Analysis and comparison of relevant mid- and long-term energy scenarios for EU and their key underlying assumptions

Transcription

1 Final report Analysis and comparison of relevant mid- and long-term energy scenarios for EU and their key underlying assumptions ENER/10/NUCL/SI For: EU DG Energy, Direct. D - Nuclear Energy, Luxembourg Team: Matthias Deutsch PhD Florian Ess Jens Hobohm Vincent Rits (project leader) Dr. Michael Schlesinger Samuel Strassburg Berlin/Basle, April 2011

2 Executive Director Christian Böllhoff President of the Administrative Board Gunter Blickle Commercial Register Number Berlin HRB B Legal Form AG (Aktiengesellschaft) according to Swiss Law Founded 1959, in Basle, Switzerland Field of Business Activity European-wide, Prognos develops practical strategies for enterprises, organisations and the public sector on the basis of authorative and objective analyses. Working Languages German, English, French Headquarters Prognos AG Henric Petri-Strasse 9 CH Basle Phone +41 (0) Fax +41 (0) info@prognos.com Other Locations Prognos AG Prognos AG Goethestrasse 85 Wilhelm-Herbst-Strasse 5 D Berlin D Bremen Phone +49 (0) Phone +49 (0) Fax +49 (0) Fax +49 (0) Prognos AG Prognos AG Schwanenmarkt 21 Sonnenstrasse 14 D Dusseldorf D Munich Phone +49 (0) Phone +49 (0) Fax +49 (0) Fax +49 (0) Prognos AG Prognos AG Friedrichstrasse 15 Square de Meeûs 37-4th Floor D Stuttgart B Brussels Phone +49 (0) Phone +32 (0) Fax: +49 (0) Fax +32 (0) Internet

3 Summary Objective and approach The objective of the project was to compare eight studies on midand long-term future energy scenarios for the European Union (EU) and to analyse main differences in their underlying assumptions with focus on the future role of nuclear power. As the project started in 2010, all analysed studies were released in 2010 the latest. The working program of the project consisted of four project steps: a) Selection of eight scenario studies by specified criteria b) Identification of key factors and driving forces in the scenario studies (input and output analysis) c) What if. : The underlying stories and premises to make the scenario happen d) Conditions and implications for nuclear power Finally, building on the outcomes of these four phases, the conclusions were drawn. Selection of scenario studies The selected energy scenarios fulfil several requirements (also according to the criteria set by the contracting body). These criteria include: a minimum time horizon until 2030 the geographical coverage of the EU-27 (or Europe). quantifiability coverage of the electricity sector Furthermore the scenarios were released quite recently (between 2008 and 2010), have international relevance, and are in totally covering a broad range of stakeholder groups. Out of a total of 28 scanned studies, eight studies were finally selected to be analysed and compared, using the criteria set up. These are: I

4 1) EU DG ENER (2010). Trends to 2030, Update 2009 (partly Update 2007) 2) IEA (2009). World Energy Outlook 2009 (with excursus to WEO 2010) 3) IEA (2010). Energy Technology Perspectives 4) Greenpeace/EREC (2010). Energy [R]evolution 5) ECF (2010). Roadmap ) Eurelectric (2009). Power Choices 7) Capros et al. (2008). Model-based Analysis of the 2008 EU Policy Package on Climate Change and Renewables. 8) FEEM et al. (2010). PLANETS-project (scenarios analysed with the WITCH-model) Models used The various models used can be differentiated by bottom-up and top-down characteristics. Applied models show properties of both groups to a greater or lesser extent. PRIMES (used in the Eurelectric, Capros et al. and EU Trends scenarios) and WITCH (used in the PLANETS-project) show several characteristics of topdown models. On the other hand, ETP-MARKAL/TIMES (IEA ETP), WEM (IEA WEO), MESAP-PlaNet (Greenpeace) and the model framework of the ECF-studies are dominated by bottom-up characteristics. Scenario studies are linked to each other, as model inputs (e.g. population, GDP, fossil fuel prices) are partly extracted from other scenario studies. The most important role in the provision of input data among the scenario studies is hold by the IEA WEO. Concerning the determination of energy supply and demand technologies, models can be classified into optimisation (PRIMES, WITCH, IEA ETP-MARKAL/TIMES, WEM) and simulation models (ECF and MESAP-PlaNet, using exogenous market shares for technologies). Furthermore, optimisation models can be divided into model types which implement individual optimisation (PRIMES) and general optimisation (WEM, ETP-MARKAL/TIMES, WITCH) frameworks. Various model types and optimisation regimes in turn lead to significant differences in the interpretation of model dynamics and scenario outcomes. A range of sub-models or external models are implemented in the applied model frameworks. Grid issues, emission trading and economy-wide sectoral effects are examples for issues covered by sub-models. However if such sub-models are used, the extent and way by which these sub-models are linked to the main model often remains unclear. II

5 Input and outcomes The baseline or reference scenarios show the impact of presently implemented policies or in some cases a continuation of present policy, whereas the alternative scenarios aim at reducing GHG emissions. Most of the studies imply an (European) Emission Trading System explicitly. The Energy Climate Foundation (Roadmap 2050) and Greenpeace/EREC (Energy[R]evolution) also made specifications on the share of renewable energies in In both studies the role of nuclear power is also predetermined. In the Roadmap 2050 it varies from 10 to 30% share of electricity generation among the (alternative) scenarios. In the Energy [R]evolution scenario, except for the baseline, nuclear power is phased out until With relative small changes in population (between -2% and +6% in 2050 compared to 2007), development of GDP per capita steadily increases as GDP grows by around 1 to 2% per year until 2030/2050. Since the oil peak price in 2008, a higher level of future oil price assumptions can be seen in the scenario studies. A (slight) increase towards 2030 can be observed in most of the studies. Gas prices remain coupled with oil prices in most of the scenarios, except for the Energy [R]evolution of Greenpeace/EREC and the alternative scenario of the FEEM-WITCH scenarios. A moderate increase of coal prices is assumed in most of the scenarios. Coal prices in the ETP Blue Map scenario though show a decreasing tendency, due to remarkable shifts from coal to cleaner energy sources. Differences in the CO 2 -certificate prices are up to the threefold. Some scenarios endogenously derive prices (e.g. eurelectric, Capros et al., EU DG ENER, IEA WEO and ETP) as a result of demand for certificates and reduction targets. Other scenarios (e.g. ECF, Greenpeace) assume an exogenous price development. Ambitious emission reduction targets induce high CO 2 -certificate prices as more expensive abatement options must be adopted. Geographical extension of emission trading systems (e.g. ECF Pathways, WEO 450 ppm) in principle leads to a slowdown in the increase of carbon prices, as more abundant and cheaper opportunities for emission reduction are available outside the EU/OECD-Europe. In most scenarios, development of power generation capacity is driven by costs of power generation technologies. Investment costs for power generation and assumptions on their development are one of the main drivers (as well as fuel and CO 2 -certificate costs) which determine these generation costs. Nuclear power plants are capital intensive. Investment costs for fossil-fired and III

6 nuclear power plants tend to decrease only slightly over the timeframe (up to 2030/2050). Significant declines of investment costs, primarily due to learning by doing, for emerging renewable technologies, such as wind onshore/offshore, solar PV, solar CSP and partly biomass, can be observed throughout the analysed studies. Without new energy policies to reduce energy demand or GHGemissions, final energy demand will increase, similar to the GDPdevelopment. With new and stringent policy measures, final energy demand can be reduced by % until Until 2030 a steady increase of energy productivity can be observed. After 2030, energy productivity has to be increased faster to fulfil the emission reduction targets. The substitution towards electricity is, besides energy efficiency measures, reducing fossil fuel demand in all alternative scenarios over the projection period. Beneath the substitution effect, electricity demand also increases due to GDP per capita growth. This outweighs efficiency measures in this field. Changes in electricity generation (development as well as structure) depend on: RES-targets set in the scenarios GHG-targets set in the scenarios bounds set for the deployment of power plants (e.g. nuclear power) competitiveness of power plants (capital costs, fixed and variable O&M-costs, fuel and CO 2 -prices) Increasing deployment of renewables is considerable in all of the alternative scenarios, especially in the E[R] advanced and ECF 80% scenarios. Other alternative scenarios extensively use CCS and nuclear power as additional abatement options in the long term. Installed capacity in the power sector is marked by a decreasing share of conventional (fossil fuelled) power plants, an absolute and relative increasing capacity of renewable energy systems and a divided development of nuclear power. Due to relatively low average load hours of renewable energy systems (RES), total installed capacity is often higher in scenarios with high shares of RES than in comparable scenarios with lower shares of RES. When looking more closely to the different capacities of power generation technologies in the scenarios, the following can be observed: IV

7 Power generation structures in the reference scenarios stay quite the same for different scenarios studies and compared to the present structure as well. Wind onshore tends to play an important role in the future power capacity mix. Capacity of biomass plants tends to increase relatively weakly, particularly in more recent scenarios. The use of PV increases rapidly after 2030 in the ECF Roadmap 2050 and Greenpeace scenarios. Regarding conventional fossil fuel plants, gas fired power plants in general remain in the mix in all scenarios. CCS and nuclear plays a role, if considered as an option, in the alternative scenarios. With currently implemented policies CO 2 -emissions can be reduced, but new policies are required to achieve significant higher targets. Underlying stories Deployment of specific technologies is mainly driven by different cost-assumption (e.g. investment costs), price-developments (e.g. fossil-fuel prices) and exogenous constraints (e.g. emission constraints and phase-out of nuclear power). The future energy supply structure is characterised by increasing shares for renewable technologies in all scenarios with ambitious emission reduction targets. These scenarios agree in the main technologies for the provision of renewable electricity and heat, namely windpower, biomass and solar-pv/solar-thermal technologies. In the absence of emission reduction targets, fossil-fuels are estimated to keep high shares in energy supply. Besides renewables, nuclear power and fossil-fired power plants with CCS (mainly emerging after 2030) are estimated to be the technologies of choice in electricity generation (in the alternative scenarios). The scenarios differ to some degree in estimations concerning the development of CCS, nuclear power and road transport. Deployment of CCS and nuclear power is depending on the development of prices (e.g. CO 2 -price, fossil-fuel prices) and exogenous constraints (e.g. phase-out of nuclear power and investment-cost increases in the deployment of CCS). Concerning road transport, development of electric vehicles (with significant shares mainly after 2030) is a distinguishing feature of the scenario studies. Energy efficiency in the end-user sector is driven by increasing applications of energy-saving technology options and improves in the alternative scenarios. However, increasing electric shares in road-transport and other end-user applications are estimated to be V

8 important means of emission reduction and lead to increasing electricity demands in the alternative scenarios. CO 2 -certificates and carbon prices are estimated to be the main policy instrument to reach emission reduction targets in most of the scenario studies. High carbon prices provide incentives to substitute CO 2 -poor technologies (e.g. renewables, nuclear power, CCS) for CO 2 -rich technologies (e.g. conventional fossil-fuels). Modelled emission trading frameworks can be mainly distinguished by different geographical coverage and different timescales for the expansion of emission trading schemes. All scenario studies confronted with high emission reduction targets expect large investments in transmission grids (estimations of up to bn cumulated grid-investment from ) and indicate the important role of technological improvements in grid technology and smart grids. However, details concerning implications for the deployment of new generation capacity, possible pathways for transmission and distribution grid investments and technological characteristics of future power grids are not provided by most of the scenario studies. Abatement costs are driven by investments for new (capitalintensive) energy supply technologies and expansions of the current grid-infrastructure. However, lower consumption of fossil fuels is estimated to compensate higher capital costs (to some extent), because high fossil-fuel prices can be avoided. A detailed comparison of abatement costs is hindered by different cost estimation conventions, timeframes and aggregation frameworks. Information on import dependency, labour market effects, environmental and social issues are rather spare. Import dependency, if considered at all, increases in the absent of emission reduction targets. Possible adverse effects from climate change (on ecosystems and the economy) are not implemented in most of the models applied. Employment is estimated to increase in green sectors and decrease in conventional fuel sectors in some studies. Fundamental changes in the behaviour (e.g. constrained optimisation and demand elasticities) of individuals are not assumed in the scenario studies for which information on this topic is available. Overview about the comparison of scenario studies In table S-1 the findings are summarized for selected issues by scenario, to display the main conformities and differences among the scenarios (see table S-1). The arrows in table S-1 reflect tendencies in the development of variables (for a specific scenario) up to 2050 and in relation to the development estimated in other scenarios. For studies with a shorter timeframe (up to 2030) the development until 2030 is evaluated. Variables for which a quantitative evaluation was not possible are described in words briefly. VI

9 Table S-1: Tendencies in the development of main variables in the scenario studies Study Timeframe up to year x Model type BU / TD Main target GHG or RES GDP p.c. Oil price Gas price Coal price CO 2 - price Model mechanism Opt or Sim ETScoverage Final energy demand Power demand Energy efficiency EEV per GDP Nuclear generation RES RES- (focus) CCS TWh TWh TWh EV CO 2 - emissions Gridinvestment Add. grids WEO Ref 2030 WEO 450ppm 2030 BU (add. TD) BU (add. TD) Opt + Sim Opt + Sim ETP BL 2050 BU Opt ETP Blue 2050 BU Opt EU DG TREN Base EU DG TREN Ref EU DG ENV BL EU DG ENV NSAT EU NSAT- CDM BU/TD mixed BU/TD mixed BU/TD mixed BU/TD mixed BU/TD mixed Opt Opt Opt Opt Opt target -80% GHG (2050) targets -74% GHG (2050) targets OECD+ (2013), CDM Wind (offshore after 2020), solar, bio (by 2030) EU-27 Wind, solar OECD+ OME EU-27, CDM EU-27, CDM Wind, bio, solar (PV/CSP after 2020) Wind, solar, bio Wind (offshore after 2020), solar, bio EU-27 Wind, bio, solar targets EU-27 Wind, bio, solar EU-27, Wind, bio, CDM solar targets ECF-Ref 2050 ECF 80% RES ECF 60% RES BU with add. TD BU with add. TD BU with add. TD Sim Sim Sim targets -80% GHG (2050) -80% GHG (2050) OECD (2020) Wind Wind OECD (offshore (2020) after 2020), OECD (2020) bio, heat pump Wind, bio, heat pump (after 2020) (after 2020) VII

10 ECF 40% RES 2050 BU with add. TD Sim -80% GHG (2050) OECD (2020) E[R] Ref 2050 BU Sim Global (long term) basic E[R] 2050 BU Sim advanced E[R] Eurelectric PowCh 2050 BU Sim 2050 BU/TD mixed Opt WITCH Ref 2100 TD Opt WITCH FB TD Opt -80% CO 2 (2050) -95% CO 2 (2050) -75% GHG (2050) ca. 500 ppm CO 2 Global (long term) Global (long term) Global (2020) Global (2012) Wind, bio, heat pump (after 2020) Wind, solar, geo Wind, solar, geo Wind (offshore after 2020), bio, solar PV (after 2020) Wind, solar Wind, solar Legend: add.: additional, BU: bottom-up, TD: top-down, Opt: optimisation, Sim: Simulation, EV: electric vehicles, bio: biomass, geo: geothermal OME: other major economies, CDM: Clean Development Mechansim, ETS: Emission Trading System, Arrows describe tendencies in the development of variables over the timeframe applied in the scenario studies. Developments are evaluated in comparison with developments in other scenario studies (e.g. a sharp increase in one variable means that the variable increases relatively sharp compared to the development in other scenario studies). (by (by 2030) 2030) Prognos 2011 Sharp increase Moderate increase Almost stable or small increase/decrease Moderate decrease Sharp decrease No information available or no development estimated in the scenario studies VIII

11 Interpretation of results Overall, the following conclusions can be drawn: Scenarios are marked by their GHG-targets. Demand Without new policy measures, energy demand will increase due to GDP growth (around 1 to 2 % per year). Electrification occurs in (almost) all scenarios. Electricity is estimated to gain shares in final energy demand, especially in scenarios confronted with ambitious GHG-targets (mainly as a substitute for fossil fuels) reaching shares of 30 to 40 % (i.e. an increase in final electricity demand of around 1 % per year). In road transport, the use of hybrid cars and electric vehicles is generally increasing towards Nuclear Power The role of nuclear power is generally an outcome of the bounds and investment cost assumptions set by the scenario developers. Without (major) restrictions, nuclear power tends to expand in Europe (reaching shares of 35 to 45 % of electricity generation) and especially worldwide. Nuclear power is estimated to gain higher importance in scenarios with ambitious GHG-targets (corresponding to an increase of around 30 % to 100 % of the currently installed capacity), unless the development is restricted exogenously. Lifetime-extension of nuclear power plants is not (explicitly) assumed in the scenario studies (except for some individual countries). Scenarios expect future nuclear power plants up to 2050 to be dominated by Generation III or III+ reactors. Detailed information about the framework in which nuclear power can develop is not given by the studies. Some studies address the importance or are aware of relevant market issues, like investment in uranium mining, capacity needs in construction and operation and final storage solutions. Renewables Absolute and relative increase of RES in the power sector (reaching shares of 40 % in the baseline scenarios, compared to up to 100 % of electricity generation in 2050 in the alternative scenarios). IX

12 Investment costs for RES decrease significantly in the scenarios, especially in the long term. Wind power, solar heat and solar PV as well as biomass show the main contributions in the deployment of RES Fossil fuel plants CCS plays an increasing role in several scenarios (from 2030, reaching shares of up to 30 % of electricity generation in 2050) and can be seen as the second main power generation technology (beneath nuclear power) to provide baseload power. In scenarios applying optimisation models, development of carbon prices is found to be crucial for the emergence of CCS. Special attention is generally given to gas-fired power plants, which are estimated to serve as dispatchable capacity in a range of the considered scenarios, but with far lower utilisation rates than currently observed. Models used The models used are characterized by complex interrelationships between and inside energy sectors, individual behaviour, policies and the whole economy. Though, the detailedness of the sectors and energy carriers varies among the models as well as the way the models are functioning and which issues are emphasized (e.g. optimisation regimes, compliance costs, use of grid sub-models). This hampers the comparison of the studies outcomes. Models used for the scenario studies can be mainly grouped into optimisation models and simulation models which use exogenously defined market shares. Optimisation models can be further split into models which apply optimisation for individual agents (stable behaviour presumed) and models applying general optimisation frameworks. If optimisation is applied, deployment of different energy supply and demand technologies is mainly affected by cost assumptions and exogenously (e.g. fossil fuel prices) or endogenously determined prices (e.g. carbon prices) and their impacts. Costs Compliance costs for emission reduction tend to be high, mainly driven by increasing capital costs (e.g. capital-intensive power generation technologies and grid costs), but decrease in X

