&EPA
United States
Environmental Protection
Agency
EPA-600/R-04-135
July 2004
Demonstration of a
Scenario Approach for
Technology
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EPA-600/R-04/135
July 2004
Demonstration of a Scenario Approach
for Technology Assessment:
Transportation Sector
by
Cynthia L. Gage, Timothy L. Johnson,
Evelyn L. Wright, Daniel H. Loughlin,
Carol L. Shay, Ronald J. Spiegel, and Leslie L. Beck
National Risk Management Research Laboratory
Air Pollution Prevention and Control Division
Research Triangle Park, NC, 27711
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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Abstract
EPA's Office of Research and Development (ORD) is pursuing an Air Quality Assessment
that will examine the potential consequences of global change on tropospheric ozone and
particulate matter in the year 2050. Technological change is one of the most important
drivers for the future of environmental air quality and global environmental change. The
National Risk Management Research Laboratory's Technology Assessment and Co-control
Team (TACT) is pursuing a scenario-oriented approach to the assessment of future
technologies and patterns of technology adoption in the transportation and electricity
generation sectors. This report presents TACT's approach and highlights early results in the
transportation sector. Scenarios considering advanced internal combustion engine vehicles,
hybrid vehicles, and hydrogen vehicles and their associated fueling infrastructures are
developed and analyzed. Preliminary emissions modeling results suggest different
technology development and penetration scenarios may have greatly differing emissions
consequences and, hence, differing air quality implications in the Air Quality Assessment
time horizon. Future work will further develop the analysis of the transportation sector,
including an assessment of the interaction between economic and technological changes,
and will expand to include an analysis of the electricity generation sector.
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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, Acting Director
National Risk Management Research Laboratory
in
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EPA Review Notice
This report has been peer and administratively reviewed by the U.S. Environmental Protection
Agency and approved for publication. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
This document is available to the public through the National Technical Information Service,
Springfield, Virginia 22161.
Disclaimer
Although every effort has been made to ensure that the data used in this report are reliable,
and although the data sources were evaluated in peer review, the US EPA has not assessed
the quality of the existing data used herein. In addition, selection and use of existing data
does not imply EPA endorsement of their sources or associated collection and analytical
methodologies.
IV
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Table of Contents
Section Page
Abstract ii
List of Figures vii
List of Tables ix
Acronyms and Abbreviations x
Executive Summary 1
Approach 1
Scenarios Investigated 3
Results 5
Conclusions 6
1 Technology Assessment and Project Scope 7
1.1 Background 7
1.2 Project Scope 10
2 General Modeling Approach Using MARKAL 13
2.1 Economic Modeling for Air Quality Assessment 13
2.2 Description of MARKAL 16
2.3 Developing the EPA's U.S. MARKAL Model 18
2.4 Using MARKAL to Develop Technological Scenarios 20
2.5 Future Technologies for Scenario Analysis 21
2.6 Emissions Consequences 22
3 Transportation Technologies 25
3.1 Hybrid Vehicles 26
3.2 Fuel Cell Vehicles 33
3.3 Hydrogen Production 42
4 Mapping Hydrogen Infrastructure Technologies into MARKAL 47
4.1 Overview 47
4.2 Methodology 49
4.3 Implementation 53
5 Scenarios 55
5.1 Introduction 55
5.2 Evolution as Usual Scenarios 56
v
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Table of Contents (concluded)
Section Page
5.3 Early Phase Hydrogen Economy Scenarios 63
6 Scenario Results and Analysis 67
6.1 EAU Scenario Outcomes 67
6.2 EPHE Scenario Outcomes 80
6.3 Comparison of EAU and EPHE 86
7 Future Work 91
7.1 Database Development 91
7.2 Expansion of Future Technologies in the Transportation Sector 91
7.3 Expansion of Future Technologies in the Electricity Generation Sector 92
7.4 Evaluating Approaches for Incorporating Economic and Learning Effects .... 93
7.5 Scenario Development and Analysis 93
7.6 Integration of MARKAL Modeling Results into the ORD Air
Quality Assessment 96
Appendices
A MARKAL 99
A. 1 System-wide Parameters 100
A.2 Energy Service Demands 100
A.3 Energy Carriers 101
A.4 Resource Technologies 101
A.5 Process and Demand Technologies 102
A.6 Environmental Variables 103
B Database Development, Review, and Calibration 105
B. 1 Data Sources 105
B.2 Peer review 106
B.3 Calibration 108
C References 113
VI
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List of Figures
Figure Page
1 An Example of a Simple Reference Energy System 17
2 Typical Hybrid Vehicle Configuration 27
3 Range of Cost Premiums for HEVs 30
4 Range of Fuel Economies for HEVs 31
5 PEM Fuel Cell Operation 34
6 Fuel Cell Vehicle with Fuel Processor 35
7 Fuel Reformer Flow Diagram 38
8 Ranges of Cost Premiums for Fuel Cell Vehicles 40
9 Ranges of Fuel Economy for Fuel Cell Vehicles 41
10 Steam Methane-Reforming Process Flow Diagram 43
11 Range of Capital Investment Costs for Steam Methane Reforming 44
12 Range of Investment Costs for Electrolysis 45
13 MARKAL RES Diagram for Centralized Steam to Methane Reforming 48
14 Per-period VMT of each Technology for the HM(C) Scenario 68
15 Per-period VMT of each Technology for the 2xH Scenario 69
16 Per-period VMT of each Technology for the GP Scenario 69
17 Per-period VMT of each Technology for the HM Scenario 70
18 Per-period VMT of each Technology for the CD Scenario 70
19 Technology Penetration per Period for HM(C) Scenario 71
20 Technology Penetration per Period for 2XH Scenario 71
21 Technology Penetration per Period for GP Scenario 72
22 Technology Penetration per Period for HM Scenario 72
23 Technology Penetration per Period for CD Scenario 73
24 Comparison of Non-Hybrid Penetrations across EAU Scenarios at 2020 and 2030 . . 73
25 Comparison of Total Hybrid Penetrations across EAU Scenarios at 2020 and 2030 . 74
26 Technology Penetration in HM(C) Scenario with 10% Price Incentive for Hybrids . . 75
27 Technology Penetration in HM(C) Scenario with ($0.50/gal) Gas Price Reduction .. 76
28 Technology Penetration in HM(C) Scenario with 15% Price Increase on Hybrids ... 76
29 Per-period Gasoline and Diesel Consumption across EAU Scenarios 77
30 PM10 Emission Reductions in 2030 Relative to the CD Scenario (20 thousand tons
PM10/yr) 78
vii
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List of Figures (concluded)
Figure Page
31 CO Emission Reductions in 2030 Relative to the CD Scenario (7590 thousand tons
CO/yr) 78
32 NOX Emission Reductions Relative to CD Scenario (300 thousand tons NOx/yr) ... 79
33 VOC Emission Reductions in 2030 Relative to CD Scenario (230 thousand tons
VOC/yr) 79
34 Per-period VMT for each Technology in the H2M Scenario 81
35 Per-period VMT for each Technology in the H2O Scenario 81
36 Technology Penetration per Period for H2M Scenario 82
37 Technology Penetration per Period for H2O Scenario 82
38 Per-period Gasoline and Fuel Use for EPHE Scenarios 83
39 Emissions Reductions for EPHE Scenarios Relative to HM(C) Scenario 84
40 Gasoline and Diesel Consumption in 2030 for the H2F Scenarios 85
41 Emission Reductions (%) in 2030 for the H2F Scenarios 85
42 PM10 Reductions in 2030 for EAU and EPHE Scenarios Relative to CD Scenario . . 87
43 CO Reductions in 2030 for EAU and EPHE Scenarios Relative to CD Scenario 88
44 NOX Reductions in 2030 for EAU and EPHE Scenarios Relative to CD Scenario ... 88
45 VOC Reductions in 2030 for EAU and EPHE Scenarios Relative to CD Scenario . . 89
Vlll
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List of Tables
Table Page
1 Technology Penetrations in 2030 for Evolution as Usual and Early Phase
Hydrogen Economy Scenarios 5
2 Emission Reductions in 2030 for Evolution as Usual and Early Phase Hydrogen
Economy Scenarios Relative to Conventionals Dominate 6
3 Personal Vehicle Technologies 25
4 Population Segment Definitions by Population and Distance 49
5 Transportation Demand Projections by Vehicle Class and Year 57
6 Summary of the Evolution as Usual Transportation Scenarios 58
7 Compact Vehicle Technology Parameter Values for HM(C) Scenario 59
8 Hybrid Vehicle Growth Constraints 60
9 Summary of the Early Phase Hydrogen Economy transportation scenarios 64
10 Compact Vehicle Technology Parameter Values for H2M Scenario 64
11 Fuel Cell Vehicle Growth Constraints 64
12 Hydrogen Production Parameter Values for H2M Scenario 65
13 Primary Sources Used in Developing the Database 105
14 Sector Peer Reviewers 107
15 Comparison of MARKAL results to AEO 2002 112
IX
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Acronyms and Abbreviations
Term
Definition
2XH scenario in which hybrid vehicles get twice the gas milage as conventionals
AC alternating [electrical] current
ACEEE American Council for an Energy Efficient Economy
AEO Annual Energy Outlook, a DOE publication
ANL Argonne National Laboratory
API American Petroleum Institute
C Elemental carbon
CAA Clean Air Act
CCRI Climate Change Research Initiative
CD scenario in which conventional vehicles dominate the market
CH4 methane
CIDI compression ignition direct injection
CMAQ Community Multiscale Air Quality
CNG compressed natural gas
CO carbon monoxide
CO2 carbon dioxide
CPPD Climate Protection Partnerships Division
DC direct [electrical] current
DOE U.S. Department of Energy
EAU evolution as usual scenario
EDF Environmental Defense Fund
EE/RE Energy Efficiency/Renewable Energy
EGAS Economic Growth Analysis System model
EIA Energy Information Administration
EMPAX Economics Model for Environmental Policy Analysis [EPA model]
EPHE early phase hydrogen economy scenario
EPRI Electric Power Research Institute
ETSAP Energy Technology and Systems Analysis Program
FCHV fuel cell hybrid vehicle
FCVs fuel cell vehicles
GHE green house equivalents
GHG greenhouse gas
GIS Geographic Information System
continued
x
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Acronyms and Abbreviations (continued)
Term
Definition
GJ Gigajoule
GP scenario in which gas price varies
GREET Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation
H2 hydrogen
H2A DOE's hydrogen Analysis workgroup
H2F scenario in which conversion to hydrogen powered vehicles is forced
H2M scenario with moderate H2 FCV market penetration
H2O scenario with optimistic H2 FCV cost and efficiency assumptions
HC hydrocarbon
HEVs hybrid electric vehicles
FDVI scenario in which hybrid vehicles are incorporated into the market
FDVI(C) scenario in which hybrids drive conventional vehicles from the market
ICE internal combustion engines
IPM Integrated Planning Model
LBNL Lawrence Berkeley National Laboratory
LEV low emissions vehicle
LP linear programming
LPG liquefied petroleum gas
MARKAL MARKet ALlocation computer model
MGA Modeling to Generate Alternatives
mpg miles per gallon
NCER National Center for Environmental Research
NEMS National Energy Modeling System (U. S. EIA)
NERL National Exposure Research Laboratory
NETL National Energy Technology Laboratory
NOX nitrogen oxides
NRMRL National Risk Management Research Laboratory
NRSA Nominal Range Sensitivity Analysis
OAQPS Office of Air Quality Planning and Standards
ORD Office of Research and Development
OTAQ Office of Transportation and Air Quality
OTT DOE's Office of Transportation Technologies
PEM proton exchange membrane
PJ pentajoules
PM paniculate matter
continued
XI
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Acronyms and Abbreviations (concluded)
Term
Definition
PM10 PM with aerodynamic diameter 10 |im or less
PM2 5 Fine PM with aerodynamic diameter 2.5 |im or less
POX partial oxidation [reformer]
psig pounds per square inch gauge
QM Quality Metrics
R&D research and development
REMI Regional Economic Models, Inc. (economic model)
RES reference energy system
RFF Resources for the Future
RTI Research Triangle International Institute
SAGE System for the Analysis of Global Energy Markets
SMR steam/methane reforming
SULEV super ultra-low emissions vehicle
SUV sports utility vehicle
TACT Technology Assessment and Co-control Team
TAG Technical Assessment Guide
ULEV ultra-low emissions vehicle
USGCRP U.S. Global Change Research Program
VMT vehicle miles traveled
VOCs volatile organic compounds
xn
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Executive Summary
The Office of Research and Development (ORD) is pursuing an Air Quality Assessment that
will examine the potential consequences of global change on tropospheric ozone and
particulate matter (PM) in the year 2050. In developing this assessment, it is recognized that
technological change is one of the most important drivers for the future of environmental air
quality and global environmental change. Within EPA, the Technology Assessment and
Co-control Team (TACT) of the National Risk Management Research Laboratory is
chartered with providing potential trajectories for technological evolution to ORD's Air
Quality Assessment. Rather than defining a "best guess" future, TACT is pursuing a
scenario-oriented approach to the assessment of future technologies and patterns of
technology adoption, with a focus on the transportation and electricity generation sectors.
This report presents TACT's approach and highlights early results in the transportation
sector. Future work will develop the analysis of the transportation sector further, including
an assessment of the interaction between economic and technological changes, and will
expand to include an analysis of the electricity generation sector.
Approach
The primary focus of TACT's analysis is examining technological change in transportation
and energy generation because these are the economic sectors that are thought to have the
greatest effect on ambient air quality. Transportation and electricity generation cannot be
studied in isolation, however, since there are important interactions both between these
sectors and the rest of the economy. Many of these interactions are related to the supply and
demand of various forms of energy. To model the U.S. energy system, TACT is using the
MARKet ALlocation (MARKAL) model (Seebregts et al., 2001), a well-established energy
system model. MARKAL is a bottom-up, linear, optimization-driven model that is easily
distributed, non-proprietary, widely used, and has an active user community. The model
provides a framework for organizing performance, cost, use, and constraint data for all
current and future technologies in the energy system being modeled. Scenarios representing
plausible storylines can be tested in MARKAL by modifying appropriate input parameters.
A set of scenarios assists in the understanding of possible future technological change.
The approach for performing technology assessments involved several phases. The first
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phase required developing the reference energy system (RES) in MARKAL. The RES is a
technology-rich database representing the economic sectors in the U.S. energy system. The
sectors which have been completed include resource supply, commercial, residential, trans-
portation, and electricity generation. The industrial sector is currently represented by a fixed
fuel demand, but work is nearly complete that will characterize this sector more completely.
The U.S. Department of Energy's (DOE) Annual Energy Outlook (AEO) was used to
construct the energy supply, demand, and technology data that made up the RES. The AEO
data were derived from the U.S. Energy Information Agency's (EIA) National Energy
Modeling System (NEMS) (U.S. EIA, 2003a) and are a nationally recognized source of
technology data. Where AEO data were not available, RES data were derived from other
widely recognized authoritative sources (e.g., Electric Power Research Institute's Technical
Assessment Guide and DOE's Office of Transportation Technology's Quality Metrics
report). In addition to defining technology parameters, emission factors have been gathered
for the RES technologies (e.g., EPA's Air Quality and Emissions Trends Report). In every
case, including the use of AEO data, the data source has been well documented with the
original data source readily available to those of interest.
Data in the completed sectors were assessed by sector appropriate reviewers. The assembled
model was then calibrated to the results of AEO 2002 (U.S. EIA, 2001). Once the industrial
sector has been completed, full calibration results from the RES will be peer-reviewed by
MARKAL modelers.
The transportation sector, the area of focus for this phase of the analysis, was then
supplemented with data characterizing potential developments in future technologies. Initial
efforts address technological change in five classes of personal vehicles—compacts,
full-size, minivans, pick-up trucks, and sport utility vehicles (SUVs)—and the associated
fuel-producing technologies. For this report, data gathering focused on hybrid vehicles,
hydrogen fuel vehicles, and the technologies required to provide a hydrogen infrastructure.
For all these technologies, literature searches have been performed to establish a range of
estimates for key MARKAL parameters such as efficiencies and capital and operating costs.
At this time, gasoline and methanol fuel cells and both biofueled cars and their associated
biofuel-production pathways are still being investigated and are not included in this report.
The next phase of the work involved characterizing "storyline" scenarios, and applying the
MARKAL model to generate results in response to each. In this context, scenarios are not
predictions of the future. Rather, each scenario is an alternative and internally consistent
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depiction of how the future may unfold, given assumptions about future economic, social,
political, and technological developments. A scenario-based approach is particularly
appropriate for assessments involving a long time horizon, such as technology assessments
linked to future air quality. Results from a selected set of these scenarios will serve as input
to the ORD Air Quality Assessment.
Scenarios Investigated
For this report, several scenarios have been investigated along two possible technological
futures: (i) evolution as usual (EAU) and (ii) early phase hydrogen economy (EPHE). The
EAU scenarios propose continued advancement of conventional internal combustion and
hybrid transportation technologies, and the EPHE scenarios investigate possible
transformations to a hydrogen-based economy in transportation.
Five primary EAU scenarios were investigated: (i) Conventional Internal Combustion
Vehicles (Conventionals) Dominate, (ii) Hybrid Market, (iii) Double Efficiency Hybrids,
(iv) Gas Price Variation, and (v) Hybrid Market Without Conventionals.
Conventional Internal Combustion Vehicles (Conventionals) Dominate—In this
scenario, both high efficiency internal combustion engines (ICE) and hybrids play a
negligible role in meeting transportation demand. The primary Conventionals Dominate
(CD) scenario applies a 7 percent "hurdle" rate premium—which reflects the reluctance of
the market to change to a new technology—on hybrids and Conventionals with increased
efficiency. Other associated scenarios that could minimize penetration of advanced
technologies include higher capital costs for advanced technologies and lower gasoline
prices than presently anticipated. Both scenarios would favor the status quo.
Hybrid Market—This scenario foresees a moderate penetration of hybrid vehicles of 10 to
15 percent in 2030. Two hybrid technology options are available representing two levels of
efficiency advancements. The success of hybrid vehicles will depend largely on their cost
and performance relative to ICE powered vehicles, consumer attitudes regarding new
technologies, manufacturing capacity, and fuel costs. Hybrid market penetration in the
MARKAL model scenarios is thus a function of hybrid capital costs and operating
efficiencies, the particular discount (or hurdle) rate applied to the technology, growth
constraints on hybrid penetration, and gasoline prices. A primary Hybrid Market scenario
incorporates growth constraints to capture the inertia of consumers moving from
conventional internal combustion vehicles to a new technology and the slow pace of
assembly line retooling. Two associated scenarios investigate the impact of a 10 percent
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reduction in hybrid capital cost (roughly equivalent to the present day tax credit) and of a 16
percent increase in capital cost for advanced efficiency conventional internal combustion
vehicles, a primary competitor of hybrids.
Double Efficiency Hybrids—In the MARKAL database, hybrids are represented in two
technology categories, one with twice the miles per gallon (mpg) efficiency (2X) of
conventional internal combustion vehicles and the other with three times the miles per
gallon (3X) of convent!onals. The Double Efficiency Hybrid scenario examines penetration
where hybrid development does not achieve the higher efficiency levels.
Gas Price Variation—The greater efficiencies of hybrids will yield long-term savings in
fuel costs. Thus, their attractiveness relative to standard ICE vehicles might be expected to
increase with the price of gasoline. The Gas Price Variation Scenario looks at the impact of
a gasoline cost about three times higher than the current average price on the penetration of
hybrids. While this value is high for the U.S. market, it is close to present European costs.
Hybrid Market Without Conventionals—It is conceivable that, with proven reliability
and reasonable cost, hybrid engines become the new conventional transportation
technology. The EAU Hybrid Market Without Conventionals [HM(C)] Scenario, therefore,
examines the impact of phasing out (via a model constraint) all conventional vehicles in
2025.
Besides the EAU scenarios, futures associated with EPHE were analyzed in MARKAL. The
primary EPHE scenarios are: (i) Hydrogen Market, (ii) Optimistic Hydrogen Market, and
(iii) Hydrogen Forcing.
Hydrogen Market—This scenario looks at a future of about 10 percent penetration of
hydrogen fuel cell vehicles (FCVs). In this scenario, hydrogen FCVs are competitive with
hybrids. Larger class FCVs become available in 2015, while compacts become available in
2020.
Optimistic Hydrogen Market—The scenario assumes that, with sufficient funds to support
research, cost effective solutions can be found for hydrogen storage and for manufacturing
cheaper fuel cell stacks. Success in the fuel cell vehicle market encourages implementation
of the necessary infrastructure, speeding the transition to a hydrogen economy. The
Optimistic Hydrogen Market (H2O) scenario investigates this by using optimistic values for
FCV costs and efficiencies.
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Hydrogen Forcing—It may be that movement to a hydrogen economy is led by actions
which seek to improve air quality by requiring specific penetrations of environmentally
friendly technologies, such as FCVs. The Hydrogen Forcing (H2F) scenarios investigate
penetrations at 2030 ranging from 10 to 50 percent.
Results
Table 1 presents the results of the primary EAU and EPHE scenarios where results are
ordered by increasing penetration of hybrids for the EAU scenarios and by increasing
penetration of hydrogen FCVs for the EPHE scenarios. With the exception of the Double
Efficiency Hybrid scenario, the majority of the hybrid technology penetration by 2030 is
from the 3X technology. As shown in this table, for the EAU scenarios, hybrids have
significant penetrations under a scenario of high gas prices and when there is a fundamental
market change away from convent!onals.
Table 1. Technology Penetrations in 2030 for Evolution as Usual and Early Phase
Hydrogen Economy Scenarios
Scenario
Conventionals Hybrids FCVs
EAU
Conventionals Dominate
Hybrid Market
Double Efficiency Hybrids
Gas Price Variation
Hybrid Market Without Conventionals
100
87
85
36
3
EPHE
Hydrogen Market
Optimistic Hydrogen Market
Hydrogen Forcing
83
80
46
0
13
15
64
97
8
6
4
0
0
0
0
0
C)
14
50
For the EPHE scenarios, hydrogen FCV penetration is only significant by 2030 under a
forcing scenario. Without forcing, penetration is less then 15 percent even when optimistic
costs and efficiencies are assumed for the FCVs. These rates are partially explained by
FCVs not being available until 2015, leaving only 15 years for market penetration. In
moving to hydrogen FCVs, market share is taken both from the hybrids and Conventionals.
The impacts of the EAU and EPHE technology penetrations on transportation sector
emissions are shown in Table 2. Significant reductions are observed for scenarios with high
penetrations of hybrids and/or hydrogen FCVs.
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Table 2. Emission Reductions in 2030 for Evolution as Usual and Early Phase
Hydrogen Economy Scenarios Relative to Conventionals Dominate.
„ . PM10a COb NOXC VOCs"
Scenario „, „, „,
Hybrid Market
Double Efficiency Hybrids
Gas Price Variation
Hybrid Market Without Conventionals
Hydrogen Market
Optimistic Hydrogen Market
Hvdroaen Porcine
EAU
15
16
58
89
EPHE
19
22
55
<1
<1
3
7
10
15
50
5
5
41
70
9
12
50
10
11
35
50
25
32
59
a PM10 = PM with aerodynamic diameter < 10 |j,m.
b CO = carbon monoxide.
c NOX = oxides of nitrogen.
d VOCs = volatile organic compounds.
