International Journal of Clean Coal and Energy, 2013, 2, 35-43
doi:10.4236/ijcce.2013.22B008 Published Online May 2013 (http://www.scirp.org/journal/ijcce)
Prospects for Renewable and Fossil-Based Electricity
Generation in a Carbon-Constrained World
Takayuki Takeshita
Transdisciplinary Initiative for Global Sustainability, The University o f Tokyo, T okyo, Japan
Email: takeshita@ir3s.u-tokyo.ac.jp
Received April 24, 2013; revised June 2, 2013; accepted April 29, 2013
ABSTRACT
In this paper, a regionally disaggregated global energy system model with a detailed treatment of the electricity supply
sector is used to derive the cost-optimal choice of electricity generation technologies for each of 70 world regions over
the period 2010-2050 under a constraint of halving global energy-related CO2 emissions in 2050 compared to the 2000
level. It is first shown that the long-term global electricity generation mix under the CO2 constraint becomes highly di-
versified, which includes coal, natural gas, nuclear, biomass, hydro, geothermal, onshore and offshore wind, solar
photovoltaics (PV), and concentrated solar power (CSP). In this carbon-constrained world, 89.9% of the electricity gen-
eration from coal, natural gas, and biomass is combined with CO2 capture and storage (CCS) in 2050. It is th en shown
that the long-term electricity generation mix under the CO2 constraint varies significantly by world region. Fossil fuels
with CCS enter the long-term electricity generation mix in all world regions. In contrast, there is a sharp regional dif-
ference in the renewable generation technology of choice in the long term. For example, the world regions suitable for
PV plants include the US, Western Europe, Japan, Korea, and China, while those suitable for CSP plants include the
Middle East, Africa, Australia, and western Asia. Offshore wind is deployed on a large scale in the UK, Ireland, Nordic
countries, the southern part of Latin America, and Japan.
Keywords: Renewable Power Sources; Fossil Power Sources; CO2 Mitigation; Regionally Detailed Analysis; Global
Energy System Model
1. Introduction
Climate change is the most pressing and threatening is-
sue facing human beings. However, the United Nations
warned in a recent report [1] that international efforts to
mitigate climate change are, at this stage, insufficient to
meet the goal of keeping global warming to below 2.0
degrees Celsius above pre-industrial levels. In order to
avoid dangerous climate change and to achieve sustain-
able development, a portfolio of rational greenhouse gas
(GHG) mitigation actions must be identified and their
implementation must be accelerated.
In response to such political needs, many research or-
ganizations have proposed desirable strategies for reduc-
ing CO2 emissions from the energy sector under a global
2.0 degrees Celsius stabilization target. They have often
indicated that the decarbonization of the electricity gen-
eration sector is an attractive CO2 mitigation option in
terms of CO2 mitigation potential and cost-effectiveness
(e.g., [2,3]). However, there are fewer studies available
conducting a regionally detailed analysis on CO2 mitiga-
tion strategies for the electricity generation sector, al-
though they have insisted that desirable CO2 mitigation
strategies are very likely to vary by region depending on
regional characteristics.
Thus, the purpose of this paper is to derive the cost-
optimal choice of electricity generation technologies in
regional detail over the period 2010-2050 under a con-
straint of halving global energy-related CO2 emissions in
2050 compared to the 2000 level. Due to space limita-
tions, the focus of this paper is confined to (1) the com-
petitiveness of renewable and fossil-based electricity
generation technologies under this constraint and (2) re-
gional differences in technology choices in the electricity
generation sector under this constraint. This is done by
using the global energy system model REDGEM70 (an
acronym for a REgionally Disaggregated Global Energy
Model with 70 regions) [4,5], which treats the electricity
supply sector in detail. For the model to be used for this
purpose, it was updated to properly consider the variabil-
ity of renewable electricity generation and flexibility
measures needed to integrate variable renewables into a
power grid .
2. Methodology
2.1. Overview of the REDGEM70 Model
REDGEM70 is a technology-rich, bottom-up global en-
Copyright © 2013 SciRes. IJCCE
T. TAKESHITA
36
ergy systems optimization model formulated as an in-
tertemporal linear programming problem (see [5] for a
schematic representation of the structure of the model).