13 the long term due to lower fossil-fuel consumption and technological improvements in renewable technologies. Electricity prices are estimated to increase in most of the studies in the medium term (more than 25% up to 2030). Some studies with high emission reduction targets expect a decrease of electricity prices in the long term (up to 2050), mainly due to the decrease of fossil-fired generation shares and technological development in renewable power generation. Comparison of compliance costs between the scenario studies is hindered by different frameworks and conventions in the quantification of costs and a lack of information on these issues. Grids Studies with ambitious emission reduction targets assume technological progress in grid technology and management and estimate that large increases in transmission capacities are needed. Transmission extension leads to additional capital costs for the power sector (e.g. cumulated grid investment of up to bn needed until 2050 in the eurelectric Power Choices scenario). Almost all studies emphasize the relevance of technologically advanced smart grids and smart metering frameworks, especially those confronted with ambitious emission reduction (although, details on the technology of choice are not determined). The linkage of grid (sub-)models and the development of generation technologies remains rather unclear in the studies analysed. Implications for distribution networks are not addressed in detail by most of the studies. Import dependency and security of supply Import dependency of the European Union is high and will increase in the absent of emission reduction measures. All of the studies agree that higher emission reduction needs result in higher shares of renewables and therefore reduce import dependency compared to scenarios with lower emission reduction. Security of resources is presumed in several studies, security of supply for electricity is assumed to be equivalent to current levels in some scenarios (if information is available at all). XI

14 Preface and acknowledgements In this report a number of European energy scenarios all published before 2011 are systematically analysed and compared. In general, the outcomes of the scenarios are based on model results and underpinned with a qualitative storyline. These outcomes vary among the scenarios, depending on the aim and parameter set, the model used and the expert s opinions. The future can not be foreseen exactly, and scenarios are also not intended to do so. Scenarios are pictures of how the future world could look like within a specified framework and under specified assumptions (EU, 1994). The scenario s purpose already legitimates certain results (e.g. compare baseline vs. target scenarios). So, the outcomes always have to be seen in the context of the scenario s purpose. The different energy models have been developed and extended extensively over the years, integrating more and more (market) elements and issues, in order to approximate more closely to the real world. Still, the models and the scenarios can only address certain aspects of the total energy system and the market in detail, depending also on the target set or specifications given by the contracting body. Other aspects are only highlighted briefly or sometimes not at all. In itself, the scenario should be consistent and comprehensible. It is not for the authors of this study to judge about the outcomes of the scenarios - although remarks will be placed to issues that seem inconsistent - but to try to understand and clearly describe the conformities and differences of the analysed scenarios. Why do the authors and modellers come to their result is therefore the main question to be answered. The results of this (attempt) are reflected in this report. We would like to express our gratitude to all the persons and organizations that have contributed to this study and report. In particularly, would like to thank Marc Deffrennes (European Commission), Dr. Christian Kirchsteiger (European Commission), the members of the ENEF Subgroup Competitiveness chaired by Dr. Didier Beutier (Areva), Dr. Peter Taylor (IEA), Dries Acke (Energy Climate Foundation), Loukianos Zavolas (Energy Climate Foundation), Sven Teske (Greenpeace), Dr. Thomas Pregger (DLR Stuttgart), Nicola Rega (eurelectric), Guiseppe Lorubio (eurelectric), Dr. Massimo Tavoni (FEEM) and Christian Dieckhoff (KIT Karlsruher Institut für Technologie) for their valuable contributions. XII

15 Content Summary Preface and acknowledgements I XII 1 Introduction Background Objective 1 2 Approach 2 3 Selection of scenario studies Introduction Criteria catalogue List of scanned studies Outcomes List of selected studies 9 4 Input parameters and outcomes Introduction Targets Socio-economic assumptions Population GDP GDP per capita Prices Other assumptions and model settings Technology assumptions Investment costs for fossil and nuclear power plants Investment costs for renewable power plants Efficiencies of power plants Life times of power plants Full-load hours of power plants Outcomes Primary energy demand Final Energy Demand Electricity Demand Electricity share of final energy Electricity generation Installed Capacity Electricity demand by sector GHG- and CO 2 -Emissions Comparison: IEA WEO 2009 and WEO Models used Interdependencies between studies Models used 70 XIII

16 5.2.1 World energy model (WEM) ETP-MARKAL/TIMES-model PRIMES-model MESAP/PlaNet-model Models used in ECF Roadmap WITCH model Comparison of models used Conclusion on Models 93 6 What if : the underlying stories to make such a scenario happen Background and objective Technological issues Type of electricity and heat generation Development in additional selected technologies Energy efficiency and energy storage Power system stability and grid issues Policy issues Emission trading Policies concerning renewables and energy efficiency Economic issues Compliance costs and investment expenses Prices Import dependency and security of supply Labour Environmental issues Social issues Implications for nuclear power Introduction Nuclear development Fuel cycle issues Market issues Financial / investment issues Safety issues Outstanding issues Conclusion Comparison of the scenario studies: overview Interpretation of results 122 Appendix 125 A.1 Literature 125 A.2 Abbreviations and acronyms 131 A.3 Conversion factors 134 A.4 Conversions of prices, handling of differing base years in index diagrams 135 A.5 Detailed data sheets 136 A.6 Approach (extended version) 142 XIV

17 Figures Figure 2-1: Project steps 3 Figure 4-1: Development of population, in millions 16 Figure 4-2: Development of GDP (Index 2007 = 100) 17 Figure 4-3: Development of GDP per capita (Index 2007 = 100) 18 Figure 4-4: Development of oil prices, in USD 2008 /barrel 19 Figure 4-5: Development of gas prices, in EUR 2008 /GJ 20 Figure 4-6: Development of gas to oil prices, in % 21 Figure 4-7: Development of coal prices, in EUR 2008 /GJ 22 Figure 4-8: Development of coal to oil prices, in % 22 Figure 4-9: Development of CO 2 -certificate prices, in EUR 2008 /t CO 2 24 Figure 4-10: CO 2 -certificate prices in 2020, in EUR2008/t CO 2 25 Figure 4-11: Figure 4-12: Figure 4-13: Figure 4-14: Figure 4-15: Figure 4-16: Figure 4-17: Figure 4-18: Comparison of current investment costs nuclear and fossil fuel plants, in USD 2008 /kw 28 Comparison of future investment costs nuclear and fossil fuel plants for 2030, in USD 2008 /kw 29 Comparison of future investment costs nuclear and fossil fuel plants for 2050, in USD 2008 /kw 30 Comparison of present investment costs for renewable power plants, in USD 2008 /kw 32 Comparison of future investment costs for renewable power plants in 2030, in USD 2008 /kw 33 Comparison of future investment costs for renewable power plants in 2050, in USD 2008 /kw 34 Development of efficiencies of new coal and lignite power plants, in % 36 Development of efficiencies of new coal and lignite power plants using ccs technology, in % 37 Figure 4-19: Development of efficiencies of gas power plants, in % 37 Figure 4-20: Life times of power plants, in years 39 XV

18 Figure 4-21: Full load hours of power plants for the years 2007, 2005 and 2010, by category, in h/a 40 Figure 4-22: Figure 4-23: Figure 4-24: Full load hours of power plants in 2020, by category, in h/a 40 Full load hours of power plants in 2030, by category, in h/a 41 Full load hours of power plants in 2050, by category, in h/a 41 Figure 4-25: Development of primary energy demand, in PJ 43 Figure 4-26: Development of final energy demand, in PJ 44 Figure 4-27: Development of GDP per final energy demand, index 2007= Figure 4-28: Final energy shares of energy carriers, in PJ 46 Figure 4-29: Final energy shares of energy carriers, in PJ 46 Figure 4-30: Final energy shares of energy carriers, in PJ 47 Figure 4-31: Final energy shares of energy carriers, in % 47 Figure 4-32: Final energy shares of energy carriers, in % 48 Figure 4-33: Final energy shares of energy carriers, in % 48 Figure 4-34: Development of final electricity demand, in TWh 50 Figure 4-35: Development of final electricity demand per capita, index 2007= Figure 4-36: Electricity share of final energy, in % 51 Figure 4-37: Electricity generation in 2020, in TWh 53 Figure 4-38: Electricity generation in 2030, in TWh 54 Figure 4-39: Electricity generation in 2050, in TWh 54 Figure 4-40: Electricity generation in 2020 by category, in % 55 Figure 4-41: Electricity generation in 2030 by category, in % 55 Figure 4-42: Electricity generation in 2050 by category, in % 56 Figure 4-43: Installed capacity in 2020, in GW 57 Figure 4-44: Installed capacity in 2030, in GW 58 Figure 4-45: Installed capacity in 2050, in GW 58 XVI

19 Figure 4-46: Installed capacity in 2020 by category, in % 59 Figure 4-47: Installed capacity in 2030 by category, in % 59 Figure 4-48: Installed capacity in 2050 by category, in % 60 Figure 4-49: Figure 4-50: Installed capacity (detailed technologies) in 2030 by category, in GW 61 Installed capacity (detailed technologies) in 2050 by category, in GW 62 Figure 4-51: Final Electricity demand in 2020 by sector, in TWh 63 Figure 4-52: Final Electricity demand in 2030 by sector, in TWh 63 Figure 4-53: Final Electricity demand in 2050 by sector, in TWh 64 Figure 4-54: Final Electricity demand in 2020 by sector, in % 64 Figure 4-55: Final Electricity demand in 2030 by sector, in % 65 Figure 4-56: Final Electricity demand in 2050 by sector, in % 65 Figure 4-57: Development of CO 2 -Emissions, in % compared to Figure 4-58: Figure 4-59: Comparison of energy prices and capacities of electricity generation 69 Comparison of final electricity consumption, total consumption and CO 2 -emissions 69 Figure 5-1: Interdependencies between studies 70 Figure 5-2: Structure of the WEM 73 Figure 5-3: Structure of the WEM Power generation module 74 Figure 5-4: General structure of a MARKAL-model (1) 76 Figure 5-5: General structure of a MARKAL-model (2) 77 Figure 5-6: Structure of the PRIMES-model 80 Figure 5-7: Structure of the energy system modelled in WITCH 87 Figure 7-1: Development of European nuclear power generation, in TWh 110 Figure 7-2: Development of European nuclear capacities, in GW 111 Figure 7-3: Development of global nuclear power generation, in TWh 112 XVII

20 Figure 7-4: Development of investment costs for nuclear power plants, in USD 2008 /kw 116 Figure A-1: Project steps 142 Figure A-2: Possible input parameter 146 Figure A-3: Output 147 Figure A-4: Indicators 148 Figure A-5: Merit-order 149 Figure A-6: Overview of the working program 154 Figure A-7: Schedule (according to the call for tender) 155 XVIII

21 Tables Table S-1: Tendencies in the development of main variables in the scenario studies VII Table 3-1: Set of criteria 4 Table 3-2: Outcomes of studies scanned by the criteria 8 Table 4-1: Overview of the main targets of the EU-27-scenarios 13 Table 4-2: Overview of policy assumptions and targets (for EU-27 or OECD Europe) 14 Table 4-3: Overview of further scenario information and assumptions 26 Table 5-1: Characteristics of models used 71 Table 5-2: Comparison of model function Geographical extension and framework 89 Table 5-3: Comparison of model function Markets and decision making 91 Table 5-4: Comparison of model function Technological framework 92 Table 5-5: Comparison of model function Policy measures 93 Table 6-1: Main electricity and heat technologies available in the alternative scenarios 96 Table 6-2: Development estimated in selected technologies 97 Table 6-3: Comparison of grid-development estimated in the studies 99 Table 6-4: Table 6-5: Table 6-6: Table 6-7: Comparison of properties of emission trading schemes considered in the studies 101 Comparison of compliance costs and estimated grid cost and investment in selected alternative scenarios 104 Comparison of electricity prices and generation costs in the scenarios analysed 105 Comparison of properties of electricity prices in the scenarios analysed 106 Table 7-1: Nuclear development, comparison of scenarios 113 Table 8-1: Tendencies in the development of main variables in the scenario studies 120 Table A-1: General information of investment costs of power plants 136 XIX

22 Table A-2: Table A-3: Investment costs of nuclear and fossil power plants, in USD 2008 /kw 136 Investment costs of renewable power plants, in USD 2008 /kw 137 Table A-4: Total final consumption in 2020, in PJ 138 Table A-5: Total final consumption in 2030, in PJ 138 Table A-6: Total final consumption in 2050, in PJ 139 Table A-7: Power generation capacities in 2030, in GW 140 Table A-8: Power generation capacities in 2050, in GW 140 Table A-9: Full load hours in 2030, in h/a 141 Table A-10: Full load hours in 2050, in h/a 141 XX

23 1 Introduction 1.1 Background 1.2 Objective In the mid-fifties the first commercial nuclear power plants became operational. As it was seen as an inexhaustible energy resource and a cheap and reliable power supply, nuclear power gained (private) market interest and its market share rose in the following decades. The number of nuclear reactors under construction worldwide peaked at almost 200 in the early eighties. Whereas the oil crisis in 1973 stimulated the use of nuclear power in countries like France and Japan, the number of orders in the US fell after 1974 and the number of cancellations rose, partly because of economic reasons. With the Three Mile Island accident in 1979, and later the Chernobyl accident, the public acceptance of nuclear power diminished. This was exacerbated by other issues like nuclear proliferation, the opposition to nuclear waste production and transport and the unclarity about final storage, hence the number of plants connected to the grid dropped. From 1990 less than 10 nuclear power reactors were connected to the grid yearly. At present 443 nuclear power reactors are in operation. In the last couple of years a renewed interest and discussion about nuclear power has come up. Issues like global warming, greenhouse gas emission reduction, fossil fuel price increases, capacity shortages and base load for energy security have put nuclear power on the agenda again. A nuclear renaissance is foreseen by some experts and institutions; this is also reflected in different future energy scenarios. All analysed studies used in this report were released before 2011 and the events at the Fukushima power plant following the earthquake and the tsunami of 11 March. The role of nuclear power in recent future energy scenarios is, however, diverse. In the reference scenario of the World Energy Outlook 2008 (IEA, 2008), the installed capacity of nuclear power increases by about 17% until 2030 compared to 2007, whereas the 2009-study foresees 28% (IEA, 2009). The International Energy Outlook (EIA, 2010) shows an increase of almost 37%. This raises the question how such different results are being affected by the implemented model, the chosen scenarios and their assumptions. The question is to be analysed within this project. The objective of the project is to compare eight recent and relevant studies on mid- and long-term future energy scenarios for the EU and to analyse main differences in their underlying assumptions mainly with regard to the likely future role of nuclear power. These assumptions could be technical, political as well as socioeconomic. 1

24 2 Approach The working program of the project consists of 4 project steps: a) Selection of scenario studies by specified criteria Out of the numerous existing scenario studies, the energy scenarios to be analyzed are selected by means of a criteria catalogue. After scanning the studies by the criteria, the last issue in selecting the scenario studies is to look at a broad covering of the main interest groups and stakeholders. The selection of the studies is discussed with and approved by the contracting body. b) Identification of key factors and driving forces in the scenario studies In phase B of the research the assumptions and parameter variations are systematically identified and listed. These could include: Socio-economic issues such as GDP, population and prices Technology issues such as efficiency, lifetime and capacity factor Policy issues such as renewables, efficiency or GHG-goals The analysis of the (different) outcomes from the scenario studies will be used to identify the strength of parameter inputs. c) What if. : The underlying stories and premises to make such a scenario happen In this phase of the project the underlying stories of each scenario are broken down consistently and placed into specific categories. Missing pieces of the scenario stories will be, if possible, completed by the contractor. Therefore a questionnaire directed to the study s authors and modellers was set up. The requirements to make such a scenario materialize as well as the general implications of the scenario are listed by categories. The categories are: Technological issues Policy issues Economic issues Environmental issues 2

25 Social issues d) Conditions and implications for nuclear power In the last part of the study, the role of nuclear power in the scenarios is analysed separately. Can specific conditions or implications for nuclear power be derived from the scenarios? And what can finally be said about the assumptions or stories of the scenarios in relation to the development and role of nuclear power? For this, following issues will be analysed: Fuel cycle issues Market issues Financial / investment issues Safety issues Outstanding issues Building on the outcomes of the four phases, the conclusion will consist in an evaluation of each scenario with regard to the development of nuclear power. The project steps are illustrated in figure 2-1. An extended description of the approach can be found in Appendix A.6. Figure 2-1: Project steps Prognos

26 3 Selection of scenario studies 3.1 Introduction 3.2 Criteria catalogue A variety of institutions, companies and universities have been setting up mid- and long-term energy scenarios, some of them already for a long time. Shell, for example, is one of the most famous and one of the first companies (early 70s) to explicitly use scenarios for generating and evaluating strategic options. Out of the numerous scenario studies existing today, eight studies to be analysed in more detail have been selected by multiple criteria. As indicated in chapter 2, a scenario has to fulfil a number of criteria in order to be selected for further investigation. These necessary criteria are listed in the first column of table 3-1. Additional criteria such as number of sectors covered as well as type of model used are added to the criteria catalogue. The criteria level of detail and international relevance, are not operationalised in objective terms (e.g. amount of pages), so therefore a subjective rating was conducted by Prognos. Table 3-1: Set of criteria Necessary criteria: A minimum time horizon until 2030 The geographical coverage of the EU-27 (or Europe) Quantifiability Coverage of the electricity sector Being up to date (recently published / when possible, not older than 2007) Available for public Further criteria: Number of sectors covered Level of detail (rating Prognos) Type of model used (top-down, bottom up) International relevance ( rating Prognos) Prognos List of scanned studies After scanning the studies by the criteria, the last issue in selecting the scenario studies to be investigated in more detail was to look at generally covering the main interest groups and stakeholders. In the beginning of the project (May / June 2010), in total 28 studies were identified by screening the internet. 1 For clarity, the 26 studies are assorted by the main interest groups and 1 Please note that this list of energy scenario studies, does not purport to cover all available energy scenarios. 4

