PSI Bericht Nr. 02-08
March 2002
ISSN 1019-0643
LA-UR-01-6941
General Energy Research Department, ENE
Electrical-Generation Scenarios for China
S. Kypreos and R. A. Krakowski1
1
On leave from the Los Alamos National Laboratory, Los Alamos, New Mexico, 87545, USA
1
Acknowledgements
The implementation of the CRETM model has been done within the ABB/AGS China Energy
Technology Program 1999-2002 MIT/ETH/PSI.
2
Contents
Abstract
Executive Summary
I. Introduction
A. Background
B. Goals
C. Scope
II. Approach
A. Key Model Definers/Drivers
B. Scenarios
C. Data Requirements, Gaps, and Uncertainties
D. Linear Optimization Model
1. Optimization versus Simulation Models
2. The China Regional Electricity Trade (Cost-Optimization) Model, CRETM
III. Results
A. Electricity Demand
B. Baseline (BHC) Scenario
C. Scenario Impacts
1. High Fuel-Price Scenario (BHH)
2. Carbon-Caps Scenarios (CHC, CHH)
3. Sulfur-Caps Scenarios (SHC, SHH)
4. Environmental (S + C Caps) Scenarios (EHC, EHH)
5. Scenario Summary
D. Single-Point Parametric Studies
1. Exogenous Energy Demand
2. Discount Rate
3. Technology Costs of Nuclear Energy
a. Impact of Nuclear-Energy Capital Costs on Market Share and Emissions
b. Carbon Taxes, Caps, and the Cost of Nuclear Energy
IV. Mainly Shandong Province
V. Results Syntheses
A. Scenario-Based Scatter Plots
B. Discount-Rate Impacts
C. Comparison with Other Studies
1. Comparisons with Studies Outside of CETP
2. Comparisons Between EEM and ESS Results
VI. Main Findings, Conclusions, and Recommendations
A. Main Findings
B. Conclusions
D. Recommendations
3. Policy
4. Modeling Advances
3
REFERENCES
NOMENCLATURE
APPENDICES
A. Summary of Key Data (Base Case BHC) Used by CRETM
B. Overview of Scenario Analysis for the Chinese Energy System; Optimization
versus Simulation Modeling Approaches
C. Description of China Regional Energy Trade Model (CRETM)
4
Abstract
The China Energy Technology Program (CETP) used both optimizing and simulation energyeconomic-environmental (E3) models to assess tradeoffs in the electricity-generation sector for
a range of fuel, transport, generation, and distribution options. The CETP is composed of a
range of technical tasks or activities, including Energy Economics Modeling (EEM,
optimizations), Electric Sector Simulation (ESS, simulations), Life Cycle Analyses (LCA,
externalization) of energy systems, and Multi-Criteria Decision Analyses (MCDA,
integration). The scope of CETP is limited to one province (Shandong), to one economic
sector (electricity), and to one energy sector (electricity).
This document describes the methods, approaches, limitations, sample results, and
future/needed work for the EEM (optimization-based modeling) task that supports the overall
goal of CETP. An important tool used by the EEM task is based on a Linear Programming
(LP) optimization model that considers 17 electricity-generation technologies utilizing 14 fuel
forms (type, composition, source) in a 7-region transportation model of China's electricity
demand and supply system over the period 2000-2030; Shandong is one of the seven regions
modeled. The China Regional Electricity Trade Model (CRETM) is used to examine a set of
energy-environment-economy E3-driven scenarios to quantify related policy implications.
3
The development of electricity production mixes that are optimized under realistically E
constraints is determined through regional demands for electricity that respond to exogenous
assumptions on income (GDP) and electricity prices through respective time-dependent
elasticities. Constraints are applied to fuel prices, transportation limits, resource availability,
introduction (penetration) rates of specific technology, and (where applicable) to local,
regional, and countrywide emission rates of CO2, SO2 and NOX. Importantly, future interregional energy flows are optimized with respect to choices of either transporting fuel (i.e.,
road, rail, ship) or electricity (wire). Results from CRETM are expressed in terms of scenariobased "visions of the future". The scenarios considered in this ESS component of the CETP
study are reference to this base line and divide according to whether the driving attributes
derive from economic policy (e.g., demand growth, discount rate), environmental policy (e.g.,
emission taxes versus emission caps, reflecting roughly a free-market versus centrally planned
economy), or technological policy (e.g., pricing and introduction rates that may favor specific
technologies like Clean Coal Technologies (CCT) versus nuclear energy, versus renewable
energy).
The primary aim of this report is to describe in detail the analytic and databases of the
CRETM and to present a series of scenario-based example results that illustrate a range of
possible futures related to electricity generation in China in general and in Shandong Province
specifically, over the next three decades. A detailed description of the CRETM and the
supporting database is given; eight "strawman", broad-coverage scenarios are described and
evaluated; interpretations of both countrywide and Shandong focused results are provided;
and future work needed to assure an optimally integrated and policy-utilitarian product are
prescribed.
5
6
Executive Summary
China generated 1,350 TWeh of electricity in the year 2000; this generation translates into a
9.5%/yr rate of increase and an installed capacity of 316 GWe (>50% of US installed
capacity). The reduced growth rates over the last few years cause by the Asian financial crisis
of 1998 seem to be returning to stronger levels of growth, indicating a strong recovery of the
national economy (Zhao, 2000): China's GDP rose by 8.2%/yr during the period January-June
of 2000 (greater than the 7.8 %/yr for 1998 and 7.1%/yr for 1999). Even with any eventual
tapering of these presently high GDP growth rates to still high growth rates of 4-5 %/yr, the
demand for electric power is expected to nearly quadruple to ~4,000 TWeh/yr by the year
2030. Significant deterioration of already seriously degraded environmental (air and water)
quality is expected if this growing demand for electrical energy is provided by coal at the
present ~75% use rate. In examining options to such a future based on conventional use of
coal, the China Energy Technology Program (CETP) used both optimizing and simulation
energy-economic-environmental (E3) models to assess tradeoffs in the electricity-generation
sector for a range of fuel, transport, generation, and distribution options. Reported herein are
methods and results associated with electric-sector optimization modeling of both China and
the focus of CETP – Shandong Province.
The Energy-Economic Modeling (EEM) component of the CETP study reported herein uses a
Linear Programming (LP) optimization model, considers 17 generation technologies utilizing
14 fuel forms (type, composition, source) in a 7-region transportation model of China's
(electricity) demand and supply system over the period 2000-2030. The China Regional
Electricity Trade Model (CRETM) is used to examine a set of E3-driven scenarios to quantify
related policy implications. The CRETM is an adaptation and extension of an earlier Harvard
University energy model (Rogers, 1999) that has been obtained through the Tsinghua
University. This model is similar in form to that used in a recent PNNL study (Chandler,
1999), which provides much of the initial, regionalized database necessary to evaluate models
of this kind. While Shandong province in northeastern China is the focus of CETP, the
CRETM allows this focus to be constructed in a countrywide context insofar as energy
resource, transportation, and environmental impact, as related to electricity generation, is
concerned. The development of electricity production under the constrained optimum
conditions is determined through regional demands for electricity that respond to exogenous
assumptions on income (GDP) and electricity prices through respective time-dependent
elasticities. Constraints are applied to fuel prices, transportation limits, resource availability,
rates of introduction (penetration) of specific technology, and (where applicable) to local,
regional, and countrywide emission rates of CO2 and SO2. Future inter-regional energy flows
are optimized according to choice of either transporting fuel (i.e., road, rail, ship) or electricity
(wire). Because of the dominance for China's coal resource, the mining, pre-combustion
cleaning, transport, and "end-of'-pipe" cleanup operations for this fuel are described in some
detail by CRETM.
Results from CRETM are expressed in terms of scenario-based "visions of the future", all of
which are described relative to a "business-as-usual:" (BAU) "point-of-departure" (POD) or
baseline case. While commonly used for these kinds of studies, the descriptor "BAU"
scenario is anything but appropriate for describing the highly dynamic economic and political
system that is China today; generally, the descriptors BAU, POD, or Baseline are used to
designate a condition that is absent of any direct or overt environmental and/or economic
7
policy having specific goals. The scenarios considered in this study are reference to this
baseline and divide according to whether the driving attributes derive from economic policy
(e.g., demand growth, discount rate), environmental policy (e.g., emission taxes versus
emission caps, reflecting a free-market versus command and control economy), or
technological policy (e.g., pricing and introduction rates that may favor specific technologies
like Clean Coal Technologies (CCT) versus nuclear energy, versus renewable energy).
Furthermore, a concerted effort has been made to interpret the results from this constrained
(economic) optimization model in terms of similar results for similar scenarios derived from
non-optimizing, but technologically and operationally more detailed, Electric Sector
Simulation (ESS) models.
The primary aim of this report is to describe the analytic models and supporting databases of
CRETM and to present a series of scenario-based example results that show a range of
possible futures related to electricity generation in China in general and in Shandong Province
specifically. To the extent that the sample results presented herein are not yet integrated with
and/or adjusted to other key components of the CETP (e.g., detailed but un-optimized electric
sector simulations, life-cycle analyses, multi-criteria decision analyses, etc.), these results
should be viewed as interim. These results, nonetheless, are crucial to an understanding of
options and impacts available to China's electricity sector in the 2000-2030 time frame
examined. Most importantly, this report is intended to communicate in concrete terms the
capabilities, limitations, and the directions of the EEM task of the CETP, with the aim of
setting realistic goals and expectations for any subsequent integration with other key
components of this project. Lastly, this document is intended to be a vehicle for
communicating EEM activities to both CETP stakeholders and those outside of but having
interest in this project. The annual technical Annex of the General Energy Department for the
year 2000 gives a short description of CRETM and a summary of interim results obtained
(Kypreos, 2000b).
On the basis of the CRETM–based modeling efforts, the following general Findings,
Conclusions, and Recommendations result:
General Findings:
Ø Coal as a Primary Fuel: China will rely on coal for electricity production, independent
of environmental policies.
Ø Advanced Generation and Emissions: Chinese RD&D on advanced generation
technology, along with integrated foreign investments, can improve energy efficiency
and, through reduced emissions, the environment.
Ø Coupled Sulfur and Carbon Emissions: Pollution related to SO2 emissions can be
reduced for moderate investments by introducing scrubbers and/or advanced-coal
technology; some reductions in CO2 automatically accompany these SO2 emission
reductions.
Ø Significant Investment to Reduce Carbon Emissions: Significant carbon-emission
reduction needs significant investments in reduced- or carbon-free generation
technologies; this policy improves local environments through reduces SO2 emissions
(secondary benefits).
8
Ø Added Cost of Sustainable Energy Less Than Cost of Pollution: The cumulative
discounted power-production cost for a more sustainable path for electricity generation
in China increases by 2-6 %/yr over the 2000-2030 time frame for the high discount
rates (RATE = 10%/yr) investigated, but these added costs needed to reduce emissions
remain below the damage cost attributed to pollution in China.
Ø Wide Differences in Energy Costs Across Scenarios: Differences in non-discounted
cost that appear across the range of scenarios examined are high, reaching levels of
around 20-30% at the end of time horizon (2030) examined.
Ø Electricity Transmission Across Regions Makes Economic and Environmental Sense:
Transmitting electricity between regions within China in general terms presents an
economic option, according to the CRETM analyses, while inter-regional transmitting
also reduces local pollution.
General Conclusion:
Ø Increased Power Demand: The demand for electrical power in China will increase
four-fold by 2030; similar demand growths are expected in Shandong Province;
Ø Pollution Control: Annual pollution cost to the Chinese economy varies anywhere
from 3-8% of GDP; ecological damage is estimated to cost potentially another 5–14%
of GDP; environmental damage roughly cancels annual economic growth; initiating
policies with region-specific emission caps and/or a sulfur-emissions permit system
across regions and economic sectors is the most efficient way to control future
emissions;
Ø Clean Coal Technologies: CRETM identifies the need to start reducing local
emissions by improving the performance of pulverized coal systems and by
introducing coal washing and/or sulfur scrubbing, while continuing with the adoption
of advanced coal systems (IGCC, supercritical steam coal);
Ø Advanced Coal-Fired Systems: Advanced coal-fired systems can be competitive with
conventional Chinese coal technology, when these advance technologies are
manufactured in China or under conditions of high coal prices and transportation cost;
Ø Fuel and Technology Diversification: Oil and gas supply options must be improved to
reduce the dependency on coal in China, especially in the coastal Provinces. The most
promising substitutes for coal are advanced gas combined cycle systems followed by
the appropriate contribution of hydropower, nuclear energy, and renewable energy
sources based on wind and small hydroelectric units;
Ø Competitive Nuclear Power: Nuclear power can be competitive in a scenario where
reactors are fabricated in China, the construction time is below 5 years, and capital
costs are at or below 1,500 $/kWe for the high discount rates assumed (10%/yr).
Nuclear energy is competitive at discount rates of ~ 5%/yr or at higher discount rates
when regional and global externalities are addressed at even moderate tax levels. The
importance of nuclear energy becomes apparent only when coal becomes more
expensive, as when placed under a carbon control policy. Without a significant share
of nuclear energy, the marginal costs of carbon control in China move to extreme and
9
almost forbidden cost ranges, weakening the hope of having China participating to
international carbon-emissions-control protocols.
General Recommendations:
Suggestions for important directions for formulating long-term energy policy for China are
made based on these findings and conclusions:
Ø Improve performance of clean coal technology;
Ø Introduce scrubbers and IGCC systems;
Ø Diversify supply by opting for gas, nuclear energy, wind, and small hydroelectric;
Ø Continue reforms to make greater use of market forces, especially in the gas and
electricity sector;
Ø Participate to Kyoto-type Protocols with commitments for carbon emissions reduction
to facilitate technology transfer, international R&DD cooperation and CDM projects.
Recommendations are also made for improving the fidelity of the CRETM-type of models
used to generate these findings, conclusions, and policy recommendations.
10
I. Introduction
A. Background
China's economy has expanded at an average rate of over 9% each year for the past two
decades, but this expansion has decreased to a still impressive 7.1%/yr in 1999 (Zhao, 2000).
Recent reports indicate that China generated 625.6 TWeh of electricity in the first half of
2000, which corresponds to an annualized growth of 10.6%. Experts claim (Zhao, 2000) that
this largest electricity growth since 1998 indeed is clear evidence that the national economy is
in a strong recovery mode. Strong growth is occurring in the southern and coastal regions of
China, with the strong demand deriving primarily by the industrial sectors (mainly steel
output, rolled steel, pig iron, and cement) along with some growth in the residential (air
conditioners, electric water heaters, home machinery and appliances) sector. Somewhat
abnormal weather (colder winters, drier and hotter summers) has also contributed to the
increased demand for energy (heating, irrigation, air conditioning, etc.). Additionally, while
recent over-capacity is vanishing, a new kind of privatized electrical energy market seems to
be emerging (Sharp, 2000). In short, the energy picture in magnitude and at sectoral levels is
dynamic, which strong industrial growth, shifts towards satisfying consumer needs, and a real
acting out of electric-sector privatization, along with increased energy efficiencies as total
capacity exceeds 300 GWe (50% of US) (Sharp, 2000).
This unprecedented string of GDP and electrical-energy expansion years, however, has
occurred with a growth in primary energy consumption that was only half that of the
productivity (GDP) growth; the primary energy required to produce a unit of GDP actually
decreased as a result of increased end-use efficiency. Air and water quality have been the
primary casualties of this growth in productivity and energy consumption. Continuing this
rapid economic expansion while simultaneously implementing effective pollution control
policies presents China with an urgent challenge. In building on significant previous work in
this area (McElroy, et al., 1999; Yu, et al., 1998; Chandler et al. 1998; Dadi, et al., 2000;
Levine, et al., 1994), the present study seeks to define the least-cost combination of
technologies and policies for China's electric power sector, which represents an essential
element in and a major driver of that country's economy.
China’s energy sector is distinctive for its heavy reliance on coal, as is indicated in an early
(1990), comprehensive global comparison given on Table I (Nakicenovic, 1998). In 1996
coal amounted to nearly 75 percent of primary energy consumption (Chandler 1998; Ni and
Nien, 1999). Additionally, large percentages of both industrial energy consumption and urban
household energy use in China are provided by coal; China leads the world in both the
production and consumption (1.2 BtonneC/yr) of coal. This consumption both has fueled the
economic growth in China over the past two decades and, being converted to secondary
energy at low efficiency with little or no emission control, has caused serious environmental
degradation both within and outside of China.
11
Table I. Comparison of Global Primary Energy Consumption Patterns and Related Patterns in 1990 (Nakicenovic, 1998).
NA (North America); LAM (Latin America and the Caribbean); AFR (Sub-Saharan Africa); MEA (Middle East and North Africa); WEU (West Europe);
EEU (Central & Eastern Europe); FSU (New States & Former Soviet Union; CPA (Centrally planned Asia and China; SAS (South Asia); PAS (Other Pacific Asia);
PAO (Pacific OECD)
GDP values are expressed in market-exchanged rates or in purchasing power parity (PPP), when in parenthesis.
Region
World
NA
LAM
AFR
MEA
WEU
EEU
FSU
CPA
SAS
PAS
PAO
World
NA
LAM
AFR
MEA
WEU
EEU
FSU
CPA
SAS
PAS
PAO
Population
GDP
Primary Energy
Billion
t$/yr
EJ/yr
5.26
20.9(25.7)
377.2
0.28
6.07(5.91)
91.7
0.43
1.09(2.03)
25.5
0.49
0.27(0.65)
12.1
0.27
0.57(1.14)
14.4
0.43
7.01(5.74)
61.1
0.12
0.30(0.71)
14.2
0.29
0.79(1.84)
58.9
1.24
0.47(2.44)
39.7
1.13
0.38(1.36)
18.6
0.43
0.66(1.51)
17.8
0.14
3.28(2.41)
22.8
k$/cap/yr
GJ/cap/yr
3.9(4.9)
71.7
21.6(21.1)
327.5
2.5(4.7)
59.3
0.55(1.3)
24.7
2.1(4.2)
53.3
16.3(6.6)
142.1
2.5(6.0)
118.3
2.7(6.3)
203.1
0.38(2.0)
32
0.33(1.2)
16.5
1.5(3.6)
41.4
23.4(17.2)
162.9
Coal
%
24.3
22
3.5
29.2
1.7
21.7
47.2
20.3
58.9
24.3
10.9
20.4
MJ/$
18.2
15.1
23.4
44.8
25.3
4.4
47.3
74.6
84.5
48.9
27
7
Oil
%
34.1
38.2
40.1
14.3
58.6
41.5
23.4
29.2
13.4
16.9
38.4
52.5
Gas
%
18.7
22.7
12.8
1.4
31.9
15.7
19.6
40.8
1.4
5.6
7.1
11.4
12
Nuclear
%
5
7
0.5
0.7
0
11.4
3.7
3.4
0
0.2
4.5
8.3
Renew.
%
17.9
10.1
53.1
54.4
7.8
9.6
5.6
6.4
26.1
52.7
39.2
7.4
kgC/GJ
16.5
17.5
11.8
11.6
20.8
16.9
19.7
17.5
17.6
10.2
12.3
16.2
C Emissions S Emissions
GtonneC/yr
MtonneS/yr
6.23
59
1.6
11
0.3
2
0.14
2
0.3
2
1.03
9
0.28
5
1.03
10
0.7
11
0.19
2
0.22
3
0.37
1
tonneC/cap/yr kgS/cap/yr
1.18
11.2
5.71
39.3
0.7
4.7
0.29
4.1
1.11
7.4
2.4
2.1
2.33
41.7
3.55
34.5
0.61
8.9
0.17
8
0.51
7
2.64
7.1
Because the low efficiencies of many coal-fired power plants in China, with much of the coal
being burned in these relatively low-capacity plants having high sulfur and ash contents,
carbon and sulfur dioxide, as well as particulate, emissions per unit of electrical energy
generated are high. The deployment of electric generation technologies that are reduced in or
relatively free of these waste products, such as advance fossil-fuel (clean-coal, natural gas)
technologies, hydroelectric, nuclear, or solar (photovoltaic or wind generators) can alleviate
these problems. These reductions in waste production, however, do not occur without
injecting other environmental and economic risks, constraints, and tradeoffs into long-term
strategic energy planning (Dadi, 2000; Chandler, 1999; McEloy, 1998): a) advance fossil
technologies require both advanced fuel forms and capital-intensive plant; b) the construction
of hydroelectric plants requires the resettlement of large numbers of people, as well as having
many potentially negative impacts on local ecosystems; c) present-day nuclear-electric
systems bring issues related primary to (capital) cost and to the disposal/containment of
radioactive waste; and d) land-use, power distribution, and cost issues are underlying issues
for other renewable technologies. Furthermore, the present electrical infrastructure in China
adds concern related to high rates of power failures, caused by excess of demand over supply,
and to poor operations and maintenance practices.
B. Goals
The goal of this study is to provide several traceable and transparent recommendations to
identify a more sustainable course for the Chinese electric power industry, while meeting
rapidly growing power needs. Although the focus of CETP is on electricity production in
Shandong Province, the present modeling activity broadens that focus into an overall,
countrywide context. The countrywide results presented herein are compared to and generally
elaborate and amplify those made by the aforementioned studies (Dadi, 2000; McElroy, et al.,
1999; Yu, et al., 1998; Chandler et al. 1998, Levine, et al., 1994). This study first projects
power demands through the year 2030 using a seven-region model of material and energy
flows related to electricity generation within China. As in these earlier studies, the
regionalized demands for electrical energy remain exogenous in the spirit of a simplified nonequilibrium approach. Price and income elasticities are used to adjust these otherwise
externally impose (regional) demands, as is described below. As for the studies reported by
Chandler et al. (1998) and by Yu et al. (1998), a linear-programming (LP) optimization model
is used to determine which combination of technologies can supply this power for the lowest
overall total present-value cost incurred over a 30-year planning horizon. The model used in
the present study is an extension and elaboration of the Harvard University China Energy and
Environmental Model, (Murray, 1998; Rogers, 1999). This multi-regional LP model of the
China electrical power system in a less-elaborated form was used by the Environmental
Resources Management (ERM) (Yu, 1998) and the Battelle Pacific Northwest Laboratory
(PNL) (Chandler, 1998) China study groups.
The "Harvard China Energy Model", as elaborated by workers at the Paul Scherrer Institut
(PSI) and hereafter referred to as the China Regional Electricity Trade Model (CRETM), is
described in Sec. II.A. The main change made to the original Harvard University model
relates to the introduction of a partial equilibrium formulation of the electricity demand based
on the use of price and income elasticities; simultaneously, an endogenous "learning-bydoing" concept (Kypreos, 2000) was introduced into CRETM. As for most energyenvironment-economics (E3) planning and polity studies of this kind, a range of relatively
"surprise-free" futures (out to the year 2030, in the present case) are explored in the form of
13
scenarios by which the economic and environmental implications of a range of interconnected
policy and technology choices are examined. As is also "standard" for studies of this kind, a
"point-of'-departure" case provides a reference to a generally "policy-free" or Business-asUsual" (BAU) view of the future. Several other scenarios related to the control of sulfur,
nitrogen, and carbon atmospheric emissions associated with electric energy generation are
posited under conditions that reflect the operation of either a centrally planned economy (e.g.,
direct emission constraints are imposed) or a (generally) free-market economy (e.g., respective
emission taxation rates are imposed on a time-varying $/tonne basis). A carbon-reduction
scenario is also described. The impact of a set of technology-based assumptions on the
implications revealed by the former set of environmentally driven scenarios are also
examined. These technology-driven scenarios consider the effects of changing costs of
natural gas and related generation technologies. Additionally, the impact on (electrical)
generation mixes and atmospheric-emission burdens of varying capital and operating (both
fixed and variable) costs associated with relatively emission-free technologies (e.g., nuclear,
and solar electric) are examined. Section II.B describes the bases, rationale, and interconnectivities of the menu of scenarios chosen to illuminate possible E3-related policy options
in China.
C. Scope
Section III presents results from scenario-based projections of China's electricity demand
through the year 2030 at five-year intervals that start with the base year of 1995. The CRETM
divides China into seven regions according to population density, energy-resource availability,
level of economic development and related conditions, and the regional urgency of impending
environmental conditions. This division follows the traditional regionalization of the country
and also takes into consideration the goals and interests of the CETP. All of these scenariodefining attributes to varying degrees are related, albeit the procedure used for analytic
implementation appears to be linear. The results of single-point parameter variations for the
BAU point-of-departure case are first reported in Sec. III.A. The sensitivity and "robustness"
of the BAU scenario to changes in fundamental drivers (e.g., basic capital costs, discount
rates, and other generally economic parameters that largely are held fixed for the subsequent
environmental- and technology-driven scenario variations) are examined. The environmental
and energy-mix impact of each of these carefully controlled scenarios are reported in Sec.
III.B. Finally, after providing a concise cost summary of each scenario, the inter-dependencies
of each are discussed in Sec. IV, as they point to possible policy and planning choices for
steering the enormous energy embodied in a growing China towards a direction of sustainable
development, as well as understanding the impact that such a coarse might have on other
countries that are interconnected through a shared, global economy in search of common
goals.
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II. Approach
The studies performed under the EEM component of CETP use an LP model and a scenariobuilding approach to identify and examine sensitivities of a range of cost-optimized material
(e.g., fuel, technologies, emissions) and electrical energy flows in a transport-connected
seven-region model of China. The E3 Technology constraints under which these optimum
material and energy flows occur over the 30-year time horizon being considered essentially
define any given scenario, as well as the overall scenario taxonomy that defines the overall
study. The description of study approach given in this section, therefore, divides naturally
into first a description of the scenarios being considered, which is then followed by a
description of the LP model per se. In that specifications of both scenario and model have
minimum data requirements, this section includes a summary description of these data
requirements and related uncertainties, gaps, and limitations.
That this study is restricted to energy use, and that the optimizations are confined to electricity
markets, must be stressed. Within this contextual limitation, therefore, this model does not
include conflicts and competitions related to fuels demands for heating in either industrial,
residential, or commercial sectors, nor are the demands from the transportation sector
reflected in the optimization of fuel and technology use to meet the demands for electrical
energy. Although inter-regional transportation of fuel is communicated to the optimization
through appropriate costs, the impact of energy used in the course of that transport process is
not included; efficiency losses are included, but the emissions associated with actual coal
transport are not. Additionally, the distribution of electrical energy to the final users, as well
as the networking and linking of networks, remains unaccounted in the CRETM electricalenergy sector model; this level of detail is left to the modeling activities of the University of
Tokyo CETP team members (Yamaji, 2000). Finally, and most importantly, price feedback
on electrical energy demand is only approximately accounted at the present level of analysis
through the use of elasticities (both price and income) to set the otherwise exogenously
determined, regional demands for electrical energy. Feedback between the electricity-sector
costs and the overall economic activity is not modeled. Within these limitations, the scenario,
data, and modeling requirements of this study are elaborated in the following sections. A
capability to model overall energy-to-economy feedback, however, is provided by the
MARKAL model (Fishbone, 1981) used also by the EEM task at both countrywide and
(Shandong) provincial levels (Eliasson, 2002).
A. Key Model Definers/Drivers
The China Regional Electricity Trade Model (CRETM) uses LP optimization techniques to
determine the mix of electricity-generation technologies needed to satisfy and exogenously
specified demand under scenario-constrained conditions. At the most simplified level, three
variables can be identified as essential to setting a constrained and optimal energy mix:
• fuel availability and prices (coal, oil, gas, and uranium, in the present case);
• environmental policies created and implemented to constrain local (particulates, SO2,
NOX); regional (SO2, NOX) , and global (CO2, NOX, CH4) emissions;
15
• technology innovation and diffusion, including through Clean Development Mechanisms
(CDMs), generally resulting in reductions of technology costs.
These three "top-level" variables combine with a set of assumptions related to demand growth
(price and income elasticities), discount rates (long-term cost of capital), and specific
technology characteristics (including construction times, operational life, economic life, fixed
and variable O&M charges, conversion efficiencies, annual load factors, etc.), and a myriad of
material-flow and environmental constraints define the scenarios to be evaluated, as
elaborated below. While endogenous learning is an available option (Kypreos, 2000a), the
impacts of technology innovation and diffusion are not applied directly in the study reported
herein as an endogenous formulation, but is indirectly applied as an exogenous time sequence
of specified technology costs in a way that suggests possible impacts of technology
innovation/diffusion. The results of single-point sensitivity studies of key variable like
economic growth rates, discount rates, and technology costs are reported in Sec. III.D.
B. Scenarios
Table II describes a taxonomy of the scenario structure adopted for this study. All cases
examined are referenced to a baseline or "point-of-departure" (POD) case that sometimes is
loosely referred to as a "business-as-usual" (BAU) scenario. This BAU, point-of-departure, or
base-case scenario assumes nominal technological advances, related technology price
reductions, and conventional assumptions on resource utilization that are not necessarily
limited by environmental or non-market (supply-demand) constraints. Furthermore, no
unusual (e.g., non-BAU) advances in demand-side management (e.g., efficiency gains,
lifestyle changes, etc.) are assumed in formulating the BAU scenario(s).
The three E3 dimensions of Table II [Environmental, Economic, and Energy (Technologies)]
form a kind of three-dimensional matrix in which specific scenarios can be defined, although
the membership of a given attribute with any one of these three dimensions in some cases is
not uniquely ascribed. Furthermore, the forcing or implementation of any given attribute
often occurs through a policy dimension, the attributes of which are also included in Table II.
Although not without ambiguity, the policy-driven three-dimensional scenario structure
suggested in Table II is useful in describing the specific (and countable) scenarios adopted by
this study, as well as possible policies needed to implement the respective combination of
scenario attributes.
The scenario taxonomy described in Table II is applied in the context of the present study in
Table III, wherein a hierarchy of 12 specific scenarios is chartered for detailed analysis using
the LP-optimizing CRETM. At the top of this hierarchy is the policy attribute (e.g., the fourth
dimension in Table II) that determines whether the exogenous demand for electricity is "high"
(H) "medium" (M), or "low" (L). For each of these possible conditions for (regional)
electricity demand, the discount rate (considered here as an economic attribute) is either
"high" (H), "medium" (M), or "low" (L). Whether emissions controls are imposed on SO2,
CO2, or SO2 + CO2 (NOX control is not considered in this study), and whether these controls
are imposed through a centralized (caps) or a market-control (taxes) mechanism determines
the two primary environmental attributes used in this study. The remaining economic
attribute determines the price of natural gas, and the only technology attribute used in the
present study establishes the viability of either of two advanced technologies, nuclear or solar
photovoltaic (PV), albeit the latter (technology attribute) is
16
Table II. Taxonomy of Scenarios Used to Describe Possible "Surprise-Free" Futures.
Scenario Attributes
Descriptions
No environmental constraints or emission limits; no or
Baseline ,Business-As Usual
nominal price controls; nominal technological
(BAU), or Point-Of-Departure
advances.
Case(s)
Environmental
Sulfur Dioxide Control;
Limits (caps) imposed on sulfur emissions and/or apply
sulfur emission fees/taxes
Nitrogen Oxide(s) Control;
Limits (caps) imposed on NOX emissions and/or apply
NOX emission fees/taxes
Carbon Dioxide Control;
Limits (caps) imposed on CO2 emissions and/or apply
CO2 fees/taxes.
Restrictions on Land and/or
Limitations imposed on the maximum percent of
Water Usage;
exploitable land and/or water use to be used
Economic
Natural Gas Policy;
Vary natural gas prices and gas technology costs.
Other Resource Prices;
Vary prices of imported uranium or oil
Discount Rates (Risks) per
Vary discount rates used, depending on time,
Technology;
technology, and installed capacity
Plant Construction and Life
Vary both parameters in conjunction with assumed
Times;
technological advances for specific technologies.
Technological
Advanced Coal, Nuclear, and/or
Model accelerated technology development by lowering
Renewable Energy Technologies; capital costs after 2005 for a range of advanced-fossil,
nuclear, and renewable energy technologies.
Advances in End-Use Energy
Vary the ratio of electrical energy demand to economic
Efficiencies;
productivity.
Technology Introduction,
Vary constraints related to kinds and introduction rates
Diffusion;
of generation technologies allowed.
Specific Technology Advances;
Vary operational conversion efficiencies, system
availability, specific waste-generation characteristics,
life-extension capabilities.
Policy
Population Growth;
Productivity (real GDP) Growth;
Resource Allocation (Land,
Water);
Import/Export Controls,
Taxation, Tariffs;
Level of External Cost Valuation;
Technology Costs (Subsidies),
Rates of Advancement/Change;
Market Form (Centrally
Controlled versus Free, DemandSide versus Supply-side
Management, Degree and Kind of
Price Regulation;
17
established largely through an economic setting. The 12 scenarios listed on Table III are less
than the 64 possible for the attributes identified. A "Global" identification (ID) system is
suggested on Table III, although for the purposes of the results presented herein the shortened
"Local" ID system also defined on Table III is used.
The embryonic scenario
mapping/identification systems suggested in Table III also provides a basis for connecting and
benchmarking results from the present optimization model with results from simulation model
used by other CETP team members (Schenler, 1998) contributing to the ESS task, as
described below in Sec. II.D.1.
As suggested in Table III, fully half of the 12 scenarios deal with control over the
environmental attribute, and, for all cases reported herein, apply only for the carbon, sulfur, or
carbon + sulfur caps (e.g., roughly, the caps are assumed to apply to centrally planned
economies versus taxes for a free-market environment). To date, these respective
environmental constraints (caps) have been applied as maximum regional (in the case of SO2)
or countrywide (in the case of CO2) emission rates, and the marginal costs when each is met
are indicate as an effective tax schedule on Table IV. It is the natural result of these LP
optimizations that the same scenario (e.g., technology mixes, total present-value of electricgeneration costs, atmospheric emission rates, etc.) results are achieved whether the emission
rate is explicitly constrained (as is done here), or a carbon and/or sulfur tax of magnitude and
schedule indicated on Table IV is applied (e.g., without an explicit cap on specific emissions).
Table IV also re-states the definition of the Local IDs used to present a limited set of results in
Sec. III.C.
18
Table III. Specific Scenarios Adopted in the Present Study of Cost Optimal Electrical Generation Mixes for China.
Scenario Definition
Scenario Attribute
Business as Usual 1
Business as Usual 2
Sulfur Dioxide Control 1
Sulfur Dioxide Control 2
Carbon Dioxide Control 1
Carbon Dioxide Control 2
Sulfur + Carbon Dioxide Control 1
Sulfur + Carbon Dioxide Control 2
Enhanced Methane Use 1
Enhanced Methane Use 2
Advanced (Nuclear) Technology Use 1
Advanced (Solar PV) Technology Use 2
Ident ifiers
Global ID
BHMOONN
BHLMHONN
SHMOONN
SHMHONT
CHMOONN
CHMHONT
EHMOONN
ELLHONT
GHMOONN
GHMH0NN
NHMOONN
PHMOONN
Local ID
BHC
BHH
SHC
SHH
CHC
CHH
EHC
EHH
Demand Discount
Fuel
Technol. Emission
Level
Rate
Prices
Costs
Cap
Policy Economic Economic Technol.
Environ.
H
H
O(C)
O
N
H
H
H
O
N
H
H
O(C)
O
C
H
H
OH
O
C
H
H
O(C)
O
C
H
H
H
O
C
H
H
O(C)
O
C
H
H
H
O
C
H
H
O(L CH4)
O
N
H
H
H((L CH4)
O
N
H
H
O
O(L Nucl)
N
H
H
O
O(L SolPV)
N
Scenario Attribute Designations
C = Cap, Constant, or Carbon
H = High
L = Low
M = Medium
N = None
O = 'Ordinary' (Baseline)
P = SolarPV
T = Tax
19
Controls
Tax
Environ.
N
N
N
N
N
N
N
N
N
N
N
N
Table IV. Scenario Identification Scheme for Case Reported Using ‘Local’ ID (Table III).
