2012

EFDA – SERF WP2011

Helena Cabal – CIEMAT Yolanda Lechon – CIEMAT Tobias Eder – IPP Markus Biberacher –iSpace Research Studio Umberto Ciorba - ENEA

[REPORT ON ENERGY SCENARIOS] The role of fusion in future energy system: a scenario analysis

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Table of contents Table of contents 2

Report on Energy Scenarios 4

1. Introduction 4

2. The ETM model 5

3. The typical characteristics of ETM 6

3.1 The industrial sector 10

3.2 Heat transmission grids 11

3.3 The nuclear fuel cycle 11

3.3.1 Implementation of existing NFC technologies 13

3.3.2 Implementation of future NFC technologies 14

3.4 Renewables and storage options 15

3.5 New biofuels and electric vehicles 21

3.6 Fusion in ETM 21

4. The scenario tree 21

5. Elasticities 23

6. Main model drivers 23

6.1 Population 24

6.2 GDP projections 25

7. Results 26

7.1 Final Consumption 26

7.1.1 The transport sector 28

7.1.2 The industrial sector 28

7.1.3 Focus: electricity consumption in Industry 29

7.1.4 The civil sector 33

7.1.5 Critical issues: the demands of the residential sector 34

7.2 Electricity Generation 37

7.3 Primary Energy and CO2 emissions 42

7.4 Indicators 46

8. Sensitivity analysis: 47

8.1. Lower levels of energy demands 47

8.2 Storage options for CSP technologies 49

8.3 New biofuels and electric vehicles 50

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8.4 Scenario analysis: nuclear fuel cycle strategies 53

8.4.1 Scenario setup 54

8.4.2 Scenario results – electricity production 54

8.4.3 Scenario results – installed capacities 56

8.4.4 Scenario results - resource availability and consumption 58

8.4.5 Scenario results – overall waste arising 58

9. Conclusions 60

References 62

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Report on Energy Scenarios

1. Introduction Fusion power has never been considered in long term energy scenarios. In 1998 the European Fusion Development Agreement (EFDA) decided to support an activity of scenarios studies based on technological bottom-up model and funded the implementation of the EFDA Times Model (ETM). The model has been developed by an external consortium of experts and delivered in 2004. The structure and data of the EFDA-TIMES model came from the SAGE1

The current version of ETM is the result of a cooperative research effort of the association participants to the EFDA –SERF (Socio Economic Research on Fusion). The aim of the scenario analyses carried out with the EFDA Times model (ETM) is to explore the possible role of Fusion in future energy systems, taking into account competition with other technologies and possible trend of evolution for the energy system.

model, which has been used by the US Department of Energy for their International Energy Outlook from 2002 to 2008.

Scenarios analyses are not forecasting but shows possible path of development for the global energy system. They depend on a wide range of external data and assumptions and it is almost impossible to take into account all the possible developments of the relevant dimensions that characterize the scenarios. This report is an attempt to systematically evaluate under which conditions fusion can play a role in future energy system breaking down scenarios components into different groups and depicting “storylines”. Storylines are linked essentially to demographic and economic conditions and to the stringency of environmental policies. Based on the framework represented by the storylines, a sensitivity analysis is carried out to assess the role of the main technological competitors for fusion.

As this is one of the first complex scenario analysis carried out through the EFDA-Times model, the results of the analysis should be considered with caution, as they are strongly dependent on a version of the model whose development is still in progress.

However, beyond the results, the first goal of the Report is to propose a methodology to analyse and define key drivers that affect the development of fusion technologies. This methodology can be easily repeated, so that results and sensitivity analysis should become more precise following the debugging and the development of the EFDA-Times model.

This work, supported by the European Communities under the contract of Association between Euratom and IPP, CIEMAT, ENEA, OAEW was carried out within the framework of EFDA. The views and opinions expressed herein do not necessarily reflect those of the European Commission.

1System to Analyze Global Energy

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2. The ETM model The EFDA TIMES (ETM) energy model is a global long term partial equilibrium model of the entire energy system. It covers the entire energy system: from energy services demands, to final consumption technologies, to transport and transformation of energy carriers until mining of primary commodities.

It uses the integrated MARKAL-EFOM system (TIMES) provided by IEA-ETSAP2 and similar global multi-regional MARKAL-TIMES models have been used for the preparation of other important long term energy technology evaluation studies3

The model dynamics is determined by a maximization of total economic surplus (which - if demands are not price elastic - is equivalent to a minimization of overall costs). Underlying assumptions require the hypotheses that energy markets are competitive and economic agents have perfect information and perfect foresight, optimizing their decisions along the entire time horizon.

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The maximization of the objective function is realized by the TIMES model generator using linear programming techniques. Linearity implies that each technology may be implemented at any level without economies of scale between inputs and outputs. Supply curves are a stepped sequence of linear functions that can be approximated by a non linear interpolation4

The EFDA TIMES model covers a time horizon from 2000 to 2100 distributed over 12 model periods of variable length. The reference energy system includes five energy consumption sectors (residential, commercial, agriculture, industrial and transportation) and two energy supply sectors (electricity production and upstream/downstream). Industry is further divided into 6 energy intensive subsectors. Technologies, in the ETM, are divided into demand technologies, transformation technologies, extraction/import technologies. The database is also divided between technologies working at the base year and the future potential technologies entering the energy system during the whole period of study to satisfy energy services demands. Technologies are characterized by a number of parameters that can be classified as:

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• Technical parameters (efficiency, technical lifetime, installed capacity and relative bounds, input and output commodities);

• Environmental Parameters (emission factor for the main pollutants CO2, CH4, N2O, SOx). • Economic parameters (Investment costs, O&M costs, sectoral hurdle rates, constraints to

technology penetration). The table below shows sectoral hurdle rates (used to evaluate the annual capital repayment by investors) in ETM model.

2Loulou (2008), Loulou et al. (2008) 3IEA (2008 and 2010), Loulou 2009. 4Loulou, R., M. Labriet, ETSAP-TIAM: the Times integrated assessment model Part I: Model structure

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Table 1. Sectoral hurdle rates in ETM

Group of technologies/region AFR AUS CAN CHI CSA EEU FSU IND JPN MEA MEX ODA SKO USA WEU Cars 30% 15% 15% 25% 25% 25% 25% 30% 15% 25% 25% 25% 20% 15% 15% Light vehicles 30% 15% 15% 25% 25% 25% 25% 30% 15% 25% 25% 25% 20% 15% 15% Trucks 20% 10% 10% 18% 18% 18% 18% 20% 10% 18% 18% 18% 15% 10% 10% Bus 20% 10% 10% 18% 18% 18% 18% 20% 10% 18% 18% 18% 15% 10% 10% Motorcycles 30% 15% 15% 25% 25% 25% 25% 30% 15% 25% 25% 25% 15% 15% 15% Hybrid Cars 36% 18% 18% 30% 30% 30% 30% 36% 18% 30% 30% 30% 24% 18% 18% Light vehicles (advanced) 36% 18% 18% 30% 30% 30% 30% 36% 18% 30% 30% 30% 24% 18% 18% Trucks (advanced) 24% 12% 12% 21% 21% 21% 21% 24% 12% 21% 21% 21% 18% 12% 12% Bus (advanced) 24% 12% 12% 21% 21% 21% 21% 24% 12% 21% 21% 21% 18% 12% 12% Residential 39% 20% 20% 33% 33% 33% 33% 39% 20% 33% 33% 33% 26% 20% 20% Commercial 33% 17% 17% 28% 28% 28% 28% 33% 17% 28% 28% 28% 22% 17% 17% Electricity and Hydrogen sector 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% Solar technologies 20% 10% 10% 18% 18% 18% 18% 20% 10% 18% 18% 18% 15% 10% 10% Industry 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10% 10%

The model is driven by energy service demands in each of the sectors mentioned above. The demands are computed from a coherent set of demand drivers (Population, GDP, SectoralProduction, per capita GDP) obtained from a general equilibrium model. This is done by choosing elasticities which describe the strength of the coupling between driverand demands: Demand = DriverElasticity:

Within the present study the set of elasticities is not varied among different scenarios while drivers are differentiated in Base drivers and High Growth drivers. The former is the set of default drivers of ETM, produced by mean of the GEM-E3 model, the latter has been produced by mean of the dynamic version of the Gtap general equilibrium model5

3. The typical characteristics of ETM

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The EFDA Times model shares many of its characteristics with the TIAM model6

ETM is a global model disaggregated in 15 world regions (plus a Global fictitious region) as shown in

. Nonetheless, apart from the inclusion of fusion technologies in the database (discussed in Chapter 6) ETM is characterized by some peculiarities that differentiate it with respect to TIAM.

Table 2. In ETM, the different regions are connected by inter-regional exchange process (trade of commodities). Transportation and transaction costs for unit of traded energy commodity are considered in the Trade template of ETM. Trade can be bi-lateral between two regions and multi-lateral between several supply and demand regions. In a bi-lateral trade, there is one inter-regional exchange process between the two regions, thus, there is a balance between both regions. A multi-lateral trade however, is based on a common marketplace for a commodity (typically, GHG emissions) which has several producing and consuming regions. The figures below show the activated links (marked cells) between regions for Coal, Electricity, Crude oil, Liquefied natural gas, natural gas transported by pipelines, Lithium, Uranium, CO2.

5Martini, C., M.C. Tommasino (2011). 6Further insights can be found in Lolulou and Labriet (2008).

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Table 2. Regions in ETM model Code Region Countries

AFR

Africa

Algeria, Angola, Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Central African Republic, Chad, Congo, Congo Republic, Djibouti, Egypt, Equatorial Guinea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Ivory Coast, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mauritius, Morocco, Mozambique, Niger, Nigeria, Reunion, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe

AUS Australia- New Zealand

Australia, New Zealand, Oceania

CAN Canada Canada

CHI China China

CSA

Central and South America

Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, St. Kitts and Nevis, St. Lucia, St. Vicent and Grenadines, Suriname, Trinidad-Tobago, Uruguay and Venezuela

EEU

Eastern Europe

Albania, Bosnia-Herzegovina, Bulgaria, Croatia, Czech republic, Hungary, Macedonia, Poland, Romania, Slovakia, Slovenia and Yugoslavia

FSU

Former Soviet Union

Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan

IND India India

JPN Japan Japan

MEX Mexico Mexico

MEA

Middle-East

Bahrain, Cyprus, Iran Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, Turkey, United Arab Emirates and Yemen

ODA

Other Developing Asia

Afghanistan, Bangladesh, Bhutan, Brunei, Chinese Taipei, Fiji, French Polynesia, Kiribati, Indonesia, North Korea, Malaysia, Maldives, Myanmar, Nepal, New Caledonia, Pakistan, Papua-New_guinea, Philippines, Samoa, Singapore, Solomon Islands, Sri Lanka, Thailand, Vanuatu and Vietnam

SKO South Korea South Korea

USA United States United States of America

WEU

Western Europe

Austria, Belgium, Denmark, Finland, France (and Monaco), Germany, Gibraltar, Greece, Greenland, Iceland, Ireland, Italy (and San Marino and Vatican City), Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland (and Liechtenstein), United Kingdom