Conclusions
A scenario approach for technology assessment has been developed and demonstrated for
several future technologies in the transportation sector. The modeling approach adopted here
allows the rapid assessment of varying assumptions about the factors that influence
technology penetration. Although the emissions modeling presented here is preliminary, the
scenarios considered indicate that different technology development and penetration
scenarios may have greatly differing emissions consequences. Consideration of a broad
range of technology scenarios is, therefore, essential for a thorough evaluation of the
potential impacts of climate change on air quality.
Additional work is required to produce a full scenario analysis of the transportation and
electricity generation sectors out to 2050. This future work includes:
• continued database development and extension to 2050,
• documentation and release of the database,
• improvements to the representation of the transportation and electricity sectors,
evaluation of approaches for incorporating economic interactions,
development of a set of alternative technology futures,
sensitivity and uncertainty analysis, and
• integration with the ORD Air Quality Assessment.
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Section 1
Technology Assessment and Project Scope
1.1 Background
In 1990, the United States Congress enacted the U.S. Global Change Research Act creating
of the U.S. Global Change Research Program (USGCRP), which has the goals of
"understanding and responding to climate change, including the cumulative effects of
human activities and natural processes on the environment." (USGCRP, 1990) Thirteen
government agencies are part of the program, including the National Science Foundation,
the Environmental Protection Agency (EPA), the National Aeronautics and Space
Administration, the Agency for International Development, the Smithsonian Institution, and
the Departments of Commerce, Energy, State, Interior, Agriculture, Health and Human
Services, Transportation, and Defense. The activities of each of these agencies in support of
the USGRCP are described on the USGCRP website. (USGCRP, 2004)
In 2001, the President announced the establishment of the U.S. Climate Change Research
Initiative (CCRI). CCRI was developed to complement the USGRCP with the goal of
supporting policy makers in the short term through "the integration of scientific knowledge,
including measures of uncertainty, into effective decision support systems." To achieve this
goal, CCRI is focusing on reducing the uncertainties in climate science and modeling,
improving the monitoring and analysis of climate change signals, and improving resources
for supporting decision-making. (USGCRP, 2001)
The EPA's primary role under these programs is to develop an understanding of the
potential consequences of global change (and particularly climate variability and change) on
human health, ecosystems, and socioeconomic systems in the United States. This
information will support stakeholders and policy makers as they decide whether and how to
respond to the risks and opportunities presented by global climate change. A central
component of the EPA's activities is to examine the interactions between global climate
change and air quality.
Global climate change will likely result in changes in regional and local weather. These
changes in meteorology may affect air pollution levels by altering (1) rates of atmospheric
7
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chemical reactions and transport processes, (2) anthropogenic emissions, including adaptive
responses involving changes in fuel combustion for power generation, and (3) biogenic
emission rates from natural sources. To assess these potential changes, the Office of
Research and Development (ORD) is pursuing an Air Quality Assessment that will explore
the potential consequences of global change on tropospheric ozone and PM through the year
2050.
Within the last century, emissions resulting from human activities have contributed to
increased ambient concentrations of tropospheric ozone and PM and have necessitated
government environmental policies such as the Clean Air Act and its amendments (CAA).
(CAA, 2002) Although the CAA is intended to improve air quality in the present and into
the future, an unknown is the interaction between future emissions levels and the
temperature and meteorological changes associated with global climate change. Making
such an assessment requires the examination of a wide range of factors, such as changes in
land use, population size and demographics, air pollution control technologies, and energy
generation and use technologies. Since many of these factors are closely linked, a systems
analysis approach may be most appropriate.
Within ORD, the National Risk Management Research Laboratory (NRMRL), the National
Exposure Research Laboratory (NERL), and the National Center for Environmental
Research (NCER) are collaborating to conduct the Air Quality Assessment. NERL will
conduct regional air quality modeling for the year 2050 using the EPA Community
Multiscale Air Quality (CMAQ) model with climate change inputs from regional climate
model simulations. In order to perform this air quality modeling, an emissions inventory for
the year 2050 must be prepared. This work will be done jointly by NRMRL and NERL.
NRMRL's role in this collaboration is to identify future technological scenarios that will
influence future emissions. NCER will be obtaining input data to the entire analysis and will
be overseeing the modeling of climate change.
Many natural, economic, and technological factors must be considered in order to create
scenarios of projected emission values. In particular, changes in the technologies that
produce emissions can be expected to play a central role in driving future air quality. To
increase the precision of environmental forecasts, it is important to improve the
characterization of technological change. In this context, the National Research Council in
1999 (NRC 1999) identified characterization of the sources and processes of technological
change as one of the seven key research pathways for the USGCRP's human dimensions of
global environmental change research area.
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NRMRL's Technology Assessment and Co-control Team is carrying out the assessment of
technological change. Although technological change has played a central role in increased
emissions, it does not exclusively produce negative impacts. New, cleaner technologies may
ameliorate many current environmental problems or prevent future problems. For example,
the emergence of hybrid and fuel cell cars is expected to offset emissions from increased
transportation demand. Forecasting technological change, such as the penetration of hybrids
and fuel cells as well as the emergence of newer technologies, is thus an important aspect of
predicting environmental quality in the future.
Forecasting technological change is an inherently uncertain process, however.
Technological change is driven by many factors, including economic change, research and
development (R&D) level of effort and success rates, energy resource supply and price,
consumer preferences, and policy changes, none of which can be predicted with certainty.
Rather than defining a "best guess" future, TACT is pursuing a multidimensional,
scenario-oriented approach to the assessment of future technologies and patterns of
technology adoption. This scenario-based approach involves the development of a set of
alternative, plausible futures that seek to characterize the range of possible realizations of
the future.
TACT's work is focused on the transportation and electricity generation sectors since these
are the largest contributors to criteria pollutants. Transportation accounts for about 50
percent of emissions of the ozone precursors nitrogen oxides (NOX) and volatile organic
compounds (VOCs) and 25 percent of fine particulate matter (PM25). Electricity generation
accounts for 25 percent of NOX and about 6 percent of PM25. (U.S. EPA, 2000) Both of
these sectors are characterized by a wide array of possible future technologies with very
different and uncertain environmental, economic, and social implications. Although there
have been many reports from the national labs, university research programs, and trade
groups on the future of technologies in these sectors, none of these assessments have
systematically synthesized these many dimensions. Thus, the goal of TACT is to provide a
comprehensive technological assessment, identifying future technological scenarios and
facilitating the evaluation of these scenarios within ORD's Air Quality Assessment.
The time frame for completion of the Air Quality Assessment is 2010. TACT is in the
process of finalizing a methodology and demonstrating this methodology with preliminary
results. The methodology and preliminary results are being subjected to a peer-review
process to ensure that the approach taken is practical and defensible. TACT is also working
with other members of the ORD Air Quality Assessment team to plan and coordinate
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modeling activities.
This report presents TACT's approach for quantifying technological evolution and
highlights early results in the transportation sector. The scenarios presented here consider
the impacts of R&D developments, fuel prices, consumer preferences, and technology
policies on the penetration of technologies within the personal vehicles subsector. Future
work will further develop the analysis of the transportation sector, including an assessment
of the interaction between economic and technological changes, and expand to include an
analysis of the electricity generation sector. In addition, TACT will explore issues related to
sensitivity analysis and characterizing the effects of uncertainty on forecasts of
technological change.
1.2 Project Scope
Early on, TACT recognized that forecasting technological change in the transportation and
electricity generation sectors could not be done successfully if these sectors were treated in
isolation from the rest of the U.S. energy system. Competition from other sectors for the
same fuel resource supplies can impact the viability of a new technology, and
implementation of new technologies can have consequences in other sectors.
The potential penetration of new technologies is a function of both economic factors (e.g.,
supply, demand, and pricing) and non-economic factors (e.g., environmental benefits, local
and national legislative actions, social and political concerns). Thus, in developing
technological assessments, it is important to include some consideration of both categories
of factors.
Finally, TACT realized that to adequately combine all these considerations, an approach
must be defined which allows the identification and evaluation of a range of plausible
futures. Several viable technological paths can be chosen from this set for use in the ORD
Air Quality Assessment. These technological paths will be selected such that they represent
the range of potential plausible outcomes and so that they are internally consistent with
respect to assumptions about future economic, social, political, and technological
developments. TACT will use a model of the U.S. energy system to develop these scenarios.
The scenarios will characterize potential technological futures through the year 2050. In
addition, TACT will develop and execute plans for examining the sensitivities of outcomes
to various assumptions, as well as the effects of uncertainties on technological forecasts.
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To model the U.S. energy system, TACT is using MARKAL, a well-established energy
system model. MARKAL provides a framework, called the reference energy system (RES),
for organizing performance, cost, use, and constraint data for all current and future
technologies in the energy system being modeled.
Since MARKAL is a least-cost optimization model, it is capable of selecting those
technologies that most cost-effectively meet demand and emissions constraints. This
information is useful in identifying if or when in the future specific technologies are
expected to penetrate their markets based on economic considerations. The effect of various
assumptions and policies such as financial incentives can also be explored to evaluate their
effect on the economics of new technologies. MARKAL also includes emissions data for
relevant technologies. MARKAL modeling is discussed in more detail in Section 2.
Overall, the TACT team's objectives for MARKAL modeling include:
• developing the U.S. reference energy system representation in the MARKAL model,
• determining which future technologies will be considered for scenario analysis and
gathering and incorporating the necessary MARKAL data,
• determining economically plausible future technology scenarios, and,
• determining the pollutant emissions from those scenarios.
Results of this work will be applied for two purposes. In the longer term, MARKAL outputs
of technology penetrations will be used by other groups in ORD's Air Quality Assessment
for the next phases of that work. In the immediate term, the scenario runs will be used to
support discussions of the system-wide impacts of technology choices on emissions. In
addition, the scenarios can be paired with other modeling works to help elucidate the
plausible storylines that yield outputs comparable to those earlier works.
TACT's focus on characterizing technological change in the transportation sector will
continue through 2004. Alternative fuels and vehicle designs will be investigated to
determine their influence on emission rates, and the time profile for the market penetration
of these technologies will be determined. Characterization of public transit and freight
technologies will also be improved. For the outputs needed from this work in 2006, there
will be a greater emphasis on electricity production. Changes/improvements in fossil fuel
electricity generation, alternative electricity generation technologies, and market penetration
of these technologies will all be examined and incorporated into emissions modeling.
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The sections that follow delineate TACT's approach to technology scenario analysis and
describe some early, illustrative results in the personal vehicles sub-sector:
Section 2 describes the general approach used by TACT to model the U.S. Energy
System. The section includes a more detailed description of MARKAL modeling,
the approach to generating scenarios, and preliminary ideas related to conducting
sensitivity and uncertainty analyses.
• Section 3 describes the work that has been carried out to characterize personal
vehicle technologies and to forecast the use of these technologies through the year
2035.
• Section 4 focuses on efforts to incorporate hydrogen-powered fuel cell vehicles and
the related infrastructure into the model. This discussion illustrates how new
technologies can be integrated into the U.S. Reference Energy System.
Section 5 applies the technologies described in Section s 3 and 4 to identify future
technological scenarios. In particular, these scenarios examine factors that affect the
adoption of hybrid and fuel cell powered personal vehicles.
• Section 6 presents MARKAL modeling results for the scenarios specified in Section
5. The discussion illustrates how MARKAL results can be interpreted to better
understand the various interactions and drivers for technological change.
Section 7 discusses future activities, including improvements to the representation of
the transportation and energy sectors; extension of the database to 2050; continued
database development, documentation, and release; the development of a set of
alternative technology futures; and sensitivity and uncertainty analysis.
Two appendices are included to provide more details about various aspects of the project.
Appendix A focuses on modeling with MARKAL, including a discussion of the MARKAL
representation of the RES. Appendix B discusses MARKAL database development,
peer-review, and calibration.
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Section 2
General Modeling Approach Using MARKAL
This section provides an introduction to the MARKAL model. Topics that are discussed
include an overview of economic models for air quality assessments, a description of
MARKAL, a description of the development and calibration of an EPA U.S. MARKAL
model, an overview of the scenario-based approach that is taken in MARKAL modeling, a
discussion on the incorporation of future technologies, and a description of the assessment
of the emissions consequences associated with technological change.
2.1 Economic Modeling lor Air Quality Assessment
Economic models have been used extensively in the context of global climate change
assessment. These applications typically have involved the projection of future green house
gas emissions by modeling the effects of economic sector growth and anticipated
technological changes. Weyant (2000) and Edmonds et al. (2000) provide reviews and
comparisons of these applications. Economic models have also been used in regulatory
applications. For example, the EPA's Office of Air Quality Planning and Standards uses
data from the Regional Economic Models, Inc. economic model in conjunction with the
Economic Growth Analysis System emissions projection system to forecast future emissions
of criteria air pollutants. This approach has been applied to project emissions through the
year 2020. In contrast, ORD's Air Quality Assessment must evaluate pollutant emissions
through the year 2050.
A variety of factors differentiate the economic models used in climate change assessments.
These factors include are discussed below.
Representation of technologies (top-down or bottom-up)—Bottom-up models explicitly
represent energy-using technologies. Each technology is characterized by information such
as capital costs, operations and maintenance costs, energy inputs, outputs to meet various
demands, emissions, efficiency, and lifetime. A bottom-up model uses this information to
make technological selections into the future based upon criteria such as cost-effectiveness
and constraints on availability. Top-down models, in contrast, model supply and demand
within and across economic sectors. These models often include assumptions about
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technological improvement, but do not represent technologies explicitly. Top-down models
can also include factors, such as household savings and investment in research and
development, that typically are not incorporated into bottom-up models.
Scope (single-sector or multi-sector)—Some economic models may represent only one
sector of the economy. For example, the Integrated Planning Model (IPM) represents only
the electricity sector (Clean Air Markets, 2004). IPM is not able to capture the effects of
electricity prices on non-electricity sector technological decisions. Further in contrast, some
economic models represent multiple sectors or may even attempt to represent all relevant
sectors. Representation of multiple sectors allows the interaction among those sectors to be
evaluated.
Time horizon (short term or long term)—Models that have been used in regulatory
applications often extend only to the year 2020 or 2030. Models used in the assessment of
global climate change typically must have a much longer horizon, extending to the year
2050 or further.
Geographic resolution (global, national, multi-regional, or regional)—The geographical
scale of economic models can differ greatly. For example, global models often represent the
interactions among the economies of different countries but are not able to provide results at
a sub-country level. In contrast, a national model may ignore economic interaction with
other countries or may represent those interactions very simplistically.
Incorporating feedbacks on demands (static or elastic)—Economic models often produce
estimates of energy prices (e.g., dollars per gallon of gasoline). In reality, changes in prices
will likely result in changes in demand. Although top-down models are more likely to
include such elasticity relationships, this information can also be incorporated into
bottom-up models.
Problem representation (linear or nonlinear)—Economic models may be linear or nonlinear.
Linear models represent the relationships among various factors in the model with linear
equations. Nonlinear models allow much more complicated representations of interactions.
Linearization has the potential to over simplify the modeled relationships, losing the ability
to account for economies of scale and adding uncertainty to model predictions. Linear
models have advantages, however; they are more readily solved, the results are often more
transparent, and data collection requirements are simplified. Further, linear representation
may not be worse than a nonlinear representation when the available data are not sufficient
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to provide a good characterization of the necessary nonlinear functions.
Solution procedure (simulation or optimization)—Economic models are typically either
simulation models or optimization models. The goal of a simulation model is to describe
some phenomenon, which is energy system behavior in this case. In the context of an air
quality assessment, a simulation model would forecast future emissions. Optimization
models, in contrast, are often used as prescriptive models. These models typically include an
objective (e.g., minimize cost) and a set of constraints. A solution procedure is used to
identify the solution that best meets these criteria. An optimization model might be used to
identify what one should do (e.g., the technological mix that is expected to most
cost-effectively meet an emissions reduction). It should be noted that optimization models
potentially could be used in a descriptive sense by constraining the various decision
variables. Also, the objective of minimizing cost can be interpreted as driving a simulation
toward an economically feasible, cost-effective solution.
Given these various factors, TACT was interested in identifying a model with the following
characteristics:
• a bottom-up approach such that technological changes can be characterized
explicitly,
• the flexibility to be used at various national or regional scales,
a flexible time horizon that facilitates use in ORD's global climate change air quality
assessment,
• an optimization-based structure such that various objectives and constraints could be
explored,
• a track record of successful applications,
• an active user community that could be tapped for feedback and support,
a transparent structure in which assumptions and the processes driving analysis
results are readily apparent,
• the ability to share the model (at low or no cost) with other interested parties, and,
• the ability to run the model in-house.
This last characteristic is important because the process of developing and calibrating a
model is inherently an iterative process, with the modeler learning more about the problem
as it is being modeled and tested. These iterations often can be carried out in a more timely
and effective manner if carried out in-house.
Given these various goals, TACT made the decision to use the MARKet ALlocation
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(MARKAL) model for ORD's Air Quality Assessment (ECN, 2004). MARKAL is a
bottom-up, linear, optimization-driven model that is non-proprietary, easily distributed,
widely used, transparent, and that has an active user community. Although available
MARKAL data at the onset of the project was not regionalized, did not extend to 2050, and
did not include the emissions of criteria pollutants, the model itself is highly flexible and
supports such modifications. Also, while the base version of MARKAL does not support
elastic demands, MARKAL has been extended by various parties; a version called
MARKAL-Elastic Demand and MARKAL-PE include elastic demands, and
MARKAL-MACRO provides interaction with a macro-economic model of the economy.
2.2. Description of MARKAL
MARKAL was developed in the late 1970s at Brookhaven National Lab in response to the
oil crisis. In 1978, the International Energy Agency adopted MARKAL and created the
Energy Technology and Systems Analysis Program (ETSAP). ETSAP is a group of
modelers and developers that meets every six months to discuss model developments,
extensions, and applications. MARKAL therefore benefits from an unusually active and
interactive group of users and developers, adding substantially to its credibility. MARKAL
is currently in use by more than 40 countries for research and energy planning. In addition,
the EIA recently adopted the MARKAL framework as the basis for its System for the
Analysis of Global Energy Markets (SAGE) model. SAGE is used to produce EIA's annual
International Energy Outlook.
MARKAL is a data-driven, energy systems economic optimization model. The user inputs
the structure of the energy system to be modeled, including resource supplies, energy
conversion technologies, end use demands, and the technologies used to satisfy these
demands. The user must also provide data to characterize each of the technologies and
resources used, including fixed and variable costs, technology availability and performance,
and pollutant emissions. MARKAL then uses straightforward linear programming
techniques to calculate the least-cost way to satisfy the specified demands, subject to any
constraints the user wishes to impose. Outputs of the model include a determination of the
technological mix at intervals into the future, estimates of total system cost, energy demand
(by type and quantity), estimates of criteria and greenhouse gas (GHG) emissions, and
estimates of energy commodity prices.
The basis of the MARKAL model framework is a network diagram called a reference
energy system, which depicts an energy system from resource to end-use demand (Figure 1).
The RES divides an energy system up into four stages. The three technology stages
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represented in MARKAL are resource, transformation, 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 miles traveled. Energy carriers interconnect the stages.
Resource
technologies
Process
technologies
Conversion
technologies
Demand
technologies
End-use
demand
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Figure 1. An Example of a Simple Reference Energy System.
The first technology stage, resource technologies, represents all flows of energy carriers into
and out of the energy system. These include imports and exports, mining and extraction, and
renewable energy flows. The second technology stage, transformation technologies, is
subdivided into two classes: conversion technologies, which model electricity generation,
and process technologies, which change the form, characteristics, or location of energy
carriers. Process technologies include oil refineries, hydrogen production technologies, and
pipelines. The final technology stage, demand technologies, are those devices that are used
to directly satisfy the final RES stage, end-use service demands. Demand technologies
include vehicles, furnaces, and electrical devices.
Energy carriers are the various forms of energy consumed and produced in the RES and can
include coal variants (e.g., with different sulfur content), crude oil, refined petroleum pro-
ducts, electricity to different grids, and renewable energy (e.g., biomass, solar, geothermal,
hydro). The model requires that the total amount of energy produced be at least as much as
that consumed. The various technologies in a MARKAL model are inter-connected by
energy carriers flowing out of one or more technologies and into others.
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The MARKAL RES concept offers a significant enhancement over single sector energy
technology models because it allows technologies and sectors to interact through the
interconnections in the RES. For example, a technology that relies heavily on natural gas for
fueling transportation technologies may shift the relative prices of fuels to the commercial,
industrial, and residential sectors, potentially leading to a shift away from natural gas for
some end uses. However, this means that even though TACT is currently only assessing
technologies for transportation and electricity production, the RES database describing all
significant end-use sectors, as well as all necessary upstream resource supplies and
technologies, is needed.
2.3 Developing the EPA's U.S. MARKAL Model
The first objective in developing an EPA U.S. MARKAL model was to develop a database
describing the RES. For the EPA U.S. MARKAL model, the database is being developed to
describe technologies and end-use demand for the sectors of resource supply, transportation,
commercial, residential, industrial, and electricity generation. For each technology
represented in these sectors, the values for data parameters describing the technology's cost,
technical performance, and availability are being obtained. A full listing of MARKAL data
parameters appears in Appendix A. This section briefly describes the database development
process. Further details appear in Appendix B.
The EPA U.S. MARKAL database is developed from a MARKAL database produced in
1997 by Brookhaven National Laboratory for the U.S. Department of Energy (DOE). All
sectors have been thoroughly revised and updated, although the original values were
maintained for several technologies that were outside this study's focus areas. Wherever
possible, data for updating the RES database have been drawn from DOE's AEO 2002 and
the input data to NEMS runs used to produce the AEO 2002.
AEO data were selected for the RES because it is a nationally recognized source of
technology data and widely used where reference or default data are required. It presents
mid-term forecasts of energy prices, supply, and demand. The projections are based on
results from EIA's NEMS and are based on federal, state, and local laws and regulations in
effect at the time of the model run. (U.S. EIA, Site 1) Where AEO data were not available in
a form appropriate to the MARKAL RES needs, RES data were derived from other widely
recognized authoritative sources. In every case, including the use of AEO data, the data
source has been well documented with the original data source readily available to those of
interest.
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In the transportation sector, personal vehicle technology data were drawn from the DOE's
Office of Transportation Technologies (OTT) Quality Metrics (QM) assessment. QM
describes the analytical process used in estimating future energy, environmental, and
economic benefits of DOE's Energy Efficiency and Renewable Energy (EE/RE) programs.
QM has been an active annual DOE EE/RE-wide analysis and review procedure since 1993.
(U.S. DOE, 2002) Section 3 presents a list of the personal vehicle technologies extracted
from the QM report. Two additional vehicle technology characterizations were derived from
a report titled Technical Options for Improving the Fuel Economy of U.S. Cars and Light
Trucks 2010-2015 (DeCicco et al., 2001).
Data for the electricity sector were drawn from NEMS with supplemental data pulled from
the Electric Power Research Institute (EPRI) Technical Assessment Guide (TAG). The TAG
is a standard reference work for the energy industry that characterizes key electric
generation technologies and their operation, costs, environmental impacts, etc.