27 stakeholders by whom or for whom the scenarios are set up. It should be remarked that the institutions or organisations listed below may not have carried out the project themselves, but only may be the contracting bodies. In the appendix, the full references are shown. (Inter-)Governmental institutions: EIA (2010). International Energy Outlook 2010 EU DG ENER (2010). Trends to 2030, Update 2009 IEA (2010). Energy Technology Perspectives IEA/NEA (2010). Technology Roadmap; Nuclear Energy European Parliament / EU DG Internal Policies (2009). Future Energy Systems in Europe IAEA (2009). Energy, Electricity and Nuclear Power Estimates for the Period up to 2030 IEA (2009). World Energy Outlook EU DG TREN (2008). Trends to 2030, Update 2007 NEA (2008). Nuclear Energy Outlook 2008 EU DG Research (2006). World Energy Technology Outlook. WETO - H2 EU DG TREN (2006). Scenarios on energy efficiency and renewables EEA (2005). European environment outlook Non-governmental organisations: ECF (2010). Roadmap 2050 EREC (2010). RE-thinking 2050 Greenpeace/EREC (2010). Energy [r]evolution WEC (2007). Deciding the Future: Energy Policy Scenarios to 2050 Industry: ExxonMobil (2009). Outlook for Energy; A View to During the project time the World Energy Outlook 2010 was released. The main differences between the 2009 and 2010 version of the World Energy Outlook are discussed briefly in Chapter

28 IHS Global Insight (2008). European Energy and Environmental Outlook Shell (2008). Shell energy scenarios to 2050 PWC (2006). The World in 2050 Industry associations: Eurelectric (2009). Power Choices Euracoal (2007). The future role of coal in Europe Eurelectric (2007). The Role of Electricity Research / academic consortia: FEEM et al. (2010). PLANETS: Probabilistic Long-term Assessment of New Energy Technology Scenarios Capros et al. (2008). Model-based Analysis of the 2008 EU Policy Package Energy Watch Group (2008). Renewable Energy Outlook 2030 ISIS et al. (2006/2009). NEEDS New Energy Externalities Development for Sustainability Öko-Institut (2006). The Vision Scenario for the European Union ECN (2005). The next 50 years: Four European energy futures 3.4 Outcomes A number of scanned studies only show the (quantitative) results of the scenarios for the whole world. In those cases, they do not differentiate by region. If Europe is defined as a region mainly two definitions are chosen: OECD Europe EU15: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom plus Czech Republic, Hungary, Iceland, Norway, Poland, Slovak Republic, Switzerland, Turkey 6

29 EU-27 EU-15: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom plus NM-12: Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovak Republic, Slovenia In older energy scenario studies, worked out before 2007, the EU still consisted of 25 countries. When comparing the different scenario studies, the definition of Europe has to be taken into account. Studies only covering the world as a whole were not selected for further investigation (exclusion criteria). Almost all studies use models which to some degree contain bottom-up properties. 3 Studies for the European Union, as well as other European energy scenario studies often rely on the PRIMESmodel from Greece E3MLab/NTUA (Capros et al.) which is characterised of a mixed representation of bottom-up and topdown properties. On the one hand, this may affect the broadness of the outcomes, as the same model is used. On the other hand, the influence of different input or scenario parameters on the outcomes can be examined in depth. The FEEM-WITCH model used in the Planets-study contains some distinctive features (e.g. timeframe up to 2100, implementation of technological development) which cannot be found in the other scenario studies. The numbers of sectors covered, as well as the level of detail vary among the scanned studies, as table 3-2 indicates. 3 Top-down models are more commonly used for climate action analyses. 7

30 Table 3-2: Outcomes of studies scanned by the criteria Geographical coverage Coverage of the sectors Type of model Nr. Year of publication Time horizon Amount of scenarios (sensitivities) World (Inter-)Governmental institutions: 1 EIA (2010). International Energy Outlook (+4) x (OECD) (EU-19) x x x x x x x x EU DG TREN / ENER (2008, 2010). Energy trends to 2030, Update 2007 and Update / / 2 x x x x x x x x x x +++ x 3 IEA (2010). Energy Technology Perspectives (+4) x x (x) x x x x x x x +++ x 4 European Parliament / DG Internal Policies (2009). Future Energy Systems in Europe x x x x x 3 + x + 5 IEA/NEA (2010). Technology Roadmap; Nuclear Energy Based on IEA (2010) Based on IEA (2010) IAEA (2009). Energy, Electricity and Nuclear Power Estimates for the Period up to x x x (Nuclear) IEA (2009). World Energy Outlook x (OECD) x x x x x x x x (x) +++ x 8 NEA (2008). Nuclear Energy Outlook x (OECD) (Nuclear) 0 + x + 9 EU DG Research (2006). World Energy Technology Outlook. WETO - H x x x x x x x 4 ++ x EU DG TREN (2006). Scenarios on energy efficiency and renewables (+2) (EU-25) x x x x x x x 6 ++ x EEA (2005). European environment outlook x x x x x x x 5 + x + Non-governmental organisations: 12 ECF (2010). Roadmap (+1) x x x x x x x? x (x) ++ x 13 EREC (2010). RE-Thinking (+1) x x x (Renewable heat) x (RES-E) 2 + x ++ (x) 14 Greenpeace/EREC (2010). Energy [r]evolution x (OECD) x x x x x x x x +++ x 15 WEC (2007). Deciding the Future: Energy Policy Scenarios to x (x) Industry: 16 ExxonMobil (2009). Outlook for Energy; A View to x x x x x x 4 + x (?) IHS Global Insight (2008). European Energy and Environmental Outlook x x x x x x x x x 6 ++ x (?) + 18 Shell (2008). Shell energy scenarios to x x x x x x 4 + x (?) PWC (2006). The World in x x 0 + x (?) + Industry associations: 20 Eurelectric (2009). Power Choices (+ base) x x x x x x x 5 ++ x x ++ x 21 Euracoal (2007). The future role of coal in Europe x x x 1 ++ x + 22 Eurelectric (2007). The Role of Electricity / (EU-25) x x x x x x 5 + x ++ Research / academic consortia: 23 FEEM et al. (2010). Probabilistic Long-term Assessment of New Energy Technology Scenarios x x x x x x x x x x (x) x ++ x 24 Capros et al. - EU DG ENV (2008). Model-based Analysis of the 2008 EU Policy Package x x x x x x x x 6 ++ x x ++ x 25 Energy Watch Group (2008). Renewable Energy Outlook x (OECD) x x x x x 4 ++ x + 26 Öko-Institut (2006). The Vision Scenario for the European Union (EU-25) x x x x x x x 6 + x + 27 ECN (2005). The next 50 years: Four European energy futures x 0 + x + 28 ISIS et al. ( ). NEEDS New Energy Externalities Development for Sustainability 2006*/ x x + ++ Europe EU-27 Quantifiability Residential Commercial / Services Industrial Transportation Power Other conversion sectors Amount of sectors covered Level of detail (rating Prognos) Bottom-up Top-down None (International) Relevance (rating Prognos) Short list for selection * year of scenario analysis Prognos

31 3.5 List of selected studies Out of a total of 29 scanned studies, eight studies were finally selected to be analysed and compared, using the criteria set up. The selection was discussed with and approved by the contracting body and the European Nuclear Energy Forum (ENEF) Competitiveness Subgroup. These eight studies, each with a short description of its purpose or scenarios, are: (Inter-)Governmental institutions EU DG ENER (2010). EU energy trends to 2030, Update 2009 The Baseline scenario determines the development of the EU energy system under current trends and policies. In addition to its role as a trend projection, the Baseline scenario is a benchmark for scenarios on alternative policy approaches or framework conditions (e.g. higher energy import prices, renewables and climate policies). The Reference scenario is based on the same macroeconomic, price, technology and policy assumptions as the baseline. In addition to the measures reflected in the baseline, it includes policies adopted between April 2009 and December 2009 and assumes that national targets under the Renewables directive 2009/28/EC and the GHG Effort sharing decision 2009/406/EC are achieved in The Reference scenario, which includes the mandatory emission and energy targets set for 2020, can serve as a benchmark for policy scenarios with long term targets. IEA (2010). Energy Technology Perspectives The goal of the analysis in this book is to provide an IEA perspective on the potential for energy technologies to contribute to deep emission reduction targets and the associated costs and benefits. It uses a techno-economic approach to identify the role of both current and new technologies in reducing CO 2 emissions and improving energy security. The ETP 2010 Baseline scenario reflects developments that would occur with the energy and climate policies that have been implemented to date. It follows the World Energy Outlook 2009 Reference scenario for the period 2007 to In addition to the Reference scenario, a BLUE MAP scenario, together with a number of variants, has been developed which examine how the introduction of existing and low carbon technologies can reduce global energy-related CO 2 emissions to half their current levels by The BLUE scenarios are consistent with a long-term global rise in temperatures of two to 9

32 three degrees Celsius, but only if the reduction in energyrelated CO 2 emissions is combined with deep cuts of other greenhouse gas emissions. IEA (2009). World Energy Outlook 2009 The results of the analysis presented here aim to provide policy makers, investors and energy consumers alike with a rigorous, quantitative framework for assessing likely future trends in energy markets and the cost-effectiveness of new policies to tackle climate change, energy insecurity and other pressing energy-related policy challenges. (Ref. scenario) More specifically, this report is intended to inform the climate negotiations by providing an analytical basis for the adoption and implementation of commitments and plans to reduce greenhouse-gas emissions. (450 Scenario) Non-governmental organisations: ECF (2010). Roadmap 2050 The objectives of the Roadmap 2050 are: a) to investigate the technical and economic feasibility of achieving at least an 80% reduction in greenhouse gas (GHG) emissions below 1990 levels by 2050, while maintaining or improving today s levels of electricity supply reliability, energy security, economic growth and prosperity; and b) to derive the implications for the European energy system over the next 5 to 10 years. Greenpeace/EREC (2010). Energy [r]evolution The report demonstrates how the world can get from where we are now, to where we need to be in terms of phasing out fossil fuels, cutting CO 2 while ensuring energy security. This includes illustrating how the world s carbon emissions from the energy and transport sectors alone can peak by 2015 and be cut by over 80% by Industry associations: Eurelectric (2009). Power Choices The EURELECTRIC Power Choices study was set up to examine how the vision, of cutting GHG-emissions by 75% in 2050, can be made reality. Power Choices looks into the technological developments that will be needed in the coming decades and examines some of the policy options that will have to be put in place within the EU to attain a deep cut in carbon emissions by mid-century. The study develops two alternative scenarios for the EU-27 countries during the period: Baseline, assuming all existing policies are pursued; and Power Choices, which sets a 75% reduction target for greenhouse gases across the entire EU economy. 10

33 Research / academic consortia: FEEM et al. (2010). PLANETS: Probabilistic Long-term Assessment of New Energy Technology Scenarios PLANETS is a research project funded by the European Commission under the Seventh Framework Programme. The project is meant to devise robust scenarios (total 10) for the evolution of low carbon energy technologies in the next 50 years. A suite of six energy-economy-climate modelling groups (DEMETER, ETSAP-TIAM, GEMINI-E3, PEM/TEAMS, TIAM- ECN, WITCH) analysed the implications of several climate policies under a wide set of assumptions about national commitments and the use of international carbon off sets. Capros et al. (2008). Model-based Analysis of the 2008 EU Policy Package on Climate Change and renewables (for EU DG ENV) The RES effort sharing problem cannot be examined without consideration of the emission reduction effort sharing and vice versa. Both the RES and the reduction of energy demand are among the options for meeting both the GHG and the RES targets. The PRIMES energy system model has been used as an impact assessment tool. A large series of tentative target differentiation schemes have been evaluated with respect to their implications on the Member-States energy systems and in terms of energy costs and prices. The previous version of the EU-study Trends to 2030 (update 2007) serves as baseline for the Capros et al. (2008)-study. By selecting the Capros-study, the differences between the 2009 updated version of the Trends to 2030 and the 2007-version becomes clear. Throughout the report, the Trends to 2030 Update study is to some extent analysed separately from the Capros-study. The selected studies represent different stakeholder groups, have been published recently, show various quantitative results, covering Europe and the electricity sector and are generally well know and discussed internationally. The internationally relevant International Energy Outlook (EIA, 2010) has not been selected as the focus is not on Europe in particular, and since it is to some extent comparable to the World Energy Outlook. 11

34 4 Input parameters and outcomes 4.1 Introduction 4.2 Targets In phase B of the research the assumptions and parameter variations are systematically identified and listed. The analysis of the (different) outcomes of the scenario studies will be used to identify the strength of parameter inputs. It is important to point out that figures provided in this report highlight developments from 2005 to However, some studies analyse timeframes up to 2030 and in the FEEM-WITCH scenarios, the timeframe is extended to Therefore development from 2050 to 2100 for this study is not covered in the following figures. This has to be kept in mind concerning the comparison of different scenario studies. All scenario studies use a baseline scenario to show the impact of presently implemented policies (e.g. until 2009), or in some cases a continuation of present policy. These baseline scenarios are used as a reflection to the impacts or effects of alternative scenarios. The alternative scenarios aim at reducing GHG emissions. The targets are generally in line with the official EU target of -20% in 2020 compared to the level of 1990, and the long term EU-target of around -80% (or more) until Whereas the baseline scenarios are forecasting scenarios, the alternative scenarios are (to some extent) backcasting scenarios. Table 4-1 shows an overview of the scenarios and its targets. The information, if not indicated otherwise, holds true for the EU-27. In line with the GHG-reduction targets, most studies imply explicitly an (European) Emission Trading System (ETS, see chapter for more details). As the current EU policy aims at a 20% share of renewable energy in 2020, this is assumed in some scenario studies as well. The Energy Climate Foundation (Roadmap 2050) and Greenpeace/EREC (Energy[R]evolution) also made specifications on the share of renewable energies in In the Roadmap 2050 renewable specifications are set for the power sector. In the advanced Energy[R]evolution scenario the renewable shares to meet a CO 2 -reduction of 95% (compared to 1990) are depending on sector and category between 85% and 98% in In both studies the role of nuclear power is also predetermined. In the Roadmap 2050 it varies from 10 to 30% share of electricity 12

35 generation among the (alternative) scenarios. In the Energy [R]evolution scenario, except for the baseline, nuclear power is phased out until Table 4-2 provides an overview about the main policy assumptions and targets implemented in the scenarios. Table 4-1: Overview of the main targets of the EU-27-scenarios Study Scenario Short name Target(s) IEA World Energy Reference Scenario No specific targets Outlook IEA Energy Technology Perspectives EU Energy trends to 2030 Update 2009 EU Energy trends to 2030 Update ppm Scenario Baseline Scenario Blue Map Scenario WEO Ref OECD Eur WEO Ref WEO 450ppm ETP BL OECD Eur ETP Blue Map OECD Eur to stabilise worldwide GHGconcentration below 450ppm No specific targets to stabilise worldwide GHGconcentration below 450ppm Baseline Scenario EU Trends 2009 BL Policies until April 2009 Reference Szenario EU Trends 2009 Ref Policies until December 2009 Baseline Scenario EU Trends 2007 BL No specific targets ECF Roadmap 2050 Baseline 80% RES pathway 60% RES pathway 40% RES pathway ECF BL ECF 80% RES ECF 60% RES ECF 40% RES No specific targets -80% GHG of 1990 levels by 2050 Ditto Ditto Greenpeace/EREC Energy [R]evolution Reference scenario Revolution Advanced Revolution E[R] Ref E[R] E[R] Adv No specific targets -80% CO 2 of 1990 levels by 2050, phasing out nuclear power Close to 100% RES share of primary energy demand, - 95% CO 2 of 1990 levels by 2050, phasing out nuclear power Eurelectric Power Choices Baseline Scenario Power Choices Eur BL Eur PowCh No specific targets -40% GHG of 1990 levels by 2030 and -75% by 2050 FEEM et al. WITCH-Model (Planets Project) Reference Scenario First-Best Scenario 3p2 FEEM-WITCH Ref FEEM-WITCH FB 3p2 No specific targets LLGHG 4 radiative forcing 3,2 W/m 2 during the 21st century (equals roughly 500ppm CO 2 eq) Prognos Long Lived Greenhouse Gases 13

36 Table 4-2: Overview of policy assumptions and targets (for EU-27 or OECD Europe) Short name scenario GHG or CO 2 -Emissions Renewables Nuclear Efficiency Others & remarks: WEO Ref GHG: -20% below of 1990 levels by 2020 for EU WEO 450ppm worldwide stabilisation of GHG-concentration below 450 ppm over the long run emission pathway of GHG: 30,7 Gt in 2020, 26,4 Gt in 2030, 14,5 Gt in 2050 (-31% versus 2007) GHG: -20% below of 1990 levels by 2020 for EU ETP BL OECD Eur ETP Blue Map OECD Eur EU Trends 2009 BL EU Trends 2009 Ref EU Trends 2007 GHG: -20% below of 1990 levels by 2020 for EU worldwide stabilisation of GHG-concentration below 450 ppm over the long run GHG: -20% below of 1990 levels by 2020 for EU CO 2 : -50% below 2007 by 2050 for World CO 2 : -75% below 2007 by 2050 for OECD+ Allowances for emissions of installations covered by ETS: - 21% below 2007 (limited on these installations) GHG: -20% below of 1990 levels by % RES of energy consumption by 2020 for EU 20% RES of energy consumption by % RES of energy consumption by 2020 for EU 20% RES of energy consumption by 2020 for EU 20% RES of gross final energy consumption by MS not using nuclear power, +3 MS gradual phase out 10 MS not using nuclear power, +3 MS gradual phase out 11 MS phased out and three others gradual phase out 20% reduction of primary energy achieved by improving energy efficiency by 2020 for EU 20% reduction of primary energy achieved by improving energy efficiency by % reduction of primary energy achieved by improving energy efficiency by 2020 for EU 20% reduction of primary energy achieved by improving energy efficiency by 2020 for EU Policies until mid ETS Policies until mid ETS (OECD+, OME) Policies until mid ETS Policies until mid ETS (OECD+, OME) Policies until April 2009 ETS Policies until December 2009 ETS 14