Scenario Demand
Price
SO2 Cap CO2 Cap C+S Cap
BHC
H
C
BHH
H
H
CHC
H
C
C
CHH
H
H
C
EHC
H
C
C
S
EHH
H
H
C
S
SHC
H
C
S
SHH
H
H
S
CO2 marginal cost
profiles, $/tonneCO2
1995
2000
2005
2010
2015
BHC
0
0
0
0
0
BHH
0
0
0
0
0
CHC
0
0
0
0
0
CHH
0
0
0
0
0
EHC
0
0
0
0
0
EHH
0
0
0
0
0
SHC
0
0
0
0
0
SHH
0
0
0
0
0
SO2 marginal cost
profiles, $/tonneSO2
1995
2000
2005
2010
2015
BHC
0
0
0
0
0
BHH
0
0
0
0
0
CHC
0
0
0
0
0
CHH
0
0
0
0
0
EHC
0
0
0
35.45
26.09
EHH
0
0
31.31
54.55
0
SHC
0
0
26.09
64.64
66.84
SHH
0
0
31.31
70.19
97.66
B = BAU; H = High Demand
and/or High (Double) Price;
C = Constant Price
and/or Carbon Cap;
S = Sulfur Cap;
E = S + C Cap
2020
0
0
46.18
34.94
44.07
34.94
0
0
2025
0
0
62.8
41.21
62.7
39.8
0
0
2030
0
0
32.4
24.95
32.4
25.04
0
0
2020
0
0
0
0
0
0
88.07
116.3
2025
0
0
0
0
0
0
98.08
116.42
2030
0
0
0
0
0
0
133.37
0
C. Data Requirements, Gaps, and Uncertainties
Whether a simulation (Schenler 1998) or an optimization (Kypreos, 1996; Chandler, 1996;
Murray, 1998; Rogers, 1999) model is used, the data requirement for these kinds of studies is
enormous and rarely is fully satisfied. A "top-level" indication of these data requirements is
given in Table V, which is organized along the scenario attributes described in the previous
section (e.g., Environmental, Economic, Technological, and Policy; Tables II and III). As is
the case for the scenario attributes, the boundaries between these four data-related attributes
are not without ambiguity and free of subjective judgment. The specific data needs of
CRETM as applied to this study are given in Appendix A, with the data summaries given in
the PNNL study (Chandler 1996) providing an excellent start on this ongoing task of data
acquisition and evaluation. The data needs listed in Appendix A largely reflect conditions
used to compute the baseline BHC scenario (Table III or IV).
20
Table V. Typical Data Requirements of Energy Flow Model to be Resolved on a
Temporal and Regional Basis.
Environmental Data
Effectiveness of flue-gas cleanup systems
Desirable/goal emission caps for particulates, SO2, NOX, CO2
Consequences/costs of acid rain to plants and people
Environmental costs of large-scale versus small(er)-scale hydroelectric plants
External costs of coal mining, power plant emissions (particulates, SO2,
NOX, CO2), and other fuels and land uses.
Economic (Cost) Data
Resource extraction costs versus depletion
Resource (coal) ash, sulfur, and energy (GJ/tonne) contents
Capital, fixed, and variable costs of domestic generation technologies
Costs of imported fuels (oil, gas, uranium)
Costs of imported advanced technologies (Advance Coal Conversion,
NPP SolarPV, wind turbines, combustion turbines, etc. )
Cost of coal washing/cleaning
Cost of flue-gas cleanup systems (ESP, FGD, etc.)
Cost of electricity transmission and distribution
Discount rate(s) per technology
Fuel transport costs by region and mode (rail, road, ship, pipeline, wire)
Cost of large-scale versus small(er)-scale hydroelectric plants
Technological Data
Fuel specific heating values (GJ/tonne)
Thermal-to-electric conversion efficiencies per technology
Availability (time, price) of advanced coal cleanup technologies
Availability (time, price) of advanced conversion technologies (PFVC,
IGCC, NPP, FC, SolarPV, etc.)
Availability (time, price) of advanced post-combustion technologies (ESP,
FGD, FGS, etc.)
Availability (time, price) of high-voltage AC and DC electrical transmission systems
Plant construction and life times
Policy-Related Data
Population and growth rates
Real GDP and growth rates
Electric power consumption and growth
Sectoral (economic) structure of energy demand
Price and income elasticities of (electrical) energy demand
Resource availability, extraction constraints
Installed and installable/exploitable hydroelectric resource
FC
Fuel Cell (included, not used)
ESP
Electrostatic Precipitator
FGD
Flue Gas Desulfurization
FGS
Flue Gas Scrubber
IGCC Integrated Gas Combined Cycle
NPP
Nuclear Power Plant
PFBC Pressurized Fluidized Bed (coal) Cumbustor
PV
Photovoltaic
21
D. Linear Optimization Model
1. Optimization versus Simulation Models
The application reported here ideally should adjust total demand according to changes in
energy prices or environmental conditions. Additionally and ideally, the model should take
risk aversion into consideration in making decisions under conditions of uncertainty; these
methodologies should then consider the distribution of possible outcomes and impacts and
factor these probabilities into generation of final results. While a useful tool for exploring
policy options in and between (electrical) power and environmental sectors, such a model
would be nonlinear, or require the use of linearized NLP methods, and, therefore, is not within
the scope of the present study. Under present circumstances, the regional demand for
electrical energy is scaled in time with exogenously input growths in GDP and fuel prices.
The resulting model remains linear and amenable to rapid solution using standard LP
optimization algorithm (ILOG, 1997; Brookes, 1998).
The analysis of constrained energy and material flows in the seven-region CRETM is based on
an optimization of total levelized energy costs incurred in five-year increments during the time
frame of the analysis (1995-2030). In exchange for this more restricted view of energy and
material flows and the associated environmental impacts, CRETM offers a multi-regional
view of these flows against which the impact of policy decisions on a provincial (e.g.,
Shandong) level can be assessed. Furthermore, while the LP optimization provides a measure
of departures from the (cost-) optimum energy mix and associated environmental economic
impacts through the reporting of marginal costs, the nature of the LP modeling approach
limits the study of strong departures from the reported optimum to carefully constrained
sensitivity studies. Additionally, sensitivity studies that actually vary the objective function
used in the LP optimization may also be required to satisfy "stakeholders" who may have
interests or concerns that are not easily reduced to purely economic terms.
Many of these limitations associated with the LP optimization are addressed by models based
on the (also) "bottom-up" simulation paradigm (Schenler, 1998), wherein electricity
production costs are computed according to methods used by (electric) utilities to dispatch a
spectrum of generating plants (on an hour-by-hour basis) in a way that minimizes electricity
production costs. Like the optimization modeling approach followed in CRETM, generation,
emissions, and costs are computed within the simulation model, with electricity demand and
fuel prices being exogenous to the modeling computations per se. While these utility
dispatching models are also capable of optimizing the way in which a utility allocates
electrical generation stocks that have a range of variable costs [e.g., putting the least costly (to
operate) systems on line first, followed by more expensive generators as the demand increases
throughout the day], as used in the context of the present study (Connors, 1999), this
dispatching model is applied as a modeling "engine" to create repetitively thousands of
scenarios based on combining a large number of detailed strategies with an equally large
number of uncertainty measures. Hence, while assessing the problem of electric-generation
mix at a much deeper (temporally and sectorally more detailed) level of engineering-economic
analysis than used in the MARKAL model or the simpler CRETM, the simulation modeling
approach uses judgments, opinions, views and impressions of "stakeholder" groups to shape
these thousands of strategy-based, uncertainty-adjusted scenarios into a "tradeoff frontier"
(e.g., minimum cost of electricity versus a range of CO2 emission rates). A single "optimum"
22
is not produced, but instead a range of energy mixes or choices are presented that satisfy a
given ensemble of "stakeholders" views and concerns. Appendix B elaborates on the essential
differences, similarities, and symbioses of these two (simulation versus optimization)
modeling approaches. Developing an appreciation of these differences is crucial to
interpreting and making connections between results from both, as they are used to shape
useful policy options and directions in CETP.
These two modeling philosophies and approaches are complimentary, but the capabilities and
assumptions that drive both can strongly impact the analytic results produced by both. In
presenting and comparing results from the optimization and the simulation models, it becomes
crucial that the scenario used for the former can be mapped into those used in the latter, and
that comparisons are made under conditions where similar objective functions (real in the case
of the optimization model; virtual in the case of the simulation model) are being optimized.
This single, but parallel, requirement (e.g., similar scenario being optimized using a similar
objective function) presents a challenge to this multi-model CETP study. Section III.E
presents preliminary CRETM results that have been cast into a form that ultimately can lead
to the required quantitative communication between these two different, but important,
approaches to assessing possible futures for China's electrical generation system(s).
2. The China Regional Electricity Trade (Cost Optimization) Model, CRETM
The Linear-Programming (LP) optimization model is described in this subsection, with
quantitative details given in Appendix C. The LP approach to optimization allows flexibility
in estimating the least-cost combination of power sources that will meet overall demand,
while considering a variety of constraints arising from energy supply or environmental
limitations. As discussed previously and in Appendix C, a model of limited scope that
focuses on single, object-function-dependent optima must be broadly evaluated in a carefully
structured scenario context and in comparison and symbiosis with other kinds of models
having other strengths and weaknesses (Appendix A).
The CRETM model is an optimization model based on LP methods (Williams, 1999). While
limited to the electrical energy sector, compared to full-energy-sector models like MARKAL
(Fishbone, 1981; Kypreos, 1996; Goldstein, 1995), the main attraction of CRETM is the
ability to follow inter-regional allocation and transport of fuels within the seven-region model
of China, as is illustrated schematically in Fig. 1. Like the earlier version of the Harvard
University energy model, the PSI-modified CRETM model minimizes the total cost of the
electrical system, including all costs associated with generation per se (e.g., capital, fixed, and
variable), coal cleaning, inter-regional fuel transport, inter- and intra-regional power
transmission, and, when so constrained, external costs attributable to the emission of both
pollutants (mainly SO2 and NOX) and of greenhouse gases (GHG, mainly CO2). Unlike the
still earliest version of the Harvard University energy model first developed and used to model
a seven-region China (Murray, 1998), CRETM examines a full-range of electrical generation
option, instead of being restricted to coal-fired stations and a much shorter time horizon
(Murray, 1998). This full-spectrum electricity-generation model explores a 30-year time scale
and is similar to that used by PNNL (Chandler, 1999), from which much of the data input
used herein derives. The PSI modifications to the Harvard model leading to CRETM include:
23
Ø Shandong Province is explicitly included;
Ø The time horizon can be extended to the year 2030, and a scenario-dependent input;
Ø Electricity Demand is not fixed, but is now price and income elastic;
Ø Resource, imports, and transportation costs are time dependent;
Ø Transportation costs are redefined based on mode, distance, and mass;
Ø Explicit variables on investment and capacity constraints have been introduced;
Ø Imports to explicit regions by transport mode and regional balances are now included;
Ø Technology market penetration and growth rates for expanding technologies are described
by explicit constraints;
Ø Emission of SO2, NOX, and CO2 are limited either by direct taxation (free-market model)
or explicitly (regional or countrywide) constrained (centrally planned market model);
Ø Endogenous technological learning is include whereby costs are reduced in accordance
with accumulated installed capacity of a given technology;
Ø Finally, Scenario Generation and Results Report Generation are broadened and redefined
to create directly inter-comparison graphs and tables.
The China Regional Electricity Trade Model is comprised of four basic relationships: a) the
objective function (present value of total system costs); b) mass conservation relationships
that balance the flows of fuel masses across processes and regions; c) energy conservation
relationships that connect the flow of fuel masses to energy generation and transport; and d) a
set of constraints that establish bounds on both energy and mass flows, levels at which
specific generation technologies can be deployed, and emission rates. These latter
endogenous constraints, combined with controls enforced through specific exogenous inputs
(e.g., discount rates, demand growths, GDP growths, fuel and capital unit costs, etc.), create
the various scenarios (Table III) and is used to define and/or bracket possible energyeconomic-environmental (E3) futures for Shandong Province in a countrywide context.
The computational algorithm used to evaluate CRETM, like that under which MARKAL
operates, is based on a set structure established by the GAMS operating system (Brookes,
1998). The four essential elements of CRETM described above are evaluated in terms of a set
of generation technologies, {ss}, that are driven by a set of fuels {tot(c)}, where the latter is a
(large) subset of a set of commodities {c} that in turn is defined by the addition of electricity,
ELEC, to the set {f} (e.g., {c} = tot(c) + ELEC). Table VI explicitly defines the commodity
and fuel sets, {c} and tot(c), along with other more specialized fuel subsets. Similarly, Table
VII describes the composition of the main generation technology set {ss} along with key
specialized technology subsets. These fuel and technology sets are combined to describe the
energy and material flows within and between the seven China regions depicted schematically
in Fig. 1. Finally, for each of these regions, Fig 2 arranges these flows in a way that emulates
the flow sequence Primary Fuels → Processes → Conversions → Demands used in the
original formulation of MARKAL (Fishbone, 1981). Both Figs. 1 and 2 contain elements not
24
yet modeled in the present version of CRETM [e.g., Guandong is currently modeled as part of
the region SC (Fig. 1); heating-plant deliveries to non-electric applications and waste and
resource streams related to nuclear or renewable are yet to be included]. These options are
retained in Figs. 1 and 2 as an indication of important future work.
A "top-level" summary of CRETM, as laid out in Tables VI-VII and Figs. 1—2, describes an
electrical energy sector LP model that cost-optimizes on 17 generation technologies, 16 fuel
forms (including the renewable energy forms), having both domestic and foreign fuel sources
(for oil, gas, and uranium), having 4 transportation modalities, searching over 8 time periods
into the future, examining energy and material exchanges within and between 7 regions, and
using an object function composed of 8 cost categories (i.e., domestic fuel, imported fuel, coal
cleaning, transportation, possible emission taxes on three species, capital costs, fixed
generation costs, and variable generation costs). A simple product of these six dimension
(i.e., generation technologies, fuel commodities, temporal, regional, fuel transport, and cost
categories) gives about 500,000 possibilities, all of which, for a given set of constraints and
inputs, are to converge to single (economic) optimum. Even for a relatively simplified model,
the size of the task can become daunting, particularly when the 10-15 optimizing scenarios
suggested in Table III are superposed. In addition to the enormity of this simplified problem,
the need to understand the impact of deviations, departures, or sensitivities from any
"optimum" that is reported, as well as the impact of changing the objection function, on
possible conclusions and recommendation is apparent. Appendix C outlines analytical aspects
of the model that attempts to find this optimum for the conditions (i.e., energy and mass-flow
balances, and associated constraints) described therein, with Appendix A recapitulating key
input used by CRETM [for the baseline scenario, BHC (Table III)].
Table VI. Summary of Main Fuel-Type Set and Subsets.
FUEL TYPES
coal; high-sulfur, high-ash
coal; high-sulfur, low-ash
coal; medium-sulfur, high-ash
coal; medium-sulfur, low-ash
coal; low-sulfur, high-ash
coal; low-sulfur, low-ash
oil: domestic
oil; imported
gas; domestic
gas (incl. LNG); imported
uranium; domestic
uranium; imported
renewables (fuel equivalent)
hydro
electricity
hhcoal
hlcoal
mhcoal
mlcoal
lhcoal
llcoal
oil
oilimpt
gas
gasimpt
ur
urimpt
fequ
c
X
X
X
X
X
X
X
X
X
X
X
X
X
elec
X
tot(c)
X
X
X
X
X
X
X
X
X
X
X
X
X
f(c)
X
X
X
X
X
X
X
k(c)
X
X
X
X
X
X
imp(c
X
X
X
enc
X
X
X
X
X
X
X
X
X
X
X
X
X
X
{c} = fuel-related commodity; tot(c) = {c} + elec; f(c) = fuel type other than coal; k(c) = type
of coals; imp(c) = imported coal; eng(c) = oil + gas + ur (not shown) = endogenous fuels other
than coal; imp(c) = imported fuels; and enc(c) = fuels used in electricity production for market
allocation.
25
Table VII. Summary of Main Electrical Generation Technology Sets and Subsets.
ELECTRIC PRODUCTION
TECHNOLOGIES
small-unit domestic coal
medium-unit domestic coal
large-unit domestic coal
large-unit w/ ESP domestic coal
large-unit, ESP + scrubber domestic coal
atmospheric fluidized bed combustion
integrated coal-gasification combined cycle
pressurized fluidized-bed combustion
regular/standard oil fired
combined-cycle oil fired
combined-cycle gas fired, gascc
advanced combined-cycle gas fired
solar photovoltaic
geothermal
nuclear electric
hydroelectric
wind turbine
ss
domsml
dommed
domlar
domesp
domscb
afbc
igcc
pfbc
oilreg
oilcc
gascc
gascca
pv
gt
nuclear
hydro
wind
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
s(ss) s1(ss s2(ss) s3(ss s4(ss) s5(ss) s6(s) sc(ss) soil(ss sg(s)
)
)
)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
ss = all power generation technologies
s = thermal generation technologies
s1 = renewable generation technologies
s2 = old domestic generation technologies
s3 = nuclear electric generation
technologies
s4 = hydroelectric generation technologies
s5 = all fossil-based generation
technologies
s6 = clean(er) fossil + nuclear +
renewables
sc = all coal-fueled generation
technologies
soil = all oil-based generation technologies
sg = all gas-fueled generation technologies
26
NE
NC
Inner Mongolia
Shanxi
Hebei
Beijing
Tianjin
NW
Xinjiang
Qinghai
Gansu
Shaanxi
Nignxia
SC
SA
Hunan
Jiangxi
Hubei
Henan
SW
Tibet
Sichuan
Guizhou
Guangxi
Yunnan
Hainan
Heilongjiang
Jilin
Lianing
Shandong
EA
Jiangsu
Zhejiang
Anhui
Fujian
GU
Chongqing
Guangdong
Coal by Land
Electricity
Coal by Sea
Imports
Hong Kong
Shanghai
Figure 1. Energy and material flows in an eight-region china model; for the present study, the Guangdong (GU) region has been subsumed into
the South-Central (SC) region to form an interim seven-region model.
27
PRIMARY
FUELS
PROCESSES
PREPATORY
TRANSPORT
Chem./Physical
Coal
CONVERSIONS
Coal-fired Gen.:
Cleanup
domsml, dommed,
DEMANDS
ECONOMY
Process
Heat
SO2,
NOx,
domlar, domesp,
Particulates
domscb, afbc,
Imp.
ENVIRON.
igcc
Oil
Rail
Heating Plant
Road
Ship
Imp.
oilcc
Pipe
Gas
gascc, gascca
Imp.
Uranium
CO2
Electricity
Conv., En.,
Nuclear
Fuel Fab.
Radwaste,
SF
Hydroelectric
Hydro
Land,
Renewables:
Renewables
Water
wind, gt, pv
Figure 2. Material (fuel) and energy flow diagram for a typical region; inter-regional flows are not included on this representation.
28
III. Results
This results section reported here place emphasis on: a) the driver for electricity demand
(following Sec. III.A); b) description of key results from the baseline BHC scenario [e.g.,
using the short-hand notation describe in conjunction with Table III, (B)AU, (H)igh
exogenous demand, (C)onstant gas prices] scenario (Sec. III.B); c) a summary of key results
(e.g., energy mixes, emission rates, etc.) from the eight scenarios listed in Table III (Sec.
III.C); and d) single-point parametric studies that explore sensitivities around the BHC
scenario (Sec. III.B). Approximately 1000 (Appendix A) parameters must be fixed for
CRETM to evaluate a given scenario that delivers a single optimized energy mix as a function
of time for the seven regions of China being modeled. Key technological, economic, and
operational parameters for the 17 electricity-generation technologies being considered (Table
VII) are summarized on Table VIII. Except where explicitly stated, these parameters remain
fixed for the scenario studies presented herein, with Appendix A listing key input to the
CRETM. The dozen or so key electrical-energy, capacity, and mass flow variables used in
this model to enforce respective balances and constraints are listed and defined in Table VIII.
Key results reported from the minimization of the total
Table VIII. List and Definition of Key Energy and Mass-Flow Variables Used to
Enforce Respective Balances and Constraints (Appendix C).
Key Variable
ZZ
QSELEC(i,ss,t)
QTR(i,i1,c,m,t)
Unit
M$
TWeh/yr
Mtonne/yr
QCAP(i,ss,t)
INV(i,ss,t)
ICOST(i,ss,t)
SDEP(i,t)
(a)
QS(i,c,t)
PQ(i,k,k1,t)
GWe
GWe
M$
MtonneSO2/yr
Mtonne/yr
Mtonne/yr
CQ(i,k,k1,t)
Mtonne/yr
NQ(i,k,k1,t)
QF(i,c,s,t)
Mtonne/yr
Mtonne/yr)
FQ(i,k1,t)
Mtonne/yr
(a)
Description
Objective function, minimized NPV of cost of electricity supply
Electricity supplied in region i by technology ss at time t
Commodity (fuel) transported form region i to region i1 by mode m
at time t
Capacity in region i of installed generation technology ss at time t
New capacity installed in region i of technology ss at time t
Investments in region i in technology ss at time t
Sulfur deposition in region i at time t
Supply of commodity c (fuel) in region i at time t)
Amount of coal k passing through physical treatment and
producing coal k1
Amount of coal k passing through chemical treatment and
producing coal k1
Amount of coal not receiving any pre-combustion treatment
Amount of commodity (fuel) c used by technology s in region i at
time t)
Amount of coal of type k after pre-treatment in region i at time t
not used in these computations.
Net Present Value (NPV) of all electricity generation costs over the period of the analysis
(1995-2030, in YEARPP = 5 year time intervals) include:
•
•
•
•
Primary fuel use;
Electricity production by fuel;
Installed generation capacity;
Local, regional, and countrywide atmospheric emissions under constrained or
unconstrained conditions;
• Technology- and region-dependent marginal costs of electricity for specified emission
constraints [e.g., effective (minimum) taxes for emissions control, $/tonneCO2, etc.];
29
• Capital investment and annual O&M charges for electricity by technology and region;
• Capital investment and annual O&M charges for both fuel and electricity transport across
China;
• Amount of fuel and electricity transported by a given mode (e.g., road, rail, ship, wire).
A. Electricity Demand
As is indicated by the "scenario map" given on Tables III and IV (as well as a more elaborated
map given in Table B-II of Appendix B), the exogenous electrical-energy demand for each of
the seven China regions being modeled is fixed at a value termed H ("high"). This demand
designator is relative, particularly when compared to other projections made and used in
various China electrical-sector modeling exercises (Murray, 1998; Chandler, 1998; Dadi,
2000). The demand for electrical energy in period t for region i , DELEC( i, t ) , is determined
in CRETM from the following relationship:
[1 + GGDP( i, t )] ELAGDP*YEARPP
DELEC( i, t ) = DELEC( i, t − 1)
[1 + GPRICE ( i, t )] ELAPR( t )*YEARPP
(1)
where the exogenous growth rates for fuel prices and GDP are GPRICE ( i, t ) and GGDP( i, t ) ,
respectively; the respective elasticities are ELASPR( t ) and ELAGDP ; and YEARPP is the
number of years per period. The growth in regional GDP is given by,
GDP( i, t ) = GDP( i, t − 1) *[1 + GGDP( i, t )]YEARPP
(2)
where the values for GGDP(i,t), ELAGDP, and ELAPR(t) used to generate the results reported
herein are given in Table IX.
Table IX. Summary of Regional GDP Growth Rates and Income and (Electricity) Price
Elasticities Used in this Study (assumed the same for all seven CRETM regions).
Time
Period
1995-2000
2000-2005
2005-2010
2010-2015
2015-2020
2020-2025
2025-2030
GDP Growth
Rate, %/yr
9
8
7
6
5
4.5
4
Income
Elasticity
0.65
0.65
0.65
0.65
0.65
0.65
0.65
30
Price
Elasticity
0
0
0
0.1
0.1
0.2
0.2
Figure 3 gives the sum of GDPs over all seven regions along with the time-dependence of
countrywide electricity demand that results. As a comparison, the GDP posited in the course
of arriving at the IIASA/WEC results (Nakicenovic, 1998) for China (actually, Region CPA
from the IIASA/WEC study, which includes Cambodia, Hong Kong, Democratic Republic of
Korea, Peoples Democratic Republic of Lao, Viet Nam, and Mongolia, in addition to China),
and used to generate the primary energy demands summarized on Table I, are also included on
Fig. 3. A regional breakdown of GDP(B$/yr) and Electrical Energy Intensity, EEI(MJ/$), for
the BHC scenario are shown on Figs. 4 and 5. The key cost and technology assumptions that
underlie the BHC scenario used to generate the Figs. 4 and 5 results are listed in Table X.
Lastly, Fig. 6A gives a comparison of China electrical energy demand used in this study with
projections made from a range of China and US studies, as reported by Chandler (1999); The
PNNL demand was adopted by Chandler (1999), the ERI demands correspond to those made
by the Chinese Energy Research Institute (1995); Quhua corresponds to projections from
Tsinghua University (1994), and EPRA designates projections made by the Chinese Electric
Power Research Academy (1994). Figure 6B continues this comparison of China electrical
energy demand with projections made for a range of IIASA/WEC scenarios (Nakicenovic,
1996). In the parlance of the latter global (11-region) study, Case A is a "High Growth"
scenario; Case B is "Middle Course" scenario, and Case C is an "Ecologically Driven"
scenario; Sub-cases A1, A2, and A3 correspond, respectively, "conventional and
unconventional oil and gas", "coal backstop", and "bio-nuc", whereas C1 and C2 corresponds,
respectively to "nuclear phase out" and "new (small) nuclear plants"
GDP AND ELECTRIC DEMAND VERSUS TIME FOR SCENARIO BHC
6000
GDP(CRETM)
DELEC(CRETM)
DELEC(TWeh/yr), GDP(B$/yr)
5000
4000
GDP(CRETM)
DELEC(IIASA)
DELEC(CRETM)
GDP(IIASA)
3000
DELEC(IIASA)
GDP(IIASA)
2000
1000
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 3. Exogenous time-dependence of China GDP used in Eq. (1) to compute the
regional electricity demands and summed to give the countrywide electricity
demandsalso depicted; a comparison with the GDP growth used in the IIASA/WEC
study (Nakicenovic, 1996) is also shown.
31
GDP VERSUS REGION AND TIME FOR SCENARIO BHC
6000
NW
NW
5000
SW
SW
GDP, B$/yr
4000
SC
SC
SA
3000
SA
EA
2000
EA
NE
1000
NE
0
1995
NO
NO
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 4. Regional GDP versus time for BHC scenario used in CRETM.
ELECTRICAL ENERGY INTENSITY VERSUS REGION AND TIME
35
NO
NE
ELECTRICL ENERGY INTESITY, EEI(MJ/$
30
EA
SA
25
SC
SW
NO
20
NW
SA
NE
CHINA
15
CHINA
EA
10
SC
NW
5
0
1995
SW
2000
2005
2010
2015
2020
2025
TIME
Figure 5. Regional Electrical Energy Intensity versus time for BHC scenario.
32
2030
Table X. Summary of Key Technology, Economic, and Operational Parameters Generally Fixed for Most of the Scenario
Analyses Reported Herein.
Generation Technology
Fuel ES Scrub
P
.
Domestic small (>100 MWe) plant
coal
Domestic medium (100-200 MWe) plant coal
Domestic medium (300 MWe) plant
Domestic medium (300 MWe) plant
Domestic large (300-600 MWe) ss plant
Foreign atm. fluid.-bed combst. plant
Foreign integ. gasif.comb.-cycle plant
Pressurized fluidized-bed combst plant
Traditional oil-fired plant
Combined-cycle oil-fired plant
Combined-cycle gas-fired plant
Adv. gas-turbine comb.-cycle plant
Nuclear plant
Hydroelectric plant
Wind plant
Solar photovoltaic plant
Geothermal plant
coal
coal
coal
coal
coal
coal
oil
oil
gas
gas
U
pv
x
x
x
x
x
ID
Const Oper. Econ. UnitTotal Variable
Fixed Conv.
Annual
.
Time Life Life
Cost
O&M
O&M
Eff. Load Factor
Cost
Cost
yr
yr
yr
$/kWe M$/TWeh %UTC/yr %
hr/yr(%)
domsml 2
20
15
676
5
3
27 4800 (55%)
domme 3
30
20
650
4
3
29 5000 (57%)
d
domesp 3
30
20
600
3.5
3
36 5200 (59%)
domscb 3
30
20
890
4.5
3
35 5200 (59%)
dombig
3
30
20
1100
4.5
3
42 5200 (59%)
afbc
3
30
20
900
8.5
3
38 6000 (69%)
igcc
2
20
15
1000
4.5
3
43 6000 (69%)
pfbc
3
30
20
1100
4
3
45 6000 (69%)
oilreg
2
20
15
530
2.8
3
35 4800 (55%)
oilcc
2.5
20
15
500
2.8
3
42 5600 (64%)
gascc
2
20
15
530
8
3
45 6000 (69%)
gascca 2.5
20
15
500
8
3
55 6000 (69%)
nuclear
5
30
20
1500 +
4+5
1.6
33 7000 (80%)
200
hydro
8
50
40
1200
1
1.5
33 3800 (43%)
wind
1
20
15
1200
2
1.5
33 2600 (30%)
pv
2
25
20
10000
0.07
1.5
33 2600 (30%)
gt
1
15
12
2000
0.05
1.5
33 3200 (37%)
33
CHINA ELECTRICITY DEMAND COMPARISONS
8000
BASE
7000
GENERATION, TWeh/yr
6000
Qghua1
PNNL
Qghua1
EPRA1
Qghua2
5000
4000
EPRA1
EPRA2
EPRA2
Qghua2
ERI
3000
BASE
2000
PNNL
1000
0
1995
2000
2005
2010
TIME
2015
2020
2025
2030
Figure 6A. Comparison of China electrical energy demand used in this study with
projections made from a range of China and US studies, as reported by Chandler
(1999); The PNNL demand was adopted by Chandler (1999), the ERI demands
correspond to those made by the Chinese Energy Research Institute (1995); Quhua
corresponds to projections from Tsinghua University (1994), and EPRA are projections
made by the Chinese Electric Power Research Academy (1994).
34
4000
CHINA ELECTRICITY DEMAND COMPARISIONS (IIASA)
BASE
3500
GENERATION, TWeh/yr
3000
A1
BASE
A2
A3
2500
2000
B
A1, A2, A3
C1
C2
1500
B, C1, C2
1000
500
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 6B. Comparison of China electrical energy demand used in this study with
projections made for a range of IIASA/WEC scenarios (Nakicenovic, 1996); in the
parlance of the latter Global (11-region) study, Case A is a "High Growth" scenario;
Case B is "Middle Course" scenario, and Case C is an "Ecologically Driven" scenario;
Sub-cases A1, A2, and A3 correspond, respectively, "conventional and unconventional
oil and gas", "coal backstop", and "bio-nuc", whereas C1 and C2 corresponds,
respectively to "nuclear phase out" and "new (small) nuclear plants".
Generally, the last few years have seen a wide variation in electricity-demand projections for
China, with more recent projections reflecting greater optimism based primarily on the recent
strong performance of the Chinese economy. The BHC scenario demand adopted for the
CRETM-based studies generally falling in the mid-range of these past and more recent
(optimistic) projections. The following Fig. 6C combines the Fig. 6A and Fig.6B electricitydemand projections to illustrate this point graphically, albeit in a somewhat cluttered display.
35
CHINA ELECTRICITY DEMAND PROJECTIONS
8000
7000
GENERATION, TWeh/yr
6000
5000
4000
3000
BASE
A1
A2
A3
B
C1
C2
PNNL
Qghua1
Qghua2
EPRA1
EPRA2
ERI
Qghua2
EPRA1
Qghua2
PNNL,ERA2
BASE
ERI
2000
1000
IIASA
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 6C. Combination of all electricity-demand projections given on Figs. 6A and 6B.
36
B. Baseline BHC Scenario
Use of the baseline demand for electrical energy reported in the last section (which for the
purposes of this study is given a "high" or "H" designation), nominal or "constant" (C) energy
prices, and the input values given in Appendix A and summarized in Table X, results in the
BHC scenario (Table III). For "constant" (C) fuel prices, the time-independent unit costs for
UCFUEL(i,tot) listed in Table 20 (Appendix A) are used to generate baseline scenarios BHC;
use of the time-dependent multipliers, DUCFUEL(i,tot) given in Table 22 (Appendix A)
generates the "high" (H) cost scenarios like BHH.
Table XI lists key integrated results reported by CRETM for this BHC scenario, which serves
as a "point-of-departure" (POD) scenario for subsequent scenarios, as reported in the
following section, as well as for the single-point parameter studies reported in Sec. III.D.
Table XI. Integrated Results for the BHC Scenario.
Parameter
Total Discounted Energy Cost, ENC(B$)
Total Undiscounted Energy Cost, B$
Total Electrical Demand, TWeh
Average (Undiscounted) COE, mill/kWeh
Total Discounted GDP, B$
Total Discounted ENC/GDP, %
Total Integrated CO2 Emissions , GtonneCO2
Specific CO2 Emissions, MtonneCO2/TWeh
Total SO2 Emissions, MtonneSO2
Specific SO2 Emissions, ktonneSO2/TWeh
Value
478.39
3246.58
94967.1
34.2
14378.75
3.31
60.88
0.64
632.1
6.7
The countrywide electrical-generation mix for China over the 1995-2030 study period needed
to meet the baseline demand (Fig. 3) for these BHC scenario conditions is shown on Fig. 7,
with Fig. 8 giving the time evolution of regional distribution of total electricity generation.
Total emissions of CO2, SO2, and NOX are given in Fig. 9, with time-dependence of the
specific emission rates (tonneXO2/TWeh) for China being given on Fig. 10. Most notable from
the BHC electricity-generation mix are: a) the nominally constant contribution of
hydroelectricity, with some growth after 2020; b) the miniscule contribution of nuclear power;
and c) the growing dependence on gas and oil; and d) the dominant use of domestic coal,
albeit through the growing use of clean-coal technologies (not explicitly shown in Fig. 7). As
shown in Sec. III.D.3.a, modest decreases in the capital cost of nuclear from the value given in
Table IX can dramatically increase the contribution of this technology to China's electricitygeneration mix.
37
ELECTRIC DEMAND MIX FOR SCENARIO BHC
4000
Nucl.
NUCL
3500
Geo.
HYDRO
GENERATION, TWeh/yr
3000
GAS
W ind
OIL
SolarPV
2500
Hydro
DOM. COAL
2000
Gas
1500
Oil
1000
Adv.
Coal
500
Dom.
Coal
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 7. Countrywide electrical generation mix for the BHC scenario.
4000
R EG IO N AL DEMAN D VERSUS TIME FO R SC ENAR IO B HC
NW
3500
GENERATION, TWeh/yr
SW
3000
SC
2500
SA
2000
1500
EA
1000
NE
500
NO
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 8. Regional distribution of electrical generation for the BHC scenario.
38
25
EMISSIO N RATES VERSUS TIME FOR SCEN ARIO BHC
(MtonneC O2/y r)/100
EMISSION RATE, MtonneXO2/y
MtonneSO2/y r
20
MtonneNOX/y r
15
10
5
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 9. Total CO2, SO2, and NOX emission rate for the BHC scenario.
S P E C IFIC E M IS S IO N R AT E S V E R S U S T IM E FO R S C E N AR IO B H C
SPECIFIC EMISSION RATE, xtonneXO2/TWeh
5
(MtonneC O2/TW eh)*10
ktonneSO2/TW eh
4
ktonneNOX/TW eh
3
2
1
0
1995
2000
2005
2010
2015
2020
2025
TIME
Figure 10. Specific CO2, SO2, and NOX emission rate for the BHC scenario.
39
2030
C. Scenario Impacts
The following four subsections summarize the results from the seven remaining scenarios
listed in Table IV. The scenario BHH [e.g., Basis or BAU, "high" discount rate (RATE =
10%/yr), "high" overall demand, and "high" fuel costs, wherein the time-dependent unit-fuelcost multipliers given in Table 22 of Appendix A are applied to the Scenario BHC values of
UCFUEL(i,tot), Table 20 of Appendix A)], scenarios CHC and CHH impose a carbon cap
indicated on Table XII, scenarios SHC and SHH likewise impose a sulfur, also given on Table
XII, and scenarios EHC and EHH simultaneously impose the carbon and sulfur emissions cap
indicated on Table XII.