At the time of this report, the representation of the industrial sector in the RES is still under
development. Currently, the energy consumption from this sector is constrained to values
derived from the AEO 2002 (future updates will incorporate AEO 2004 figures). Unlike
other sectors, this sector is therefore presently unable to respond to changes in energy prices.
Ongoing efforts to develop the industrial sector representation are centered on adapting the
characterization used in EIA's SAGE model . This characterization describes six energy
services within each of six industrial sectors. Additional documentation will be provided
when this sector's development work is complete.
The database is divided into time periods of equal length of 5 years per period. The current
database runs from 1995 to 2035. The eventual end-point of the database will be extended to
2055 in future work. Note that although results are needed in 2050, 2055 has been chosen as
the end-year in order to eliminate possible end-effects for year 2050 which may occur in the
model at the end-point year. Determining how to extend the database most appropriately out
to the 2055 is an important step in the next phase of the project.
As each sector of the model is completed, data characterizing the associated technologies
have been peer-reviewed for appropriateness of the data source, completeness of the
technology options, and correctness of the methodology in converting the data from the
original source to the MARKAL inputs. A separate document is under development that
discusses the comments from the reviewers and the responses/actions resulting from these
comments. That document will be a subsection of full database documentation provided to
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the future users of TACT MARKAL database. After assembling a complete representation
of the energy system, the model was calibrated against the AEO 2002 report. This process is
discussed in Appendix B. The goals of the calibration were to (i) ensure that the model was
producing reasonable results, given its input assumptions, (ii) determine whether the model
was providing a plausible, consistent representation of the key features of the U.S. energy
system, (iii) identify why the differences exist in cases where our results differ from AEO
results, and, (iv) identify any significant errors in the construction or characterization of the
RES. It should be noted that an exact calibration of MARKAL to the AEO is not practical or
desirable since the models are very different in structure and purpose. In addition, the AEO
calibration underlies all scenarios, and therefore should not be construed as a reference case.
2.4 Using MARKAL to Develop Technological Scenarios
Scenarios are images of alternative futures. They are neither predictions nor forecasts.
Rather, each scenario is one alternative, internally consistent depiction of how the future
may unfold, given assumptions about future economic, social, political, and technological
developments as well as consumer preferences. A set of scenarios assists in the
understanding of possible future developments of complex systems.
Scenarios explore plausible futures by using models to generate an outcome or set of
alternative outcomes consistent with a set of motivating assumptions, sometimes called a
storyline. This procedure allows the consequences of varying sets of plausible assumptions
to be assessed. No attempt is made to calculate every possible future with this procedure,
nor is there an attempt to assign likelihoods to alternative outcomes. Instead, the intent is to
construct a set of scenarios that together cover the range of plausible futures. The process of
developing, evaluating, and comparing a set of scenarios assists analysts and
decision-makers in understanding the range of possible futures, how these possible futures
are similar or different, and the drivers that may lead to each.
A scenario-based approach is particularly appropriate for assessments involving a long time
horizon, such as assessments linked to global climate change. Technology developments are
difficult to project over such horizons. Over a period of decades, it is not possible to predict
which technologies will achieve fundamental breakthroughs and which will not. As a result,
it is inappropriate to use the simple extrapolations that are conventionally applied in
shorter-term energy futures analyses. In both the transportation and electricity generation
sectors, several alternative potential technology trajectories that can be envisioned today
diverge greatly from current standard technologies in very different ways. Changes in
economic structures, consumer preferences, resource supplies, and other variables similarly
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lead to inherent unpredictability.
TACT plans to address the many uncertainties surrounding future technological
development in the transportation and electricity generation sectors by using the scenario
approach. Each scenario will be a MARKAL run satisfying an alternative, plausible set of
assumptions and meeting the demands of the U.S. energy system across the model time
horizon. Through the scenario assessment process, MARKAL will allow TACT to identify
the specific changes in assumptions that cause the model to switch from one technology
trajectory to another. Similarly, using techniques called modeling to generate alternatives
(MGA) will allow evaluation of the range of potential outcomes for any given set of
assumptions. This will provide some useful information about the range of possible results
that can be expected. Together, these approaches will help develop and evaluate a set of
scenarios that represents the range of possible technology futures.
Results from a selected set of these scenarios will serve as input to the ORD Air Quality
Assessment. In addition, the scenario runs will be used to present discussions of the
system-wide impacts of technology choices on emissions of criteria air pollutants through a
variety of papers and reports.
TACT will also explore several alternative approaches for evaluating the effects of
uncertainty on MARKAL outputs. One such approach will be to evaluate the MARKAL
outputs for sensitivity information. Since MARKAL is a linear programming model, outputs
called shadow prices and reduced costs are produced automatically. These provide valuable
sensitivity information such as amount a constraint would need to be modified before the
technological selections produced by MARKAL would change. The use of Monte Carlo
simulation or similar techniques will also be explored to propagate uncertainties in
MARKAL inputs through the model to obtain estimates of uncertainties in model
predictions. Development of a plan for examining uncertainty will be one of the objectives
in the next phase of this work.
2.5 Future Technologies for Scenario Analysis
To evaluate various technological pathways to 2050, future technologies for the
transportation and energy production sectors are being added to the MARKAL database.
Specific technologies in the transportation sector include FCVs, hybrid vehicles, biofuels,
and hydrogen fuel. Specific technologies for the electricity production sector will be
selected in FY'04. This report focuses on a subset of technologies for the personal vehicles
sub-sector, including gasoline internal combustion engine (ICE) and hybrid vehicles,
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hydrogen FCVs, and associated hydrogen fuel infrastructures.
The literature is being reviewed to characterize these potential future technologies and
determine the range of plausible future values for the key MARKAL parameters such as
capital and operating costs and technology efficiencies. Section 3 describe the technologies
considered in this report and the ranges of values discovered in the literature.
2.6 Emissions Consequences
An important capability of MARKAL is the ability to estimate the emissions that result from
the various activities represented in the RES. MARKAL has the capability to estimate both
the emissions of criteria pollutants as well as GHG emissions. The emissions factors used
within MARKAL were recently updated, and these new factors have been used in the results
presented later in this report.
Vehicle emissions depend on fuel, propulsion technology (e.g., ICE or fuel cell), emissions
control devices, and vehicle age (cumulative miles traveled) through degradation of control
equipment. Emissions from existing vehicles that make up the fleet at the model start year
will also change over time due to the earlier retirement of older, more polluting vehicles.
Emissions factors for existing vehicles were calculated from actual 1995 light-duty vehicle
fleet emissions based on the 1999 EPA National Air Quality and Emission Trends Report
(U.S. EPA, 2000). Vehicle stock turnover and annual vehicle miles traveled (VMT) by
vintage were calculated based on information from the Energy Information Administration
(EIA 1998, 2003b) and the Transportation Energy Data Book, Edition 21 (Davis, 2001).
Degradation estimates were based on a variety of sources depending on the pollutant,
including the EPA Federal Test Procedure, EPA's Mobile 6 model (U.S. EPA, 1999), and
the American Council for an Energy Efficient Economy (ACEEE) Green Book methodology
(DeCicco, J. andKliesch, J., 2001).
For new ICE and hybrid vehicles, emissions factors were based on standards specifications
for Tier 1, low emission vehicles (LEV), ultra low emission vehicles (ULEV), super ultra-
low emissions vehicle (SULEV), and Tier 2 compliant vehicles (U.S. EPA, 2000a). For Tier
2 compliant vehicles, emissions factors were derived from the Greenhouse Gases, Regulated
Emissions, and Energy Use in Transportation (GREET) model, developed by the Argonne
National Laboratory (ANL,2001). All hybrid vehicles were assumed to be SULEV
compliant. For all other ICE vehicles, a mix of compliance levels was assumed based on
national and state regulations. Degradation estimates were based on the Mobile 6 model,
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EPA's Tier 2/Sulfur analysis (U.S. EPA, 2000), and the ACEEE Green Book methodology.
In addition to providing emissions estimates, including emissions factors also facilitates
investigations such as the determination of technological scenarios that most cost-effectively
meet emissions reduction targets, maximize emissions reduction, or allow emissions trading
between sectors.
The next two sections provide details about alternative automobile technologies, such as
advanced ICEs, hybrids, and hydrogen fuel cells and technologies for implementing a
hydrogen infrastructure. These sections provide a template for how additional vehicle and
energy technologies will be incorporated into MARKAL. A variety of scenarios have been
developed and evaluated involving these technologies.
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Section 3
Transportation Technologies
This section describes the transportation technologies considered in TACT's scenario
analysis. Following a brief description of the vehicles in the RES database, the technologies
highlighted in the scenario analysis are described in detail. These include hybrid gasoline
electric vehicles, FCVs, and hydrogen fueling infrastructures. A description of each of the
technologies is given, followed by consideration of issues that may affect adoption of the
technologies and the potential emissions implications of technology adoption.
Table 3 is a list of the technology types available as personal vehicles in the EPA MARKAL
RES database. For future technologies, the first year of their availability is also shown. Most
of these technologies are derived from OTT's Quality Metrics report. Two additional
technologies, representing conventional internal combustion engine vehicles with
"packages" of efficiency improving technologies, are taken from DeCicco et al. (2001). The
existing gasoline and diesel fleet phases out linearly over 15 years and is unavailable to fill
new demand. MARKAL chooses a least cost mix of new technologies to fill demand as
existing vehicles retire, dependent on the scenario input assumptions.
Table 3 Personal Vehicle Technologies.
Technology
Existing gasilone
Existing diesel
Conventional
Moderate MPG3
Advanced MPG
Advanced diesel
Electric
2X hybrid
3X hybrid
flex ethanol
Gasoline fuel cell
Fuel cell
a MPG = miles per gallon.
Description
Existing auto fleet
Existing fleet (light trucks only)
Gasoline powered
8-16 mpg more than Conventional
14-23 mpg more than Conventional
8-12 mpg more than Conventional0
Electric powered
100% mpg increase over Conventional
200% mpg increase over Conventional
Fuel is E85 (gasoline/ethanol) or gasoline
FCV powered from gasoline reformer
FCV powered directlv with hydrogen
Year Available
In place
In place
2000
2010
2010
2005
2005
2005
2015
2005
2010
2020
b Increase varies with car class. Lower end of the range is for pick-up trucks.
c Advanced diesel is available for all classes. Lower end of the range is for pick-up trucks.
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Five classes of personal vehicles are represented: compacts, full-size, minivans, pick-up
trucks, and sports utility vehicles (SUVs). Market shares for these classes in the RES
database are fixed at their 2000 sales share levels: 25, 27, 7, 20, and 21 percent,
respectively. The lifetime for all vehicles is set to 15 years. Average VMT per year per
vehicle are fixed at their 1995 values of 11,203 for cars and 12,018 for trucks and SUVs. All
data from Transportation Energy Data Book, Edition 21 (Davis, 2001). Average VMT per
year values are used to convert vehicle fixed (capital and operating) costs into MARKAL
units of dollars per billion VMT per year capacity. Allowing these values to increase over
time would tend to shift model solutions towards more efficient vehicles, as variable costs of
operation would be increased relative to fixed costs.
3.1 Hybrid Vehicles
Because of their low additional capital costs, ability to make use of the existing gasoline
fueling infrastructure, and improved efficiency over conventional vehicles, hybrid
technologies are expected to compete for market share in the future. Thus, the representation
of hybrid vehicles in MARKAL is critical. Hybrid technologies and the approach for those
technologies into MARKAL are discussed in this section.
3.1.1 Description of the Technology
Electric vehicles have long been touted as a way to minimize highway pollution, but their
acceptance has been limited by performance issues and limited range. Recent improvements
in electric motors and electronic controls have helped the performance issue, and battery
improvements have helped the range issue. Nonetheless, the weight of batteries with
sufficient storage to allow an acceptable range between charges reduces the vehicle's
performance and efficiency.
Hybrid vehicles, sometimes called hybrid electric vehicles, or HEVs, are a blend of the
technology provided by ICE and electric motors. The range issue is eliminated by using an
efficient ICE to keep the batteries charged. Regenerative braking systems complement the
engine's capacity to recharge the batteries by recovering the energy normally lost as heat.
Generally, the ICE component of HEVs is a much smaller engine than would be needed to
produce all of the energy needed to power the vehicle. It is used mainly to keep the batteries
charged. The electric motors associated with the batteries produce the torque needed for
better performance than would be realized by the small engine. Further, since the engine is
not the sole source of power for the vehicle, it can be operated at conditions more amenable
to efficiency and lower pollution (e.g., optimal revolutions per minute).
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Figure 2 illustrates a typical HEV showing the primary components: internal combustion
engine (1), transmission (2), electric motor (3), electronics (4), fuel tank (5), and batteries
(6).
Figure 2. Typical Hybrid Vehicle Configuration.
3.1.2 Design Considerations
There are three major design considerations common to all HEVs:
• Propulsion system configuration ( series or parallel),
• Power unit (combustion engine), and
• Energy storage system.
Propulsion system—The combustion engine and electric motors of HEVs can be configured
in either series or parallel. In a series configuration, an electric motor is the only means of
driving the wheels. The electric motor gets its power either directly from the battery pack or
from a generator powered by an engine in much the same way as a portable generator.
Electronic controls determine how power to the motor is shared between the battery and the
engine/generator set. Since there is a direct connection of the motor to the wheels, there is
no need for a complicated transmission and clutching system, thereby reducing weight. It
also allows the engine to operate at optimal conditions since it is used only to keep the
batteries charged. It also allows the use of non-conventional engine types such as turbines,
Stirling engines, or any other means of driving the generator. A disadvantage of the series
configuration is cost. Today's ICEs are relatively inexpensive per unit of power compared to
the modern batteries, generator, and electric motors used in HEVs. Battery packs must be
27
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larger in the series configuration.
Series hybrids show their greatest advantages under relatively slow, stop-and-go driving
conditions. Here, the advantage of the high torque at low speeds outweighs the need for
efficiency at cruising speeds. Because of this, the series hybrids currently under
development are primarily for busses and other heavy duty urban vehicles.
In parallel HEVs, both the engine and the motor can drive the wheels. This ability to switch
adds complexity to the HEV by requiring a transmission and generally a clutching system.
The engine in parallel HEVs is larger than in the series configuration since it does a greater
portion of powering the vehicle, but the battery packs are smaller. Since engines are
currently less expensive than batteries and motors, these tradeoffs are cost-effective. This
cost advantage will diminish as battery and motor costs come down over time. The
automobile shown in Figure 2 is a parallel HEV.
The parallel configuration is also more suited to highway driving. Both the Honda Insight
and the Honda Civic Hybrid are parallel HEVs.
Though series and parallel are the two general classifications of HEV configuration, there
are several modifications of the two designs. One is the "split" drive train where the engine
drives one set of wheels and the electric motor drives the other. Using this configuration the
vehicle can operate in 4-wheel drive mode or can switch from engine to motor as conditions
warrant.
The Toyota Prius uses a "series/parallel" drivetrain. With this configuration, the vehicle
operates in either series or parallel mode depending on driving conditions. This requires a
coupling of the two systems using a "power split device" and computerization to determine
the series/parallel choice. This configuration incorporates some of the best of both
series/parallel configurations, but at a cost. It has the higher cost of the series configuration
because of the larger battery pack and the need for a generator, and has the added
complexity of the parallel configuration. It also requires more computing power and
electronic controls.
Power unit (combustion engine)—Conventional spark ignition engines are used almost
exclusively in today's HEVs. However, compression ignition direct injection (CIDI) or
"diesel" type engines can be used just as well and would add additional efficiency to the
HEV package. If the HEV has a series configuration, practically any type of engine can be
28
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used, including gas turbines, Stirling engines, Atkinson engines, fuel cells, etc. The ability to
use non-conventional engine types introduces the potential for non-conventional fuels (e.g.,
biomass-derived fuels). Concerns, however, have been expressed about whether CIDI
engines will be able to meet planned EPA emission regulations (Ball, 2003).
Energy storage system—Battery packs are used exclusively to store energy in today's
HEVs. These can add considerable weight to the vehicle with a consequent reduction of
efficiency. The primary considerations for batteries are (DOE, 2003a)
• High specific energy (weight-to-energy ratio),
• High peak and pulse-specific power,
• High charge acceptance (for regenerative braking systems),
• Long calendar and cycle life,
• Recycleability, and
• Abuse tolerance (safety).
The most common automotive battery today is the lead-acid storage battery found in
practically all cars. This design is sufficient for the normal electrical needs of the
conventional automobile. There also is a well-established infrastructure for manufacture and
recycling. However, lead-acid storage batteries are heavy (low specific energy), have poor
low temperature performance, and relatively short calendar and cycle life. Also, the strong
sulfuric acid integral to this technology presents safety concerns in the event of car crashes.
Because of these disadvantages, alternate battery designs have been pursued for HEVs.
The HEVs currently on the road in the largest numbers, Toyota Prius and Honda Civic and
Insight, use sealed Nickel-Metal Hydride (NiMH) modules. The NiMH battery pack is
designed to be recharged tens of thousands of times and provides potentially significant
safety advantages because the ingredients are sealed in a carbon composite case and are
essentially inert, nonflammable, and noncaustic. Disadvantages of NiMH batteries include
high cost, high self-discharge, and low individual cell efficiency. Other battery technologies
such as lithium ion, lithium polymer, and nickel cadmium also have potential for HEVs but
will require additional development to bring down cost and to mitigate other disadvantages
of the technologies.
Other interesting potential technologies for energy storage systems are ultracapacitors and
flywheels. Ultracapacitors have a higher specific energy than batteries and can deliver
strong pulses of power. They may find an application in recovery of braking energy and for
power assist during passing or hill climbing. Flywheels have the potential to store kinetic
29
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energy (e.g., from braking), are free of hazardous materials, and are not affected by
temperature extremes. However, flywheels have a low energy density. More research will be
needed to integrate these technologies into mainstream HEVs (DOE, 2003b).
3.1.3 Issues for Implementation
Several issues affect the potential for HEV adoption. These include cost and performance,
consumer acceptance, fuel infrastructure, and fuel diversity and security.
Cost and performance—The initial cost of an HEV may be a deterrent to penetration of the
technology. However, the cost penalty is expected to decrease with time as the component
technology improves and as experience with manufacturing and operation continues to
grow. Figure 3 shows estimates from various sources of HEV cost versus conventional
vehicles for the years 2005-2035. The high estimate of 36 percent is from one source that
assumes the cost of early retirement of unamortized equipment, tooling, and engineering in
2010 (Sierra, 1999).
2005
2010
2015
2020
2025
Ref 4
Ret 3
Ref. 2
Ref 5
Ref 1
Ref. 3
Ref. 1
Ref. 6
Ref. 4
Ref.
1. U.S. DOE, 2003
- 2 EPRI, 2001
3 OTA. 1995
4. Greene & Scnafer, 2003
5 Sierra. 1999
6. Friedman. 2003
0 5 10 15 20 25 30 35 40
Cost Premium over Conventionals, %
Figure 3. Range of Cost Premiums for HEVs.
Increased fuel economy over conventional vehicles will partially offset the higher initial
cost of the vehicles. Figure 4 shows a comparison of fuel efficiency ranges reported for full
size HEVs and the expected improvements. Fuel economy reported in Figure 4 came from
30
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the Research Triangle Institute (RTI, 2002). The RTI data represent a compilation of data
from various published sources.
2005
2010
2015
2020
2035
10 20 30 40 50
Miles per gallon
60
70
80
Figure 4. Range of Fuel Economies for HEVs.
Consumer acceptance—An issue affecting the adoption of all new vehicle technologies is
the degree to which consumers will accept the new technology. Because HEVs refuel in the
same way as conventional vehicles and use existing fuels (gasoline or diesel) and the
existing fueling infrastructure, HEVs are expected to require little or no change in consumer
behavior. This factor will tend to ease consumer acceptance relative to other new vehicle
technologies.
J. D. Power and Associates estimates that hybrid sales will climb to 500,000 shortly after
mid-decade when five automakers are selling them. In a survey of 5,200 new car buyers,
Power found that 60% would "definitely" or "strongly" consider buying a hybrid (JDPA,
2002).
Fuel infrastructure—The fact that HEVs utilize existing fuels and fuel infrastructures also
avoids the need for expensive investments in fueling infrastructures, an important
consideration for hydrogen fuel cell vehicles.
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Fuel diversity and security—Today's HEVs do not provide the option of using electricity
from the grid to recharge the batteries. However, the technology for switching to a plug-in
variety of HEVs exists and could be implemented. This could be an "add-on" option by
leaving space for additional battery capacity and wiring harnesses for the additional
controls. The plug-in option would allow these vehicles to take advantage of the fuel
diversity in the electricity generation sector, providing fuel security and reduced dependence
on imported petroleum. Consumers would also be able to respond to fluctuations in gasoline
and electricity prices.
In a study of consumer preferences, EPRI found that the majority of participants preferred
charging the vehicle on their own premises until the costs and benefits were explained.
Then, the preference for "plugging in" varied with price and other key attributes. The EPRI
study further showed that 35% to 46% of the respondents who drive a mid-sized vehicle
would choose an HEV over a conventional vehicle and that the market potential is sensitive
to price (EPRI, 2001, p. xxiii). The EPRI study noted that tax credits and other incentives
could offset much of a consumer's purchase and life cycle costs of HEVs (EPRI, 2001,
p.2-21).
A related convenience brought about by the marriage of electricity to conventional fuel in
HEVs is that power can be supplied as well as consumed by the vehicle. At least one
existing HEV (Toyota Estima) supplies household current through conventional outlets
(Toyota, 2003). This option would allow HEVs to serve as generators for emergency and
off-grid power (e.g., for campers), though supplying power directly to the electric grid is a
longer-term possibility. This option may increase perceived value and consumer acceptance
of such vehicles.
3.1.4 Emissions considerations
The ICE that is an integral part of the HEV can be designed to be less polluting than
conventional ICEs due to several factors. First, the HEV ICE can operate at an optimal
revolutions per minute. Though designs vary, HEVs can use the electric motors for the
high-torque demands of overcoming the inertia of a vehicle at rest. If the ICE is connected
to the drive train, then it can be used in the cruising range of the vehicle where it is more
efficient. Second, the smaller, lighter ICE heats up quickly. This reduces start-up emissions,
which is a primary challenge in reducing tailpipe emissions (EPRI, 2001, p. 3-32). In
addition to these benefits, the ICE used by HEVs is smaller, so there is less weight and,
consequently, less fuel must be used to move the vehicle. Finally, most HEVs use
regenerative braking, another fuel-saving measure. In general, improved fuel efficiency will
32
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lead to lower emissions.
A detailed modeling of emissions from HEVs is a complex task since emissions will depend
on the degree to which the ICE or the electric motor is powering the vehicle throughout the
drive cycle. Hence, emissions will be sensitive to the design of each HEV. For the purposes
of this scenario analysis, which considers the potential emissions consequences of varying
scenarios, HEVs have been modeled as SULEV vehicles. (All of the hybrid vehicles
currently on the U.S. market are certified SULEV.) TACT's emissions modeling for the
ORD Air Quality Assessment will consider the factors affecting HEV emissions in more
detail.
3.2 Fuel Cell Vehicles
Fuel cells are another technology that is expected to compete for market share in the future.