With a 5% discount rate, the model is designed to deter-
mine the cost-optimal energy strategy (e.g., the cost-op-
timal choice of technology options) from 2010 to 2050 at
10-year intervals for each of 70 world regions so that
total discounted global energy system costs are mini-
mized under constraints on the satisfaction of exoge-
nously given en ergy end-use demands, the availab ility of
primary energy resources, material and energy balances,
the maximum market growth rates of new technologies,
etc. In the model, price-induced energy demand reduc-
tions and energy efficiency improvements, fuel switching
to less carbon-intensive fuels, and CO2 capture and stor-
age (CCS) in geologic formations are the three options
for CO2 emissions reduction.
Furthermore, in the current version of the model used
in this study, there is also a constraint that global en-
ergy-related CO2 emissions in 2050 are to be halved
compared to the 2000 level. This constraint is imposed
because the Intergovernmental Panel on Climate Change
has concluded that a 50% to 80% reduction of global
CO2 emissions by 2050 compared to the 2000 level can
limit the long-term global mean temperature rise to 2.0
degrees Celsius above pre-industrial levels [6]. The
model has a full flexibility in where and how CO2 emis-
sions reduction is achieved to meet this co nstraint.
As described above, REDGEM70 uses 70 world re-
gions. Figure 1 shows how the 70 world regions are de-
fined in the model. These 70 regions are categorized into
“energy production and consumption regions” and “en-
ergy production regions”. The whole world was first di-
vided into the 48 energy production and consumption
regions to which future energy end-use demands are al-
located. The 22 energy production regions, which are
defined as geographical points, were then distinguished
from the energy production and consumption regions to
represent the geographical characteristics of the areas
endowed with large amounts of fossil energy resources.
While the 48 energy production and consumption regions
cover the global final energy consumption, all the en-
ergy-related activities except final energy consumption
are conducted in each of the two region types in the
model. Such a detailed regional disaggregation enables
the explicit consideration of regional characteristics in
terms of energy resource supply, energy demands, geog-
raphy, and climate.
Future trajectories for energy end-use demands were
estimated as a function of those for socio-economic
driving forces such as population and income in the in-
termediate B2 scenario developed by [7]. Allocation of
the energy end-use demand estimates to the 48 energy
production and consumption regions was done by using
country- and state-level statistics/estimates (and projec-
tions if available) on population, income, geography,
energy use by type, and transport activity by mode, and
by taking into account the underlying storyline of the B2
scenario that regional diversity might be somewhat pre-
served throughout the 21st century.
Assumptions on the availability and extraction cost of
fossil energy resources and uranium resources were de-
rived from [8] and [9], respectively. For non-biomass
renewable resources, electricity supply potentials and
electricity generation costs by world region are exoge-
nous inputs to the model, which were obtained from
Representative cities in energy production and consumption regions
Represe ntative s ite s in energy production regions
Figure 1. Regional disaggregation of REDGEM70.
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T. TAKESHITA 37
[10,11]. For biomass resources, the model considers not
only terrestrial biomass, but also waste biomass. The
availability of these biomass resources and excess crop-
land that can be used for energy purposes without con-
flicting with other biomass uses such as food production
was estimated for each region and each time point. They
were estimated assuming that biomass is produced in a
sustainable way so that biomass-derived energy carriers
can be regarded as carbon neutral. Data for these biomass
resources (e.g., resource availability, yield s per hectare of
land, and supply costs) are provided in [12]. These re-
source availability estimates were then allocated to the 70
model regions by using country-, state-, and site-level
statistics/estimates.
2.2. Electricity Supply Sector Submodel
The electricity supply sector submodel of REDGEM70
was developed based on the ReEDS model [13]. In this
submodel, time is subdivided within each 10-year time
period: each year is divided into four seasons, each sea-
son is divided into three diurnal time-slices, and there is
one superpeak time-slice. Given a set of input data and
constraints, this submodel determines the cost-optimal
choice of electricity generation technologies and elect ricity
storage technologies for each region and each time point
and determines their cost-optimal operation patterns for
each region, each time-slice, and each time point.
