1
SE/P3-39
Nuclear Fusion as New Energy Option in a Global Single-Regional Energy
System Model
C. Eherer 1), M. Baumann 1), J. Düweke 2), T. Hamacher 2)
1) Institute for Theoretical and Computational Physics (ITP), Graz University of Technology,
Austria
2) Max Planck Institute for Plasma Physics (IPP), Garching, Germany
e-mail contact of main author: [email protected]
Abstract. Is there a window of opportunity for fusion on the electricity market under “business as usual”
conditions, and if not, how do the boundary conditions have to look like to open such a window? This question
is addressed within a subtask of the Socio-Economic Research on Fusion (SERF) programme of the European
Commission. The most advanced energy-modelling framework, the TIMES model generator developed by the
Energy Technology System Analysis Project group of the IEA (ETSAP) has been used to implement a global
single-regional partial equilibrium energy model. Within the current activities the potential role of fusion power
in various future energy scenarios is studied. The final energy demand projections of the baseline of the
investigations are based on IIASA-WEC Scenario B. Under the quite conservative baseline assumptions fusion
only enters the model solution with 35 GW in 2100 and it can be observed that coal technologies dominate
electricity production in 2100. Scenario variations show that the role of fusion power is strongly affected by the
availability of GEN IV fission breeding technologies as energy option and by CO2 emission caps. The former
appear to be a major competitor of fusion power while the latter open a window of opportunity for fusion power
on the electricity market. An interesting outcome is furthermore that the possible share of fusion electricity is
more sensitive to the potential of primary resources like coal, gas and uranium, than to the share of solar and
wind power in the system. This indicates that both kinds of technologies, renewables and fusion power, can
coexist in future energy systems in case of CO2 emission policies and/or resource scarcity scenarios. It is shown
that Endogenous Technological Learning (ETL), a more consistent description of technological progress than
mere time series, has an impact on the model results.
1. Introduction
Facing the expected growth in energy demand on a global scale during the next decades,
whereby especially fast growing economies in the non-OECD part of the world (e.g. China)
will have an impact on the situation, the investigation of the technical and economic potential
of new energy supply technologies in possible future energy systems is of high interest.
Nuclear fusion is one of the promising new energy supply technologies due to its safe
operation, nearly inexhaustible resources and its potential for a CO2 emission free production
of base load electricity. Fusion reactors are expected to be commercially available for power
generation about the middle of the century. Due to their large unit size, they will initially
serve as base load option for electricity production. The site for the fusion experiment ITER,
which is the next step in fusion research and which might bring the proof of principle for
nuclear fusion, is currently under decision.
Within a subtask of the Socio-Economic Research on Fusion (SERF) programme of the
European Commission the central goal is to identify, whether there is a window of
opportunity for fusion on the electricity market under “business as usual” conditions, and if
not, how the boundary conditions have to look like to open such a window [1].
The most advanced energy-modelling framework, the TIMES model generator developed by
the Energy Technology System Analysis Project group of the IEA (ETSAP, [2]) has been
used to implement a global single-regional partial equilibrium energy model at the IPP
Garching in co-operation with the ITP Graz [3].
2
SE/P3-39
Within an EFDA-SERF project task, the ITP Graz studies the potential role of fusion power
in a future energy system. The work is done in co-operation with the IPP Garching and is
mainly focused on long-term scenario analyses until 2100 with special attention to nuclear
fusion and its most likely long-term competitors. Scenarios comprise possible development
paths of major driving forces of an energy system. They can be seen as story lines formulated
by the modeller or by stakeholders to assess e.g. the impact of energy policies [4].
2. Methodology
2.1. The TIMES Model Generator
The main tool to carry out the present system studies is the “The Integrated MARKAL EFOM
System” (TIMES), which is the latest energy modelling framework developed by the Energy
Technology System Analysis Project group of the IEA (ETSAP). TIMES is a so called model
generator. The modeller provides information about the structure of the energy system, the
energy technologies and all the required technical and economic data. Out of these
information TIMES generates an economic partial equilibrium model of the energy system in
terms of a mathematical programme.