There are still many challenges in reducing costs and optimizing design, and it is expected
that fuel cell vehicles will not compete for market share until at least 2015-2020, though
with unanticipated cost and performance breakthroughs this date could be earlier. This
section characterizes fuel cell vehicles and these challenges.
3.2.1 Description of technology
Different fuel cell technologies are being developed for many applications. Fuel cell designs
range from small proton exchange membrane (PEM) fuel cells that power vehicles to large
stationary power plants using molten carbonate or solid oxide fuel cells. A thorough
description of the various types of fuel cells and their operational principles can be found
elsewhere (U.S. DOE, 1994).
Although the types and designs are different, all fuel cells are electrochemical devices that
convert the chemical energy in H2 into electricity, heat, and water vapor without
combustion. A fuel cell consists of an anode and a cathode separated by an electrolyte,
which is a fluorinated Teflon-based material in the case of a PEM fuel cell.
Figure 5 shows the principle of operation of a H2/air fuel cell. The fuel (H2) and oxidant (air)
gases flow past the anode and cathode, respectively. A platinum catalyst on the anode
encourages H2 to become H2 ions by releasing electrons, which pass through an external
circuit to provide electricity. The circuit is completed by the transport of the ions by the
ion-conducting electrolyte to the cathode where they are oxidized to water. These individual
cells are electrically connected in series to form a stack with the desired voltage/current
output. If the fuel cells are operated using fossil-based fuels, then a fuel reformer is required
33
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to reform the fuel into a H2-rich mixture for use by the stack.
H,in
ELECTRIC LOAD •
AIR In
NITROGEN out
WATER out
Anode Reaction: 2 Ht,
Cathode Reaction: O, + 4 H+ + 4 e
Cell Reaction:
2 H2 + O,
• 2 H2O + electricity + heat
Figure 5. PEM Fuel Cell Operation.
The PEM fuel cell presently is the leading contender to provide power for FCVs. Its primary
advantages include a low operating temperature (-80 °C), high current densities, a fast start
capability, no corrosive fluid spillage hazard, low weight, small size, and a potentially low
cost to manufacture.
3.2.2 Fuel Cell Engine Subsystems
Figure 6 illustrates the basic functional subsystems of a FCV. Like the familiar internal
combustion engine, the fuel cell engine combines fuel and air to create power. Fuel is stored
in an external tank that can be refilled, providing the vehicle with the required range. Unlike
an internal combustion engine, however, the fuel cell engine converts the chemical energy in
the fuel (H2) directly into electricity without combustion, as described above. Since no
combustion is involved, there are no emissions other than water vapor. H2-powered FCVs
are therefore categorized as zero-emission vehicles. The electricity produced by the fuel cell
engine is supplied to the electric motor that power both the vehicle's drive wheels and
auxiliary equipment.
If the FCV is powered by a fossil-based fuel such as methanol or gasoline, an onboard fuel
processor is required to reform the fuel into a H2-rich mixture for use by the fuel cell.
Because external combustion may be required to increase the gas temperature for the reform
34
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process, realtively small emissions of CO and NOX result. Thus, a FCV with a reformer is
considered a near-zero-emission vehicle.
FUEL CELL VEHICLE
Figure 6. Fuel Cell Vehicle with Fuel Processor.
As shown in Figure 6, a number of subsystems are required to make the fuel cell engine
operate. These various subsystems include the fuel cell array, air delivery, fuel delivery,
cooling system, electrical system, control system, and electric traction drive. These are
discussed briefly below.
Fuel cell array—The fuel cell array is the heart of the fuel cell engine. It is composed of a
number of PEM fuel cell stacks arranged to provide the required power at the desired
voltage and amperage. Internal manifolds direct the flow of fuel, air, and coolant through the
array.
Air delivery system—The air delivery system is one of the most critical subsystems. It
provides air to the fuel cell array at a flow and pressure corresponding to power demand. As
more power is demanded from the fuel cell array, higher pressure and flow must be provided
to generate power. The air delivery system can either be a pressurized design or a design
that operates at ambient pressure. In the pressurized case, the fuel cell engine is designed to
provide maximum power at a pressure in the vicinity of 30 pounds per square inch gauge
(psig). Air from the outside is drawn in through a filter by an electrically driven compressor
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and increased to full operating pressure by a turbocompressor. The turbocompressor is
powered by energy recovered from the exhaust air from the engine. Air flow through the
engine is also used to remove the water that is produced by the electrochemical reaction.
An ambient pressure fuel cell engine is also being developed. At near-atmospheric pressure,
only a blower and its drive motor are required. Although ambient pressure operation
eliminates the need for a compressor, turbocompressor, and related equipment, the size of
the fuel cell array and related manifolds are larger. The efficiency gain made possible by the
higher performance of pressurized stacks tends to be offset by the parasitic power
requirement of the compressor. Thus the cost and efficiency trade-offs between pressurized
and ambient air delivery are difficult to quantify.
Fuel delivery system—For an H2 FCV, fuel may be stored as high-pressure compressed H2
gas that is stored in lightweight composite cylinders or as a cryogenic liquid. Metal hydride
and carbon nanotube storage systems are also under research and development. If the fuel is
methanol or gasoline, a fuel processor or reformer must be included in the fuel delivery
system. A reformer can efficiently deliver H2 to the fuel cell array by splitting the
hydrocarbon molecule. A reformer produces trace emissions (CO and NOX) as it burns some
of the hydrocarbon to provide the necessary heat of reaction. The reformer also adds cost,
weight, and complexity to the overall engine system.
Electrical system—The electrical system provides the power interface between the fuel cell
array and the electrical equipment for the engine and vehicle. An inverter is required to
convert the direct current (DC) power produced by the fuel cell stack into alternating current
(AC) power for use by an induction motor. Although a DC motor could be utilized and the
inverter eliminated, an AC induction motor is usually the motor of choice because its small
size, ruggedness, reliability, and cost advantages.
Other subsystems include the cooling system to maintain the fuel cell operating temperature
and the control system to coordinate operation of all other systems.
3.2.3 Hybridization
As with internal combustion engines, it is possible to add batteries for additional storage
capability to form a fuel cell hybrid vehicle (FCHV). The FCHV subsystems would be the
same as those in a FCV (see Figure 6) but with more batteries and a more sophisticated
control system. Some advantages of a hybrid configuration might include:
• regenerative braking to recover the kinetic energy normally dissipated as heat during
36
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braking,
• power during cold start to eliminate the need for near-instant start up of the fuel
processor for gasoline- or methanol- powered fuel cell engines, and
additional batteries as a power boost for acceleration and hill climbing, thereby
allowing a smaller and less expensive fuel cell engine.
FCHVs will be considered in TACT future analyses.
3.2.4 Fuels and Processors
The three types of FCVs being developed are a direct H2 FCV, an alcohol (methanol)
design, and a gasoline version. In a direct H2 FCV, the H2 can be stored on-board as a high
pressure gas or in the form of a metal hydride. Weight, storage density, and
charge/discharge cycles issues have not been totally resolved for metal hydrides, however.
Therefore, cost estimates in this report are based on the high pressure tank option. This
subsection describes onboard reforming of methanol and gasoline to H2. Building a
large-scale H2 infrastructure—consisting of production, storage, transmission, distribution,
and delivery—are discussed in Section 3.3.
Methanol is a good H2 carrier for on-board reforming because it is a liquid at room
temperature and ambient pressure. Even though methanol has some properties different from
gasoline (e.g., methanol is hygroscopic and corrosive), it could be handled in much the same
manner as gasoline. As a result, the development of a methanol fueling infrastructure may
be significantly cheaper than for H2. Since methanol is a very simple molecule (a single
carbon atom linked to three hydrogen atoms and one oxygen-hydrogen bond), releasing the
H2 is easier to accomplish than with other liquid fuels such as gasoline.
In addition to methanol reformer FCVs, there is current effort to develop a direct methanol
fuel cell. No reformer is needed in this case, as the methanol is injected directly to the fuel
cell's anode, where it is oxidized to CO2, releasing H2 ions and electrons. This technology is
not considered in the current scenario analysis, but will be considered in future analyses.
Gasoline can also be used as a H2 source, but it is more difficult to reform than methanol.
Additionally, very low sulfur gasoline with low aromatics is required so that reformer and
fuel cell stack catalysts are not contaminated. If very low sulfur fuel is not available,
onboard desulfurizer units must be used reduce fuel sulfur to the approximately 30 parts per
billion by volume level necessary for high reformer performance and fuel cell stack
endurance.
37
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Figure 7 presents a fuel processor flow diagram. On-board processing (reforming) of H2-rich
fuels can be accomplished by essentially three techniques: steam reforming, partial
oxidation (POX), and auto-thermal.
VAPORIZER
— (
REFORMER
'
BURNER/
HEATER
T
TAIL C5AS
EXHAUST
H
SHIFT
CONVERTER
k
CO
REMOVER
H (to anodes of
' * 1uel cell stack)
Figure 7. Fuel Reformer Flow Diagram.
Steam Reformers—Steam reforming combines fuel with steam over a catalyst, producing H2
and CO. This technology yields a very high concentration of H2, but a shift converter can
also be added to further increase the concentration by converting the CO to more H2 and
CO2. Generally, this endothermic reaction must be powered by burning some of the fuel to
maintain the proper reformer temperature, and generating the heat required to power the
reaction can result in long start-up times. After start-up, the heat can be supplemented by hot
exhaust from the fuel cell's cathode. Some of the required heat can be supplied also by
burning the anode exhaust gas (only about 85% of the H2 produced is utilized by the fuel
cell stack). Only simple hydrocarbons such as methanol or ethanol can be processed by
steam reforming. Thus, this approach does not offer full fuel flexibility. Additionally, the
reformer catalyst is extremely susceptible to poisoning from contaminates such as sulfur. It
does, however, offer the potential for the lowest cost and smallest size. Methanol-based
steam reformers have been demonstrated on-board in FCVs.
POX reformers—POX systems operate at much higher temperatures than steam reforming.
They have the advantage that operation is possible with a variety of fuels such as gasoline,
methanol, or ethanol. The process combines fuel with O2 to produce H2 and CO via an
38
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exothermic reaction. As with steam reformers, POX reformers use subsequent water-gas
shift converters to convert the CO to more H2 and CO2. The reaction can provide a very fast
response to transients, but additional equipment may be required to remove excess heat.
Typically, prototype POX reformers only require a few seconds to "light-off and begin the
reaction. No POX reformers have been demonstrated in on-board FCVs.
Autothermal reformers—Autothermal reforming combines fuel with steam and air and is a
mixture of the POX and steam reforming processes. It combines the reactions of steam
reforming and POX such that the exothermic heat from the POX provides the heat for the
steaming reforming process to proceed. This allows for a lower operating temperature of the
reformer. This procedure produces a more concentrated H2 gas stream than POX but less
concentrated than the steam reforming process. The reformer is fuel flexible.
3.2.5 Issues for Implementation
Issues affecting the implementation of FCVs include the cost and performance of fuel cell
engines, the expense and logistics associated with developing a hydrogen infrastructure, the
availability and price of natural gas, and the availability of platinum.
Cost and performance—A principal challenge facing PEM fuel cell engine developers is to
reduce costs. Although several subsystems are involved in a fuel cell engine as outlined
above, the fuel cell stack is the major cost component associated with the engine. Three
major challenges in reducing the cost of the stack are reducing the cost of the electrode
plates, reducing the amount of platinum on the electrodes, and developing a cheaper but
effective electrolyte membrane. To date, the cost of producing an entire fuel cell engine
(stack, fuel processor, on-board clean-up, controls, etc.) is projected to be about $300/kW at
mass production and based on current technology. However, achieving the cost production
target of competitiveness with internal combustion engines (around $50/kW) will probably
require additional technical innovation to find pathways for significant cost reduction.
Several cost analyses (e.g., ADL, 2000) have been conducted of the mass production capital
cost estimates for the FC V drive train. These studies indicate that the drive train of a
H2-powered FCV will cost around $2000 more than a conventional vehicle. Another $200 to
$900 can be added to that total for methanol- and gasoline-powered FCVs as a result of the
onboard reformer and related equipment. Depending on the FCV type (small car, large car,
minivan, sport utility vehicle, or van/pickup truck), the estimated capital cost ratio (capital
cost for a FCV divided by the capital cost for a conventional vehicle in the same year)
ranges between 1.15 and 1.4. When FCV vehicles are introduced, the cost ratio is expected
39
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to be at the upper end of this range, diminishing to the lower end as the technology matures.
Figure 8 shows ranges of reported estimates of FCV capital costs, normalized to similar
parameter values for a conventional vehicle. Figure 9 contains ranges for fuel economy. For
the capital cost values, the high end of the ranges refer to when the FCVs are initially
introduced, while the low end of the ranges represent the cost of more mature FCVs after
several years of commercial production. The high end of the ranges for fuel economies,
however, are for mature FCVs, with the lower ranges the initial values. Sections 5 and 6
discuss scenarios examining the effects of variation in FCV cost and performance.
2010
2 2020
2030
Hef 4
Ref 3
Ref 5
Ref. 1
Ref
1 US DOE, 2002
2 Edwards el al. 1999
3. Lipman. 1999
4 Weiss at al , 2000
5 Thomas et al., 1998
10 20 30 40
Cost Premium, %
50
60
Figure 8. Ranges of Cost Premiums for Fuel Cell Vehicles.
Hydrogen infrastructure—For large-scale penetration of direct H2 FCVs, a H2 infrastructure
consisting of production, storage, transmission, distribution, and delivery would be required.
Development of such an infrastructure could be extremely costly. These issues are
considered further in Section 3.3 and Section 4.
Natural gas availability and price—Natural gas is the most frequently proposed feedstock
for producing methanol for FCVs (although alternate pathways will be considered in
TACT's upcoming biofuels scenario analysis). The process for conversting natural gas to
methanol is well known, and methanol converters are commercially available. However,
40
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Methanol
Gasoline
Ref. 2
Ref 3
Ref.
1. U.S. DOE, 2002
2. Edwards et al.. 1999
3. Weiss etal., 2000
4. Thomas et al., 1998
Ref. 1
20 40 60 80
Miles per Gallon-Equivalent
100
120
Figure 9. Ranges of Fuel Economy for Fuel Cell Vehicles.
using natural gas as a feedstock would make the methanol production cost extremely
dependent on the cost of natural gas, which is expected to be quite unpredictable over a
typical 30-year planning cycle. In addition, the natural gas share of electricity generation is
widely predicted to increase substantially over the coming two decades (Hester, 2000; EIA,
2003a). MARKAL's RES enables the modeler to examine the potential consequences of
these competing demands for natural gas. These issues will be considered in TACT's
scenario analyses for methanol FCVs.
Platinum availability—Another issue that has been raised regarding FCVs is the amount of
platinum required for FCV catalysts. Borgwardt (2000) is pessimistic relative to the issue of
whether platinum supply can meet demand when large numbers of FCVs enter the market.
3.2.6 Emissions considerations
For each type of FCV, well-to-wheels emissions of VOCs, CO, NOX, and CO2 can be
estimated (Weiss et al.. 2000). This section considers the impact of FCV designs on vehicle
emissions. TACT's MARKAL analyses will examine full well-to-wheels emissions. For
vehicle emissions, FCVs provide the opportunity for significant emissions reductions over
internal combustion engine and hybrid designs because combustion is essentially eliminated,
although a small amount of combustion occurs for steam reformers. Additionally, the
catalytic-based reformer processes require local cleanup of certain contaminants, (e.g., CO).
The H2-powered FCV would be a true zero emission vehicle because only water vapor is
41
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emitted as a result of the electrochemical reaction inside the fuel cell stack. Alcohol- and
gasoline-powered FCVs are not zero emission vehicles. Both vehicle types produce small
combustion emissions of CO and NOX, and evaporative VOC emissions are negligible.
However, these emissions are expected to be well below the most stringent proposed
emissions specifications, including California's near zero emission vehicle specifications
(CARS, Site 1).
Some of extremely low emissions associated with fuel cell engines are a result of other
required cleanup processes. For example, typical PEM fuel cell stacks require less than 10
ppmv CO in the feed stream or the anode gets "poisoned," resulting in lower output power.
Fuel processors produce CO at levels from 2000 to 5000 ppmv, requiring CO removal as
illustrated in Figure 7. Although there are many commercial methods available for CO
reduction, they are unlikely candidates for fuel cell engines because of their complexity and
low product recovery. Therefore, a considerable amount of ongoing research is being
conducted to develop a suitable CO clean-up process. In one approach, a preferential
oxidation reactor unit oxidizes CO with added air in preference to the H2 in the fuel stream.
Because some H2 can be lost the process, it is very important to achieve the lowest practical
concentration of CO in the shift reaction and the highest possible CO tolerance of the fuel
cell's anode.
As noted above, the fuel cell stack can only consume around 85 percent of the H2 delivered
by the fuel processor. A combustor burns the excess H2 from the anode exhaust and thereby
delivers a clean exhaust stream from the system. The waste energy obtained from the
combustor can be utilized in the fuel reforming process to aid in rapid warm-up (see Figure
7) and effective operation of the reformer. Additionally, this process can remove trace
hydrocarbons and CO from the exhaust.
3.3 Hydrogen Production
The large-scale adoption of direct H2-powered, FCVs would require a H2 infrastructure be
established to support the fuel demand. A variety of options exist for instituting H2 as a
transportation fuel. These choices impact efficiency, emissions, cost, and other factors. One
of the primary decision points is where to produce the H2. Options include off-site produc-
tion at a centralized plant, on-site production at the fuel station, or at home. As with other
transportation fuels produced off-site at centralized plants, H2 must be transported by truck,
rail, or pipeline to refueling stations. Production on-site requires the transport of the feed-
stock fuels—natural gas or methanol, electricity, and water—to the local fueling station
where the hydrogen is then produced.
42
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3.3.1 Description of the technology
Hydrogen can be produced (1) thermochemically from fossil fuels or biomass, (2) electro-
lytically from water, or (3) photolytically from water. The primary thermochemical method
for H2 production is steam/methane reforming (SMR). This process produces one mole of
CO2 for each four moles of H2, but it also requires energy for the reaction to take place.
Figure 10 shows a typical SMR flow diagram. The majority of the H2 is formed in the
reformer where methane (CH4) reacts with water to form CO and H2 in a high temperature,
high pressure reaction. Heat from the exiting gases can be recovered to preheat the feed to
the reformer. In the shift reactor, the CO is further processed with steam to form CO2 and
additional H2. The pressure swing adsorber separates the CO2 and unreacted methane and
water to yield H2 more than 99 percent pure.
Steam
HeatHemoval
CO,
1
Natural
Gas »
Feedstock
Sulfur
Removal
t
Catalytic
Steam
Reforming
1 ;
Jatural
-0i
Meth
CO Shift
Reaction
ine OH G33
PrcGGurc
Swing
Adsorption
fias Fuel
Figure 10. Steam Methane-Reforming Process Flow Diagram.
Currently, H2 is used at the point of production for ammonia manufacture and petroleum
refining with less than 5 percent distributed for off-site use. In a H2 economy, SMR is
proposed for off-site, centralized plants and for on-site fuel-stations. Figure 11 shows ranges
of the capital investment costs for SMR with large capacities representative of centralized
plants and smaller capacities applicable to on-site fueling stations.
As discussed above, the reforming process can also be used with methanol and gasoline to
produce H2. Another H2 production option for biomass and coal feedstocks involves
gasification to produce a syngas. The syngas can then be additionally processed
thermochemically to increase the H2 fraction. Other thermochemical technologies include
43
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10.000+
£ 1.000+
->
f
n
|
u 100+
10+
1
10 15 20 25 30
Capital Cost per Unit Capacity ($/TJ/yr)
35
40
Figure 11. Range of Capital Investment Costs for Steam Methane
Reforming.
(a) partial oxidation of hydrocarbons, (b) thermocatalytic decomposition of hydrocarbons,
and (c) biomass or organic waste pyrolysis. Future H2 scenario analyses may include some
of these technologies.
The primary electrolytic process involves alkaline electrolysis. This process uses electricity
to breakdown water into hydrogen and oxygen. In an alkaline electrolyzer, the electrolyte is
concentrated potassium hydroxide. Electrolysis produces a low-pressure H2 gas that must be
compressed or liquified for transport and use. Electrolysis has been proposed as a produc-
tion process for on-site fuel stations and at-home applications. Figure 12 shows ranges of
capital investments for alkaline electrolysis stations. The emissions implications of
electrolytically-produced H2 are highly sensitive to the electricity generation method.
Another electrolysis process involves proton exchange membranes (PEM). This is basically
a reverse process of the PEM fuel cell. In the electroylsis process, water is added on the
positive side of the cell, and an electric charge is imposed across the membrane. This
induces the movement of H2 ions through the membrane to the negative side where they link
up with electrons to produce H2 gas. On the positive side, oxygen is expelled and replaced
with more water.
44
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10.000+
I
1to50
1
10 20 30 40 50
Capital Cost per Unit Capacity ($/TJ/yr)
70
Figure 12. Range of Investment Costs for Electrolysis.
Photolytic processes are long-term possibilities for H2 production. These options include (a)
photobiological such as algal production and (b) photoelectrochemical production from
water. These processes are still at the research stage, and their future practicability is
unknown.
3.3.2 Issues for implementation
Distribution—Hydrogen as a liquid or a gas contains only 25 to 30 percent of the energy per
unit volume of gasoline and natural gas. Thus, for centralized H2 production, distribution (by
truck, pipeline, or rail) presents substantial additional costs. In addition, there are significant
costs in compressing or liquefying the H2 for storage and subsequent distribution. Capital
costs for this equipment are also significant.
With on-site production, distribution becomes an issue of the "H2 carrier". For example,
methane is the H2 carrier for the SMR process since the methane must be distributed to the
fuel station for reforming. Other H2 carriers include gasoline, methanol, and ammonia.
Storage—Another issue to resolve when using H2 fuel is storage, which takes place first in
the plant, second in the transport equipment (i.e., truck or rail when centralized plants are
used), and third in the car itself. The low density of H2 gas requires larger tank volumes for
45
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similar miles traveled when compared to petroleum. Transportation efficiencies can be
improved by storing H2 either (1) as a compressed gas, (2) as a liquified gas (with the
associated additional energy requirements to liquify), (3) on metal hydrides, or (4) on carbon
nanotubes. These approaches are not capable of completely offsetting hydrogen's energy
density limitations, however. For example, for an equivalent energy content of gasoline,
storage requirements for liquid H2 and compressed H2 gas are 6 to 8 times and 6 to 10 times
more, respectively.
The hydride and nanotube H2 storage options are adsorption processes. Hydrides require
high temperature heat to release the adsorbed H2, whereas lower temperatures may be
required for release from nanotubes. Research on hydride and nanotube storage options is
ongoing. See Section 4 for a discussion of how H2 distribution and storage technologies
have been mapped into the MARKAL modeling framework.
Natural gas availability and price—As discussed in Section 3.2.5, natural gas for SMR H2
production would have to compete with existing and anticipated uses, particularly in the
electricity generation sector. Hydrogen prices would also depend on fluctuations in natural
gas price.