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Figure 1. Trade links for relevant commodities in ETM (part 1) ~TradeLinksCOAHCO AFR AUS CAN CHI CSA EEU FSU GLB IND JPN MEA MEX ODA SKO USA WEU MINRNW IMPEXPAFR 1 1 1AUS 1 1 1 1 1 1 1 1CAN 1 1 1 1CHI 1 1CSA 1 1 1EEU 1FSU 1 1 1 1GLBINDJPNMEAMEXODA 1 1 1 1SKOUSA 1 1 1 1 1WEUMINRNWIMPEXP~TradeLinksELCC AFR AUS CAN CHI CSA EEU FSU GLB IND JPN MEA MEX ODA SKO USA WEU MINRNW IMPEXPAFR 1AUSCAN 1CHI 1 1CSAEEU 1 1 1FSU 1 1 1 1GLBIND 1 1JPNMEA 1 1 1 1MEX 1ODA 1 1SKOUSA 1 1WEU 1 1 1 1MINRNWIMPEXP~TradeLinksGASLNG AFR AUS CAN CHI CSA EEU FSU GLB IND JPN MEA MEX ODA SKO USA WEU MINRNW IMPEXPAFR 1 1 1 1 1 1 1 1 1AUS 1 1 1 1 1 1 1 1CANCHICSA 1 1 1EEUFSU 1 1 1 1GLBINDJPNMEA 1 1 1 1 1 1 1 1MEXODA 1 1 1 1 1SKOUSA 1WEU 1MINRNWIMPEXP~TradeLinksGASNGA AFR AUS CAN CHI CSA EEU FSU GLB IND JPN MEA MEX ODA SKO USA WEU MINRNW IMPEXPAFR 1 1AUSCAN 1CHI 1CSAEEU 1FSU 1 1 1 1 1GLBINDJPNMEA 1 1 1MEX 1ODASKOUSA 1 1WEU 1MINRNWIMPEXP

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Figure 2. Trade links for relevant commodities in ETM (part 2) ~TradeLinksNUCLIT AFR AUS CAN CHI CSA EEU FSU GLB IND JPN MEA MEX ODA SKO USA WEU MINRNW IMPEXPAFRAUSCANCHICSAEEUFSUGLB 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1INDJPNMEAMEXODASKOUSAWEUMINRNWIMPEXP~TradeLinksNUCURA AFR AUS CAN CHI CSA EEU FSU GLB IND JPN MEA MEX ODA SKO USA WEU MINRNW IMPEXPAFR 1 1AUS 1 1 1 1 1 1 1 1 1 1CAN 1 1 1 1 1 1 1CHICSAEEUFSU 1 1 1 1 1 1GLB 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1INDJPNMEAMEXODASKOUSA 1WEUMINRNWIMPEXP~TradeLinksOILCRD AFR AUS CAN CHI CSA EEU FSU GLB IND JPN MEA MEX ODA SKO USA WEU MINRNW IMPEXPAFR 1 1 1 1 1 1 1 1 1 1 1 1AUS 1 1 1 1CAN 1 1 1 1 1 1 1 1CHI 1 1 1 1 1 1 1 1 1 1 1CSA 1 1 1 1 1 1 1 1 1 1EEUFSU 1 1 1 1 1 1 1 1 1 1 1GLBINDJPN 1 1 1 1 1 1 1 1MEA 1 1 1 1 1 1 1 1 1 1 1 1 1MEX 1 1 1 1 1 1 1ODA 1 1 1 1 1 1 1 1 1 1 1SKOUSA 1 1 1 1 1 1 1 1 1 1 1WEU 1 1 1 1 1 1 1 1 1 1 1MINRNWIMPEXP~TradeLinksTOTCO2 AFR AUS CAN CHI CSA EEU FSU GLB IND JPN MEA MEX ODA SKO USA WEU MINRNW IMPEXPAFR 1AUS 1CAN 1CHI 1CSA 1EEU 1FSU 1GLBIND 1JPN 1MEA 1MEX 1ODA 1SKO 1USA 1WEU 1MINRNW 1IMPEXP 1

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The model is calibrated on IEA Energy Statistics for the year 2000 and includes several parts of the energy system modeled with extreme detail (the so-called sub-res).

In recent years the technological database of the ETM has been considerably improved together with some relevant structural changes mainly in the industrial sector.

3.1 The industrial sector The industrial sector is represented in ETM by five energy-intensive subsectors and an aggregate residual subsector combining other types of industry.

Following the definitions corresponding to the IEA energy statistics, the areas identified are: 1.IIS: Iron and Steel Industry (ISIC Group 271 and Class 2731);

2.INF: Non-Ferrous Metals (ISIC Group 272 and Class 2732);

3.ICH: Chemical and Petrochemical industry (ISIC Division 24);

4.INM: Manufacture of other non-metallic minerals (ISIC Division 26);

5.ILP: Pulp, Paper, and printing of paper products (ISIC divisions 21 and 22);

6.IOI: All other industrial sectors.

A residual subsector is called ONO (Other non-specified Consumption) including consumption related to the industrial sector but not easily attributable to a specific branch and NEO (Industrial and other non-energy uses) containing the non-energy uses from all the sectors. Initially in TIMES every branch in the model industry was characterized by six generic energy services demands, expressed in terms of final consumption of PJ:

1. Steam (Industrial Steam - IS) 2. Process Heat (Process Heat - IP) 3. Driving force (Machine Drive - IM) 4. Electro-chemical processes (Electro-Chemical Process - IE) 5. Semi-finished products / non-energy use (Feedstocks - IF) 6. Other (Other - IO).

Since most of the energy sector comes from the production of certain goods defined "high intensity good" at a later time the main manufacturing processes have been considered in detail calibrating energy demands on their physical volume of production. The result is a more realistic approach, which describes the most important industrial processes, while the remaining energy consumption is embedded in the generic production of energy service. Within each energy-intensive sub-sector, the industrial structure of the Reference Energy System has a hybrid character. The technologies that represent the most important industrial processes use materials other than energy. These technologies can use as inputs either directly a fuel (process heat) or generic energy services (machine drive, industrial steam). The use of commodities as an input material to the final product is also allowed (this is the case of the paper industry where inputs for the production of paper are generic energy services and different types of pulp produced and used by different processes).

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The six services are provided by industrial energy technologies with different levels of efficiency, each powered by a particular fuel. Every sector has its own technologies for the production of the energy services, except for the production of steam and the driving force whose technologies of production are in common between all the sub-sectors,given the commonality of costs, efficiencies and processes.

3.2 Heat transmission grids Heat transmission grids have been included in ETM following an innovative approach. Heat production by CHP plants has been considered as a virtual heat pump. It means that part electricity generated in condensing mode is converted into heat at an efficiency factor that is the inverse of the power-loss ratio. Instead of operating a physical heat pump by electricity, part of the steam in the turbine is sent to a heat exchanger and the district heating network rather than the low-pressure turbine and the power generator.

Interpreting CHP as virtual heat pumps makes it much easier to integrate CHP and heat supply from power stations with CCS into a heat market, where also individual heat pumps become increasingly important. The various heat supply technologies will compete on efficiencies, fuel price and requirement for investment in house installation as well as city-wide infrastructure.

3.3 The nuclear fuel cycle The implementation of the nuclear fuel cycle (NFC) in ETM7

The front-end of the NFC accounts for the whole chain of processes from mining, milling and conversion of natural uranium over enrichment up to fuel production and parts of the reprocessing steps. Additionally, uranium and plutonium from the decommissioning of nuclear weapons are implemented as fuelling resources. Seven different reactor technologies are implemented into the model to facilitate the generation of scenario analysis comparing different technological development pathways in a sense of most reasonable combinations. The technologies representing the back-end of the NFC offer options for direct disposal of spent fuel (e.g. to represent a once through cycle, “OTC”) and range of reprocessing technologies to represent various reprocessing strategies up to closed fuel cycles. The involved material flows are tracked on an element group basis, allowing for the analysis of overall waste production focussing on produced volumes, radiotoxicity and proliferation risks.

provides a technology rich representation of the current global nuclear sector and selected future fission technology pathways. This embraces the front-end, the reactor technologies themselves as well as the back-end of the NFC and thus allows for scenario analysis considering a wide range of possible future fission strategies and a detailed evaluation of related costs, resource consumption and waste production.

Figure 3: Main processes and commodities of the NFC in ETM

7Further insights can be found in Eder and Bustreo (2011).

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3.3.1 Implementation of existing NFC technologies Figure 2 shows the implemented processes and commodities representing the reference NFC, e.g. the existing fission technologies inside the blue frame. The front-end of the NFC consists of natural uranium mining technologies (MINNFCNAU1-4) that include mining, milling and conversion into U3O8(NFCNAU) and represent different cost levels and stocks according to. The production of uranium oxide fuel (UOX) for the light water reactor (LWR) technology(ENFCNPPLWR) requires enriched uranium with a content of 4.25 % 235U (NFCULX). The enrichment process (NFCFPDUEN) produces enriched uranium NFCULX and depleted uranium (NFDUDP), the variable O&M cost of this process are set to 132 $2000/kgHM. Subsequently, UOX fuel (NFCFULLUX) is produced (NFCFPDLUX) from NFCLUX, the variable O&M costs of the production process are set to 200$2000/kgHM. The NFCUDP from enrichment is used together with plutonium from reprocessing (NFCREPLMXPU0) for mixed oxide fuel (MOX) production (NFCFPDLMX), whereby an enrichment of 8.1 %Pu and variable O&M costs of 1300 $2000/kgHMfor the production process is defined.

Table 3: Global nuclear reactor stock, installed capacities in 2000 [GWe]8AFR

AUS CAN CHI CSA EEU FSU IND JPN MEA MEX ODA SKO USA WEU

2 0 13 2 3 10 31 2 42 0 2 0 13 99 123

The global reactor stock (see Table 3) is modelled as generic LRW technology (ENFCNPPLWR), either fuelled by UOX or up to 50 % by MOX from reprocessing of spent UOX fuel (NFCSFULUX). The burn-up rate of LWR is set to 50 GWd/tHM, the power plant efficiency is 34.2 % for the existing reactor stock and increases up to 36.0 % in 2010 for new reactors. The investment costs for LWR are regionalized according to Table 4, the fixed O&M costs are 46.5 $2000/kWe; both (INV and FIXOM) decrease by 0.26 % p.a.

Table 4: Regionalized investment cost for LWR [$2000/kWe] AFR AUS CAN CHI CSA EEU FSU IND JPN MEA MEX ODA SKO USA WEU 1895 3158 3185 1500 1895 2289 2289 1895 2368 1895 1895 1895 1342 2668 3710

For each fuel type (UOX and MOX), a reactor core technology is implemented (NFCNPCLUX and NFCNPCLMX) that reflects the irradiation of different nuclear fuels in the reactor core producing process heat (NFCHETLUX and NFCHETLMX) and different types of spent fuel (NFCSFULUX and NFCSFULMX). The heat produced is consequently converted into electricity (ELCC) by the actual power plant technology (NFCNPPLWR). For the further treatment of NFCSFULUX, the model offers two options: The first is the so-called “once-through cycle” (OTC), where spent fuel is stored indefinitely in a final waste deposit (NFCWDPSFULUX). Thereby, different waste components are distinguished for tracking and evaluation purposes: Uranium (NFCWDPSFUU00), plutonium (NFCWDPSFUPU0), minor actinides (NFCWDPSFUMA0) and fission products (NFCWDPSFUFP0). The second option given by the model is to reprocess the spent UOX fuel with PUREX (NFCREPPURLUX) or advanced PUREX (NFCREPPRALUX) processes and to use the extracted Pu (NFCREPLMXPU0) for MOX fuel production. The variable O&M costs for

8 Source: http://world-nuclear.org/NuclearDatabase [08/2011]

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reprocessing are set to 900 $2000/kgHM and are equal for all reprocessing technologies. The high level waste (NFCREPPURLUXHLW) arising from reprocessing is separated into four waste components, analogous to the spent fuel final deposit: Uranium (NFCWDPHLWU00), plutonium (NFCWDPHLWPU0), minor actinides (NFCWDPHLWMA0) and fission products (NFCWDPHLWFP0).