In the solution of a TIMES model the quantities and prices of the various commodities of the
energy system are in equilibrium, i.e. their prices and quantities in each time period are such
that at those prices the suppliers produce exactly the quantities demanded by the consumers
[5]. This equilibrium is present at every stage of the energy system from primary energy
forms to final energy demands. In reality, demands will reduce or increase compared to a
reference case, when prices rise and fall, respectively. TIMES offers the feature to make
energy demands price sensitive, by assigning elasticities to them. The demands can thus selfadjust within a certain bandwidth endogenously within the model, allowing a bona fide
supply-demand equilibrium. Due to elastic demands, the energy model has the chance to
balance welfare losses caused by a more expensive electricity mix with welfare losses caused
by demand reductions, leading to an optimal distribution of the two with respect to the net
total surplus of the system.
In a TIMES model energy technologies are explicitly modelled in detail in terms of
technological and economic data, which is a bottom-up description of the energy system. The
scope of TIMES models is beyond purely energy related issues. The representation of
materials and environmental emissions related to the energy system is possible. Thanks to the
explicitness of the representation of technologies and fuels, a TIMES model can be
constructed to analyse energy-environmental policies.
The energy system in a TIMES model is depicted by flows of energy carriers through energy
technologies, modelled by the concept of a Reference Energy System (RES). Energy, material
flows and emissions are described by commodities, which are transformed by processes into
each other. In this way, the whole path from primary energy to final energy or even energy
services can be modelled.
TIMES offers a number of advanced modelling features for a sophisticated representation of
the energy system (e.g. variable model period lengths, flexible time slices and storage
processes, accurate and realistic depiction of cost payments and investments, flexible
processes, Endogenous Technological Learning, data decoupled from period definition, age
dependent parameters, vintaging, multiple model regions linked by inter-regional trade) [6],
[7], [8].
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3
FIG. I. STRUCTURE OF THE TUG-IPP MODEL.
2.2 The TUG-IPP Global Energy Model
The developed global model depicts the energy system starting from extraction of primary
energy carriers over conversion, refining and distribution to the various end-use sectors
demanding final energy. In addition, a simplified description of the transport sector is
included. At the moment, the focus of the model structure lies on the supply side (especially
electricity production), while the demand side is viewed at in an aggregated way. Although,
the model is single-regional at the present state, the energy demands are split into OECD and
non-OECD due to the expected differences in development in these two world regions.
Figure I gives an overview of the structure of the TUG-IPP model.
The time horizon of the model is the long-term future. It reaches from 1990 to 2100, divided
into 12 model periods of 10 years length each. The resolution is annual, no seasonal or even
smaller time divisions are made. Past investments existing prior to the model time horizon are
accounted for.
The set of electricity and central heat production technologies includes a variety of present
and future technology types: nuclear fusion power (HCLL, Model AB, [9]), nuclear fission
power, coal and gas plants, CO2 sequestrating technologies, hydro power, solar thermal
power, photovoltaics, on- and offshore wind turbines, geothermal power from hard dry rock
and aquifers, biomass and biogas plants, fuel cell plants.
To achieve a more realistic representation of technological diffusion and growth, dynamic
growth constraints are being used. Such constraints link the development of a technology in
one period with its status in the previous period. In reality, the growth of a technology is
dependent on its former development. So, by the use of dynamic growth constraints
unrealistic effects (e.g. the taking over of the whole market in one single period, or the
alternating use of two technologies) can be prevented, leading to smoother transitions from
one technology to another one and smoother transitions between periods.
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SE/P3-39
3. Baseline Scenario
An energy scenario consists of a set of coherent assumptions about future trajectories of the
drivers of an energy system. It describes a possible way in which the future may enfold [4].
Long-term energy models like the TUG-IPP model are explorative tools for the investigation
of such possible futures based on contrasted scenarios. In a TIMES model, a scenario consists
of different types of inputs: energy demands, primary resource potentials (e.g. coal and
lignite, natural gas, crude oil, uranium), a policy setting, and the description of a set of
technologies [5].