Table XII Carbon and Sulfur Emission Caps Imposed on the Eight Scenarios. Listed on
Table IV (non-shown cases are unconstrained).
Scenarios
Time, t
2000
2005
2010
2015
2020
2025
2030
CHC, CHH
SHC, SHH
EHC, EHH
EHC, EHH
CO2TARGET(t) SO2TARGET(t)
MtonneCO2/yr
MtonneSO2/yr
1300
10
1500
12
2000
13
1800
14
1600
14
1500
13
1500
12
As is indicated on the lower part of Table IV, the effective carbon or sulfur taxes resulting
from the respectively imposed emission caps are equal to the marginal costs associated with
the imposition (and meeting) of these emission constraints. In this regard, domestic controls
of carbon emissions particularly can take one of three forms (Edmonds, et al., 1999): a) taxes
on emissions, carbon, energy, fossil fuels, or any activity related to the use of fossil fuels; b)
"command-and-control" regulations that either directly limit specific emissions or prescribe
certain technologies (e.g., fuel efficiencies); or c) the allocations of emissions allowances that
can be traded between countries or regions having differing marginal cost for emissions
abatement. The examples reported below fall into the category of regulation through
"command-and-control". Parametric studies of carbon-emissions control through direct
taxation are reported in Sec.III.D.3. Consideration of the more efficient trading of emissions
is not within the scope of the present study, as important as it may eventually become in the
global control of CO2 emissions (Edmonds, et al., 1999).
1. High Fuel-Price Scenario (BHH)
The impact of increasing the price of fuel by the time- and region-dependent factors indicated
on Table 22 of Appendix A on the countrywide energy electrical generation mix for China is
illustrated for this BHH scenario in Fig. 11. The impact of these fuel-price increases on CO2,
SO2, and NOX emissions is depicted relative to the BHC baseline scenario on Fig. 12.
Comparison of the BHC versus BHH scenarios shows that the kind of change in price
structure involved with the BHC Þ BHH transition is accompanied by a strong switch to
advanced coal technologies, the introduction of some renewable generation in the form of
40
wind, and a modest increase in the used of nuclear energy, which nevertheless remains a small
contributor to the overall electric-generation portfolio for China for the base-case (costs)
assumptions made (Table X). As is seen from Fig. 12, modest (~15-20%) decreases in CO2
and NOX emissions in the year 2030 result from these price increases, but SO2 emissions in
2030 decrease by nearly 40% as a result of the decreased use of "Domestic Coal" generation
technologies. In assessing these results, however, the absence of price-economy (GDP)
feedback in the CRETM must be recognized, but this feedback is expected to be of secondary
importance; more important are price effects on demand.
ELECTRIC DEMAND MIX FOR SCENARIO BHH
4000
Nucl.
W IND
NUCL
3500
W ind
HYDR
GENERATION TWeh/yr
3000
GAS
Hydro
2500
OIL
ADV. COAL
2000
Gas
Oil
1500
DOM. COAL
1000
Adv. Coal
500
Dom.
Coal
0
1995
2000
2005
2010
2015
2020
YEAR
Figure 11. Generation mix for BHH scenario (Table IV).
41
2025
2030
EMISSIONS VERSUS TIME AND FUEL-COST SCENARIO
25
EMISSION RATES, MtonneSO2,NOX/yr
MtonneCO2/yr/100
BHC(CO2)
BHC(SO2)
20
CO2
SO2
BHC(NOX)
BHH(CO2)
BHH(SO2)
15
BHH(NOX)
SO2
10
CO2
NOX
5
NOX
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 12. Comparison of emission rates for CO2, SO2, and NOX as the price of fuel is
increased from scenario BHC to BHH.
2. Carbon-Caps Scenarios (CHC, CHH)
Carbon emission limits where applied, as is indicated in Table XII for both the "H"
[DUCFUEL(i,tot) as given in Table 22 of Appendix A] and the "C" [DUCFUEL(i,tot) = 1]
fuel-price conditions, with both demand and discount rates remaining at levels used in the "H"
mode; scenarios CHC and CHH (Table IV) result. These CO2 emission constraints were
applied separately and in conjunction with the SO2 emission constraints. Figures 13 and 14
give evolution of the electrical generation mix for both the CHC and the CHH scenarios. The
comparison of CO2, SO2 and NOX emissions for the CHC scenario with the counterpart BHC
baseline scenario is given in Fig. 15. The marginal cost of CO2 emission is described as a
function of time on Fig. 16, which also makes a comparison with results from the other
carbon-constraining scenarios considered in this study. From Fig. 15 it is noted that for the
constant prices (CHC and EHC) the marginal costs are higher in that more coal-fired
technologies must be displaced compared to the CHH or EHH cases, when substitution for
coal has been taking place in the BAU cases.
42
4000
E L E C T R IC D E M AN D M IX F O R S C E N AR IO C H C
N UC L
GENERATION, TWeh/yr
N u c l.
W IND
3500
Geo.
3000
W in d
H Y D RO
S o la r P V
2500
O IL
GA S
2000
H y d ro
A D V. C OAL
1500
Gas
O il
1000
D OM. C OAL
A d v. C o a l
500
0
1995
D om.
C oal
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 13. Generation mix for CHC scenario (Table IV): (C)arbon constraint with
(High) discount rate and (C)onstant fuel prices.
4000
E L E C T R IC D E M AN D M IX F O R S C E N A R IO C H H
NU C L
W I ND
Nuc l
3500
Geo
GENERATION, TWeh/yr
3000
W in d
HY D RO
2500
S o la r P V
GA S
2000
Hy dro
OIL
1500
AD V. C OAL
Gas
O il
1000
D OM. C OAL
A d v. C o a l
500
D om . C oal
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 14. Generation mix for CHH scenario (Table IV): (C)arbon constraint with
(High) discount rate and (H)igh fuel prices.
43
E MIS S IO N S V E R S U S T IME AN D C AR B O N C AP S S C E N AR IO
25
C O2
B HC (C O2)
S O2
B HC (NOX )
20
C HC (C O2)
tonneCO2/yr/100
EMISSIONS, tonneSO2,NOX/yr,
B HC (S O2)
C HC (S O2)
15
C HC (NOX )
10
NOX
5
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 15. Comparison of CO2, SO2, and NOX emission rates between scenarios BHC
and CHC as a function of time.
M AR G IN AL C O 2 C O S T V E R S U S T IM E AN D S C E N AR IO
MARGINAL EMISSION COST, $/tonneCO
70
C HC (C O2 )
60
E HC ( C O 2)
50
C HH( C O 2)
E HH( C O2)
40
30
20
10
0
1 99 5
2 00 0
2 00 5
2 01 0
T IM E
2 01 5
2 02 0
2 02 5
Figure 16. Time evolution of marginal costs for CO2 emissions for the range
of carbon-constrained scenarios considered.
44
2 03 0
3. Sulfur-Caps Scenarios (SHC, SHH)
In parallel to the foregoing presentation of carbon-constrained results, Figs 17-20 give
respectively the electric-generation mixes reported by CRETM for the sulfur-constrained
scenarios with H and C fuel-price conditions, with both being based on the "high" (H)
discount rate (RATE = 10%/yr) and demand assumptions. Figures 17 and 18 summarize the
electrical-energy mixes for SHC and SHH scenarios, Fig. 19 compares emission rates of CO2,
SO2, and NOx for the SHC and baseline BHC scenarios, and Fig. 20 gives the time
dependence of all marginal costs associated with the sulfur-constrained cases.
4000
ELECTRIC DEMAND MIX FOR SCENARIO SHC
NUCL
Nucl
3500
GENERATION, TWeh/yr
3000
HYDRO
Geo
GAS
W ind
2500
OIL
SolarPV
2000
Hydro
ADV. COAL
Gas
1500
DOM. COAL
1000
Oil
Adv. Coal
500
Dom. Coal
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 17. Generation mix for SHC scenario (Table IV): (S)ulfur constraint with (High)
discount rate and (C)onstant fuel prices.
45
E L E C T R IC D E MAN D MIX FO R S C E N AR IO S H H
4000
W IND
NUC L
Nuc l
3500
Geo
HY D RO
GENERTATION, TWeh/yr
3000
W ind
GA S
2500
S olarP V
OIL
A D V . C OA L
2000
Hy dro
Gas
1500
D OM. C OA L
1000
Oil
A dv. C oal
500
D om.
C oal
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 18. Generation mix for SHH scenario (Table IV): (S)ulfur constraint with (High)
discount rate and (H)igh fuel prices.
E M IS S IO N S V E R S U S T IM E AN D S U L F U R C AP S S C E N AR IO
25
B HC ( C O2)
C O2
S 02
20
B HC ( NOX )
S HC ( C O2)
TonneCO2/yr/100
EMISSIONS, TonneSO2,NOX/yr
B HC ( S O2)
S HC ( S O2)
15
S HC ( NOX )
10
C
5
NOX
0
1995
2000
2005
2010
T IM E
2015
2020
2025
2030
Figure 19. Comparison of CO2, SO2, and NOX emission rates between scenarios BHC
and SHC as a function of time.
46
MARGINAL SO2 COSTS VERSUS TIME AND SCENARIO
140
EMSSION COST, $/tonneSO2
120
100
80
SHC(SO2)
EHC(SO2)
SHH(SO2)
EHH(SO2)
60
40
20
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 20. Time evolution of marginal costs for SO2 emissions for the range of sulfurconstrained scenarios considered. Notice that the low sulfur cap imposed for the year
2030 gives zero marginal control cost in the high fuel price scenario.
4. Environmental (S + C Caps) Scenarios (EHC, EHH)
Finally, Figs. 21 and 22 give time evolution of the electrical generation mix for China under
conditions where both of the previously reported carbon and sulfur emission constraints are
applied simultaneously for the "high" (H) discount rate assumption and both fuel-price (H and
C) scenarios. The comparison of emission rates for the "constant" (C) fuel-price condition
with emissions for the baseline BHC scenario is given in Fig. 23. The previously referenced
Figs 16 and 20 complete the comparison of the respective marginal costs for carbon and/or
sulfur emission. These marginal costs are equivalent to the "taxes" needed to achieve (within
the confines and limitation of CRETM) the emission rates given on Figs. 17, 20, or 23.
47
E L E C T R IC IT Y D E M A N D M IX F O R S C E N A R IO E H C
4000
N u c l.
W IN
NUC L
3500
Geo.
W in d
GENERATION, TWeh/yr
3000
HY D RO
S o la r P V
2500
GAS
Hy dro
2000
O IL
1500
AD V. C OAL
O il
1000
D OM. C OAL
A d v.
C oal
500
0
1995
Gas
D om.
C oal
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 21. Generation mix for EHC scenario (Table IV): Both sulfur and carbon
emission constraints are applied (E = S + C) with (High) discount rate and (C)onstant
fuel prices.
4000
E L E C T R IC IT Y D E M A N D M IX F O R S C E N A R IO E H H
GENERATION, TWeh/yr
NUC L
Nuc l
W IND
3500
Geo
3000
W in d
HY D RO
S o la r P V
2500
GAS
Hy dro
2000
OIL
Gas
A D V . C OA L
1500
O il
1000
DOM. COAL
A d v.
C oal
500
0
1995
D om.
C oal
2000
2005
2010
2015
2020
2025
2030
YEAR
Figure 22. Generation mix for EHH scenario (Table IV): Both sulfur and carbon
emission constraints are applied (E = S + C) with (High) discount rate and (H)igh fuel
prices.
48
EMISSIO NS VERSUS TIME AND C + S CAPS SCENARIO
25
BHC(CO2)
BHC(SO2)
EMISSION, MTonneSO2,NOX/yr
MTonneCO2/yr/100
20
SO2
C O2
BHC(NOX)
EHC(CO2)
EHC(SO2)
EHC(NOX)
15
10
NOX
5
0
1995
2000
2005
2010
TIME
2015
2020
2025
2030
Figure 23. Comparison of CO2, SO2, and NOX emission rates between scenarios BHC
and SHC as a function of time.
5. Scenario Summary
While neither the model nor this investigation is sufficiently comprehensive to warrant
detailed analyses, the array of seven scenario results so far reported nonetheless can helpfully
be expressed relative to the eighth baseline BHC scenario to give some sense of the cost
versus emissions trade-off associated with each of the approaches considered. To that end,
Fig. 24 summarizes the CO2 and SO2 emission rates for the eight scenarios considered,
whereas Fig. 25 presents the percentage changes in total discounted (again, for all scenarios
reported a RATE = 10%/yr discount rate was used, with Sec. III.D.2 reporting the single-point
impact of increasing and decreasing this baseline discount rate) energy cost computed by
CRETM in comparison to the percentage change in the total accumulated emission over the
1995-2030 time frame of this exercise for either CO2 or SO2. Generally, it is seen that higher
fuel prices (BHH) reduce sulfur emission rates compared to the BHC lower fuel-cost scenario
because of the introduction of advanced coal technologies and the substitutability for coal.
Also, the introduction of carbon constraints (e.g., CHH, CHC, etc. scenarios) reduces sulfur
emissions as a secondary benefit.
49
CO2 EMISSIONS VERSUS SCENARIO
2500
BHC
BHC,SHC
BHH
CO2 EMISSION RATE, MtonneCO2/yr
2000
CHC
CHH
SHH,BHH
EHC
1500
EHH
SHC
CHC, CHH,
EHC, EHH
SHH
1000
500
0
1995
2000
2005
2010
TIME
2015
2020
2025
2030
Figure 24A. Summary of CO2 emission rates for the eight scenarios.
S02 EMISSIONS VERSUS SCENARIO
25
SO2 EMISSIONS, MtonneSO2/yr
20
15
BHC
BHH
CHC
CHH
EHC
EHH
SHC
SHH
BHC
BHH
SHH SHC
10
CHC
EHC
CHH
EHH
5
0
1995
2000
2005
2010
2015
2020
TIME
Figure 24B. Summary of SO2 emission rates for the eight scenarios.
50
2025
2030
PER C EN T A G ES T O T A L C O 2 R ED U C T IO N ,
T O T A L C O ST IN C R EA SE
18
PERCENTAGE
16
14
12
10
8
6
4
2
% CO 2
0
B HH
CHC
CHH
% ZZ
E HC
E HH
S HC
C O 2 DEC.,
C O ST INC.
S HH
SCENARIO
Figure 25A. Percentage change in total (1995-2030) CO2 emissions (decrease) in
relationship to percentage increase in present-value of total energy costs ZZ, (10%/year
discount rate over the period 1995-2030) for seven scenarios (Table IV) relative to the
baseline BHC scenario.
PERCENTAGES TOTAL SO2 REDUCTION,
TOTAL COST INCREASE
45
PERCENTAGES
40
35
30
25
20
15
10
5
%SO2
0
BHH
CHC
CHH
%ZZ
EHC
EHH
SCENARIOS
SHC
SO2 DEC.,
COST INC.
SHH
Figure 25B. Percentage change in total (1995-2030) SO2 emissions (decrease) in
relationship to percentage increase in present-value of total energy costs (10%/year
discount rate over the period 1995-2030) for seven scenarios (Table IV) relative to the
baseline BHC scenario.
51
D. Single-Point Parametric Studies
The eight scenarios (e.g., one baseline and seven fuel-cost and emission variations) variations
provide a broad range of possible futures for China's electric generation system over the next
three decades, albeit many of the "fixed" scenario attributes can strongly impact the magnitude
and the structure of that future. To advance understanding of the role played by some of these
key variables, a series of single-point parametric studies were conducted. These studies
varied: a) the energy demand [as driven by the assumed GDP growth rate, Eqs. (1) and (2)];
b) the rate at which expenditures are discounted to determine the present-value of total
(electrical) energy costs to be minimized (RATE = 0.10/yr for the present computations); c)
and the costs of key, non-carbon-emitting technologies. The results from these single-point
parameter variations are reported in the following subsections.
1. Exogenous Energy Demand
The growth rate of electricity demand in China assumed for the BHC baseline scenario is seen
from Fig. 6C to fall within the mid-field of a range of recent projections. While the ERI
projections (for Shandong Province) provide a common basis for all components of the CETP,
investigations using CRETM considered a range of demands that were both above (HH) and
below (L, LL, and LLL) the baseline "high" (H) demands used to generate the BHC and
derivative scenarios.
Figure 26 gives the range of GDP and associated growths in electricity demand over the span
of the five demand cases considered: LLL Þ HH, with the baseline BHC and all of its seven
derivative scenarios being based on the "high" (H) GDP and associated energy demand case.
The impact of varying this demand on the countrywide generation mix is shown in Fig. 27,
with the LL demand leading to an optimized (and unconstrained) electricity-generation system
for China that is comprised almost entirely hydroelectric and domestic coal, to an extent that
is even greater than for the baseline BHC scenario. At the very high demand limit posited by
the HH case, domestic coal is displaced by gas and oil, with some growth in nuclear energy
and renewable energy sources; this regime needs more thorough analysis. These results (e.g.,
applied constraints) do not reflect recent concerns (Gong, 2000) concerning increased oil
prices and the undermining of sustainable development and energy security if coal gives way
too fast to oil and gas, and options that incorporate great use of clean coal technologies
(CTTs), particularly the use of coal-water mixture (CWM) technologies.
52
G D P G R O W T H S C E N A R IO S
14000
H
12000
L
GDP (B$/yr)
10000
LL
HH
LLL
8000
HH
6000
H
4000
L
LL
2000
LLL
0
1995
2000
2005
2010
2015
2020
2025
2030
YE A R
Figure 26A. Range of growths assumed for China GDP, with the "high" (H) level being
adopted for the present study.
G D P - D R IV E N E L E C R IC IT Y D E M A N D S C E N A R IO S
7000
H
ELECTRICITY DEMAND, TWrh/yr
6000
5000
L
LL
HH
LLL
4000
HH
H
3000
L
LL
2000
LLL
1000
0
1995
2000
2005
2010
2015
2020
2025
2030
YE AR
Figure 26B. Range of possible countrywide electrical energy demand growth scenarios
based on the relationships given in Eqs. (1) and (2); the H growth is used to generate the
BHC baseline scenario.
53
G E N E R AT IO N M IX V E R S U S T IM E F O R H D E M AN D ( B H C )
4000
HY D RO
3500
NUC L
HY D RO
NUC L
GENERATION, TWeh/yr
3000
GAS
RE N
O IL
2500
GA S
2000
D OM. C OA L
1500
O IL
1000
C OLA
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 27A. China countrywide mix of electric generation technologies for the baseline
BHC scenario (same as Fig. 7).
G E N E R AT IO N M IX V E R S U S T IM E F O R L L L D E M AN D G R O W T H
2000
HY D RO
1800
HY D RO
1600
NUC L
NUC L
GENERATION, TWeh/yr
1400
OIL
RE N
GAS
1200
1000
GA S
D O M. C OA L
800
OIL
600
C O LA
400
200
0
1995
D OM
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 27B. China countrywide mix of electric generation technologies for electricalenergy demands that are below (case LLL in Fig. 25) those used to generate the baseline
BHC scenario.
54
G ENERATION MIX VERSUS TIME FO R HH DEMAND GROW TH
7000
HYDRO
6000
HYDRO
NUCL
NUCL
GENERATION, TWe/yr
5000
REN
GAS
4000
GAS
OIL
3000
OIL
2000
ADV. COAL
1000
COLA
DOM. COAL
DOM
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 27C. China countrywide mix of electric generation technologies for electricalenergy demands that are above (case HH in Fig. 25) those used to generate the baseline
BHC scenario.
As is discussed in Sec. II.D.1 and elaborated in Appendix B, significantly different approaches
and levels of intuitive judgment characterize the optimization versus simulation approaches to
modeling complex energy systems. The latter typically examines thousands (if not tens of
thousands) of "scenarios" with an aim being the culling by experience and judgment those
scenarios that either might "appeal" to a given set of stakeholders, or represent an acceptable
compromise among a set of different stakeholders. This process typically divides the task into
ranges or sets of possible futures to be attained by a set of strategies, with typically the sum
futures + strategies = scenarios (Schenler, 1996). In simplified (two-dimensional) form, the
inset on Fig. B-1 illustrates a cost-versus-emissions "phase space" in which the simulation
modeling processes collects possible scenarios. From this collection some kind of operational
"frontier" might form in a way that defines or is suggestive of an edge of a region of "best"
solutions. The optimization modeling approach, on the other hand, focuses on constrained,
cost-based optimization of a more limited number of optimized scenarios, thereby assuring
this limited set of optimized scenarios resides near an operational frontier of the kind the
simulation modeling activity attempts to define through stakeholder experience, opinion, or
(in the case of multi-stakeholder groups) compromise vis-a-vis "membership" functions.
Additionally, new approaches are being developed (Kann, 2000) for optimization models to
perform sensitivity analyses of data uncertainties (distributions) using Monte Carlo sampling
techniques, thereby leading to distributions of possible values of the objective function. This
55
approach allows decisions to be made under conditions of uncertainty that account for the risk
aversion(s) of targeted stakeholder groups.
The limited number of parametric studies reported herein can be expressed in a way (e.g., cost
versus emissions) that facilitates and joins the understanding brought by both modeling
approaches to understanding better and feasible approaches to steering the development of
electric-generation technologies needed to meet China and Shandong Province energy
demands. To this end, Fig. 28 gives a correlation of total (integrated) CO2 and SO2 emissions
with present value of total energy costs (in China and Shandong, respectively) for the case
where variations are driven by the range of energy demands, per Fig. 27. Likewise, Fig. 29
gives the correlation of normalized (e.g., cost per unit energy generated) cost versus emission
for these two cases. Finally, Fig. 30 describes the variation of specific CO2 and SO2
emissions (per unit of energy generated) with the average unit cost of energy used in Fig. 29.
It should be noted that depending on the degree to which emissions and/or costs are
normalized, the "scatter plots" given in Figs. 28-30 may or may not show correlation (i.e., a
monotonic relationship between adjacent points and any ordering provided by the root driver
of these points.). These "scatter plots" are presented here in the spirit of initiating correlations
between the optimization and simulation modeling approaches to modeling the electrical
energy future(s) of Shandong Province and of China.
Finally, the exogenous rate of electrical-energy demand shown on Fig. 25 were determined
and used to correlate total cost, generation, average unit costs, and total integrated emissions
of CO2 and SO2. This correlation is shown on Fig. 31. Interestingly, while all extrinsic
parameters evaluated over the 1995-2030 time frame of the optimization expectedly grow
with increased demand, the average cost of energy, <COE> ~ ENC/GEN, remains relatively
insensitive to the assumed growth in demand for electrical energy, perhaps even showing a
slight minimum at ~4%/yr.
56
O P T IM AL E N E R G Y C O S T S V E R S U S 3 0 - YE AR C O 2 A N D S O 2 E M IS S IO N S
F O R C H IN A ( G D P G R O W T H - R A T E S C E N AR IO D R IV E R , 1 0 % / y r D IS C O U N T
R ATE )
75000
HH
70000
Mtonne CO2, SO2*100
CO2 and SO2 EMISSIONS,
65000
60000
H
L
55000
50000
45000
C O2
LL
40000
S O2*100
LLL
35000
30000
300
350
400
450
500
550
600
P R E S E N T -V ALU E O F E N E R G Y C O S TS , E N C (B $)
Figure 28A. Correlation of China countrywide integrated CO2 and SO2 emissions over
the period 1995-2030 with present value of total energy costs for a range of demand
scenarios (LLL Þ HH, Fig. 25).
O P T IM A L E N E R G Y C O S T S V E R S U S 3 0 - Y E A R C O 2 A N D S O 2
E M IS S IO N S F O R S H A N D O N G P R O V IN C E ( G D P G R O W T H - R A T E
D R IV E R , 1 0 % / y r D IS C O U N T R A T E )
7000
6500
Mtonne CO2, SO2*100
CO2 and SO2 EMISSIONS
6000
5500
5000
4500
4000
CO2
3500
SO2*100
3000
25
30
35
40
P R E S E N T -V AL U E O F E N E R G Y C O S T S , B $
45
Figure 28B. Correlation of Shandong Province integrated CO2 and SO2 emissions over
the period 1995-2030 with present value of total energy costs for a range of demand
scenarios (LLL Þ HH, Fig. 25).
57
O P T IM A L E N E R G Y C O S T S V E R S U S 3 0 - Y E A R C O 2 A N D S O 2 E M IS S IO N S
F O R C H IN A
( G D P G R O W T H - R A T E D R IV E R , 1 0 % / y r D IS C O U N T R A T E )
80000
60000
Mtonne CO2, SO2*100
30-YEAR CO2 AND SO2 EMISSIONS
70000
50000
40000
30000
20000
CO2
SO2*100
10000
0
33
3 3 .5
34
3 4 .5
35
35 .5
36
A V E R A G E E N E R G Y C O S T S , < C O E > ( m ill/ k W e h )
Figure 29A. Correlation of China countrywide integrated CO2 and SO2 emissions over the
period 1995-2030 with average unit energy costs (ratio of total undiscounted costs to total
electric generation) for a range of electricity-demand scenarios (LLL Þ HH, Fig. 25).
O P T IM A L E N E R G Y C O S T S V E R S U S 3 0 - Y E A R C O 2 A N D S O 2
E M IS S IO N S F O R S H A N D O N G P R O V IN C E
( G D P G R O W IT H - R A T E D R IV E R F O R 1 0 % / y r D IS C O U N T R A T E )
7000
5000
Mtonne CO2, SO2*100
30-YEAR CO2 AND SO2 EMISSIONS
6000
4000
3000
2000
CO2
1000
S O2*100
0
32
3 2 .5
33
3 3 .5
34
3 4.5
35
A V E R A G E E N E R G Y C O S T S , < C O E > ( m ill/ k W e h )
Figure 29B. Correlation of Shandong Province integrated CO2 and SO2 emissions over the
period 1995-2030 with average unit energy costs (ratio of total undiscounted costs to total
electric generation) for a range of electricity-demand scenarios (LLL Þ HH, Fig. 25).
58
O P T IM A L S P E C IF IC C O S T S V E R S U S 3 0 - Y E A R S P E C IF IC E M IS S IO N S
F O R C H IN A ( G D P G R O W T H - R A T E D R IV E R , 1 0 % / y r D IS C O U N T R A T E )
0.7
kgXO2/kWeh (SO2*100)
SPECIFIC CO2 AND SO2 EMISSIONS
0.8
0.6
0.5
0.4
0.3
C 02
0.2
0.1
SO2
0
33
3 3 .5
34
3 4 .5
35
3 5.5
36
A V E R A G E E N E R G Y C O S T , < C O E > ( m ill/ k W e h )
Figure 30A. Correlation of China countrywide integrated CO2 and SO2 emissions over
the period 1995-2030 normalized to total energy generation (e.g., specific emissions) with
average unit energy costs (ratio of total undiscounted costs to total electricity generation)
for a range of demand scenarios (LLL Þ HH, Fig. 25).
O P T IM A L E N E R G Y C O S T S V E R S U S 3 0 - Y E A R C O 2 a n d S O 2 E M IS S IO N S
F O R S H A N D O N G P R O V IN C E
( G D P G R O W T H - R A T E D R IV E R , 1 0 % / y r D IS C O U N T R A T E )
1
kgXO2/kWeh (SO2*100)
SPECIFIC CO2 and SO2 EMISSIONS
1 .2
0 .8
0 .6
0 .4
C 02
0 .2
SO2
0
32
3 2 .5
33
AV E R AG E
3 3 .5
34
3 4 .5
35
E N E R G Y C O S T S . < C O E > ( m ill/ k W e h )
Figure 30B. Correlation of Shandong Province integrated CO2 and SO2 emissions over
the period 1995-2030 normalized to total energy generation (e.g., specific emissions) with
average unit energy costs (ratio of total undiscounted costs to total electricity generation)
for a range of demand scenarios (LLL Þ HH, Fig. 25).
59
COSTS AND (30-YEAR) EMISSIONS VERSUS NOMINAL DEMAND GROW TH
FOR CHINA, 10% /yr DISCOUNT RATE
ENC(B$), GEN(TWeh)/1000,
<COE>(mill/kWeh)*10, CO2(MtonneCO2)/100,
SO2(MtonneSO2)
800
ENC(B$)
700
GEN/1000
600
<COE>*10
500
CO2/100
400
SO2
300
200
100
0
0
1
2
3
4
5
6
7
NOMINAL ENERGY DEMAND GROW TH RATE, % /yr
Figure 31. Summary of dependence of key integrated variables on the growth rate of
electricity demand for China: ENC(B$) = present-value of total energy generation over
the period 1995-2030; GEN(TWeh) = total generation over the period 1995-2030;
<COE>(mill/kWeh) = average (undiscounted) cost of electricity; CO2(MtonneCO2) and
SO2(MtonneSO2) = total integrated emissions of these respective gases.
60
2. Discount Rate
The canonical discount rate used for all scenarios is RATE = 0.10/yr, and is expected to have
the greatest impact on those technologies characterized by high capital costs and long
construction times (e.g., present-day nuclear energy). To develop an greater appreciation of
the influence discount rate has on the optimal energy mixes for both China and for Shandong
Province, the parameter RATE(1/yr) was varied over the range {0,0.15}. Taking conditions
that are integrated over the computational period, Fig. 32 gives the dependence of total
(discounted) energy costs, ENC(B$), average energy costs, <COE>(mills/kWeh), and total
CO2 and SO2 emissions on RATE(1/yr) over the range indicated for both China and for
Shandong Province. While ENC expectedly decreases as RATE increases, the ratio of total
undiscounted cost to total energy generation, <COE>, increases because of increasing total
capital charges and the interest paid during construction. When the present-value of total
energy costs are expressed as a fraction of the present value of total GDP taken over the 35year computational period, this indicator increases with increasing discount rate for both
China and Shandong Province. As is shown in Fig. 33, a nominal slope of 0.22
%(ENC/GDP)/%(RATE) is indicated. These results pertain to the assumption of no feedback
of the exogenously varied discount rate and the economy, as is modeled through the GDP and
the assumed connection with energy demand growth [Eq. (1)]. The slope given on Fig. 33 can
be explained assuming an exponential growth model for China. For example if the
exponential growth of energy demand, GDP, and the cost of energy is assumed to be,
respectively, λi ( i = D, G, C ), then the indicator ξ = ENC / PVGDP can be defined as
function of the initial (1995) values of demand, GDP, and COE and the rates, and it is given
by:
(1 − e − ( DR −λDC )T ) /( DR − λ DC )
,
ξ = DMDo * COEo / GNPo ⋅
(1 − e −( DR −λG )T ) /( DR − λG )
where T = 2030 - 2000 = 30 years is the time period of interest, DR is the assumed discount
rate (RATE) and λ DC = λ D + λC . The discounted energy cost and GDP are obtained by the
integration of the exponential functions:
T
T
0
0
ENC ( B$) = ò DMD(t ) * COE (t ) * e − DR*t dt = DMD0 ⋅ COE 0 ⋅ ò e λDG *t * e − DR*t dt
T
T
0
0
PVGDP( B$) = ò GDP(t ) * e − DR*t dt = GDP0 ò e −λG *t ⋅ e − DR*t dt .
For constant fuel prices and cost over time: λDC = λD + λC = a ⋅ λG . Assuming also some
typical parameters for China shown next, the numerical values of the integrals can easily be
obtain assuming that coal power stations play the dominant role in power generation.
61
λDC = a ⋅ λD = 0.7 ⋅ 0.06 = 0.041 / yr
DR0 = 0.1 and
DMD0 = 1,350
λG = 0.061 / yr
TWeh
COE0 = 40 mill / kWeh, GDP0 = 1,000 B$ and T = 30 yr
It can be verified that for the conditions under which these parametric studies where
conducted, the slope of ENC/PVGDP versus RATE is positive and depends mainly on the cost
of electricity generation. The fraction of electricity cost, for the simplified example given
above, increases from 3% at RATE = 5%/yr to 3.8% at RATE = 10%/yr, and to 4.6% at RATE
= 15%/yr, which all compare well with the results of Figure 33.
In the spirit of relating total costs to emissions in a way that connects with the simulation
(ESS) modeling efforts, Figure 34 gives a correlation that results when total integrated
emissions of CO2 and SO2 over the period 1995-2030 are plotted against the present value of
total energy costs, ENV, for both China and Shandong Province, as driven by the variations in
discount rate, RATE(1/yr). Figure 35 gives a similar correlation of CO2 and SO2 emissions
with average energy costs, <COE>. The results given in both Figs. 34 and 35 are presented
as "scatter plots" that might result from uncertainties in discount rates, although lines are used
to connect points within respective sets. On these scatter plots the direction of decreasing
ENC(B$) correlates with decreasing discount rate, whereas the direction of increasing
<COE> correlates with increasing discount rate (Fig. 32). Generally, Fig. 32 shows only the
weakest of integrated 30-year CO2 and SO2 emissions correlation with discount rate, for
reasons discussed below. This behavior is replicated in the emissions versus cost correlation
plots at both China countrywide and Shandong Provincial levels (Figs. 34 and 35), with weak
negative correlation of emission with ENC and weak positive correlation with ENC at the
China countrywide level possibly being indicated. Generally, for low discount rates the
advanced coal, nuclear energy and (to some extent) hydroelectric technologies are more
readily introduced and CO2 emissions decrease. Again, the direction of decreasing RATE in
Fig. 33 is that of increased present-value of total electric energy costs, ENC.
The shift in generation mix as the discount rate is varied above and below the RATE = 10%/yr
value, for 4%/yr to 15%/yr, is illustrated in Fig. 36. The share fractions of key generation
technologies in the last period (2030) are shown on Fig. 37 as a function of discount rate. The
reason for the previously noted weak or non-existent correlation of both CO2 and SO2
emissions with discount rate becomes evident from the general dominance of domestic coal
(DOM, Fig. 36)) for all ranges discount rate examined. Increased discount rates generally
push out of the market advanced (costly) technologies like nuclear and advanced coal (COLA)
for the capital (in the case of nuclear and renewable energy sources) costs assumed. Very low
values of discount rate favor nuclear over advanced coal, but the relative market shares at lowto-intermediate discount rates cause these (30-year) market shares to reverse, with both for all
intents and purposes being eliminated for the RATE = 10%/yr baseline or above discount
rates. For very high discount rates and the prices of gas and oil assumed, the latter dominates
market share for these relatively small fractions of total electrical energy provided by sources
other than domestic coal, which dominates the market (Fig. 36). Lastly, for the baseline
capital costs assumed, renewable energy sources (mainly solar PV and wind) remain minor
players in these China electrical-energy mixes.
62
C O S T S AN D E M IS S IO N S V E R S U S D IS C O U N T R AT E F O R C H IN A AT
L E V E L - H D E M AN D
1600
E N C (B $ )
CO2(Mtonne/100), SO2(Mtonne)
ENC(B$), <COE>mill/kWeh),
1400
< C O E > *1 0
1200
C 02/100
SO2
1000
800
600
400
200
0
0
0 .0 2 5
0 .0 5
0 .0 7 5
0 .1
D IS C O U N T R AT E , 1 /y r
0 .1 2 5
0 .1 5
Figure 32A. Dependence of present-value of total energy costs, ENC(B$); average cost
of electricity, <COE>(mill/kWeh); and the total integrated (1995-2030) emissions for
both CO2 and SO2 on discount rate for China.
C O S T S A N D E M IS S IO N S V E R S U S D IS C O U N T R A T E
F O R S H A N D O N G P R O V IN C E A T L E V E L - H D E M A N D
E NC (B $)
100
<COE>
C 02/100
80
SO2(Mtonne)
ENC(B$), <COE>(mill/kWeh), CO2(Mtonne/100),
120
SO2
60
40
20
0
0
0.025
0.05
0.075
0 .1
D IS C O U N T R A T E , 1 /y r
0.125
0.15
Figure 32B. Dependence of present-value of total energy costs, ENC(B$); average cost of
electricity, <COE>(mill/kWeh); and the total integrated (1995-2030) emissions for both
CO2 and SO2 on discount rate for Shandong Province.