3.3.2 Implementation of future NFC technologies Future NFC options are implemented as a consequent development of the NFC comprising advanced reprocessing and reactor technologies. The relevant processes and commodities are schematically drawn in Figure 2 (outside the blue frame).

A generic fast reactor (FR) technology (ENFCNPPFMX) is available from 2025 on with an efficiency of 40.3 % and an availability factor of 85 %.The related investment costs are assumed to be equal to that of ENFCNPPLWR, the fixed O&M costs are20 % higher. The reactor is fed by Pu-based MOX fuel (NFCNPPFMX) that is produced from 58 % reprocessed Pu (NFCREPFMXPU0) and 42 % depleted uranium (NFCUDP) from uranium enrichment. The Pu is extracted from spent LWR MOX by either PUREX (NFCREPPURLMX) or advanced PUREX reprocessing (NFCREPPRALMX), both with variable O&M costs of 900 $2000/kgHM.

An advanced breeder reactor (ABR) technology (ENFCNPPABR) is available from 2040 on, with investment costs of 2600 $2000/kWe (not regionalised), decreasing by 0.26 % p.a. and fixed O&M costs being 3.5 % of the investment costs. The technology’s availability factor is 85 %, the efficiency is defined as 38.1 %. Two different fuelling options for ABR have been implemented through different core technologies: The first core technology (NFCNPCBTU, “advanced burner reactor”) has a breeding rate of 0 and a fuel burn-up value of 185 GWd/tHM. It is fuelled by a MOX kind of fuel (Pu and U) and is fed by natural uranium. The second core technology (NFCNPCBTR, “integral fast reactor”) forms a self-sustained reactor with a breeding ratio equal to 1, fuelled by transuranic (TRU)and depleted uranium from enrichment. The transuranic fuel component is produced by UREX (uranium extraction) reprocessing (NFCREPURXLUX) of spent LWR UOX (NFCSFULUX) with variable O&M costs of 900 $2000/kgHM. Pyro-reprocessing processes were “internalised” into both of the core technologies for modelling purposes, accordant costs were added proportionately to the fuel production processes (NFCFPDBTU and NFCFPDBTR, respectively). Hence, the variable O&M costs for ABR fuel production are comparably high: 46,087 $2000/kgHMadd for NFCFPDBTU and 38,145 $2000/kgHMadd for NFCFPDBTR. On the other hand, the fuel efficiency (heat produced per tHMadd in the reactor core technology) is accordingly high (NFCNPCBTU: 83.8 PJ/tHMadd, NFCNPCBTR: 84.5 PJ/tHMadd).tHMadd is the additional fuelling material, equal to the high level waste (HLW) production of the internal pyro-reprocessing cycle, and is not equal to the reactor core loading. The high level waste produced by pyro-reprocessing is the direct spent fuel output of the reactor core technologies (NFCBTUHLW and NFCBTRHLW) and is further sent to final deposit as HLW.

Accelerator driven systems (ADS) are available from 2040 on with investment costs of 2860 $2000/kWe, being 110 % of ABR investment costs (not regionalised), decreasing by 0.26 % p.a. The O&M costs are set to 3.5 % of the investment cost. The availability factor for both reactor technologies (ENFCNPPATR and ENFCNPPAMA) is 80 %; the efficiency of ENFCNPPATR is 32.7 %, the one of ENFCNPPAMA 31.3 %. The aim of using ENFCNPPATR technology is to burn

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the transuranic elements (NFCREPURXLUXTRU) from spent LWR UOX that is reprocessed using uranium extraction(UREX) technologies (NFCREPURXLUX).The ENFCNPPAMA technology allows for burning minor actinides (NFCREPPRALUXMA0, NFCREPPRALMXMA0, NFCREPPRAFMXMA0) separated by advanced PUREX reprocessing (NFCREPPRALUX, NFCREPPRALMX, NFCREPPRAFMX) of spent LWR UOX, LWR MOX and FR MOX fuel. For both technologies – like for the ABR – the pyro-reprocessing processes were internalised within the reactor core technologies, again resulting in higher variable O&M costs for fuel production processes (NFCFPDAMA: 117,570 $2000/kgHMadd, NFCFPDATR: 78,175 $2000/kgHMadd) and efficiencies (NFCNPCAMA: 85.3 PJ/tHMadd, NFCNPCATR: 86.5 PJ/tHMadd). The spent fuel component from the reactor core technologies is the high level waste produced by the internal pyro-reprocessing (NFCAMAHLW, NFCATRHLW) and sent to final deposit.

3.4 Renewables and storage options The technological database has been enriched with new Concentrating Solar Power (CSP) technologies with thermal storage facilities.

3.4.1. CSP Processes added

There are two commercial kinds of CSP with storage nowadays: central tower and cylindrical-parabolic trough collectors with different storage capacities. Updated information on commercial CSP plants with storage has been gathered and three processes have been built to include the different storage options in the solar thermal facilities into the model.

Process ESOLTHC205: CSP with low storage

This process has been built with data from existing facilities and corresponds to a central tower CSP plant with 1 hour storage (http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=38 and http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=39).

Figure 3.PS10 and PS20 CSP power plants.Source.http://www.nrel.gov/

Process ESOLTHC305: CSP with medium storage

This process has also been built with data from existing facilities and corresponds to a cylindrical-parabolic trough plant with 7.5 hour storage (http://www.nrel.gov/csp/solarpaces/project_detail.cfm/projectID=3).

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Figure 4.Andasol CSP power plant. Source.http://www.nrel.gov/

Process ESOLTHC405: CSP with high storage

This process has been built with data from a recently opened solar tower plant with 15 hour storage, Gemasolar (http://www.torresolenergy.com/TORRESOL/gemasolar-plant/en).

Figure 5.Gemasolar CSP power plant. Source.http://www.torresolenergy.com

Data gathered and used to build those technologies are shown in the following table:

ESOLTHC205 ESOLTHC305 ESOLTHC405AF SD 0.57 0.99 1AF SN 0 0.1 0.77AF ID 0.56 1 1AF IN 0 0.1 0.72AF WD 0.5 1 1AF WN 0 0.08 0.67INVCOTS_2010 (€2005/kW) 3098 6151 8152INVCOST_2020 (€2005/kW) 1859 3998 4891INVCOST_2030 (€2005/kW) 1487 3279 3913FIXOM 91 133 240LIFE 25 40 40STORAGE 1h 7.5 h 15 hSTART 2006 2008 2011

Table 5.Economic and technical data for solar thermal power plants with storage.

3.4.2. Suitable areas for solar thermal facilities

Concentrating solar power plants can not be built in all the regions of the model. Only areas with direct normal irradiance above 1800 kWh/m2 are suitable for the installation of these plants. A map showing DNI values in the globe is shown in the following figure:

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Figure 6. Global DNI values

Further to this limitation, other areas are excluded. These areas are protected areas and areas with slope higher than 2.1%. From the remaining areas, only areas classified as bare and sparsely vegetated areas (CORINE, GLC 2000) are considered to be suitable for the installation of this type of plants.

Suitable areas are shown in the following map.

Figure 7. Suitable areas in the World

Suitable areas in each of the region of the model and maximum production of solar electricity in these areas considering a 16% solar to electricity efficiency are quantified in the following table.

18

REGION Suitable area in km² PJ/a AFR 1295182 1757687 AUS 530145 818702 CAN 15133 14224 CHI 147048 217054 CSA 149491 185403 EEU 291 129 FSU 534074 539181 IND 6225 8624 JPN 84 83

MEA 400666 569754 MEX 11442 16053 ODA 98530 142649 SKO 2 2 USA 21745 30037 WEU 350 240

Table 6. Suitable area and maximum electricity production of solar thermal facilities by region.

These restrictions of maximum CSP electricity that can be produced in each region have been introduced in the model in the scenario file UC_ESOLTHC_100.xls.

3.4.3. Availability factors solar thermal facilities

Availability factors are also dependent on the location of the CSP power plants as well as on the season of the year.

These availability factors have been calculated for the suitable areas in each region, season and time slice for a CSP plant with no storage. Results are shown in the following figure and table.

Figure 8. Annual average availability of CSP plants without storage.

19

AF day Intermediate Summer Winter AnnualAFR 0.53 0.56 0.45 0.52 AUS 0.47 0.37 0.52 0.46 CAN 0.27 0.37 0.11 0.28 CHI 0.33 0.44 0.21 0.34 CSA 0.37 0.28 0.44 0.37 EEU 0.30 0.38 0.15 0.30 FSU 0.37 0.47 0.21 0.37 IND 0.47 0.46 0.44 0.46 JPN 0.37 0.40 0.26 0.35 MEA 0.50 0.57 0.39 0.49 MEX 0.49 0.50 0.37 0.46 ODA 0.42 0.47 0.30 0.41 SKO 0.38 0.40 0.31 0.37 USA 0.46 0.49 0.31 0.43 WEU 0.36 0.45 0.21 0.36

World average 0.47 0.49 0.41 0.46

Table 7. Day time availability factors of a CSP technology without storage during the day.

During the night, the availability factors of this technology are 0 in all the regions.

In order to estimate the availability factors of the CSP technologies that incorporate energy storage the following consideration has been done. It has been added the availability increase that would result of having the additional storage hours in each case in the following manner:

Seasonal availability factor with storage = seasonal availability factor without storage + number of h of storage / 24

In this way, the availability factors of the three technologies with storage for each region, season and time slice are the following:

AF day Intermediate Summer Winter Annual Intermediate Summer Winter Annual Intermediate Summer Winter AnnualAFR 0.62 0.63 0.54 0.60 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 AUS 0.55 0.46 0.60 0.54 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 CAN 0.35 0.43 0.24 0.37 0.90 0.83 1.00 0.91 1.00 1.00 1.00 1.00 CHI 0.41 0.51 0.31 0.42 0.96 0.96 0.99 0.97 1.00 1.00 1.00 1.00 CSA 0.46 0.38 0.51 0.46 1.00 1.00 0.98 1.00 1.00 1.00 1.00 1.00 EEU 0.39 0.44 0.26 0.38 0.93 0.86 1.00 0.92 1.00 1.00 1.00 1.00 FSU 0.45 0.53 0.32 0.45 1.00 0.97 1.00 0.99 1.00 1.00 1.00 1.00 IND 0.55 0.54 0.53 0.54 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 JPN 0.45 0.47 0.36 0.43 1.00 0.94 1.00 0.98 1.00 1.00 1.00 1.00 MEA 0.59 0.64 0.48 0.58 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 MEX 0.57 0.57 0.47 0.54 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 ODA 0.50 0.54 0.41 0.49 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 SKO 0.46 0.47 0.41 0.45 1.00 0.94 1.00 1.00 1.00 1.00 1.00 1.00 USA 0.54 0.57 0.41 0.51 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 WEU 0.44 0.51 0.32 0.44 0.99 0.95 1.00 0.98 1.00 1.00 1.00 1.00