This submodel explicitly takes into account the vari-
ability of wind an d so lar p hotovo ltaics (PV) power, flex i-
bility measures needed to integrate these variable re-
newables into a power grid (e.g., installing additional,
flexible generation capacities and/or electricity storage
technologies on the grid), and the costs associated with
such measures. In contrast, it is assumed that concen-
trated solar power (CSP) plants have some form of heat
storage to deliver power on demand and thus do not in-
crease the operating reserve requirement [2]. This sub-
model also accounts for the cost of constructing adequate
transmission capacity from renewable distributed gen-
erators to the nearest grid that must be built specifically
to carry their generation.
Table 1 shows the input data for electricity generation
technologies other than non-biomass renewables, while
Table 2 shows the input data for CO2 capture technolo-
gies for power plants (which represent the bulk of the
Table 1. Data for electricity generation technologies other than non-biomass renew ablesa.
Technologies Capital costb
(US$2000/kW) Conversion efficiencyb
(%, LHV basis) Maximum capacity
factor (%) Technical lifetime
(years)
Coal-fired steam cycle 1,500-1,170 43.0-52.0 85 40
Coal IGCC 1,680-1,300 46.0-54.0 85 40
Coal IGCC-SOFC 2,080-1,830 56.0-60.0 85 30
Oil-fired steam cycle 760-640 43.0-52.0 85 30
NGCC 630-530 57.0-63.0 85 30
NGCC-SOFC 1,260-1,060 66.0-70.0 85 30
Light-water nuclear r eactor 2,350-2,100 34.0-37.0 85 40
Biomass-fired steam cycle
using wood chips 1,880-1,460 24.9-34.0 85 30
using wood pellets 1,790-1,400 28.5-34.0 85 30
using grain residues 2,120-1,660 24.9-34.0 85 30
using sugarcane residues
(a uniform mixture of bagasse and trash) 1,880-1,460 22.1-30.2 (el ect ricity)
18.5 (heat) 85 30
using black liquor 4,420-3,440 12.4-13.0 (electricity)
61.0 (heat) 85 30
using municipal wastes 1,630-1,270 22.7-31.0 85 30
Biomass IGCC
using wood chips 2,390-1,530 37.6-45.0 85 30
using wood pellets 2,300-1,470 39.8-47.1 85 30
using sugarcane residues
(a uniform mixture of bagasse and trash) 2,780-1,780 32.4-37.9 (electricity)
18.5 (heat) 85 30
using black liquor 2,010-1,280 25.4-28.7 (electricity)
45.7 (heat) 85 30
Biogas-fueled gas engine for CHP applications 3,200-1,650 35.9-40.0 (electricity)
84.0 (total efficiency) 85 20
a Sources: [3,12,14-21]. LHV = lower heating value; IGCC = integrated gasification combined cycle; NGCC = natural gas combined cycle; CHP = co mbined
heat and po wer; SOFC = solid oxide fuel cell. b These ranges denote the assumed evolution of the parameter values over the period 2010-2050. All cost num-
bers are based on North American manufacture and construction.
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38
overall CCS costs). The input data for six types of sta-
tionary fuel cell technologies for CHP applications are
not presented here because of space limitations. The ac-
tual model inputs for capital costs vary by word region
by applying a region-specific plant location factor (see
[22] for the location factor values). Table 3 shows the
global technical potential of six non-biomass renewable
electricity technologies at different cos t ca teg orie s. Th es e
non-biomass renewable resource availability estimates
were allocated to the 48 energy production and consump-
tion regions based on data provided by [23]. Although
not shown here, electricity generation technologies are
also characterized by their load-following, quick-start,
and operating/sp inning reserve capabilities.
This submodel includes batteries and hydro-pumping
as electricity storage technologies. The capital cost of
batteries is assumed to decrease from 1,790 US$2000/kW
in 2010 to 1,290 US$2000/kW in 2050, while their round
trip efficiency is assumed to increase from 77% in 2010
to 82% in 2050 [13]. On the other hand, there is assumed
to be potential for approximately 1,000 GW of pumped
storage capacity worldwide, while the overall efficiency
of pumped storage is assumed to be 80% [2]. The cost of
pumped storage systems is assumed to be 20% to 200%
higher than that of equivalent unpumped hydropower
systems depending on the cost category [2].