63
PV EN C R ELATIVE TO G D P VER SU S D ISC O U N T R ATE FO R
SH AN D O N G PR O VIN C E AN D C H IN A
FO R LEVEL-H D EMAN D
5
PV ENC/GDP, %
4
3
2
Shandong
1
C hina
0
0
0.025
0.05
0.075
0.1
0.125
0.15
D ISCO U N T R ATE, 1/yr
Figure 33. Dependence of the present value of total energy cost, ENC, as a percent of the
present value of total (integrated and discounted over the computational time frame)
GDP on discount rate for both China countrywide and Shandong Province.
O P T IM AL E N E R G Y C O S T S V E R S U S 30 -YE AR C O 2 AN D S O 2
E M IS S IO N S F O R C H IN A F O R L E V E L -H D E M AN D ( D IS C O U N T R AT E D R IV E R )
800 00
C O2
Mtonne CO2, SO2*100
CO2 AND SO2 EMISSIONS,
750 00
700 00
S O2* 100
650 00
600 00
550 00
500 00
450 00
400 00
0
2 00
400
6 00
8 00
1 000
1 200
1 400
1 600
P R E S E N T -V AL U E O F E N E R G Y C O S T S , B $
Figure 34A. Dependence of total integrated (1995-2030) CO2 and SO2 emissions for
China countrywide on present-value of total energy costs, ENC(B$), as the discount rate
is varied.
64
O P T IM U M E N E R G Y C O S T S V E R S U S 3 0- YE AR C O 2 AN D S O 2
E M IS S IO N S F O R S H AN D O N G P R O V IN C E F O R L E V E L -H D E M AN D
(D IS C O U N T - R AT E D R IV E R )
75 00
C O2
Mtonne CO2, SO2*100
CO2 AND SO2 30-YEAR EMISSIONS
80 00
70 00
S O2 * 10 0
65 00
60 00
55 00
50 00
45 00
40 00
0
20
40
60
80
1 00
12 0
P R E S E N T - V AL U E O F E N E R G Y C O S T S , B $
Figure 34B. Dependence of total integrated (1995-2030) CO2 and SO2 emissions for
Shandong Province on present-value of total energy costs, ENC(B$), as the discount rate
is varied.
O P T IM A L S P E C IF IC E N E R G Y C O S T S V E R S U S E M IS S IO N S F O R
C H IN A A N D L E V E L - H D E M A N D ( D IS C O U N T - R A T E D R IV E R )
Mtonne CO2, SO2*100
TOTAL 30-YEAR EMISSIONS,
80000
75000
C O2
70000
S O2*100
65000
60000
55000
50000
45000
40000
15
20
25
30
35
40
45
A V E R A G E E N E R G Y C O S T S , < C O E > ( m ill/ k W e h )
Figure 35A. Dependence of total integrated (1995-2030) CO2 and SO2 emissions for
China countrywide on average cost of energy, <COE>(mill/kWeh), as the discount rate is
varied.
65
O P T IM U M S P E C IF IC C O S T V E R S U S 3 0 - YE A R E M IS S IO N S F O R
S H A N D O N G P R O V IN C E F O R L E V E L - H D E M A N D ( D IS C O U N T R A T E D R IV E R )
Mtone CO2, SO2*100
TOTAL 30-YEAR EMISSIONS
8000
7500
CO2
7000
S O2*100
6500
6000
5500
5000
4500
4000
15
20
25
30
35
40
45
A V E R A G E E N E R G Y C O S T , < C O E > ( m ill/ k W e h )
Figure 35B. Dependence of total integrated (1995-2030) CO2 and SO2 emissions for
Shandong Province on average cost of energy, <COE>(mill/kWeh), as the discount rate is
varied.
G E N E R AT IO N M IX V E R S U S T IM E F O R R A T E = 1 0 % /y r ( B H C )
4000
HY D RO
3500
HY D RO
NUC L
NU C L
GENERATION, TWeh/yr
3000
GAS
RE N
O IL
2500
GAS
2000
D OM. C OAL
1500
O IL
1000
C OLA
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 36A. Countrywide mix of electric-generation technologies for the baseline BHC
scenario (same as Fig. 7).
66
G E N E R AT IO N MIX V E R S U S T IME F O R R AT E = 4% /yr
4000
HY D RO
3500
NUC L
3000
GENERATION, TWe/yr
HY D RO
RE N
NUC L
2500
ADV.
C OA L
2000
1500
GA S
GA S , OIL
1000
OIL
D OM . C OA L
C OLA
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
TIM E
Figure 36B. Countrywide mix of electric-generation technologies for the BHC scenario
with a very low discount rate (RATE = 0.04/yr).
G E N E R ATIO N V E R S U S TIME F O R R AT E = 15% /yr
4000
HY D RO
3500
HY D RO
NUC L
NUC L
GENERATION TWeh/yr
3000
GA S
RE N
2500
OIL
GA S
2000
D OM. C OA L
1500
OIL
1000
C OLA
500
0
1995
D OM
2000
2005
2010
2015
2020
2025
2030
T IME
Figure 36C. Countrywide mix of electric-generation technologies for the BHC scenario
with a very high discount rate (RATE = 0.15/yr).
67
GENERATION FRACTIONS IN 2030 VERSUS DISCOUNT RATE
0.2
GENERATION SHARE FRACTIONS
IN 2030, Fxxx
0.18
0.16
FNUCL
0.14
FCOLA
FOIL
FCOLA
FGAS
FOIL
FREN*1000
FNUCL
FGAS
0.12
0.1
0.08
0.06
FREN*1000
0.04
0.02
0
0.01
0.04
0.06
0.08
0.1
0.12
0.15
DISCOUNT RATE, RATE(1/yr)
Figure 37. Dependence of share fractions of key generation technologies in the last
computational period (2030) on discount rate (hydroelectric generation, HYDRO, is
include under and dominates generation from renewables, REN).
68
3. Technology Costs of Nuclear Energy
The cost of any given generation technology takes the form of capital (annual charges), fixedoperating charges, variable-operating charges, fuel charges (called out separately here from
variable operating costs), D&D charges (Decommissioning and Decontamination charges
incurred so far only for nuclear, although long-term use of coal portends unique heavy-metal
contamination issues). Additionally, charges related to waste disposition are incurred, but so
far these costs have been internalized to any great extent only for nuclear energy primarily
through the annual charges incurred for fuel. Given the key, largely exogenous (in the case of
CRETM) economic drivers (e.g., GDP growth, related growth in energy demand, both in kind
and in magnitude, "learning-by-doing" improvements in costs, and the intensity by which
energy is used to provide certain services, etc.), these technology costs are by far the strongest
determinants of cost-optimized electricity-generation mixes versus time. For this reason, the
sensitivity of the baseline BCH generation mixes to a range of technology costs for a range of
important technologies have been examined and are reported in this subsection.
Nuclear energy is selected in this study as the generation technology with which to examine
the coupling between technology costs and emissions, since it is a major source of relatively
carbon- and sulfur-free electrical energy while having large uncertainties related primarily to
capital costs. Advanced fossil-fuel generation technologies should similarly be examined, and
parametric sensitivity studies of this kind should also be made for other technologies in this
category. The primary aim here, however, is to illustrate the sensitivity and inter-connectivity
of market-share fractions and attendant CO2 and SO2 emissions for optimization models like
CRETM, rather than to advance a given technology.
The costing structure in CRETM for nuclear energy is aggregated at a high level to reflect
only capital costs [Table 35 of Appendix A, COSTCAP($/kWe)], both variable- and fixedoperating costs [Table 34 of Appendix A, COSTVAR(mill/kWeh) and COSTFIX(expressed as
%/yr of COSTCAP)], fuel charges [ Table 20 of Appendix A, UCFUEL($/tonneHM of
fabricated fuel (assemblies), including disposal charges)] and a Decommissioning and
Decontamination (D&D) cost, COSTDD($/kWe). Typically, a heating value corresponding to
a low-enriched-uranium (LEU) fuel burn-up of BU = 50 MWtd/kgIHM is assumed, nuclear
fuel charges are fixed at UCFUEL = 1,000 $/kgHM, and a maximum growth rate of
MAXGR(NUCL) = 8%/yr was imposed.
No other fuel-cycle options or related
concerns/uncertainties/problems (e.g., fuel supply, safety, proliferation risks, long-term
environmental issues related to the disposal of radioactive waste, etc.) where considered at the
present level of study. The impact of these other NFC (Nuclear Fuel Cycle) charges warrants
more serious examination, when and if a NFC model of sufficient detail (Trellue, 2000) is
incorporated into CRETM.
Table XIII summarizes a series of "nuclear-oriented" cases examined using CRETM; these
cases are designated/identified here as nucl_xx. For the cases investigated, COSTCAP,
COSTDD, MAXGR, and (CO2, SO2) emissions control mode (e.g., emissions caps versus
taxes) where parametrically varied, and the tradeoffs between technology costs, market share,
and rates of CO2 and SO2 emissions were determined as a function of the shifts in electricitygeneration mixes that emerge. Results from the Series A variations shown on Table XIII
indicate little impact of the overall electricity-demand growth constraint on the deployment of
nuclear energy for the parameters examined. After reporting on the impact of capital cost on
69
the market share taken by nuclear energy, as described under the baseline BHC scenario
conditions, and the impact on both CO2 and SO2 emissions (Series D, Table XIII), the share
impact of the cost of nuclear when either carbon taxes are imposed or the corresponding levels
of CO2 and SO2 species-specific emissions caps or targets are enforced is examined.
Table XIII Values of Key Parameters Used in Generating a Series of Nuclear-based
Parametric Sensitivity Studies of Cost, Market-Share and Emissions Tradeoffs.
Scenario
nucl_xx
(d)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Series
(a)
(b)
ENC
M$
MAXGR COSTCAP COSTDD
1/yr
$/kWe
$/kWe
CTAX
(c)
BHC
477389.96
0.08
1600
200
$/tonneCO2/5yr
0
A
477380.96
471292.52
535771.76
605262.34
605262.34
587878.23
566816.64
540338.45
504758.25
517916.53
530040.51
511326.61
515837.83
494600.89
479773.75
478388.13
472181.46
4.770E+05
4.761E+05
474308.61
471494.75
466095.95
494600.91
0.35-0.20
0.35-0.20
0.35-0.20
0.35-0.20
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
1600
1200
1200
1600
1600
1600
1600
1600
1600
1600
1600
1400
1500
1600
1600
1400
1200
1500
1400
1300
1200
1100
1600
200
100
100
200
200
200
200
200
200
200
200
100
150
200
200
100
100
200
200
200
200
200
200
0
0
50
50
50
25
10
5
2
3
4
3
3
CO2target(5)
SO2target(5)
SO2target(5)
SO2target(5)
0
0
0
0
0
SO2CO2target(5)
B
C
D
E
(a) Run series: Series A examines mainly growth rate constraints imposed on the deployment of nuclear
technologies; Series B examines carbon tax and COSTCAP constraints; Series C examines the imposition of
emission caps at a level corresponding to emissions reported for the taxation rate of CTAX = 5
$/tonneCO2/5yr; and Series D examines the direct impact of COSTCAP(NUCL) reductions relative to the
baseline scenario BHC. Finally, Series E imposes the CO2 and SO2 emissions from nucl_09 as respective
caps, and in a sense is an EHC type scenario, whereas nucl_15 is a CHC type scenario, and nucl_16 - nucl_18
are SHC type scenarios.
(b) Total present value of energy costs incurred over the period 1995-2030; the objective function.
(c) Rate of linear application of carbon taxes, starting in the year 2005.
(d) Baseline scenario BHC.
a. Impact of Nuclear Capital Cost on Market Share and Emission Rates
Starting with the baseline scenario BHC, the capital cost of nuclear energy was decreased
from the COSTCAP(NUCL) = 1,600 $/kWe value to gain some insight into the sensitivity of
this parameter on determining the electricity-generation mix. Figure 38 gives the impact on
nuclear market share for four lower capital costs (1,200 – 1,600 $/kWe). For all other costs
70
held fixed, as given in Appendix A for the BHC scenario, the CRETM results suggest that
decreases in the capital cost of nuclear energy to the range 1,200-1,300 $/kWe solely on a cost
basis would result in this technology becoming important (e.g., >30% of total electric
generation in 2030). The sensitivity of the market-share fraction for nuclear energy, fNE, to
COSTCAP and time is illustrated in Fig.39, which gives the share fraction as a function of
COSTCAP($/kWe) for a range of times. The second frame of Fig. 39 shows an approximate
linear relationship between fNE and COSTCAP on logarithmic coordinates, indicating a
relationship of the form f NE ~ 1 / ( COSTCAP ) n , where the exponent n is in the range 6.7-8.4.
Finally, with such large share fraction projected for low-cost (and publicly acceptable) nuclear
energy, large impacts of power plant carbon and sulfur emission are expected, as is indicated
on Fig. 40.
G E N E R AT IO N M IX F O R S C E N AR IO n u c l_0 1 ( B H C )
C O S T C AP ( N U C L ) = 1 6 00 $ /k W e
4 0 00
3 5 00
GENERATION, TWeh/yr
HY D RO
HY D RO
NUC L
NUC L
3 0 00
GA S
OIL
2 5 00
RE N
GA S
2 0 00
1 5 00
OIL
D OM . C OA L
1 0 00
C O LA
5 00
0
1 9 95
D OM
2 00 0
2 00 5
20 1 0
20 15
2 02 0
2 0 25
2 03 0
T IM E
Figure 38A. Generation mix for BHC scenarios when the capital cost of nuclear energy
is COSTCAP(NUCL) = 1,600 $/kWe (baseline BHC scenario, Fig. 7).
71
G E N E R A T IO N M IX F O R S C E N A R IO n u c l_ 1 9
C O S T C AP ( N U C L ) = 1 50 0 $ /k W e
4000
HY D RO
HY D RO
3500
GENERATION, TWeh/yr
NUC L
NUC L
3000
GAS
O IL
2500
2000
RE N
GAS
1500
O IL
D OM. C OA L
1000
C OLA
500
0
1995
DOM
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 38B. Generation mix for BHC scenarios when the capital cost of nuclear energy is
decreased from COSTCAP(NUCL) = 1,600 $/kWe (baseline BHC scenario) to 1,500 $/kWe.
G E N E R A T IO N M IX F O R S C E N A R IO n u c l_ 2 0
C O S T C A P (N U C L ) = 1 4 0 0 $ /k W e
4000
HY D RO
HY D RO
GENERATION, TWeh/yr
3500
GA S
NUC L
3000
NUC L
RE N
2500
2000
GAS
OIL
1500
O IL
D OM. C OAL
1000
C OLA
500
0
1995
D OM
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 38C. Generation mix for BHC scenarios when the capital cost of nuclear energy is
decreased from COSTCAP(NUCL) = 1,600 $/kWe (baseline BHC scenario) to 1,400 $/kWe.
72
G E N E R A T IO N M IX F O R S C E N A R IO n u c l_ 2 2
C O S T C AP (N U C L ) = 1200 $/kW e
4000
HY D RO
HY D RO
3500
GENERATION, TWeh/yr
NUC L
3000
RE N
NUC L
2500
GAS
2000
1500
GAS
O IL
O IL
1000
D OM. C OAL
C OLA
500
D OM
2030
2025
2020
2015
2010
2005
2000
1995
0
T IM E
Figure 38D. Generation mix for BHC scenarios when the capital cost of nuclear energy is
decreased from COSTCAP(NUCL) = 1,600 $/kWe (baseline BHC scenario) to 1,200 $/kWe.
N U C L E A R F R A C T IO N V E R S U S C O S T C A P A N D T IM E
0 .6
NUCLEAR SHARE FRACTION
2000
0 .5
2005
0 .4
2010
0 .3
2015
0 .2
2020
0 .1
2025
0
1600
2030
1500
1400
1300
1200
1100
C O S T C A P (N U C L ), $ /K W e
Figure 39A. Dependence of nuclear-energy share fraction, fNE, on the capital cost and
time.
73
N U C L E A R F R A C T IO N V E R S U S C O S T C A P A N D T IM E
NUCLEAR SHARE FRACTION
1
2000
2005
2010
2015
0.1
2020
2025
2030
0.01
3.2041
3.1761
3.1461
3.1139
3 .0 7 9 2
3.041
lo g [ C O S T C A P ( N U C L ) ]
Figure 39B. Re-plot of Fig. 39A on logarithmic coordinates, indicating a strong
dependence of fNE on COSTCAP of the form f NE ~ 1 / ( COSTCAP ) n where n is in the
range 6.7 - 8.4.
C O 2 E M IS S IO N R A T E V E R S U S T IM E A N D C O S T C A P ( N U C L )
2500
$ / kW e
CO2 EMISSION RATE, MtonneCO2/y
1600
2000
B H C , 1 6 0 0 $ / kW e
1500
1400
1400
1300
1300
1200
1500
1200
1100
1000
1100
500
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 40A. Impact of reduced cost of nuclear energy on the rate of CO2 emissions.
74
S O 2 E M IS S IO N R A T E V E R S U S T IM E A N D C O S T C A P ( N U C L )
SO2 EMISSION RATE, MtonneSO2/y
25
$ /kW e
1600
20
B HC , 1 60 0 $/kW e
1500
1300
1400
1300
15
1200
1200
1100
10
1100
5
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 40B. Impact of reduced cost of nuclear energy on the rate of SO2 emissions.
75
b. Carbon Taxes, Emission Constraints, and the Cost of Nuclear Technologies.
The previous section considered only a single-point variation of the (capital) cost of nuclear
technologies without imposing constraints on CO2 or SO2 emissions, either through taxes or
caps. As is indicate by Edmonds (1999), neither approach (to carbon control) are
economically efficient as non-reflecting a market mechanism, with some form of permit
trading being preferred. The cost of carbon emission reduction under full-trade conditions will
be equivalent to the imposed tax rate, and the regional reduction of carbon emissions will be
exactly the same as the case of full trade for the same total CO2 reduction for China.
Recognizing the assumptions underlying these conditions, as well as the inability of CRETM
in its present form to deal with permit trading, carbon taxes were imposed at linearly
increasing rates, CTAX($/tonneCO2/5yr) in the form of a "probe" with which to investigate
and illustrate the sensitivities of both CO2 and SO2 emissions and the resulting mixes of
generation technologies over the analysis period 1995-2030. These parametric variations are
grouped under Series B in Table XIII. For all cases where the rate of carbon tax increases,
CTAX, was increased), the BHC scenario conditions were maintained (Table III, and
Appendix A).
C H C .nucl_01 (C TAX = 0, B H C )
4000
HYD RO
3500
ANNUAL GENERATION, TWeh/yr
NUC L
HY D RO
NUC L
3000
GA S
OIL
2500
REN
2000
GA S
DOM. C OAL
1500
OIL
1000
C OLA
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 41A. Time evolution of generation technology mix for the BHC scenario (CTAX
= 0 $/tonneCO2/5yr, same as Fig. 7, designated here as scenario CHC.nucl_01).
76
C H C . n u c l_ 1 0 ( C T A X = 2 $ / t o n n e C O 2 / 5 y r )
4000
HY D RO
ANNUAL GENERATION, TWeh/yr
3500
HY D RO
NUC L
NUC L
3000
GAS
RE N
2500
O IL
2000
GAS
C OLA
1500
O IL
D OM. C OAL
1000
C OLA
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 41B. Time evolution of generation technology mix for a carbon tax imposed at a
linear rate CTAX = 2 $/tonneCO2/5yr: Scenario CHC.nucl_10.
C H C . n u c l_ 1 1 ( C T A X = ( 3 $ / t o n n e C O 2 / 5 y r )
4000
HY D RO
ANNUAL GENERATION, TWeh/yr
3500
HY D RO
NUC L
3000
2500
GAS
2000
C OLA
NUC L
RE N
O IL
GAS
1500
O IL
DOM. COAL
1000
C OLA
500
DOM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 41C. Time evolution of generation technology mix for a carbon tax imposed at a
linear rate CTAX = 3 $/tonneCO2/5yr: Scenario CHC.nucl_11.
77
C H C .n u c l_ 1 2 ( C T AX = 4 $ /t o n n e C O 2 /5 y r )
4000
HY D R O
3500
ANNUAL GENERATION, TWeh/yr
H Y D RO
NU C L
3000
NU C L
RE N
2500
GA S
2000
GAS
O IL
1500
O IL
C OLA
1000
C OLA
D OM. C OA L
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 41D. Time evolution of generation technology mix for a carbon tax imposed at a
linear rate CTAX = 4 $/tonneCO2/5yr: Scenario CHC.nucl_12.
C H C . n u c l_ 0 9 ( C T A X = 5 $ / t o n n e C O 2 / 5 y r )
4000
HYD RO
3500
ANNUAL GENERATION, TWeh/y
HY D RO
NUC L
3000
2500
REN
NUC L
2000
GAS
GAS
1500
O IL
C OLA
O IL
1000
C OLA
D OM. C OAL
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 41E. Time evolution of generation technology mix for a carbon tax imposed a
linear rate CTAX = 5 $/tonneCO2/5yr : Scenario CHC.nucl_09.
The last three scenarios in Series B on Table XIII fix the rate at which carbon taxes are
imposed to CTAX = 5 $/tonneCO2/5yr and then decreased the cost of nuclear technology over
the range 1,400-1,600 $/kWe. The impact on overall generation mix when the cost of nuclear
energy is reduced in the presence of a carbon penalty is indicated on Fig. 42.
78
If the rates of CO2 emissions are imposed as an emissions-cap constraint and the carbon tax
removed, as was done for the CTAX = 5 $/tonneCO2/5yr case nucl_15 listed in Table XIII, the
generation mixes for the two cases are identical. Taking the SO2 emissions from the
CHC.nucl_09 as a target, the SHC-like (Table III) scenario SHC.nucl.16 results.
Parametrically varying the cost of nuclear technologies, as is indicated for the Series C studies
listed in Table XIII, leads to the mixes of generation technologies described on Fig. 43. These
results show the impact of nuclear energy costs when sulfur constraints of the kind and
magnitude described above are applied.
Finally, using the CO2 emissions rates reported for the carbon tax rate CTAX = 5 $/tonne
CO2/5yr (scenario CHC.nucl_09), and the associated SO2 emission rates, both as caps
constraints without taxes per se, the electricity-generation mix given in Fig. 44 results. This
case is listed as Series E in Table XIII, and for the otherwise BHC baseline conditions is an
EHC-like (Table III) scenario and is designated here as EHC.nucl_24.
C H C . n u c l_ 1 1 ( C T A X = 3 $ / t o n n e C O 2 / 5 y r )
4000
HY D RO
3500
ANNUAL GENERATION, TWeh/yr
HY D RO
3000
NUC L
2500
GAS
2000
C OLA
NUC L
RE N
O IL
GAS
1500
O IL
D OM. C OAL
1000
C OLA
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 42A Time evolution of generation technology mix for a carbon tax imposed at a
linear rate CTAX = 3 $/tonneCO2/5yr for COSTCAP(NUCL) = 1,600 $/kWe,
COSTDD(NUCL) = 200 $/kWe: Scenario CHC.nucl_11.
79
C H C .n u c l_ 1 4 ( C T A X = 3 $ /t o n n e C O 2 /5 r ;
C O S T C A P ( N U C L ) = 1 4 0 0 $ /k W e , C O S T D D ( N U C L ) = 1 0 0 $ / k W e )
4000
HY D RO
3500
ANNUAL GENERATION, TWeh/yr
HY D RO
NUC L
3000
NUC L
2500
RE N
GAS
2000
GAS
O IL
1500
O IL
C OLA
1000
C OLA
D OM . C OA L
500
0
1995
D OM
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 42B. Time evolution of generation technology mix for a carbon tax imposed at a
linear rate CTAX = 3 $/tonneCO2/5yr for [COSTCAP(NUCL) = 1,500 $/kWe,
COSTDD(NUCL) = 150 $/kWe: Scenario CHC.nucl_14.
S H C . n u c l_ 1 6 ( C T A X = 0 , S O 2 T A R G E T F R O M C H C . n u c l_ 0 9 )
4000
HYD RO
3500
ANNUAL GENERATION, TWeh/y
NUC L
HYD RO
NUC L
3000
GAS
REN
2500
O IL
2000
C OLA
GAS
1500
O IL
1000
DOM. C OAL
C OLA
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 43A. Time evolution of generation technology mix for SO2 emissions constrained
to those reported for Scenario CHC.nucl_09, but without carbon taxes,
[COSTCAP(NUCL) = 1,600 $/kWe, COSTDD(NUCL) = 200 $/kWe]:
Scenario
SHC.nucl_16
80
S H C . n u lc _ 1 7 ( S O 2 T A R G E T F R O M n u c l_ 0 9 ,
C O S T C A P (N U C L ) = 1 4 0 0 $ /k W e , C O S T D D (N U C L ) = 1 0 0 $ /k W e )
4000
HYD RO
3500
ANNUAL GENERATION, TWeyr/yr
HYD RO
NUC L
3000
NUC L
REN
2500
C OLA
2000
GAS
GAS
1500
O IL
O IL
1000
C OLA
DOM. COAL
500
DOM
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 43B. Time evolution of generation technology mix with SO2 emissions
constrained to those reported for Scenario CHC.nucl_09, but without carbon taxes
[COSTCAP(NUCL) = 1,400 $/kWe, COSTDD(NUCL) = 100 $/kWe]: Scenario
SHC.nucl_17.
EHC .nucl_24 (CTAX = 0, C O 2 AN D SO 2 TARG ETS TH O SE O F CH C .nucl_9 (CTAX = 5
$/tonneC O 2/5yr)
4000
HYD RO
3500
HYD RO
NUC L
GENERATION, TWeh/yr
3000
REN
NUCL
2500
GAS
2000
GAS
1500
OIL
OIL
1000
COLA
C OLA
DOM. COAL
500
D OM
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 44. Time evolution of generation technology mix with both CO2 and SO2
emissions constrained to those reported for scenario CHC.nucl_09, but without carbon
taxes [COSTCAP(NUCL) = 1,600 $/kWe, COSTDD(NUCL) = 200 $/kWe]: Scenario
EHC.nucl_24.
81
The evolution of countrywide energy mixes depicted on Figs. 41-44 show a richness in form
and magnitude of each generation component as the cost of fossil energy is (selectively) made
more expensive though: a) a progressively increasing carbon tax; b) related but independent
levels of emission caps or constraints are imposed; c) or the cost of an important noncarbon/non-sulfur emitting technology is made to undergo relatively modest decreases in cost
(albeit, not constrained by other less-economic problems with which nuclear energy must
deal.). Figure 45 re-capitulates the CO2 and SO2 emission rates associated with most of the
CHC, SHC, and/or EHC variations represented on Table XIII. To single out individual
responses, the two frames in Fig 46 give first the impact of carbon taxes on CO2 emissions for
the same constraints (caps) imposed on SO2 emissions, and, secondly, the impact of
moderately reduced costs (and other than growth rate, no other constraints) for nuclear energy.
CO2 EMISSIONS VERSUS TIME AND SCENARIO
2500
CTAX($/tonneCO2.5yr)
nucl_xx
BHC, CTAX = 0
1
CO2 EMISSION RATE, tonneCO2/y
2000
2
6
7
3
3, 1500 $/kW e
8
9
10
1500
4
11
12
13
14
5
1000
3, 1400 $kW e
500
25
10
50
0
1995
2000
2005
2010
TIME
2015
2020
2025
2030
Figure 45A. Summary of CO2 emission rates for the scenarios listed in Table XIII and
represented in Figs 41-44.
82
SO2 EMISSIONS VERSUS TIME AND SCENARIO
25
CTAX($/tonneCO2/5yr)
nucl_xx
1
6
7
8
9
10
11
12
13
14
SO2 EMISSION RATE, tonneSO2/y
20
15
BHC, CTAX = 0
2
3
4
3, 1500 $/kWe
5
10
3, 1400 $/kWe
5
10
25
50
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 45B. Summary of SO2 emission rates for the scenarios listed in Table XIII and
represented in Figs 41-44.
CO2 EMISSIONS WITHOUT/WITH CTAX(5 $/tonneCO2/5yr)
AND SAME SO2 EMISSIONS
2500
nulc_xx,
CTAX($/tonneCO2/5yr)
BHC
1
CO2 EMISSION RATE, tonneCO2/y
2000
9
16
0, SO2 TARGET FOR CTAX = 5
1500
5
1000
500
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 46A. Impact of carbon taxes on CO2 emission rates for the same constraint
imposed on SO2 emissions (e.g., those given by CHC.nucl_09), for the scenarios
indicated.
83
CO2 EMISSIONS VERSUS TAXES, TARGETS, AND NE COSTS
2500
nulc_xx,
CTAX($/tonneCO2/5yr)
BHC
1
2000
9
0, SO2 TARGET FOR CTAX = 5
CO2 EMISSIONS, tonneCO2/yr
16
17
0, S02 TARGET FOR CTAX = 5,
1400 $/kWe
18
1500
0, SO2 TARGET FOR CTAX = 5,
1200 $/kWe
5
1000
500
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 46B. Impact of carbon taxes, emission caps (targets), and nuclear costs on CO2
emission rate for the scenarios indicated.
84
Whether CO2 and/or SO2 emissions are constrained by direct taxation or indirectly by caps
that force the use of more expensive technologies, the overall (discounted, total) energy cost,
ENC(B$), will increase, give that other effects like "leaning-by-doing" (Kypreos, 2000a,b),
AEEI-related effects, etc. are not included. The incremental increase in ENC(B$) with the
increase in avoided total carbon emitted over the 1995-2030 computational period is
illustrated in Fig. 47. A nominally linear behavior having an average slope of ~24 $/tonneCO2
is indicated. Finally, the incremental reduction in carbon emissions with increasing carbon
tax from a number of earlier model studies has shown some correlation (Nordhaus, 1991) with
a saturating analytic function. Figure 48 plots the fractional reduction in CO2 emissions with
various carbon taxes applied to the BHC baseline case. These results identify that Chinese
carbon reduction potential at the same costs and levels as suggested by Nordhaus is difficult to
obtain. This observation could be explained in terms of the high discount rates imposed in
CRETM, the restriction of analysis to the electricity-generation system (versus the full energy
system represented by Nordhaus), and the high cost of alternative generation technologies in
China that must compete against cheap Chinese coal. The maximum penetration rate of
nuclear energy is restricted to 8 %/yr. Changing the growth rate to 12 %/yr leads to almost
identical results to these of Nordhaus.
INCREASED PV TOTAL ENERGY COST VERSUS AVOIDED CO2 EMISSIONS
OVER THE PERIOD 1995-2030
140000
120000
INCREASED ENC, M$
100000
80000
60000
40000
20000
23.8 $/tonneCO2
0
0.0
1141.9
3964.5
10493.0
20064.8
44362.7
51855.0
53795.2
AVOIDED CO2, toneCO2
Figure 47. Correlation of added cost of (total, discounted) energy on total CO2 emitted
over the computational period 1995-2030.
85
CO2 EMISSION RATE REDUCTION VERSUS CO2 TAX AND TAX RATE
0.7
DRCO2/RCO2
0.6
5
0.5
10
0.4
20
30
0.3
40
0.2
50
0.1
Nordhaus
0
0
20
40
60
80
100
CO2 TAX, $/tonneCO2
Figure 48. Correlation of fractional reduction in CO2 emission with level of carbon tax
as determined from a constant carbon tax rate applied to the BHC baseline cases after
the year 2020, along with a comparison of the analytic correlation reported by Nordhaus
(1991). Nuclear energy is assumed to penetrate with an annual maximum growth rate of
8%/yr. Increasing this rate to 12 %/yr gives almost the same fractional reduction of
carbon emissions as in the Nordhaus case.
86
IV. MAINLY SHANDONG PROVINCE
The focus of the previous sections has been on China countrywide, with an aim towards
explaining the bases, workings, capabilities, and limitations of the China Regional Energy
Transport Model (CRETM). The database used to characterize the eight scenarios adopted by
the EEM task of the CETP (Table III-IV) at the time of this writing was evolving and differs
somewhat from the one used herein. Table XIV recapitulates the eight wide-ranging scenarios
adopted (re: Table III). Figure 49 gives the baseline countrywide demand for electrical energy
used for all scenarios listed in Tables III or XIV. A wide range of options for and opinions on
this key, driving issue can be found, as is seen from Fig. 6. The countrywide electrical-energy
demand given on Fig. 49 results from the regional GDP growths assumed, and the algorithm
given by Eq. (1). On Fig. 49 is also given the countrywide demand for electricity as projected
from the MARKAL-CH model of Chen, et al. (2000).
The present section focuses onto the subject of the CETP - Shandong Province. The
electricity-generation mixes and associated SO2 and CO2 emission rates resulting from the
eight scenarios listed in Tables III, IV, or XIV, to which the countrywide generation-mix
projections given in Figs. 7, 11, 13-14, 17-18, and 21-22 correspond, are given in the
following subsection for Shandong Province. The assumed (exogenous) growth in GDP and
the computed electricity demand that results are given in Fig. 50. Also shown are recent
projections for Shandong Province made by the Energy Research Institute (ERI) (Gao, 2000).
Table XIV. Summary of (Economic, Technological, Environmental) Attributes for Eight
Scenarios.
Scenario(a) Discount(b)
Price(c)
SO2 Cap(d) CO2 Cap(e)
BHC
H
C(constant)
BHH
H
H(increasing)
CHC
H
C(constant)
C
CHH
H
H(increasing)
C
SHC
H
C(constant)
S
SHH
H
H(increasing)
S
EHC
H
C(constant)
S
C
EHH
H
H(increasing)
S
C
(a) B = baseline; H = high; C = constant or carbon; S = sulfur; E = C + S
Order: Emissions:Discount:Price
(b) H = 10%/yr discount rate
(c) C = Table 20; H = Table 22; Appendix A
(d) S = caps from Table 9B; Appendix A
(e) C = caps from Table 9A; Appendix A
87
C R E T R V ER SU S MAR K AL-C H D E MAN D C O MP AR IS O N
4000
GENERATION, TWe/yr
3500
C RE TR
3000
MARKA L-C N
2500
2000
1500
1000
500
0
1995
2000
2005
2010
2015
2020
2025
2030
T IME
Figure 49. Countrywide China electrical energy demand used to drive CRETM [Eq.
(1)], and comparison with projection from MARKAL-CN model (Chen, 2000).
SH AN D O N G P R O V IN C E G D P C O MP AR IS O N S
B E TW E EN E R I PR O JEC TIO N S AN D C R ETM
500
ERI
GD P (C RETM)
400
GDP(B$/yr)
GD P (ERI)
300
C RE TM
200
100
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 50A. GDP projections for Shandong Province used to generate the results in this
report, as well as more recent projections made by the Energy Research Institute (Gao,
2000).
88
SH AN D O N G PR O VIN C E ELEC TR IC ITY D EMAN D C O MPAR ISO N
B ETW EEN C R ETM AN D ER I PR O JEC TIO N S
300
ERI
GENERATION, DELEC(TWeh/yr)
D ELEC (C RETM)
250
D ELEC (ERI)
C RETM
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 50B. Electricity demand projections for Shandong Province used to generate the
results in this report, as derived from Eq. (1), as well as more recent projections made by
the Energy Research Institute (Gao, 2000).