World average 0.56 0.57 0.50 0.55 1.00 0.99 1.00 1.00 1.00 1.00 1.00 1.00

AF night Intermediate Summer Winter Annual Intermediate Summer Winter Annual Intermediate Summer Winter AnnualAFR - - - - 0.16 0.16 0.11 0.14 0.78 0.84 0.69 0.77 AUS - - - - 0.09 0.05 0.11 0.08 0.72 0.62 0.81 0.71 CAN - - - - - - 0.04 - 0.52 0.62 0.50 0.53 CHI - - - - - - - - 0.58 0.72 0.51 0.59 CSA - - - - - 0.01 - - 0.62 0.56 0.71 0.62 EEU - - - - - - 0.02 - 0.55 0.63 0.50 0.55 FSU - - - - - - 0.02 - 0.62 0.78 0.52 0.62 IND - - - - 0.09 0.05 0.11 0.09 0.72 0.73 0.68 0.71 JPN - - - - - - 0.01 - 0.62 0.66 0.54 0.60 MEA - - - - 0.13 0.16 0.08 0.12 0.75 0.87 0.64 0.74 MEX - - - - 0.12 0.07 0.06 0.08 0.74 0.78 0.62 0.71 ODA - - - - 0.04 - 0.05 0.03 0.67 0.77 0.58 0.66 SKO - - - - 0.01 - 0.05 - 0.63 0.66 0.58 0.62 USA - - - - 0.08 0.04 0.04 0.05 0.71 0.79 0.58 0.68 WEU - - - - - - 0.01 - 0.61 0.74 0.52 0.61

World average - - - - 0.10 0.10 0.08 0.09 0.72 0.77 0.67 0.71

ESOLTHC205 ESOLTHC305 ESOLTHC405

Table 8.Regional day/night AF for the CSP technology with storage in scenario file ESOLTHC_AF

3.5 New biofuels and electric vehicles In the transport sector, after an economic, technical and environmental data gathering, existing biofuels in the model have been updated and new biofuels have been included. Particularly relevant is the chain of new biofuels that allows for bio-ethanol and bio-diesel production from woody residuals, crops, biomasses and agricultural residuals

The new technologies and commodities added are:

Production of bioethanol from wood and agricultural residues (UBLSETH00), biodiesel from crops (UCRPDST00), biodiesel from wood and agricultural residues (UBSLDST00), and biomethanol from wood and agricultural residues (UBSLMET00). Biodiesel (BIODST) and biomethanol (BIOMET)

Also hybrid electric-gasoline and electric-diesel cars have been added to the transport technologies portfolio of the model.

The two new transport technologies added are:

Hybrid electric car with gasoline (TRAHYBG05), and hybrid electric car with diesel (TRAHYBD15)

Bio-ethanol and bio-diesel are then mixed with methanol and diesel and used by demand technologies. The impact on CO2 reduction of this enrichment of the transport sector is noticeable in many scenarios.

3.6 Fusion in ETM The Fusion sector is entirely modeled: from lithium extraction to electricity production by fusion plants.

Global lithium resources are estimated in 12Mt (2.4x10^8 PJ) while extraction and enrichment costs are estimated to be 4.5x10^-3 M$/PJ. Mined lithium is transformed in fuel and used in two types of fusion power plant whose characteristics are shown9

Table 9. Main parameters of the fusion technologies in ETM model

in table 9.

Source: Han et al. Fusion Engineering and Design 84 (2009).

4. The scenario tree The scenarios presentedin thisreport aim tohighlightthe possible evolutionof theglobalenergy systemover a very long time horizon. Thesescenarioscannot beconsidered asforecastsbecause toomany factors influencethe evolutionof the system andtheiruncertaintydoes notallowto formulatereliableexpectations.

On the contrary, the scenarios show a range of possible evolutions of the global energy system. We hypothesize that each scenario is characterized by different dimensions10

9HAN, W. E. et al., Fusion Engineering and Design 84 (2009).

: demographic,

22

economic, technological and political and for each dimension we select one or more critical variables whose different developments, combined with the evolution of the other critical variables characterize the scenario tree.

The analysisis dividedinto two steps:

1. Referencescenarios: built on alternatives for Economic, demographic and environmental policies issues

2. sensitivity analysis on technological evolution and availability of Fusion’s main competitors.

In the construction of Baseline scenarios, among themany dimensions ofthe possible evolutionof the energy system, demographic and economic growth as well as stringency of environmental policies have beenconsidered particularlyrelevant; when the key factors are combined together in order to represent possible future evolutions, a set of “storylines” is produced.

In this Report two different sets of model drivers (GDP, Population and sectoralproduction) have been used in order to account for different evolution path for demographic and economic variables.

The two sets of drivers have been produced by mean of the dynamic version of the General Equilibrium model for global trade and production, Gtap – model11

The severity of climate change mitigation policies is represented in Baseline scenarios using two alternative global bounds for CO2 emission: the weaker bound allow a level of emission that leads to a concentration of 550 ppm at the end of the century, the stronger emission constraint is consistent with a concentration level of 450 ppm in 2100. In addition, a price-based policy option, without quantitative caps on emission is considered. The carbon tax representative for this kind of environmental option is differentiated among regions and grows over time. In OECD regions the CO2 tax rises from 20$/tCO2 in 2020 to 50$/tCO2 in 2100. The amount of the tax is halved for non OECD regions

.

The resulting set of storylines (or scenario tree) is reported below.

Table 10. Storylines for the ETM model Drivers/Scenario BASE

450ppm BASE 550ppm BASE Tax HG 450ppm HG 550ppm HG tax

Economic and demographic growth

GEM E3 drivers

GEM E3 drivers GEM E3 drivers Gtap

High Growth drivers

Gtap

High Growth drivers

Gtap

High Growth drivers

Environmental policies

450 ppm 550 ppm CO2 Tax 450 ppm 550 ppm CO2 Tax

10EurEnDel 11 The methodology for the calculation of the new set of drivers is described in ENEA WP10-SER-ETM-7 Final Report.

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Starting from these six storylines a sensitivity analysis has been carried out. The sensitivity analysis aims to explore the role of some key technologies in competition with fusion generation. In particular the sensitivity analysis has been focused on:

• Renewable Energy Sources and Land-use;

• Fission technologies;

Fission technologies in the six baseline scenarios are not bounded, this implies that the only constraints affecting fission penetration are Uranium availability and cost comparison with competing technologies.

5. Elasticities As already mentioned, ETM energy service demand are produced by mean of exogenous sets of drivers and elasticities.

A comparison with the TIAM version distributed by ETSAP in 2011 shows that the two models share the same set of elasticities with the exception of transport demand (slightly higher in ETM).

The elasticities link the model drivers to energy services demand. Since the literature on the elasticity of energy service demands is scarce, robustness of elasticities assumptions for the ETM has been investigated in the framework of SERF Workprogramme 200512

6. Main model drivers

. Main conclusion of the investigation stated that the default elasticities of the ETM model are consistent with literature estimates for elasticities to energy demands. Nonetheless, the choice of the correct driver for each energy service demand is a quite sensitive one that has to be carried out with care.

The default set of driver for ETM is the one produced with the GEM-3 model13

In the framework of ETM Workprogramme 2011 a set of alternative drivers has been evaluated by mean of the Gtap(General Trade Analyses Programme) model

. The set of drivers produced for ETM by mean of the GEM-3 model is aligned with drivers used in TIAM at 2050 but has GDP projections considerably higher at 2100 for AFR, CHI, SKO and ODA.

14

Several drivers are used to project model’s demands: population, GDP, GDP per capita, number of households and sectoralproduction. A comparison of drivers projections is reported below for Population and GDP.

for two alternative hypothesis of development (High Growth and Low growth). Low growth drivers produced with the G-tap model for ETM are closer to TIAM drivers while the High growth drivers enhance the differences even further.

12ENEA, A set of consistent scenarios for the TIMES/EFDA model. Final Report of task TW2-TRE-FESA/A2 (2005) 13 For a detailed description see Ordecsys 2004. 14 Martini Tommasino 2011

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6.1 Population In this section, Population projectionsfor the two set of Gtap driversare compared with Population projections of the default ETM drivers and with those used in four relevant IPCC scenarios (A1, A2, B1, B2)15

The graphs below show that:

. The sets are shown in the graphs below that compare population indexes (year 2000=1)at year 2050 and 2100.

1) The default projections of ETM represent a medium estimate with respect to the High and Low Gtap estimates for the vast majority of the regions in ETM.

2) The Low and High Gtap Scenarios are very extreme compared with the projections used in the IPCC’s Special Report on Emission Scenarios (SRES). The low growth scenario is not too far from SRES B2 assumptions for most of the regions.

3) At 2050 the differences between the High growth projections and SRES A1 are still low while at 2100 they increase considerably. It has to be considered that the projections of the Gtap model are taken by the High and Low population scenarios of the UN (2008)16

.

Figure 9. Population projections for selected scenarios (2050)

15 IPCC, Special Report on Emission Scenarios, IPCC, 2000. In the text it is often called SRES. 16 Martini, Tommasino (2011)

25

Figure 10. Population projections for selected scenarios (2100)

6.2 GDP projections GDP projections vary considerably between regions both at 2050 and at 2100. It is worth noting that Gtap results are produced for the period 2000-2050. After 2050 they have been extrapolated hypothesizing a constant rate of increase equal to the maximum rate registered in the period 2000-205017

Figure 111. GDP projections for selected scenarios (2050)

. Underlying hypothesis concerning factor productivityare relevant in determining differences in regional results and it is very hard to draw a univocal interpretation of the graphs shown below. As a general rule it is worth nothing that at 2100 SRES projections for non OECD regions are considerably higher than ETM default and Gtap drivers. On the contrary, there seem to be a convergence between the various models in assessing the GDP growth in OECD regions.

17The same approach has been used for sectoral production indexes.

26

Since, drivers are produced exogenously by mean of consistent general equilibrium models, it is hard to define which set is more likely or more affordable than others. Nonetheless, the differences are slighter for 2050 and increase up to 2100.

Figure 13. GDP projections for selected scenarios (2100)

7. Results

7.1 Final Consumption Total final consumption in 2100 increases by a factor of 2.5 in the BASE scenarios and by approximately a factor 3.7 in the High growth scenarios. In the same period global GDP increases by a factor 8 in the Base scenarios and a factor 9 in the High growth scenarios.

In the first part of the century, final consumption decreases in Western Europe and Japan in the three Base scenarios. China, Other Developing Asian countries and Middle East countries accounts for nearly 55-60% of total increase in all the BASE scenarios both in the first and in the second half of the century.

Figure 124. Total Final consumption by region in the three BASE scenarios

0

100000

200000

300000

400000

500000

600000

700000

800000

2000 2050 2100 2000 2050 2100 2000 2050 2100

BASE_450 BASE_550 Base Tax

OTHER

WEU

USA

IND

ODA

CHI

27

In the High growth scenarios, that forecast a sustained path of growth also for developed regions, the increase in consumption is more widespread and the three regions mentioned above account for approximately 45% of total increase.