There are two important constraints imposed to inte-
gration of variable renewables into a power grid. First,
the planning reserve margin constraints ensure that the
sum of (1) the capacities of dispatchable generators and
storage technologies and (2) the capacity value of vari-
able renewable generators as represented by their effec-
tive load-carrying capability is larger than the annual
peak load plus a reserve margin. The capacity value of
each type of variable renewable electricity generation
technology was calculated for each region and each time
point using the approach proposed by [13,24]. Second,
the operating reserve constraints ensure that the sum of
the capacities of spinning reserves, quick-starts, and
storage technologies is larger than the normal operating
reserve requirement plus that imposed by variable re-
newables. The normal operating reserve requirement and
the additional operating reserve requirement per unit ca-
pacity of variable renewable generators were calculated
for each region, each time-slice, and each time point us-
ing the approach proposed by [13].
Table 2. Data for CO2 capture technologies for new pow er plantsa.
Technologies Incremental capital costsb
(US$2000/t-C/year) Electric power requiredb
(MWh/t-C) CO2 capture efficiency
(%)
Post-combustion ca pt ure from coal steam cycle plants 240-210 1.077-0.861 90
Oxyfuel combustion c a p tu re from coal steam cy c l e p l a nts 340-300 1.077-0.861 95
Pre-combustion capture from coal IGCC plants 130-110 0.861-0.646 90
Post-combustion capture from NGCC plants 370-330 1.535-1.343 90
Oxyfuel combustion capture from NGCC plants 470-420 1.814-1.633 95
Pre-combustion capture from biomass IGCC plants 280-250 0.702-0.526 90
Pre-combustion captur e f rom black liquor IG C C p la nt s 160-140 0.373-0.280 90
a Sources: [3,12,17,19,20]. b These ranges denote the assumed evolution of the parameter values over the period 2010-2050. All cost numbers are based on
North American manufacture an d constructi on. The incremental capital costs of CO2 capture are assumed to be 60% higher for existing power plants than for
new power plants, while the electric p ower consump tion for CO2 capture is a s sumed to be 32% higher for existing power p lants than for new power plants [12].
Table 3. Technical potential of non-biomass renewable electricity supply in 2050 by cost category, in TWh/yeara.
Renewable electricity type < 30
US$2000/MWh 30 – 50
US$2000/MWh 50 – 100
US$2000/MWh 100 – 150
US$2000/MWh 150 – 200
US$2000/MWh
Hydroelectricity 3,151 6,673 4,065 0 0
Geothermal electricity 82 1,183 11,236 0 0
Onshore wind powerb 1,254 23,891 70,562 9,570 0
Offshore wind powerb 0 127 1,436 4,827 0
CSPc 0 42,842 180,753 52,377 0
Solar PV powerc 54 450,314 14,601 5,032 0
a Sources: [10,11]. b The technical potential of onshore and offshore wind power is assumed to increase at an annual rate of 0.54% and 0.42%, respectively, until
2050, while the generation costs of onshore and offshore wind power are assumed to decrease at an annual rate of 1.04% and 1.03%, respectively, until 2030.
c The technical potential of CSP and PV is assumed to increase at an annual rate of 0.48% and 1.14%, respectively, until 2050, while the generation costs of
CSP and PV are assumed to decrease at an annual rate of 3.52% and 4. 59%, respec tively, until 2050.
Copyright © 2013 SciRes. IJCCE
T. TAKESHITA 39
3. Results and Discussion
3.1. Globally Aggregated Results for Electricity
Generation
Before examining the simulation results on a regional
basis, the focus is placed on the globally aggregated re-
sults. Figures 2(a) and (b) show the evolution of the
global electricity generation for the business-as-usual
(BaU) case without any CO2 constraints and the case
with the CO2 constraint used here (called the CO2 con-
straint case hereafter), respectively. The results indicate
that among the four sectors (i.e., industry, transport,
buildings, and electricity generation), the electricity gen-
eration sector makes the largest contribution to reducing
cumulative global CO2 emissions over the period 2010-
2050 from those in the BaU case, accounting for 64.7%
of the total emissions reduction. This is the reason why
there is a significant change in the choice of electricity
generation technologies between the two cases. Renew-
ables, CCS, and nuclear are the three largest contributors
to CO2 emissions reduction from the electricity genera-
tion sector in the CO2 constraint case.