3.3.3 Emissions
Typical SMR process emissions are direct emissions from the process itself and indirect
emissions at the power plant as a result of electricity requirements for the process. The
primary direct emission is CO2, which is the waste product of the reforming process. Other
emissions include unreacted CH4 and CO and small quantities of NO2 and PM. For the
electrolysis process, all emissions are indirect. As mentioned earlier, these will depend
sensitively on the electricity generation method and will be significant when fossil fuels are
the fuel choice for electricity generation. The use of renewable energy sources would
appreciably reduce the environmental burden of the electrolysis process. Combined with
zero emissions from direct H2 FCVs, renewable-powered electrolysis has the potential for
significant reductions in air pollution. However, this is widely expected to be the most
expensive option for H2 production in the near to midterm.
This section has touched upon many of the infrastructure requirements necessary for
H2-powered FCVs to be practical. Thus, any MARKAL scenarios in which H2-powered fuel
cells are considered would need to include a representation of a H2 infrastructure. Section 4
describes the mapping of such an infrastructure into MARKAL. The approach demonstrates
how geographical and demographic information can be used to inform the model.
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Section 4
Mapping Hydrogen Infrastructure
Technologies into MARKAL
An important factor in the adoption of H2 fuel cell technologies is the existence of a
cost-effective infrastructure for distributing H2. Since it is not possible for such an
infrastructure to quickly appear, one can expect a phased implementation. The early phase
of a H2 infrastructure development can consist of several technologies. This section provides
an overview of the methodology used to characterize and map these technologies into the
U.S. EPA MARKAL database. Critical implementation decisions and outstanding issues are
identified. The methodology described here is important not only for modeling the H2
infrastructure, but also serves as a template for modeling other geographically distributed
resources within MARKAL. For this reason, more detail is provided regarding implemen-
tation than elsewhere in this report.
4.1 Overview
Hydrogen production may occur at a central location or at the refueling station. If centrally
produced, such as by steam methane reforming, the H2 fuel will have to be transported to
demand centers via pipeline or truck. If by truck, this will likely require conversion of the H2
from approximately 200 psi to a more dense form that is more cost-effectively transported.
For the early-phase H2 infrastructure development, truck transport of liquid H2 is assumed.
Alternatively, H2 transport can be avoided by producing it directly at the refueling station
(i.e., a gas station), either by electrolysis or steam methane reforming. In all cases, the H2
fuel that is delivered to the vehicle is assumed to be compressed gas at 5000 psi. An
additional alternative is the production of H2 gas at residences. This process requires
electricity to fuel electrolysis. Residential H2 production can be performed continuously or
only at night, when electricity prices are typically lower. In this phase of the analysis,
vehicles are modeled with onboard storage of compressed H2 gas. Future scenarios will
consider additional onboard storage technologies.
Thus, the relevant technologies necessary to represent the H2 infrastructure include
• Central steam methane reformer,
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• Liquid H2 trucking,
• H2 gas pipeline,
Steam methane reformer at the fueling station,
• Alkaline electrolysis at the station,
• Night-time only alkaline electrolysis at the fueling station,
• Electrolysis at residence,
• Night-time only electrolysis at residence, and
• Automotive fueling stations.
The linkages between these various H2 technologies are represented in Figure 13.
Non-hydrogen Input Symbols
Elsctncily +
Natural uas A
Diesel •
Hydrogen State
- - Gas
Liquid H£ ^—^—
Compressed H2 Gas
Llectrolysis at Residence
(full day npe rail nn)
Eleclrulysis at Residence
(night operation only)
Figure 13. MARKAL RES Diagram for Centralized Steam Methane
Reforming.
Although Figure 13 depicts one H2 station (representing delivery to only a single population
segment), the costs of making H2 fuel available for use in FCVs is considered in the U.S.
EPA database for 12 representative segments of the population. The characterization of
these 12 representative population segments is shown in Table 4.
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Table 4. Population Segment Definitions by Population and Distance.
Segmem
Numbei
1
2
3
4
5
6
7
8
9
10
11
12
£ Population Range
Low End
500,000
100,000
50,000
100,000
50,000
100,000
50,000
100,000
50,000
100,000
50,000
50.000
High End
infinity
500,000
100,000
500,000
100,000
500,000
100,000
500,000
100,000
500,000
100,000
100.000
Distance from Central
Plant
Low End
0
0
0
30
30
60
60
120
120
240
240
480
High End
30
30
30
60
60
120
120
240
240
480
480
960
Number of Vehicles in
Segment
2000 Census
52,934,840
1,374,163
1,421,821
4,338,047
4,121,994
5,487,342
6,009,105
3,315,001
4,048,789
580,851
837,577
100.979
%
62.6
1.6
1.7
5.1
4.9
6.5
7.1
3.9
4.8
0.7
1.0
0.1
The 12 population segments are not defined as specific geographical regions, but represent
instead different population groupings that share similar characteristics. These character-
istics include factors that affect the costs of H2 distribution, specifically the size and density
of the populations and proximity to central production facilities. The methodology for
modeling H2 production and delivery is discussed in the following section.
4.2 Methodology
Aspects of the methodology described here include: modeling transportation costs, locating
centralized plants and refueling stations, calculating transportation distances, and
characterizing trucking and pipelines.
4.2.1 Modeling hydrogen transportation costs
An important component of mapping the H2 infrastructure into MARKAL was the
characterization of the transportation costs for supplying H2 to different segments of the
population. This characterization was critical because distances to each refueling station
may vary substantially and, because the costs of H2 transport, may be a high portion of the
overall costs of supplying H2 to the vehicle. Hydrogen is more costly to deliver compared to
other fuels such as gasoline because of its low energy density—even when compressed or
liquefied.
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The distance to each population segment is important to consider when evaluating the costs
of transport from a central facility. In order to calculate the distances, it is necessary to know
approximately where centralized plants and refueling locations will be located.
4.2.2 Central plant locations
An important factor in calculating transportation costs is the distance from a segment of the
population to a centralized H2 plant. The siting of such plants should include consideration
of issues such as the location of feed stocks (e.g., electricity, natural gas, or diesel fuel) and
the balancing of production and transportation costs. In the absence of better information, a
heuristic approach has been applied to locate central plants.
An assumption in this heuristic approach was that central plants were assumed to be located
in large urban areas. This assumption is reasonable because transportation costs would
necessitate the location of central plants near larger centers of demand. An urban area is an
official Census 2000 term that, in general, is a contiguous area that has a population density
of greater than 1,000 people per square mile at the census block level.
In deciding where hydrogen plants might be located, the following guiding rule was
adopted: a central H2 production plant would serve at least 400,000 cars, equivalent to 165
tons of H2 per day. This size plant is meant to equate roughly to a size that could achieve
significant economies of scale.
A procedure was developed and followed to locate plants of this capacity or greater. Using
Geographic Information System (GIS) tools, plants were located in central locations where
at least 500,000 people would be served, corresponding to approximately 200,000 cars
(assuming all cars were to use H2). This cutoff of 200,000 cars is lower than the 400,000
value stated in the previous paragraph because it is assumed that these plants will also serve
nearby areas with delivery by pipeline or truck. The additional H2 demanded will raise the
total demand to more than 400,000 cars.
Given this information, MARKAL will select the amount of centralized plant capacity based
on numerous considerations, including factors unrelated to H2 modeling (e.g., oil prices).
Care therefore must be taken to confirm that the model results make sense (e.g., that a plant
located in an urban area with 200,000 cars does indeed serve nearby areas). If not, the
costing of the centralized plant may have to be adjusted.
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4.2.3 Refueling station locations
The location of refueling stations is important for estimating trucking and long-distance
pipeline distances, as well as for estimating the costs of local delivery by pipeline. In this
study, H2 refueling stations are assumed to be dispersed throughout the census-defined urban
areas. This assumption is reasonable since according to Census 2000, 81 percent of vehicles
(85 million of a total of 105 million) in the nation are owned by residents living within
urban areas.
4.2.4 Distance calculations with GIS
GIS tools were used to approximate pipeline and trucking distances from the central plant to
the refueling station. First, following the guiding rule for locating central plants, a database
of central plant locations were identified and entered into a GIS map of the United States.
Then, an Arc View layer of census-defined urban areas was added. Combined with the urban
area map was a database indicating the size of each urban area. Using tools available in
Arc View GIS, the fraction of the population falling within a certain distance range of a
centralized plant and within a certain urban area size classification could be determined. The
12 population segments and their defining characteristics, population range, and distance
range are presented in Table 4.
4.2.5 Trucking
The distances used to estimate truck transport costs to each population segment were based
upon the distance intervals as given in Table 4. The distance to the population segment was
evaluated as the midpoint of the interval. This estimate was doubled to account for an empty
return trip. For example, for population segment 1, which was defined (in part) by a distance
from the central plant of between zero and 30 miles, the midpoint is 15 miles. The round trip
distance was assumed to be 30 miles.
The GIS procedure used rectilinear estimates of the distance, which will tend to under-
estimate the total round trip distance. However, a greater portion of the urban area within the
distance interval is assumed to be nearer to the central plant than the midpoint distance; this
will tend to have the opposite bias. Thus, in the absence of more detailed information, the
midpoint was selected.
The trucking option actually consists of a set of technologies. Trucking, as implemented in
MARKAL, assumes that the H2 produced at the central plant will be liquefied, placed into
diesel-fueled tanker trucks, driven to the refueling station, volatilized, and dispensed to the
H2 vehicle. The costs and efficiencies of the entire trucking option reflect the costs and
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efficiencies of all of these processes.
4.2.6 Pipelines
Hydrogen can be transported via pipeline as an alternative to being trucked from the central
plant to the fueling stations. Like the trucking option, this option represents all the processes
involved with taking the H2 in the form that it is produced at the central plant to its form as it
is dispensed. Therefore, in addition to the pipelines and the associated right-of-ways, the
pipeline option includes compressor stations along the way, compressing and dispensing
equipment at the refueling station, and the associated energy requirements.
Delivery by pipeline can be simplified to consist of (i) delivery from a central plant to a city
gate, followed by (ii) local distribution from the city gate to the refueling stations (Ogden
1999). In this study, the city gate corresponds to an urban area. The cost of long-distance
transmission is primarily a function of the length of the pipeline to the gate and total demand
of the urban area.
In this preliminary work, the length of a long-distance pipeline required to reach each urban
area gate is approximated in the same fashion as for trucking, with the exception that
pipelining is a one-way trip. If costs need to be more precisely estimated, a more detailed
analysis could be undertaken to anticipate a more realistic layout of the networks, perhaps
modeled on natural gas distribution networks. That kind of analysis, however, would
involve more complexity than is warranted here because the costs of the long-distance
transmission appear to be considerably less than the costs of local distribution (Ogden
1999).
According to Ogden (1999), the cost of local distribution (e.g., H2 delivery from the gate to
the refueling stations) is a strong function of the density of vehicles using H2. The local
distribution costs begin to rise sharply for areas with vehicle densities less than 300 cars per
square mile. Above a density of 400 cars per square mile, the costs of local distribution per
unit of energy transmitted begins to level off at roughly $2/GJ of H2 (Ogden, 1999). This
cost estimate of $2/GJ of H2 assumes costs of $1 million per mile of pipeline. If pipeline
costs were $250,000 per mile, then the costs would be somewhat lower, about $1.5/GJ.
The above information is useful for estimating the costs of H2 delivery to a census-defined
urban area. The car density of an urban area has been reported to roughly correspond to the
car density at which the pipelining becomes economical: 400 cars per square mile (Ogden
1999). Assuming the national average for car ownership, approximately 4 cars per 10
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persons, the delineation of urban areas roughly corresponds to areas with vehicle densities
of 400 cars per square mile (1000 times 4/10). Although this only applies to cars using H2
fuel, Ogden (1999) also suggests that the pipelines are more economical if designed for a
large and stable demand. Therefore, it is reasonable to assume that delivery will be carried
out by truck until the time that the sufficient density of cars using H2 is reached.
Information is not yet available that allows for adjustment of costs by the total demand of
the urban area. For example, it should be less costly to deliver H2 to refueling stations in
large urban areas than small urban areas due to the nature of gas transmission economics. In
the absence of such information, the estimate of $2/GJ is used as the central estimate.
4.3 Implementation
After characterizing the various H2 infrastructure technologies and population segments, the
next step was to integrate this information into the MARKAL model. This involved
modifying the RES to incorporate the relevant technologies and energy carriers for each
population segment. Technologies included in the RES were those shown in Figure 13.
Information about the cost and efficiency of fueling station equipment was included for each
transportation technology. However, fueling stations were represented in the RES as dummy
nodes that aggregate the amounts of H2 produced via different options. Use of dummy nodes
allowed the total dispensed amount for a population segment to be calculated and
constrained.
Hydrogen-related energy carriers represented in the RES included H2 as a gas, liquid, and
compressed gas. Mass balances for each technology were created that take compression into
account.
Next, efficiency and cost data for H2 FCVs were characterized. Hydrogen FCVs were then
added to the RES. In this implementation, H2 fuel cell technologies were considered for the
personal vehicle sector only. Hydrogen FCVs were then allowed to compete with other
personal vehicle technologies to meet VMT demand.
In addition to this representation, several modeling issues were addressed with constraints in
MARKAL. These included limits on VMT and rate of growth.
VMT limits—Hydrogen supplied to each population segment cannot exceed the total VMT
demand for each segment. Appropriate MARKAL constraints were therefore necessary.
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Rate of growth limits—Because of its linear programming formulation, MARKAL may
predict a rate of vehicle adoption that is more rapid than is practical. For example,
MARKAL does not model consumers' hesitation in purchasing a new, unproven technology
or the need for a gradual ramp-up in manufacturers' production capacity. To represent these
factors, a MARKAL growth constraint was used to limit the rate of growth. The growth
constraint limited initial-year adoption to 1 percent of total personal vehicle VMT.
Hydrogen FCV penetration was then restricted to grow by no more than 300 percent in the
following 5-year period. In the next two 5-year periods, growth could be no more than 220
percent and 160 percent, respectively.
Other issues have been identified that may arise when modeling some technological
scenarios involving a H2 infrastructure. These issues include the interplay between trucking
and pipelines, natural gas distribution, and the chicken-and-egg problem.
Chicken and egg problem—People will not purchase a new vehicle unless there is a place to
refuel, but there will not be a place to refuel until there are people driving H2 fuel cell cars.
Often, the question is framed as, "How many initial stations are needed to overcome the
chicken and egg problem?" (Melaina 2003) Thus, a small number of pre-existing stations
may be required for some scenarios in order for growth to occur.
Trucking—Hydrogen transport via trucks is expected to precede use of pipelines, though the
potential for pipelines to show up in the solution before trucks is possible. In cases where
this arises, a constraint can be placed on the model forcing the early introduction of
trucking.
Distribution of natural gas—Natural gas reforming of methane is limited by the capacity of
the natural gas distribution network. A constraint could be added for each population
segment that restricts the amount of H2 to be produced from steam methane reforming.
Using the methodology and implementation described in this section, market penetration
and demand for H2 FCVs could be estimated for each of the 12 population segments,
considering sector-specific characterizations of H2 infrastructure costs. This methodology
and implementation are preliminary, however, and are expected to be refined as the project
progresses.
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Section 5
Scenarios
5.11ntroduction
The previous sections provided a general introduction to the transportation technologies
examined in this report, focusing on findings from the literature and how these conclusions
are being mapped into the MARKAL database. This section serves as a bridge between this
research and the model results discussed in Section 6 by describing a number of scenarios
distilled from this background material. These scenarios serve as "what if story lines that
characterize possible technological futures for the transportation sector. It is important to
stress that they should not be interpreted as predictions about technology parameters, rates
of market penetration, or emission trajectories. Rather, the scenarios pose such questions as,
"If the investment cost of a compact hybrid automobile is $30,000 in 2030, how much of the
market do these vehicles capture?" Note that one may also work backward in an exploratory
sense and ask, "What range of investment costs for a new hybrid compact yields a minimum
market penetration of 25 percent for these vehicles in 2030?" MARKAL returns results as
consequences about these particular assumptions as they play out in the model's
energy-economic framework (which serves as a "container" for a more comprehensive set of
assumptions). The scenario assumptions, of course, are plausible, but should not be taken as
an endorsement of a particular research finding or range of values.
Given what is known or can safely be assumed about the potential for alternative transpor-
tation technologies and the economic and lifestyle trends which drive transportation
demand, the course of technological evolution over the next few decades will likely be
bounded by two general scenarios. The first and most conservative of these bounds does not
look much different from the present: gasoline continues to fuel most vehicles, with the
gradual introduction of more advanced ICEs and gasoline-electric hybrid vehicles. The
market penetration of each would be a function of its relative cost and efficiency, the price
of gasoline, government policy, and consumer environmental concerns. Moving slightly
away from this bound, one might see greater use of fuels such as diesel, natural gas, and
methanol. Vehicle power trains and their supporting infrastructure, however, will look
familiar near this end of the scenario spectrum.
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The opposite end of the scenario spectrum sees a more radical shift in the transportation
infrastructure, with a movement away from fossil fuels to a hydrogen-based economy. At
the far end of this spectrum, for instance, vehicles would be powered by H2 fuel cells. For
this future to be realized, fundamental changes must occur in the supply and distribution
networks, as well as in consumer acceptance of a vehicle technology quite different from
that which has dominated transportation for the last century. The importance of niche mar-
kets (e.g., H2 fuel cell buses) as proving grounds for the new technologies as well as the role
of transitional technologies (e.g., gasoline fuel cells) would likely become apparent in the
successful evolution to this future.
These bounds, of course, assume that the nature of transportation demand does not change.
Individuals, for instance, continue to prefer the convenience of a personal vehicle, and
freight continues to be moved by a combination of rail, air, and a surface truck fleet. Within
these bounds, however, exists a range of technological paths, with significant implications
for the future use of fossil energy as well as the nature of atmospheric pollutant and green-
house gas emissions. Although the particular path that transportation technologies take will
be influenced by political, economic, and social factors that cannot easily be captured in a
model like MARKAL, the modeling framework can be used to explore why one path (all
other things being equal) might be preferred over another.
This report begins such an analysis by examining two general sets of scenarios: a series of
"Evolution as Usual" (EAU) developments concerned with the continued advancement of
conventional ICE and hybrid transportation technologies, and an "Early Phase Hydrogen
Economy" (EPHE) transition that builds on the EAU assumptions to examine how the trans-
formation to a hydrogen-based economy would affect transportation. Note that the present
analysis does not give full consideration to interactions with model variables outside the
transportation sector of the economy (which is particularly important with regard to the
supply of alternative fuels). As discussed in Section 7, Future Work, this more compre-
hensive analysis is the goal of the TACT project, but awaits completion and refinement of
the full MARKAL database. This report aims to provide a rigorous, though restricted,
demonstration of the model's capabilities.
5.2 Evolution-as-Usual Scenarios
Section 3 described the present set of personal vehicle technologies included in the
MARKAL database. The model characterizes each technology by its availability date,
investment cost, fixed and variable operating costs, efficiency, discount (hurdle) rate,
growth rate limit, and emission factors for a range of pollutants (see Appendix A for a full
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description of the TACT MARKAL database and model). In addition to variations in these
technology-related parameters, assumptions about the price of transportation fuels (inclu-
ding applicable taxes) provide the basis of the EAU scenarios described here. Note that
transportation demand projections are fixed, including demand for particular vehicle classes
(i.e., consumer preferences for compacts, fullsize cars, minivans, pickups, and SUVs do not
vary across the EAU scenarios). Table 5 summarizes these exogenous vehicle demand
assumptions used in the MARKAL database. Travel demand is denoted in billion vehicle
miles traveled (BVMT). Note that while AEO-derived demand projections are employed
here, future analyses will consider alternative travel demand projections and examine
sensitivity to these assumptions.
The EAU scenarios focus on those factors driving the balance between gasoline-fueled ICE
vehicles (conventional and advanced mpg) and gasoline-electric hybrids. Relative to con-
ventional gasoline-fueled vehicles, their advanced counterparts achieve a 14-23 mpg effi-
ciency improvement, while 2X and 3X hybrids represent 100 and 200 percent increases in
mileage, respectively. For the purposes of this report, both hybrid technologies are assumed
to meet SULEV emission criteria. All ICE and hybrid engine technologies are available
across the five vehicle classes (Table 5). Future EAU scenarios will examine additional
transportation technologies, including gasoline and methanol fuel cells as well as the use of
compressed natural gas (CNG), liquefied petroleum gas (LPG), ethanol, and methanol as
fuels. Data for these technologies are not final, and their integration in the current MARKAL
database is incomplete. Note that electric-powered vehicles remain in the EAU scenarios
though, as parameterized, cannot compete with more conventional technologies and,
therefore, do not enter the market.
Table 5. Transportation Demand Projections by Vehicle Class and Year.
Vehicle
Class
Compacts
Full Size
Minivans
Pickups
SUVs
Percent
25.0
26.9
7.4
19.7
21.0
2000
585.9
629.5
172.0
461.2
490.7
Am
2005
665.8
715.3
195.4
524.1
557.6
lual Dema
2010
746.4
801.9
219.1
587.6
625.1
nd (in BVJ
2015
830.8
892.5
243.9
654.0
695.8
Ml) lor year
2020
909.2
976.7
266.9
715.7
761.4
2025
994.1
1067.9
291.8
782.5
832.5
2030
1086.7
1167.5
319.0
885.4
910.1
Total 100.0 2340 2659 2981 3318 3631 3970 4340
57
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Several considerations drove selection of the EAU scenarios, including a desire to calibrate
the MARKAL model to the EIA's AEO transportation results, a question about the condi-
tions under which hybrid vehicles achieve significant market penetration, and an interest in
the effects of sustained changes in gas prices. The model parameters varied and ranges of
values explored—including those that were adjusted without appreciable effect—are
discussed where relevant. Table 6 summarizes the EAU scenarios.
Table 6. Summary of the Evolution as Usual Transportation Scenarios.
EAU Scenario Description
Hybrid Market Examines the vehicle-specific factors driving hybrid market
penetration while competing with conventional technologies
Conventionals Dominate Examines the circumstances under which alternative vehicle
technologies do not penetrate the transportation market
Hybrid Market without Assesses hybrid market penetration when manufacturers phase out
Conventionals conventional vehicles by 2020
2X Hybrids Explores the conditions under which high-efficiency hybrids (3X) do
not enter the market
Gas Price Variation Investigates the effects of higher gas prices
5.2.1 Hybrid Market [HIVKOI Scenario
The EAU Hybrid Market [HM(C)] scenario examines the impact of moderate hybrid growth
under competition with Conventionals. The success of hybrid vehicles will depend largely on
their cost and performance relative to ICE-powered vehicles—including advanced effi-
ciency (miles per gallon)—and various "green" alternatives, consumer attitudes regarding
the environment and adoption of new technologies, manufacturing capacity, and fuel costs.
Hybrid market penetration in the MARKAL model scenarios will therefore be a function of
hybrid investment costs and operating efficiencies, the particular discount, or hurdle, rate
applied to the technology, growth constraints on hybrid penetration, and gasoline prices. The
remainder of this section describes the parameter settings relevant to the EAU Hybrid
Market Scenario. Detail on parameters not varied here is also provided as changes in their
values lie behind the storylines of the subsequent EAU scenarios.