Figure 15. Total Final consumption by region in the three High Growth scenarios

0100000200000300000400000500000600000700000800000900000

1000000

2000 2050 2100 2000 2050 2100 2000 2050 2100

HG Tax HG_450 HG_550

PJ

OTHER

WEU

USA

IND

ODA

CHI

Figure 16. Total Final consumption by sector in the three BASE scenarios

0

100000

200000

300000

400000

500000

600000

700000

800000

2000 2050 2100 2000 2050 2100 2000 2050 2100

BASE_450 BASE_550 BASE_tax

PJ

Non Energy

Agricolture

Commercial

Residential

Transport

Industry

In the Base scenarios, Industry and Transport sector account for 72-76% of total consumption increase in the first part of the century and between 54-58% of total increase in the second half of the century.

The transport sector is the main responsible of final consumption increase in the first period while industry consumption grows steadily between 2050 and 2100. The dynamic in the High growth scenarios is quite similar.

28

Figure 17. Total Final consumption by sector in the three High Growth scenarios

0

200000

400000

600000

800000

1000000

1200000

2000 2050 2100 2000 2050 2100 2000 2050 2100

HG_tax HG_450 HG_550

PJ

Non Energy

Agricolture

Commercial

Residential

Transport

Industry

7.1.1 The transport sector On a global scale, the sector accounts for about 30% of total final consumption both in 2000 and in 2100. The shares are similar in all the three Base scenarios.

Between 2000 and 2100, total final consumption in the transport sector grows by a factor 2.4 in the Base scenarios to satisfy a growth of service demand in 2100 that is fourfold for road transport and nine times higher for aviation and navigation.

In one century, electricity demand in the transport sector grows by 18EJ in each of the three scenarios. The remaining increase in consumption (90-100EJ) is covered by oil products and biofuels whose market share is obviously related to the tightness of environmental constraints. In the most favourable case (450ppm), biofuels consumption grows by 60EJ in 100 years.

The dynamic in the High growth scenarios is quite similar, with an even higher contribution of biofuels in Transport when the constraints are tight. In the high growth scenarios, final consumption rises by a factor of 3 in 2100 to match the growth of service demand in 2100 that is five times for road transport and 11 times for aviation and navigation showing that the trend of a progressively more efficient use of final energy is a common feature of the six scenarios. Shares of sector final consumption are lower than those observed in the Base scenarios (about 25%).

7.1.2 The industrial sector The sector accounts for about 31% of global final consumption in 2000 and about 34%in 2100. The share is higher in the Base tax scenario (37%).

Between 2000 and 2100, the fuel mix in industry changes considerably in three BASE scenarios, with a decrease of natural gas consumption both in absolute and relative terms up to a share of 5% in 2100, and a growing importance for coal (with an increase in absolute terms of 65EJ, 32EJ, 17EJ in the Tax, 550ppm, 450 ppm scenarios, respectively). Electricity consumption dominates the fuel mix at the end of the century, rising from 18EJ in 2000 up to 153EJ, 158EJ and 170EJ in the Tax, 550ppm and 450ppm scenarios, respectively. The increasing use of coal in the scenarios with tight

29

constraints is due to the adoption of CCS technologies in some key industrial production (namely dry clinker and autoproduction of electricity).

The dynamic in the High growth scenarios is quite similar, with an even higher contribution of electricity when the constraints are tight. The share of sector consumption in the High growth scenarios is higher than in the Base case (41%, 39% and 38% in the Tax, 550ppm and 450 ppm scenarios, respectively).

7.1.3 Focus: electricity consumption in Industry Focusing on final consumption of electricity, we observe that the industrial sector is the major responsible for the increasing electrification rate. It accounts for more than 50% of the total increase of electricity consumption to 2050 in all the scenarios and for an average of 68% and 73% in the second part of the century for the base and high growth scenarios, respectively.

Figure 18. Industry global demands by sector in the six Reference scenarios. Index number 2000=1

0

2

4

6

8

10

12

14

16

2000 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

BASE ICH

BASE IIS

BASE ILP

BASE INF

BASE INM

BASE IOI

HG ICH

HG IIS

HG ILP

HG INF

HG INM

HG IOI

Figure 18 shows the increase of global industry demands (in practice, the indexes of production by sector). In the high growth scenarios all the sectors (except Pulp and paper) show an increase of production to 2100 that is more than tenfold the base year level. In the Base scenarios the sectors can be classified as “slow” (Iron and Steel, Non ferrous and Pulp and paper) and “fast growing” (other industries, Chemicals and Non metal) whose production at 2100 is 6-8 times the level of 2000.

The figures from Figure 19 to Figure 23 show, for each industrial sector, the energy intensity (En_) and the electricity intensity (El_) for unit of demand (approximately, for unit of production).

In all the sectors energy is used more efficiently while electricity increases its relevance in the fuel mix (the electricity intensity decrease always at a slower rate with respect to the energy intensity).

It is worth noting that:

30

• electricity intensity is related to the stringency of environmental constraints and to the path of growth, in all the sectors;

• a complete electrification of the Machine-drive service and an increased use of the service itself is a common characteristic for all the sectors (particularly in Other industries that are mainly mechanics, textiles, food and beverages);

• electricity intensity is always decreasing, except in the Pulp and paper sector and in Iron and steel production;

The increase of electricity intensity in Iron and steel production is explainable by the growing demand of electrochemical processes related to Electric Arc Furnaces (EAF). With different paths and different mix in the various scenarios, EAF with Direct Reduction or EAF with scrap gradually replace Basic oxygen furnaces.

The increase in the Pulp and paper sector is related to the switch from chemical to mechanic processes and to the transformation of recycled paper (the latter particularly used in the 450 ppm scenarios).

The increase in electricity intensity shown for the non metal sector in the 450ppm scenarios and in the 550HG, is related to dry clinker production. When production with CCS starts to enter the market (sooner in the 450ppm scenarios, later in the 550HG) substituting the technologies without CCS, an increasing input of electrochemical processes related to CCS is required.

Summarizing, even when electricity intensity is decreasing, the total consumption increases over time. The level of activity seems to dominate over an intensity (decreasing) effect.

The level of production forecasted by macroeconomic models is quite high both in the base and in the high growth scenarios. The sector “Other Industries” and the chemical sector will increase their production of nearly 30 times with respect to the base year in some developing regions (CHI, ODA, AFR). Nonetheless, the levels of production are evaluated in a very simple way: the driver chosen is the index of sectoral production coming from the general equilibrium models, the elasticity in developing regions is equal to 1 until 2050 and slightly lower after that date (0,8 in most of the cases) to allow for an effect of autonomous energy efficiency improvement. In the developed regions the elasticity is less than one even in the first part of the century. Reliability of general equilibrium models in a very long time horizon is an interesting issue but far beyond the scope of this report so, the values of the drivers will be taken for granted but alternative paths will be checked.

31

Figure 19. Electricity intensity (El_) and Energy intensity (En_) in the Chemical sector. Index numbers (2000=1)

0

0.2

0.4

0.6

0.8

1

1.2

2000 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

CH_El_BASE_450

CH_El_BASE_550

CH_El_BASE_tax

CH_El_HG_450

CH_El_HG_550

CH_El_HG_tax

CH_En_BASE_450

CH_En_BASE_550

CH_En_BASE_TAX

CH_En_HG_450

CH_En_HG_550

CH_En_HG_Tax

Figure 20. Electricity intensity (El_) and Energy intensity (En_) in the Iron and Steel sector. Index numbers (2000=1)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

2000 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

IS_El_BASE_450

IS_El_BASE_550

IS_El_BASE_tax

IS_El_HG_450

IS_El_HG_550

IS_El_HG_tax

IS_En_BASE_450

IS_En_BASE_550

IS_En_BASE_TAX

IS_En_HG_450

IS_En_HG_550

IS_En_HG_Tax

32

Figure 21. Electricity intensity (El_) and Energy intensity (En_) in the Pulp and paper sector. Index numbers (2000=1)

0

0.2

0.4

0.6

0.8

1

1.2

2000 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

LP_El_BASE_450

LP_El_BASE_550

LP_El_BASE_tax

LP_El_HG_450

LP_El_HG_550

LP_El_HG_tax

LP_En_BASE_450

LP_En_BASE_550

LP_En_BASE_TAX

LP_En_HG_450

LP_En_HG_550

LP_En_HG_Tax

Figure 22. Electricity intensity (El_) and Energy intensity (En_) in the production of Non ferrous metals. Index numbers (2000=1)

0

0.2

0.4

0.6

0.8

1

1.2

2000 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

NF_El_BASE_450

NF_El_BASE_550

NF_El_BASE_tax

NF_El_HG_450

NF_El_HG_550

NF_El_HG_tax

NF_En_BASE_450

NF_En_BASE_550

NF_En_BASE_TAX

NF_En_HG_450

NF_En_HG_550

NF_En_HG_Tax

33

Figure 13. Electricity intensity (El_) and Energy intensity (En_) in the production of Non metallic minerals. Index numbers (2000=1)

0

0.2

0.4

0.6

0.8

1

1.2

2000 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

NM_El_BASE_450

NM_El_BASE_550

NM_El_BASE_tax

NM_El_HG_450

NM_El_HG_550

NM_El_HG_tax

NM_En_BASE_450

NM_En_BASE_550

NM_En_BASE_TAX

NM_En_HG_450

NM_En_HG_550

NM_En_HG_Tax

7.1.4 The civil sector On a global scale, the Commercial and the Residential sectors account for about 27% of total final consumption in 2000. The share decreases to approximately 14-17% in 2100 in the six references scenarios.

Between 2000 and 2100, total final consumption in the civil sector grows by a factor 1.6-1.7 in the Base scenarios to satisfy a factor 2.6 growth of service demand in 2100.

In one century, the fuel mix changes considerably:

• Oil products, from a share of 21% in 2000, completely phase out at the end of the century;

• Natural gas, from a share of 33% in 2000 reduces its contribution to 20%, 17% and 12% in the tax 550ppm and 450 ppm scenarios, respectively;

• Heat, in the various scenarios covers a share of 8-15% and its penetration is related to the tightness of environmental constraints;

• Coal share is stable at 5-6% over the entire period; it is used mainly in the residential sector for heating, cooking and hot water production.

• Electricity dominates the fuel mix at the end of the century and its share rises from31% in 2000 up to 60%, 65% and 70% in the Tax, 550ppm, 450ppm scenarios, respectively.

In the high growth scenarios, final consumption rises by a factor of 2 in 2100 to match a threefold growth of service demand in 2100 showing that the trend of a progressively more efficient use of final energy is a common feature of the six scenarios. The dynamic of the fuel mix in the High growth scenarios is quite similar to that of the Base scenarios, with slightly higher contribution of heat, and coal and a slightly lower share of electricity. Both heat and electricity penetration are related to the tightness of environmental constraints.

34

7.1.5 Critical issues: the demands of the residential sector The drivers for the demand in the residential sector are shown in table 11.

Table 11. Drivers for the residential sector

GDP POP HOU GDPP PAGR PISNF PCHEM POEI POI PSER Constant GDPPHOU RH1 Y RH2 Y RH3 Y RH4 Y RC1 Y Y RC2 Y Y RC3 Y Y RC4 Y Y RWH Y RL1 Y RL2 Y RL3 Y RL4 Y RK1 Y RK2 Y RK3 Y RK4 Y RRF Y Y RCW Y Y RCD Y Y RDW Y Y REA Y Y ROT Y Y When drivers are differentiated, a red mark indicates AFR, CHI, CSA, EEU, FSU, IND, MEA, MEX, ODA, SKO

Depending on the region and on the driver used, elasticities assume different values in the range 0.5-1.2; in all the cases the elasticities sharply drops after 2050 to account for saturation effects.