37 EU DG ENV BL EU DG ENV Policy scenarios GHG: -20% below of 1990 levels by 2020 ECF BL GHG: -20% below of 1990 levels by 2020 for EU ECF 80% RES GHG: -80% below of 1990 levels by 2050 ECF 60% RES GHG: -80% below of 1990 levels by 2050 ECF 40% RES GHG: -80% below of 1990 levels by % RES of energy consumption by % RES of energy consumption by 2020 for EU 80% RES of power generation by % RES of power generation by % RES of power generation by MS not using nuclear power, +3 MS gradual phase out 11 MS not using nuclear power, +3 MS gradual phase out 10% nuclear of power generation by % nuclear of power generation by % nuclear of power generation by % reduction of primary energy achieved by improving energy efficiency by 2020 for EU Policies until 2006 ETS Policies until 2006 ETS: Specific assumptions depending on scenario Policies until mid ETS ETS (OECD+, OME) ETS (OECD+, OME) ETS (OECD+, OME) E[R] Ref No specific targets or policies mentioned E[R] CO 2 : -80% below of 1990 levels by 2050 Phasing out E[R] Adv CO 2 : -95% below of 1990 levels by 2050 High RES share: Close to fully renewable energy system by 2050 Phasing out Eur BL Germany and Belgium phased out Eur PowCh GHG: -40% below of 1990 levels by 2030 and -75% by 2050 FEEM-WITCH LLGHG radiative forcing 3,2 W/m 2 during the 21st century (equals roughly 500ppm CO 2 eq) 20% RES of energy consumption by 2020 Germany and Belgium phased out No exogenous constraint ETS: Emission Trading System GHG: Greenhouse Gas LLGHG: Long Lived Greenhouse Gases MS: Member States OME: Other Major Economies (Brazil, Russia, South Africa and the countries of the Middle East) RES: Renewable Energy System/Source Policies until mid ETS Policies until mid ETS (all sectors) Prognos

38 4.3 Socio-economic assumptions Population Concerning the development of population, most scenarios use projections of the United Nations. These projections are based on birth and mortality rates. Differences in the outcomes, as shown in Figure 4-1, are mainly due to unequal geographical coverage. As in OECD Europe population increases until , population in the EU-27 stabilises a couple of years before. The difference in population between OECD Europe and EU-27 grows from about 50 millions in 2010 to millions in Figure 4-1: Development of population, in millions Mio OECD Europe EU IEA Statistics EU-27 IEA Statistics OECD Eur WEO WEO OECD Eur ETP OECD Eur EU Trends 2007 EU Trends 2009 Eurelectric E [R] OECD Eur ECF FEEM-WITCH* * EU-25 plus Norway Switzerland Prognos GDP The studies analysed show a steady increase of GDP of around 1 to 2% per year until 2030/2050. The financial crisis is taken into account in the projections of GDP, except for the old EU Trends to 2030 (2007-version) and the EU DG ENV scenarios. The two main sources used are: The EU-projections and the IEAprojections. The developments of GDP in the Greenpeace/EREC Energy [R]evolution as well as the ECF Roadmap 2050 are, until 2030, based on the IEA World Energy Outlook For the period own projections are implied. The Power Choices study bases its development on the (new) EU-projections (trends to 2030). In FEEM-WITCH, GDP is lower in the alternative 16

39 scenario by 2050 (although the gap is decreasing up to 2100, which is not displayed in figure 4-2). GDP is partly determined exogenously in the model used by these scenarios. Comparability of the data sets is somewhat complicated as the studies use: Different types of GDP: real term vs. real term PPP (purchasing power parity) Different currencies (USD vs. Euro) 5 Various (currency) base years: 2000 vs vs etc. Different geographical coverage: EU-27 vs. OECD Europe To overcome the main issues an index is set up, to compare the GDP-growth is set even to 100 as the last indicated statistical year is often In some cases data had to be interpolated. The methodology applied in the conversion of different currencies and base years is described in Annex 4. Figure 4-2: Development of GDP (Index 2007 = 100) Index 2007 = without crisis with crisis USD WEO WEO OECD Eur ETP OECD Eur E [R] OECD Eur EUR EU Trends 2007 EU Trends 2009 Eurelectric ECF FEEM-WITCH* Ref FEEM-WITCH* FB 3p2 * EU-27 plus Norway, Switzerland Prognos GDP per capita With relative small changes in population (between -2% and +6% in 2050 compared to 2007), development of GDP per capita (figure 4-3) shows a similar path as development of GDP (figure 4-2). In the ECF-study the per capita development of GDP is even steeper (figure 4-3) than GDP development, as this study assumes 5 Information on development of real exchange rates is rarely provided. 17

40 a lower population level after ca compared to the 2007-level. This is somewhat remarkably, as their GDP projections are in line with those of the IEA, which assume an increase of population. Figure 4-3: Development of GDP per capita (Index 2007 = 100) Index 2007 = without crisis 100 with crisis USD WEO WEO OECD Eur ETP OECD Eur E [R] OECD Eur EUR EU Trends 2007 EU Trends 2009 Eurelectric ECF FEEM-WITCH* Ref FEEM-WITCH* FB 3p2 * EU-25 plus Norway, Switzerland Prognos Prices Oil Since the oil peak price in 2008, a higher level of future oil price assumptions can be seen in the scenario studies. While the 2007 version of the EU Trends to 2030 estimated an oil price of around 70 USD/barrel (in real terms), recent studies show a range of around 90 to 120 USD/barrel between 2030 and Only Greenpeace/EREC considers an oil price that increases to 150 USD/barrel, in Oil-prices in the Greenpeace/EREC and ECF-scenarios are assumed to stay constant after The Greenpeace/EREC scenarios use a higher price sensitivity case from the WEO 2009, which explains their relatively high oil-price trajectory. In the alternative scenarios of the IEA, oil prices are endogenously determined and stabilise by 2030 (WEO 450 ppm), respectively decrease after 2030 (ETP Blue Map) due to weaker demand. Fossil-fuel prices in the FEEM scenarios are derived from marginal costs of extraction, which are in turn determined by current and cumulative extraction, including a regional mark-up. This explains their sharp increase after 2030 (especially in the Reference case with higher oil demand). 18

41 Throughout all other studies, oil prices do not respond to lower demand in the alternative scenarios, caused by the substitution of fossil fuels by renewables and energy efficiency improvements. To show the long term development, and neglect short term price volatility, the comparison of oil prices in the different scenarios only displays the period from 2015 onwards (figure 4-4). Figure 4-4: Development of oil prices, in USD 2008 /barrel USD 2008 / bbl BP Brent WEO Ref WEO 450ppm ETP BL ETP Blue EU Trends 2007 EU HOG BL EU Trends 2009 Eurelectric E [R] ECF FEEM-WITCH* Reference FEEM-WITCH* FB 3p2 * EU-25 plus Norway, Switzerland Prognos 2011 Gas Gas prices in the different scenarios, as indicated in figure 4-5, show a more diffuse development. Until 2030 most scenarios presume increasing gas prices. Apart from different assumptions for the development, differences in the price level, which vary between 7 and 15 /GJ, may also be linked to regional differences (e.g. prices at German border or UK border). In the alternative scenarios of the IEA, the prices of gas, as for oil, stabilise or decrease after 2030 due to weaker energy demand, while in the Reference case gas prices increase in respond to increasing demand (from e.g. additional gas-fired power plants). Gas prices in FEEM-WITCH are determined by current cumulative extraction and increase significantly after Throughout all other studies, gas prices are often assumed to stay linked to oil prices. This explains the similar tendencies of oil and gas prices in the Eurelectric, ECF, and the EU DG ENV/EU Trends studies. Gas prices in the Energy[R]evolution scenarios show a steeper increase for the period until 2050 compared to the other 19

42 studies and are based on the WEO higher price sensitivity case for fossil fuels. Figure 4-5: Development of gas prices, in EUR 2008 /GJ USD2008 / GJ BP Gas Import DE WEO Ref ETP BL EU Trends 2007 EU Trends 2009 E [R] FEEM-WITCH* Reference WEO 450ppm ETP blue EU HOG BL Eurelectric ECF FEEM-WITCH* FB 3p2 * EU-25 plus Norway, Switzerland Prognos 2011 Gas prices remain coupled with oil prices in most of the scenarios (see figure 4-6), except for the Energy [R]evolution of Greenpeace/EREC and the alternative scenario of the FEEM-WITCH scenarios. The latter estimates a higher increase of gas prices than oil prices, probably motivated by high gas demand and relatively low oil demand. 20

43 Figure 4-6: Development of gas to oil prices, in % 110% 100% 90% 80% gas price / oil price 70% 60% 50% 40% 30% 20% 10% 0% BP Gas Import DE / Oil price Brent WEO Ref WEO 450ppm ETP BL ETP blue EU Trends 2007 EU HOG BL EU Trends 2009 Eurelectric E [R] ECF FEEM-WITCH* Reference FEEM-WITCH* FB 3p2 * EU-25 plus Norway, Switzerland Prognos 2011 Coal A moderate increase of coal prices is assumed in most of the scenarios. Coal prices in the ETP Blue Map scenario show a decreasing tendency, due to remarkable shifts from coal to cleaner energy sources. Onwards from 2030, an opposing trend compared to the oil price development (slowdown in the decrease of coal prices vs. accelerating decrease of oil prices), can be observed in the ETP Blue Map scenario. This could reflect the increasing importance of CCS for power generation, fuel transformation and industrial production and resulting higher coal demand. Coupling with the oil price is not as strong as with the gas price (figure 4-8). However, differences do exist between the studies. Most studies (e.g. Eurelectric, EU Policy) expect coal prices to increase at far lower rates than oil prices. In contrast to the other studies, the Energy [R]evolution of Greenpeace/EREC (again, using the WEO high fossil fuel prices sensitivity) and the alternative scenario of the FEEM-WITCH scenarios show a stronger increase of coal prices than oil prices from In the case of the WITCH alternative scenario, this development can be interpreted as a result of the increasing importance of CCS. 21

44 Figure 4-7: Development of coal prices, in EUR 2008 /GJ USD2008 / GJ NW EU market price coal WEO Ref WEO 450ppm ETP BL ETP blue EU Trends 2007 EU HOG BL EU Trends 2009 Eurelectric E [R] ECF FEEM-WITCH* Reference FEEM-WITCH* FB 3p2 * EU-25 plus Norway, Switzerland Prognos 2011 Figure 4-8: Development of coal to oil prices, in % 50% 45% 40% 35% coal price / oil price 30% 25% 20% 15% 10% 5% 0% NW EU market price coal /brent oil price WEO Ref WEO 450ppm ETP BL ETP blue EU Trends 2007 EU HOG BL EU Trends 2009 Eurelectric E [R] ECF FEEM-WITCH* Reference FEEM-WITCH* FB 3p2 * EU-25 plus Norway, Switzerland Prognos

45 CO 2 -certificates Differences in the CO 2 -certificate prices are up to the threefold. Some scenarios endogenously derive prices as a result of demand for certificates and reduction targets (e.g. Eurelectric, Capros et at.). Other scenarios (e.g. ECF, Greenpeace Energy[R]evolution) assume an exogenous price development. The following factors are expected to be important determinants of carbon prices (if prices are endogenously determined): Emission reduction targets Cost assumptions for realisable GHG-abatement options Design of the trade system for emission certificates, particularly its sectoral and geographical coverage and the availability of options like Clean Development Mechanism 6 (CDM) Development of fossil energy prices. Effectiveness of other policy measures which aim at reducing CO 2 -emissions (e.g. energy efficiency policies). Different levels of emission prices influence the competitiveness of technologies, particularly in the power sector (chapter 4.5.4). Ambitious emission reduction targets induce high CO 2 -certificate prices as more expensive abatement options must be adopted (e.g. ETP blue, WEO 450 ppm, Power Choices). The ETP blue line scenario assumes CO 2 -prices up to 175 $/t CO 2 in 2050 (with for the power sector at a certain reduction target exponential increasing costs). In Power Choices, CO 2 -certificates in 2030 are cheaper than in WEO 450 ppm and ECF Pathways, which can be partly traced back to higher emissions up to 2030 in this scenario (figure 4-57). The sharp increase of CO 2 -certificate prices in the Power Choices scenario from 2030 partly results from the removal of mandatory RES-targets after 2020 in this scenario. Therefore, carbon prices seem to gain high importance to deliver required emission reductions by Geographical extension of emission trading systems (e.g. WEO 450 ppm) in principle leads to a slowdown in the increase of carbon prices, as more abundant and cheaper opportunities for emission reduction are available outside the EU-27/OECD-Europe. However, this effect seems to be outweighed by price increases due to ambitious emission reduction targets. 23

46 Relatively low prices for emission certificates in the Energy[R]evolution scenarios may be partly determined by the implicit assumption (given in the questionnaires), that the process of emission trading remains unclear and is not able to help renewable energy expansion (and is not supposed to be an important instrument in the model). Figure 4-9: Development of CO 2 -certificate prices, in EUR 2008 /t CO EUR 2008 / t Prognos 2011 In the EU DG ENV-scenarios, different options for emissionabatement are compared and the resulting effects for CO 2 -prices are analysed in detail (figure 4-10). Beneath the existence (or nonexistence, as in the EU BL scenario) of ambitious emission reduction targets, availability of CDM is estimated to be the most important determinant of CO 2 -prices in these scenarios. Availability of CDM-options leads to decreasing CO 2 -prices, as lower-cost opportunities for emission reduction can be found in countries covered by the CDM-mechanism. The possibility to trade RES-credits throughout EU member states induces the exploitation of emission reduction options to be geographically optimized to some extent. This in turn causes lower demands for CO 2 -certificates (to reach the targets) and leads to lower carbon prices (NSAT vs. RSAT scenario). In the EU CES scenario, overall cost-efficiency is assumed to be reached, which leads to lower CO 2 -prices (compared to the RSAT and NSATscenarios). High oil and gas prices (HOG CES) provide incentives for individuals to substitute fossil fuels, which in turn reduces demand for emission credits. Therefore, CO 2 -prices reach a level close to scenarios which assume the availability of CDM-options (HOG CES vs. CES CDM). 24

47 Figure 4-10: CO 2 -certificate prices in 2020, in EUR2008/t CO EUR 2008 / t Prognos Other assumptions and model settings Table 4-3 summarizes further relevant scenario information and assumptions. As indicated above, the different base / statistical years and price units hamper the comparison of the scenarios. 25

48 Table 4-3: Overview of further scenario information and assumptions Study / Short name scenario Base / statistical year Prices in Discount rate WEO for investments in RES USD2005 GDP: USD2008 PPP 10% ETP for CO 2 -eq USD2008 GDP: USD2008 PPP 10% EU Trends CO 2 : EUR2005 Fuel: USD2005 GDP: EUR2005 EU Trends CO 2 : EUR2008 Fuel: USD2008 GDP: EUR2008 8% large utilities 20% individuals 8,2% (2005)-9,0% ( ) power and steam gen. companies 9,5% (2005)-10,5% ( ) small companies 12% industry, service, agriculture 17,5% households 12% trucks and inland navigation 8% public transport Same as in EU Energy trends 2007 EU DG - ENV 2005 EUR2005 subjective discount rate 12-20% consumer 8-9% utilities ECF Roadmap 2007/2010 Costs: USD2010 CO 2, fuel: USD2008 GDP: EUR2010 Greenpeace/Erec Energy [R]evolution 2007 Fuel: EUR2005 CO 2 : USD2008 Eurelectric Power Choices 2005/2010 Fuel, CO 2 : EUR2008 Costs: EUR2005 GDP: EUR2005 9% for RES FEEM WITCH (Planets Project) 2005 EUR2005 3% (declining to 2% in 2100) Prognos Technology assumptions This chapter concentrates on quantitative assumptions of technology development. Further (more qualitative and comparative) information about technology assumptions is provided in the detailed description of the storylines (see chapter 6). 26

49 4.4.1 Investment costs for fossil and nuclear power plants In most scenarios the development of power generation capacity is driven by power generation costs for the different technologies. Investment costs (beneath fuel, CO 2 -certificate costs, interest rates, life times) are one of the main drivers which determine generation costs. As comparable data for levelised generation costs are only available for few scenarios, the graphs below show the detailed development for investment costs. The range of investment costs can be caused by different geographical coverage. Whereas the IEA studies give worldwide cost ranges, the other studies focus on costs for Europe. Another reason for differences of prices can be found in the inclusion of different technologies and plant sizes. This explains (partly) the wide range for investment costs in EU DG TREN data. Other reasons such as different safety standards or different inclusion of costs components could not be extracted from the database. Costs for gas-fired plants show a small range and are the lowest among the technologies analysed. Little or no further cost degression is assumed. Nuclear plants are capital intensive and costs show large ranges and also large differences between studies. Investment costs are estimated to show a constant but small decline. Detailed interpretations concerning nuclear investment costs can be found in chapter 7. Costs for coal- and lignite-fired plants lie in between those of gas and nuclear plants and show a significant decline until Greenpeace Energy[R]evolution and ECF-scenarios display lowest cost assumptions for coal-fired plants but limit their development by constraints (as part of the scenario definition, e.g. fossil share in ECF and reduced lifetime in E[R]). The ECF-scenarios show lowest cost assumptions for CCS. The Greenpeace Energy[R]evolution scenario assumes CCStechnology not to become cost-effective at all and FEEM-WITCH assumes an increase of CCS-costs due to rising costs for CO 2 - storage. 27