In the CO2 constraint case, renewables account for a
considerable share of the global electricity generation:
the share of biomass, hydro, geothermal, onshore wind,
offshore wind, solar PV, and solar CSP in the global
electricity generation in 2050 is 5.1%, 15.0%, 2.5%,
10.3%, 2.0%, 6.2%, and 6.2%, respectively. In total, the
share of renewables in the global electricity generation in
the CO2 constraint case increases from 20.6% in 2010 to
47.3% in 2050, while that in the BaU case decreases
from 20.6% in 2010 to 20.3% in 2050.
On condition that the resulting CO2 is properly cap-
tured and sequestered, fossil fuels remain an important
source of the global electricity gen eration throughout the
time horizon even under the stringent CO2 emissions
reduction constraint. In the CO2 constraint case, the share
of coal and natural gas in the global electricity generation
in 2050 is 10.2% and 18.6%, respectively. The increasing
deployment of CCS allows for the continued reliance on
fossil fuels in the electricity generation sector in the CO2
constraint case: the global penetration rate of CCS in
fossil-based electricity generation technologies increases
to 75.5% in 2050. Furthermore, clean coal technologies
gain increasing impor tance in this CO2-constrained world:
the share of coal IGCC plants in the total global coal-
based electricity generation increases to 23.5% in 2030
and to 90.4% in 2050 in the CO2 constraint case. This is
because coal IGCC plants are estimated to become more
economical than coal-fired steam cycle plants if CCS
becomes necessary [25].
3.2. Identifying World Regions where Each
Technology Is Economically Attractive
In this section, a regionally detailed analysis is carried
out to identify world regions where each electricity gen-
eration technology is economically attractive in the CO2
constraint case. This is done to better understand regional
differences in technology choices in the electricity gen-
eration sector in this case, which are examined in Section
3.3. Figures 3(a) and (b) show the cost-optimal technol-
ogy choices in the electricity generation sector in the CO2
constraint case by world region for 2030 and 2050, re-
spectively.
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
2010 2020 2030 2040 2050
Worldelectricityge n erat io n (TWh/year)
Hydrogen
SolarCSP
SolarPV
Windoffshore
Windonshor e
Geoth ermal
Hydro
Biomass+CCS
Biomass
Nu cl ear
Natura l gas+CCS
Natura l gas
Oil
Coal+CCS
Coal 0
5000
10000
15000
20000
25000
30000
35000
40000
45000
2010 2020 2030 2040 2050
Worldelectricityge n erat io n (TWh/year)
Hydrogen
SolarCSP
SolarPV
Windoffshore
Windonshor e
Geoth ermal
Hydro
Biomass+CCS
Biomass
Nu cl ear
Natura l gas+CCS
Natura l gas
Oil
Coal+CCS
Coal
(a) (b)
Figure 2. Global electricity generation in the BaU case (a) and the CO2 constraint case (b).
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T. TAKESHITA
40
(a)
(b)
Figure 3. Cost-optimal technology choices in the electricity generation sector in the CO2 constraint case by world region in
2030 (a) and 2050 (b)a.(a Towers indicate representative sites in energy production regions, while crosses indicate representa-
tive cities in energy production and consumption re gions. )
Fossil fuels retain their position as an important sou rce
of electricity generation in many world regions until 2050.
Coal-based electricity generation technologies continue
to be important until 2050 in regions such as India, the
southern part of Africa, the Former Soviet Union, Oce-
ania, the US, China (excluding western China), Eastern
Europe, and Indonesia. Although not explicitly shown
here, China, western Asia (mainly India), and the south-
ern part of Africa take the lead in the deployment of coal
IGCC plants. On the other hand, natural gas-based elec-
tricity generation technologies continue to be important
until 2050 in reg ions such as the Middle East, Chin a, the
US, Europe, the southern part of Latin America, the
Former Soviet Union, Japan, and West Africa. Coal-rich
countries such as the US, India, China, Russia, and Aus-
tralia take the lead in the deployment of CCS from elec-
tricity generation.
Hydro, onshore wind, and solar PV constitute one of
the pillars of the long-term generation system in many
parts of the world. In the case of hydro, it accounts for a
large share of electricity generation in regions such as
southeastern Asia, Canada, Latin America, Russia, Eu-
rope, India, inland central and southern China, tropical
central Africa, the US, Korea, Oceania, and Japan. In the
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T. TAKESHITA 41
case of onshore wind, it penetrates into the generation
system in regions such as China, the southern part of
Latin America, Western Europe, the Former Soviet Un-
ion, and North America. In the case of solar PV, it pene-
trates into the generation system in regions such as Jap an,
the US, China (excluding western China), Western Eu-
rope, Korea, parts of the Former Soviet Union, and
southeastern Asia. This result is comparable to that of the
International Energy Agency [2], which projected that
solar PV would grow very significantly in solar-rich in-
dustrialized countries (particularly in the US) and China.