Some categories of demands exhibits a dramatic increase even in the BASE scenarios. However, the demands, normalized on a per capita basis and conveniently regrouped (energy services are all expressed in the same unit – useful PJ) are quite high but do not seems unreasonable. Considering the base year consumption in the US as representative of a very high standard of service demand, we observe that in 2100 the standard is not overcome in the majority of the cases (Figures 24 to 28).

35

Figure 24. Residential cooling – Per capita service demand (index number: USA 2000=1)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6AF

RAU

SCA

NCH

ICS

AEE

UFS

UIN

DJP

NM

EAM

EXO

DA SKO

USA

WEU AF

RAU

SCA

NCH

ICS

AEE

UFS

UIN

DJP

NM

EAM

EXO

DA SKO

USA

WEU

HG BASE

2000

2050

2100

Figure 25. Residential Hot water – Per capita service demand (index number: USA 2000=1)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

AFR

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Figure 26. Residential Heating and cooking – Per capita service demand (index number: USA 2000=1)

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Figure 27. Residential demands mainly related to electricity (RL, RCD, RCW, REA, RDW) – Per capita service demand (index number: USA 2000=1)

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Figure 28. Residential Refrigeration – Per capita service demand (index number: USA 2000=1)

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The high level of per capita demand shown in table xk for CHI, SKO, EEU and CAN are mainly related to Residential Lighting. The unlikely amount of service demanded depends on several factors:

• a dramatic increase of drivers; • an insufficient drop of elasticity to account for saturation effects; • the attribution of energy consumption for other purposes to the base year demand for

lighting (no statistical data are available on fuel consumption for type of demand and fuels are splitted using reasonable guesses).

The magnitude of the overestimation of lighting demands can be roughly evaluated as follows: • the absolute value of service demands whose per capita index exceeds 0.8 are proportionally

reduced; • the corresponding demands for electricity are estimated dividing by the efficiency of the

standard halogen lamp (RL1HSI005). The procedure leads to a correction (reduction) of final energy demand in 2100 of 18EJ in the Base scenarios and 9.7EJ in the HG scenarios. The magnitude of the error (6% of electricity production in Base and 2% in HG scenarios) do not change the conclusions that have been drawn on the role of fusion in the electricity market.

7.2 Electricity Generation Figure 29 to Figure 31 show global electricity production in the three BASE scenarios. In 2100, Electricity production increases by a factor of 6 with respect to the current level of production, reaching a value of about 300 EJ. This amount is in the range reported by the SRES B1 and B2 scenarios, but is much smaller than the level of production reached by the A family of SRES scenarios.

38

Figure 29 shows global electricity production by fuel for the BASE Tax scenario.

In the BASE Tax scenario coal still plays a relevant role accounting for 8% of total electricity production in 2100. Coal becomes a transition technology between 2050 and 2080, when most of LWR reactors phase out and new base load capacity is needed. Due to the lower levels of the carbon tax, new capacity is installed mainly in Asia (IND and ODA accounts for 2/3 of the installed capacity in 2050). At a global level, after a peak reached in 2070, coal fuelled generation is progressively displaced by nuclear production (fusion and ABR) and maintains a noticeable contribution only in Middle East where it progressively substitutes natural gas and oil plants after 2060.

Natural Gas fired electricity generation increases considerably in the first part of the century; it represents a share of about 25% of electricity production in 2050 when it is widely diffused in all the regions except China. In the second half of the century production declines, partially displaced by renewables and nuclear technologies.

The share of Hydropower production grows from 18% in 2000 to 28% between 2050 and 2070 and shrinks back to 20% in 2100. The maximum potential is reached at the middle of the century in all the regions and no absolute increase is possible in latest periods. Hydropower represents however a key source of electricity for Canada and Central and South America where it covers more than 60% of electricity production in 2100.

RES production increases considerably, rising from a negligible share at the beginning of the century up to 23% in 2100. The increase is due to onshore wind. Off shore production is a transition option in some regions (USA and developing Asia) between 2050 and 2080, while CSP plays a minor role and is used only in Africa, China and India.

In the first part of the century nuclear fission represents a share of 20-30% of total electricity production. Between 2040 and 2060 the share decreases due to the partial replacement of LWR capacity that goes out of production.

Between 2040 and 2050 ABR gradually starts to replace LWR confirming the key role of nuclear energy that, at the end of the century, covers a share of 10% and is diffused in all the regions.

Fusion power enters the market since 2050, when basic power plants are installed in India, China, Western Europe, USA, Japan, Mexico and in Other developing Asian countries. The technology represents still a niche option and produces a share of electricity of about 1% between 2050 and 2070. The massive contribution of Fusion power to global electricity production starts in 2070 when Advanced plants are available and the technology is widespread in all the regions except Central and South America and Middle East. The share of Fusion production rises to 20% in 2090 and to 36% in 2100.

Fusion penetration is quite relevant in Western Europe (40% of total production in 2100), in USA (31% of total production in 2100) and in Asia where it represents more than 60% of 2100 electricity production for India and South Korea, more than 50% for China and India and about 45% for Japan.

39

Results change if stringent emission targets reinforce the effect of the carbon tax (Figure 30 and Figure 31).

In the 550ppm scenario, generation from fossil fuels phases out at the end of the century; it is replaced by fission and electricity generation from RES (mainly on shore wind) that, at the end of the century, account for 17% and 21% of global production, respectively.

The amount of production increases to 322 EJ in the 450ppm scenario, due to the higher penetration of electric technologies in the sectors of final demand.

In this scenario coal fired generation phases out at 2050 and gas at 2080. The gap is filled by fission production and renewables with an increase also for CSP and off shore wind generation; the share of production is 22% at 2100 for both fission and renewables.

In the 450ppm scenario CCS (mainly gas fired) enter the solution from 2050. Even though the contribution to global electricity production is still negligible (almost 1% in the period 2050-2100), it sustains the growth of production in some crucial regions (India, China, Korea, Former Soviet Union and Eastern Europe).

In both scenarios the share of fusion remains at 36-38% of total production in 2100.

Figure 32 to Figure 34 show global electricity production in the three High Growth scenarios. In 2100, Electricity production , compared to production of the base year, increases by a factor of 8 in the tax scenario and by a factor of 9 in the two scenarios with emission constraints.

The amount of production in 2100 (on average, more than 400 EJ for the High Growth scenarios) is in the range reported by the SRES B2 and A2 scenarios, but is much smaller than the level of production reached by other scenarios of the A family in SRES.

In the high growth scenarios, more stringent emission constraints determine progressively higher levels of electricity demand. Since the stock of power plants at the end of the century is really clean, the substitution of fossil fuels with electricity is a quite logical choice in the sectors of final consumption.

40

Figure 14. Global electricity generation mix in the BASE Tax scenario

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Figure 15. Global electricity generation mix in the BASE 550 ppm scenario

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Figure 16. Global electricity generation mix in the BASE 450 ppm scenario

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41

Figure 17. Global electricity generation mix in the HG Tax scenario

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Figure 18. Global electricity generation mix in the HG 550 ppm scenario

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Figure 19. Global electricity generation mix in the HG 450 ppm scenario

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42

Some of the considerations drawn in the description of the Base scenarios, still hold for the High Growth case. In particular:

Coal-fired generation plays a relevant role in the Tax scenario (particularly in China, India and Other Developing Asian at the middle of the century), it reaches a maximum of production in 2070, and is progressively displaced by nuclear production (fusion and ABR). It still maintains a noticeable contribution only in Middle East, where progressively substitutes natural gas and oil plants after 2060. In the 550 ppm scenario coal fired generation (and natural gas generation) phases out in 2060 and, in the 450 ppm scenario, ten years earlier.

Natural Gas fired electricity generation is relevant and widely diffused in the first part of the century; in the second half of the century production declines, partially displaced by renewables and nuclear technologies.

Hydropower production reaches the maximum potential at the middle of the century in all the regions and no absolute increase is possible in latest periods.

Fission production is approximately constant until 2060. Fusion and ABR electricity production penetrates massively at the end of the century. The higher level of demand and the stringency of environmental constraints plays in favor of these two sources. At 2100 fusion represents almost 37% of electricity production in the three Hg scenarios, while the share of fission is 16% in the tax scenario and about 28% in the constrained scenarios.

In the three scenarios RES production increases considerably, rising from a negligible share at the beginning of the century up to 20% in 2100. The absolute amount of production is higher than in the Base scenarios.

Differently from what has been observed in the Base scenarios, the HG scenarios show:

• a considerable amount of CSP production in the final part of the century in the 450 ppm scenario.

• A relevant role for CCS technologies in the 550ppm scenario. They account for 11-14% of global production between 2060 and 2070 but play an important role at regional level: 49% in China, 38% in India, almost 20% in Eastern Europe and Middle East at 2060.

It is worth noting that only gas fired CCS technologies enter the solution mainly for the higher efficiency in comparison with CCS coal technologies. Finally, it should be remarked that a given amount of emissions from CCS plants cannot be sinked and is emitted in atmosphere, penalizing the contribution of CCS technologies in more stringent 450ppm scenarios.

7.3 Primary Energy and CO2 emissions Primary energy increases in 2100 approximately by a factor 2.5 in the Base scenarios and by almost a factor 4 in the High growth scenarios with environmental constraints, slightly less (factor 2.4) in the HG Tax scenarios (Figure 35). There can be found similarities in the evolution of the fuel mix in all the scenarios: at 2050 the increase of primary production relies mainly on natural gas and renewables; at 2100 the increase is due to nuclear (both fission and fusion) energy. Oil loses importance while coal does not disappear from the global energy mix, playing an important role in the medium term even in the scenarios with environmental constraints.

43

Figure 20. Global Primary energy by source in the six Reference Scenarios

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Nuclear (fission)

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The Figure 36 shows the evolution of global CO2 emissions related to the three BASE scenarios.

The scenarios exhibit a common path until 2030 when emission decreases for the partial substitution of coal with natural gas and renewables. In subsequent periods the emission trend differs considerably.

Figure 21. Global CO2 Emission in the three Base scenarios.

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When constraints are tight as in the 450 ppm scenarios, emissions are forced to drop since the earlier periods and halves in 2100 with respect to the year 2000. Nuclear fission, renewables and CO2 sequestration permits the fulfillment of the target. It is worth noting that constraints related to 550ppm concentration are ineffective until 2050, and emission follow the same ascending trend in both the 550 ppm High growth and the Base scenario between 2030 and 2060, mainly due to the increased use of coal. Starting from 2060, in the 550ppm scenario emissions drops thanks to sequestration, to renewables, and to nuclear technologies that displace coal in power generation

44

sector. In 2100 emissions are slightly lower than base year levels. On the contrary, emissions in the Tax scenario, rise by a factor 1.5 with respect to the base year: in this scenario the contribution of sequestration is more than 5 times less that in the 450 ppm scenario.

Similarly, in the high growth scenarios (Figure 37), emissions declines when an admixture of nuclear and renewable energy replaces fossil sources and sequestration is used in upstream, industry and power generation.

The emissions in the Tax scenario rise at a sustained rate up to 2080, later they decrease at a level that in 2100 is twice that of the base year.