The eight frames in Fig. 51 give the evolving electricity-generation mixes for Shandong
Province for the scenarios listed in Table XIV. The CO2 and SO2 emission rates associated
with each scenario are depicted in Fig. 52. Generally, a richness in technology mixes and
options is revealed as the Shandong Province electrical-energy mix is optimized under
variations and combinations of the two main scenario attributes examined: fuel costs and
emission caps. Common to all eight scenarios is the early phase-out of small domestic coal
plants (domsml, Table VII), by design, followed by a phased departure of medium (dommed)
and large (dombig) coal plants without emissions cleanup technologies. Depending on the
cost of fuel and the level and kind of emissions caps, large coal-fired plans with electrostatic
precipitators (domesp) fill a significant part of the generation mix. For reasons that remain to
be explained, large domestic coal-fired plants with flue-gas scrubbers (domscb) do not
penetrate for the conditions assumed for these computations. The generation-mix variations
observed in Fig. 51 generally divide most strongly first along lines determined by whether a
"constant" (C) or a "high" (H) fuel cost is exogenously enforced. Secondly, for a five fuelcost scenario, the generation mix varies according to the kind (e.g., C, S, or E = C + S) of caps
enforced. While integrated gas combined cycle (igcc) plants under high-fuel-cost scenarios
take an important market share, as do advanced gas combined-cycle plants (gascca),
combined-cycle plants fueled by oil barely appear in the generation mix. For all cases where
carbon emission caps are enforced (e.g., scenarios CHC, CHH, EHC, EHH), transfer of
electricity generation (trans) from outside of Shandong Province is required to maintain the
exogenous demand under such emission-constrained condition. In one scenario examined
(EHH), for a period of time between ~2010-2020, Shandong Province is able to reverse this
flow and to export electrical energy (Fig. 51H).
89
Table XV. Summary of Integrated Emission and Cost Impacts for the Eight Scenarios
Examined.
SCENARIO
BHC
BHH
CHC
CHH
SHC
SHH
EHC
EHH
ENC
TOTAL CO2 TOTAL SO2
M$
MtonneCO2 MtonneSO2
478392.53
60874.41
632.12
507053.21
58543.84
562.51
484859.56
50713.73
445.92
512320.89
50777.74
450.14
478989.99
60469.14
482.82
507501.62
58273.71
482.31
487878.31
50714.09
441.76
512362.21
50727.42
432.79
<COE>
DENC/DC02 DENC/DS02
mill/kWeh
$/tonneCO2 $/tonneSO2
34.19
0
0
38.19
12.29
411.85
35.92
0.64
34.74
38.93
3.36
186.46
34.31
1.47
4.01
38.24
11.19
194.33
35.87
0.93
49.83
38.96
3.35
170.44
G E N E R A T IO N M IX F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E F O R S C E N A R IO B H C
300
w in d
hy dro
n u c le a r
gt
pv
gasc c a
gasc c
o ilc c
o ilr e g
p fb c
ig c c
a fb c
dom sc b
dom esp
d o m b ig
dom m ed
dom sm l
GENERATION TWeh/yr
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 51A. Generation mix for scenario BHC [Baseline (no CO2 or SO2 emission
constraints), (H)igh discount rate, (C)onstant fuel costs].
90
G E N E R A T IO N M IX F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E F O R S C E N A R IO B H H
300
w in d
hy dro
n u c le a r
gt
pv
gasc c a
gasc c
o ilc c
o ilr e g
pfbc
ig c c
afbc
dom sc b
dom esp
d o m b ig
dommed
dom sml
GENERATION, TWe/yr
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 51B. Generation mix for scenario BHH [Baseline (no CO2 or SO2 emission
constraints), (H)igh discount rate, (H)igh (increasing) fuel costs].
G E N E R A T IO N M IX F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E F O R S C E N A R IO C H C
300
tr a n s
w in d
hy dro
n u c le a r
gt
pv
gasc c a
gasc c
o i lc c
o i lr e g
p fb c
ig c c
a fb c
dom sc b
dom esp
d o m b ig
dommed
dom sm l
GENERATION, TWeh/yr
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 51C. Generation mix for scenario CHC [CO2 emission constraints via caps),
(H)igh discount rate, (C)onstant fuel costs].
91
300
G E N E R A T IO N M IX F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E F O R S C E N A R IO C H H
tr a n s
w in d
hy dro
n u c le a r
gt
pv
gasc c a
gasc c
o ilc c
o ilr e g
p fb c
ig c c
a fb c
dom sc b
dom esp
d o m b ig
dommed
dom sm l
GENERATION, TWeh/yr
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 51D. Generation mix for scenario CHH [CO2 emission constraints via caps),
(H)igh discount rate, (H)igh (increasing) fuel costs].
G E N E R A T IO N M IX F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E F O R S C E N A R IO S H C
300
w in d
hy dro
n u c le a r
gt
pv
gasc c a
gasc c
o i lc c
o i lr e g
p fbc
ig c c
a fbc
domsc b
dom esp
d o m b ig
dommed
domsml
GENERATION, TWeh/yr
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 51E. Generation mix for scenario SHC [SO2 emission constraints via caps),
(H)igh discount rate, (C)onstant fuel costs].
92
G E N E R A T IO N M IX F O R S H A N D O N G P R O V IN C E V E R S U S
T IM E F O R S C E N A R IO S H H
300
w in d
hy dro
n u c le a r
gt
pv
gasc c a
gasc c
o i lc c
o i lr e g
p fb c
ig c c
a fb c
dom sc b
dom esp
d o m b ig
dommed
dom sm l
GENERATION, TWeh/yr
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 51F. Generation mix for scenario SHH [SO2 emission constraints via caps),
(H)igh discount rate, (H)igh (increasing) fuel costs].
G E N E R A T IO N M IX F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E F O R S C E N A R IO E H C
300
tr a n s
w in d
hy dro
n u c le a r
gt
pv
gasc c a
gasc c
o i lc c
o i lr e g
p fb c
ig c c
a fb c
dom sc b
dom esp
d o m b ig
dom med
dom sm l
GENERATION, TWwh/yr
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 51G. Generation mix for scenario EHC [CO2 + SO2 emission constraints via
caps), (H)igh discount rate, (C)onstant fuel costs].
93
G E N E R A T IO N M IX F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E F O R S C H E N A R IO E H H
300
tr a n s
w in d
hy dro
n u c le a r
gt
pv
gasc c a
gasc c
o i lc c
o i lr e g
pfbc
ig c c
afbc
dom sc b
dom esp
d o m b ig
dom m ed
dom sm l
tr a n s
GENERATION, TWeh/yr
250
200
150
100
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 51H. Generation mix for scenario EHH [CO2 + SO2 emission constraints via
caps), (H)igh discount rate, (H)igh (increasing) fuel costs.
C O 2 E M M IS IO N R A T E S F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E A N D S E N A R IO
250
BHC
BHC ,
SHC
CO2 EMISSION RATE, MtonneCO2/yr
BHH
200
CHC
CHH
EHH
SHC
150
BHH,
SHH
SHH
EHC
C HC ,
EHC
EHH
100
C HH
50
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 52A. Comparison of CO2 emission rates for each of the eight scenarios listed in
Table XIV and described in Fig. 51.
94
S O 2 E M IS S IO N R A T E S F O R S H A N D O N G P R O V IN C E
V E R S U S T IM E A N D S C E N A R IO S
S02 EMISSION RATE, MtonneSO2/yr
2 .5
2
1 .5
BHC
BHH
C HC
C HH
SHC
SHH
EHC
EHH
BHC
B HH
C HC
1
E HC
C HH
E HH
SHC ,
SHH
0 .5
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 52B. Comparison of SO2 emission rates for each of the eight scenarios listed in
Table XIV and described in Fig. 51.
Figure 52 gives a composite summary of CO2 and SO2 emission rates for the eight scenarios
considered. Generally, for a given fuel-cost scenario attribute (H or C), imposition of an SO2
emissions constraint leads to little benefit in the form of reduced CO2 emissions. Table XV
summarizes the integrated emissions results, as well as impacts on total present value of
energy costs, ENC. The average cost of energy, <COE>(mill/kWeh), determined by dividing
the total electrical energy generation over the period 1995-2030 by the total undiscounted
energy costs, is also given on this table, along with other incremental parameters. These data
pertain to China as a whole; similar information that is specific to Shandong remains to be
reported. At this (countrywide) integrated level, the cost versus emissions impacts of the eight
scenarios examined are best shown as changes relative to the baseline BHC case. Figure 53
gives these results for both CO2 and SO2 emissions. As noted previously, the main
discriminator between these eight scenarios is the fuel cost. The scenario points on Fig. 53
have been artificially connected according to those scenarios with high (increasing) fuel costs
versus those with constant fuel cost. The relationships between scenarios on this costemissions "phase space" may be distorted for some cases by the fact that the relative cost
changes refer to discounted costs at a substantial (H or "high" for RATE = 10%/yr) rate. To
eliminated this distortion, Fig. 54 is include to show the impacts of these emission-versus-cost
scenario trades using undiscounted total energy costs (again, incurred over the computational
period 1995-2030). Finally, for the undiscounted costs, Fig. 55 represents these aggregated
cost-versus-emissions scenario correlation for Shandong Province. As is indicated on Fig.
51(C,D,G,H), for those scenarios with carbon emission constraints, meeting these constraints
at the Shandong Province level under minimum total cost conditions requires the import if
electrical energy in the out years of the optimization. The cost of this imported energy was
estimated using the marginal costs for Shandong Province, and these costs added to the sums
used to generate Fig. 55. Figure 56 gives the marginal costs reported by CRETM as a
function of time for both China and for Shandong Province, for the eight scenarios being
95
considered in this study. Comparison of Figs. 54 and 55 indicate a generally larger range of
cost and emissions impacts at the Shandong Province level compared to China countrywide
averages.
R E L A T IV E C O S T C H A N G E V E R S U S R E L A T IV E C O 2 C H A N G E
RELATIVE CO2 EMISSION DECREASE
0 .2 5
C HINA
C HH,
E HH
C HC
0 .2
E HC
0 .1 5
0 .1
S HH
0 .0 5
S HC
ZZ( B H C ) = 4 4 8 B $
C O 2 = 6 0 . 9 G to n n e C O 2
S O 2 = 6 3 2 . 1 M to n n e S O 2
B HC
0
0
0 .0 1
0.02
0 .0 3
0 .0 4
B HH
0.05
0 .0 6
0 .0 7
R E L A T IV E T O T A L ( D IS C O U N T E D , 1 0 % ) C O S T IN C R E A S E
Figure 53A. Relative discounted total energy cost versus CO2 emissions impacts for the
eight scenarios examined.
R E L A T IV E C O S T C H A N G E V E R S U S R E L A T IV E S O 2 C H A N G E
RELATIVE SO2 EMISSION DECREASE
0.5
E HH
C HINA
0 .45
E HC
0.4
C HH
C HC
0 .35
S HH
0.3
S HC
0 .25
0.2
0 .15
B HH
0.1
0 .05
B HC
0
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
R E L A T IV E T O T A L ( D IS C O U N T E D , 1 0 % ) C O S T IN C R E A S E
Figure 53B. Relative discounted total energy cost versus SO2 emissions impacts for the
eight scenarios examined.
96
R E L A T IV E C O S T C H A N G E V E R S U S R E L A T IV E C O 2 C H A N G E
RELATIVE CO2 EMISSION DECREAS
0.25
C H IN A
C HC ,
C HH
C HH,
E HH
0 .2
0.15
0 .1
0.05
S HH,
B HH
B HC ,
S HC
0
0
0 .0 2
0 .0 4
0.06
0.08
0 .1
0 .1 2
0 .1 4
R E L A T IV E T O T A L ( U N D IS C O U N T E D ) C O S T C H A N G E
Figure 54A. Relative undiscounted total energy cost versus CO2 emissions impacts for
the eight scenarios examined: China countrywide.
R E L A T IV E C O S T C H A N G E V E R S U S R E L A T IV E S O 2 C H A N G E
RELATIVE S02 EMISSION DECREASE
0 .5
C HINA
E HH
0.45
E HC ,
C HC
0 .4
C HH
0.35
S HH
0 .3
S HC
0.25
0 .2
0.15
B HH
0 .1
0.05
B HC
0
0
0.02
0 .0 4
0 .0 6
0.08
0 .1
0 .1 2
0.14
R E L A T IV E T O T A L ( U N D IS C O U N T E D ) C O S T IN C R E A S E
Figure 54B. Relative undiscounted total energy cost versus CO2 emissions impacts for
the eight scenarios examined: China countrywide.
97
R E LATIVE C O S T C H AN G E V ER S U S R E LATIVE C O 2 C H AN G E
RELATIVE CO2 EMISION DECREASE
0.35
C HC , E HC
S HA ND ONG
P ROV INC E
0.3
C HH
0.25
E HH
0.2
0.15
0.1
0.05
SHH
BHH
BHC , S HC
0
0
0.05
0.1
0.15
0.2
R E LATIV E TO TAL (U N D IS C O U N TE D ) C O S T IN C R E AS E
Figure 55A. Relative undiscounted total energy cost versus CO2 emissions impacts for
the eight scenarios examined: Shandong Province.
RELATIVE CO ST CHANG E VERSUS RELATIVE SO 2 CHANG E
RELATIVE SO2 EMISSION DECREASE
0.45
SHANDONG
PROVINCE
EHC
0.4
CHC
0.35
SHH
0.3
EHH
CHH
0.25
0.2
SHC
0.15
BHH
0.1
0.05
BHC
0
0
0.05
0.1
0.15
0.2
RELATIVE TOTAL (UNDISCO UNTED) CO ST INCREASE
Figure 55B. Relative undiscounted total energy cost versus SO2 emissions impacts for the
eight scenarios examined: Shandong Province.
98
MARG INAL CO ST O F ELECTRICTIY FOR
SHANDO NG PRO VINCE VERSUS TIME AND SCENARIO
MARGINAL COE, mill/kWeh
60
50
40
30
BHC
BHH
CHC
CHH
SHC
SHH
EHC
EHH
CHC,
EHC
CHH
EHH
BHH
SHH
BHC,
SHC
20
10
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 56A. Dependence of marginal costs of energy on time and scenario for China.
60
MARGINAL COE, mill/kWeh
50
40
30
MARGINAL CO ST OF ELECTRICITY FO R CHINA
VERSUS TIME AND SCENARIO
BHC
BHH
CHC
CHH
SHC
SHH
EHC
EHH
CHH,
EHH
CHC
BHH
SHH
BHC,
EHC
20
10
0
1995
2000
2005
2010
2015
2020
2025
2030
T IM E
Figure 56B. Dependence of marginal costs of energy on time and scenario for Shandong
Province.
99
V. RESULTS SYTHESES
This section synthesizes key results reported in Sec. III into a form that is better compared to
other modeling tasks within CETP. This section also explores further some of the implications
suggested by the preliminary results given so far (e.g., the impact of discount rates on market
penetration of more capital-intensive technologies like nuclear and hydroelectric power). This
section generally extends and synthesizes the modeling results presented in Secs. III-IV.
Results of the optimizations model (EEM task) are organized into a form comparable to the
results anticipated from the Electric simulation model (ESS).
The focus of the results reported in Sec. III has been placed on China countrywide, with an
aim towards explaining the bases, workings, capabilities, and limitations of the China
Regional Energy Transport Model (CRETM). The database (Table X) used to characterize
the eight scenarios adopted by the EEM task of the CETP (Table II. and III.) is evolving and,
therefore in the future is expected to differ somewhat from the one used herein. A wide range
of options and opinions can be found on this key, driving issue, as is seen from Fig. 5.
The single-point parametric studies reported in Sec III.D are comprised of 19 nuclear
variations (Series B-E, Table XI), 6 discount-rate variations (ignoring the unrealistically low
RATE = 1%/yr case, which was examined only to test model limits), and five electricity
demand growth rate cases (Fig. 25). Together with the baseline BHC scenario (Table III),
about which the variations where conducted, a total of 31 ENC-minimizing "scenarios" result.
In the spirit of the non-optimizing Electric-Sector Simulation (ESS) modeling component of
CETP, these 31 EEM scenarios are captured on an energy-emissions "scatter" plots given on
Figs 57-60 for China and for Shandong Province, respectively. The average electrical-energy
generation cost, <COE>(mill/kWeh) computed over the period of the optimization
(1995,2030) was selected to describe the cost dimension, and the total, integrated CO2
emissions, MCO2(MtonneCO2), was chosen to measure the emissions dimension. The
integrated CO2 emissions is correlated with the integrated emissions of SO2 for both China
and for Shandong Province (Figs. 58 and 60, respectively). Since most of the 31 cases
represented on these scatter plots were evaluated for a discount rate of RATE = 10 %/yr, the
corresponding RATE variation curve included on Figs. 57 and 59 (<COE> versus integrated
CO2 emissions) provides an approximate scale whereon the Centrum of the remaining cluster
of points would shift if the corresponding value of RATE was increased or decreased above
the BHC base case value of RATE = 10%/yr. This cluster of points represents a kind of
minimum ENC-MCO2 "frontier", which under the ideal conditions being described by CRETM
limits access to the lower regions of this <COE>-MCO2) phase space. As for most economic
or trade-off frontiers of this kind, the realities of the technological and social world can limit
the approach of actually achievable cost-emission goals to some distance above this trade-off
frontier.
Once the identity of each cluster of points is identified, as shown on the lower frames of Figs.
57-60, the trends become more-or-less self evident: a) the CTAX points expectedly move to
higher <COE>, lower MCO2 regions as CTAX($/tonneCO2/5yr) is increased; b) modest
decreases in the cost of nuclear (Table XI) can push the frontier to desirable regions of lower
<COE> and lower MCO2, albeit a more detailed model of the nuclear fuel cycle (Trellue,
2000) than presently incorporated into CRETM is needed to access costs related (primarily) to
waste and nonproliferation concerns; c) the availability of moderately cheaper nuclear in
combination with an effective carbon tax (CTAX = 3 $/tonneCO2/5yr, COSTCAP(NUCL) =
1,400 $/kWe) push back the cost-emissions frontier without increasing the average cost of
100
AVERAGE ENERGY COST VERSUS TOTAL CO2
EMISSIONS FOR CHINA
50
AVERAGE COST OF ENERGY, <COE>(mill/kWeh)
SCEN
45
RATE
GGDP
40
NUCLA
35
CTAX
30
CAPS
UTC
25
ENV
20
0
10000
20000
30000
40000
50000
60000
70000
80000
TOTAL CO2 EMISSIONS, MCO2(MtonneCO2)
Figure 57A. Collection of 31 <COE>-MCO2 couplets from the single-point parametric
variations reported in Sec. III.D. for China countrywide, indicating a minimum-ENC
"frontier” for the BHC parameters (Table X) and variations thereon (Table XIII, Fig.
26, Fig. 31)
AVERAGE ENERGY COST VERSUS TOTAL CO2
EMISSIONS FOR CHINA
50
AVERAGE ENERGY COST, <COE>(mill/kWeh)
SCEN
0.15
45
RATE
BHH,SH
CHH,EHH
CTA
GGDP
40
0.12
CHC,EHC
35
CTAX+UT
C
GGD
CTAX+UTC
NUCLA
CTAX
BHC,SHC
UTC
0.08
30
CAPS
SO2(TAR.)
0.06
25
UTC
0.04
20
0
10000
20000
30000
40000
RATE(1/
yr)
50000
60000
ENV
70000
80000
TOTAL CO2 EMISSIONS, MCO2(MtonneCO2)
Figure 57B. Collection of 31 <COE>-MCO2 couplets from the single-point parametric
variations reported in Sec. III.D for China countrywide, identifying each minimum-ENC
cluster.
101
CORRELATION OF TOTAL CO2 WITH TOTAL SO2 EMISSIONS
800
CHINA
SCEN
TOTAL S02 EMISSIONS, MSO2(MtonneSO2)
700
RATE
600
GGDP
500
NUCLA
400
CTAX
300
CAPS
200
UTC
100
ENV
0
0
10000
20000
30000
40000
50000
60000
70000
80000
TOTAL CO2 EMISSIONS, MCO2(MtonneCO2)
Figure 58A. Collection of 31 MSO2-MCO2 couplets from the single-point parametric
variations reported in Sec. III.D for China countrywide, indicating a minimum-ENC
"frontier” for the BHC parameters (Table X) and variations thereon (Table XIII, Fig.
26, Fig. 31).
CORRELATION OF TOTAL CO2 WITH TOTAL SO2 EMISSIONS
800
CHINA
SCEN
700
BHC
TOTAL SO2 EMISSIONS, MSO2(MtonneS)
RATE
GGDP
600
UTC
500
BHH
GGDP
SHC,SH
H
CHH,EHC,EHH,
400
RATE
NUCLA
SO2 TAR.
CTAX
300
CAPS
200
UTC
100
NUCLA, CTAX
ENV
0
0
10000
20000
30000
40000
50000
60000
70000
80000
TOTAL C02 EMISSIONS, MCO2(MtonneCO2)
Figure 58B. Collection of 31 <COE>-MCO2 couplets from the single-point parametric
variations reported in Sec. III.D for China countrywide, identifying each minimum-ENC
cluster.
102
AVERAGE ENERGY COST VERSUS TOTAL CO2 EMISSIONS
FOR SHANDONG PROVINCE
50
AVERAGE ENERGY COST, <COE>(mill/kWeh)
SCEN
45
RATE
GGDP
40
NUCLA
35
CTAX
30
CAPS
UTC
25
ENV
20
0
1000
2000
3000
4000
5000
6000
7000
TOTAL CO2 EMISSIONS, MCO2(MtonneCO2)
Figure 59A. Collection of 31 <COE>-MCO2 couplets from the single-point parametric
variations reported in Sec. III.D for Shandong Province, indicating a minimum-ENC
"frontier” for the BHC parameters (Table X) and variations thereon (Table XIII, Fig.
26, Fig. 31).
AVERAGE ENERGY COST VERSUS TOTAL CO2 EMISSIONS
FOR SHANDONG PROVINCE
50
AVERAGE ENERGY COST, <COE>(mill/kWeh)
SCEN
45
RATE
BHH,SHH
GGDP
40
CTAX
EHH
35
NUCLA
BHC
CHH
CTAX
UTC
CTAX+UTC
30
CHC,EH
GGD
P
UTC
SO2 TAR.
CAPS
UTC
25
RATE
ENV
20
0
1000
2000
3000
4000
5000
6000
7000
TOTAL CO2 EMISSIONS, MCO2(MtonneCO2)
Figure 59B. Collection of 31 <COE>-MCO2 couplets from the single-point parametric
variations reported in Sec. III.D for Shandong Province, identifying each minimum-ENC
cluster.
103
CORRELATION OF TOTAL CO2 WITH TOTAL SO2 EMISSIONS
80
SHANDONG PROVINCE
SCEN
TOTAL SO2 EMISSIONS, MtonneSO2
70
RATE
60
GGDP
50
NUCLA
40
CTAX
30
CAPS
20
UTC
10
ENV
0
0
1000
2000
3000
4000
5000
6000
7000
TOTAL CO2 EMISSION, MtonneCO2
Figure 60A. Collection of 31 MSO2-MCO2 couplets from the single-point parametric
variations reported in Sec. III.D for Shandong Province, indicating a minimum-ENC
"frontier” for the BHC parameters (Table X) and variations thereon (Table XIII, Fig.
26, Fig. 31).
CORRELATION OF TOTAL CO2 WITH TOTAL SO2 EMISSIONS
80
SHANDONG PROVINCE
TOTAL SO2 EMISSIONS, MtonneSO2
SCEN
RATE
70
GGDP
RATE
CHH,EHH
60
BHC
BHH
SHC
SHH
50
CHC
EHC
40
GGDP
NUCLA
SO2(TAR.)
CTAX
30
UTC
CAPS
ENV
20
UTC
10
ENV
NUCLA, CTAX
0
0
1000
2000
3000
4000
5000
6000
7000
TOTAL CO2 EMISSION, MtonneCO2
Figure 60B. Collection of 31 <COE>-MCO2 couplets from the single-point parametric
variations reported in Sec. III.D for Shandong Province, identifying each minimum-ENC
cluster.
104
energy; d) the application of SO2 limits alone can push back the frontier without incurring
significant increases in average cost of electricity; e) few-percent changes in the discount rate
used has a large impact on the vertical (<COE>) position of the cost-emission trade-off
frontier. These findings require more extensive analysis, but the cost-emissions trade-off
frontier of the kind suggested in Fig. 49 can provide a excellent means for joining,
interpreting, and utilizing EEM and ESS results for policy-making purposes.
A. Impact of Discount Rates on High-Capital Cost Technologies
On the basis of the results given in Sec. III.D.2, and Figs. 57-60 in Sec. V.A, the impact of
discount rate, RATE(%/yr), on the market share taken by capital-intensive technologies like
nuclear energy and hydroelectric can be dramatic. These technologies require the borrowing
and investment of large capital stocks prior to and during installation, and long before the
electricity-generation revenue streams begin to flow. Furthermore, but less importantly from a
market-share viewpoint, technologies like nuclear leave a long-lived legacy that also requires
the expenditure of capital and O&M costs long after that technology as been retired. The rate
at which these costs can be discounted has a stronger impact on technologies based on coal,
where the major non-external costs are largely incurred at the time the fuel is burned and
converted to generally non-externalized waste products and revenue-generating electricity.
This section examines the impact of discount rate on the market share of both nuclear and
hydroelectric power for both the Base-Case BHC scenario and the CHC (carbon caps)
scenario, particular attention is given to the relationship between such impacts and the
reduction of integrated CO2 emissions.
Figures 61 and 62 give the generation mix for both scenarios BHC and CHC (Table III) and
three values of discount rate, RATE = 5, 10, and 15%/yr. These two figures show the full
range of the 17 possible generation technologies available in CRETM. The Base Case
105
GENERATION MIX, TWeh/yr
4500
GENERATION MIX VERSUS TIME FOR
SCENARIO BHC AND RATE = 5%/a
4000
3500
3000
2500
2000
1500
1000
500
0
1995 2000 2005 2010 2015 2020 2025 2030
wind
hydro
nuclear
gt
pv
gascca
gascc
oilcc
oilreg
pfbc
igcc
afbc
domscb
domesp
dombig
dommed
domsml
TIME
Figure 61A. Generation mix versus time for scenario BHC and RATE = 5%/yr discount
rate.
GENERATION MIX, TWeh/yr
4500
GENERATION MIX VERSUS TIME FOR
SCENARIO BCH AND RATE = 10%/a
wind
hydro
nuclear
gt
pv
gascca
gascc
oilcc
oilreg
pfbc
igcc
afbc
domscb
domesp
dombig
dommed
domsml
4000
3500
3000
2500
2000
1500
1000
500
0
1995
2000 2005
2010
2015
2020 2025
2030
TIME
Figure 61B. Generation mix versus time for scenario BHC and RATE = 10%/yr discount
rate.
106
GENERATION MIX, TWeh/yr
4500
GENERATION MIX VERSUS TIME FOR
SCENARIO BHC AND RATE = 15%/a
wind
hydro
nuclear
gt
pv
gascca
gascc
oilcc
oilreg
pfbc
igcc
afbc
domscb
domesp
dombig
dommed
domsml
4000
3500
3000
2500
2000
1500
1000
500
0
1995
2000 2005
2010
2015
TIME
2020 2025
2030
Figure 61C. Generation mix versus time for scenario BHC and RATE = 15%/yr discount
rate.
GENERATION MIX, TWeh/yr
4500
GENERATION MIX VERSUS TIME FOR
SCENARIO CHC AND RATE = 5%/a
wind
hydro
nuclear
gt
pv
gascca
gascc
oilcc
oilreg
pfbc
igcc
afbc
domscb
domesp
dombig
dommed
domsml
4000
3500
3000
2500
2000
1500
1000
500
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 62A. Generation mix versus time for scenario CHC and RATE = 5%/yr discount
rate.
107
GENERATION MIX, TWeh/yr
4500
GENERATION MIX VERSUS TIME FOR
SCENARIO CHC AND RATE = 10 %/a
4000
3500
3000
2500
2000
1500
1000
500
0
1995 2000 2005 2010 2015 2020 2025 2030
wind
hydro
nuclear
gt
pv
gascca
gascc
oilcc
oilreg
pfbc
igcc
afbc
domscb
domesp
dombig
dommed
domsml
TIME
Figure 62B. Generation mix versus time for scenario CHC and RATE = 10%/yr discount
rate.
GENERATION MIX, TWeh/yr
4500
GENERATION MIX VERSUS TIME FOR
SCENARIO CHC AND RATE = 15%/a
4000
3500
3000
2500
2000
1500
1000
500
0
1995 2000 2005 2010 2015 2020 2025 2030
wind
hydro
nuclear
gt
pv
gascca
gascc
oilcc
oilreg
pfbc
igcc
afbc
domscb
domesp
dombig
dommed
domsml
TIME
Figure 62C. Generation mix versus time for scenario CHC and RATE = 15%/yr discount
rate.
108
corresponds to BHC with RATE = 10%/yr. The richness of generation mixes brought out by
this combination of carbon caps and discount rate serves as a good example of the variability
of futures available to China under the simplified conditions where policies driven by
environmental constraints combine with policies that lead to specific levels of future valuation
as reflected in the discount rate are sole arbitrators of choice based on this level of constrained
optima. Generally, whatever the level of carbon-emissions constraint, lower discount rates
favor capital-intensive technologies like nuclear energy and hydroelectric. The imposition of
a carbon-emission constraint with low-to-moderate discounts rates leads to substantial
generation shares being taken by nuclear and hydro, with these two technologies by 2030
providing half the BHC Base-Case demand of DMD = 3,800 TWeh/yr in that out year. As the
discount-rate creeps upward in the presence of carbon-emission constraints, both oilcc and
gascca technologies emerge as optimal choices, with wind for the first time making an
appearance in the energy mix by 2025. While both oilcc and gascca technologies make
important contributions in the out years even for the Base Case, a contribution that grows
substantially in the presence of carbon-emission constraints and high discount rates, the role
played by these technologies is strongly diminished for low discount rates with or without
carbon-emission constraints.
To elaborate further on this carbon-constraint versus discount-rate trade off, the integrated
emissions, average electricity-generation costs, and technology generation fractions in the out
year (2030) have been organized in the following figures. First, Figures 63 and 64 give the
out-year generation mix for the BHC (Base-Case) and CHC scenarios, respectively, in both
continuum and histogram form. While both nuclear energy and hydroelectricity play
increasing roles in the out years as RATE is decreased, when a carbon-cap is enforced,
advanced coal-, gas-, and oil-fired technology find strong niches; generally, these niches are
filled at the expense of conventional coal technologies. The impact of discount rate on
average energy costs, <COE>, and integrated CO2 and SO2 emission is shown for both BHC
and CHC scenarios on the respective frames in Fig. 65. Generally, the impact on average
energy cost of the BHC versus CHC scenario on <COE> is small, as is shown in the explicit
comparison given in Fig. 66A, albeit, the impact of discount rate on the (optimized) average
energy cost is strong. The remaining frames in Fig. 66 give respectively the impact of
discount rate on the integrated CO2 and SO2 emissions and on the out-year share fractions for
NUCL, HYDRO, and REN generation technologies, with all given in direct comparison
between the BHC and CHC scenarios. Generally, the imposition of carbon caps results in a
15-20% reduction in CO2 and SO2 emission relative to the BHC scenario, depending on the
discount-rate determined generation mix. (Fig. 66B). While decreasing the discount rate
increases the out-year market share of capital-intensive nuclear generation for both BHC and
CHC scenarios, nuclear energy expectedly penetrates more when competing with the carboncaps-induced advance fossil technologies (gasscca, oilcc) than with conventional coal-fired
technologies under BHC conditions (Fig. 66C), eventually reaching 30-35% market share in
2030 for very low discount rates (e.g., ~5%/yr). Hydroelectric generation is constrained both
by high capital requirements and capacity limitation, so its market share grows more slowly
with decreasing capital or the application of carbon caps (Fig. 66D). Lastly, Fig. 66E shows
that the renewable energy sources make only minor inroads into the China electricity market
for the unit capital costs assumed, and almost all of the REN capacity introduced occurs when
carbon caps are imposed (CHC scenario) and discount rates are high, where nuclear
deployment is hamstrung by its large upfront capital requirements and long construction
times.
109
GENERATION MIX VERSUS DISCOUNT RATE
IN 2030 FOR SCENARIO BHC
GENERATION MIX, %
100%
90%
HYDRO
80%
NUCL
70%
REN
60%
GAS
50%
40%
OIL
30%
COLA
20%
10%
DOM
0%
5
6.5
7.5
8.5
10
12.5
15
DISCOUNT RATE, RATE(%/a)
Figure 63A. Generation mix in 2030 versus discount rate for BHC scenario: continuum.
100%
GENERATION MIX VERSUS DISCOUNT RATE IN 2030
FOR BHC SCENARIO
HYDRO
90%
GENERATION MIX, %
80%
NUCL
70%
REN
60%
GAS
50%
40%
OIL
30%
20%
COLA
10%
DOM
0%
5
6.5
7.5
8.5
10
12.5
15
DISCOUNT RATE, RATE(%/a)
Figure 63B. Generation mix in 2030 versus discount rate for BHC scenario; histogram.
110
GENERATION MIX VERSUS DISCOUNT RATE
IN 2030 FOR SCENARIO CHC
100%
HYDRO
GENERATION MIX, %
90%
NUCL
80%
70%
REN
60%
GAS
50%
40%
OIL
30%
20%
COLA
10%
DOM
0%
5
6.5 7.5 8.5
9
9.5 9.9
10
11 12.5 15
DISCOUNT RATE, RATE(%/a)
Figure 64A. Generation mix in 2030 versus discount rate for CHC scenario: continuum.
GENERATION MIX, %
100%
GENERATION MIX VERSUS DISCOUNT RATE IN 2030
FOR CHC SCENARIO
90%
HYDRO
80%
NUCL
70%
REN
60%
50%
GAS
40%
OIL
30%
COLA
20%
10%
DOM
0%
5
6.5 7.5 8.5
9
9.5 9.9
10
11 12.5 15
DISCOUNT RATE, RATE(%/a)
Figure 64B. Generation mix in 2030 versus discount rate for CHC scenario; histogram.
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COST OF ENERGY AND INTEGRATED EMISSIONS
VERSUS DISCOUNT RATE IN 2030 FOR BHC SCENARIO
700
<COE>*10(mill/kWeh),
CO2/100(MtonneCO2),
SO2(MtonneSO2)
600
500
400
300
<COE>*10
200
CO2/100
100
SO2
0
5
6.5
7.5
8.5
10
12.5
15
DISCOUNT RATE, RATE(%/a)
Figure 65A. Average generation cost and integrated CO2 and SO2 emissions versus
discount rate for BHC scenario.
COST OF ENERGY AND INTEGRATED EMISSIONS
VERSUS DISCOUNT RATE IN 2030 FOR CHC SCENARIO
700
<COE>(mill/kWeh)*10,
CO2(MtonneCO2)/100,
SO2(MtonneSO2)
600
500
400
300
<COE>*10
200
CO2/100
100
SO2
0
5
6.5
7.5
8.5
9
9.5
9.9
10
11
DISCOUNT RATE, RATE(%/a)
12.5
15
Figure 65B. Average generation cost and integrated CO2 and SO2 emissions versus
discount rate for CHC scenario.
112
AVERAGE COST OF ELECTRICITY VERSUS TIME
FOR BHC AND CHC SCENARIOS
50
<COE>(BHC)
<COE>(mill/kWeh)
45
<COE>(CHC)
40
35
30
25
5
6.5
7.5
8.5
10
12.5
DISCOUNT RATE, RATE(%/a)
15
Figure 66A. Average cost of generation versus discount rate and BHC versus CHC scenario.
INTEGRATED EMISSIONS IN 2030 VERSUS DISCOUNT
RATE AND BHC VERSUS CHC SCENARIO
700
INTEGRATED EMISSIONS,
CO2(MtonneCO2)/100,
SO2(MtonneSO2)
600
500
400
300
SO2(BHC)
200
SO2(CHC)
CO2/100(BHC)
100
CO2/100(CHC)
0
5
6.5
7.5
8.5
10
12.5
15
DISCOUNT RATE, RATE(%/a)
Figure 66B. Integrated emissions versus discount rate and BHC versus CHC scenario.
113
NUCLEAR SHARE FRACTION
IN 2030, FNUCL(BHC,CHC)
NUCLEAR SHARE IN 2030 VERSUS DISCOUNT RATE
AND BHC VERSUS CHC SCENARIOS
0.4
FNUCL(BHC)
0.35
FNUCL(CHC)
0.3
0.25
0.2
0.15
0.1
0.05
0
5
6.5
7.5
8.5
10
12.5
DISCOUNT RATE, RATE(%/a)
15
Figure 66C. Nuclear-energy generation fraction in 2030 versus discount rate and BHC
versus CHC scenario.