In regions where onshore wind and/or solar PV are de-
ployed, natural gas-based electricity plants and/or flexi-
ble hydroelectricity plants play a major role as a backup
for these variable renewables.
In contrast, biomass-based electricity generation tech-
nologies (mainly biomass IGCC plants), geothermal
electricity generation technologies, and offshore wind
electricity generation technologies participate visibly in
the electricity generation mix in a limited number of
world regions in the long term. In 2050, biomass (most
of which is combined with CCS) enters the electricity
generation mix in biomass-rich regions such as Latin
America and Europe. In the case of geothermal, it enters
the long-term electricity generation mix in countries
around the “Ring of Fire” (e.g., Indonesia, the Philip-
pines, Central America, and the western coast of the US),
rift zones (e.g., East Africa), and Russia. In the case of
offshore wind, it enters the long-term electricity genera-
tion mix in Nordic countries, Latin America, the UK and
Ireland, Japan, and Korea.
Solar CSP has a large share of electricity generation in
the widespread Sunbelt regions, which include the Mid-
dle East, Africa, Australia, the southern US, and western
Asia. In these regions with high direct solar radiation
(excluding the southern US), solar PV is excluded from
the electricity generation mix. This result is plausible
because CSP electricity is estimated to be cheaper than
solar PV electricity in the Sunbelt regions [2] and be-
cause flexibility measures need to be taken to integrate
variable solar PV electricity into a power grid, which
worsen its overall economics.
3.3. Regional Differences in Technology Choices
in the Electricity Generation Sector
Taking into account the findings of Section 3.2, regional
differences in long-term technology choices in the elec-
tricity generation sector in the CO2 constraint case can be
summarized as follows:
The long-term generation system in the US is charac-
terized by diversified sources of electricity generation
such as coal, natural gas, hydro, onshore wind, and solar
PV. In addition, so lar CSP plays an important role in the
long-term generation system in the southern US, while
geothermal is deployed on a large scale in the long-term
generation system in the western coast of the US. On the
other hand, the Canada’s generation system continues to
be dominated by hydroelectricity. The Central America’s
long-term generation system is highly diversified and
depends largely on geothermal electricity, while onshore
and offshore wind play an important role in the genera-
tion system in the southern part of Latin America. The
long-term generation system in other parts of Latin
America is characterized by a large dependence on hydro
and biomass and a small dependence on fossil fuels.
A distinctive feature of the long-term generation sys-
tem in Western Europe and the southern part of Eastern
Europe is that its components include hydroelectricity
plants, onshore wind electricity plants (except in Turkey,
Greece, Cyprus, and Eastern European countries), bio-
mass-based electricity plants, and solar PV electricity
plants (except in the UK, Ireland, and Nordic countries)
and that it depends much less on coal-based electricity
plants. Offshore wind is a significant part of the long-
term generation system in the UK, Ireland, and Nordic
countries. The Former Soviet Union’s long-term genera-
tion system varies significantly by region. Coal, hydro,
and geothermal are common sources of electricity gen-
eration in Russia in the long term, while onshore and
offshore wind are deployed on a large scale in the
long-term generation system in the western part of the
Former Soviet Union.
Natural gas continues to dominate the Middle East’s
generation system. Also, the long-term generation system
in the Middle East and Africa is characterized by a large
dependence on solar CSP. Coal and hydro play marginal
roles in the generation system in the Middle East and
North Africa, while natural gas plays an important role
only in the West Africa’s generation system among
Sub-Saharan Africa. Geothermal is deployed on a large
scale in the East Africa’s long-term generation system,
while coal with CCS has a large share of the long-term
generation system in the southern part of Africa.
The China’s long-term generation system can be clas-
sified into three types. First, onshore wind and solar PV
participate visibly in the generation system in northern
and eastern China, but hydro has a very small participa-
tion in it. Second, the generation system in inland central
and southern China is highly diversified, in which coal,
natural gas, nuclear, hydro, onshore wind, and solar PV
have a visible participation. Third, the western China’s
generation system is dominated by natural gas and on-
shore wind.