In the 550ppm scenario emissions rise up to 2030 and subsequently decrease to a level slightly lower than in the base year. When constraints are tighter, the rise of emissions terminates in 2020 and in 2100 they are half the level of 2000.

Figure 22. Global CO2 Emission in the three High Growth scenarios

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Capture and sequestration helps considerably in reaching the environmental targets; Figure 26 shows the amount of CO2 captured in the six reference scenarios. Not surprisingly the contribution of carbon sequestration increases as the demand rise and the environmental constraints becomes tighter. It is worth noting that in the scenarios with a cap on emissions, 60-70% of sequestration is related to the industrial sector (mainly clinker production and large CHP plants).

45

Figure 23. Captured CO2 - including afforestation- (Mt CO2)

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7.4 Indicators The six baseline scenarios depict possible paths of evolution of the energy system: main data and indicators that characterize the scenarios are summarized in table 12.

Table 12. Global indicators and data for the six energy scenarios.

Scenario BASE Tax BASE 550ppm BASE 450ppm HG Tax HG 550ppm HG 450ppm Drivers GEM E3 GEM E3 GEM E3 Gtap HG Gtap HG Gtap HG

Environmental constraints CO2 Tax 550 ppm 450 ppm CO2 Tax 550 ppm 450 ppm

GDP (Billions US$ 2000) 2000 32149 2050 89055 89055 89055 134279 134279 134279 2100 245374 245374 245374 305406 305406 305406

Population (Billions) 2000 6182 2050 9239 9239 9239 10455 10455 10455 2100 10423 10423 10423 15270 15270 15270

Primary Production (EJ) 2000 385 2050 642 679 795 1101 1215 1282 2100 904 967 1022 1307 1484 1482

Energy Intensity (MJ/US$ 2000) 2000 12.0 2050 7.2 7.6 8.9 8.2 9.0 9.5 2100 3.7 3.9 4.2 4.3 4.9 4.9

Final consumption (EJ) 2000 269 2050 457 455 431 670 631 622 2100 714 679 685 995 967 994

Electricity production (EJ) 2000 49 2050 140 140 139 192 193 203 2100 283 296 323 393 428 471

Electricity /Primary energy 2000 0.13 2050 0.22 0.21 0.18 0.17 0.16 0.16 2100 0.31 0.31 0.32 0.30 0.29 0.32

Emissions (Mt CO2) 2000 20603 2050 27589 27015 14433 42825 29638 14433 2100 34864 19595 11075 48914 19595 11075

Carbon intensity (kt/PJ) 2000 53.5 2050 43.0 39.8 18.2 38.9 24.4 11.3 2100 38.6 20.3 10.8 37.4 13.2 7.5

In all scenarios, energy intensity decreases as an effect of the introduction of more efficient conversion technologies that become available as the economic conditions improve. Unexpectedly, the impulse towards the reduction is not higher when the environmental constraints are more stringent: in these scenarios, a great variety of mitigation options are required, even those that are carbon free but less efficient (for example CCS). The results also show that the effect of energy

47

efficiency is maybe underestimated, given the limited options in the end-use technologies database of ETM. This is one of the issues that requires an improvement of ETM.

Carbon intensity, declines substantially in all the scenarios. As expected, the high growth scenarios show lower values of the indicator with respect of the corresponding Base scenarios. The two tax scenarios, characterized by price signals without quantitative restrictions, leave the model free to set the optimal level of emission corresponding to an effective cost allocation of resources: they show similar evolution paths of the indicator.

In all scenarios, the paths of the electrification rate are similar, increasing up to 0.3 at the end of the century. Despite the decrease of energy intensity, electricity intensity and absolute electricity demand increase in response to the environmental constraints.

The share of electricity on total final consumption is 18% in the base year. It is strongly related to the stringency of environmental constraints and in 2100 increases up to 40% in the two tax scenarios, to 44% in the 550ppm scenarios and to 47% in the 450ppm scenarios.

8. Sensitivity analysis:

8.1. Lower levels of energy demands In order to verify the robustness of the results concerning fusion penetration, three scenario with a very low path of growth have been run.

The scenarios combine the low growth drivers already described and the three environmental policy options and constraints used for the main scenario analysis of this report. Further changes have been performed in order to flatten the drivers’ growth even more:

1. Extrapolation of economic data for the period 2050-2100 has been performed maintaining the average (instead of the maximum) increase of sector production and GDP provided by the Gtap model for the period 2000-2050;

2. Residential lightning demands have been linked to the number of households: since elasticity is equal to 1.2 for the first part of the time horizon and to 0.7 for the second part, current levels of demand are slightly increased until a level of saturation, later they drops progressively for behavioral reasons.

Scenario results are shown in the following figures. The focus of this paragraph is the role of fusion in the global electric market and the figures 39 and 40 show the electricity production in the cases of low growth of energy demand. Other results will not be discussed in detail.

48

Electricity production amounts to 220 EJ in 2100 in the three scenarios. It is about 25% lower than in the Base case and comparable with the B1 SRES scenario. It is worth nothing that at this level of demand, the 550ppm constraints are ineffective and environmental policies are driven by the carbon tax; 550 ppm scenario and the tax scenario are in practice equivalent and results will be shown only for the Tax case.

The evolutionary path of the production mix is similar to the one described for the Base scenarios, with the following differences:

• with a lower level of demand the share of Hydropower increases considerably; • given a lower amount of production needed at the end of the century, ABR penetration is

slower; • the lower level of demand slightly affects also fusion production both in absolute and

relative terms;however, fusion still maintains a considerable role when the advanced plant becomes available;

• CCS enter the solution when constraints are tighter (but CCS for large CHP industrial auto producers is always chosen in the optimal solution mix).

Figure 24. Global electricity generation mix in the Low Growth Tax scenario

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Figure 25. Global electricity generation mix in the Low Growth450 ppm scenario

49

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8.2 Storage options for CSP technologies In order to explore the role of storage in the penetration of renewable technologies and the possible competition with fusion technologies, a sensitivity analysis has been performed. The High Growth 450 ppm scenario with the new CSP technologies with thermal storage has been considered as the baseline and an alternative scenario without these technologies has been also considered.

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Figure 26. Electricity production in the baseline scenario for sensitivity analysis

50

Figure 27. Electricity production in the alternative scenario for sensitivity analysis

When storage is available the share and the electricity production with solar increases but this is not done at the expense of fusion which also increases its share. The share of gas, coal and wind remain the same in both scenarios, while fission and CCS shares and production decrease.Total electricity production grows 2%.

8.3 New biofuels and electric vehicles Following, the analysis of the impact of new alternative biofuels and technologies in transport by buses and trucks, is presented. There are 15 bus and 56 truck (14 of each: heavy, light, medium and commercial) technologies in the model. Both, buses and trucks, can, theoretically, use biofuels (blended with fossil fuels), diesel, gasoline, electricity, hydrogen, LPG, and natural gas.

The following scenarios have been tested:

- No alt fuels scenario: base scenario without alternative fuels - Alt fuels scenario: base scenario with alternative fuels - 450ppm + alt fuels scenario: Alt fuels scenario with a CO2 restriction of 450 ppm

Buses

In a scenario with no possibility of alternative fuels consumption, the dominant fuel is gasoline until 2080 when it is gradually substituted by methanol and, in a minor proportion, by gas.

Buses- no alt fuels

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Figure 28. Fuel consumption in buses. No alt fuels scenario.

51

When alternative fuels are available, the behaviour is similar to the previous scenario with no gas penetration at all and with biomethanol instead of methanol.

Buses- alt fuels

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Figure 29. Fuel consumption in buses. Alt fuels scenario

Finally, in a scenario with the possibility of alternative fuels use and a limit in CO2 concentrations of 450ppm, the gasoline continues being the main fuel until 2060. Afterwards, biomethanol enters the system substituting quickly the gasoline until it disappears in the last periods.

Buses- 450 ppm+alt fuels

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Figure 30. Fuel consumption in buses. 450ppm + alt fuels scenario

Emissions in the three scenarios are shown in next figure.

CO2 emissions- Buses

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Figure 31. CO2 emissions from buses. All scenarios

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According to the fuel consumption, in the first scenarios, emissions start decreasing in 2080 when biomethanol enters the system while in the 450ppm scenario, the emissions reduction begins in 2060.

Trucks

In a scenario with no alternative fuels, the fuel consumption in trucks is similar to that in buses, with a predominant use of gasoline and the introduction of methanol that here anticipates and starts in 2060. Also natural gas consumption anticipates and there is a higher share of diesel than in buses in the whole period.

Trucks- no alt fuels

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Figure 32. Fuel consumption in trucks. No alt fuels scenario.

When alternative fuels are available, trucks start consuming biomethanol in 2010, this consumption increases until it equals and exceeds gasoline in 2070 and 2080 respectively.

Trucks- alt fuels

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Figure 33. Fuel consumption in trucks. Alt fuels scenario

When environmental restrictions are set, biomethanol enters the system also in 2010 but biomethanol consumption equals and exceeds gasoline before than in the previous scenario, in 2040 and 2050 respectively, being almost the only fuel consumed in 2100 (84% share).

53

Trucks- 450ppm+alt fuels

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Biomethanol Diesel/Biodiesel Ethanol Gasoline Gas

Figure 34. Fuel consumption in trucks. 450ppm + alt fuels scenario

According to these consumptions, emissions start decreasing earlier in the 450ppm scenario when diesel consumption decreases until it disappears, and biomethanol almost equals gasoline consumption. When alternative fuels are available but there is not CO2 concentration restrictions, emissions decrease at the same time that diesel consumption disappears, but then start increasing again until 2040 due to an increase in gasoline use. Afterwards, with the gradually substitution of gasoline by biomethanol, emissions start decreasing again.

In the scenario with no restrictions and no alternative fuels, emissions reach the highest level between 2050 and 2060 and then start decreasing when other fuels such as methanol enter the system.

CO2 emissions- Trucks

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Figure 35. CO2 emissions from trucks. All scenarios

8.4 Scenario analysis: nuclear fuel cycle strategies The described implementation scheme of existing and future fission technologies allows analysing a broad spectrum of future fuel cycle strategies in the context of the long-term development of the global energy system. Selected strategies have been implemented into scenario setups. In the following section, the main characteristics of the different fuel cycle scenario setups are described.

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8.4.1 Scenario setup The following scenarios are implemented analogical to the proposed fuel cycle strategies in (18

Scenario

),a detailed description is available within this document.

Scenario description OTC

· once-through cycle only (with generic LWR) · 150 kt of NFCNAU equivalent from military inventories

MOX

· scenario based on “OTC” · LWR technology is fed by up to 50 % by MOX fuel · >= 50 % of spent UOX is reprocessed · additional 150 t of Pu from military inventories for LWR MOX

MOX_FR

· scenario based on “MOX” · additional fast reactor technology from 2025 on (ENFCNPPFAR) · 100 % of spent LWR MOX is reprocessed

MOX_FR_ABR1

· scenario based on “MOX_FR” (no repro constraint after 2030) · ABR and UREX reprocessing from 2040 on (ENFCNPPBTR) · ABR fuelled by transuranic and natural U

MOX_FR_ABR2

· scenario based on “MOX_FR” · ABR from 2040 on (ENFCNPPBTU) · ABR fuelled by natural U

MOX_FR_ADS1

· scenario based on “MOX_FR” (no repro constraint after 2030) · ADS and UREX from 2040on (ENFCNPPATR) · ADS fuelled by transuranic (TRU) from LWR and NFCNAU

MOX_FR_ADS2

· scenario based on “MOX_FR” · ADS and advanced PUREX from 2040 on (ENFCNPPAMA) · ADS fuelled by minor actinides (MA)

8.4.2 Scenario results – electricity production All the fuel cycle scenario results presented in the following section are calculated based on the base growth 550 ppm scenario.