50 Figure 4-11: Comparison of current investment costs nuclear and fossil fuel plants, in USD 2008 /kw nuclear coal coal+ccs lignite lignite+ccs gas gas+ccs ETP* EU Trends 2009 ECF** FEEM-WITCH ETP EU Trends 2009 ECF** Eurelectric FEEM-WITCH E[R]ev ETP EU Trends 2009 ECF** Eurelectric FEEM-WITCH EU Trends 2009 E[R]ev EU Trends 2009 ETP EU Trends 2009 ECF** Eurelectric FEEM-WITCH E[R]ev ETP EU Trends 2009 ECF** USD 2008 / kw * values for nuclear plants in ETP are given for US Prognos 2011 ** interest during construction included 28

51 Figure 4-12: Comparison of future investment costs nuclear and fossil fuel plants for 2030, in USD 2008 /kw nuclear coal coal+ccs lignite WEO 450ppm EU Trends 2009 ECF** FEEM-WITCH EU Trends 2009 ECF** Eurelectric FEEM-WITCH E[R]ev EU Trends 2009 ECF** Eurelectric FEEM-WITCH EU Trends 2009 E[R]ev lignite+ccs gas gas+ccs EU Trends 2009 EU Trends 2009 ECF** Eurelectric FEEM-WITCH E[R]ev EU Trends 2009 ECF** USD 2008 / kw * values for nuclear plants in ETP are given for US Prognos 2011 ** interest during construction included 29

52 Figure 4-13: Comparison of future investment costs nuclear and fossil fuel plants for 2050, in USD 2008 /kw nuclear coal coal+ccs lignite lignite+ccs gas gas+ccs ETP* EU Trends 2009 ECF** FEEM-WITCH ETP EU Trends 2009 ECF** Eurelectric FEEM-WITCH E[R]ev ETP EU Trends 2009 ECF** Eurelectric FEEM-WITCH EU Trends 2009 E[R]ev EU Trends 2009 ETP EU Trends 2009 ECF** Eurelectric FEEM-WITCH E[R]ev ETP EU Trends 2009 ECF** * values for nuclear plants in ETP are given for US Prognos 2011 ** interest during construction included Investment costs for renewable power plants USD 2008 / kw Investment costs for renewable power plants have a larger range and show larger differences between the studies compared to estimations for fossil and nuclear power plants. Most renewable technologies show a sharp decline with rising total installed power capacity. Wind onshore, hydro and biomass can be considered as well established renewables and show lower investment cost declines compared to emerging technologies (e.g. solar PV, wind offshore). Hydro and biomass investment costs lie in between costs for coal and coal with CCS technologies and below costs for nuclear plants. Hydro power shows, compared to other renewable, relatively low investment costs and most scenarios assume small or no decline of investment costs over time. In contrast to other studies, Greenpeace Energy[R]evolution estimates rising investment costs with the assumption that future plants also have to cope with more extreme weather events as a consequence of climate change and 30

53 must be integrated in a more sustainable way into the landscape. Current cost 7 assumptions vary only little between studies. Investment costs for onshore wind plants are among the lowest and show relatively small deviations between studies. Greenpeace Energy[R]evolution estimates investment costs to decrease at a similar level as for gas-fired plants until Offshore plants are more expensive but show a faster decline of costs. Photovoltaic shows large differences in current investment costs and a steady decline of costs during the projection period. In the WEO (2009) current costs are estimated to be higher than in the Energy[R]evolution scenarios as well as in the ECF scenarios and also considerably higher than in the ETP (2010). This difference may be partly explained by different base-years for investment cost estimations. 5 ECF estimates relatively low costs during the whole projection period. Current costs for thermal solar plants are above USD/kW and show a strong decline over time. WEO 2009 estimates costs to be even lower than costs of PV in contrast to all other studies. Investment costs for biomass plants show a small range and little differences between studies, with the exception of the WEO 2009 which assumes higher investment costs. Investment costs for geothermal plants as well as tide and wave plants are assumed to decrease rapidly towards Differences in costs among the studies can be explained by differences in assumed technologies (e.g. Enhanced Geothermal Systems vs. hydrothermal). Figures 4-14 to 4-16 provide an overview about investment costs for renewable power technologies implemented by the scenario studies analysed. 7 The year for which current costs are provided depends on the study. ETP, EU DG TREN 2009, ECF, Eurelectric provide costs for 2010, E[Rev] for 2007 and WEO for

54 Figure 4-14: Comparison of present 8 investment costs for renewable power plants, in USD 2008 /kw hydro WEO 450ppm ETP EU Trends 2009 ECF* FEEM-WITCH E[R]ev wind onshore WEO 450ppm ETP EU Trends 2009 ECF* E[R]ev wind offshore WEO 450ppm ETP EU Trends 2009 ECF* E[R]ev solar PV WEO 450ppm ETP EU Trends 2009 ECF* E[R]ev solar CSP biomass WEO 450ppm ETP EU Trends 2009 ECF* E[R]ev WEO 450ppm ETP EU Trends 2009 ECF* E[R]ev geothermal WEO 450ppm ETP EU Trends 2009 ECF* E[R]ev tide and wave WEO 450ppm ETP EU Trends 2009 E[R]ev USD 2008 / kw * values for nuclear plants in ETP are given for US Prognos 2011 ** interest during construction included 8 See footnote 7. 32

55 Figure 4-15: Comparison of future investment costs for renewable power plants in 2030, in USD 2008 /kw hydro WEO 450ppm EU Trends 2009 ECF* E[R]ev wind onshore WEO 450ppm EU Trends 2009 ECF* E[R]ev E [R] Adv wind offshore WEO 450ppm EU Trends 2009 ECF* E[R]ev solar PV WEO 450ppm EU Trends 2009 ECF* E[R]ev E [R] Adv solar CSP WEO 450ppm EU Trends 2009 ECF* E[R]ev E [R] Adv biomass WEO 450ppm EU Trends 2009 ECF* E[R]ev geothermal WEO 450ppm EU Trends 2009 ECF* E[R]ev E [R] Adv tide and wave WEO 450ppm EU Trends 2009 E[R]ev E [R] Adv USD 2008 / kw * values for nuclear plants in ETP are given for US Prognos 2011 ** interest during construction included 33

56 Figure 4-16: Comparison of future investment costs for renewable power plants in 2050, in USD 2008 /kw hydro ETP EU Trends 2009 ECF* E[R]ev wind onshore ETP EU Trends 2009 ECF* E[R]ev E [R] Adv wind offshore ETP EU Trends 2009 ECF* E[R]ev solar PV ETP EU Trends 2009 ECF* E[R]ev E [R] Adv solar CSP ETP EU Trends 2009 ECF* E[R]ev E [R] Adv biomass ETP EU Trends 2009 ECF* E[R]ev geothermal ETP EU Trends 2009 ECF* E[R]ev E [R] Adv tide and wave ETP EU Trends 2009 E[R]ev E [R] Adv USD 2008 / kw * values for nuclear plants in ETP are given for US Prognos 2011 ** interest during construction included 34

57 4.4.3 Efficiencies of power plants (Assumptions on) The development of efficiencies of new power plants plays an important role for the development of fuel consumption of fossil-fired power plants and therefore emissions as well as on the profitability of different technologies. Comparison is hampered by different definition of efficiencies (gross vs. net) in the various studies as well as the different technologies assumed. PLANETS-WITCH, ECF and E[R] provide an average gross efficiency of new plants for each fuel / type, whereas ETP and EU Trends 2009 provide net efficiencies for specific technologies. Figures 4-17 to 4-19 show the efficiencies for fossil plants assumed in this studies. For the sake of clarity, efficiencies of EU Trends 2007 have not been included in the diagrams but are taken into account in the written comparison. The development of efficiencies is set externally and does not show any difference between the various scenarios in one study. In general, efficiencies increase slightly over time, only the PLANETS-WITCH-study assumes efficiencies which are constant over time. The efficiencies of coal and lignite fired power plants increase around 5 till 10 %-points until 2050 compared to present values. In comparison to EU Trends 2007, EU Trends 2009 uses lower initial efficiencies (2005) but is generally more optimistic about improvements and finally reaching similar levels in In detail, coal-fired plants with CCS-technology are estimated to reach lower efficiencies than in EU Trends 2007 (around 3 %-points). Efficiencies for gas-fired combined cycle plants and lignite IGCCplants are assessed more optimistically (no important change in assessment). While EU Trends 2007 estimates gas-fired peak devices to show important efficiency improvements EU Trends 2009 estimates very low improvements (2 %-poinsts instead of 8 %-points). 35

58 Figure 4-17: Development of efficiencies of new coal and lignite power plants, in % 70% 60% current coal lignite 50% 40% 30% 20% 10% 0% EU 09: EU Trends 2009, Conv.: conventional,st : steam turbine Prognos 2011 SC: supercritical, USC: ultra supercritical, PCC: pulverised coal combustion, FBC: fluidised bed combustion, IGCC: integrated gasification combined cycle Due to new technologies for fossil plants (e.g. integrated gasification combined cycle coal-fired plants reaching over 50% and combined cycle gas-fired plants reaching over 60%) important efficiency improvements versus current technology (+15%-points) are projected. Equipment with CCS-technologies leads to a reduction of efficiency (-10%-points in 2030, reducing to coal: -5%-points and gas -7%-points in 2050) due to the internal consumption of electricity for CCS. 36

59 Figure 4-18: Development of efficiencies of new coal and lignite power plants using ccs technology, in % 70% 60% current coal ccs lignite ccs 50% 40% 30% 20% 10% 0% EU 09: EU Trends 2009, SC: supercritical, PCC: pulverised coal combustion, Prognos 2011 IGCC: integrated gasification combined cycle, comb: combustion Figure 4-19: Development of efficiencies of gas power plants, in % 70% current gas gas ccs 60% 50% 40% 30% 20% 10% 0% EU 09: EU Trends 2009, Prognos 2011 NGCC: natural gas combined cycle 37

60 4.4.1 Life times of power plants The economical life time of a power plant, determines, next to investment costs and interest rates, the capital costs as part of the total generation costs. As economical life times (depreciation period) are rarely specified, figure 4-20 compares assumptions on technical life times 9 (operation period) only. Depending on the approach the economical life time is set even the technical life time. Assumptions on life times show differences up to 50% between the studies (e.g. solar PV in EU Trends compared to PLANETS- WITCH). Next to the most enduring hydro plants with a life time up to 60 years, nuclear plants are run for up to 50 years in EU Trends while ECF (45 years) and PLANETS-WITCH (40 years) assume shorter life times. Coal-fired plants are assumed to be run for 40 years besides EU Trends 2009 assuming 30 years and E[R] adv in which they get only the permission to be run for half their technical life time. Gas-fired power plants generally have lower life times than coal-fired plants (about 10 years less). Life times of fossil plants show no difference whether equipped with CCS or not. The comparison of life times for renewables, other than hydro power, shows high differences between the studies. EU Trends 2009 estimates life times for PV to be only 15 years and considerably higher lifetimes for wind plants (25 years). In contrast PLANETS-WITCH and ECF estimate life times of solar PV and wind plants to be equal at 25 / 30 years. Life times of solar CSP and geothermal plants are assumed to lie in the same range as fossil plants. In comparison to EU Trends 2007 the updated version of 2009 assumes lower life times for fossils and longer life times for renewables. 9 Mostly no further specification about life time is made and life times provided seem to rely to technical life times. 38

61 Figure 4-20: Life times of power plants, in years years 60 hydro geothermal solar csp solar pv wind biomass gas coal nuclear ECF EU Trends 2007 EU Trends 2009 PLANETS-WITCH E [R] / E[R] Ref E [R] Adv Full-load hours of power plants Prognos 2011 The comparison of full load hours (figure 4-21 to 4-24) provides further understanding of the functioning of the applied model framework. The determination was partly hindered by differing data on power generation and capacity, using gross and net values (EU Trends 2007, EU Trends 2009, EU ENV, Eurelectrics). Nuclear plants have full load hours of typically h and more. Some scenarios assume no or small change in full load hours, whereas ETP Blue Map scenario, ECF, Eurelectrics and EU Trends 2007 assume rising full load hours reaching around 8000 h towards Average full load hours of renewables are typically between h and h. It s level depends on the mix of technologies used. Whereas Solar PV achieve typically around full load hours, biomass plants are used with up to full load hours. The low level is therefore caused by intermittent properties of wind and solar power plants. Variations are mainly due to different assumptions on implementation of technologies. Scenarios with high shares of renewables tend to have lower average full load hours of renewables. For a more detailed comparison of full load hours of single technologies see appendix

62 Figure 4-21: Full load hours of power plants for the years 2007, 2005 and 2010, by category, in h/a fullload hours nuclear fossil renewables * PLANETS-WITCH: EU-25+NO+CH Prognos 2011 Figure 4-22: Full load hours of power plants in 2020, by category, in h/a nuclear renewables fossil fossil - CCS 2020 full load hours *: PLANETS-WITCH: EU-25+NO+CH Prognos

63 Figure 4-23: Full load hours of power plants in 2030, by category, in h/a nuclear renewables fossil fossil - CCS 2030 full load hours * PLANETS-WITCH: EU-25+NO+CH Prognos 2011 Figure 4-24: Full load hours of power plants in 2050, by category, in h/a nuclear renewables fossil fossil - CCS 2050 full load hours * PLANETS-WITCH: EU-25+NO+CH Prognos 2011 Fossil plants are partly used for base-load and peak-load resulting in present full load hours of about h. Several reference 41

64 scenarios assume rising full load hours reaching e.g. up to 5800 h in the ECF Reference. ETP Blue Map shows the influence of CCS on full load hours. Plants capturing emissions are used more intensively and remaining (gas-fired) plants without CCS are mainly used for peak-load, resulting in low full load hours (around 300 h compared to h for CCS-plants) A detailed view on CCS technologies (appendix 5.3) shows higher full load hours of gas-fired CCS plants than of coal-fired CCS plants despite the higher fuel prices which seems to be induced by the very high level of carbon prices (compare figure 4-9). PLANETS-WITCH sets full load hours of fossil plants generally at h and constant over time, which is in strong contradiction compared to the other studies. 4.5 Outcomes Primary energy demand Future levels of primary energy demand have to be seen in relation to final energy demand and technologies applied. While the baseline or reference scenarios show slightly increasing primary energy demands, the alternative scenarios aiming to reduce GHG-emissions generally show declining demands. This is a result from additional energy efficiency measures in these scenarios. E.g. both of the Energy[R]evolution scenarios assume ambitious energy efficiency policies and significantly increasing market shares for energy efficient (end-user) technologies as well as for renewable energy technologies (without heat losses) being deployed at a fast pace. In other studies (e.g. FEEM-WITCH and Eurelectric Power Choices), large reductions in primary energy demand are expected not until

65 Figure 4-25: Development of primary energy demand, in PJ PJ EU-27 EU OECD Eur WEO Ref WEO Ref OECD Eur WEO 450ppm ETP BL OECD Eur ETP Blue OECD Eur EU Trends 2007 EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv PLANETS WI Ref* PLANETS WI FB 3p2* * EU-25 plus Norway, Switzerland Prognos Final Energy Demand Without new energy policies to reduce energy demand or GHGemissions, final energy demand will increase, similar to the GDPdevelopment. With new and stringent policy measures, final energy demand can be reduced by 20-25%, until Displayed differences in height in the early phase of the time horizon (e.g. 2010) are related to different definitions of the term (including or excluding non-energy use), which can be seen in the different statistical values (figure 4-26). High deployment of renewables in power generation (with efficiencies of up to 100 % by definition) influences the development of final energy demand in relation to primary energy demand. For example, final energy demand in the WEO 450ppm and EU DG ENV/EU Trendsscenarios show a higher increase compared to the development of primary energy demand. It has to be noted that in some cases differences in efficiency assumptions may lead to major deviations. For example, ambitious energy efficiency measures are implemented exogenously in the Energy[R]evolution scenarios (even in the medium term up to 2020) and chosen by individuals in the Eurelectric Power Choices scenario (especially after 2030). This results in a significant decline of final energy demand as well as primary energy demand, 43

66 because measures aim at reducing energy demand of endconsumers. Primary energy demand shows a smaller decrease in relation to final energy demand in the Eurelectric Power Choices scenario (most notably after 2030). This can be partly traced back to the deployment of CCS in this scenario. In this context, differences to the Energy[R]evolution of scenarios Greenpeace/EREC are considerable. This could be due to the fact that in the Energy[R]evolution-scenarios, CCS is not taken into account as an option to reduce CO 2 -emissions. Figure 4-26: Development of final energy demand, in PJ PJ EU-27 EU-27 IEA** EU OECD Eur IEA** WEO Ref** WEO 450ppm** WEO Ref OECD Eur** ETP BL OECD Eur** ETP Blue OECD Eur** EU Trends 2007 EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv ECF Ref** PLANETS WI Ref* PLANETS WI FB 3p2* * EU-25 plus Norway, Switzerland Prognos 2011 ** including non-energy IEA ex-post, WEO, ETP, ECF Until 2030 a steady increase of energy productivity can be observed (see figure 4-27). After 2030, energy productivity has to be increased faster to fulfil the emission reduction targets. The alternative scenarios more than double energy productivity between 2010 and 2050 whereas the reference scenarios only show about half the improvement. Only ECF assumes the same path for the reference and the alternative scenarios. Eurelectric assumes the highest rise in energy productivity between 2030 and 2050 with 3.1 % per year. 44