In the baseline scenario of the TUG-IPP model the final energy demands are based on the
projections of IIASA-WEC Scenario B, which are characterised by moderate growth [4].
Recent numbers for primary resource potentials were included ([10], [11], [12], [13], [14])
and the maximum share of electricity generated by wind and solar power was given an upper
limit of 30%. In the baseline no policies like emission caps or taxes are assumed and no
GEN IV nuclear fission breeding technologies are available. Commercial fusion power plants
are assumed to be available in the mid of the century. For investments a discount rate of 5%
has been chosen for all model periods.
Figure II shows the results for the electricity production mix in the baseline scenario. One can
observe that coal technologies without CO2 sequestration dominate electricity production in
2100 (47%). Nuclear fission contributes quite a lot to the electricity mix (up to 33% in 2060).
It starts declining about 2070 due to ongoing depletion of the uranium resources available in
the baseline. Gas turbines and fuel cells satisfy the peak load demand for electricity (ca. 20%
of the demand). Solar power, wind turbines, biomass plants and hydro power produce
together 24% of the electricity in 2100, while fission accounts for only 7% of the production.
Nevertheless, without any emission policies, the chosen development path favours
inexpensive electricity from coal. In the described setting of the baseline, which is chosen
quite conservative, fusion power cannot compete as base load option with coal and nuclear
power and enters the global energy mix with only 35 GW in 2100.
4. Scenario Variations
To investigate the influence of different boundary conditions on the potential of fusion power
a number of scenario variations have been carried out and analysed. Compared to the baseline
all combinations of the following scenario assumptions have been tested: different cumulative
CO2 emission constraints leading to a certain long-term stabilisation level [15], an upper limit
on the cumulative amount of CO2 sequestration, different upper limits on the maximum share
of solar and wind power, different potentials of primary resources, existence of nuclear
fission breeding technologies (Table I).
For the analysis of key boundary conditions the total electricity produced from fusion power
plants has been compared to the total cumulative production over the whole model horizon.
The observations derived from the results of all possible scenario variations (in total 72
variations) can be summarised as follows.
SE/P3-39
5
Generated electricity by fuel - baseline
80,000.00
Other
70,000.00
Wind
Solar
60,000.00
Electricity [TWh]
Hydro
Fission
50,000.00
Existing LWR
Biomass
40,000.00
Fuel cell
Gas + Seq.
30,000.00
Gas
Fusion
20,000.00
Coal + Seq.
Coal + Lig.
10,000.00
Existing
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Model period
FIG. II. ELECTRICITY PRODUCTION BY FUEL IN THE BASELINE.
TABLE I: SCENARIO VARIATIONS.
Assumption
Cumulative CO2 emission constraints
Maximum amount of cumulative
sequestrated CO2
Maximum share of electricity from wind
and solar power
Amount of primary resources
GEN IV Nuclear fission breeding
technology
Variations
450 ppm / 550ppm / no limit
300 Giga tonnes carbon / no limit
30% / 60%
65% / 100% / 200% (of baseline)
yes / no
If nuclear fission breeding technologies are available as energy option (at 30% higher
investment cost, 50% higher operation and maintenance cost and 100% higher activity costs
than LWRs [16]), nuclear fusion power does not enter the electricity mix in any of the
scenarios. In the cases where breeder reactors are not available, fusion power can get a market
share in 32 of 36 cases. In 24 of these 32 cases CO2 emission constraints are present,
confirming the expected impact of emission policies on the role of fusion power. The amount
of fusion power is also dependent on the amount of primary resources and the maximum
shares of wind and solar power, while the limit on cumulative sequestrated CO2 has no
impact. Furthermore, it can be seen, that the amount of resources has a higher impact on the
share of fusion power than an imposed upper limit on the share of wind and solar power. The
maximum share of cumulative electricity production that can be reached by fusion power in
scenarios without fission breeding technologies is 14.3%.
