent) contribute the rest.  NOx emissions remain
at their constrained limits, while SC>2 falls to 40 percent of its upper bound. Opening nuclear
investment to advanced technologies, while maintaining the gas and nuclear investment cost
mark-ups, increases nuclear output to over 25 percent ("Adv Nuc + NGA MU + 2x Nuc Inv").
CCS once again plays a trivial  role in meeting electric power demand. Electric sector criteria
pollutants remain essentially unchanged. Finally, improvements in CCS costs and efficiencies
("Conv Nuc + NGA MU + 2x Nuc Inv + Better CCS") lower gas-fueled generation somewhat
when only conventional nuclear investment is allowed, at the expense of higher electric sector
SO2 emissions (approximately  two-thirds of their constrained value).
                                                                                    86

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         fin
         O
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            20
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         I  10
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         O
            2°000
          Conventional
          Retrofit CCS
          New CCS
                                                                       Renewable
                                                                       Gas
                                                                       Coal
                                                                       Nuclear
                   2010
2020
2030
    Figure 6.6: Electricity generation per-period by technology class for the 50 percent
 emissions scenario (electric sector COi emissions held constant at 50 percent of 1995 levels
    from 2015 on) under the relaxed wind growth constraints with nuclear investment
      restricted to conventional technologies (the "Conv Nuc" scenario in Figure 6.5).

These results provide an indication of how nuclear power, wind generation, and CCS might fare
against each other.  The last set of scenarios, as noted, is tentative pending improvements in the
U.S. EPA's National MARKAL modeling framework. The results, however, suggest that wind
and nuclear together could displace a significant portion (60 percent) of fossil-fuel  electric
generation, though  the co-control emissions benefits  of this  substitution  may  not  be that
substantial and depend largely on how existing  coal plants  are used. An  important caveat is
whether and how system operators of electricity  grids could  manage the intermittent output of
wind  serving  such  a  large fraction  of demand. Future  work on the EPANMD includes  a
representation of backup generation to complement intermittent resources.

In conclusion, several key results emerge from this section's integrated technology assessment:
   •   The penetration of advanced technologies  does not necessarily yield a significant criteria
       pollutant benefit.  Under  all but the most radical departures from BAU  electric sector
       carbon trajectories, for instance,  nuclear power and CCS merely replace the new coal and
       gas capacity  that are needed to meet increasing electricity demand. The model maintains
       its existing coal plants through their useful  lifetime, and criteria  pollutant  emissions
       remain near their Clean Air Act limits as a result.  These existing plants, which are free of
       amortized  investment  costs, remain  too economical to  retire as baseload capacity—an
       observed trend noted in the literature (e.g., Ellerman, 1996).  This picture  changes  when
       electric sector carbon emissions depart furthest from their BAU levels and CC>2 emissions
                                                                                     87

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from baseload plants declines.   Even  in these cases, however,  the  decrease in NOx
emissions is more modest than that seen in SC>2.
CCS is dominated by investment in new nuclear technologies as well  as additional gas-
fired generation and wind power.  Only  when favorable  CCS cost and  efficiency
assumption combine with high nuclear investment cost and gas price scenarios does CCS
approach its modeled growth constraints over the full time horizon in which it is available
(2015-2030).  Furthermore, given that CO2  capture and geologic  sequestration are both
untested at even the modest scales adopted here, these results are probably optimistic.
Even with a rudimentary conception of wind resources, wind generation in the EPANMD
competes for a significant share of electric  power output when the model incorporates
more favorable growth constraint assumptions for new turbine investment.  These growth
limits are modest compared to some predictions, though a more realistic representation of
unit dispatch is needed to capture the technology's intermittency (beyond lowering the
annual availability of wind turbines, as is done here) and evaluate its actual potential.
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                                       Section 7

       Limitations, Future Model Development, and Analysis Directions

While  the  model  results  provide  insight into  future energy  technology  pathways, they are
nonetheless based on a model, and all models have limitations  that introduce  caveats. This
section identifies and  discusses some of the limitations  associated with the EPANMD  and
MARKAL  model. Planned strategies and future work to address these limitations are interwoven
in the discussion. The report concludes with next steps towards fulfillment of ISA-W's role in
EPA's Air Quality Assessment.