HYDRO SHARE IN 2030 VERSUS DISCOUNT RATE
AND BHC VERSUS CHC SCENARIO
HYDRO SHARE FRACTION
IN 2030, FHYDRO(BHC,CHC)
0.18
0.16
0.14
0.12
0.1
0.08
0.06
FHYDRO(BHC)
0.04
0.02
FHYDRO(CHC)
0
5
6.5
7.5
8.5
10
12.5
DICOUNT RATE, RATE(%/a)
15
Figure 66D. Hydroelectric generation fraction in 2030 versus discount rate and BHC
versus CHC scenario.
114
REN SHARE IN 2030 VERSUS DISCOUNT RATE
AND BHC VERSUS CHC SCENARIO
1.0E+00
REN SHARE FRACTION
IN 2030, FREN(BHC,CHC)
FREN(BHC)
1.0E-01
FREN(CHC)
1.0E-02
1.0E-03
1.0E-04
1.0E-05
5
6.5
7.5
8.5
10
12.5
DISCOUNT RATE, RATE(%/a)
15
Figure 66E. Renewable generation fraction in 2030 versus discount rate and BHC versus
CHC scenario.
B. Comparison with Other Results
This section compares selected CRETM result with those to reported by the ESS task. It is
expected that differences will be encountered both as a result of fundamental differences
between the optimization and simulation modeling approaches (Appendix B gives more
operational detail is included in the non-optimizing simulation models) as well as inevitable
differences in databases and driver assumptions. Although attempts have been made to
minimize the latter, since both efforts were developed and conducted in parallel, post-study
data and assumption reconciliation will be needed. In large part, this early inter-comparisons
are made to catalyses this reconciliation process. In addition to this preliminary ESS-EEM
results comparison, , a few comparisons are made between CRETM (EEM) results and
related work reported outside CETP; the latter is first presented.
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1.
Comparison with Studies Outside of CETP
The PEW Center on Global Climate Change (DADI, 2000) has recently reported a study on
electrical-generation options in China. A few key results reported therein are used to compare
with CRETM projections of CO2 and SO2 emissions. Figure 67 compares the Base-Case
(BHC scenario, Table III) countrywide electricity demands used in both studies. Reasonable
agreement indicated, albeit the PEW study projects only to the year 2015. On the basis of
these preliminary comparisons, reasonable agreement can be claimed for two comparable
scenarios for China energy futures.
ELECTRICITY DEMAND VERSUS TIME
ELECTRICITY DEMAND, TWeh/yr
4500
4000
3500
3000
2500
2000
1500
DMD(CRETM)
1000
DMD(PEW)
500
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 67. Comparison of electricity demands between the CRETM Base Case and that
used in the PEW study (DADI, 2000).
116
CO2 EMISSIONS VERSUS TIME AND SCENARIO
CO2 EMISSION RATE,
MtonneCO2/yr
2500
2000
1500
1000
CO2(CRETM, BHC)
CO2(PEW-BASE)
500
CO2(CRETM, CHC)
CO2(PEW,C-CAP)
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 68A. Comparison of CO2 between CRETM BHC (Base Case) and CHC (carbon
caps) scenarios and comparable scenarios reported by the PEW study (DADI, 2000).
SO2 EMISSIONS VERSUS TIME AND SCENARIO
S02 EMISSION RATE,
MtonneSO2/yr
25
20
15
10
SO2(CRETM, BHC)
SO2(PEW, BASE)
5
S02(CRETM, CHC)
SO2(PEW,C-CAP)
0
1995
2000
2005
2010
2015
2020
2025
2030
TIME
Figure 68B. Comparison of SO2 between CRETM BHC (Base Case) and CHC (carbon
caps) scenarios and comparable scenarios reported by the PEW study (DADI, 2000).
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2.
Comparisons Between EEM and ESS Results
Comparisons between MARKAL, CRETM, ESS and the ETM models have been made; these
comparisons have been conducted only at the most aggregated level and only for Shandong
province. Figure 69 makes this comparison of CRETM and the results of the other models on
the basis of: a) specific SO2 emissions versus undiscounted unit costs per kWh; b) total CO2
specific emissions versus undiscounted unit electrical-energy costs (per-kWeh); and c) the
specific (per-kWeh) SO2 and CO2 emissions. The CRETM optimized results correspond to
the eight scenarios listed in Table XV and the parametric variations listed in Table XII; these
comparisons are plotted in Figs. 57-60. The ESS results reflect (un-optimized) scenarios, as
described in Appendix B. The ETM and MARKAL results will be described in the final
document of the CETP project (Eliason, 2002).
Comparison Cost/SO2
6
5
4
c/kWh
ESS
CRETM
3
MARKAL
ETM
2
1
0
0
2
4
6
8
10
12
14
g SO2/kWh
Figure 69A. Comparison of CRETM, MARKAL, ETM and ESS results based on
specific SO2 emissions versus unit cost of electricity for Shandong Province.
118
Comparison Cost/CO2
6
5
c/kWh
4
ESS
CRETM
3
MARKAL
ETM
2
1
0
0
200
400
600
800
1000
1200
g CO2/kWh
Figure 69B. Comparison of CRETM, MARKAL, ETM and ESS results based on
specific CO2 emissions versus unit cost of electricity for Shandong Province.
Comparison SO2/CO2
14
12
g SO2/kWh
10
ESS
8
CRETM
MARKAL
6
ETM
4
2
0
0
200
400
600
800
1000
1200
g CO2/kWh
Figure 69C. Comparison of CRETM, MARKAL, ETM and ESS results based on
specific CO2 and CO2 emissions of Shandong Province.
As expected, differences are noted in the constellation of scenario points that summarize
EEM, ESS and ETM modeling efforts. These differences can be explained when the different
119
assumptions made for the technology costs, efficiencies, discount rates, and fuel prices are
taken into account.
The CRETM results shown on Fig. 69 include costs and emissions computed under a variety
of environmental policies, pricing, and discount-rates assumptions, as discussed in Secs. III,
IV, V.A, and V.B. On the contrary, when similar assumptions are used in these other
modeling efforts (MARKAL-Sh, ETM, ESS), results are similar. Thus, the 4%/yr discount
rate case of CRETM compares well with the MARKAL cases (RATE = 5%/yr in that case).
The ETM cases, while the RATE = 1%/yr case is the cheapest and the RATE = 15%/yr is the
most expensive of all cases studied.
In case of the MARKAL results, two groups of costs-versus- emissions results are seen; the
lower group that assumes constant fuel prices, and the higher group that is generated on the
basis of escalating fuel prices. The simulation model used to generate the ESS results uses
slightly declining fuel prices. These ESS cost-versus-emissions results included also demandside management (DSM) options that have a minor influence on the average electricitygeneration cost, but can result in significant emissions reductions. The BAU scenarios
reported from the optimizing MARKAL modeling effort have costs that are comparable to
those used in the in ESS simulation model. Only at lower emissions, cost estimations of
MARKAL are significantly higher than costs reported from the ESS study because the
alternative technologies needed to achieve these lower emissions are significantly cheaper in
the case of the ESS model. Finally, the ESS effort reports runs with significantly lower CO2
emissions than the best reported from the MARKAL model, and this difference is a result
mainly of DSM options in the ESS model that reduce demands. Other cost differences, apart
from the above-mentioned differences in fuel-cost assumptions and discount rates, are due to
the imperfectly harmonized technology data of the two models. Specifically,
Ø ESS has technical lifetimes for all (new) plants of 40 years versus 30 years in both
MARKAL and CRETM.
Ø ESS assumes higher efficiencies for its electricity-generation plants; for coal-fired plants
the average effect is in the order of 5% (e.g., 31.5% versus 30%), corresponding to
roughly 0.5 mills/kWeh. For gas-fired plants the effect is more dramatic; at a gas price of
3 $/GJ this difference in efficiencies translates into a difference of nearly 3 mills/kWeh in
fuel cost for advanced combined-cycle (CC) plants.
These points are sufficient to explain the difference in the graphs between MARKAL,
CRETM, and ESS models.
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VI. Findings, Conclusions, and Recommendations
A. Findings
Ø Increased Power Demand: The demand for power in China will increase four-fold by
2030. This demand will reach, and maybe go above, the 4,000 TWeh level by 2030,
which represents a four-fold increase from 1995 levels of consumption. Similar levels
of growth are expected in Shandong Province.
Ø Pollution Control: According to the “December 2000” report by U.S. Embassy in
Beijing, annual pollution costs the Chinese economy varies anywhere from 3-8% of
GDP, based on estimates of various Chinese and Western scholars (Xu Songling,
1998, Vaclav Smil and Mao Yushi, 1998, World Bank, 1997). Air pollution and acid
rain causes damages to human health and buildings and acidification of soil and lakes.
Ecological damage is estimated to cost potentially another 5–14% of the annual GDP.
Even at the lower bounds of these estimates, environmental damage roughly cancels
annual economic growth. Initiating policies with region-specific emission caps and/or
sulfur-emission permit system across regions and sectors is the most efficient way to
control future emissions in China and Shandong Province.
Ø Clean Coal Technologies: Promising solutions for efficient power generation in China
and control of sulfur emissions are identified using CRETM. The model identifies the
need to start reducing local emissions by improving the performance of pulverized
coal systems and by introducing coal washing and/or sulfur scrubbing, while
continuing with the adoption of advanced coal systems like IGCC power plants and
eventually supercritical steam coal.
Ø IGCC is Attractive When Manufactured in China: Advanced coal-fired systems can be
competitive with conventional Chinese coal technology, when these advance
technologies manufactured in China or under conditions of high coal prices and
transportation cost. This development will help to improve efficiency and it will result
to enhanced clean coal technology use.
Ø Fuel and Technology Diversification: To improve the energy-supplying system of
China by either diversifying supply options or by establishing a rich portfolio of
technologies is of primary importance. Oil and gas supply options must be improved to
reduce the dependency on coal, especially in the coastal provinces. The most
promising substitutes for coal are advanced gas combined cycle systems followed by
the appropriate contribution of hydropower, nuclear energy, and renewable energy
sources based on wind and small hydroelectric units.
Ø Competitive Nuclear Power: Nuclear power can be competitive in a scenario where
reactors are fabricated in China, the construction time is below 5 years, and capital
costs are at or below 1,500 $/kWe for the high discount rates assumed (RATE =
10%/yr). Nuclear energy is competitive at discount rates of RATE ~ 5%/yr or at higher
discount rates when regional and global externalities are addressed at even moderate
tax levels. The importance of nuclear energy becomes apparent only when coal
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becomes more expensive, as when placed under a carbon control policy. Without a
significant share of nuclear energy, the marginal costs of carbon control in China move
to extreme and almost forbidden cost ranges, weakening the hope China participating
to international carbon-emissions-control protocols.
B. Conclusions
Results from studies using the CRETM (seven-region optimisation model of China electric
sector, including Shandong Province) are based on the set of techno-economic data, and price
assumptions, fossil-fuels resource availability and imports, as well as projections of the
technical potential and performance of renewable energy technology and use. Recognizing the
uncertainties that characterize these data and, therefore, the associated results (e.g., optimal
energy mixes, fuel compositions, emission rates, costs, etc.), general and hopefully useful
remarks and conclusions can be formulated. These conclusions are listed as follows:
Ø Coal as a Primary Fuel: China will rely on coal for electricity production, independent
of environmental policies
Ø Advanced Generation and Emissions: Chinese RD&D on advanced generation
technology, along with integrated foreign investments, can improve energy efficiency
and, through reduced emissions, the environment.
Ø Coupled Sulfur and Carbon Emissions: Pollution related to SO2 emissions can be
reduced for moderate investments by introducing scrubbers and/or advanced-coal
technology; some reductions in CO2 automatically accompany these SO2 emission
reductions.
Ø Significant Investment to Reduce Carbon Emissions: Significant carbon-emission
reduction needs significant investments in reduced- or carbon-free generation
technologies; this policy improves local environments through reduces SO2 emissions
(secondary benefits).
Ø Added Cost of Sustainable Energy Less Than Cost of Pollution: The cumulative
discounted power-production cost for a more sustainable path for electricity generation
in China increases by 2-6 %/yr over the 2000-2030 time frame for the high discount
rates (RATE = 10%/yr) investigated, but these added costs needed to reduce emissions
remain below the damage cost attributed to pollution in China.
Ø Wide Differences in Energy Costs Across Scenarios: Differences in non-discounted
cost that appear across the range of scenarios examined are high, reaching levels of
around 20-30% at the end of time horizon (2030) examined.
Ø Electricity Transmission Across Regions Makes Economic and Environmental Sense:
Transmitting electricity between regions within China in general terms presents an
economic option, according to the CRETM analyses, while inter-regional transmitting
also reduces local pollution.
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C. Recommendations
1. Policy
Suggestions for important directions for formulating long-term energy policy for China:
Ø Improve performance of clean coal technology;
Ø Introduce scrubbers and IGCC systems;
Ø Diversify supply by opting for gas, nuclear energy, wind, and small hydroelectric;
Ø Continue reforms to make greater use of market forces, especially in the gas and
electricity sector;
Ø Participate to Kyoto type Protocols with commitments for carbon emissions reduction
(only when China attains sufficient economic development) to facilitate technology
transfer, international R&DD cooperation and CDM projects.
2. Model Advancement
On the basis of the CRETM development and evaluations experience so far accumulated as
part of CETP, important limitations and areas of improvement have surfaced. In addition to
crucial interface areas between EEM, ESS, LCA, and MCDA modeling activities, important
issues and areas of integration within the EEM task itself have emerged. Finally, within the
CRETM itself, a number of developments and enhancements were identified as important
future work. It is emphasized that the majority of effort needed to accomplish these tasks and
goals cannot be accomplished within the temporal and funding limitations that presently
define the CETP. These limitations, nevertheless, do not preclude listing of work remaining to
be completed, even if that work may not be made under the present CETP. Such a list is
given below:
Ø Fuel Transportation: The model presently forces flows of material based on modal
flow-variables that consider the official Chinese statistics and the assumption that
changes occur smoothly over time; capacities variables per se and load factors are not
modeled. In any case, since it is not possible to model demand and supply for all
products transported in China, constraints on the maximum load imposed by the
transportation of fuels are legitimate.
Ø Electricity Transmission: The CRETM transports energy by wire across regions under
the assumption of average pricing and losses per unit of km and smooth capacity
growth.
Ø Technology Diffusion: Advanced "learning-by-doing" R&D models need to be
integrated into the determination of technology costs. While some progress has been
made in this important area, the model used to achieve this goal requires better
collaboration with Chinese institutions.
123
Ø Partial Equilibrium: Advanced partial economic equilibrium models are applied to
endogenize electricity demand as a function of price feedback under conditions of an
evolving electric generation energy mix; price elasticities for the Chinese conditions
must be elaborated in cooperation with Chinese Institutes.
Ø Nuclear Energy: The fidelity of the models currently used needs to be enhanced,
particularly as related in a China context to the dependence of fuel price on resource
depletion, the flows of environmentally and proliferation-risky materials for a given
set of nuclear-fuel-cycle (NFC) assumptions, the introduction of newer technologies to
deal with (perceived or real) proliferation, waste, and/or safety issues so that these are
reflected more accurately in all cost components (e.g., capital, variable, fixed,
decommissioning, transmutation, waste disposal, etc.) (Trellue, 2000; Brogli, 2001);
the growth-rate constraints now enforced (8%/yr, or ~50% increase each five-year
period) needs to be reconciled with both technical and financial limits; the capability
to include advanced NFC modeling methods and databases exist, but the resources
within the present CETP are not sufficient.
Ø External Costs: Environmental externalities estimated in the CETP project should be
explicitly included in the objective function used in CRETM. For that reason
particulate material and NOX that are not yet treated, at either source or at consequence
levels, must be included. Formulating then, the damage cost as a non-linear function of
emission level can help to integrate the results of Environmental Impact Assessment
group in the analyses with CRETM.
Ø Other Renewable Energy Sources: The issues and constraints of land availability/
competition/"footprint", and the distribution of energy and peaking/capacity/storage
issues need better modeling methods and more realistic assumptions; MARKAL-style
balances would be useful here.
Ø Hydroelectric: The cost and licensing issues related to big versus small hydroelectric
generation need to be addressed, particularly with respect to evolving regional and
cultural differences [e.g., in the face of environmental, economic (longer construction
periods), and adverse public responses, the trend in South America is towards the
construction of smaller hydroelectric installations, versus China, is pursuing a largedam policy that to a great extent is permitted by an economy that until recently has
been less affected by global trends and a culture where protestations of impacted
populations is not as strong.
The CRETM offers the advantages of a fast-running, relatively transparent E3 optimization
model that describes a regionalized China. Its main limitation is the narrowness of the energy
sector being examined - only electricity production and consumption. Nevertheless, within
this limitation, Province-specific analyses can be conducted and expressed in a countrywide
context. With the improvements suggested above, the CRETM can be applied with great
policy and planning utility to a range of China Provinces, once the experience of the
Shandong study has been gathered, integrated, and advertised. Furthermore, through the
application of user-friendly graphical-user interfaces (GUI), the CRETM can be put at the
disposal of policymakers through a PC/laptop platform. In this way, guidance and policy
analyses can be provided directly to Provincial Stakeholders in a countrywide context using a
flexible and standardized modeling tool. While many of the tasks listed above are crucial to a
124
more profound application of these model results to the generation of useful energy policy,
most of these items fall outside the budget presently allocated to the EEM task.
125
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128
Nomenclature
A
AEEI
AFBC
All Advanced generation technologies
autonomous energy efficiency improvement
atmospheric fluidized-bed combustion generation technology;
Table VI
AFR
Sub-Saharan Africa
B, BAU
business as usual
C
scenario attribute related to caps, constant prices, or carbon control, also
subscript designator for energy cost.
CC
Combined Cycle
CAPUTIL(ss)
full-power hours per year for generation technology ss
CC(k,k1)
unit cost of chemically clean coal of type k to produce coal of
type k1, $/tonne
CCT
Clean Coal Technology
CDM
Clean Development Mechanism
COSTCAP(ss)
capital cost of generation technology, $/kWe
COSTDD
cost of Decontamination and Decommissioning, $/kWe
COSTFIX(ss)
fixed cost for operating generation technology ss, annual fraction of
capital cost expenditure
COSTVAR(ss)
variable cost associated with operating generating technology ss,
M$/TWeh
CO2EF(tot)
specific CO2 emission rate for fuel type tot(c), tonneCO2/tonne
CO2TARGET(t)
CO2 emission target, MtonneCO2/yr
CPA
Centrally Planned Asia and China
CQ(i,k,k1,t)
rate of (coal) fuel of type k passing through chemical cleanup
treatment to produce (coal) fuel of type k1 in region i at time t,
Mtonne/yr
CRETM
China Regional Electricity Trade Model
CRF(RATE,TLIFE) capital recovery factor, 1/yr; Eq.(B-H''')
CTAX(i,t)
carbon tax, $/tonneCO2
CWM
Coal-Water Mixture technologies
c
fuel + electricity commodity index; Table V.
D
subscript designator for energy demand, DMD.
D&D
decontamination and decommissioning.
DC
designator for D + C
DELEC(i,t)
annual electrical energy demand in region i at time t
DISCPP
inter-period discounting factor
DMD(TWeh.yr)
electric energy demand.
DOMGAS
domestic gas; Table VI
DOMOIL
domestic oil; Table VI
DOMUR
domestic uranium; Table VI
DOMESP
domestic, large-sized coal-fired generation technologies fitted
with electrostatic precipitators, Table VI
DOMLAR
domestic, large-sized coal-fired generation technologies,
Table VI
DOMMED
domestic, medium-sized coal-fired generation technologies,
Table VI
DOMSCB
domestic, large-sized coal-fired generation technologies fitted
with electrostatic precipitators plus (sulfur) scrubbers, Table VI
DOMSML
domestic, small-sized coal-fired generation technologies, Table VI.
129
DR(1/yr)
E, EMI
E3
EA
EEU
EFF(s,c)
ID
IDC
IGCC
discount rate, same as RATE.
Environmental (C + S)
energy, economics, environment
Eastern China region; Fig. 1
Eastern Europe
efficiency of generation technology s in converting fuel commodity
c to electricity
Energy-Economics Model (Optimization Model, either single or multiregion, either full energy sector or electric-energy sector.
Electric Sector Simulation
electricity commodity, Table V
total discounted energy cost (value of objective function).
efficiency of intra-regional supply of demand DELEC(i,t)
fuel equivalent for all (GT, HYDRO, PV, WIND) renewable
energies; Table V
unit cost of imported fuel of type f (non-coal) transported by mode
m, $/tonne(coal, oil), $/m3(gas)
(coal) fuel of (type k in region i at time t after chemical or physical
cleaning (pre-combustion treatment), Mtonne/yr
maximum fuel capacity for cleaning, Mtonne/yr
Former Soviet Union
maximum fuel capacity to be utilized, Mtonne/yr
fuel commodity index; Table V
combined-cycle gas-fired generation technology; Table VI
advanced combined-cycle gas-fired generation technology;
Table VI
Gross National Product
greenhouse gas
subscript designator for GDP
geothermal generation technology; Table V
Guangdong China region; Fig. 1
Graphical User Interface
scenario attribute designator related to hing demand or discount rate
high-sulfur, high-ash coal; Table V
high-sulfur, low-ash coal, Table V
heavy metal (uranium fuel)
heating value of fuel tot(c), GJ/tonne (actually kcal/kg units are
used in the computer model, replacing the conversion coefficient
value of 3.6 with 860 in Eq. (B-9)
Harvard University
hydroelectric generation technology; Table VI
capital investment cost for technology times capital recovery,
annualized cost brought to the year t including interest during
construction factor, CRF(RATE,TLIFE), for technology ss in
region i at time t, M$/yr; Eq.(B-10H'')
Identifier
interest during construction
integrated (coal) gasification combined cycle generation
IIASA
International Institute for Applied Systems Analysis
EEM
ESS
ELEC
ENC(M$)
ETATT(i,t)
FEQU
FIMP(m,t)
FQ(i,k,t)
FQHIGH(t,i,k)
FSU
FUELHIGH(t,i,tot)
f(c)
GASCC
GASCCA
GDP
GHG
G
GT
GU
GUI
H
HHCOAL
HLCOAL
HM
HSTD(tot)
HU
HYDRO
ICOST(i,ss,t)
130
IMPGAS
IMPOIL
IMPUR
INV(i,ss,t)
i, i1
k(c), k1(c)
L
LAM
LEU
LLCOAL
LHCOAL
LP
M
MCO2(MtonneCO2)
MARKAL
MAENC(t,ss)
MAXGR(ss)
MEA
MHCOAL
MLCOAL
m
N
NA
NE
NO
NOXEF(ss,tot)
NOXTARGET(t)
NPV
NQ(i,k,k1,t)
NTAX(i,t)
NW
NUCLEAR
O
OECD
OILCC
OILREG
P
PAO
PAS
pc
PC(k,k1)
PHASOUT(t)
PNNL
technology, Table VI
imported gas; Table V
imported oil; Table V
imported uranium
investment per period in new technology of type ss in region i at
time t, GWe/period;
region index; Fig. 1; also general idex
index for coal fuel of type k; Table V
scenarios designator related to low demand or discount rate
Latin America and the Caribbean
low-enriched uranium
low-sulfur, low-ash coal; Table V
low-sulfur, high-ash coal; Table V
linear program
scenario designator related to medium demand or discount rate
integrated CO2 emissions over model time period (1995-2030)
Market Allocation model
technology limits as percent of total annual generation
maximum growth rate of certain technologies, 1/yr
Middle East and North Africa
medium-sulfur, high-ash coal; Table V
medium-sulfur, low-ash coal; Table V
transport mode (rail, road, ship, pipeline, wire)
scenario attribute designator related to no emissions tax or caps
North America
northeast China region; Fig. 1
north (central) China region; Fig. 1
specific NOX emission rate, tonneNOX/tonne
NOX emission target, MtonneNOX/yr
Net Present Value
rate of (coal) fuel of type k not passing through any cleanup
treatment to produce (coal) fuel of type k1 =k in region i at time t,
Mtonne/yr
NOX tax, $/tonneNOX
northwest China region; Fig. 1
nuclear-fueled generation technology; Table VI
scenario attribute designator for "ordinary" or BAU conditions
Organization for Economic Cooperation and Development
combined-cycle oil-fired generation technology; Table VI
regular/standard oil-fired generation technology; Table VI
scenario designator related to solar PV advanced generation technology
Pacific OECD
Other Pacific Asia
personal computer
cost of physically cleaning coal of type k to produce coal of
type k1, $/tonne
phase-out rate of "bad" technology as fraction of 1995 capacity
Pacific Northwest National Laboratory
131
PQ(i,k,k1,t)
PRCT(t)
PFB
PSI
PV
QCAP(i,ss,t)
QF(i,c,s,t)
QSELEC(i,ss,t)
QTR(i,i1,c,m,t)
QIMP(i,f,m,t)
QS(i,c,t)
RATE
RCW(k,k1)
R, REN
RES
RENEW(t,i,s1)
S
SA
SAS
SC
SEFF(ss)
SO2EMISS(i,t)
SO2TARGET(t)
STAX(i,t)
SUM
SW
SDEP(i,t)
s(ss)
sc(ss)
sg(ss)
soil(ss)
ss
s1(ss)
s2(ss)
s3(ss)
s4(ss)
s5(ss)
s6(ss)
rate of (coal) fuel of type k passing through physical cleanup
treatment to produce (coal) fuel of type k1 in region i at time t,
Mtonne/yr
percent coal cleaning of total coal annual flow
Pressurized Fluidized Bed coal combustion
Paul Scherrer Institut
present value, referred to the year 1995; or solar photovoltaic solar
generation technology; Table VI
electric generation capacity of technology ss installed in region i at
time t, GWe/yr: Eq.(B-8)
Fuel commodity c used by generation technology s in region i at
time t, Mtonne/yr; Eq.(B-3,4)
annual electricity supplied to region iby technology ss
at time t, TWeh/yr; Eq. (B-5)
rate of transport of commodity c from region i to region i1 by
transport mode m at time t, Mtonne/yr; Eq.(B-5)
rate of import of fuel f(c) into region i by mode m at time t,
Mtonne/yr; Eq.(B-4)
rate of supply of commodity (fuel) c to region i at time t, not
including electricity, Mtonne/yr; Eq.(B-1)
discount rate, 1/yr
ratio of output to input coal mass associated with all (chemical or
physical cleaning process that delivers coal of quality k to quality k1
Renewable
Reference Energy System
capacity limits on renewable energy generation technologies, GWe
Sulfur (SO2 caps, taxes)
Shandong China region; Fig. 1
South Asia
South-central China region; Fig. 1
specific SO2 emission rate, tonneSO2/tonne
regional SO2 emission limits, MtonneSO2/yr
SO2 emission target, MtonneSO2/yr
SO2 tax, $/tonneSO2
general summation
Southwest China region; Fig. 1
sulfur deposition rate in region i at time t, MtonneSO2/yr
set of all thermal generation technologies; Table V
set of all coal-fueled generation technologies; Table V
set of all gas-fueled generation technologies; Table V
set of all oil-fueled generation technologies; Table V
set of all generation technology; Table V
set of all renewable generation technologies; Table ?V
set of all older domestic coal-fuel generation technologies;
Table V
set of all nuclear generation technologies; Table V
set of all hydroelectric generation technologies; Table V
set of all fossil-fueled generation technologies; Table V
set of all "clean" (fossil-fueled + nuclear + renewable) generation
technologies; Table V
132
T
t
TCH
TCONST(ss,t)
TLIFE(ss,t)
TMAX
TOT(c)
TR(m,i1,i)
TRM(c,m)
TRMI(c,m)
TCW(c,m)
UCTRP(c,m,i,i1,t)
WEC
WEU
WIND
x, x'(1/yr)
YEARPP
ZIMP(i,t)
ZDOM(i,t)
ZWAS(i,t)
ZTRP(i,t)
ZTAX(i,t)
ZVAR(i,t)
ZFIX(i,t)
ZCAP(i,t)
ZZ(t)
ZZTT(B$)
λ i (1/yr)
scenario attribute designator describing application of emissions tax
time (period) index
Technologies
construction time for technology ss at time t, yr
capitalization or economic life of technology ss at time t, yr
cardinal of {t}
set if all thermal fuels, Table V
indication of a transportation link between region i and i1 mode m
measure of ability to transport fuel commodity c via mode m
measure of ability to import and transport fuel commodity c via
mode m [restricted to fuel commodities in set f(c), coal is not
imported]
efficiency (non-loss fraction) in transporting fuel commodity c via
transport mode m
unit cost of transportation of fuel commodity c from region I to
region i1, $/tonne/km
World Energy Council
Western Europe
wind generation technology, Table VI
general discount, escalation, or inflation rates
years per period
annual charges for imported (non-coal) fuels, M$/yr
annual charges for all domestic fuels, M$%.yr
annual charges for washing all coal fuels, M$/yr
annual charges for transporting all fuels, M$/yr
annual charges for emissions (SO2, CO2, NOX), M$/yr
annual variable operating charges, M$/yr
annual fixed operating charges, M$/yr
annual capital cost charges, M$/yr
objective function, total present value (1995) of all costs associated
with the total China electrical supply, M$; (Eq. (B-9,10).
total undiscounted costs associated with the total China electrical
supply.
exponential rate for i = D, C, or G.
KEY ENERGY CONVERSIONS FACTORS
Joule
1 Ws; 1018 J/EJ; 1015 J/PJ; 109 J/GJ; 106 J/MJ
tce
tonne of coal equivalent = 29.3 GJ
toe
tonne of oil equivalent = 42 GJ
bbl(oil)
barrel of oil = 6.11 GJ
Mm3(gas)
million m3 natural gas = 0.036PJ
133
Appendix A. Summary of Key Data (Base Case BHC) Used by China Regional
Energy Model (CRETM)
Summarized in this Appendix are key data drivers of CRETM. These data pertain primarily
to the Base Case (BHC, Table III) conditions. Many of these data were derived from other
sources and are subject to modification as refinements, new insights, and new directions
emerge. The primary intent here is to give a firm and transparent basis for the preliminary
parametric analyses presented in this complete, but nonetheless interim, report on a sevenregion, 30-year time horizon model of the countrywide Chinese electrical energy sector, with a
focus on Shandong Province.
Table 1. Time Periods Modeled by CRETM.
Time Periods in Planning-Horizon Variable,
t (1995-2030)
1995
2000
2005
2010
2015
2020
2025
2030
Table 2. Seven Regions Modeled by CRETM.
Coal-Producing Nodes at a Provincial Level
Region, i
NO
NE
EA
SA
SC
SW
NW
Description
NOrth
NorthEast
EAst
ShAndong
SouthCentral
SouthWest
NorthWest
Table 3. Vital Statistics for Seven Regions Modeled by CRETM.
STAT(i,param); Vital Statistics for China
Population, POPN(i)/1000 People,
2
AREA(i)/1000km ,
2
Population DENSITY(i)/km
Region, i
POPN
AREA
NO
139910
1572.2
NE
103850
757.2
EA
268180
638.5
SA
80000
153
SC
333990
1007
SW
190630
2317.8
NW
86070
3140.3
134
DENSITY
88.99
172.81
439.9
552.86
331.67
82.25
27.41
Table 4. Elasticities of Demand and Price.
Demand Elasticity of Income, ELAGDP
0.65
Demand Elasticity of Price
Year
1995
2000
2005
2010
2015
2020
2025
2030
ELAPRICE(t)
0
0
0
0
0.1
0.1
0.2
0.2
Table 5. 1995 Electricity Demand by Region.
Electricity Demand by Region in Period 1995
Region (i)
QELEC95(i)
TWeh/yr
NO
NE
EA
SA
SC
SW
NW
Total
165.8
121.3
225.3
74
240.3
127.8
52.6
1007.1
Table 6A. Lower Bound on Forced Penetration of Technologies enc.
MAENCLO(t,enc); Forced Penetration Fraction via Lower
Bounds on Technologies
Time, t
GAS
NUCLEAR
OIL
1995
0.01
0.01
0.01
2000
0.01
0.01
0.01
2005
0.01
0.01
0.01
2010
0.01
0.01
0.01
2015
0.01
0.01
0.01
2020
0.01
0.015
0.015
2025
0.01
0.015
0.015
2030
0.01
0.015
0.015
135
COAL
0
0
0
0
0
0
0
0
HYDRO
0
0
0
0
0
0.1
0.12
0.12
Table 6B. Upper Bound on Forced Penetration of Technologies enc.
MAENCUP(t,enc); Forced Penetration Fraction as Upper Bounds
on Technologies
Time, t
COAL
HYDRO
1995
0.8
0.2
2000
0.8
0.2
2005
0.8
0.22
2010
0.8
0.23
2015
0.8
0.25
2020
0.85
0.25
2025
0.85
0.25
2030
0.85
0.25
Table 7. Assumed annual marginal electricity Price Growth by Region and Time.
For the partial equilibrium version of the models this is an endogenous parameter.
Annual Price Growth Rate, GPRICE(t,i)
Time, t
NO
NE
EA
1995
0.01
0.01
0.01
2000
0.01
0.01
0.01
2005
0.01
0.01
0.01
2010
0.01
0.01
0.01
2015
0.01
0.01
0.01
2020
0.01
0.01
0.01
2025
0.01
0.01
0.01
2030
0.01
0.01
0.01
SA
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
136
SC
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
SW
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
NW
0
0
0
0
0
0
0
0
Table 8. Annual Average Economic Growth Rates per Scenario.
Annual Average Economic Growth
Rate, GGDP(t,i), 1/yr
Demand Level
TIME, t
NO
Low-Low-Low, LLL
1995
0.1
2000
0.055
2005
0.04
2010
0.035
2015
0.03
2020
0.025
2025
0.02
2030
0.015
Low-Low, LL
1995
0.1
2000
0.06
2005
0.05
2010
0.04
2015
0.04
2020
0.03
2025
0.03
2030
0.035
Low, L
1995
0.1
2000
0.075
2005
0.06
2010
0.06
2015
0.04
2020
0.04
2030
0.04
High, H
1995
0.1
2000
0.09
2005
0.08
2010
0.07
2015
0.06
2020
0.05
2025
0.045
2030
0.04
High-High, HH
1995
0.1
2000
0.1
2005
0.1
2010
0.095
2015
0.09
2020
0.08
2025
0.07
2030
0.06
NE
0.1
0.055
0.04
0.035
0.03
0.025
0.02
0.015
0.1
0.06
0.05
0.04
0.04
0.03
0.03
0.035
0.1
0.075
0.06
0.06
0.04
0.04
0.04
0.1
0.09
0.08
0.07
0.06
0.05
0.045
0.04
0.1
0.1
0.1
0.095
0.09
0.08
0.07
0.06
137
EA
0.1
0.055
0.04
0.035
0.03
0.025
0.02
0.015
0.1
0.06
0.05
0.04
0.04
0.03
0.03
0.035
0.1
0.075
0.06
0.06
0.04
0.04
0.04
0.1
0.09
0.08
0.07
0.06
0.05
0.045
0.04
0.1
0.1
0.1
0.095
0.09
0.08
0.07
0.06
SA
0.1
0.055
0.04
0.035
0.03
0.025
0.02
0.015
0.1
0.06
0.05
0.04
0.04
0.03
0.03
0.035
0.11
0.075
0.06
0.06
0.04
0.04
0.04
0.1
0.09
0.08
0.07
0.06
0.05
0.045
0.04
0.1
0.1
0.1
0.095
0.09
0.08
0.07
0.06
SC
0.1
0.055
0.04
0.035
0.03
0.025
0.02
0.015
0.1
0.06
0.05
0.04
0.04
0.03
0.03
0.035
0.1
0.075
0.06
0.06
0.04
0.04
0.04
0.1
0.09
0.08
0.07
0.06
0.05
0.045
0.04
0.1
0.1
0.1
0.095
0.09
0.08
0.07
0.06
SW
0.1
0.055
0.04
0.035
0.03
0.025
0.02
0.015
0.1
0.06
0.05
0.04
0.04
0.03
0.03
0.035
0.1
0.075
0.06
0.06
0.04
0.04
0.04
0.1
0.09
0.08
0.07
0.06
0.05
0.045
0.04
0.1
0.1
0.1
0.095
0.09
0.08
0.07
0.06
NW
0.1
0.055
0.04
0.035
0.03
0.025
0.02
0.015
0.1
0.06
0.05
0.04
0.04
0.03
0.03
0.035
0.1
0.075
0.06
0.06
0.04
0.04
0.04
0.1
0.09
0.08
0.07
0.06
0.05
0.045
0.04
0.1
0.1
0.1
0.09
0.09
0.08
0.07
0.06
Table 9A. Emissions Constraints for CO2.