Figure III shows the results for generated electricity in case of an energy scenario containing
cumulative CO2 emission constraints leading to a long-term stabilisation level of 550 ppm
[15]. Furthermore, it is assumed that no GEN IV breeders are available and high shares of
solar and wind power (60%) are allowed. In this scenario fusion power can get a considerable
market share of 9.6% of the cumulative electricity production over the model time horizon.
Fission fades out due to depletion of uranium.
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6
Fusion Energy Scenario - Generated electricity by fuel
550ppm, unlimited sequestration, up to 60% wind and solar, no GEN IV fission breeder
80,000.00
Other
70,000.00
Wind
Solar
Electricity [TWh]
60,000.00
Hydro
Fission
50,000.00
Existing LWR
Biomass
40,000.00
Fuel cell
Gas + Seq.
30,000.00
Gas
Fusion
20,000.00
Coal + Seq.
Coal + Lig.
10,000.00
Existing
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Model period
FIG. III. ENERGY SCENARIO WITH CO2 EMISSION CONSTRAINTS.
In 2100 fusion power (31%) and coal technologies with CO2 sequestration (9%) account for
40% of electricity production. Hydro, solar, wind and geothermal power take 40% of the
market. The demanded peak load (20%) is produced by heron turbine fuel cell plants.
5. Impact of Endogenous Technological Learning (ETL)
In long-term energy models, one of the important issues is to describe the future development
of the technological progress. Among the parameters that are important for the
characterisation of a technology are the specific investment costs [17]. Some years ago, in
energy models the cost development was considered as being a function of time, i.e. a
technology becomes cheaper with elapsing time, also if it is not used. The progress of a
technology was an exogenous assumption, though based on historic experiences. This
sometimes led to unreasonable results, especially in perfect foresight models. In these models,
a technology was chosen because of its competitive price in model periods at the end of the
time horizon, without being in use before.
Historical experience has shown that the progress of a technology is related to the knowledge
accumulated through the construction or use of this technology [18], [19]. Thus, it has
become more common to treat the technological progress of a technology in an energy system
model in an endogenous way. In the TIMES model generator technological learning is
endogenised by linking the specific investment costs of a technology to its cumulative
installed capacity via the Experience Curve concept [20], [21]. This feature has been applied
to the electricity producing technologies and the impact on the baseline and selected scenarios
was investigated.
To study the impact of ETL on the scenario results, three different scenarios were analysed,
the baseline scenario and two scenarios with a considerable share of electricity generated by
fusion power. The results show that the endogenous description of the specific cost
development has a significant impact. The main benefit is gained by technologies based on
solar power, especially PV cells which can enter already the baseline scenario with a high
market share (in terms of generated electricity). Also wind power and biomass plants can
increase their market share. In all scenarios, the total amount of generated electricity
increased, and the cost of electricity decreased.
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SE/P3-39
The increased electricity production of these technologies compensates the production by
some other technologies. Coal power plants have the biggest decrease in production, but also
the production of fusion power plants declines. For example, the small share of fusion in the
baseline scenario vanishes completely. In the case of high CO2 emission constraints
(450 ppm), the share of cumulative fusion power drops from 12% to 9%, whereas the in case
of 550 ppm it drops from 10% to only 3%.
Nevertheless, a major drawback of ETL is the tremendous increase of computer resources if
one wants to include more than a few (5-10) learning technologies in an energy model. This is
also the reason, why at the moment not all 72 scenario variations presented in section 4 could
be investigated with ETL.