The use  of national  rather than regional inputs to MARKAL is  perhaps  the most  significant
limitation affecting the analysis. The EPANMD is a national database that does not contain
region-specific  data.  The  lack of regional data manifests itself in three key limitations. First,
important regional differences in resource supplies,  energy service demands,  and technology
availability  cannot be  represented.  An example of where  this limitation  would affect model
results is in an evaluation of renewables,  such as biomass  and wind.   In  the  EPANMD,
renewable resources are represented by aggregated national supply  curves, if at all. For example,
biomass  resources are represented  by a single 7-step supply curve that  aggregates biomass
resources across the nation. Wind does not have a supply curve, but rather  an assumed capacity
factor (which implies the strength of the wind resource) of 35 percent. These representations do
not account for widely varying resource quantities and  costs from  one region to another. To
address regional differences explicitly, ISA-W is developing a  nine-region  MARKAL database
(EPA9R) that will account for variations in supply, demand, and technologies between the nine
U.S. Census regions.  We expect to use the EPA9R database to examine renewables in the future.

Second, the EPANMD does not include transportation costs for coal or other resources. As with
renewables,  transportation  costs   will  affect  regional   technology   penetration,  and   by
extrapolation, the  aggregate  technology  penetration at the national level. In particular, the
inclusion of transportation costs will have the greatest effect  on energy  resources with  low
energy density,  such as biomass or low grade coals. It is not cost-effective to ship these resources
on transnational  scales, but such  transport  may be occurring  implicitly within EPANMD
modeling. Transportation costs for energy resources will be included in EPA9R.

Third, air pollution regulations that have been implemented on  a state or regional level must be
modeled at a national level within EPANMD. Air pollution is a problem that has multiple scales.
Damages from  air pollution occur at  the local  scale, affecting  human health,  agriculture,  and
ecosystems. Some of the pollutant emissions contributing to air pollution are from local sources,
but others typically are emitted from upwind sources both  within and outside the state.  In this
context,  federal air quality regulations, including the Clean Air Act (CAA) and the Clean Air
Interstate Rule (CAIR), contain provisions that reduce emissions at the local and regional scales.
For example, the CAA introduces national ambient air quality standards that must be met at the
local level.  States must develop plans for reducing emissions to these standards, so these plans
typically result  in local- or state-level emissions reductions.  To address regional- and national-
scale transport of emissions, the CAA also place NOx and 862 emissions  limits on individual
boilers. Utilities are given the option of trading emissions credits so that the  emissions reductions
                                                                                      89

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occur in aggregate, but, through purchasing emissions credits, individual boilers may exceed the
limit. CAIR was also developed to reduce the transport of pollutants from one state to another. In
CAIR, states within the eastern U.S. are assigned budgets that account for the effects of their
emissions  on downwind states  within the  region.  States have  considerable  freedom  in
determining how to comply with their budgets.

The  regional- and state-level complexities of these regulations complicate modeling emissions
regulations within EPANMD. Resulting projections  for the use of emissions controls can be
quite different from what is seen "on the ground." As a  result of CAIR, for example, most coal-
fired boilers  east of the Mississippi are expected  to use SCR controls for NOx. By not
representing the regional requirements of CAIR, however, EPANMD favors SNCR, which has
lower costs but also lower NOX removal efficiencies. A state or regional representation of control
requirements  would produce results that are closer  to what will  actually be implemented. With
the  development of EPA9R, ISA-W  expects to  be  better  able  to  represent regional-level
implementation of national rules.  State-level differences will still be difficult to capture within
EPA9R.