The cost to purchase a hybrid vehicle and the savings in fuel costs (a function of engine effi-
ciency) the technology offers will be dominant parameters driving hybrid market penetra-
tion. Future hybrid investment costs and efficiencies will be a function of technical advances
(the learning that takes place in design and manufacturing), sales volumes (economies of
scale), and targeted government subsidies. Starting with the OTT QM parameters, the
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HM(C) scenario assumed values of these parameters that yielded moderate hybrid growth in
2030. By way of illustration, Table 7 compares 2X and 3X hybrid technology parameters for
compact vehicles with their conventional and advanced ICE counterparts, as well as AEO
values. Note that the numbers used here are at the low end of the spectrum summarized from
the literature in Section 3 of this report.
Table 7. Compact Vehicle Technology Parameter Values for HM(C) Scenario.
Period beginning in Year
vehicle lype
2000
2005
2010
2015
2020
2025
2030
Investment Cost ($1000, 1999)
2X Hybrid
3X Hybrid
Conventional ICE
Advanced ICE
AEO Hybrid
25.31
NAa
19.25
NA
26.89
25.31
NA
20.24
NA
26.89
24.77
NA
20.64
22.25
22.74
24.10
26.20
20.96
22.59
22.25
23.76
25.46
21.21
22.86
22.62
22.27
23.34
21.21
22.86
b
22.27
23.34
21.21
22.86
b
Efficiency (mpg)
2X Hybrid
3X Hybrid
Conventional ICE
Advanced ICE
AEO Hvbrid
44.50
NA
31.02
NA
46.36
44.50
NA
23.96
NA
45.10
55.21
NA
34.50
54.17
44.33
60.27
79.72
34.44
54.07
43.79
64.52
92.90
34.41
54.02
43.64
68.82
103.23
34.41
54.02
b
68.82
103.23
34.41
54.02
b
a NA = not available in this time period.
b AEO 2002 data available only to 2020.
HM(C) includes two sub-scenarios designed to explore the factors driving ICE-hybrid
competition. The first of these examines the effects of a 10 percent reduction in 2X hybrid
investment costs (approximately $2500) offered during the first two model periods, a
discount roughly equivalent to the tax break currently available on US hybrid vehicle
purchases. The second sub-scenario adds a 16 percent mark-up on advanced efficiency ICE
vehicles—the increase needed to reduce the alternatives to a choice between conventional
ICE and hybrid vehicles.
Beyond cost considerations, consumer demand for hybrids will depend on the willingness of
vehicle owners to invest in a new and unproven technology, a perceptual issue that the
model captures through a technology-specific discount rate (i.e., a risk premium). The
HM(C) scenario maintains the model's 5 percent discount rate for all technologies, though
this is varied in subsequent EAU scenarios.
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Even with rapid acceptance and an affordable sticker price, a rapid transition to hybrid
vehicles is unlikely. The natural turnover of the existing (conventional ICE) vehicle fleet
will slow hybrid penetration, as will the inability of manufacturers to retool assembly lines
overnight regardless of demand. The model captures this inertia through a growth rate
constraint that caps hybrid vehicle miles traveled in a given period at a declining percentage
of the previous period's available capacity (see Table 8). 2X Hybrids can enter the market at
0.5 percent of demand for each vehicle class during the first period of their availability
(2000), while 3X hybrids are limited to 1.0 percent when introduced in 2015. Note that the
growth rate constraints apply only to hybrid vehicles and do not affect conventional or
advanced ICE technologies.
Table 8. Hybrid Vehicle Growth Constraints.
Period Beginning in Year
2010 2015 2020 2025 2030
Growth Constraint (»/„ of 2QQ m 6Q 3Q 3Q
prior period utilization)
a Per period growth is constrained to the given percentage of the previous period's available capacity.
Finally, gasoline prices will influence hybrid-ICE competition. The extent to which fuel
savings compensates for the greater hybrid investment cost, given that hybrid gasoline
consumption is equally affected by higher prices, is the focus of a subsequent EAU scenario.
The HM(C) Scenario maintains the model's average $1.5 gasoline price.
The following sections describe the remaining EAU scenarios. For the sake of brevity, only
significant departures from the parameter settings described above are discussed. It is
therefore tempting to think of the EAU HM(C) Scenario as a "base" or "reference" case.
The TACT team, however, cautions against this interpretation. The HM(C) numbers
represent one possible view of the future, selected in this case to examine the potential for
moderate hybrid growth in an overall transportation scenario similar to the AEO. This state
of the world is a consequence of model assumptions, the selection of which was driven by
particular questions. The assumptions and results are no more (or less) likely than those that
follow from the other EAU scenarios. Attention should be focused on the validity of the
particular questions and assumptions, rather than on endorsement of particular results.
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5.2.2 Conventionals Dominate (CD) Scenario
The EAU Conventionals Dominate (CD) Scenario represents a replication of the EIA's
AEO transportation reference case. Exact replication of the AEO numbers, of course, is
neither possible nor desirable. The AEO, however, is a widely cited standard, and therefore
serves as a useful check on the TACT modeling results. Similar findings provide confidence
in the MARKAL model's input and structural assumptions, while divergences offer a
chance to explore how differences between TACT and AEO assumptions affect scenario
outcomes. Note that the AEO time horizon is 20 years, whereas the MARKAL database
currently extends to 2035. Section 6 takes this into account in its comparison of model
scenarios.
The AEO reference scenario describes a world in which advanced technologies play a
marginal role in meeting transportation demand (hybrids, for instance, achieve a market
share of just under 2 percent in 2020). One can image at least three situations under which
all advanced efficiency vehicles like hybrids might fail to achieve a significant market share,
leaving conventional ICE technologies to meet transportation market demand (once again,
future EAU scenarios will broaden the alternatives to include fuels other than gasoline).
Consumers, for instance, may prove reluctant to adopt a new technology, a preference
reflected in a higher associated discount rate. The Conventionals Dominate scenario,
therefore, examines the effects of a 12 percent hurdle rate on both advanced efficiency ICE
and hybrid vehicles—a 7 percent risk premium on the 5 percent rate applied to conventional
vehicle technologies (12 percent was found to be the minimum discount rate that eliminated
hybrid penetration).
The analysis explores the remaining situations in which conventional vehicle technologies
might dominate as alternative hypotheses. The first of these sub-scenarios examines the
effect of higher vehicle purchase prices—in this case, an across-the-board 15 percent
mark-up in advanced efficiency ICE and hybrid investment costs. In contrast, the second
alternative strikes at the primary (economic) advantage all advanced technology
technologies offer—their greater efficiency and consequent fuel cost savings. Very low gas
prices, if sustained, would discourage the purchase of advanced efficiency ICE and hybrid
vehicles. The analysis, therefore, looks at the impact of a long-term $1.00/gallon gas price (a
$0.50/gallon reduction from prior assumptions).
5.2.3 Hybrid Market Without Conventionals (HM) Scenario
The counterpart to the EAU Conventionals Dominate Scenario is one in which vehicle
manufacturers and buyers make a complete switch to hybrid technologies. It is conceivable
61
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that, with proven reliability and reasonable cost, hybrid engines become the new
conventional transportation technology. Manufacturers would realize the savings of tooling
their assembly lines for a single technology, which would help bring costs in line with
traditional ICE vehicles. Such a transition might be seen as part of the natural evolution of
transportation technologies, even if consumers did not demand the fuel savings and
environmental benefits of hybrid engines. The EAU Hybrid Market Without Conventionals
(HM) Scenario therefore examines the impact of phasing out (via a model constraint) all
conventional vehicles in 2025. Note that the entry rate on the 2X hybrid growth constraint
had to be increased from 0.5 to 1.0 percent of the initial period demand in order to allow
sufficient market penetration in subsequent years.
5.2.4 2X Hybrid (2xH) Scenario
The MARKAL database currently includes two hybrid vehicle types, characterized by their
efficiencies relative to their ICE counterparts. These hybrid options are available across
vehicle classes, though their availability dates differ; see Table 7. 2X hybrids, with a 100
percent efficiency improvement on ICE vehicles, are available in 2000, while their 3X
higher-efficiency counterparts can enter the transportation market in 2015. The 2X Hybrid
(2xH) Scenario examines hybrid market penetration when only the 2X option is offered.
This scenario therefore represents a situation where the performance of hybrid engines
remains below the more optimistic goals set by the U.S. DOE.
5.2.5 Gas Price Variation (GP) Scenario
Finally, hybrids offer the perceptual advantage of owning an environmentally-friendly
vehicle. More pragmatically, of course, their greater efficiencies will yield long-term
savings in fuel costs. The attractiveness of hybrids relative to standard ICE-powered
vehicles might, therefore, be expected to increase with the price of gasoline. The extent to
which this advantage is realized, however, depends on both vehicle efficiency and the extent
to which owners value future operating cost reductions vis-a-vis a more immediate increase
in purchase price (a function of the potential buyer's implicit discount rate). The Gas Price
Variation (GP) Scenario approaches this issue by examining how a gas price of $4.5/gallon
affects the conventional-hybrid balance.
Section 6 discusses results from each of these EAU storylines. Once again, the goal is not to
present a favored view of the world either in terms of model inputs (how hybrid vehicles, for
instance, might be expected to perform in 2030) or outputs (the shape of the transportation
sector's emission trajectory). Each storyline corresponds to a particular question, and the
value lies not in an isolated set of results, but in comparing answers across scenarios.
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5.3 Early Phase Hydrogen Economy Scenarios
The EAU scenarios focused exclusively on factors that might shift the balance between
future ICE and hybrid vehicle market penetration. The EPHE scenarios build on this
analysis by adding H2 fuel cells as a third high-efficiency vehicle technology. In the
long-term, several drivers may lead the transportation sector to adopt H2 as a fuel, including
a potential need to
• achieve further reductions in environmental emissions,
• reduce the transportation sector's reliance on oil imports,
• reduce GHG emissions through sequestration of carbon dioxide at a centralized
fossil fuel-based H2 plant, and
• shift the fuel for future vehicles from fossil fuels to renewable resources.
The EPHE scenarios do not explore these drivers directly. Rather, they seek to examine the
near-term consequences of introducing H2 as a fuel in the U.S. transportation sector and,
therefore, estimate the added costs of adopting H2 fuel cell vehicles. In addition, because
there are virtually no tailpipe emissions from H2 FCVs, the reduction in transportation sector
emissions will reflect the avoidance of emissions from vehicles displaced by their H2
equivalents. In the next phase of this work, TACT will examine emissions from a complete
life cycle perspective, one that fully accounts for the possible increase in emissions
associated with H2 production. The MARKAL modeling framework is uniquely suited for
this type of systems-level assessment.
The full set of assumptions behind the EAU scenarios apply to the EPHE storylines. The
EPHE scenarios, however, go beyond a focus on vehicle technologies and examine the
makeup of the H2 infrastructure. Centralized facilities, for instance, might produce H2 for
distribution to refueling stations by truck or pipeline; alternatively, refueling stations might
produce H2 on-site by electrolysis or steam methane reforming. Section 4 described how the
EPHE H2 infrastructure has been mapped into the MARKAL framework, and the three sets
of EPHE scenarios include assessment of these infrastructure differences and their impact on
resource consumption. Table 9 and the following sections summarize the EPHE scenarios.
Future TACT work will examine additional H2 production pathways and a wider variety of
parameter values for H2 production and distribution technologies.
5.3.1 Hydrogen Market Scenario (H2M)
Equivalent to the EAU Hybrid Market Scenario with Conventionals [HM(C)], H2M
examines the effects on ICE and hybrid vehicle market shares, as well as emissions, for a
moderate H2 fuel cell market penetration. Tables 10 and 11 show the parameter values for
63
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Table 9. Summary of the Early Phase Hydrogen Economy Transportation Scenarios.
EPHE Scenario Description
Hydrogen Market Examines the impact of a moderate H2 fuel cell vehicle market
penetration
Optimistic Hydrogen Assesses the impact of optimistic H2 production and distribution
efficiencies as well as H2 vehicle costs and efficiencies
Hydrogen Forcing Forces the VMT by H2 FCVs in year 2030 and beyond to be greater
than or equal to some fraction (e.g., 10%, 20%, 30%, 40%, 50%) of
the total 2030 demand
the compact H2 FCVs used in the H2M scenario. Note that, although compact FCVs do not
become available until 2020, larger classes (for which design issues are simpler to resolve)
are available in 2015. Table 12 summarizes the assumptions associated with H2 production.
For the purpose of emission calculations, H2 FCVs are treated as zero-emission vehicles.
Table 10. Compact Vehicle Technology Parameter Values for H2M Scenario.
Period beginning in Year
Vehicle lype
2000
2005 2010
2015 2020
2025
2030
Investment Cost ($1000, 1999)
H2FCV
AEO H9 FCV
NAa
NA
NA NA
84.46 50.94
NA 27.58
34.73 27.11
24.4
b
24.4
b
Efficiency (mpg gasoline equivalent)
H2FCV
AEO H, FCV
NA
NA
NA NA
50.94 49.73
NA 92.9
48.99 48.43
103.23
b
103.23
b
a NA = not available in this time period.
b AEO 2002 data available only to 2020.
Table 11. Fuel Cell Vehicle Growth Constraints.
Period Beginning in Year
2010 2015 2020 2025 2030
Growth Constraint (% of 2QQ m 6Q 3Q 3Q
prior period utilization)
a Per period growth is constrained to the given percentage of the previous period's available capacity.
64
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Table 12. Hydrogen Production Parameter Values for H2M Scenario.
1999 $Million/PJ/yr
Description
Central steam methane reformer
Steam methane reforming at station
Alkaline electrolysis at station
Alkaline electrolysis at station (night operation only)3
Electrolyzer - residential (24 hour operation)
Electrolyzer - residential (night-only operation)3
Investment Cost
20.56
87.71
65.39
130.78
124.66
249.33
O&M Costs
1.88
3.86
0.31
0.31
3.05
3.05
Because equipment capacities are measured in petajoules of output per year, equipment designated for night only
operation has twice the investment cost.
5.3.2 Optimistic Hydrogen Scenario (H20)
The EPHE H2O scenario investigates a future of faster movement towards a H2 economy by
examining the impact of optimistic assumptions regarding H2 FCV costs and efficiencies. In
this scenario, research finds cost effective solutions to H2 storage and transport issues, and
manufacturing develops cheaper fuel cell stacks making cars more cost competitive. Success
in the FCV market thus encourages implementation of the necessary infrastructure.
5.3.3 Hydrogen Forcing Scenario (H2F)
Without major scientific breakthroughs or very large subsidies, H2 is not likely to be
competitive as a transportation fuel in the near term. The EPFIE H2F scenarios, therefore,
force a H2 FCV market share in 2030. The scenarios, which look at penetration rates ranging
from 10 to 50 percent (in 10 percent increments), capture potential shifts between
electrolysis and steam methane reforming as sources of supply for an increasing H2 demand.
The associated changes in gasoline and natural gas consumption are of particular interest.
65
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66
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Section 6
Scenario Results and Analysis
The previous section identified several known factors that will shape the path along which
transportation technologies evolve. Different combinations of these factors yielded possible
futures and the section translated the resulting storylines into specific MARKAL scenarios.
This section takes the conditions outlined in each scenario as a set of starting assumptions
and examines their consequences as they play out in the MARKAL modeling framework.
The first two sections examine the Evolution as Usual (EAU) and Early Phase Hydrogen
Economy (EPHE) results, respectively. The third section approaches the long-term goal of
the TACT project by offering a comparative analysis of these futures. When complete, the
MARKAL database will allow an integrated assessment of all transportation technologies
and will identify optimal paths based on their full life cycle implications—how the
consequences of meeting a given level of transportation demand cascade through the entire
energy economy.
6.1 EAU Scenario Outcomes
The goal of the TACT assessment is a comparative analysis of possible futures; hence, this
section is organized around results, not specific EAU scenarios. Three sets of transportation
scenario outcomes are of interest within the larger scope of this report: the particular
technology paths along which transportation demand is met through 2030, the corresponding
levels of fuel consumption, and the emission profiles that follow. As described in Section 5,
the EAU scenarios focus on the circumstances driving the competition between conven-
tional and advanced ICE vehicles and their hybrid counterparts. Attention should, therefore,
be focused on how the conditions outlined in each scenario affect the choice of vehicle
technology and resulting demand for gasoline and then on how the extent to which hybrid
vehicles enter the market affects transportation-related emissions.
Figures 14 through 18 show how individual vehicle technologies contribute to meeting
demand across time in terms of vehicle miles traveled for each EAU scenario, while Figures
19 through 23 express the same information as a percentage of per-period demand.
Comparisons across scenarios are made in Figures 24 and 25 where market penetrations for
67
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5000
4500
4000
3500
H 3000
c 2500
O
2X hybrid
advanced diesel
advanced yasoline ICE
Conventional
existing-,
2000
2005
2010 2015
Year
2020
2025
2030
Figure 14. Per-period VMT of each Technology for the HM(C) Scenario.
advanced ICE vehicles and hybrids (2X and 3X combined), respectively, are shown in 2020
and 2030. Although the TACT project is ultimately concerned with emissions several
decades out (2030 in this report; 2050 in future analyses), the evolution of transportation
technologies between now and then is of interest because it will affect overall fuel
consumption and, hence, aggregate emissions.
The most fundamental question to ask of these results is simply what shifts the balance
between ICE-powered vehicles and hybrids in favor of the latter. The Hybrid Market
Scenario [HM(C)] was derived from OTT assumptions about future transportation
technology costs and performance and illustrates moderate hybrid growth (Figure 14). By
2030, hybrids meet approximately 13 percent of demand, with 2X vehicles accounting for
two-thirds of the hybrid market share. Advanced efficiency ICE vehicles meet the remaining
transportation demand.
68
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C
o
CO
5000
4500
advanced yasoline ICE
conventional -\
2000
2005
2010 2015
Year
2020
2025
2030
Figure 15. Per-period VMT of each Technology for the 2XH Scenario.
5000 i
c
0
CD
4500
4000
3500
3000
2500
3X hybrid -
2X hybrid
advanced diesel
advanced gasoline ICE
conventions
2000
2005
2010 2015
Year
2020
2025
2030
Figure 16. Per-period VMT of each Technology for the GP Scenario.
69
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1995
2000 2005
2010 2015
Year
2020
2025 2030
Figure 17. Per-period VMT of each Technology for the HM Scenario.
5000
4500
1995 2000
2005
2010 2015
Year
2020 2025
2030
Figure 18. Per-period VMT of each Technology for the CD Scenario.
70
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1995
existiny
conventional
advanced
gasoline ICE
advanced diesel
2X hybrid
3X hybrid
2000
2005
2010 2015
Period
2020
2025
2030
Figure 19. Technology Penetration per Period for HM(C) Scenario.
1995 2000 2005 2010 2015
Period
2020 2025
2030
existing
gasoline ICE
advanced diesel
Figure 20. Technology Penetration per Period for 2XH Scenario.
71
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existing
conventional
advanced
gasoline ICE
advanced diesel
2X hybrid
3X hybrid
1995 2000 2005 2010 2015 2020 2025 2030
Period
Figure 21. Technology Penetration per Period for GP Scenario.
1995
• existing
Kj conventionals
•advanced
gasoline ICE
~~| advanced diesel
~| 2X hybrid
r~| 3X hybrid
2000
2005
2010 2015
Period
2020
2025
2030
Figure 22. Technology Penetration per Period for HM Scenario.
72
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1995
2000
2005
2010 2015
Period
2020
2025
existing
conventionals
2030
Figure 23. Technology Penetration per Period for CD Scenario.
- 70
c
o
'J
O
111
o
100%
2020
100%
2030
Year
Figure 24. Comparison of Non-Hybrid Penetrations across EAU Scenarios at 2020 and
2030.
73
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2020
2030
Year
Figure 25. Comparison of Total Hybrid Penetrations across EAU Scenarios at 2020
and 2030.
The two HM(C) scenarios probe the extent to which relative investment costs determine
hybrid penetration. Starting with hybrids, a 10 percent investment cost reduction on 2X
vehicles during the 2000 and 2005 time periods—equivalent to today's hybrid vehicle tax
credit—increases aggregate 2030 hybrid market share to nearly 28 percent, with all the
growth coming in 2X vehicles at the expense of their advanced efficiency ICE counterparts
(Figure 26). Likewise, a 16 percent increase in advanced efficiency ICE investment costs
blocks their market entry and boosts hybrid penetration to 31 percent. Conventional ICE and
advanced diesel vehicles meet the remaining transportation demand.
The remaining EAU scenarios expand the HM(C) analysis. The 2X Scenario (2XH), for
instance, examines a technology path that excludes 3X hybrids. In this situation where the
DOE's more optimistic technology development goals are not reached, hybrids achieve the
same overall 2030 hybrid market share as HM(C), about 13 percent (Figure 20). The EAU
Gas Price Scenario (GP), in contrast, shifts the 2X-3X balance and achieves a significant
hybrid penetration—a 64 percent market share that favors 3X hybrids when the price of
gasoline reaches $4.5/gallon (advanced mpg ICE vehicles meet the remaining demand; see
74
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existing
Conventionals
advanced
gasoline ICE
advanced diesel
2X hybrid
3X hybrid
1995
2000
2005
2010 2015
Period
2020
2025
2030
Figure 26. Technology Penetration in HM(C) Scenario with 10% Price Incentive for
Hybrids.
Figure 21). The Hybrid Market Without Conventionals Scenario (HM) goes further; 3X
hybrids capture nearly 83 percent of the transportation market when manufacturers phase
out all ICE-powered vehicles, with 2X hybrids and advanced diesel-fueled vehicles meeting
the remaining demand (Figure 22).
Advanced efficiency vehicles therefore remain competitive unless gas prices nearly triple
(GP) or gasoline-electric hybrid engines become the new "conventional" power train (HM).
Note that the EAU Conventionals Dominate Scenario (CD) postulated three situations under
which ICE vehicles might entirely out-compete their hybrid equivalents: a higher hurdle rate
on all non-conventional transportation technologies (including advanced efficiency ICE-
powered vehicles), a sustained reduction in gas prices (to, say, $1.0/gallon), and a
higher-than-expected [15 percent over HM(C)] hybrid investment cost. The CD scenario in
Figure 23 captures the effects of the first situation, and Figures 27 and 28 illustrate the
effects of the latter two situations.
Figure 29 compares gasoline consumption across the EAU scenarios and serves as the
bridge between the technology paths illustrated previously and the corresponding emission
profiles presented in Figures 30 through 33. In all scenarios except CD, gasoline
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1995
2000
2005
2010 2015
Period
2020
2025
2030
existiny
convent ionals
advanced
gasoline ICE
advanced diesel
2X hybrid
3X hybrid
Figure 27. Technology Penetration in HM(C) Scenario with ($0.50/gal) Gas Price
Reduction.
existing
yasoline ICE
advanced dissel
1995 2000 2005 2010 2015 2020 2025 2030
Period
Figure 28. Technology Penetration in HM(C) Scenario with 15% Price Increase on
Hybrids.