6. Conclusion/Outlook
Under the quite conservative baseline assumptions fusion only marginally enters the model
solution in 2100 (35 GW) and it can be observed that coal and gas technologies account for a
large part of the electricity production in 2100. Scenario variations show that the role of
fusion power is strongly affected by the availability of GEN IV fission breeding technologies
as energy option and by CO2 emission caps. The former appear to be a major competitor of
fusion power while the latter open a window of opportunity for fusion power on the
electricity market. An interesting outcome is furthermore that the possible share of fusion
electricity is more sensitive to the potential of primary resources like coal, gas and uranium,
than to the share of solar and wind power in the system. This indicates that both kinds of
technologies, renewables and fusion power, can coexist in future energy systems in case of
CO2 emission policies and/or resource scarcity scenarios. For instance, in case of CO2
emission constraints leading to a long-term stabilisation level of 550 ppm in the atmosphere,
fusion power is able to produce 31% of the electricity in 2100, while another 40% are
produced by solar, wind, hydro and geothermal power.
It was shown that Endogenous Technological Learning (ETL), a more consistent description
of technological progress than mere time series, can have an impact on the model results. The
application of ETL imposes high needs on computer resources, even in case of just a few
learning technologies, thus at the moment its application is limited.
In the next phase of the activities, more in depth analyses and informative results will be
obtained making use of the new long-term 15-regions global energy model, which was
developed under EFDA contract and has been released in October 2004 [5], [22]. The TUGIPP model will be used and maintained further for quick checks, comparisons and exploration
of modelling features that are only applicable in smaller models because of computer
resources.
7. Acknowledgements
Besides the Austrian EURATOM Association and EFDA, we especially would like to thank
the Friedrich Schiedel Foundation for Energy Technology and the Austrian Ministry of
Science (BM:BWK) for their significant support of this research project.
8. Disclaimer
This work, supported by the European Communities under the Contract of Association
between EURATOM and the Austrian Academy of Sciences, was carried out within the
SE/P3-39
8
framework of the European Fusion Development Agreement. The views and opinions
expressed herein do not necessarily reflect those of the European Commission.
Appendix 1: References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
EFDA-STAC, Fusion Technology work programme 2005, 2004.
ETSAP homepage, http://www.etsap.org, referenced in November 2004.
Eherer, C., Baumann, M., Long-Term Technology Based Modelling with TIMES,
Interim Report, EFDA/SERF sub task TW3-TWE-FESA/A, Garching, June 2004
Nakicenovic, N., Grübler, A., and McDonald A., Global Energy Perspectives,
Cambridge University Press, Cambridge, UK, 1998.
Haurie, A., Kanudia, A., Loulou, R., van Regemorter, D., and Vaillantcourt, K., The
EFDA World Model. Draft Final Report prepared for EFDA, ORDECSYS,
HALOA/KANORS and KUL, 2004.
TIMES, The "New" ETSAP E3 MODEL System Documentation, Working Paper, The
IEA-ETSAP TIMES Working Group, 2000.
Mäkelä, J., Development of an Energy System Model of the Nordic Electricity
Production System, Master Thesis, Helsinki, 2000.
Eherer, Ch., Fusion as a new energy technology in a long-term TIMES model of the
energy system, Friedrich Schiedel project, project report, Graz-Garching, 2004.
Ward, D.J., Baumann, M., Report on the first assessment of an HCLL plant model,
Culham, March 2004.
BGR, Reserven, Ressourcen und Verfügbarkeit von Energierohstoffen 2002,
Rohstoffwirtschaftliche
Länderstudien
Heft
XXVIII,
Bundesanstalt
für
Geowissenschaften und Rohstoffe, Hannover 2003.
Erdmann, G., Wirtschaftliche Vorraussetzung der energetischen Nutzung von
Biomasse, TU Berlin, 1998.
Fischer, G., Schrattenholzer, L., Global Bioenergy Potentials through 2050, Biomass &
Bioenergy 20, pages 151-159, 2001.
Kaltschmitt, M, Hartmann, H., Energie aus Biomasse – Grundlagen, Techniken,
Verfahren, Springer Verlag, Berlin-Heidelberg, 2001.
Lako, P., Eder, H., de Noord, M., Reisinger, H., Hydropower Development with a
Focus on Asia and Western Europe, Overview in the framework VLEEM 2, ECN report
C—03-027, Netherlands, 2003.
IPCC, Special Report on Emissions Scenarios, Nakicenovic and Swart (eds).,
Cambridge University Press, 2000.