The western Asia’s generation system is characterized
by a small dependence on natural gas and wind (except
in the northern part of western Asia). Not solar CSP but
solar PV participates in the long-term generation system
in eastern and southern India with the eastern India’s
Copyright © 2013 SciRes. IJCCE
T. TAKESHITA
42
generation system relying heavily on coal with CCS. In
contrast, solar CSP plays an important role in the
long-term generation system in other parts of western
Asia. Solar CSP and solar PV co-exist in the long-term
generation system in the northern part of western Asia.
The southeastern Asia’s long-term generation system
is characterized by a large dependence on hydro, partici-
pation of solar PV, and absence of wind. Geothermal is
deployed on a large scale in the long-term generation
system in Indonesia and the Philippines. There are simi-
larities in the long-term generation system in Japan and
Korea, that is, these systems consist mainly of natural gas,
nuclear, hydro, offshore wind, and solar PV. In addition,
geothermal constitutes one of the pillars of the Japan’s
long-term generation system. On the other hand, coal
with CCS, hydro, and solar CSP are the major compo-
nents of the Oceania’s long-term generation system.
4. Conclusions
Using the regionally disaggregated global energy system
model with a detailed treatment of the electricity supply
sector, this paper has derived the cost-optimal choice of
electricity generation technolog ies in regional detail over
the period to 2050 under the constraint of halving global
energy-related CO2 emissions in 2050 compared to the
2000 level. The majo r find ings and notes are su mmarized
below:
First, all the seven renewable power sources consid-
ered in this study and the two fossil power sources, coal
and natural gas, will become cost-competitive in the
long-term global generation system under the stringent
CO2 constraint used here. For these two fossil fuels to
remain important power sources throughout the time ho-
rizon even under the stringent CO2 constraint, the CO2
resulting from fossil fuel co mbu stion /co nversio n needs to
be properly captured and sequestered. According to the
simulation results, fossil-based power plants with CCS
produce 75.5% of the total global fossil-based electricity
generation in 2050. Among all the renewable power
sources, hydro will continue to account for the largest
share of the global electricity generation, followed by
onshore wind, solar PV, solar CSP, and biomass. Off-
shore wind and geothermal sources are less competitive
than the other renewable pow e r s ources.
Second, a future generation system in each world re-
gion will evolve over time in a very different manner to
form a regionally highly diverse electricity supply struc-
ture. For example, the long-term generation syste m in the
northeastern US and northern and eastern China consists
mainly of coal, natural gas, nuclear, onshore wind, and
solar PV, while that in Japan and Korea consists mainly
of natural gas, nuclear, hydro, offshore wind, and solar
PV. The main components of the Western Europe’s
long-term generation system include natural gas, nuclear,
biomass, hydro, onshore and/or offshore wind, and solar
PV. Solar CSP plays an important role in the long-term
generation system in the Middle East, Africa, Australia,
the southern US, and western Asia, while hydro has a
large share of the long-term generation system in Canada,
Latin America, inland central and southern China, and
southeastern Asia. Such a large regional difference in
long-term generation systems is caused mainly by re-
gional characteristics in terms of primary energy resource
endowments, the need for a backup for variable renew-
ables, the seasonal and daily load pattern, and the eco-
nomics of CCS.
The results of this study must be interpreted with the
following two limitations in mind. First, although the
model solutions presented here are optimal in terms of
cost under the stringent CO2 constraint, electricity gen-
eration systems in the “real world” are designed to meet
not only the goals of cost-effectiveness and reduced
GHG emissions, but also other goals (e.g., of improved
energy security and increased public acceptance), which
are important but very difficult to consider in bottom-up
optimization models like REDGEM70. Second, policy
actions, targets, and incentives to reduce GHG emissions,
to promote renewables, etc. have a large impact on the
choice of technologies in the “real world”, which are
only partially considered in the model due to the lack of
worldwide information. Nevertheless, this study is novel
and significant in that the findings obtained from the re-
gionally disaggregated results have important implica-
tions for the cost-optimal deployment of different elec-
tricity generation technologies under CO2 constraints in
many countries and regions.
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