76
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30.000 -,
25.000
tfl
_ 20.000
V
V)
0)
T3
C
C8
15.000
.E 10,000
o
5000
- HU(C)
-CD
-HM
-GP
1995 2000 2005 2010 2015 2020 2025 2030
Period
Figure 29. Per-period Gasoline and Diesel Consumption across EAU Scenarios.
consumption peaks relatively early (in the 2005 time frame) and then declines as a combina-
tion of more efficient advanced ICE vehicles and hybrids enters the market and the useful
life of pre-2000 vintage vehicles ends. The decline is sustained for the scenarios with
substantial 3X hybrid penetration (HM and GP); otherwise, gasoline consumption begins a
modest upward trend after 2020 as the increase in transportation demand (miles traveled)
overcomes the improvement in vehicle fleet efficiency in determining total fuel require-
ments. By 2030, transportation sector gasoline demand for the CD scenario is nearly 80
percent above the scenario with the second greatest fuel consumption (2XH) and is more
than twice that of several others. Improvements in overall vehicle efficiency, achieved via
diffusion of both advanced efficiency ICE and hybrid vehicles, therefore have a significant
impact on resource requirements, with the magnitude of the reduction in gasoline
consumption directly proportional to hybrid penetration.
The emission results paint a similar picture. Figures 30 through 33 compare PM10 (PM with
aerodynamic diameter 10 |im or less), CO, NOX, and VOC emissions (respectively) across
scenarios. The figures express emissions for the 2030 time period relative to the CD
scenario in which conventional ICE vehicles dominate the transportation market. In all
cases, the all-hybrid HM scenario (83 percent high-efficiency 3X vehicles) achieves the
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HM(C)
HM 2xH
Scenario
Figure 30. PM10 Emission Reductions in 2030 Relative to the CD Scenario
(20 thousand tons PM10/yr).
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Figure 31. CO Emission Reductions in 2030 Relative to the CD Scenario (7590
thousand tons CO/yr).
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Figure 32. NOX Emission Reductions Relative to CD Scenario (300
thousand tons NOx/yr).
HM(C)
HM 2xH
Scenario
Figure 33. VOC Emission Reductions in 2030 Relative to CD Scenario (230
thousand tons VOC/yr).
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largest relative emission reduction, followed by GP, 2XH, and the moderate hybrid HM(C).
In short, the greater the hybrid market penetration, the greater the emissions reduction in
2030 relative to a conventional ICE-only world. Maximum differences range from nearly 90
percent for PM10 to 17 percent for CO, with reductions of 70 and 50 percent, respectively,
forNOxandVOCs.
The Section 6.3 provides further discussion of the EAU results in its comparison with the
EPHE scenario findings. The results from the latter, which represent the TACT's first steps
toward its long-term goal of a comprehensive hydrogen economy analysis, are presented
next.
6.2 EPHE Scenario Outcomes
The EPHE scenarios add a H2 fuel infrastructure and H2 FCVs to the EAU framework. The
EPHE storylines—especially the Hydrogen Market (H2M) and Optimistic Hydrogen (H2O)
scenarios—first ask how these additions displace hybrid and advanced efficiency ICE
vehicles and ask then how these shifts in technology market penetration impact transpor-
tation sector emissions. Beyond this comparison, the EPHE Hydrogen Forcing Scenarios
(H2F) look at how the need to meet a given H2 vehicle market share affects the means of
supplying H2 fuel. This section addresses these questions, while the following section draws
more general conclusions from a comparison of the EAU and EPHE scenarios.
Figures 34 and 35 show how the H2M and H2O assumptions affect vehicle technology
market share in meeting actual per-period transportation demand (in billion VMT); Figures
36 and 37, respectively, express the same data on a percentage basis. By 2030, H2 FCVs in
the H2M scenario achieve a modest (9.3 percent) market penetration; the more optimistic
H2O assumptions increase this value slightly (to 13.7 percent). Advanced efficiency ICE
vehicles meet better than 80 percent of transportation demand for both EPHE scenarios,
whereas the total hybrid market penetration remains below 10 percent. Thus the introduction
of H2 FCVs takes market share primarily from hybrids. An 8 percent increase in all H2
vehicle investment costs is sufficient to drive FCVs out of the market, reproducing the EAU
HM(C) (Hybrid Market with Conventionals) scenario.
Gasoline consumption across EPHE H2M and H2O scenarios mimics that of the EAU
storylines (Figure 38). Fuel use peaks early and declines for several model periods before
leveling off after2020. Once H2 FCVs enter the market, they capture a substantial part of the
growth in transportation demand over subsequent periods, while the market share of
advanced efficiency ICE vehicles plateaus. Gasoline consumption changes accordingly.
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5000 -,
4500
4000
o
CD
advanced diesel
conventionals —i
2025 2030
Figure 34. Per-period VMT for each Technology in the H2M Scenario.
5000
4500
1995 2000 2005 2010 2015 2020 2025 2030
Figure 35. Per-period VMT for each Technology in the H2O Scenario.
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1995
2000
2005
2010 2015
Period
2020
2025
2030
existing
oonventionals
advanced
yasoline ICE
advanced diesel
2X hybrid
3X hybrid
H2 FCVs
Figure 36. Technology Penetration per Period for H2M Scenario.
1995
2000
2005
2010 2015
Period
2020
2025
2030
existing
conventionals
advanced
gasoline ICE
advanced diesel
2X hybrid
3X hybrid
H2 FCVs
Figure 37. Technology Penetration per Period for H2O Scenario.
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Figure 38. Per-period Gasoline and Fuel Use for EPHE Scenarios.
Figure 39 expresses the H2M and H2O emissions for 2030 relative to the HM(C) values.
The modest displacement of gasoline-fueled vehicles (ICE and hybrid) by their H2 fuel cell
counterparts yields relative emissions reductions that range between 5 percent for both PM10
and NOX, to 10 percent for CO and 16 percent for VOCs, given the H2M assumptions. The
more optimistic H2O assumptions increase these reductions to 8 (PM10 and NOX), 14 (CO)
and 24 percent (VOCs). Again, these numbers reflect decreases in transportation sector (i.e.,
tailpipe) emissions, and are therefore a function of the displacement of one vehicle techno-
logy (or fuel) by another. Future TACT work will examine the lifecycle emissions assoc-
iated with the full H2 fuel cycle, particularly those that occur "upstream" with H2 production.
Significant H2 consumption might also indirectly affect emissions outside the H2 fuel chain.
Sufficient demand for H2 would affect the price—and, therefore, consumption—of natural
gas and electricity across the economy. With completion of the electric sector database,
MARKAL will allow TACT to examine the associated impacts on emissions.
The EPFIE scenarios as currently modeled, however, provide an initial look at how H2 fuel
might be supplied. All H2M and nearly all H2O hydrogen, for instance, is produced at local
refueling stations by steam methane reforming (electrolysis at the refueling station provides
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25 n
PM10
CO NOX
Pollutant
VOCs
Figure 39. Emissions Reductions for EPHE Scenarios Relative to HM(C) Scenario.
nearly 10 percent of the H2O scenario H2). Given the EPHE H2M assumptions, forcing a
minimum H2 vehicle market share (up to 50 percent by 2030) does not affect the preference
for steam methane reforming. This technological preference, however, does divert an in-
creasing share of natural gas into H2 production as the market share of H2 FCVs grows. The
EPHE H2M Scenario, for instance, requires nearly 4 percent of the natural gas consumed
economy-wide in 2030 in order to achieve a 9 percent vehicle penetration, whereas a forced
50 percent market share requires over 15 percent of the total gas consumed to meet its H2
needs. Subsequent TACT analysis will explore the economic implications of this diversion.
The EPHE forcing scenarios also illustrate how gasoline consumption and emissions vary
with market entry and penetration of H2 FCVs. Figure 40 compares 2030 gasoline
consumption across the five H2 FCV forcing runs. The largest decrease from a conventional
ICE-only world comes with the entry of hybrids as shown in Figure 29. Comparing Figures
38 and 40 shows that the adoption of H2 FCVs yields further reductions in gasoline
consumption only when their market share begins to exceed 20 percent. Emissions relative
to the EAU CD scenario vary inversely with adoption of hydrogen FCVs, and production of
PM10, CO, NOX, and VOCs decreases to half of EAU CD levels when forced penetration
reaches 50 percent (Figure 41).
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Figure 40. Gasoline and Diesel Consumption in 2030 for the H2F Scenarios.
10
20 30 40
Penetration Rate at 2030, %
50
VOCs
Figure 41. Emission Reductions (%) in 2030 for the H2F Scenarios.
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Apart from the macroeconomic issues hinted at here, future EPHE scenario work will
examine the transition to a hydrogen economy. Gasoline fuel cells, for instance, would
likely play an important role in the early diffusion of transportation fuel cell technology. The
complete MARKAL transportation database will provide this more comprehensive picture
of the route from the present to various transportation futures.
6.3 Comparison ofEAU and EPHE
The EAU and EPHE scenario analyses tell an incremental story about the diffusion of
alternatives to conventional ICE vehicles and the resulting impact on transportation sector
emissions. Variations in assumptions about factors thought likely to drive future preferences
for conventional and advanced efficiency ICE vehicles versus their hybrid counterparts
yielded the EAU scenarios. The EPHE storylines built on this analysis by introducing a H2
fuel infrastructure and hydrogen FCVs.
As the previous section noted, advanced efficiency ICE vehicles continue to meet at least
four-fifths of transportation demand in 2030 if cost and performance numbers for the
technologies included here fall near their assumed values (given, as well, the larger set of
assumptions embedded in the MARKAL modeling framework). These values are derived
from the recent literature and, therefore, reflect current thinking. MARKAL, in turn,
provides a consistent means of determining the consequences of these assumptions whuch,
in this case, indicate that, all other things being equal, gasoline-electric hybrid and hydrogen
FCVs do not achieve more than a combined 20 percent market share before 2030. Situations
such as a significant increase in gasoline prices (the EAU GP scenario) affects these conclu-
sions, and it is not hard to imagine technological, economic, and political factors—some that
necessarily fall outside of this modeling framework—that might easily change the outcome.
Hence, the results presented here should not be taken as predictions about an unknowable
future.
The market penetration of hybrid and H2 fuel cell technologies is constrained in both EAU
and EPHE scenarios by a combination of high investment costs and rate of growth con-
straints. Although the latter may seem to be an artifact of the model, the growth constraints
reflect the gradual nature of historical technology diffusion and recognize that supporting
infrastructure must evolve in tandem with end-use technology. As captured here, more
radical transportation infrastructure change includes the development of a system to provide
H2 fuel, but also recognizes the need for vehicle manufacturers to commit to a new tech-
nology and retool their assembly lines.
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Incorporating learning and economy-of-scale effects in the model might yield a more
favorable outcome for hybrids and H2 fuel cells as investment costs and efficiencies would
likely improve with market penetration, which would in turn lead to even greater penetration
in subsequent time periods. The EAU and EPHE results described above are, therefore, con-
servative in the sense that they do not capture these dynamics endogenously (i.e., MARKAL
takes all per-period costs and efficiencies as input assumptions and cannot change their
values to reflect growing investment). MARKAL-ETL (Endogenous Technology Learning)
incorporates technological learning in an extended version of the basic MARKAL model
and may be adopted for future TACT analysis.
Notable across the scenarios, however, is a tendency to select higher efficiency technologies
in the transportation sector. In most scenarios, these higher efficiency vehicles are predom-
inantly advanced efficiency ICE vehicles, with hybrids and H2 fuel cells achieving a small
but stable market share by 2030. In these preliminary emissions modeling results, these
selections have a significant impact on transportation sector emissions. Figures 42 through
45 show emission reductions for the EAU and EHPE scenarios, all expressed relative to the
EAU conventional ICE-only (CD) scenario. The greatest reductions are seen in PM10, with
significant changes in VOCs and, for a couple of scenarios in particular, in NOX as well. The
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two hybrid-heavy EAU scenarios (HM and GP) achieve the largest relative decreases in
PM10, NOX, and VOCs. The availability of hydrogen FCVs, on the other hand, yields the
largest CO reductions relative to a world constrained to conventional ICE vehicles. A more
thorough and sophisticated emissions analysis will be necessary to determine the likely
extent and distribution of such reductions in these and other scenarios.
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Section 7
Future Work
This report has described the process TACT will use to evaluate technology scenarios for
their impact on future air emissions. Example results are provided for personal vehicles, an
important component of the transportation sector.
The purpose of this section is to describe the next steps toward the goal of providing com-
prehensive energy system assessments from future technology changes in the transportation
and electricity generation sectors. Anticipated steps are described below, including con-
tinued database development and extension to 2050, documentation, and release; improve-
ments to the representation of the transportation and electricity sectors; evaluation of
approaches for incorporating economic interactions; the development of a set of alternative
technology futures, sensitivity and uncertainty analysis; and integration with the ORD Air
Quality Assessment.
7.1 Database Development
One of the primary products of this work will be the public release of the U.S. Reference
Energy System database. The final development activities for this release involve imple-
menting the refinements to the industrial sector (as discussed in Section 2), completion of
the emission factor component of the database, and development of supporting documen-
tation, including documentation of the calibration process. Each of these activities is
on-going. The database will be extended to 2050, in order to support TACT's analyses for
the ORD Air Quality Assessment and will be updated to AEO 2004. The database as a
whole will also be reviewed by MARKAL modelers. (To this point, review has been on a
sector basis, by sector-appropriate energy experts who are not necessarily MARKAL users.)
The database and supporting documentation are expected to be ready for release in the fall
of2004.
7.2 Expansion of Future Technologies in the Transportation Sector
As discussed in Sections 3 through 6, significant work has been done to characterize
personal vehicle fuels and technologies. Additional work in this area will be to include
biofuels such as methanol, ethanol, and biodiesel. Conversion processes for forming biofuels
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are presently being investigated, as are the associated transportation technologies, such as
methanol- powered FCVs. Additional work outside the personal vehicle subsector may
include analyses of future technologies and fuels for freight and mass transit.
7.3 Expansion of Future Technologies in the Electricity Generation
Sector
As core database and scenario work on the transportation sector begins to near completion,
the TACT team will shift its focus to electric power generation. Modeling of the U.S.
electric sector will continue to follow the same pattern of activities that TACT has pursued
with transportation. Although much of the electric sector portion of the MARKAL database
is in place, emission coefficients for combustion technologies, for instance, must be added
and an approximate calibration to AEO numbers accomplished. Following completion of the
database, TACT electric power specialists—in consultation with other knowledgeable
individuals within and outside of the EPA—will assemble a series of advanced technology
scenarios akin to the transportation EAU and EPFIE storylines.
A scenario-development philosophy similar to that described in this report will guide the
electric sector work. Evolution as Usual storylines examining gradual improvements in coal-
and natural gas-fired generators, as well as wind, solar, nuclear, and other contemporary
non-fossil energy sources will be constructed. These scenarios will be compared to electric
sector futures that represent a more radical shift from an evolution of the current U.S. power
generating infrastructure. Technology scenarios that fall into the latter category include CO2
capture and sequestration, which promises to be an important route to a more comprehensive
hydrogen economy. As envisioned by the DOE's FutureGen initiative, H2 would be pro-
duced via coal gasification or natural gas reforming at centralized plants, with the resulting
CO2 injected into an underground geological formation. The H2 would then be available for
use in turbines for electric power generation, or it could be sold for transportation use.
Further electric sector scenarios might focus on more radical improvements in solar
technologies and even the transition to a distributed power generating infrastructure (using,
for instance, microturbines or fuel cells) provides further scenario options. A complete
MARKAL model will allow an integrated lifecycle analysis of both transportation and
electric sector technologies, one that will trace the implications of different scenarios along
the chain of energy technologies from resource extraction, through processing and
transformation to end-use.
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7.4 Evaluating Approaches for Incorporating Economic and
Learning Effects
Energy and vehicle demands are currently determined exogenously from MARKAL. There-
fore, changes in energy prices is not currently captured although they would undoubtedly
have an effect on these demands. Several approaches for capturing elasticity in demand are
available. One such approach is to use a version of MARKAL called MARKAL-Elastic
Demand. This model would allow energy service demands to be sensitive to price changes
through price elasticities. Another potential approach would be to link MARKAL to an
economic model. For example, a version of MARKAL called MARKAL-MACRO has been
linked to a macro-economic model, allowing the effects of energy prices on economic
growth and energy demand to be characterized. Alternatives to this approach include linking
MARKAL with other economic models, such as Regional Economic Models, Inc. (REMI)
or EPA's Economics Model for Environmental Policy Analysis (EMPAX).
Likewise, the present TACT MARKAL model takes all technology cost and efficiency
parameters as fixed input assumptions that do not change with market share. The results
discussed in this report, therefore, do not reflect the cost and performance improvements
that come with the widespread adoption of a new technology. MARKAL-ETL (Endogenous
Technology Learning), however, extends the base model to capture learning dynamics and
economy-of-scale effects and may be adopted for future TACT analysis. The practicality,
advantages, and disadvantages of each of these approaches will be explored.
7.5 Scenario Development and Analysis
Once the extension of the database to 2050 is completed and the database finalized,
additional scenario runs will be performed in the transportation sector. From these scenarios,
several storylines will be chosen in consultation with other research groups participating in
the ORD Air Quality Assessment. Then the transportation futures (both technology options
and penetrations) from these storylines will be provided for the next phase of the air quality
work.
The TACT team will perform additional investigations on these scenarios analyzing the cost
implications of technology choices. Several papers and presentations are anticipated from
this work. Some of the hydrogen work will be provided to DOE's Hydrogen Analysis (H2A)
workgroup. This group consists of members from national labs, universities, federal
agencies and stakeholders with its mission to "Improve the transparency and consistency of
approach to analysis, improve the understanding of the differences among analyses, and
seek better validation from industry."
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7.5.1 Generation of alternative future scenarios
MARKAL selects control technologies based on least cost. Thus, the MARKAL results are
a prediction of the most inexpensive approach that could be taken (based on assumptions,
etc.), but do not necessarily predict what will occur. Since corporations and consumers tend
to act in such a way to reduce costs to themselves, it can be argued that the MARKAL
results will identify tendencies. For the Air Quality Assessment, however, the goal will be to
predict and characterize the ramifications on air quality of alternative technological futures.
This implies that the model will be used to predict possible futures, a task for which the
traditional use of least-cost optimization is not well suited.
An alternative approach is to modify MARKAL to perform a variant of least cost optimi-
zation called Modeling to Generate Alternatives, or MGA. MGA techniques provide an
efficient means to develop a small set of distinctively different, yet reasonable, solutions to
an optimization problem. In the context of MARKAL modeling, these alternatives can
represent alternative technological futures.
In order to carry out a MGA analysis, the least cost solution is first identified. Next, the
MARKAL objective function is modified to maximize the difference from the least cost
solution, and a bound is placed on cost (e.g., 10 percent greater than the least cost). The
model is then used to identify an alternative solution. The process is repeated, with the new
objective being to maximize the difference from both the least cost and first alternative.
Additional alternatives can be generated until a sufficient number have been identified or
until no additional alternatives sufficiently different from the solutions already identified can
be generated.
An advantage of MGA approaches is that they represent only incremental modifications to
the original model formulation. Further, the similarity or difference among the alternatives
can provide valuable information not available from only a least-cost solution. For example,
if all of the alternatives that meet future air quality constraints involve the adoption of a
hydrogen infrastructure, this suggests that such an infrastructure may be necessary to
achieve the desired results. If, in contrast, a variety of very different solutions meet the air
quality constraint, this suggests much more flexibility.
7.5.2 Sensitivity analysis
Given the set of plausible technological futures, an important next step will be to conduct a
sensitivity analysis. Sensitivity analyses are useful in understanding how individual inputs
and assumptions affect model results. For example, a sensitivity analysis may suggest that
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the effect of a parameter such as gasoline prices at the pump has a much greater impact on a
particular technological outcome than does discount rate. Sensitivity analysis is also of use
in identifying those inputs that have the greatest affect on outputs, allowing the values for
those inputs to be refined in further analyses.
Sensitivity analysis approaches can be classified as being either brute-force or implicit. One
of the most common types of brute force sensitivity analysis is Nominal Range Sensitivity
Analysis (NRSA). Using NRSA, one first identifies the inputs to be considered in the
sensitivity analysis. Next, the endpoints of the plausible range for each input are identified.
One would then conduct a new MARKAL run for each endpoint of each selected input, with
the other inputs held at their baseline values. Thus, if there were 10 inputs considered in the
sensitivity analysis, 20 runs would be required. The results could then be plotted or
presented in a table to illustrate the response to changes in each parameter.
Implicit sensitivity analysis does not make use of iterative runs as in the brute force
approaches, but instead examines the outputs of the MARKAL solutions to infer sensitivity
information. For example, typical linear programming (LP) solutions (like those generated
by MARKAL) include information that characterizes
• how much the optimal solution will change with an incremental change to the
bounds on each constraint, and
• the amount that any constraint can be changed before the optimal solution changes.
An advantage of implicit techniques is that they do not require additional runs to
provide this information.
These techniques are expected to provide information that will be useful in understanding
and evaluating MARKAL results. Thus, a major step will be to design and carry out a
sensitivity analysis.
7.5.3 Uncertainty analysis
Modeling activities often involve a high degree of uncertainty. Sources of uncertainty
include measurement error, sampling bias, spatial and temporal averaging, and imperfect
model formulation. Failure to account for these uncertainties may suggest to analysts and
decision-makers that there is a higher degree of precision in model results than is actually
the case.
Uncertainty analysis involves the characterization of how uncertainties in the inputs to an
assessment affect uncertainties in the outputs of the assessment. Thus, one outcome of an
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uncertainty analysis is a representation of the uncertainty in each critical assessment output.
Depending on the level of characterization of uncertainties in inputs, this representation may
range from a qualitative descriptor, to a set of high and low bounds, to a probability density
function. The analysis may also result in a ranking of uncertain inputs that characterizes
their relative influence on uncertainties in outputs. Such information is useful in allocating
resources most efficiently for increasing the precision in assessment outputs.
Uncertainty analysis approaches typically involve propagation of uncertainties through a
model. Propagation techniques fall into the categories of analytical, approximation, and
numerical. Analytical techniques are useful for problems involving linear summations and
simple statistical distributions for inputs. Approximation methods are similar, but allow
application to a wider range of problems through Taylor series expansions or similar
approaches. These techniques are very limited for problems that are nondifferentiable,
however. Numerical propagation algorithms are computationally intensive, but are well
suited to address most uncertainty analysis problems, including those that are nonlinear,
non-differentiable, or that involve empirical descriptions of uncertainty. Monte Carlo
simulation is a commonly used technique in this class. Using regression-based approaches,
Monte Carlo results can be analyzed to provide sensitivity information. For example,
standardized regression coefficients provide the relative impact of changes in each input on
changes in each output.
A short-term task for uncertainty analysis is to evaluate the types of uncertainty information
available for various inputs to the assessment. For example, inputs may best be character-
ized as ranges, with alternate values, or as statistical functions. Based on how uncertainties
are characterized, the team will identify the most appropriate propagation and analysis app-
roaches. These approaches will take into account the linear nature of the MARKAL model,
and will likely involve Monte Carlo simulation, followed by regression analysis. Alternative
approaches will be evaluated for applicability as well. Once a scheme has been selected, the
uncertainty analysis will be carried out and the results characterized both tabularly and
graphically. The approach and results of the uncertainty analysis will be characterized and
reported along with other project documentation.