In the BaU cases bounds are high and inactive.
CO2TARET(t); Target for CO2 Emissions
Time, t
CO2TARGET(t)
(MtonneCO2/yr)
1995
2000
2005
2010
2015
2020
2025
2030
1000
1300
1500
8000
8000
8000
8000
10000
Table 9B. Emissions Constraints for SO2.
In the BaU cases bounds are high and inactive.
SO2TARGET(t); Target for SO2 Emissions
Time, t
SO2TARGET(t)
(MtonneSO2/yr)
1995
2000
2005
2010
2015
2020
2025
2030
100
100
100
100
100
100
100
100
Table 9C. Emissions Constraints for NOX.
In the BaU cases bounds are high and inactive.
NOXTARGET(t); Target for NOX Emissions
Time, t
NOXTARGET(t)
MtonneNOX/yr)
1995
2000
2005
2010
2015
2020
2025
100
100
100
100
100
100
100
2030
100
138
Table 10. SO2 Emission Constraints by Region and Time.
In the BAU cases bounds are high and inactive.
SO2EMISS(i,t); Limit to SO2 Emissions,
MtonneSO2/yr
Region, I
1995
2000
2005
NO
100
100
100
NE
100
100
100
EA
100
100
100
SA
100
100
100
SC
100
100
100
SW
100
100
100
NW
100
100
100
2010
100
100
100
100
100
100
2015
100
100
100
100
100
100
2020
100
100
100
100
100
100
2025
100
100
100
100
100
100
2030
100
100
100
100
100
100
100
100
100
100
100
Table 11. Emission Targets for CO2, SO2, and NOX.
In the BaU cases bounds are high and inactive.
Time, t
CO2TARGET(t)
(MtonneCO2/yr)
SO2TARGET(t)
(MtonneSO2/yr)
NOXTARGET(t)
(MtonneNOX/yr)
1995
2000
2005
2010
2015
2020
1000
1300
1500
8000
8000
8000
100
100
100
100
100
100
100
100
100
100
100
100
2025
8000
100
100
2030
10000
100
100
Table 12A. Carbon Tax Rate per Region and Time.
CTAX(I,t), $/tonneCO2, Carbon Tax per Region
and Time
Region, I /Time, t
1995
2000
2005
NO
0
0
0
NE
0
0
0
EA
0
0
0
SA
0
0
0
SC
0
0
0
SW
0
0
0
NW
0
0
0
2010
0
0
0
0
0
0
0
139
2015
0
0
0
0
0
0
0
2020
0
0
0
0
0
0
0
2025
0
0
0
0
0
0
0
2030
0
0
0
0
0
0
0
Table 12B. Sulfur Tax Rate per Region and Time.
STAX(I,t), $/tonneSO2, Sulfur Tax per Region
and Time
Region, I /Time, t
1995
2000
2005
NO
0
0
0
NE
0
0
0
EA
0
0
0
SA
0
0
0
SC
0
0
0
SW
0
0
0
NW
0
0
0
2010
0
0
0
0
0
0
0
2015
0
0
0
0
0
0
0
2020
0
0
0
0
0
0
0
2025
0
0
0
0
0
0
0
2030
0
0
0
0
0
0
0
2015
0
0
0
0
0
0
0
2020
0
0
0
0
0
0
0
2025
0
0
0
0
0
0
0
2030
0
0
0
0
0
0
0
Table 12C. Nitrogen Tax Rate per Region and Time.
NTAX(i,t), $/tonneNOX Nitrogen Tax per
Region and Time
Region, I /Time, t
1995
2000
2005
NO
0
0
0
NE
0
0
0
EA
0
0
0
SA
0
0
0
SC
0
0
0
SW
0
0
0
NW
0
0
0
2010
0
0
0
0
0
0
0
Table 13. Transportation Capabilities by Fuel and Model.
MODETRPT(c,m), Capability to Transport Each Fuel
Fuel, c / mode, m
ROAD
RAIL
SHIP
HHCOAL
1
1
1
HLCOAL
1
1
1
MHCOAL
1
1
1
MLCOAL
1
1
1
LHCOAL
1
1
1
LLCOAL
1
1
1
OIL
1
1
1
GAS
OILimpt
1
GASimpt
LGNimpt
1
UR
1
1
1
URimpt
1
1
1
ELEC
140
PIPE
WIRE
1
1
1
1
1
Table 14. Import Transportation Mode by Region.
MODEIMPR(f,i,m) Imports of Fuel by Region
and Mode
Mode, m
ROAD
RAIL
SHIP
Fuel, c / Region, i
OILimpt
NO
1
NE
1
EA
1
SA
1
SC
1
SW
NW
GASimpt
NO
1
NE
1
EA
1
SA
1
SC
1
SW
NW
URimpt
NO
1
1
NE
1
1
EA
1
1
SA
1
1
SC
1
1
SW
1
NW
1
PIPE
WIRE
1
1
1
1
Table 15. Cost of Fuel Imports.
FIMP(m,f) Cost of Fuel Imported by
Mode,
$/tonne(coal,oil),
3
$/1000m (gas)
Mode, m / Fuel, f OILimpt GASimpt
PIPE
137
125
SHIP
132
120
RAIL
Urimpt*
0
0
* Cost of Uranium is included in the fuel cost of nuclear Energy
141
Table 16. Transportation Links.
TRANSPORT(m,i,i1) , Transport Link Between Nodes
[from i to i1 exports, from i1 to i
imports]
Mode, m / Region, i
RAIL
NO
NE
EA
SA
SC
NO
0
1
1
1
1
NE
1
0
1
1
1
EA
1
1
0
1
1
SA
1
1
1
0
1
SC
1
1
1
1
0
SW
1
1
1
1
1
NW
1
1
1
1
1
SHIP
NO
NE
EA
SA
SC
NO
0
1
1
1
1
NE
0
0
1
1
1
EA
0
0
0
1
1
SA
0
0
0
0
1
SC
0
0
1
1
0
SW
0
0
1
1
0
NW
0
0
0
0
0
PIPE
NO
NE
EA
SA
SC
NO
0
1
1
1
1
NE
0
0
1
1
1
EA
0
0
0
1
1
SA
0
0
0
0
1
SC
0
0
1
1
0
SW
0
0
1
1
1
NW
0
1
1
1
1
WIRE
NO
NE
EA
SA
SC
NO
0
1
1
1
1
NE
1
0
1
1
1
EA
1
1
0
1
1
SA
1
1
1
0
1
SC
1
1
1
1
0
SW
1
1
1
1
1
NW
1
1
1
1
1
ROAD
NO
NE
EA
SA
SC
NO
0
1
1
1
1
NE
1
0
1
1
1
EA
1
1
0
0
1
SA
1
1
0
0
1
SC
1
1
1
1
0
SW
1
1
1
1
1
NW
1
1
1
1
1
142
SW
1
1
1
1
1
0
1
SW
0
0
0
0
0
0
0
SW
0
0
0
0
0
0
0
SW
1
1
1
1
1
0
1
SW
1
1
1
1
1
0
1
NW
1
1
1
1
1
1
0
NW
0
0
0
0
0
0
0
NW
0
0
0
0
0
0
0
NW
1
1
1
1
1
1
0
NW
1
1
1
1
1
1
0
Table 17. Average Distance Between Regions.
ADIST(I,I1), km, Average Distance Between
Regions
Region, i / Region, i1
NO
NE
EA
NO
0
2000
1500
NE
2000
0
3600
EA
1500
3600
0
SA
500
600
500
SC
2100
3800
900
SW
2500
4500
2300
NW
3100
5300
4600
SA
500
600
500
0
500
1800
3200
SC
2100
3800
900
500
0
1200
3900
SW
2500
4500
2300
1800
1200
0
3200
Table 18. Transportation Efficiencies (Losses).
TCW(c,m) . Transport Efficiency;
Part of fuel c Remaining after transportation
by Mode m
Fuel, c / Mode, m ROAD
RAIL
SHIP
HHCOAL
0.95
0.95
0.95
HLCOAL
0.95
0.95
0.95
MHCOAL
0.95
0.95
0.95
MLCOAL
0.95
0.95
0.95
LHCOAL
0.95
0.95
0.95
LLCOAL
0.95
0.95
0.95
OIL
0.99
0.99
0.98
GAS
OILimpt
0.98
GASimpt
UR
1
1
1
URimpt
1
1
1
ELEC
PIPE
WIRE
0.98
0.98
0.98
0.98
0
0
0.92
Table 19. Transportation Costs.
TRCPERKM(c,m); Transportation Costs, Units:
COAL($/tonne km);
3
OIL($/tonne km); NG($/km km); UR($/tonne km);
ELEC(M$/TWeh/km)
Fuel, c / Mode, RAIL
ROAD
SHIP
m
HHCOAL
0.006
0.012
0.002
OIL
0.007
0.014
0.004
OILimpt
0.007
0.014
0.004
GAS
0
0
0
GASimpt
0
0
0
UR
0.024
0.048
0.008
URimpt
0.024
0.048
0.008
ELEC
0
0
0
143
PIPE
0
0.005
0.005
0.018
0.018
0
0
0
WIRE
0
0
0
0
0
0
0
0.003
NW
3100
5300
4600
3200
3900
3200
0
Table 20. Unit Cost of Fuel.
UCFUEL(i,tot); Cost of Fuel Mined; Units: $/tonneCOAL, $/tonneOIL;
3
$/km GAS
(a) fuel charges for UR included in variable costs,
COSTVAR(ss,t)
Region, i HHCOAL HLCOAL MHCOAL MLCOAL LHCOAL LLCOAL
NO
12.46
13.33
12.65
13.54
13.03
13.94
NE
13.89
14.86
14.1
15.09
14.52
15.54
EA
14.72
15.75
14.94
15.99
15.39
16.47
SA
14.72
15.75
14.94
15.99
15.39
16.47
SC
16.97
18.16
17.23
18.44
17.75
18.99
SW
18.99
20.32
19.28
20.63
19.86
21.25
NW
10.09
10.79
10.24
10.96
10.55
11.29
(a)
include as part of variable annual operating costs for NUCLEAR
Table 21. Dynamics Parameter for Imported Fuels.
DFIMP(t,m,f); (unitless) Dynamics
Parameter for Imported Cost of Fuel by
Transportation Mode
Mode, m / Time, t
OILimpt
GASimpt
PIPE
1995
1
1
2000
1.1
1.1
2005
1.2
1.2
2010
1.35
1.35
2015
1.5
1.5
2020
1.65
1.65
2025
1.75
1.75
2030
1.9
1.9
SHIP
1995
1
1
2000
1.1
1.1
2005
1.2
1.2
2010
1.35
1.35
2015
1.5
1.5
2020
1.65
1.65
2025
1.75
1.75
2030
1.9
1.9
RAIL
1995
1
2000
1
2005
1.1
2010
1.1
2015
1.1
2020
1.1
2025
1.1
2030
1.1
URimpt
1
1
1.1
1.1
1.1
1.1
1.1
1.1
144
OIL GAS UR^(a)
108.4
90
0
106.6
90
0
111.4
90
0
111.4
90
0
132.5
90
0
137.1
90
0
100.1
90
0
Table 22. Dynamics of Relative Fuel Costs.
DUCFUEL(t, tot,i); Dynamics of Relative Cost of Fuels or Imports by
Region
Fuel, tot / Time, t
NO
NE
EA
SA
SC
HHCOAL
1995
1
1
1
1
1
2000
1.1
1.1
1.1
1.1
1.1
2005
1.2
1.2
1.2
1.2
1.2
2010
1.35
1.35
1.35
1.35
1.35
2015
1.5
1.5
1.5
1.5
1.5
2020
1.65
1.65
1.65
1.65
1.65
2025
1.75
1.75
1.75
1.75
1.75
2030
1.9
1.9
1.9
1.9
1.9
OIL
1995
1
1
1
1
1
2000
1.25
1.25
1.25
1.25
1.25
2005
1.35
1.35
1.35
1.35
1.35
2010
1.46
1.46
1.46
1.46
1.46
2015
1.56
1.56
1.56
1.56
1.56
2020
1.66
1.66
1.66
1.66
1.66
2025
1.77
1.77
1.77
1.77
1.77
2030
1.88
1.88
1.88
1.88
1.88
GAS
1995
1
1
1
1
1
2000
1.1
1.1
1.1
1.1
1.1
2005
1.15
1.15
1.15
1.15
1.15
2010
1.22
1.22
1.22
1.22
1.22
2015
1.25
1.25
1.25
1.25
1.25
2020
1.33
1.33
1.33
1.33
1.33
2025
1.4
1.4
1.4
1.4
1.4
2030
1.44
1.44
1.44
1.44
1.44
OILimpt
NO
NE
EA
SA
SC
1995
1
1
1
1
1
2000
1.25
1.25
1.25
1.25
1.25
2005
1.35
1.35
1.35
1.35
1.35
2010
1.46
1.46
1.46
1.46
1.46
2015
1.56
1.56
1.56
1.56
1.56
2020
1.66
1.66
1.66
1.66
1.66
2025
1.77
1.77
1.77
1.77
1.77
2030
1.88
1.88
1.88
1.88
1.88
GASimpt
1995
1
1
1
1
1
2000
1.1
1.1
1.1
1.1
1.1
2005
1.15
1.15
1.15
1.15
1.15
2010
1.22
1.22
1.22
1.22
1.22
2015
1.25
1.25
1.25
1.25
1.25
2020
1.33
1.33
1.33
1.33
1.33
2025
1.4
1.4
1.4
1.4
1.4
2030
1.44
1.44
1.44
1.44
1.44
145
SW
NW
1
1.1
1.2
1.35
1.5
1.65
1.75
1.9
1
1.1
1.2
1.3
1.5
1.6
1.7
1.9
1
1.25
1.35
1.46
1.56
1.66
1.77
1.88
1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1
1.1
1.15
1.22
1.25
1.33
1.4
1.44
SW
1
1.25
1.35
1.46
1.56
1.66
1.77
1.88
1
1.1
1.1
1.2
1.2
1.3
1.4
1.4
NW
1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1
1.1
1.15
1.22
1.25
1.33
1.4
1.44
1
1.1
1.1
1.2
1.2
1.3
1.4
1.4
Table 22.(Cont.) Dynamics of Relative Fuel Costs.
DUCFUEL(t, tot,i); Dynamics of Relative Cost of Fuels or Imports by
Region
Fuel, tot / Time, t
NO
NE
EA
SA
SC
UR
1995
1
1
1
1
1
2000
1
1
1
1
1
2005
1
1
1
1
1
2010
1
1
1
1
1
2015
1
1
1
1
1
2020
1
1
1
1
1
2025
1
1
1
1
1
2030
1
1
1
1
1
SW
NW
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Table 23. Dynamics of Relative Transportation Costs.
DUNTRNSP(t,m); Dynamics (Relative) Transportation Costs by Mode
Time, t / Mode, m
ROAD
RAIL
SHIP
PIPE
1995
1
1
1
1
2000
1.05
1.05
1.05
1
2005
1.1
1.1
1.1
1
2010
1.16
1.16
1.16
1
2015
1.21
1.21
1.22
1
2020
1.27
1.27
1.29
1
2025
1.35
1.35
1.35
1
2030
1.4
1.4
1.4
1
Table 24. Residual (post-1995) Capacity.
RESID(t); Residual Capacity per Period,
Relative to 1995
Time, t
Resid(t)
1995
0.6
2000
0.4
2005
0.2
2010
0
2015
0
2020
0
2025
0
2030
0
146
WIRE
1
1
1
1
1
1
1
1
Table 25. Heat Content of Various Fuels.
HSTD(tot); kcal/kg; Heat Content for Standard Chinese Fuel
(Standard coal has heat contend of 29.31 GJ/tonne)
Fuel, tot
HHCOAL
HLCOAL
MHCOAL
MLCOAL
LHCOAL
LLCOAL
OIL
GAS
OILimpt
GASimpt
UR
URimpt
HSTD(tot)
5000
5500
5000
5500
5000
5500
10000
9310
10000
9310
1.03E+09(a)
1.03E+09(a)
(a) based on unit mass of prepared fuel element an a burn-up of
40 MWd/kgHM.
Table 26. Minimum Fuel Capacity to be Utilized per region.
FUELLOW(i,tot); Minimum Fuel Capacity to Be Utilized for
Region i, r
in Mtonne/yr or 1000 m^3(GAS)/y (Upper Bound in Use of
Resource)
Region, I HHCOAL HLCOAL MHCOAL MLCOAL
LHCOAL
NO
0
0
0
0
NE
0
0
0
0
EA
0
0
0
0
SA
0
0
0
0
SC
0
0
0
0
SW
0
0
0
0
NW
0
0
0
0
147
0
0
0
0
0
0
0
LLCOAL
0
0
0
0
0
0
0
OIL
GAS
0
0
0
0
0
0
0
UR
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Table 27. Maximum Fuel Capacity to be Utilized per Region and Time.
FUELHIGH (t,i,tot) Maximum Fuel Capacity to Be Utilized for
3
Region i, in Mtonne/yr or 1000m (GAS)/yr units.
(Upper Bound in Use of Resource)
Time, t / Region, HHCOAL HLCOAL MHCOAL MLCOAL LHCOAL LLCOAL OIL GAS
I
1995
NO
2
2
108
87
37
18 11.4
2
NE
1
1
11
5
41
23
75
6
EA
2
3
51
36
7
1 31.1
3
SA
2
3
51
36
7
1 31.1
3
SC
11
1
38
24
21
4 13.4
2
SW
34
1
49
7
19
1
1.9
13
NW
2
2
3
14
10
4 17.3
4
2000
NO
2
3
122
99
41
21
12
2
NE
1
1
11
5
42
23
76
6
EA
2
3
52
37
7
1
35
3
SA
2
3
52
37
7
1
35
3
SC
11
1
38
24
22
4
15
2
SW
36
1
52
8
20
1
8
13
NW
2
2
4
14
11
4
20
4
2005
NO
3
4
159
129
54
27
12
3
NE
1
1
12
5
43
24
76
7
EA
2
3
53
37
7
1
35
4
SA
2
3
53
37
7
1
35
4
SC
11
1
39
24
22
4
15
3
SW
38
1
54
8
21
1
8
15
NW
2
2
5
19
14
6
40
18
2010
NO
4
4
197
159
67
33
12
3
NE
1
1
12
5
44
24
76
7
EA
2
3
30
28
6
1
20
4
SA
2
1
14
10
2
0
15
2
SC
11
2
40
25
23
4
15
3
SW
39
1
57
8
22
1
12
21
NW
3
3
6
24
18
7
60
32
2015
NO
4
5
211
171
72
36
10
3
NE
1
1
12
5
44
24
70
7
EA
2
3
54
38
8
1
30
4
SA
2
3
54
38
8
1
30
4
SC
11
2
40
25
23
4
15
3
SW
41
1
59
9
22
1
12
23
NW
4
4
7
29
21
9
75
50
148
UR
0.5
0
0
0.02
0.05
0.04
0.5
0
0
0.02
0.05
0.04
0.6
0
0
0.03
0.08
0.08
0.6
0
0
0.03
0.08
0.08
0.8
0.01
0.01
0.04
0.09
0.1
Table 27(Cont.). Maximum Fuel Capacity to be Utilized per Region and Time.
FUELHIGH (t,i,tot) Maximum Fuel Capacity to Be Utilized for
Region i,
3
in Mtonne/yr or 1000 m (GAS)/yr (Upper Bound in Use of
Resource)
Time, t / Region, HHCOAL HLCOAL MHCOAL MLCOAL LHCOAL LLCOAL OIL GAS
I
2020
NO
4
5
224
182
76
38
8
3
NE
1
1
12
6
45
25
65
7
EA
2
3
54
38
8
1
30
4
SA
2
3
54
38
8
1
30
4
SC
11
2
40
25
23
4
15
3
SW
42
1
61
9
23
1
12
25
NW
4
4
8
33
24
10
90
58
2025
NO
4
5
237
192
80
40
6
3
NE
1
1
12
6
45
25
65
7
EA
2
3
54
38
8
1
30
4
SA
2
3
54
38
8
1
30
4
SC
11
2
40
25
23
4
15
3
SW
42
1
61
9
23
1
12
25
NW
4
4
8
33
24
10
90
58
2030
NO
4
5
250
202
84
42
4
3
NE
1
1
12
6
45
25
65
7
EA
2
3
54
38
8
1
30
4
SA
2
3
54
38
8
1
30
4
SC
11
2
40
25
23
4
15
3
SW
42
1
61
9
23
1
12
25
NW
4
4
8
33
24
10
90
58
149
UR
0.9
0.03
0.03
0.06
0.11
0.14
1
0.05
0.05
0.08
0.13
0.17
1.1
0.1
0.1
0.1
0.15
0.25
Table 28. Maximum Coal Cleaning Capacity per Region and Time.
FQHIGH (t,i,k1) Maximum Fuel (Coal) Cleaning Capacity in Region i;
3
in Mtonne/yr or 1000 m (GAS)/yr
Time, t / Region, i / Fuel k1 HHCOAL
HLCOAL MHCOAL MLCOAL LHCOAL
1995
NO
2
2
108
87
37
NE
1
1
11
5
41
EA
2
3
51
36
7
SA
2
3
51
36
7
SC
11
1
38
24
21
SW
34
1
49
7
19
NW
2
2
3
14
10
2000
NO
2
3
122
99
41
NE
1
1
11
5
42
EA
2
3
52
37
7
SA
2
3
52
37
7
SC
11
1
38
24
22
SW
36
1
52
8
20
NW
2
2
4
14
11
2005
NO
3
4
159
129
54
NE
1
1
12
5
43
EA
2
3
53
37
7
SA
2
3
53
37
7
SC
11
1
39
24
22
SW
38
1
54
8
21
NW
2
2
5
19
14
2010
NO
4
4
197
159
67
NE
1
1
12
5
44
EA
2
3
54
38
8
SA
2
3
54
38
8
SC
11
2
40
25
23
SW
39
1
57
8
22
NW
3
3
6
24
18
2015
NO
4
5
211
171
72
NE
1
1
12
5
44
EA
2
3
54
38
8
SA
2
3
54
38
8
SC
11
2
40
25
23
SW
41
1
59
9
22
NW
4
4
7
29
21
150
Table 28(Cont.). Maximum Coal Cleaning Capacity per Region and Time.
FQHIGH (t,i,k1) Maximum Fuel (Coal) Cleaning Capacity in Region i;
3
in Mtonne/yr or 1000 m (GAS)/yr
2020
NO
NE
EA
SA
SC
SW
NW
4
1
11
2
2
42
4
5
1
2
3
3
1
4
224
12
40
54
54
61
8
182
6
25
38
38
9
33
76
45
23
8
8
23
24
NO
NE
EA
SA
SC
SW
NW
4
1
11
2
2
42
4
5
1
2
3
3
1
4
237
12
40
54
54
61
8
192
7
25
38
38
9
33
80
46
23
8
8
23
24
NO
NE
EA
SA
SC
SW
NW
4
1
11
2
2
42
4
5
1
2
3
3
1
4
250
12
40
54
54
61
8
202
8
25
38
38
9
33
84
47
23
8
8
23
24
2025
2030
Table 29. Efficiency for Fuel to Electricity Conversion per Technology
EFF(s,tot); Efficiency of Technology s to Convert Fuel tot to Electricity
Technol., s HHC HLC MHC MLC LHC LLC OIL GAS OILimpt GASimpt UR
URimpt
DOMSML
0.26 0.27 0.26 0.27 0.26 0.27
DOMMED
0.28 0.29 0.28 0.29 0.28 0.29
DOMESP
0.33 0.35 0.33 0.35 0.33 0.36
DOMSCB
0.32 0.34 0.32 0.34 0.32 0.35
DOMBIG
0.39 0.39
0.4 0.4 0.41 0.42
AFBC
0.37 0.37 0.37 0.38 0.38 0.38
IGCC
0.42 0.42 0.43 0.43 0.45 0.45
OILREG
0.35
0.35
OILCC
0.42
0.42
GASCC
0.45
0.45
GASCCA
0.55
0.55
NUCLEAR
0.33
0.33
PFBC
0.42 0.42 0.42 0.42 0.42 0.42
151
Table 30. Maximum Renewable Energy Capacities.
RENEW(t,i,s1); Maximum Renewable
Capacity , GWe
to Be Utilized in Region i,
Time, t / Region i / Technol. s1
HYDRO
1995
NO
1.24
NE
4.45
EA
8.8
SA
0.2
SC
20.1
SW
11.74
NW
5.92
2000
NO
6.9198
NE
11.9945
EA
17.9022
SA
0.3
SC
67.4349
SW
232.3433
NW
41.9377
WIND
PV
GT
0.01295
0.005055
0.003155
0.003155
0.0015
0
0.015
0.00875
0.00055
0.003
0.003
0
0
0.0165
0.02
0.01
0.02
0.02
0.0105
0
0.0271
15
4.5
8
8
10
0
5.5
25
25
10
10
10
0
25
20.25
6.75
2.25
2.25
33.75
4.5
157.5
Table 30(Cont.). Maximum Renewable Energy Capacities.
RENEW(t,i,s1); Maximum Renewable Capacity , GWe
to Be Utilized in Region i,
2020
HYDRO
WIND
NO
6.9198
15
NE
38
4.5
EA
17.9022
8
SA
0.3
8
SC
67.4349
10
SW
232.3433
0
NW
41.9377
5.5
2030
NO
6.9198
15
NE
38
4.5
EA
17.9022
8
SA
0.3
8
SC
67.4349
10
SW
232.3433
0
NW
41.9377
5.5
Table 31. Capacity Factor for HYDRO.
LOADHYDR(i); Regional
Load Factors for HYDRO, hr/yr
Region, I
LOADHYDR
NO
2000
NE
3000
EA
2000
SA
2000
SC
4000
SW
5000
NW
3000
152
PV
25
25
10
10
10
0
25
GT
20.25
6.75
2.25
2.25
33.75
4.5
157.5
25
25
10
10
10
0
25
20.25
6.75
2.25
2.25
33.75
4.5
157.5
Table 32. Capacity Factors per Technology.
CAPUTIL(ss); Capacity, hr/yr
[1992 National Average 52%,
thermal and hydro combined]
Technol. ss
CAPUTIL(ss)
DOMSML
4800
DOMMED
5000
DOMESP
5200
DOMSCB
5200
DOMBIG
5200
AFBC
6000
IGCC
6000
PFBC
6000
OILREG
4800
OILCC
5600
GASCC
6000
GASCCA
6000
NUCLEAR
7000
HYDRO
3800
WIND
2600
GT
3200
PV
2600
Table 33. Lifetimes for Construction, Technologies, and Economics.
CONSLIFE(ss,life); Technology Construction Period and
Lifetimes, yr
Technology, ss / life
CONSTR
LIFE
DOMSML
2
20
DOMMED
3
30
DOMESP
3
30
DOMSCB
3
30
DOMBIG
3
30
AFBC
3
30
IGCC
3
30
OILREG
2
20
OILCC
2.5
20
GASCC
2
20
GASCCA
2.5
20
NUCLEAR
5
30
HYDRO
8
50
WIND
2
20
PV
2
25
GT
2
15
PFBC
3
20
153
ECLIFE
20
30
30
30
30
30
30
20
20
20
20
30
50
20
25
15
20
Table 34. Initial (1995) Installed Electrical Capacity by Region.
QCAP95(i,ss) Initial Installed Capacity of Thermal
Technology ss, GWe*
Region, i DOMSML DOMMED DOMBI DOMESP DOMSCB OILREG
G
NO
8.4
15.61
8.21
0.019
NE
7.62
11.56
2.2
EA
10
10
18
0.1
SA
2
9.8
1
2
0.08
SC
7.88
13.8
7.385
5.24
SW
5.14
5.15
2.96
0.72
NW
2.36
3.43
1.8
Region, i GASCC GASCCA NUCLE HYDRO
WIND
PV
AR
NO
1.24 0.01295 0.00875
NE
4.448 0.005055 0.00055
EA
0.3
8.6 0.003155
0.003
SA
0.08
0
0
0.07
0
0
SC
1.8
20.038
0.0015
0
SW
0.23
0
11.7
0
0
NW
5.92
0.01
0.01
*Total installed capacity in 1994 was 195.3 GWe
(NUCLEAR + COAL)
Table 35. Capital Costs of Key Technologies.
COSTCAP(ss); Capital Costs of
Installed Technology, ss , M$/GWe
Technology., ss
COSTCAP(ss)
DOMSML
676
DOMMED
650
DOMESP
600
DOMSCB
890
DOMBIG
890
AFBC
900
IGCC
900
OILREG
530
OILCC
500
GASCC
530
GASCCA
500
NUCLEAR*
1600
HYDRO
1200
WIND
1200
PV
10000
GT
2000
PFBC
1000
154
OILCC
0.132
0.191
0.33
0.1
2.106
0.073
0.074
GT
0.002
0.001
0.002
0
0
0
0.027
Table 36. Relative Decrease in Capital Costs with Time.
DECOSTCAP(ss,t); Factor Decrease in COSTCAP
Assumed With Time
Technol., ss 1995 2000 2005
2010 2015 2020
DOMSML
1
1
1
1
1
1
DOMMED
1
1
1
1
1
1
DOMESP
1
1
1
1
1
1
DOMSCB
1
1
1
1
1
1
DOMGIG
1
1
1
1
1
1
AFBC
1
1
1
1
1
1
IGCC
1
1
1
1
1
1
OILREG
1
1
1
1
1
1
OILCC
1
1
1
1
1
1
GASCC
1
1
1
1
1
1
GASCCA
1
1
0.9
0.9
0.9
0.9
NUCLEAR
1
1
0.9
0.9
0.9
0.9
HYDRO
1
1
1
1
1
1
WIND
1
0.9
0.9
0.85
0.85
0.8
PV
1
0.5
0.4
0.35
0.3
0.27
GT
1
1
1
1
1
1
PFBC
1
1
1
1
1
1
Table 37. Variable Operating Costs per Technology
COSTVAR(ss); Variable Operating Costs of Technology ss,
M $/TWeh; 300MW cost is 2.0 + 0.5 for
ESP
COSTVFIX(ss); Fixed Operating Costs of Technology ss,
fraction/yr of COSTCAP(ss
Technology, ss
COSTVAR(ss)
COSTFIX(ss)
DOMSML
5
0.03
DOMMED
4
0.03
DOMESP
3.5
0.03
DOMSCB
4.5
0.03
DOMBIG
4.5
0.03
AFBC
8.5
0.03
IGCC
4
0.03
OILREG
2.8
0.03
OILCC
2.8
0.03
GASCC
8
0.03
GASCCA
8
0.03
NUCLEAR
9
0.03
HYDRO
1
0.016
WIND
2
0.015
GT
0.05
0.015
PV
0.07
0.015
PFBC
4.5
0.015
155
2025
1
1
1
1
1
1
1
1
1
1
0.9
0.9
1
0.75
0.25
1
1
2030
1
1
1
1
1
1
1
1
1
1
0.9
0.9
1
0.7
0.2
1
1
Table 38. CO2 Emission Yield.
CO2EF(tot); Emission Factor for CO2,
tonneCO2/tonne Fuel Burned*
Fuel, tot
CO2EF(tot)
HHCOAL
1.89
HLCOAL
2.28
MHCOAL
1.91
MLCOAL
2.3
LHCOAL
1.93
LLCOAL
2.32
OIL
3.08
GAS
2.05
OILimpt
3.08
GASimpt
2.05
UR
0
URimpt
0
* Alignment factor of 0.783 applied
Table 39. SO2 Emission Yield.
SO2EF(s,tot) SO2 Emission factor, tonneSO2/tonne Fuel
Burned
(HCOAL 3% S, LCOAL 1% S, MCOAL 2% S)
Technol., ss HHCOAL HLCOAL MHCOAL MLCOA LHCOAL LLCOAL OIL GAS OILimpt GASimpt
L
DOMSML*
0.051
0.051
0.04
0.04
0.017
0.017
DOMMED*
0.051
0.051
0.04
0.04
0.017
0.017
DOMESP*
0.051
0.051
0.04
0.04
0.017
0.017
DOMBIG*
0.01
0.01
0.0068 0.0068 0.0034 0.0034
DOMSCB
0.01
0.01
0.0068 0.0068 0.0034 0.0034
PFBC
0.0051
0.0051
0.004
0.004 0.0017 0.0017
AFBC
0.00765 0.00765
0.006
0.006 0.00255 0.00255
IGCC
0.00255 0.00255
0.002
0.002 0.00085 0.000085
OILREG
0.01
0.0075
OILCC
0.01
0.0075
GASCC
GASCCA
* Correction factor of 0.633 applied to these values to match SO2 emissions
reported for starting year (1995).
156
Table 40. SO2 Regional Transfer Function.
TRANSFER(i,i1); SO2 Emission Transfer Function
(from row to column)
Region, i/i1
NO
NE
EA
SA
NO
0.77
0.07
0.09
0.03
NE
0.11
0.88
0.01
0
EA
0.15
0.02
0.5
0.26
SA
0.15
0.02
0.26
0.5
SC
0.05
0
0.1
0.05
SW
0.05
0
0.01
0
NW
0.13
0
0
0
SC
0.03
0
0.07
0.07
0.75
0.08
0
SW
0.01
0
0
0
0.05
0.86
0.04
NW
0
0
0
0
0
0
0.82
Table 41. NOX Emission Yield.
NOXEF(s,tot); NOX Emission Factor, tonneNOX /tonne Fuel
Burned
Technol., ss HHCOAL HLCOAL MHCOAL MLCOAL LHCOAL LLCOAL OIL GAS OILimpt GASimpt
DOMSML
0.003
0.003
0.003
0.003
0.003
0.003
DOMMED
0.003
0.003
0.003
0.003
0.003
0.003
DOMESP
0.003
0.003
0.003
0.003
0.003
0.003
DOMBIG
0.003
0.003
0.003
0.003
0.003
0.003
DOMSCB
0.003
0.003
0.003
0.003
0.003
0.003
PFBC
0.001
0.001
0.001
0.001
0.001
0.001
AFBC
0.001
0.001
0.001
0.001
0.001
0.001
IGCC
0.002
0.002
0.002
0.002
0.002
0.002
OILREG
0.01
0.011
OILCC
0.01
0.011
GASCC
0.011
0.011
GASCCA
0.001
0.001
Table 42. Cost of Physical and Chemical Washing of Coal.
PC(k,k1); Cost of Physical Coal Washing
from Start to Final Product, $/tonne
CC(k.k1); Cost of Physical Coal Washing
from Start to Final Product, $/tonne [ ]
k / k1
HLCOAL
MLCOAL
HHCOAL
1.4
2.5
HLCOAL
1.54
MHCOAL
1.4
MLCOAL
LHCOAL
LLCOAL
[23.74]
[23.74]
2.2
2
1.4
157
Table 43. Loss Fraction from Coal Washing.