67 Figure 4-27: Development of GDP per final energy demand, index 2007= Index 2007= WEO Ref WEO 450ppm WEO Ref OECD Eur ETP BL OECD Eur ETP Blue OECD Eur EU Trends 2007 EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv ECF Ref PLANETS WI Ref* PLANETS WI FB 3p2* * EU-25 plus Norway, Switzerland Prognos 2011 Figures 4-28 to 4-33 show final energy shares of energy carriers estimated by the scenario studies. Electricity is substituting fossil fuels (beneath energy efficiency effects, already mentioned in above) in all alternative scenarios over the projection period. Substitution is particularly large in the long term and in those scenarios which estimate electric vehicles to gain large market shares in transportation (e.g. Power Choices, E[R] advanced). As a result of this substitution effect, fossil fuels (especially oil) decrease significantly. Compared to shares of renewable in electricity, shares of renewables in final energy (e.g. biomass) are relatively low. 45

68 Figure 4-28: Final energy shares of energy carriers, in PJ other renewables biomass heat electricity gas oil coal PJ Figure 4-29: Final energy shares of energy carriers, in PJ Prognos other renewables biomass heat electricity gas oil coal PJ Prognos

69 Figure 4-30: Final energy shares of energy carriers, in PJ other renewables biomass heat electricity gas oil coal PJ Figure 4-31: Final energy shares of energy carriers, in % Prognos % 90% 80% 70% 60% 50% other renewables biomass heat electricity gas oil coal % 30% 20% 10% 0% Prognos

70 Figure 4-32: Final energy shares of energy carriers, in % 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% other renewables biomass heat electricity gas oil coal Figure 4-33: Final energy shares of energy carriers, in % Prognos % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% other renewables biomass heat electricity gas oil coal Prognos

71 4.5.3 Electricity Demand Looking at electricity demand, a steady increase can be seen in all scenarios. Substitution towards electricity is especially relevant for scenarios with high GHG-reduction targets. Decarbonisation of power generation and substitution of electricity for fossil fuels in transportation (e.g. electric vehicles) and buildings (e.g. heat pumps) enables a significant part of the decarbonisation needs. Substitution is either induced by cost-optimization, for individuals (DG ENV, EU Trends, Eurelectric) or for the whole region (e.g. ETP, FEEM-WITCH), or determined exogenously (e.g. Energy[R]evolution). Beneath this substitution effect, electricity demand increases due to population growth (see figure 4-34), higher income and economic activity (displayed by GDP-development in figure 4-2). Figure 4-34 clearly shows that reductions in electricity demand due to energy-efficiency policies are outweighed by additional demand caused by the factors mentioned above. Furthermore, Figure 4-35 shows that in all scenarios, electricity demand growth is larger than population growth (due to high economic activity and the substitution effect mentioned above). The shape of the electricity demand trajectory in the Eurelectric Power Choices scenario can be explained by the choices of individuals between different energy sources each fifth year. As long as electricity remains the cheapest source to fulfil their energy needs they adapt their choices in favour of electricity ( ) and partly switch to other opportunities if cost-efficient alternatives are available ( ). The same mechanism is applied in the EU DG ENV and DG TREN scenarios. 49

72 Figure 4-34: Development of final electricity demand, in TWh TWh EU-27 OECD Eur WEO Ref WEO 450ppm WEO Ref OECD Eur ETP BL OECD Eur ETP Blue OECD Eur EU Trends 2007 EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv ECF PLANETS WI Ref* PLANETS WI FB 3p2* * EU-25 plus Norway, Switzerland Prognos 2011 Figure 4-35: Development of final electricity demand per capita, index 2007= Index 2007 = EU-27 OECD Eur WEO Ref WEO Ref OECD Eur WEO 450ppm ETP BL OECD Eur ETP Blue OECD Eur EU DG TREN EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv ECF PLANETS WI Ref* PLANETS WI FB 3p2* * EU-25 plus Norway, Switzerland Prognos

73 4.5.4 Electricity share of final energy Currently, the share of electricity demand in total final energy demand is around 20 to 25 %. It slightly increases by 2030 and more rapidly after While demand for other energy carriers, mainly in the heat sector, can be stabilised or even reduced, electricity demand grows with higher population and GDP. Moreover, a substitution towards electricity can be observed (see also figure 4-28 for a detailed representation of final energy shares). Depending on the competiveness and efficiency potentials of electricity compared to other energy carriers the 2050 shares increase to between 27 and 45 % in the different scenarios (see figure 4-36). Figure 4-36: Electricity share of final energy, in % 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% EU-27 OECD Eur WEO Ref WEO 450ppm WEO Ref OECD Eur EU Trends 2007 EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv ECF Ref PLANETS WI Ref* PLANETS WI FB 3p2** * EU-25 plus Norway, Switzerland Prognos Electricity generation Changes in electricity generation (development as well as structure), which are shown in figure 4-37 up to figure 4-42 depend on: RES-targets set in the scenarios 51

74 GHG-targets set in the scenarios bounds set for the deployment of power plants (e.g. nuclear power) competitiveness of power plants (capital costs, fixed and variable O&M-costs, fuel and CO 2 -prices) Deployment of renewables is considerable in all of the alternative scenarios, especially in the E[R] advanced and ECF 80% scenarios. Other alternative scenarios extensively use CCS and nuclear power as additional abatement options in the long term. In the medium term, up to 2030 and especially up to 2020, differences between the alternative scenarios are relatively small. To some degree, this effect reflects the sluggishness of the energy system. Differences between the EU DG ENV-scenarios show the effects from different emission trading frameworks on the structure of power generation: Within the applied modelling framework, CDM lowers carbon prices in the EU, therefore conventional fossilfuelled power generation is more competitive and corresponding shares are higher. RES-trading enables cheaper opportunities for renewable power generation. Therefore deployment of renewables is higher. Of course, even in the medium term, differences between alternative and reference scenarios are considerable: Ambitious scenarios generally show higher shares for renewable and nuclear power, with the exception of the Energy[R]evolution scenarios (where nuclear power plants are generally phased-out and power generation is restricted to be 95% renewable in the long term). In the long term, even differences between alternative scenarios are considerable. In the Power Choices, ETP Blue Map and FEEM-WITCH scenarios, nuclear power plants are estimated to obtain a high relevance in reaching emission reduction targets set. Nuclear power plants are expected to be an economic option to provide baseload power in these scenarios, whereas fossil-fuelled plants are mainly used for load following (gas-fired plants with low load factors), with the exception of coal-fired plants with CCS. With regard to the structure of power generation, differences in the alternative scenarios are due to different geographical coverage (OECD-Europe vs. EU-27) and differences in the estimated competitiveness of CCS/fossil fuels vis-à-vis nuclear power and renewable power generation. For example, in the WITCH FB 3.2 scenario, decreasing interest rates and relatively low investment costs (as well as restrictions for technological learning in the deployment of CCS) lead to considerable deployment of nuclear power compared to other scenarios (see also chapter 6.2.2). Furthermore, ETP and WITCH cover OECD-Europe (including Norway and Switzerland, both with high RES-shares), while Power 52

75 Choices covers the EU-27 member states (endowed with lower current renewable shares). Concerning CCS, figures 4-37 to 4-40 clearly show that deployment starts in the period from 2020 to 2030, but is generally estimated to gain importance not before Deployment of CCS is of importance for all alternative scenarios (except E [R]), but significantly higher in the ECF-pathways (with exogenously set shares for CCS). In the Power Choices scenario, high carbon prices seem to be the main driver for the development of renewables and CCS. Of course, high carbon prices do favour nuclear power as well, but without sufficiently high carbon prices, CCS would perhaps not emerge at all. The outcomes of the basic and advanced Energy[R]evolution scenarios are significantly different. This is a result of the assumed general phase-out of nuclear power and the non-consideration of CCS in these scenarios. Figure 4-37: Electricity generation in 2020, in TWh renewables fossil - CCS fossil nuclear TWh * EU-25 plus Norway, Switzerland Prognos

76 Figure 4-38: Electricity generation in 2030, in TWh renewables fossil - CCS fossil nuclear TWh * EU-25 plus Norway, Switzerland Prognos 2011 Figure 4-39: Electricity generation in 2050, in TWh renewables fossil - CCS fossil nuclear TWh * EU-25 plus Norway, Switzerland Prognos

77 Figure 4-40: Electricity generation in 2020 by category, in % 100% 90% 80% 70% 60% 50% 40% renewables fossil - CCS fossil nuclear % 20% 10% % * EU-25 plus Norway, Switzerland Prognos 2011 Figure 4-41: Electricity generation in 2030 by category, in % 100% renewables fossil - CCS fossil nuclear % 80% % 60% % % % 20% 10% % 5 3 * EU-25 plus Norway, Switzerland Prognos

78 Figure 4-42: Electricity generation in 2050 by category, in % 100% renewables fossil - CCS fossil nuclear % 80% % % 50% 40% 30% % 10% 0% * EU-25 plus Norway, Switzerland Prognos Installed Capacity Installed capacity in the power sector is marked by a decreasing share of conventional (fossil fuelled) power plants, an absolute and relative increasing capacity of renewable energy systems and a divided development of nuclear power. Due to relatively low average load hours of renewable energy systems (RES), total installed capacity is generally higher in scenarios with high shares of RES than in comparable scenarios with lower shares of RES. Throughout all alternative scenarios, Eurelectric Power Choices is the scenario with the highest share of installed capacity for fossilfuelled power plants (and electricity generation as well, see figures 4-32 to 4-34) in the long term. Compared to figure 4-43 to 4-48, differences for conventional fossil-fired power plants (without CCS) are remarkable in the ETP Blue Map and Eurelectric Power Choices scenarios. The main reason for this is the need for dispatchable fossil-fired power plants (mostly gas-turbines) to serve as peakload-capacity (with low operating hours) assumed in these scenarios. Nuclear phase-out is taken into account in a range of studies, but restricted to countries where such policies were implemented at the time of writing (with the exception of a general phase-out assumed in the basic and advanced Energy[R]evolution scenarios). Therefore implications from phase-out policies are only modest (see also chapter 7.1 for a more detailed description of this topic). Generally, nuclear power plants show relatively high 56

79 generation shares and low shares of installed capacity, indicating their role as baseload capacity. Figure 4-43: Installed capacity in 2020, in GW renewables fossil - CCS fossil nuclear 2020 GW , no data for renewables * EU-25 plus Norway, Switzerland Prognos 2011 EU Trends 2007, EU Trends 2009, EU ENV, Eur PowCh: net values elsewhere gross values 57

80 Figure 4-44: Installed capacity in 2030, in GW renewables fossil - CCS fossil nuclear 2030 GW no data for renewables * EU-25 plus Norway, Switzerland Prognos 2011 EU Trends 2007, EU Trends 2009, EU ENV, Eur PowCh: net values elsewhere gross values Figure 4-45: Installed capacity in 2050, in GW renewables fossil - CCS fossil nuclear GW no data for renewables * EU-25 plus Norway, Switzerland Prognos 2011 EU Trends 2007, EU Trends 2009, EU ENV, Eur PowCh: net values elsewhere gross values 58

81 Figure 4-46: Installed capacity in 2020 by category, in % 100% renewables fossil - CCS fossil nuclear % 80% 70% % 50% % 30% 20% % 0% EU Trends 2007, EU Trends 2009, EU ENV, Eur PowCh: net values elsewhere gross values Prognos 2011 Figure 4-47: Installed capacity in 2030 by category, in % 100% renewables fossil - CCS fossil nuclear % 80% 70% 60% 50% % 30% % 10% 0% EU Trends 2007, EU Trends 2009, EU ENV, Eur PowCh: net values elsewhere gross values Prognos

82 Figure 4-48: Installed capacity in 2050 by category, in % 100% 90% renewables fossil - CCS fossil nuclear % 70% % 50% % 30% 20% 10% 0% EU Trends 2007, EU Trends 2009, EU ENV, Eur PowCh: net values elsewhere gross values Prognos 2011 When looking more closely at the different capacities of power generation technologies (as displayed in figures 4-49 and 4-50 for the years 2030 and 2050), the following can be observed: Power generation structures in the reference scenarios stay quite the same for different scenarios studies and compared to the present structure as well Wind onshore tends to play an important role in the future power capacity mix Capacity of biomass plants tends to increase relatively weakly, particularly in more recent scenarios The use of PV increases rapidly after 2030 in the ECF Roadmap 2050 and Greenpeace scenarios. Regarding conventional fossil fuel plants, gas fired power plants in general remain in the mix in all scenarios CCS, if considered as future option, plays a role in the alternative scenarios These aspects should be taken into account in addition to the points mentioned above. 60

83 Figure 4-49: Installed capacity (detailed technologies) in 2030 by category, in GW GW add backup other res solar csp solar pv solar wind offshore wind onshore wind biomass hydro gas ccs lignite ccs coal ccs ccs oil gas lignite coal fossil nuclear Prognos

84 Figure 4-50: Installed capacity (detailed technologies) in 2050 by category, in GW GW add backup other res solar csp solar pv solar wind offshore wind onshore wind biomass hydro gas ccs lignite ccs coal ccs ccs oil gas lignite coal fossil nuclear Prognos Electricity demand by sector Sectoral electricity demand is influenced by economic activity levels, the extent of substitution from fossil-fuels to electricity and efficiency improvements. Figure 4-51 to figure 4-53 show that until 2020, the scenarios estimate no considerable differences in the development of sectoral electricity demands (again, partly reflecting the sluggishness of the energy system), but projections start to be more diverse from Differences can be found for all sectors, with the most significant deviations occurring in the transport sector. Throughout the scenarios with ambitious emission reduction targets, all sectors reduce their electricity demand compared to the baseline or reference scenario, with the exception of the transport sector. This development is due to the increasing importance of electric vehicles generally assumed in the alternative scenarios, at least in the long term. However, power demand is increasing 62

85 compared to current levels. The magnitude of the deployment of electricity in transportation is clearly different between the scenarios, with the Power Choices and the Energy[R]evolution advanced scenarios being most optimistic concerning the deployment of electric vehicles. Figure 4-51: Final Electricity demand in 2020 by sector, in TWh Transport Industry Residential Serv./agricul TWh Prognos 2011 Figure 4-52: Final Electricity demand in 2030 by sector, in TWh Transport Industry Residential Serv./agricul TWh Prognos

86 Figure 4-53: Final Electricity demand in 2050 by sector, in TWh Transport Industry Residential Serv./agricul TWh Prognos 2011 Figure 4-54: Final Electricity demand in 2020 by sector, in % 100% Transport Industry Residential Serv./agricul % 80% 70% 60% 50% 40% % % 10% 27 0% Prognos

87 Figure 4-55: Final Electricity demand in 2030 by sector, in % 100% 90% Transport Industry Residential Serv./agricul % 70% % 50% 40% 30% 20% 10% % Prognos 2011 Figure 4-56: Final Electricity demand in 2050 by sector, in % 100% 90% 80% 70% 60% 50% 40% Transport Industry Residential Serv./agricul % 63 20% 10% 0% Prognos

88 4.5.8 GHG- and CO 2 -Emissions Reduction of (energy related) CO 2 -emissions, as illustrated in figure 4-57, is, except for the baseline scenarios, determined by the targets set. With currently implemented policies, CO 2 - emissions can be reduced, but new policy measures are required to achieve significant higher targets. Overall reduction of CO 2 /GHG-emissions is exogenously set in the long term alternative scenarios. Therefore, resulting values of emissions in 2050 are not or hardly influenced by the dynamics in the modelling of these scenarios. Figure 4-57: Development of CO 2 -Emissions, in % compared to 1990 Reduction compared to % 20% 10% 0% -10% -20% -30% -40% -50% -60% -70% -80% -90% -100% EU-27 Eurostat EU-27 IEA OECD Eur IEA WEO Ref WEO 450ppm WEO Ref OECD Eur ETP BL OECD Eur ETP Blue OECD Eur EU Trends 2007 EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv ECF Ref ECF 80/60/40 % RES Note: in ECF-Study GHG-emissions Prognos 2011 * EU-25 plus Norway, Switzerland 66

89 4.6 Comparison: IEA WEO 2009 and WEO 2010 During the project work, the IEA WEO 2010 was released. As a full inclusion into the comparison was not possible at that stage of the project, only the main differences between the new scenarios in relation to those of WEO-2009 will be discussed here. The geographical focus lies on Europe (here OECD Europe). In WEO 2009 a Reference Scenario (Ref 2009), in which governments are assumed to persist on their existing policies and measures related to the energy sector is compared with the 450 Scenario (450_2009). Therein a set of policies is introduced to stabilise the concentration of greenhouse gases in the atmosphere below 450 parts per million carbon dioxide equivalent (ppm CO 2 - eq) 10. WEO 2010 presents projections for three scenarios: A Current Policies Scenario (CurrPol_2010) uses a similar methodology as the WEO-2009 Reference Scenario but includes numerous new policies enacted between mid 2009 and mid A new 450 Scenario (450_2010) describes same as in WEO-2009, a path to stabilise at 450 ppm CO 2 -eq. But it takes into account the delayed action of mitigation policies as a result of not achieving a comprehensive agreement on limiting emissions at the UN climate summit in Copenhagen in December WEO-2010 includes a third scenario, the New Policies Scenario (NewPol_2010), which assumes the introduction of new measures that have already been announced but not yet been formally adopted and implemented. The results lie between the other two scenarios. The following discussion concentrates on the directly comparable CurrPol_2010 and Ref_2009 as well as 450_2009 and 450_2010. The main difference of the WEO-2010 scenarios compared to those of the WEO 2009 is a faster recovery of the economy after its crises, which results in higher energy demands and greenhouse gas emissions in the early stages of the projection period. Total final consumption in WEO-2010 s scenarios is slightly higher in 2020 greenhouse gas emissions are slightly higher as well. In order to stay below 450 ppm, emissions must be cut faster (-3,6 % p.a. instead of -2,9 % p.a.) after 2020 and reach a level lower that of WEO ppm scenario To enable the steep decrease in emissions in the 450_2010 Scenario total final consumption decreases slightly between 2020 and 2030 as well in contrast to a marginal increase in 450_2009. While oil prices in the 450_2010 Scenario remain unchanged from 450_2009, in the CurrPol_2010 Scenario oil prices lie about 10% higher and rise faster compared to Ref_2009. The IEA motivates the latter by stating that higher oil prices are needed to choke off 10 The concentration of greenhouse gases is determined by the integrated emissions over time. The target therefore doesn t determine the emission level for a particular year but limits the sum of all current and future emissions. 67