18Bustreo, C. EFDA TIMES report 2011

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Scenario Scenario results – global electricity production

OTC

In the OTC scenario, the electricity production from fission is limited due to the availability of conventional uranium resources and military uranium inventories. From 2030 on, the fission share declines until 2100.

MOX

Through the additional reprocessing option, the electricity production from fission in LWR is increased by 10 % relative to the OTC scenario. From 2030 on itshows a similar trend in decrement.

MOX_FR

Having fast reactors available from 2025 on, the global electricity production from fission increases up to 2040. Under the given constraints (minimum 50 % of UOX reprocessed), the electricity production from fission decreases from 2050 on, mainly due to resource availability (reprocessed MOX). The overall electricity production from fission increases by 33 % relative to the OTC scenario as FR increase resource efficiency considerably.

MOX_FR_ABR1

The availability of ABR1 technology from 2040 on is not impacting the electricity production from fission considerably asits focus lies on the transmutation (TRU burning) of waste. Slightly higher fission contributions in the end of the century and lower contributions in the mid-century can be observed, the overall increase of electricity production from fission compared to the OTC scenario amounts to 44 %.

MOX_FR_ABR2

With ABR2 technology available from 2040 on, the electricity production from fission increases substantially from 2040 on as this technology is fuelled by depleted uranium that is available in large quantities as a by-product of uranium enrichment for LWR. The overall increase in global electricity production from fission is 183 %, relative to the OTC scenario. A decrease of fossil contribution from the mid-century on as well as a decrease of renewable and fusion share in the end of the century can be observed.

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MOX_FR_ADS1

With ADS technology, also available from 2040 on, waste transmutation (ADS1: transuranic elements) is focussed rather than electricity production. Following, the electricity production share from fission develops similar to MOX and MOX_FR scenario with an overall increase of 28 % relative to the OTC scenario.

MOX_FR_ADS2

Similar to the MOX_FR_ADS1 scenario, in the ADS2 scenario the overall share of electricity production from fission is increased only by 36 % compared to the OTC scenario. Also for ADS2, the transmution is on the transumtation of waste (MA burning).

8.4.3 Scenario results – installed capacities

Scenario Scenario results – installed capacities

OTC

In the OTC scenario, the global installed LWR capacity increases up to 2030 and decreases due to scarce uranium resources to a marginal level in 2100.

MOX

The MOX scenario shows a similar development as the OTC scenario, whereby the installed capacity is slightly higher than in the OTC scenario from 2020 on.

MOX_FR

In the MOX_FR scenario, FRs are installed from 2030 on and increase the overall installed fission capacity from 2040 on. Generally, the FR capacity follows the installed capacity of LWRs due to the reprocessing chain. LWR have a higher contribution from 2050 on compared to the MOX scenario, a

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prolongation of LWR era is achieved.

MOX_FR_ABR1

In the MOX_FR_ABR1 scenario, FRs are replaced by ABR1 technology installed from 2040 and increasing up to 2070, following the development of LWR capacities (reprocessing chain) afterwards. It can be observed that a considerable prolongation of LWR era can be achieved with a contribution up to 2100. The overall installed fission capacity is lower than in the MOX_FR scenario at mid-century, with a smaller decrease until the end of the century.

MOX_FR_ABR2

The attractiveness concerning costs and resource consumption of ABR2 technology leads to a considerable increase of installed capacities from 2050 on. In the MOX_FR_ABR2 scenario, FRs and LWRs are replaced by ABR2 until the end of the century. The overall installed fission capacity quadruples up to 2080 and then decreases due to fusion entering the market.

MOX_FR_ADS1

The ADS1 technology, mainly focussing on transmutation (TRU burning) replaces FR technology from 2040 on and shows a similar influence on LWR capacities as ABR1. In comparison to the ABR1 scenario, the overall installed fission capacity is slightly lower, the prolongation effect on the LWR stock is similar.

MOX_FR_ADS2

The ADS2 technology is focussing on the transmutation (MA burning) of waste from either LWR UOX, LWR MOX or FR MOX. It reduces the overall MA waste production compared to the MOX_FR scenario but increases the overall fuel cycle costs. As a consequence, the installed nuclear fission capacity is slightly lower in the mid-century and LWR usage is prolonged.

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8.4.4 Scenario results - resource availability and consumption The NFC basically relies on three primary resources: Uranium from mining of natural uranium ore, highly enriched uranium from the decommissioning of nuclear weapons and plutonium from military inventories. The data about global resources of uranium are taken from19

Table

and sum up to 6.3 Mt U3O8. The regionalization according to ETM regions and different cost levels is shown in

13.

Table 13: Identified global uranium resources

AFR AUS CAN CHI CSA EEU FSU IND JPN MEA MEX ODA SKO USA WEU SUM 40 k$ / kt 172.3 0.0 366.7 67.4 139.9 0.0 50.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2946 80 k$ / kt 144.1 1612.0 80.7 82.6 102.8 0.5 723.2 0.0 0.0 111.8 0.0 41.8 0.0 39.0 7.0 1662 120 k$ / kt 587.6 61.0 37.9 21.4 57.8 15.9 578.4 80.2 6.6 7.3 0.0 12.3 0.0 168.4 27.3 902 160 k$ / kt 45.6 6.0 59.4 0.0 1.5 18.8 384.8 0.0 0.0 2.2 1.8 7.6 0.0 264.7 109.9 2946

The globally available highly enriched uranium from nuclear warheads is estimated to 150 kt natural U3O8 equivalent, the according cost level is set to 80 k$ / kt, as fuel form military inventories is sold at market price. The available Plutonium resources from nuclear weapons decommissioning amount to 0.15 kt, and it is supposed being equal to plutonium from reprocessed UOX20

Uranium and Plutonium resource availability is a crucial point for the future of the nuclear fission sector. All the fission scenarios presented above are commonly using all the available uranium until 2100. Hence, uranium resources are a limiting factor for the further growth of today’s nuclear fission technologies. As can be seen in the scenarios “OTC” and “LWR_MOX”, the available conventional resources are exhausted completely until the end of the century limiting fission contribution form mid-century on within the electricity market. On the one hand, a serious long-term fission contribution in the electricity market strongly depends on the available future technologies (as seen in “LWR_FR_BTR2” scenario) and on the other hand the availability of unconventional uranium resources (e.g. from seawater) or the introduction of a thorium cycle could cause similar effects.

.

8.4.5 Scenario results – overall waste arising The implementation of the different NFC strategies allows for the tracking of waste material flows on the basis of four different (aggregated) components: Uranium (U), plutonium (PU), minor actinides (MA) and fission products (FP). Hence, the waste arising (caused by spent fuel and high

19 OECD / NEA. “Uranium 2009: Resources, Production and Demand”. 2010 20 Source: http://www.world-nuclear.org

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level waste from reprocessing) in each NFC scenario can be examined in further detail. Depending on the mixture (and the internal element and isotope composition) of the four waste components, the characteristics concerning half-life, radiotoxicity, proliferation risk and costs for treatment and disposal differ.

In the following section, an overview over the waste arising within the different NFC scenarios is provided (for more details see EFDA TIMES report on NFC implementation). Figure 51 shows the overall waste arising for different NFC scenarios. From the OTC over MOX up to the MOX_FR scenario, the overall waste production is slightly growing caused by the additional depleted uranium used for MOX and FR MOX fuel production. As a consequence of reprocessing, also the share of HLW increases in both of the latter scenarios. As can be seen in Figure 52, the waste arising related to electricity production shows an opposite trend. In the MOX_FR_ABR1 scenario, the overall waste production increases due to additional natural uranium demand for ABR fuel production, whereby the increase of waste production per electricity produced is comparatively lower. The most efficient scenario concerning the relation of waste per electricity and the overall waste production is the MOX_FR_ABR2 scenario: The waste per electricity production rate is less than half of all the other scenarios, only the production of high level waste exceeds the levels of the other scenarios by far. Both ADS scenarios (MOX_LWR_ADS1 and MOX_FR_ADS2) represent politically induced transmutation strategies with comparatively high waste arising.

Figure 36: Overall waste arising in fuel cycle scenarios

Figure 37: Waste arising related to electricity production for NFC scenarios

The different components of waste arising, namely fission products (FP), plutonium (PU) and minor actinides (MA), are depicted for each scenario in Figure 53 and Figure 54. The comparison between the scenarios OTC and MOX shows that the amount of FP is reduced to less than the half, PU is reduced slightly and MA increase. This trend continues by introducing a second reprocessing step in the MOX_FR scenario: FP are reduced to less than 1/3, PU is reduced by more than factor four

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and on the other hand MA increase. In the MOX_FR_ABR1 scenario, also MA are reduced below OTC levels, PU arising is less than in the MOX_FR scenario and only the arising of FP exceeds slightly the MOX_FR case. In the MOX_FR_ABR1 scenario, similar reductions in PU and MA arising are achieved, only PU production nearly doubles compared to the MOX_FR(_ABR1) scenario. In the MOX_LWR_ADS1 scenario, similar transmutation efficiencies as seen in theMOX_FR_ABR1 can be achieved. As both focus on TRU burning, the achieved reduction of minor actinides is about factor 2-3 compared to scenario MOX_FR. The MOX_FR_ADS2 contributes to minor actinides reduction compared to the MOX_FR scenario but on the other hand, fission product rising remains at very high levels.

Figure 38: Overall production of waste components in NFC scenarios

Figure 39: Production of waste components related to electricity production in NFC scenarios

9. Conclusions According to the results of the present Report, fusion represents a key technological option for future energy system. The key driver for fusion penetration is a concern for climate change. The adoption of environmental measures, even in the weaker form of a CO2 tax differentiated among regions and with a moderate path of growth is sufficient to push fusion into the electricity market at the end of the century. Fusion results seem robust against different paths of economic growth, even the very low paths shown in the sensitivity analysis. Fusion role is also linked to the tightness of environmental constraints.

Main competitors are Renewables and ABR that penetrates the market some decades in advance. The sensitivity analysis shows that the availability of storage technologies for renewable electricity generation technologies does not influence the penetration of fusion into the electric system. The

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higher penetration of solar electricity observed in the sensitivity analysis is done at the expense of ABR and CCS.

ETM model results are somehow biased to limited hypothesis of energy efficiency and energy savings. This issue represents one of the line of future improvement that deserves additional work. The first sector of investigation should be the residential, where, for some critical regions, a couple of service demands assume values suspiciously high. The magnitude of the possible error is however limited and do not affects the conclusion drawn upon the role of fusion.

The main source of the increase in electricity demand, according to the model, is the industrial sector, whose levels of activity will dominate the gain in electricity efficiency and that will show some increases in the electricity intensity related mainly to the Iron and Steel production and to the Pulp and paper sector.

The analysis highlights the need of innovative technologies in the electric sector in 2070-2080 when coal is used as a transition technologies for some years. The progressive growth of capacity of ABR partially resolves the problem.

CCS is a key mitigation option in most of the scenarios that have been shown. It is tipically related to the industrial sector both for cogeneration and for cement production.

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