RCW(k,k1); Coal Washing Fraction
Weight Loss
k / k1
HLCOAL
MLCOAL
HHCOAL
0.8
0.79
HLCOAL
0.99
MMCOAL
0.8
MLCOAL
LHCOAL
LLCOAL
0.78
0.98
0.79
0.98
0.8
158
Appendix B. Overview of Scenario Analysis for the Chinese Energy System;
Optimization versus Simulation Modeling Approaches
B.I. Introduction
The scenario analysis approach followed by the Energy Systems Analyses group of PSI and by
other energy and environmental analyses groups in Europe, the USA, Japan, and in other
countries around the world, uses engineering and economic optimization techniques. The
optimization methodology uses an engineering-based, "bottom-up" model with integrated
resource planning that links the "bottom-up" energy model to “top-down” economic models to
drive economic growth and demands for energy services. This coupling is provided through
the use of energy-price and energy-income (GDP) elasticities. Decision making under
uncertainty concerning future economic developments and environmental/energy policy can
also be performed by applying stochastic analyses with hedging options. This latter extension,
however, will not be made in the initial phases of this CETP study component. Although the
methodology is based on optimization techniques, "tradeoff analyses" between the system
costs and other considerations related to security of supplies and to specific elements of
environmental policies being implemented play a central role in assessing the policy
implications emerging from these optimization models. Within the context of CETP, a
scenario-based simulation model is also being applied (Schenler, 1998) to expand
understanding of possible futures for the Chinese electrical energy system. The central aim of
this appendix is to describe the scenario approach adopted by the PSI optimization models, as
well as to highlight differences and complementarities between this approach and that used by
non-optimizing simulation models.
Although the primary intent here is only to outline the scope, approach, and interrelationships
of the scenarios used to explore a range of China energy futures, some mention should be
made of the overall scope of the optimization-based Energy-Economy Modeling (EEM)
activity in relationship to the simulation-based Electric Sector Simulation (ESS) activity. The
EEM task is following three optimization modeling pathways: a) adaptation and extension of
an existing (Rogers, 1998, 1999; Chandler, 1999) seven-region optimization and trade model
of China's electricity sector, as described in the main body of this document; b) a single-region
(full energy sector, not just electricity) MARKAL model of China; (WenYing Chen and
Kypreos, 2000); and c) a single-region (again, full energy sector) MARKAL model of
Shandong Province. While the ultimate focus is placed on the latter modeling approach
(Shandong-MARKAL), efforts to date on both model development and scenario development
has been with the China Regional Energy Trade Model (CRETM). The descriptions given
herein generally reflect this current state of development, in that the Shandong modeling effort
will be broadened and improved when examined in the context of China's Energy-EconomicEnvironmental (E3) needs as a whole. The ESS and scenario development related to these
simulation-model activities (Schenler, 1998), on the other hand, are limited to the electricalenergy sector and to Shandong Province.
159
B.II. Optimization versus Simulation Models (Murray and Rogers, 1998)
The main difference between the optimization approach described here and other scenariobased analyses using the simulation techniques is that the former considers simultaneously a
broad range of existing and future (electricity-generation) technological options as described
by the Reference Energy System (RES) of a given country or region. The “best” (e.g., costoptimal) technological choice is identified in terms of all options available for a given set of
objectives and management constraints. Simulation techniques can consider a broader set of
technological options in greater technological and operational detail, but the ESS
methodology applies and compares user-defined solutions without any direct indication that
the solution is optimal. Additionally, both methodologies consider different instruments for
introducing environmental and energy policy, while technologies can also evolve in different
ways as consequence of policies that focus R&D or open markets to competition. For both
modeling approaches these assumptions are expressed systematically using scenario-building
techniques that describe a range of relatively “surprise-free” futures in terms of key
uncertainties and options associated with a set of top-level scenario attributes.
The use of optimization versus simulation models for energy planning is characterized by the
well-known trade off between complexity and flexibility. The degree to which reality is
approached does not necessarily vary monotonically with either. Following Murray and
Rogers (1998), Figs. B-1 and B-2 illustrate the essential features of both approaches to
constructing visions of the future through scenario building. Optimization models search for a
set of minimum-cost technologies that will meet specific demand (level and distribution), fuel,
emission, deployment rate, resource-distribution etc. constraints. These models are generally
limited in spatial and temporal resolution of the power system. Simulation models, on the
other hand, determine the cost of meeting demand for a specific set of investment scenarios,
are specific to this range of energy scenarios, and use the characteristics of technology options
to compare different scenarios in meeting demand, cost, and environmental goals. In contrast
to the optimization models, which give one optimal solution (along with associated marginal
costs and associated optimal energy technology mixes) for a given set of technology
characteristics, simulation models compare a given set of user-defined, intuited solutions
without any direct indication that the solution is economically optimal. The procedure
followed uses the optimization model to give optima for a given set of user-defined scenarios,
and thereby performs a similar but more limited range of parametric trade studies.
160
Cost Analyses
COST
SIMULATION MODEL
Environmental
(Emissions)
Analyses
x x o +x +o
o ++
x
ox
o
x + x
+xx+
++ x
o
EMISSIONS
DEFINE DIFFERENT TECHNOLOGICAL CHOICES
(SCENARIO GENERATOR)
• Base Case
• Emissions Control
• Advanced Technologies
• etc.
Thermal/Hydro
Capacity Ratio
Transmission/
Distribution Losses
Capacity/Availability
Factor
Generation Base
Expansion
Fuel Heating Value
Demand for Extra Power
Figure B-1. Schematic diagram of information flow for a simulation model (ESS task).
161
OPTIMIZATION MODEL
MINIMIZE TOTAL SYSTEM COST
(Capital, Variable and Fixed O&M, Fuel, Transportation
Transmission, Cost of Unmet Demand, External Costs)
SCENARIO
GENERATOR
CONSTRAINTS
OPTIMAL RESULTS
• Costs
• Energy Mixes
FUEL
GENERATION
MARGINALS
CAPACITY
DEMAND
EMISSIONS
TECHNOLOGY
INNOVATION/DIFFUSION
Figure B-2. Schematic diagram of information flow for an optimization model (EEM
task); typical composition and focus of each of the constraints displayed above include:
Fuel:
• do not exceed fuel capacity or resource;
• transport only fuel that has been extracted;
• choice of coal versus gas (for specific Provinces).
Generation:
• convert fuel to electricity under given efficiency and capacity limits;
• generation limited to/by capacity.
Capacity:
• choices of new capacity to meet generation demand;
• vintaging and related loss of old capacity.
Demand:
• generation meets demand;
• limitation on / consequences of unmet demand.
Emissions:
• local versus regional versus global constraints/impacts;
• caps versus taxes;
Technology Diffusion/Innovation:
• introduction/deployment rates;
• learning constraints.
162
B.III. Scenarios
B.III.A. Scenario Structure and Attributes
While not unique and having a tendency to vary somewhat with modeling methodology (e.g.,
EEM versus ESS), the present application of optimization models examines scenarios that are
categorized in terms of technology, economic, and environmental attributes. Included under
the first attribute is (advanced) coal, gas, nuclear, and renewable generation technologies, with
the introduction of any given technology into a given scenario being controlled primarily
through technology-specific operational and economic parameters (primarily cost, efficiency,
emission rates, and introduction/deployment rate). The economics attribute is characterized
by assumptions and uncertainties related primarily to fuel-fuel prices (related to both resource
and policy-driven clean-up considerations), electrical energy demand (e.g., primarily through
GDP growth and attendant price and income elasticities), and discount rates. Lastly, the
environmental attribute is characterized in this study primarily by the choice of imposed
emission limits or caps (sometimes associated with centrally planned economies) versus taxes
(sometimes associated with free-market economies) to control SO2 (local, regional) or CO2
(countrywide, global) emissions. While the allocation of caps versus taxes is sometimes
related to particular economy structure, examples where both instruments are used within the
same economy paradigm can be sited.
Two points should be emphasized with respect to the scenario attributes used to create and
structure the scenario paradigm used in this study. First, in some cases membership of a given
parameter or related variation (uncertainty) to a given attribute may not be unique.
Consideration of the combination of all technology scenarios with all environmental
constraints, however, is expected to make this membership clearer. Secondly, the application
of “policy” per se to influence the course of any given scenario applies in one form or another
to each of the three scenario attributes identified above. Additionally, uncertainty is
connected with parameters in any of these three E3 attributes. The level and importance of
these uncertainties in driving possible futures, however, varies greatly among attributes.
The goal of analyses that use optimization models is not to give "the best" long-term strategy,
since uncertainties, particularly beyond a decade or two, make such a determination tenuous.
Instead, analyses focus on the evaluation of robust scenarios expressed in terms of a range of
possible future uncertainties associated with the three key scenario attributes proposed for this
EEM-based study. Additionally, all decision analyses performed under conditions of
uncertainty identify the best "hedging" strategy in view of these uncertainties. This tack is
taken by both the optimization and the simulation modeling approaches. In the case of the
former, this goal will be accomplished by maximizing the expected surplus of producer and
consumer goods under given uncertainties, by assigning different probabilities for the future
states [e.g., fuel prices, discount rates, and/or (electrical) energy demand (or the demographic
and econometric factors that determine that demand) and for the different sets of tradeoffs and
connectivities related to imposed energy and environmental constraints].
As suggested above, the existing composition of technological options, the socio-economic
assumptions, the demands for energy services, and the expected environmental and energy
policy constraints are central components of scenario analyses. These “policy” elements are
expressed in terms of technological, economic, and environmental attributes. Along with
163
stakeholder input regarding technological alternatives and characteristics, input of a general or
“top-level” nature is required to specify with minimum ambiguity attribute parameters for all
scenarios being considered. Additionally, a set of common assumptions at this level of
scenario definition is crucial to the linkage of the optimization and the simulation modeling
approaches being use in CETP. This scenario-based methodology has been developed by the
IEA/OECD ETSAP project over the last two decades for MARKAL-based applications, it has
been applied by different groups on the national, regional and local planning, and it is
designed for evaluation and communication of crucial results to external stakeholder advisory
groups, including the ultimate decision makers.
Table B-I.. Listing of High-Level Scenario Characteristics, Variants, and Drivers
Essential Elements Used to Define Scenarios, as Applied in Table B-II:
•
BAU (B)
Business as Usual in terms of technology implementation, energy and environmental
policy;
•
SO2 (S)
putting emphasis on SO2 control (global/ regional target or eventually external costs);
•
CO2 (C)
putting emphasis on CO2 control and trade (More open China to international
cooperation and trade with technology transfer, low interest rates and investments in
infra structures for energy and environmental control);
•
EMI
(E = C + S) putting emphasis on both pollutants to study synergism and
secondary benefits;
•
COAL (K)
acceleration in the use of more efficient and economic coal power
stations;
•
GAS (G)
faster introduction of gas conventional/advanced technologies, new
transmission lines and liquefied natural gas imports;
pipelines, new
•
NUCL (N)
higher market shares of nuclear energy induced through economic
instruments;
•
REN (R)
higher market shares for renewable energy induced through economic
instruments;
•
TCH (A)
higher market shares for all advanced technologies.
Key Scenario Variants:
•
Demands for Energy and Electricity [ High(H) versus Medium(M) versus Low(L) demand developments];
•
Fuel Prices [ Nominal(N) versus High (H, doubling prices) in the next two decades];
•
Penetration of Advanced Technologies [ High(H) versus Low(L) success for the four generation
technologies listed on Table B-II as part of the third and fourth scenario sets.];
•
Environmental Policy [Control of SO2(S) and CO2(C); to what level should emission controls be set, or at
what level should external costs or taxes be introduced versus time and emittant?; what are the impacts of
advanced generation technologies on achieving specific emission goals?]
General Scenario Drivers:
•
Expected economic and population growth rates, for China and Shandong;.
•
Income and price elasticity to define the de-coupling of economic growth and energy use;
•
Structural changes in the economy;
•
Real discount rates and pay-back times on investments.
•
Fuel prices and availability for endogenous productions and international imports;
•
Energy and Environmental policy; options and instruments;
•
Public R&D policy on advance technology development.
164
B.III.B. Overall Considerations
One central theme covers the incorporation of any scenario into the study portfolio that
describes possible futures for electricity generation and use in China for CRETM, ChinaMARKAL, or Shandong-MARKAL models over the next three decades - the identification of
robust routes to satisfying a growing per-capita electrical energy demand at costs that will not
unduly hinder overall economic growth while simultaneous mitigating generation and
emission impacts on both populations and the environment. The impacts of any given policydriven scenario attribute (e.g., technology, economic, and/or environmental) in fulfilling the
goals laid out by this theme are measured relative to a "point-of-departure" or base case. In
that this reference case generally evokes the use of currently known technologies and fuel
resources without extensive emission controls (some coal washing and flue-gas scrubbing of
SO2 and NOX may be imposed), the term "business-as-usual" (BAU) is applied to this case(s).
Table B-III applies the categorizations defined on Table B-I, and represents an elaboration of
the working scenario list given on Table III. A range is given for the limited number of
economic attributes [e.g., electricity demand (population growth, demographics, capital and
labor efficiency and growth, etc., (Ho, Jorgenson, and Perkins, 1998), fuel costs, discount
rate]; Table B-II shows two BAU cases for nominal (N) and high (H) fuel costs, both with
high (H) electricity demand and discount rates for the cases so far examined.
A direct imposition of SO2 and/or CO2 emission constraints (caps) without changing the
BAU technology assumptions generates the second group of six scenarios, depending on
whether nominal (N) or high (H) fuel costs are imposed. All six scenarios in this second
group also assume high (H) electricity demand associated with high economic growth and a
moderate penetration of measures for demand management, and for high discount rates. A
range, however, is indicated for these scenario drivers on Table B-I. The marginal costs
($/tonneCO2 or $/tonneSO2) that result from evaluating this second scenario set give the tax
rate needed to achieve the same emission reductions at the same overall impact on total
present-valued energy cost relative to the BAU case (Table B-II or Table III) . Again, a range
of Low → Medium → High variations in these economic attributes is considered. The primary
intent of this second set of scenarios having a variety of externally imposed caps on emissions
is to provide a baseline for the third set of scenarios wherein advanced technologies without
specific emission caps (or taxes) are introduced to achieve like environmental policy goals or
endgame.
The third grouping of scenarios is comprised of policy-driven improvements for each of four
key generation technologies using the following fuels: coal (IGCC, PFB, etc.); gas (CC);
nuclear (safe, competitive configurations), and renewable energy sources (mainly wind and
solar PV). The level of cost reduction and efficiency increases needed to achieve emissions
comparable to those reported in the second scenario group (e.g., BAU technologies with
emission caps) will be examined. Each of these four key generation technologies, therefore,
will be combined with each of the environmental policies to identify and explain the potential
benefits of R&D and other policies that favor advanced technologies in improving the
economic and environmental performance of China's electricity sector. When all four
advanced generation technologies are combined together with and without CO2 and/or SO2
emissions caps, the fourth and last set or grouping of technologies indicated on Table B-II
results. As for the other scenario groups, all of the technology-based scenarios evaluated to
date correspond to high demand, nominal fuel costs, and high discount rates. Variations of
these economic scenario attributes must also be made to quantify the impacts of related future
uncertainties; Sec. III.D. reports on parameter variations of a few of the key scenarios
attributes [e.g., exogenous electricity demand (GDP growth), discount rate, technology costs].
165
Appendix C. Description of CRETM
C.1. Overview
The PSI-modified version of the Harvard University electrical energy flow model (Rogers,
1999; Chandler, 1998) is called CRETM, the China Regionalized Trade Model. This model is
an optimization model based on Linear Programming (LP) methods (Williams, 1999). While
limited to the electrical energy sector, compared to full energy sector models like MARKAL
(Fishbone, 1981; Kypreos, 1996, Goldstein, 1995), the main attraction of CRETM is the
ability to follow inter-regional allocation and transport of fuels within the seven-region model
of China (Fig. 1). Like the version of the HU model reported by Chandler (1998), the CRETM
model minimizes the total cost of the electrical system, including all costs associated with
generation per se (e.g., capital, fixed, and variable), coal cleaning, inter-regional fuel
transport, inter- and intra-regional power transmission, and, when so constrained, external
costs attributable to the emission of both pollutants (mainly SO2 and NOX) and of greenhouse
gases (GHG, mainly CO2).
The CRETM model is comprised of four kinds of relationships: a) the objective function
(present value of total system costs, as elaborated above); b) mass conservation relationships
that balance the flows of fuel masses across processes and regions; c) energy conservation
relationships that connect the flow of fuel masses to energy generation and transport; and d) a
set of constraints that establish bounds on both energy and mass flows, levels at which
specific generation technologies can be deployed, and emission rates. These latter endogenous
constraints combine with controls enforced through specific exogenous inputs (e.g., discount
rates, demand growths, GDP growths, fuel and capital unit costs, etc.), to create the various
scenarios (Table III) used to define and/or bracket possible energy/economic/environmental
(E3) futures.
These four essential elements of the CRETM model are evaluated in terms of a set of
generation technologies, {ss}, that are driven by a set of fuels {f(c)}, where the latter is a
(large) subset of a set of commodities {c} that is defined by the addition of electricity, ELEC,
to the set {f} (e.g., {c} = {f} + ELEC). Table V. defines the commodity and fuel sets, {c} and
{f}, along with other more specialized fuel subsets. Similarly, Table VI. Defines the main
technology set {ss} along with key specialized technology subsets. These fuel and technology
sets are combined to describe the energy and material flows within and between the seven
China regions described in Fig. 1. Finally, for each of these regions, Fig 2. arranges these
flows in a way that emulates the flow sequence Primary Fuels → Processes → Conversions →
Demands used in the original formulation of MARKAL (Fishbone, 1981).
On the basis of the formalism described above and established by Tables IV-VI and Figs. 1-2,
the following subsections summarize the CRETM model in algebraic form, as numerically
evaluated using the General Algebraic Modeling Systems language, GAMS (Brooke, 1998),
and the CPLEX (ILOG, 1999) LP solver. In terms of component and options resolution, the
present state of the CRETM model, along with predecessor versions, emphasize coal. While
this emphasis is justified on the basis of the enormous coal resources in China, other advanced
technologies, including clean-coal technologies as well as the development and deployment of
generation technologies that use reduced- or non-carbon fuels will require the (fairly straightforward) expansion of modeling detail for these other technologies. In view of recent
evaluations of the impacts of continued and extensive coal burning using contemporary
166
technologies (McElroy, 1998; Sweet, 1999), more detailed exploration of alternatives seems
warranted.
A "top-level" summary of the CRETM model, as laid out in Tables V-VI and Figs. 1-2
describes an electrical energy sector LP model that cost-optimizes on 17 generation
technologies, 16 fuel forms (including the renewable energy forms), having both domestic and
foreign fuel sources (for oil, gas, and uranium), having 4 transportation modalities, searching
over 8 time periods into the future, examining energy and material exchanges within and
between 7 regions, and using an object function composed of 8 cost categories (i.e., domestic
fuel, imported fuel, coal cleaning, transportation, possible emission taxes on three species,
capital costs, fixed generation costs, and variable generation costs). A simple product of these
six dimension (i.e., generation technologies, fuel commodities, temporal, regional, fuel
transport, and cost categories) gives about 500,000 possibilities, all of which for a given set of
constraints and inputs are to converge to single optimum. Even for a fairly simplified model,
the size of the task can become large, particularly when the 10-15 optimizing scenarios are
superposed. Aside from the enormity of this simplified problem, also apparent is the need to
understand the impact of deviations/departure from any "optimum" that is reported, as well as
the impact of changing the objection function on possible conclusions and recommendation.
In any case, the following subsection outlines the model that attempts to find this optimum for
the conditions described.
C.2. Model Description
The following descriptions are heavily dependent on the notations and definitions given in the
list of Nomenclature. This notation follows closely that used in the GAMS model, instead of
using algebraically more a conventional system. In the cause of brevity, many of the
definitions given in the Nomenclature are not extensively repeated in the following text.
Generally, monetary, (fuel) mass, power, and (electrical) energy flows are expressed,
respectively, in M$/yr, Mtonne/yr, GWe, and TWeh/yr units. Arguments associated with a
given variable relate: regions to the set index {i}; the fuel or commodity to the set index {c}
or related subsets (Table VI); generation technologies to the set index {ss} or related subsets
(Table VI.); and time periods to the set index {t}. For example, the rate at which coal of type
k(c) is supplied in region i at time t to produce a (cleaner) coal of type k1(c) is given by
PQ(i,k,k1,t) in units of Mtonne/yr; or the demand for electricity in region i for electricity from
a generation technology ss at time t is given by QELEC(i,ss,t) in units of TWeh/yr. Some
monetary flows, however, are given in per-period units, with YEARPP = 5 years/period in all
results reported herein.
C.2.1. Mass and Material Balances and Related Constraints
Coal is supplied to pre-combustion physical cleaning, PQ(i,k,k1,t), chemical cleaning,
CQ(i,k,k1,t), or no cleaning at all, NQ(I,k,k1,t), at a rate QS(i,k,t), and the following precleaning mass balance must be satisfied:
QS ( i, k , t ) = å ( PQ( i, k , k1, t ) + CQ( i, k , k1, t ) + NQ( i, k , k1, t )
k1
167
)
(C-1)
Given that the ratio of coal mass out of a given cleaning process that converts coal of quality k
to coal of quality k1 is RCW(k,k1), which in the present model is taken as independent of the
cleaning process, the quantity of coal of quality k1 after the pretreatment processing must
satisfy the following mass balance:
FQ( i, k1, t ) = å ( RCW ( k , k1) *( PQ( i, k , k1, t ) + CQ( i, k , k1, t ) ) + NQ( i, k , k1, t ) ) .
(C-2)
k
The mass balance for coal-based fuels requires that the fuel commodity used by generation
technologies in set {s} be less than or equal to that provide from the above-listed pretreatment plus the difference between regional transportation inflow and outflow. This fuel
balance for the coal-burning technologies is given by:
å QF (i1, k1, s, t ) ≤ FQ (i1, k1, t ) + å QTR (i, i1, k1, m, t ) * TR (m, i, i1) * TRM (k1, m) * TCW (k1, m)
s
i, m
− å QTR (i1, i, k1, m, t ) * TR ( m, i1, i ) * TRM (k1, m),
i, m
(C-3)
where TR(m,i,i1) acknowledges the existence of a transport link between region i and i1,
TRM(k1,m) reflects the ability to transport fuel (coal) type k1 via transport mode m, and
TCW(k1,m) is a material ("non-loss") efficiency for fuel (coal) type k1 and transport mode m.
The sum on the left of this constraint is contributed by only terms having non-zero conversion
efficiencies, EFF(s,k1). A similar constraint as that given by Eq. (C-3) reflects the fuel
balance on non-coal-burning technologies [e.g., the commodity set f(c), Table V.], with an
added term related to import of such commodities as oil, gas, and uranium. This constraint is
given by
å QF ( i1, f , s, t ) ≤ QS (i1, f , t ) + å QTR(i, i1, f , m, t ) * TR( m, i, i1) * TRM ( f , m) * TCW ( f , m)
s
i ,m
− å QTR( i1, i, f , m, t ) * TR( m, i1, i ) * TRM ( f , m) + å QIMP( i1, f , m, t )TRMI ( f , i1, m)
i,m
m
(C-4)
where the QS(i1,f,t) represents the rate of local supply of fuel commodity f(c), and the term
TRMI(f,i1,m) acknowledges the existence of or ability to import fuel f(c) into region i1 by
transport mode m. Equations (B-1)-(B-4) describe the essential elements of the mass-flow
parts of the CRETM model.
B.2.2. Electricity Flows and Conservation
Given that QTR(i,i1,c,m,t) represents the transport of commodity c from region i to region i1
via transport mode m at time t, the special case when c is the electricity commodity ELEC
(Table V) leads to the following constraint being imposed on the overall regional electricity
demand, where now the balance is expressed in TWeh/yr units.
168
å QSELEC(i1, ss, t ) + å QTR(i, i1, ELEC, m, t ) * TR( m, i, i1) * TRM ( ELEC, m) * TCW ( ELEC, m)
s
i ,m
+ å QTR( i1, i, ELEC, m, t ) * TR( m, i1, i ) * TRM ( ELEC, m) ≥ DELEC( i1, t ) / ETATT ( i1, t )
i ,m
(C-5)
In this expression QSELEC(i1,ss,t) is the annual electricity supply to region i1 by generation
technology ss at time t, and DELEC(i1,t) is the corresponding (exogenous) demand for
electricity, with ETATT(i,t) being transport/distribution efficiency within a given region. .
Given that each generation technology has an annual capacity of CAPUTIL(ss) full-power
hours per year, and that the installed capacity is QCAP(i,ss,t) in region at time t (in GWe
units), the following expression is an important part of the "energy balance" enforced in the
HUPSIM model:
QELEC( i, ss, t ) ≤ QCAP( i, ss, t ) * CAPUTIL( ss ) / 1000
(C-7)
Again, QELEC(i,ss,t) has TWeh/yr units. Finally, the last contribution to the energy-balance
part of the model increases the available capacity for generation technology ss by summing for
each region i new generation inventories added to the existing electricity-generation stock,
INV(i,ss,t). These new inventories are added to the capacity existing in the starting year
(1995) that is diminished by the fraction RESID(t), which is a exogenous variable that forces
retirement of capacity existing prior to the start year (1995). The capacity balance under these
condition is given as follows:
t
QCAP( i, ss, t ) = RESID( t ) * CAP( i, s,1995) + å INV ( i, ss, t1)
(C-8)
t1
In this summation, t1 starts either at the first period or (t-TLIFE/YEARPP) which ever is
larger, where TLIFE(ss) is the lifetime of generation technology ss, and as usual t and t1 are
ordinals of the particular period designator, with the time separating periods being YEARPP.
Finally, and most important to the material-energy connection in CRETM is the relationship
between electrical energy supplied, QSELEC(i,s), and the quantity of fuel f(c) or commodity c
consumed in creating that supply, QF(i,tot,s,t). This connection between energy and mass is
made through the specific heating rate for the fuel in set t(c), HSTD(tot) having GJ/tonne
units, and the thermal-to-electricity conversion efficiency for that fuel being used by
technology s, EFF(s,tot). The resulting constraint between mass and energy takes the
following form:
QSELEC( i, s, t ) ≤ å QF ( i, tot , s, t ) * HSTD( tot ) * EFF ( s, tot ) / 3.6
(B-9)
tot
Again, the units are TWeh/yr for QSELEC, Mtonne/yr for QF, and GJ/tonne for HSTD, giving
the conversion factor 3.6.
169
C.2.3 Objective Function
The objective function to be minimized in this version of CRETM is the cost of all electrical
generation for all regions discounted at a rate RATE over the period of the study (1995,2030),
ZZ(M$). Given the cost of all electricity generation in region i at time t, Z(i,t), the objective
function is given by,
ZZ = å
i,t
Z ( i, t )
(1 + RATE ) YEARPP*( t −1)
(C-10)
where again YEARPP is the number of year per period, and t here is the ordinal of the
sequence of these time periods. The annual charges for electricity generation, Z(i,t), is
composed of the following eight components: a) fuel extraction and import charges, ZIMP(i,t);
b) cost of domestic fuel, ZDOM(i,t); c) fuel (coal) pre-treatment (cleanup) costs, ZWAS(i,t); d)
transportation charges, ZTRP(i,t); e) emission charges (taxes, if applied to SO2, NOX, and/or
CO2), ZTAX(i,t); f) variable charges associated with generation, ZVAR(i,t); g) fixed charges
associated with generation, ZFIX(i,t); and h) the sum of annualized capital charges from the
period of first installation (including interest charges during construction, IDC) to the end of
the time horizon, which will be either at the end of the plant economic life or the year 2030,
which ever comes first, ZCAP(i,m). Since these annual charges at any given t are incurred
during the time period YEARPP in between ordinal years, discounting by a factor
DISCPP =
YEARPP
å 1 / (1 + RATE )
t1
is first used to bring this costs back to the year t before
1
Eq.(B-9) is used to bring all costs back to the base year (1995). In the order presented above,
the components of Z(i,t) are as follows (refer to Nomenclature for specific variable
definitions):
Z IMP = å DISCPP * QIMP( i, f , m, t ) * TRMI ( f , i, m) * FIMP( m, f )
(C-11A)
Z DOM = å DISCPP * QS ( i, tot , t ) * UCFUEL( i, tot )
(C-11B)
ZWAS = å DISCPP * [CQ( i, k , k1, t ) * CC( k , k1) + PQ( i, k , k1, t ) *PC( i, k , k1)]
(C-11C)
f ,m
tot
k , k1
Z TRP =
å DISCPP * QTR( i, i1, c, m, t ) * UCTRP( c, m, i, i1, t )
(C-11D)
i1, c , m
Z TAX = CTAX ( i, t ) * DISCPP * å CO2 EF ( tot ) * QF ( i, tot , s, t ) +
tot , s
STAX ( i, t ) * DISCPP * å SO2 EF ( s, tot ) +
(C-11E)
tot , s
NTAX ( i, t ) * DISCPP * å NOXEF ( s, tot ) * QF ( i, tot , s, t )
tot , s
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ZVAR = DISCPP * å COSTVAR( ss ) * QSELEC( i, ss, t )
(C-11F)
Z FIX = DISCPP * å COSTFIX ( ss ) * QCAP( i, ss, t )
(C-11G)
ss
ss
Z CAP = DISCPP * å
t + TLIFE / YEARPP
å
ss
t1
ICOST ( i, ss, t )
(1 + RATE ) ( t1− t )*YEARPP
(C-11H')
COSTCAP( ss ) * INV ( i, ss, t ) TCONST
1
ICOST ( i, ss, t ) = CRF ( RATE , TLIFE ) *
å
t1
TCONST ( ss )
t 1=1 (1 + RATE )
(C-11H'')
CRF ( RATE , TLIFE ) =
RATE
1 − 1 / (1 + RATE ) TLIFE
(C-11H''')
The annualized cost of investment, ICOST(I,ss,t) incurred from the addition to the generating
inventory of type ss in region i at time t, INV(I,ss,t), uses a capital recovery factor
CRF(RATE,TLIFE) to express the annual charges for capital in terms of the year t of that
investment, while accounting for the interest incurred during construction under an
assumption of a linear expenditure rate of the total capital outlay, COSTCAP(ss)*INV(I,ss,t)
over the construction period TCONST. A similar expression for ICOST(i,ss,1995) is used for
this first year of the optimization, by only the fraction 1 - RESID(1995) of "residual" stock is
capitalized at the starting boundary. Additionally, the economic lifetime determines the CRF,
and not simply the operational lifetime of the plant (Table A33), and nuclear costs include the
expenses associated with decommissioning.
C.2.4. Key Constraints
While the mass and energy (actually mass-to-energy) balances described in Secs. C.2.1 and
C.2.2. represent the heart of this LP energy flow model and are essential to the search for
generation mixes that minimize the cost-based objective function described in Sec. C.2.3.,
other constraints that are endogenous to the model are needed. In addition to the exogenously
fixed input variables (e.g., fuel and technology costs and their rates of change with time,
regional and temporal variations of electricity demand, tax structures, discount rate, etc.), the
endogenous constraints listed in this subsection are essential to force both objective and
subjective realism and other forms of stakeholder bias into the results, as well as to achieve or
force some of the scenario attributes listed generally in Table II and specifically in Table III.
While not a complete description of what is probably the most model-specific part of this
model, the examples included below give a good sense of the way in which influence over the
model results is controlled for either investigative, political, or stakeholder reasons. Given that
the nature of the optimization model does not allow the impacts and implications of offoptimum results (i.e. these simply are not part of the scenario for the optimization model, as
they are for the simulation model, as discussed in Sec. II.C.1.), it becomes crucial to
understand the ways in which these constraints enter into the optimization. In addition to the
generic constraints summarized below, upper and lower limits that are technology specific are
also part of the art of modeling, but these are not listed. The format used below is to give a
terse statement of the endogenous constraint (in italics) that is followed by an algebraic
statement of the constraint as approximately used to generate the numerical results.
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All "good" fossil, nuclear, and renewable technologies shall grow in time:
QSELEC( i, s6, t ) ≥ QSELEC( i, s6, t − 1)
(C-12A)
Renewable technologies will have a controlled rate of introduction, based on capacities by
region:
QCAP( i, s1, t ) ≤ RENEW ( t , i, s1)
(C-12B)
where RENEW(t,,s1) is a time-dependent, exogenous limit imposed on the generation
capacities of the renewable technologies (Table VI, s1 = {PV, WIND, HYDRO, GT}) that
follow a maximum allowed penetration rate.
Maximum oil supply shall be limited to 30% of domestic oil production plus imports:
å [QS ( i, oil , t ) + QS ( i, oili, t )] ≤ 0.3å [ FUELHIGH ( i, oil , t ) + FUELHIGH ( i, oili )]*
i
i
(C-12C)
where FUELHIGH(i,tot,t) is an externally imposed limit on the capacity to use fuel tot(c) in
region i at time t. This constraint takes into account uses of oil other than the supply of direct
energy, such as transportation and chemicals.
Total cleaning capacity of coal type k shall be limited to FQHIGH(t,i,k):
å [ PQ(i, k , k1, t ) + CQ( i, k , k1, t )] ≤ FQHIGH ( t , i, k )
(C-12D)
k1
where FQHIGH(t,i,k) is an exogenous input.
Total coal treatment at time t is a fraction PRCT(t) of total coal mass flow:
å [ PQ( i, k , k1, t ) + CQ( i, k , k1, t )] ≥ PRCT ( t ) * å [ PQ( i, k , k1, t ) + CQ( i, k , k1, t ) + NQ( i, k , k1, t )]
i ,k ,k1
i , k ,k 1
(C-12E)
where the fraction PRCT(t) increases from 0.15 in 1995 by 0.01 per year (0.05/period)
increments.
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Gradually phase-out "bad" generation technologies s2 (Table VI) as an decreasing fraction
PHASOUT(t) of 1995 capacity:
QCAP( i, s2, t ) = PHASOUT ( t ) * QCAP( i, s2,1995)
(C-12F)
Use of coal or hydro must be less than a factor MAENC(t,ss) of total electricity generation:
å
QSELEC( i, sc, t ) ≤ MAENC( t , coal ) * å QSELEC( i, ss, t )
(C-12G')
å
QSELEC( i, s4, t ) ≤ MAENC( t , hydro ) * å QSELEC( i, ss, t )
(C-12G'')
i ,sc
i ,sc
i , ss
i , ss
Similar MAENC-like restrictions, which are exogenous inputs, are also imposed on oil and
gas usage as a function of time and source (e.g., domestic versus imported). These constraints
are introduced to simulate the needs of other stakeholders.
Regional limits of SO2 emissions at a level of SO2EMISS(i,t):
å QF ( i, tot, s, t ) * SO2 EFF ( s, tot ) ≤ SO2 EMISS ( i, t )
(C-12H)
tot .s
Country-wide target (limits) on SO2, NOX, and CO2 emissions:
å QF ( i, tot , s, t ) * SO2 EFF ( s, tot ) ≤ SO2TARGET (t )
(C-12I')
å QF ( i, tot , s, t ) * NOXEFF ( s, tot ) ≤ NOXTARGET ( t )
(C-12I'')
å QF ( i, tot , s, t ) * CO2 EFF ( s, tot ) ≤ CO2TARGET ( t )
(C-12I''')
i , tot , s
i , tot , s
i , tot , s
It is noted that the SO2 and NOX constraints are applied regionally, whereas the CO2
constraint usually is applied countrywide. When these constraints are binding the solution the
marginal cost of eliminating the last tonne of a given pollutant, corresponds to the tax needed
to give the same level of pollutant as the caps. It corresponds also to the price of permits
when trade between regions is possible and regional pollution rights are specified such that
correspond to the same total national pollution bound.
A series of other region-specific constraints related to how and where (coal) is use and
restriction on the transport mode are also imposed. These constraints and the ones describe
above can have a strong impact on the "optimal" generation mixes reported by a model of this
kind, and the policy implications such results might have.
173
The version of CRETM used for the CETP analysis is extended to include the following
options, which will be described in the CETP Book and CD (Eliasson, 2002):
Ø Endogenous learning;
Ø Partial Equilibrium; and
Ø A peak capacity constraint that defines needs for peak load and reserve margins.
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