90 demand to bring it into balance with supply. Gas prices in the 450_2010 scenario lie 5% below WEO-2009 values in 2020 and catch up slightly until In relation to oil, gas decouples (70% instead of 80%) in the CurrPol_2010 Scenario, whereas it stays coupled in the 450 scenario. Prices for CO 2 -certificates in WEO lie considerably below prices in WEO-2009 (-30% in the 450 Scenario). Final electricity consumption for the 450 Scenarios remains unchanged for 2020 and lies slightly below WEO-2009 in In the CurrPol Scenario the value in 2020 is slightly higher. Total power generation capacity for 2030 in the scenarios of WEO is slightly higher than in last year s scenarios. Nuclear power capacities for OECD Europe are 6 % to 9 % higher in the 2010 version than in the 2009 version, partly because of the extension of the operational lifetime of nuclear power plants in Germany 11 ). Power generation capacities of renewables remain unchanged in 2020 and are slightly smaller in Coal power plants generally play a more important role (up to +18 % in 450 Scenario for 2020). Gas-fired power plant capacity in the CurrPol Scenario is 6 to 7 % higher than in the Ref Scenario. In the 450 Scenario gas is slightly higher in 2020 but decreases in absolute terms and in comparison to WEO-2009 until Figure 4-58 and figure 4-59 provide a comparison for the main results of both the WEO 2009 and the WEO Worldwide: +13% (CurrPol- Ref) and +9% (450) compared to WEO 2009, corresponding to +37% (CurrPol), and +94% compared to Concerns over energy security, rapidly rising demand, climate change and local pollution are driving a resurgence of interest in nuclear power in many countries, the IEA (2010) states. 68

91 Figure 4-58: Comparison of energy prices and capacities of electricity generation USD 2008 / barrel crudeoil imports USD 2008 / GJ natural gas import EU GW WEO WEO WEO 2010 CurrPol WEO 2009 Ref WEO 2010 NewPol steam coal imports OECD WEO WEO WEO 2010 CurrPol WEO 2009 Ref renewables gas nuclear WEO WEO WEO 2010 CurrPol WEO 2009 Ref oil coal Prognos 2011 Figure 4-59: Comparison of final electricity consumption, total consumption and CO 2 -emissions TWh 3,6 3,5 3,4 3,3 3,2 3,1 Final electricity consumption PJ 56,0 55,0 54,0 53,0 52,0 51,0 50,0 49,0 Total final consumption Bill. t CO 2 -eq 4,0 3,5 3,0 2,5 2,0 1,5 1,0 CO 2 -emissions 3,0 48,0 0,5 2, , WEO WEO WEO 2010 CurrPol WEO 2009 Ref WEO 2010 NewPol 0, Prognos

92 5 Models used 5.1 Interdependencies between studies Several studies use outputs from other studies as inputs in the scenario formulation and calculation. Two main studies can be identified providing inputs to a range of other scenario analysis: the IEA World Energy Outlook and the EU Trends to The IEA Energy Technology Perspectives, the Roadmap 2050 of the ECG, Eurelectric s Power Choices and Greenpeace/EREC s Energy [R]evolution are partly based on the World Energy Outlook. Input and output of the EU Trends to 2030 (update 2007 and 2009) are used for the Eurelectric-study and the version of 2007 for the study of Capros et al. (2008) on the energy and climate-policy package (EU DG ENV 2008). The relations between the studies are illustrated in figure 5-1. Figure 5-1: Interdependencies between studies UN-demography data OECD, IMF, World Bank (economic data) GDP growth, interest rates IEA WEO 2009 Ref 450 ppm Eurelectric 2010 Power Choices Baseline Baseline follows Trends 2009 Baseline follows WEO until 2030 IEA ETP 2010 EU Trends 2009 EU Trends 2007 Baseline Ref Baseline Blue Map GDP, fossil fuel prices, CO 2- intensities Ref FEEM-WITCH FB 3p2 EU DG ENV 2008 Baseline Policy scenarios Baseline ECF 2010 Fossil fuel prices, carbon prices, CO 2-intensities, Baseline follows WEO until 2030 Pathways Greenpeace/EREC 2010 Ref E[R] Prognos Models used Generally, the following description of the models refers to a single core-model used in each scenario study. For some scenario studies (e.g. WEO, ETP and Power Choices), one single model can be clearly identified as the fundamental instrument in the 70

93 formulation of the scenarios. On the other hand, some scenario studies (e.g. ECF, Energy[R]evolution) use a more diversified model background. However, a detailed description of all models used for additional questions of minor importance for this study (e.g. specific sectoral economic effects analysed in additional general equilibrium models) would go beyond the scope of this work. Therefore, the description focuses on one model for each scenario study, pointing out the usage of additional models, when necessary. This is true for all the scenario studies, with the exception of the ECF-scenarios, where the model-background is too diversified to solely focus on one model. Furthermore, the models described in this chapter are in constant development and appropriate descriptions of the models used for the analysed scenario studies were not always available. Descriptions of the models therefore refer to the most current model reports available. Thus, the functions and geographical coverage described here may differ from these applied in the scenario studies (but, unless otherwise specified, deviations are estimated to be relatively small). In most of the scenario studies, models used can be classified as bottom-up (i.e. including an exact representation of technologies), although some of them also show properties of top-down models or use additional top-down models for specific questions. The WITCH-model of FEEM shows more top-down attributes, although it contains a more detailed representation of energy technologies than conventional top-down models. Table 5-1: Characteristics of models used Study Models used Type of model Characteristic WEO ETP World Energy Model ETP MARKAL/TIMES Bottom-up-model (with additive top-down model) Bottom-up-model EU DG TREN PRIMES Mixed representation: Bottomup and top-down model EU DG ENV ECF Roadmap Greenpeace/EREC Energy[R]evolution Eurelectric Power Choices PRIMES, Prometheus a.o. McKinsey Power Generation Model Mixed representation: Bottomup and top-down model Bottom-Up-Model (with additive top-down model) Simulation Optimization (lead costs) Partial market equilibrium Partial market equilibrium Simulation MESAP/PlaNet Bottom-up model Simulation PRIMES Mixed representation: Bottomup and top-down model FEEM (Planets) WITCH-model Top-down with hybrid (bottomup) energy sector Partial market equilibrium General market equilibrium Prognos

94 5.2.1 World energy model (WEM) The following description refers to the WEM for the WEO 2010 (IEA, 2010b) and differs from the older WEM applied for the WEO 2009 in some points (mainly data on taxes/subsidies, improved price formulation, technological learning, other technological characteristics, better granularity of the models). The description of the WEM, as well as the descriptions provided for the other models, additionally takes into account information given by the responsible organizations in the questionnaires or in supplementary personal communication. In note form, the World energy model features the following characteristics: General description WEM was implemented in 1993 to generate detailed sector-by sector and region-by-region projections of the worldwide energy system. The model covers 24 world regions and is used for the timeframe up to 2030 in the WEO 2009 (the timeframe was extended to 2035 for the WEO 2010). The main framework parameters are: Economic growth Demography (population growth rates and rural/urban split) International fossil fuel prices Taxes and subsidies (taxes remain unchanged over the projection period, whereas subsidies are assumed to be slightly reduced) Technical and economic characteristics of (future) technologies Input data for the WEM come from the energy statistics of the IEA, the UN World Population Prospects and from OECD, IMF and World Bank studies on economic growth. Model type The structure of the World energy model is illustrated in figure

95 Figure 5-2: Structure of the WEM Source: adapted from IEA, 2009 The WEM incorporates six main modules: Final energy demand (with sub-models covering residential, services, agriculture, industry, transport with further detailed sub-models as well as non-energy use). Power and heat generation Refinery/petrochemicals and other transformation Policies Fossil-fuel supply CO 2 -emissions Investment Demand-side parameters are generally derived from an econometric estimation. The supply-side is implemented via costrelated decisions as well as via existing government plans and policies. Some sub-models (e.g. refinery) balance supply and demand through optimisation processes. The model is integrated interlinked to a general equilibrium model (IMACLIM-R), a model for local-pollutants (IIASA-GAINS) and the WorldRES-model concerning the development of renewable technologies. Representation of energy markets Final energy demand in the WEM sub-models is driven by economic activity, population, elasticities and income. These demand-related parameters are estimated using an econometric 73

96 approach (including data from the period ), taking into consideration expert s judgement. Fossil-fuel supply is considered in detail. E.g. oil-supply is implemented by a field-by-field representation, including upstream projects planned and announced, with specific production profiles and decline rates. Concerning the investment-criteria for future fields, countries are differentiated by their openness to international investments (with investment-paths of countries closed to international investment being driven by national policies only). Expert s judgement is used for the estimation of the future relevance of non-conventional oil-supply. To calculate the structure of future power supply, levelised cost modelling is implemented in the WEM. Investment decisions are based on long-run marginal costs and the relative competitiveness of different technologies (taking into account existing government plans and policies). Trade in emission credits is determined using a carbon-flow model, taking into account regional marginal abatement costs and additional constraints (e.g. requirements to undertake a proportion of abatement domestically). Representation of technologies Figure 5-3 provides an overview about the power generation module in the WEM and the relationships between the cost and technological variables. Figure 5-3: Structure of the WEM Power generation module Source: adapted from IEA,

97 The WEM-framework incorporates regional load curves to implement the time patterns of electricity demand. These load curves determine current power generation and new plant requirements, taking into consideration retirement of existing plants. Additionally, grid constraints are implemented as annual restrictions, which limit the realisable potential of renewables. Technological change for renewable technologies is external to the WEM-model, calculated in an additional WorldRES-model (estimating technological development, implemented by a decrease in costs, using the increase in production and sales of a technology). However for the WEO 2010, a sub-module was developed which addresses global learning by technology. Representation of energy and environmental policies Marginal abatement cost curves are developed for each world region and summed to build a global abatement cost curve ETP-MARKAL/TIMES-model General description The following description refers to a range of documentations, namely Loulou et.al. (2005), Dielen & Taylor (2007) and the ETP 2010 provided by the IEA (2010a). The MARKAL (MARket ALlocation) model-family was developed over 30 years by the Energy Technology Systems Analysis Programme (ETSAP). TIMES (The Integrated MARKAL-EFOM- System), which is used in the ETP for some regions, was developed combining inputs from the MARKAL- and EFOMmodels. Multi-regional MARKAL-models, which cover 15 world regions and mainly described the energy conversion sectors, are used in the ETP. End-use sectors (agriculture, buildings, industry and transport) are analysed using additional bottom-up simulation models. In some regions, even more detailed modelling is implemented (e.g. TIMES used for the European power sector at the country-level). The ETP-models cover the timeframe from 2000 to In TIMES, general framework parameters are: Demography Economic growth, interest and discount rates (representing real-world rates and no social discount rates) Policies Technical and economic characteristics of (future) technologies Future sources and potential of primary energy supply 75

98 Existing stocks of energy-related equipment Fossil-fuel prices (until 2030 from the WEO 2009) Model type ETP-MARKAL/TIMES are technology-rich bottom-up models, using linear programming for the optimisation of a mix of energy technologies to serve demand. The energy system is reproduced from primary energy to end use (incorporating about 1000 individual technologies). A Reference Energy System (RES) is constructed with available data of the current energy system. This RES contains the current energy sector, modelled through a network of processes that are linked to flows of energy carriers and materials. Additionally, the model is enriched by detailed demand-side models. Figure 5-4 displays the structure of the energy-system modelled using the MARKAL-framework. Figure 5-4: General structure of a MARKAL-model (1) Source: adapted from Johnson (U.S. EPA), 2004 Optimisation of the energy system is implemented through a least cost assessment of available technologies. This approach is characterized by a minimization of total discounted system costs (including CO 2 -costs) via investment decisions in a perfectforesight manner. In doing so, material substitution or material efficiency potentials are not assessed (e.g. elasticities of inputsubstitution are not considered in MARKAL). Furthermore, nonrational decisions, and decisions including effectiveness, equity, timing and risk (apart from higher discount rates) are not taken into account in the MARKAL-framework. Figure 5-5 shows an overview about the optimisation structure in the MARKAL-model. 76

99 Figure 5-5: General structure of a MARKAL-model (2) Source: adapted from BNL, 2004 Macroeconomic factors (e.g. exchange rates) and effects stemming from these factors are generally not taken into account. Moreover, feedback-effects linking changes in the energy system to the rest of the economy (e.g. economic activity) are also not considered in the MARKAL-models. Representation of markets Energy demand is calibrated to the WEO and driven by e.g. sectoral GDP and population. The demand path for energy and product services is fixed and must be met. Energy supply options are implemented via energy supply curves. Furthermore, energy saving supply options compete with real supply options in fulfilling energy services needs (therefore reducing real energy demand). Representation of technologies Extraction and provision of primary energy supply, conversion into final energy carriers and conversion of final energy carriers into useful energy or energy services is modelled explicitly by processes. These processes represent black-boxes characterised by physical inputs and outputs of energy and materials, cost-data, lifetimes and other attributes. Costs are mainly structured into the three components investment costs (annualized over the economic lifetime, taking into account lead times and related cost-components), fixed annual costs (proportional to installed capacity) and variable costs (proportional to production volume). Greenhouse gas emissions and other emissions are considered as additional outputs in the modelframework. 77

100 TIMES used for the ETP incorporates a regional representation of European countries, connected by transmission lines. Furthermore, regions are linked by energy and material trading and by emission trading variables. Efforts for grid expansion are approximated by additional costs of renewables for different distance-from-demand-centres classes. Grid reinforcement due to wind power expansion is implemented via additional costs depending on the potential exploited for each region. The representation of electricity demand and supply in TIMES takes into account fluctuations across the year, accounting for differences between baseload and peak-demand and the need for different plants to fill the load curve for different regions. These characteristics are implemented in TIMES via sub-yearly timeslices (e.g. winter and summer demand/supply, enlarged by a safety factor/peak reserve factor). The definition of time-slices is principally also available as an option in the MARKAL-framework (for electricity and heat). Furthermore TIMES is able to model storage options, consuming commodities in one period and releasing them in another period (compared to the MARKALframework, TIMES offers a more general storage process, not restricted to electricity as stored commodity, but applicable to all commodities). Assessment of present and future characteristics of technology options (e.g. emerging technology options) and their potential is based on expert s information (IEA Implementing Agreements and other sources). Potentials are implemented via constraints in the model. Energy efficiency characteristics are considered via technological developments and cost optimisation through choices between energy demand technologies. Additionally, resource availability and maximum rates for the implementation and decay of technologies are implemented as model-constraints. Representation of energy and environmental policies Several types of emission pricing, emission trading systems and emission regulations are considered in ETP-MARKAL. These possibilities allow the implementation of energy and climate policies, penalising technologies which lead to emissions and shifting optimal technology portfolios in the direction of lowemission technology options. Thus, the model is able to implement both micro-measures (e.g. targeted subsidies to a range of technologies) and broader policy targets (e.g. emission taxes) into the technology-rich portfolio. Carbon leakage (e.g. industrial relocation) is taken into account, leading to higher industrial relocation estimates than calculated by econometric approaches. 78

101 5.2.3 PRIMES-model The description of the PRIMES model refers to the models used in the EU DG ENV and DG TREN scenarios (described in E3M-LAb, 2010 and Capros et.al, 2008). Later versions (e.g. the version used in the Eurelectric scenarios) of the model could differ slightly from the following model description. General description PRIMES has been developed by the Economic-Energy- Environment Modelling Laboratory (E 3 MLab) operating within the Institute of Communication and Computer Systems of the National Technical University of Athens (ICCS/NTUA) since PRIMES is calibrated with EUROSTAT data for the years 1995, 2000 and 2005, and is principally used for the time frame to 2030 (but was extended in recent years for the time frame up to 2050, e.g. for the Eurelectric scenarios). The model covers the EU-27 member countries; however, PRIMES is also capable to include countries of the Western Balkans, Switzerland, Norway and Turkey. The main framework parameters exogenous to PRIMES are: GDP, economic activity (calculated within GEM-E3, an additional general equilibrium framework) and interest rates (incl. risk premiums specific for each sector and technology) World energy supply and fossil fuel prices (fossil-fuel prices are calculated within PROMETHEUS, an additional model framework) Policies Technical and economic characteristics of (future) energy supply technologies (however, technological change can be calculated endogenously as well) Habits of energy consumption Potentials of primary energy supply, sites for new power plants Model type PRIMES is characterised by a mixed representation structure. On the one hand, technologies are implemented using a bottom-up representation. Thus, demand and supply technologies and their technical-economic properties are represented in an engineering based framework. On the other hand, decision making is described by microeconomic behaviour for each agent (multiple consistent economic decisions for each sector and household). In doing so, PRIMES incorporates a market-oriented scenario analysis of energy supply and demand. Figure 5-6 displays the simplified interrelations in the PRIMES model-framework. 79

102 Figure 5-6: Structure of the PRIMES-model Economy Demand Prices Supply Environment Source: adapted from E3M-Lab, 2007 Representation of markets Due to the representation of decision making at every stage of the energy system, multiple partial market equilibria for energy supply and demand are calculated. For each sector, an individual decision maker is assumed to optimize an economic objective function (utility maximization for households and profit maximization for other sectors). Consumption and investment decisions (taking into account possible constraints, which affect optimization paths) are made between a range of energy products and energy technologies. No overall social planner or overall least cost optimization of the whole energy system is implemented. The model therefore determines the equilibrium by finding the prices of each energy form such that the quantity producers find best to supply matches the quantity consumers wish to use. Decisions in the model are taken in regard to the operation of existing equipment and investment in new equipment (for consumers and suppliers). Replacement of technologies due to retirement or premature replacement is possible. Electricity and steam producers decide on supply in accordance with the optimal pattern of supply behaviour. Long-run marginal costs are taken into account for capital expansion decisions (strategic capacity expansion), whereas dispatching and plant commitment (operational plant selection and utilisation) is based on short-run marginal costs. Electricity tariffs per consumer type are determined using the Ramsey-Boiteux methodology. Therefore, market competition influences price-building and mark-ups on prices are reflecting assumptions about the prevailing market competition regime. There exists a static equilibrium within each time period which is repeated in a time-forward path under dynamic relationships (capital accumulation is used for the linkage of time periods). 80