7.6 Integration of MARKAL Modeling Results into the ORD Air
Quality Assessment
A range of technological change scenarios developed by TACT ultimately will be integrated
into ORD's Air Quality Assessment. Thus, it is important that any outputs that are generated
include the appropriate information to inform that assessment and be in a format that can
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readily be integrated. To ensure that this is the case, TACT has begun to work with members
of EPA's National Exposure Research Laboratory (NERL) and Office of Air Quality
Planning and Standards (OAQPS) to define the various linkages between the models that
will be used in the assessment. This ongoing examination of the ORD Air Quality
Assessment modeling framework has identified a number of issues that must be considered.
Examples include:
• Pre- and post-processing modules will be needed for many of the models, so
MARKAL technological outputs can be assimilated.
• To ensure that the economic assumptions used in our MARKAL runs are consistent
with those used to develop inventory growth factors, and to consider the effects of
energy price changes on sector growth, it may be desirable to run MARKAL and an
economic model iteratively, converging to an equilibrium economic condition.
• The current model used to make emissions projections, the Economic Growth
Analysis System (EGAS), uses a simple, regression-based approach for correlating
the relationship between economic growth and emissions. MARKAL potentially
provides a more realistic projection. EGAS should therefore be modified to use
projection results from MARKAL.
Discussions with OAQPS and NERL will continue until all such issues have been identified
and plans for addressing those issues have been determined.
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Appendix A
MARKAL
A MARKAL database uses a variety of data parameters to describe each element of the
RES. A small number of system-wide parameters are also used to tell the model how to
handle technologies across the RES. The general categories of data required for a MARKAL
model are
• System-Wide Parameters,
o discount rate
o seasonal/day-night fractions
o electric reserve margin
• Energy Service Demands,
• Energy Carriers,
• Costs,
o resource
o investment
o fixed
o variable
o fuel delivery
o hurdle rates
• Resource Technologies,
o resource supply steps
o cumulative resources limits
o installed capacity
o new investment
• Process and Demand Technologies, and
o fuels in/out,
o efficiency
o availability
• Environmental Impacts
o Unit emissions per resource
o technology
o investment.
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This appendix provides a brief description of each of the main types of data required by a
MARKAL model. A full description of the parameters is available upon request and will be
published separately as part of the complete database documentation.
A.1 System-Wide Parameters
System-wide, or global, parameters are assumptions that apply to the entire model. Two
important, system-wide aspects of the model are
• Cost discounting—Costs are to be provided in MARKAL for the supply of energy
resources and the building and operating of technologies. All costs must be entered
in the same monetary unit (U.S. 1995 dollars for the U.S. MARKAL model). All
input costs along with those reported from the model are then discounted to a
common year. The user must specify this common year and the discount rate to be
used.
• Subdivision of the year into load fractions—MARKAL subdivides the year into
three seasons Z (Z = summer, winter, intermediate) and two times of day Y (Y =
day, night). The fraction of the year that is to be assigned to each season and
day/night is provided by the user. These subdivisions of the year determine the
default percentage of the year for the construction of the electricity and
low-temperature heat demands. The user must specify the fraction of the year that is
to be assigned to each of these six subdivisions.
A.2 Energy Service Demands
Energy service demands describe the requirement for specific end-use energy services to be
delivered to individuals and the economy. Examples of energy services include residential
lighting, personal automotive transport, and industrial process heat. The demand for an
energy service does not refer to consuming a particular energy commodity, but rather to
providing services such as manufacturing steel, moving people, lighting offices, and heating
homes. These energy services are measured in units of useful energy, which may vary with
sector. For example, in the U.S. model, demand for the majority of transport services is
measured in miles traveled, while the demand for industrial process energy is measured in
petajoules (PJ).
MARKAL is a demand driven model. In most formulations of the model, the objective is to
satisfy all of the energy service demands at the least possible cost, subject to a variety of
system and user-imposed constraints. Each demand is met by the sum of the output from all
technologies that serve that demand. For example, the demand for personal travel can be
serviced by a variety of cars and light trucks. For the standard MARKAL model, demand for
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energy services must be specified exogenously by the user. In other model variants,
MARKAL-MACRO and MARKAL-Elastic Demand, demand levels are determined
endogenously in response to prices.
Key demand related data includes
• projections for useful energy demand services by sector, and
• The load shape of the demand pattern by season/day-night, when the sector includes
demand devices that consume electricity or low-temperature heat.
A.3 Energy Carriers
Energy carriers are the various forms of energy produced and consumed in the RES depicted
in a MARKAL model. Energy carriers can include fossil fuels, such as coal with different
sulfur content, crude oil and oil products, electricity to different grids, synthetic fuels
produced by model processes, and renewable energy (e.g., biomass, solar, geothermal,
hydro). Energy carriers provide the interconnections between the various technologies in a
MARKAL model by flowing out of one or more technologies and into others. The model
requires that the total amount of each energy carrier produced is greater than or equal to the
total amount consumed.
All energy carriers are tracked annually with the exceptions of electricity (which is divided
into three seasons and day/night) and low-temperature heat (which is tracked by seasons).
Key energy carrier related data includes
• Overall transmission efficiency (usually 1 except for electricity and low-temperature
heat grids) for all energy carriers, and
• For electricity and low-temperature heat:
o investment and operation and maintenance cost for transmission and distribution
systems,
o reserve margin, or amount of installed capacity above the highest average annual
demand (usually higher than the traditional utility reserve margin because it is
the level above the average peak period load, not the peak itself).
A.4 Resource Technologies
Technology characterizations are the heart of a MARKAL model. Resource technologies
represent all flows of energy carriers into and out of the system, including imports and
exports, mining and extraction, and renewable energy flows. These technologies are
generally characterized using stepwise supply curves that indicate how much of a resource
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can be obtained at each of a set of prices during each model period. For example, in the U.S.
model, imported electricity is modeled using a three-step curve, whereas mining various
grades of coal is represented using eight-step curves.
Key resource technology data includes
• Bounds indicating the size of each step on each resource supply curve (These bounds
might arise for technical reasons, such as a limitation on the amount of oil that can
be produced from a particular reservoir in a given year, or for economic reasons.),
• A corresponding resource supply cost for each supply step, and
• Cumulative resources limits indicating the total amount of a resource supply step
that can be delivered over the entire modeling horizon (e.g., total proven size of a
petroleum reservoir).
A.5 Process and Demand Technologies
Process technologies are those that change the form, characteristics, or location of energy
carriers. Examples of process technologies in the U.S. model include oil refineries and
hydrogen production technologies. A subcategory of the process technologies is the
conversion technologies, which model electricity and low temperature heat production.
Demand technologies are those devices that are used to directly satisfy end-use service
demands, including vehicles, furnaces, and electrical devices. These technologies are
characterized using parameters that describe technology costs, fuel consumption and
efficiency, and availability.
Key process and demand technology data include
• Technology costs,
o cost of investing in new capacity,
o fixed operating and maintenance (O&M) costs for installed capacity,
o variable O&M costs according to the operation of installed capacity,
o fuel delivery costs corresponding to any sectoral difference in the price of an
energy carrier,
• Energy carriers into and out of each technology,
• The technical efficiency (usually defined as the ratio between the sum of energy
carrier or useful energy service outputs to the sum of energy carrier inputs),
• The model year in which the technology first becomes available for investment,
• Availability factors (for process technologies) and capacity utilization factors (for
demand technologies) that describe the maximum percent annual (or
season/day-night) availability for operation or a fixed percent annual (or
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season/day-night) capacity utilization per unit of installed capacity,
• The current existing installed capacity,
• Limits on capacity in the form of incremental new investment (absolute or growth
rate) or total installed capacity (Such bounds may be set for economic, technical,
behavioral, or other reasons.), and
• Hurdle rates, or technology specific discount rates, that can be used to represent
non-economic, behavioral aspects of investment choices (e.g., consumer preferences,
expectation of very rapid rates of return, information gaps). Often the "real world"
does not make decisions based strictly upon the least-cost perspective that
MARKAL uses. These impediments to the market can be represented to MARKAL
as technology-specific discount rates, higher than the systemwide discount rate, for
such technologies.
A.6 Environmental Variables
MARKAL has the capacity to track the production or consumption of environmentally
relevant quantities according to the activity, installed capacity or new investment in capacity
of a resource or technology. This capacity has most often been used to track emissions of
traditional pollutants such as CO2, NOX, sulfur oxides, VOCs, and particulates. However, it
could also be used to track consumption of land or other resources or the removal of
pollutants from the system.
Key environmental variable related data (expressed in terms of pollutant emissions) include
• Emissions per unit of technology activity, installed capacity, or new investment,
• Emission constraints, which can take the form of a cap on total emissions in a year or
a cumulative cap on emissions over the entire modeling horizon, if desired,
• Taxes, which can be applied to each unit emitted, by sector/technology, if desired.
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Appendix B
Database Development, Review, and Calibration
This Appendix describes the EPA MARKAL RES database development, including data
sources, peer review, and calibration.
B.1 Data Sources
Wherever possible, data was taken from NEMS input data underlying the AEO 2002 (U.S.
EIA, 2001). AEO data was selected for the RES because it is a nationally recognized source
of technology data, widely used where reference or default data are required. In some cases,
AEO data were not available in a form that could be utilized for the EPA MARKAL model.
The table below lists the data sources used for each sector as well as the number of
technologies/resources in each sector.
Table 13. Primary Sources Used in Developing the Database.
Sector
Transportation
Commercial
Residential
Industrial
Electricity
Resource
Supply
Data Source
OTTQM
DeCiccoetal.,2001
NEMS
NEMS
SAGE (under
development)
NEMS
EPRI TAG
NEMS
Data Quality3
A
B
A
A
A
A
C
A
Number of Technologies/Resources
15 personal vehicles in 5 size classes; 40
other passenger & freight technologies
300
135
-100
45
25 coal types, 10 imported petroleum
products, domestic and imported oil and
natural gas
a Data quality definitions can be found in the Quality Assurance Plan (Shay et al., 2003)
The AEO is a nationally recognized source of technology data that is widely used where
reference or default data is required. It presents mid-term forecasts of energy prices, supply,
and demand. The projections are based on results from EIA's NEMS (U.S. EIA, 2003a) and
are based on federal, state, and local laws and regulations in effect at the time of the model
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run. (EIA, Site 1.)
Because the majority of the RES data used in these analyses are coming from EIA's NEMS
database, the quality level of the data drawn from them is of particular interest. EIA has
performance standards to ensure the quality (i.e., objectivity, utility, and integrity) of
information it disseminates to the public. Quality is ensured and maximized at levels
appropriate to the nature and timeliness of the disseminated information. EIA also strives for
transparency about information and methods in order to improve understanding and to
facilitate reproducibility of the information.
For a complete description of EIA's Quality Guidelines see EIA, Site 2.
For the transportation base case sector, the data are drawn from the U.S. DOE's OTT QM
assessment. QM describes the analytical process used in estimating future energy,
environmental, and economic benefits of U.S. DOE EE/RE programs. QM seeks to monitor
and measure the impacts of all DOE EE/RE programs and to summarize their overall
national effects. Quality Metrics has been an active annual DOE EE/RE-wide analysis and
review procedure since 1995 (U.S. DOE, 2002).
Data for the electricity sector was drawn from NEMS with supplemental data pulled from
the EPRI TAG (EPRI, 1993). EPRI is a non-profit energy research consortium providing
scientific research, technology development, and product implementation for the energy
industry. The TAG is a standard reference work for the energy industry that characterizes
key electric generation technologies and their operation, costs, environmental impacts, etc.
Ongoing efforts to develop the industrial sector representation are centered on adapting the
characterization used in EIA's SAGE model (U.S. DOE, 2003c). This characterization
describes six energy services within each of six industrial sectors. Additional documentation
will be provided when this sector's development work is complete.
Data were then aggregated and transformed into MARKAL units as necessary.
B.2 Peer Review
Each sector's data and documentation was then sent to several experts in that sector for
review. Peer review questions included:
• Has an appropriate data source been used for the sector?
• Has that data been used appropriately?
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• Do the relative costs and performance of the technologies/resources look reasonable?
• Are there technologies that should have been included that were not, or that have
been included that should not?
Table 14 lists the peer reviewers by sector.
Table 14. Sector Peer Reviewers.
Sector
Invited Accepted Responded
Individuals
Residential
11
Transportation
Resource Supply 13
Electricity
Commercial
16
11
John Cymbalsky (EIA/DOE)
Jonathon Koomey (LBNL3)
Jim Sullivan/Glenn Chinery (EPA/CPPDb)
Roger Gorham (EPA/OTAQC)
Therese Langer (ACEEE)
Steve Plotkin (ANLd)
John DiCicco (EDFe)
Don Hanson (ANL)/Marc Melaina (U.
Mich)
Floyd Boilanger (DOE/NETI/)
Casey Delhotal (EPA/CPPD)
Russell Jones (API8)
John Conti/Kaydes (EIA/DOE)
Floyd Boilanger (DOE/NETL)
Dallas Burtraw (RFFh)
Russell Noble (Southern Companies)
Jim Sullivan (EPA/CPPD)
Harvey Sachs (ACEEE)
Erin Boedecker (EIA/DOE)
Jonathon Koomey (LBNL)
a LBNL = Lawrence Berkeley National Laboratory.
b CPPD = Climate Protection Partnerships Division.
c OTAQ = Office of Transportation and Air Quality.
d ANL = Argonne National Laboratory.
e EDF = Environmental Defense Fund.
f NETL = National Energy Technology Laboratory.
g API = American Petroleum Institute.
h RFF = Resources for the Future.
In general, peer review responses indicated that the data sources and TACT's use of the data
were appropriate. Several minor errors and omissions were identified and corrected. The
reviewers also made several suggestions for future technologies that could be examined
through scenario analysis in sectors beyond transportation. A document describing the peer
review comments and our responses in greater detail will be provided with the database
documentation when the database is released.
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B.3 Calibration
Following the incorporation of peer review comments and any necessary changes into the
RES database, the model was run for comparison and calibration to AEO 2002 results. AEO
2002 was selected as a calibration benchmark for two reasons. First, the Annual Energy
Outlook is a nationally recognized short to mid-term energy technology and consumption
forecast, widely used where a reference forecast is required. Second, much of our RES data
was derived from AEO 2002 input data.
The goals of the calibration are
• to ensure that the model is producing reasonable results, given its input assumptions,
• to determine whether the model is providing a plausible, consistent representation of
the key features of the U.S. energy system,
• in cases where our results differ from AEO results, to be able to identify why the
differences exist, and
• to identify any significant errors in the construction or characterization of the RES.
Comparing model results to AEO 2002 encompassed total energy consumption for each
(AEO Table 1), by sector (AEO Table 2), and within sector by use (AEO Tables 4-9 and
Supplemental Tables). First, it was determined whether or not broad trends (upward,
downward, or changing over the time horizon) were tracked by MARKAL model results.
Then, the degree of quantitative match between MARKAL results and AEO 2002 was
determined.
NEMS, the model used to produce AEO 2002, differs in many respects from MARKAL. In
general, NEMS sectors are modeled in more detail, more aspects of consumer and producer
behavior are simulated, and the model is generally more conservative about switching fuels
and technology types than is MARKAL. Therefore, unconstrained MARKAL results are not
expected to match AEO results exactly.
In some cases, constraints were added to force MARKAL to track AEO more closely. The
decision to use constraints to force MARKAL to track AEO involves trade-offs between
desired model characteristics. On the one hand, it is desirable to make sure that MARKAL's
behavior is realistic in that it represents real constraints and inflexibilities in the energy
system. On the other hand, AEO results are a simulation of NEMS modelers' judgment
about the most likely direction of the energy system, whereas we are using MARKAL to
explore a variety of scenarios for the system's future evolution. Therefore, it is not desirable
to force MARKAL to track AEO so closely that it lacks the flexibility to respond with
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different outcomes to differing input assumptions.
Constraints were added where there is an underlying feature of the energy system that an
unconstrained MARKAL run does not represent. These constraints have been highlighted
within the model and made easily adjustable by the user. Documentation of these constraints
and the spreadsheets necessary to adjust them will be provided when the model is released.
Examples of these constraints include fuel switching, personal vehicle classes, and
availability of electricity generation from renewable sources.
Fuel switching—In the commercial and residential sectors, NEMS contains built-in
mechanisms that inhibit fuel switching for end-use applications where more than one fuel is
available. In both sectors, NEMS tracks new floorspace separately from existing. In the
commercial sector, when selecting the technologies for existing floor space, NEMS requires
a significant percentage of these to use the same fuel as did the previous technologies
serving that space. In the residential sector, NEMS imposes a cost representing investment
in necessary technologies (e.g., ductwork) when fuels are switched in existing homes. The
MARKAL database described here does not track new and existing space separately, so
constraints have been added limiting the rate of fuel switching in the sectors over time. For
commercial and residential space and water heating, 1995 fuel splits are constrained to
historical values. These constraints are then relaxed by 3 percent each model period. This
relaxation rate is adjustable by the user.
Personal vehicles size classes—An unconstrained MARKAL run would satisfy all demand
for personal vehicle travel using the least cost options, which in most cases would be
compact cars. In order to prevent this unrealistic behavior, the model has been constrained
to maintain the 1995 model year market shares in its purchases throughout the model time
horizon. This split is adjustable by the model user. Unlike fuel switching, this constraint is
not allowed to relax over time because the model would simply switch back to compact cars.
Renewable electricity generation technology availability—Cost and performance
characterizations of renewable electricity generation technologies were derived from AEO
2002 input assumptions. These costs assumptions are adjusted within NEMS according to
yearly and cumulative capacity installations, representing the effects of technology learning
(decreasing costs) and of site quality, necessary transmission network upgrades, and market
pressures from competing land uses (all increasing costs). In practice, this means that the
costs assumptions that have been put into MARKAL are appropriate only for a limited
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increasing capacity. To represent this limitation, total installed capacity of these
technologies is constrained to follow AEO projections. (In practice, these constraints are not
binding in the scenario results we report on here.) These characterizations and constraints
will be replaced by a thorough scenario analysis considering technology cost and
performance and resource availability when the electricity generation scenario analysis is
performed.
In representing the broad trends of energy consumption out to 2020, MARKAL runs
described here track AEO. The largest deviation occurs in the electricity generation sector,
where MARKAL runs are consistently consuming more coal and less natural gas than AEO
projects. It is anticipated that this deviation will disappear when emissions and emission
control technologies in this sector are extensively reviewed and updated during the first six
months of 2004.
Total energy consumption in the commercial sector is within 10 percent of AEO values. For
the two major fuels in the commercial sector, electricity consumption is within 10 percent
and natural gas is within 20 percent. Within specific end uses, MARKAL consumption
differs by more than 20 percent from some AEO values. This difference primarily arises
because NEMS differentiates between 11 different commercial building types, whereas
MARKAL treats the commercial sector as a single unit. Because some equipment types are
applicable to only certain building types, NEMS achieves a more detailed picture of the use
of equipment types by building types. Because the commercial sector is not the focus area
here, that tracking AEO commercial sector energy use at the broad level was deemed
sufficient for the purposes of this report. Commercial sector calibration will be revisited in
future analyses.
In the residential sector, total fuel consumption is within 5 percent of AEO values,
electricity consumption is within 10 percent, and natural gas consumption is within 10
percent until 2020, at which point it is within 20 percent. The major deviation of MARKAL
results from AEO results in this sector is that MARKAL chooses to exercise its fuel
switching option, making its most significant investments in new water heating capacity in
LPG-fueled devices, more than doubling LPG consumption for this end-use over the AEO
time horizon. By contrast, AEO results do not project any increase in LPG-fueled water
heating. This difference arises because NEMS imposes a significant distribution cost on
LPG to the residential sector which this MARKAL database does not presently replicate.
Because the residential sector is not the focus area here, this degree of conformity between
MARKAL and AEO results was deemed appropriate. Fuel distribution costs will be
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revisited in future analyses.
In the transportation sector, energy consumption by subsector for non-light-duty applica-
tions is within 10 percent of AEO values for trucks, buses, air, passenger rail, and water
freight. For rail freight, the difference between our results in AEO values grows to 20
percent by 2020. This difference arises because of the way efficiencies for diesel rail freight
technologies, which were retained from the 1997 DOE MARKAL database, were mapped
into MARKAL. Technology characterizations for non-light-duty transport applications will
be reviewed and updated as necessary in future analyses. Consumption of the two major
fuels in the non-light-duty sub sectors, jet fuel and diesel fuel, is within 10 percent of AEO
values.
NEMS represents light-duty vehicles very differently from these OTT-derived MARKAL
representations. NEMS represents a number of vehicle component technologies (including
engines, transmission, and tire types) and a variety of efficiency improvements and opti-
mizes vehicle packages built from these technologies during its runs. Therefore, not all of
the scenario results are expected to track AEO results exactly with respect to technology
choice and fuel consumption. In addition, these scenarios have focused on gasoline-fueled
vehicles and hydrogen FCVs and have excluded from consideration the variety of alternative
fuels that AEO considers, including alcohol-based and natural gas-based fuels. (These
alternative fuels will be examined during the next phase of the transportation scenario
analysis.) Finally, these analyses have considered a wider range of scenarios than AEO,
involving considerable variation in fuel prices and technology price, performance and
availability.
The Conventionals Dominate scenario resembles AEO most closely in its mix of vehicles
(AEO projects 94 percent conventional cars and 89 percent conventional light trucks in
2020), and its light-duty gasoline consumption closely tracks AEO projections (see Table
15). Other scenarios that feature greater penetration by more efficient vehicles will show
lower gasoline consumption than AEO projections. The Hybrid Market HM(C) scenario
results are shown for comparison. They deviate significantly from AEO values as more
efficient vehicles penetrate the market from 2010 on.
The electricity generation sector is the most complex to calibrate because of availability
constraints and base load and peaking requirements. In the electricity generation sector, total
generation is within 5 percent of AEO values in each model year. As noted above, these
MARKAL runs are consistently consuming up to 25 percent more coal (and less natural gas)
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Table 15. Comparison of MARKAL results to AEO 2002.
Light-Duty Gasoline Consumption (PJ)
scenario
AEO 2002
Conventionals Dominate
Hybrid Market [HM(C)1
2000
15,548
15,573
15.564
2005
17,098
17,463
17.104
2010
18,743
19,082
15.916
2015
20,251
20,435
13.591
2020
21,477
21,763
11.514
than AEO projections. Emissions control device characterizations and requirements are still
in development, and it is believed that this discrepancy will disappear when that work is
complete. In addition, MARKAL's petroleum-fired generation does not fall off as quickly as
AEO projects and renewable generation does not increase as quickly as AEO projects.
Considerable effort will be devoted to the characterization of technologies in the electricity
generation sector as TACT moves into scenario analysis in this sector, and TACT expects to
resolve these calibration issues in the process.
Because the electricity generation sector is not a major consumer of petroleum (the primary
transportation fuel), calibration issues in the electricity generation sector are not expected to
affect transportation results, with the exception of the early hydrogen economy scenarios.
Because hydrogen production processes consume electricity and/or natural gas (a major fuel
in the electricity generation sector) changes in electricity sector results, particularly as they
affect the prices of electricity and natural gas, will affect hydrogen scenario results. These
issues will be further examined as TACT works with hydrogen and electricity generation
scenarios.
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Appendix C
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