Mooz, W.E., Siegel, S., A Comparison of the Capital Costs of Light Water Reactor and
Liquid Metal Fast Breeder Reactor Power Plants, Prepared for the U.S. Arms Control
and Disarmament Agency, R-2441-ACDA, The Rand Corporation, 1979.
Bareto, L., Technological Learning In Energy Optimisation Models And Deployment
Of Emerging Technologies, Dissertation, ETH Zürich, 2001.
Dutton, J., Thomas, A., Academy of Management Review – Treating Progress
Functions as a Managerial Opportunity, Academy of Management Review Vol. 9, No.2,
235-247, 1984.
Argote, L., Epple, D., Learning Curves in Manufacturing, Science Vol. 247, Febr. 1990.
Wene, C., Experience Curves for Energy Technology Policy, IEA, Paris, 2000.
Neij, L., et.al., Experience Curves - A Tool for Energy Policy Assessment, Lund, 2003.
Lechon, Y., Cabal, H., Varela, M., Saez, R., Eherer, C., Baumann, M., Düweke, J.,
Hamacher, T., Tosato, G.C., A Global Energy Model with Fusion, contribution
submitted to the SOFT conference, Venice, Italy, 2004.
Nuclear Fusion as a New Energy Option in a Global SingleRegional Energy System Model
C. Eherer1, M. Baumann1, J. Düweke2, T. Hamacher2
1 Institute
for Theoretical and Computational Physics (ITP), Graz University of Technology, Petersgasse 16/II, 8010 Graz, Austria
2 Max Planck Institute for Plasma Physics (IPP), Boltzmannstr. 2, 85748 Garching, Germany
Introduction
Introduction
Baseline
Baseline Scenario
Scenario (cont.)
(cont.)
™
™ Socio-Economic
Socio-Economic Research
Research on
on Fusion
Fusion (EFDA)
(EFDA)
™
™ Investigation
Investigation of
of potential
potential role
role of
of fusion
fusion power
power in
in
future
future energy
energy scenarios
scenarios
™
™ Identifying
Identifying boundary
boundary conditions
conditions for
for fusion
fusion
900.00
™
™ Consistent
Consistent description
description of
of specific
specific cost
cost
development
development
800.00
700.00
Transport fuels
Final energy [EJ]
600.00
Methodology
Methodology
Endogenous
Endogenous Technological
Technological
Learning
(ETL)
Learning (ETL)
Final energy demands - baseline
Generated electricity by fuel - with ETL - baseline
Residential electricity
Residential heat
500.00
80,000.00
Commercial electricity
400.00
Commercial heat
Other
70,000.00
Industrial electricty
300.00
Wind
Industrial heat
Solar
60,000.00
200.00
Electricity [TWh]
Hydro
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Model period
Fission
50,000.00
Existing LWR
Biomass
40,000.00
Fuel cell
Gas + Seq.
30,000.00
Gas
Fusion
20,000.00
Coal + Seq.
Coal + Lig.
Primary energy - baseline
10,000.00
1,600.00
Existing
1990
1,400.00
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Model period
Wind energy
Uranium
1,200.00
Solar energy
Natural gas
1,000.00
Energy [EJ]
™
™ The
The Integrated
Integrated MARKAL
MARKAL EFOM
EFOM System
System (TIMES)
(TIMES)
™
™ Energy
Energy Technology
Technology System
System Analysis
Analysis Project
Project
(ETSAP)
(ETSAP) of
of the
the IEA
IEA
™
™ Model
Model generator
generator
™
™ Partial
Partial equilibrium
equilibrium (supply-demand)
(supply-demand)
™
™ Total
Total surplus
surplus maximisation
maximisation
™
™ Multi-period,
Multi-period, perfect
perfect foresight
foresight
™
™ Technology-rich
Technology-rich bottom-up
bottom-up description
description of
of the
the
energy
energy system
system
™
Energy
flows,
materials,
emissions
™ Energy flows, materials, emissions
™
™ Analysis
Analysis of
of energy-environmental
energy-environmental policies
policies
™
™ Advanced
Advanced features
features
100.00
Fusion Scenario 1 - Generated electricity by fuel - with ETL
Lignite
Hydro energy
80,000.00
Coal
800.00
Geoth. heat (deep)
Other
70,000.00
Geoth. heat (aquifer)
600.00
Wind
Deuterium + Lithium
¾
¾ Price
Price sensitive
sensitive demands
demands
¾
¾ Flexible
Flexible technology
technologydescription
description
¾
¾ Endogenous
EndogenousTechnological
TechnologicalLearning
Learning
Solar
60,000.00
Crude oil
400.00
Hydro
Biomass (wood + res.)