Other limitations are unrelated to  the  lack of regional specificity in the  EPANMD. First,
MARKAL is not an electric dispatch model: the user is limited to exactly six time slices in which
to specify all  demands (see Shay, et al. 2006). The time  slices represent two diurnal time periods
(day and night)  as well  as three seasons (summer,  winter, and intermediate); the 6 time slices
come from the combination of times of day and seasons enumerated above. The specification of
demands according to these time slices results in a flat demand profile compared with the more
conventional  load duration curve used to specify demand in electric dispatch models. The result
is that MARKAL tends  to favor baseload  units with  high capital costs  and low marginal costs
(e.g., coal and nuclear)  over units with higher marginal costs that are better suited to meeting
peak and  shoulder demand (e.g., gas turbines and wind). ISA-W is  addressing this MARKAL
limitation by  funding the development of flexible time  slices, so the user can specify as many
time slices as desired to more accurately represent the profile of electricity load. The  expanded
time slices will be included in the EPA9R model.

The   EPANMD's simple representation  of  the  nation's  existing  coal-fired generating
infrastructure is  an additional limitation. The database  contains just three types of pre-existing
coal  plants: bituminous, sub-bituminous, and lignite steam.  In reality, a diverse variety of coal
plants exist, with performance  and emissions  that are  highly dependent on their vintage and
detailed characteristics of the coal used to supply the plants.  Future development will include an
expanded representation these existing technologies.

Finally, the  analysis excludes consideration  of efficiency  improvements  as well  as non-
traditional generating technologies, like  combined heat  and power, which could supply a large
share of the nation's electricity in the coming  decades.  End-use efficiency improvements, in
particular, are promising as they could significantly reduce electricity demand by 2030 (Laitner,
et al. 2005).  Ignoring the potential for a  significant demand reduction, as this analysis does,
biases conclusions in favor of advanced generating technologies like nuclear power  and CCS
under certain scenarios.  The non-BAU carbon trajectories, for instance, through their impact on
the cost  of electricity,  would likely spur as  much  investment in  end-use energy  efficiency
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measures as they do in alternative supply-side technologies.  Likewise, the analysis does not
consider the impact of radical technological innovation, such as the emergence of "nano-bio-
info" technologies, on future energy demand (see Laitner, 2006, for an overview).  Surprises in
principle are difficult to anticipate, and this assessment makes no attempt to cover all scenarios
that may play out in the future.

Future Work
With the transportation and electricity sector assessments complete, work will involve using the
EPANMD within a variety of applications. One such application is the development of future-
year emissions scenarios for the EPA Office of Research and Development's Global Change Air
Quality Assessment. These scenarios will be used to project criteria pollutant emissions out to
the year 2050. The emissions will then be used within an air quality model to characterize the
impacts of increased energy service demands and technological change on regional air quality.

ISA-W also plans  to use the EPANMD to assess the prospects and implications of particular
technologies. Using formal sensitivity analysis techniques, MARKAL can be used to identify the
conditions in which a  particular technology is competitive,  the sectoral and cross-sector fuel
implications of the penetration  of the technology, and the resulting  emissions. An example of
such an analysis is the  recently  completed evaluation of hydrogen fuel cell vehicles (Yeh et al.
2006).   Additional technologies that we expect to assess with the  EPANMD include advanced
nuclear power, coal gasification, and plug-in gasoline-electric hybrids.

Another potential  application for the EPANMD is in the  area  of risk  assessment and risk
management.   Within a  probabilistic framework,  MARKAL can  be used to identify  the
conditions that lead to  poor air  quality or  depletion  of specific fuel resources.  The model  can
then be used to identify technological pathways to minimize these risks.

Through these  and other  applications, we expect EPANMD, and  later, EPA9R,  to  play  an
important role in EPA's efforts to understand the linkage between energy and emissions and use
this understanding to protect the environment and human health into the future.
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                                      Section 8

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