200.00
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Model period
Electricity [TWh]
Biomass (energy crops)
Fission
50,000.00
Existing LWR
Biomass
40,000.00
Fuel cell
Gas + Seq.
30,000.00
Gas
Fusion
20,000.00
The
-IPP Model
TUG
The TUGTUG-IPP
Model
2030
2040
2050
2060
2070
2080
2090
2100
Model period
Hydro
¾
¾ Split:
Split:OECD,
OECD,Non-OECD
Non-OECD
Biomass
80,000.00
Fuel cell
Gas + Seq.
30,000.00
Wind
om
.E
lc
om .
.H
ea
t
d.
Elc
.
In
d.
H
e
R e at
s.
Elc
.
R
es
.H
Tra eat
ns
po
rt
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Model period
Fusion
Fusion in
in Energy
Energy Scenarios
Scenarios
Fusion Scenario 1 - Generated electricity by fuel
450ppm, unlimited sequestration, up to 60% wind and solar, no GEN IV fission breeder
90,000.00
CO2
Other
80,000.00
Wind
Transport
70,000.00
Solar
Electricity [TWh]
Hydro
60,000.00
Fission
Existing LWR
50,000.00
Biomass
Fuel cell
40,000.00
Gas + Seq.
Gas
30,000.00
Fusion
20,000.00
Coal + Seq.
Coal + Lig.
Final energy demands for
OECD and Non-OECD
10,000.00
Existing
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Model period
Baseline
Baseline Scenario
Scenario
™
™ Conservative
Conservative assumptions
assumptions on
on potential
potential of
of wind
wind
and
and solar
solar power
power (30%
(30% of
of generated
generated electricity)
electricity)
™
™ Dynamic
Dynamic growth
growth constraints
constraints for
for all
all sectors
sectors
™
emission caps
caps and/or
and/or taxes
taxes
™ No
No CO
CO22 emission
™
™ Discount
Discount rate:
rate: 5%
5%
Fusion Scenario 2 - Generated electricity by fuel
550ppm, unlimited sequestration, up to 60% wind and solar, no GEN IV fission breeder
80,000.00
Other
70,000.00
Wind
Solar
60,000.00
Electricity [TWh]
¾
¾ Geothermal,
Geothermal,biomass,
biomass,hydropower
hydropower
Gas + Seq.
Gas
Coal + Seq.
Existing
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Model period
™
™ Share
Shareof
of cumulative
cumulativefusion
fusionpower:
power:
CO2
™
™ Based
Based on
on IIASA-WEC
IIASA-WEC Scenario
Scenario BB final
final energy
energy
projections
projections
™
™ Resource
Resource constraints
constraints (fossils,
(fossils, uranium)
uranium)
™
™ Limited
Limited potentials
potentials according
according to
to literature
literature
Fuel cell
Coal + Lig.
Electricity &
Heat
Production
Demand side
Biomass
30,000.00
Fusion
¾¾ Scenario
Scenario1:
1:13.7%
13.7%(from
(from1990
1990on),
on),19.6%
19.6%(from
(from2050
2050on)
on)
¾¾ Scenario
Scenario2:
2:9.6%
9.6%(from
(from1990
1990on),
on),13.6%
13.6%(from
(from2050
2050on)
on)
Sectoral
Heating
Existing LWR
40,000.00
10,000.00
Grids
CO2
Fission
50,000.00
20,000.00
Sectoral Elc.
Dem.
Electrolysis
Hydro
Electricity [TWh]
Existing
Solar
60,000.00
Coal + Seq.
10,000.00
CO2
Refining &
Conversion
Other
70,000.00
Gas
Coal + Lig.
In
C
C
ic
ity
He
Ele
at
ctr
icit
y
t
ctr
Ele
H
ea
Fu
els
ew
ab
R
en
Existing LWR
40,000.00
Fusion
Dynamic
Dynamic growth
growth constraints
constraints
Price
Price sensitive
sensitive energy
energy demands
demands
Endogenous
Technological
Endogenous Technological Learning
Learning (ETL)
(ETL)
Supply side
Fusion Scenario 2 - Generated electricity by fuel - with ETL
Fission
50,000.00
20,000.00
rg
y
y
2020
Solar
60,000.00
En
e
rg
2010
Wind
le
En
e
2000
Other
70,000.00
Global
Global MARKAL/TIMES
MARKAL/TIMES energy
energy model
model
Long-term
Long-term (1990-2100,
(1990-2100, 12
12 periods)
periods)
Single
Single world
world region
region
Final
Final energy
energy demands
demands
¾
¾ Refining,
Refining,conversion,
conversion,sectoral
sectoralheat
heatproduction,
production, basic
basic
grids,
grids, transport
transport
Pri
m
ary
Existing
1990
™
™ Focus
Focus on
on supply
supply side
side (electricity
(electricity sector)
sector)
™
™ Other
Other sectors
sectors
™
™
™
™
™
™
10,000.00
80,000.00
Electricity [TWh]
™
™
™
™
™
™
™
™
Coal + Seq.
Coal + Lig.
Generated electricity - baseline
™
™ Baseline
Baseline
¾
¾ Coal,
Coal, gas
gasand
andnuclear
nucleartechnologies
technologies account
accountfor
foraa
large
large part
partof
ofthe
theelectricity
electricityproduction
production
¾
Only
a
small
fraction
of
fusion
in
2100
(35
GW)
¾ Only a small fraction of fusion in 2100 (35 GW)
™
™ Role
Role of
of fusion
fusion is
is affected
affected by
by
¾
¾ Availability
Availabilityof
of GEN
GENIV
IVfission
fissionbreeding
breedingtechnologies
technologies
at
ataacertain
certaincost
cost level
level
¾
Cumulative
CO
emission
constraints
¾ Cumulative CO22 emission constraints
™
™ Resource
Resource potentials
potentials affect
affect fusion
fusion more
more than
than
share
share of
of solar
solar and
and wind
wind power
power
™
™ ETL
ETL as
as aa consistent
consistent description
description of
of progress
progress has
has
an
an impact
impact
™
The
new
EFDA-TIMES
energy
model
™ The new EFDA-TIMES energy model
¾
¾ 15
15 world
world regions
regions
¾
¾ Trade
Tradebetween
between regions
regions
¾
¾ Soft-link
Soft-linkto
tomacroeconomic
macroeconomicframework
framework (general
(general
equilibrium
equilibriummodel
modelGEM-E3)
GEM-E3)
Hydro
Fission
50,000.00
Existing LWR
Biomass
40,000.00
Fuel cell
Gas + Seq.
30,000.00
Gas
Acknowledgements
Acknowledgements
Fusion
20,000.00
Coal + Seq.
Coal + Lig.
10,000.00
Existing
1990
Conclusions/Outlook
Conclusions/Outlook
2000
2010
2020
2030
2040
2050
Model period
2060
2070
2080
2090
2100
™
™ Friedrich
Friedrich Schiedel
Schiedel Foundation
Foundation for
for Energy
Energy
Technology,
Technology, Austria
Austria
™
Austrian
Ministry
of
Science
™ Austrian Ministry of Science
™
™ OEAW-EURATOM
OEAW-EURATOM Association
Association