103 Additionally, a separate market-model for emission rights is implemented in the PRIMES model. Representation of technologies There exists a modular design of fuel-supply (gas/biomass submodel), energy conversion (power/steam sub-model) and end-user demand (with sub-models, representing behaviour of specific agents). Transmission of electricity is modelled via the representation of interconnections among countries (DC linear electricity network model). Furthermore, one country represents one single load node. Thermal capacities and reactance of electricity networks are implemented in the interconnections for present and future time periods. Additionally, transmission losses are taken into account, decreasing over time due to technological progress. Gas supply is modelled via dynamic market modelling. Infrastructure, consumers and suppliers are implemented in an oligopolistic market model, with one TSO for each country and third party access on networks. Representation of energy and environmental policies Emission and RES-targets are implemented by two different constraints, which influence individual decision making of the agents. Carbon value: shadow price of the GHG constraint, i.e. marginal costs for the last unit of GHG reduction to fulfil the GHG target (constraint). This value feeds into decision-making of agents, increasing the perceived costs of carbon emissions and encouraging substitution away from carbon-intensive energy forms. RES value: shadow price of the RES constraint, i.e. marginal costs of the last unit of RES expansion to fulfil the RES target (constraint). Feeds into decision making of agents, increasing the perceived revenues from renewable energy technologies. Therefore, the RES-value comprises characteristics of a virtual subsidy (can be thought of e.g. the development of infrastructure or better public acceptance) which encourages substitution of RES for non-res energy forms MESAP/PlaNet-model The MESAP/PlaNet-model can be briefly described by the following points (the description below is based on Schlenzig, 1998 and Greenpeace/EREC, 2010c): 81

104 General description MESAP/PlaNet was originally developed by the IER at the University of Stuttgart and is now maintained by the German company Seven2one Informationssysteme GmbH. MESAP (Modular Energy System Analysis and Planning Environment) is an energy-system analysis toolbox. MESAP PlaNet calculates energy and emission balances for any kind of reference energy systems (RES). Currently, 2005 and 2007 are used as the calibration year of the model and projections are possible until MESAP/PlaNet covers the world regions OECD Europe (alternatively EU-27), Middle East, China, Transition economies, OECD North America, Latin America, Africa, India, Developing Asia and OECD Pacific. Model calibration is based on the IEA World Energy Outlook 2009 (WEO 2009). Framework parameters for MESAP/PlaNet are: Key macroeconomic and energy indicators from the WEO 2009 General structure of the energy system, defined by the Reference Energy System (RES) Policies Fossil fuel prices (WEO 2009), extrapolated after 2030 Intensities of final energy demand (modelled by Ecofys) Technical and economic characteristics of (future) technologies (e.g. capacity factors, emission factors, fuel-mix of processes, investment costs) Model type MESAP/PlaNet is a simulation (accounting framework) model for supply scenarios. A Reference Energy System (RES) is developed as the structuring principle, reproducing the real topology of the energy system using a network design approach. In doing so, all flows of goods and transformations from resources to energy services are included. The module PlaNet Flow is used for the simulation and balancing of physical flows of inputs and outputs (incl. emissions) in the transformation of energy carriers. Market shares, energy demand and supply determine the structure of the energy system in this accounting framework. PlaNet Cost is another module within which necessary capacity additions and cost calculations (capital costs, fuel costs, O&M costs and system costs) are determined based on the PlaNet Flow structure. 82

105 Representation of technologies Transformation of energy carriers and energy services is modelled via network-representation including exact representations of input-output flows. Each transformation step is modelled as a process, partly with different possible technology options and interlinked to other processes. For example, a coal power plant is modelled as a process which uses inputs (e.g. coal) and these inputs are in turn produced by other processes (e.g. coal extraction). Therefore, these two processes are interlinked via flows of coal. On the other hand, electricity is produced by coal power plants, but also by other technology options (e.g. nuclear power plants), thus market shares are needed to determine the contribution of each technology to the output electricity. For the Greenpeace-scenarios, the choice of technologies was exogenously determined, combined with some effort to minimize total costs of the energy system. Interventions (e.g. policy measures) in the energy system are modelled by the modification of market shares for specific processes. Learning curves and growth rates of technologies are assumed exogenously in the model. Future technologies must be included in the master-res and can be switched on/off through the determination of market shares (e.g. market-share of 0). Implications of the estimated power supply structure for the electricity network are estimated externally in another study (Greenpeace/EREC, 2010d). The grid model used is built in DIgSILENT PowerFactory and comprises a DC load flow calculation and power flow optimisation. Results from load flow calculation show where the grid will be overloaded and provide information about useful future capacity additions for the transmission grid. In doing so, the geographical disaggregation of power plants is partly based on an external study of DLR (DLR, 2005). Representation of energy and environmental policies Energy and climate policies are mainly implemented through the determination of market shares. Global potentials for renewable energy (e.g. sustainability criteria for biomass) are estimated comprehensively and constrain the development of renewable energy carriers Models used in ECF Roadmap 2050 General description The ECF-scenarios use a diversified model background, comprising three main models: 83

106 Modelling of electricity generation is done using the McKinsey Power Generation Model. The KEMA/ICL grid model is used for modelling the electricity network infrastructure. Economic sectors and interrelations between them are modelled using a computable general equilibrium model from Oxford Economics. The model background comprises the EU-27 region, with additional consideration of the rest of the world in the general equilibrium model (e.g. emission trading and market for clean technologies) and covers the timeframe up to Framework parameters: Data from the WEO up to 2030, extrapolated afterwards (e.g. final energy consumption and GDP-growth). Fossil fuel prices Technical and economic characteristics of (future) technologies Model type The Power Generation Model comprises a bottom-up representation for power generation technologies and associated costs. Outputs from the power sector model are market shares of different power generating technologies, capital and operational expenditures and implications for electricity prices. Economic sectors of the EU-27 countries are implemented by a top-down representation for the EU-27 economy, focusing on supply and energy sectors, using a general equilibrium approach of Oxford Economics. This model contains relationships between different sectors, modelling their input- and output-flows and their weight in the economy. Outputs from this model are economic activity, represented by GDP, energy prices and inflation, employment and competitiveness of the European Union (compared to the rest of the world). Carbon shadow prices trigger investment in the energy sector. Impacts on the European network infrastructure are analysed by the KEMA/ICL grid model, which comprises a regional clustering of the EU-27 transmission network. Representation of technologies Market shares for bundles of technologies (nuclear, fossil-fired and renewable power plants) in the power sector are implemented as fixed values in the alternative scenarios (the so-called pathways). Power transmission is represented by the KEMA/ICL grid-model, 84

107 comprising nine interconnected regions, each with one centre of gravity (node), including necessary net transfer capacity requirements. Using this approach, inter- and intraregional transmission capacities are quantified. Cost-optimization, considering the trade-off between transmissioninvestment, generation investment and operating costs, is applied in the KEMA Grid Model. To support decarbonisation, the need for additional flexible thermal plants is minimized and the use of additional intermittent renewable energy is maximized. Additionally, the model takes into account security requirements (maintaining today s level of security of power supply) and a system reliability analysis. Distribution grids are excluded from this analysis. Technological development is determined by exogenously given learning rates, tested with key industry players. These learning rates are used for the estimation of characteristics of future capital and operational costs WITCH model General description WITCH (World Induced Technical Change Hybrid model) has been developed by the Fondazione Eni Enrico Mattei (FEEM), which is a research institution in full operation since WITCH is one of the main modelling tools developed within FEEM s sustainable development research programme. The model is solved numerically in GAMS (General Algebraic Modelling Software). The following description is based on Bosetti et.al (2009) and FEEM et al. (2010). The world economy is disaggregated into twelve regions. Aggregation is not always geographically determined (e.g. KOSAU contains South Korea, South Africa and Australia, due to similarities in income levels and energy demand structures). WITCH is calibrated using data from 2005 and covers the timeframe up to 2100 in five-year steps. Main framework parameters are: Population growth (UN-projections) Total factor productivity, rate of time preference Elasticities of substitution (following literature and econometric calculations) Hydro-production and non-electric coal consumption 85

108 Technological development (partly, e.g. improvement of efficiencies of coal- and gas-fired power plants and capacityfactors of wind and solar are given exogenously) Model type WITCH is a dynamic top-down, optimal growth model with a hybrid representation of the energy sector. The model calculates an intertemporal general equilibrium solution for the world economy. The energy sector is fully integrated with the rest of the economy (represented by one generic sector) by input-output relationships and characterised by a bottom-up specification. Economic growth can be calculated endogenously in the model by using sectoral outputs (although the possibility to estimate economic growth using exogenous growth of total factor productivity is also available). At the regional dimension, a game theoretical setup of investment decisions is represented. For each region, one forward-looking agent (social planner) maximises the regions intertemporal social welfare function (regional present value of long term per capita consumption), strategically and simultaneously to other regions. In doing so, agents choose the optimal dynamic path of control variables, namely investment in different capital stocks, R&D, energy technologies and consumption of fossil fuels. Regions interact through the existence of economic and environmental global externalities. The intertemporal equilibrium is calculated as an open-loop Nash equilibrium or, alternatively, as a cooperative solution. Cooperative solutions can be interpreted as first best solutions (global optimality due to the internalisation of all externalities), whereas a non-cooperative solution (optimal from a regional point of view) represents a second-best solution. Additionally, an integrated assessment module is implemented in WITCH to provide the possibility for a dynamic framework of climate change modelling (adverse effects from climate change on regional economies are taken into account). Representation of markets Prices for oil, coal, gas and uranium depend on current and cumulative extraction, plus a regional mark-up to mimic different regional costs (endogenous determination). Rebound effects from changing fuel prices due to shifts in fuel demand are implemented across all regions. The use of coal is limited to developing regions and assumed to decrease exogenously. Trade in carbon permits is represented using an iterative determination of equilibrium carbon prices, starting with an initial allocation of permits. Regions interact through one global carbon market, marginal abatement costs are equalized across regions. 86

109 Representation of technologies Technologies in the energy sector are implemented by an exact representation (using Leontief production functions with zero elasticity of input substitution). Inputs for each technology (e.g. nuclear power plants) are capital, fuel and operation & maintenance. The energy sector is furthermore distinguished in electricity generation and non-electric energy carriers, providing energy services as a potential substitute for capital and labour. Different technologies are combined using production functions with constant elasticity of substitution in a so-called nesting structure. Therefore, production in the WITCH may represent conventional production processes or processes which just combine different technologies. Figure 5-7 provides an overview about the structure of the energy system modelled in WITCH. Numbers beyond the energy aggregates represent assumed substitution elasticities (e.g. EN, i.e. energy is produced combining electricity EL and non-electric energy NEL with a substitution elasticity of 0,5). Figure 5-7: Structure of the energy system modelled in WITCH Source: FEEM (2011) There exists one backstop technology in the electricity sector and one in the non-electric energy sector. Both of them are carbon-free technologies and therefore represent important options for emission abatement. The backstop technology in the non-electric energy sector is substituting oil and can be thought of some form of next generation biofuels or carbon-free hydrogen. This technology provides the main mitigation option in the long term. In 87

110 the electricity sector another backstop-technology offers the opportunity to replace nuclear power plants. The economy modelled is influenced by exogenous Hicks-neutral technological change 12. Costs for new investments and maintenance in power generation are region specific and constant over time, except for renewable and backstop technologies as well as energy efficiency. Wind and solar technologies in the generation of electricity are characterized by learning by doing, i.e. the installation of new capacity reduces investment costs for these technologies. On the other hand, learning by researching improves energy efficiency of final production and reduces unit costs of the two backstop technologies. Learning by researching is enabled by investment in R&D. WITCH also accounts for international spillover of knowledge, reflected by the assumption that information and best practices quickly circulate worldwide. Thus, regional investment decisions in R&D affect worldwide investment costs. In WITCH, interest paid for the capital stock is equal to the marginal product of capital plus the depreciation rate. The marginal product of capital is decreasing over time due to the accumulation of capital and interest rates decline. Thus, more capital intensive technologies gain a comparative advantage over less intensive ones and tend to be preferred. However technical progress may prevent interest rates from declining by increasing the productivity of capital. This effect has various effects for different world regions: industrialized countries in general show lower interest rates than developing regions over the whole projection period. An integrated assessment module explicitly accounts for the (adverse) effects from climate change on regional economies. This sub-module can be turned on/off and was not activated in the model runs for the PLANETS project. Regional GHG-emissions are modelled to increase global mean temperature. Representation of energy and environmental policies Environmental policies are implemented by emission stabilization targets, leading to increasing world carbon prices. Carbon prices provide incentives to reallocate resources towards energy innovation (investment in R&D) and low carbon technologies. For example, energy efficiency improvements are induced in this way, because investments in R&D improve the energy efficiency of production Comparison of models used Most of the models use data from the WEO (e.g. macroeconomic framework variables) and UN-data (e.g. data comprising demography). Sometimes additional General Equilibrium Models are used to take into account sectoral effects for the whole economy, 12 Hicks-neutral technical change is characterised by the relationship,, with a(t) representing a time-dependent factor which increases the productivity of inputs (here K and L) exogenously (Dupuy, 2006). 88

111 Model Geographical extension WEM Global (24 regions) ETP MARKAL, TIMES PRIMES McKinsey Power Generation Model MESAP/PlaNet Global WITCH Global (15 regions, TIMES with countryspecific resolution) besides implications for the energy sector. This is also the case regarding environmental effects (e.g. local pollutants or scarcity of resources), which are in some cases evaluated using additional sub-models. Geographical extension and exactness is quite different for the models analysed. Most of them are global models. Therefore the EU-27 region (or parts of it) is represented as one region among others. Furthermore, some models use specific submodels to analyse interrelationships within these regions. Demography, economic growth/activity, fossil fuel prices and characteristics of (future) technologies are framework parameters for most of the studies (i.e. exogenously determined outside the model). Table 5-2 provides a comparison of the different models used in these fields. EU-27 (incl. CH, NO and Eastern Europe) EU-27 (countryspecific resolution) Global (12 world regions) Table 5-2: Comparison of model function Geographical extension and framework Related studies, models and research UN (demography) OECD, IMF, World Bank (economic data) IIASA GAINS (local pollutants) IMACLIM-R (GE-model) World RES WEO (economic growth, fossil fuel prices) WEO PROMETHEUS and POLES (fossil fuel prices) IIASA GAINS (local pollutants) EUROPOP 2008 (demography) GEM E3 (GE-model, dynamic link) WEO 2009 Oxford Economics (GE-model) KEMA/ICL (grid model) WEO (macroeconomics, energy indicators, population) Ecofys (Energy demand) German biomass centre (biomass potential) Institute for Sustainable Futures German Aerospace Center (vehicle technologies) UN-data (demography) EPA/EMF21 (non-co 2 emissions) Framework parameters Demography Economic growth Policies World fossil fuel prices Demography Economic growth Policies Characteristics of technologies Potential of primary energy supply Demography Economic growth and interest rates Characteristics of technologies World fossil fuel prices Potential of primary energy supply Policies and habits n/a Energy demand Characteristics of technologies Policies General structure of the energy system Demography Total factor productivity Habits of energy consumption, elasticities (partly) Policies Characteristics of technologies Prognos

112 The implementation of decisions determining energy supply can be distinguished between models which use an optimizing tool (e.g. PRIMES, ETP, WITCH) and models using a simulation tool (e.g. MESAP/PlaNet). Optimization tools can be further distinguished in individual optimization (optimization at every stage of the energy supply process, e.g. PRIMES) and general optimization models (optimization for the energy sector or the economy as a whole, e.g. ETP and WITCH). Most of the approaches not only take into concern the current period, but also future periods, through the determination of future investment paths. Future investment decisions in the power sector are generally based on total (generation) costs. It is not obvious whether investment decisions in the models are based on (short run) marginal costs and the resulting competitiveness of technologies (e.g. whether investments in the power sector taking into account merit-order properties of energy markets and resulting revenue expectations). This would implicate the disaggregation of time periods and a dynamical linkage of power markets and power generation structure. Applying such frameworks in combination with policy assumptions (e.g. priority for renewable) could have implications for the future power generation structure and lead to other scenario results respectively. Some scenario studies estimate demands for energy services using econometric approaches (e.g. IEA WEO), while others take into account elastic demands (e.g. PRIMES, TIMES) responding to prices of energy services. Individual (producer and consumer) behaviour is often assumed to be stable for future periods in the models. PRIMES takes into account Ramsey pricing methodology for electricity prices (i.e. prices are influenced by market power) as well. Table 5-3 provides an overview about these topics. 90

113 Table 5-3: Comparison of model function Markets and decision making Model Energy supply determination Energy demand and price determination Consideration of the time dimension WEM Investment based on LRMC 13 ; national development plans, expert s judgement Econometric estimation (economic activity, energy prices, income, policy) Structure of energy demand and taxes determine prices Regional load curves Marginal power generation costs for electricity prices ETP MARKAL, TIMES Overall cost-minimization, perfectforesight investment Calibrated to WEO, demand path for energy services fixed Elastic energy services demand in TIMES Sub-yearly time-slices Modelling of storage options Annual electricity load curve by region PRIMES Individual optimization for each sector Individual optimization (utility or profit) for each sector and household Load profiles resulting from individual uses of energy Partial equilibrium prices and Ramsey-pricing for electricity McKinsey Power Generatio n Model, KEMA/ICL Exogenous shares for technology bundles General Equilibrium modelling? Regional demand curves (using historical load curves) Hydro optimization, storage utilization and flexibility of demand considered MESAP /PlaNet Exogenous shares Ecofys-model (key-drivers: population, GDP-growth, energy intensity) Extreme events of power supply/demand analysed externally (renewable 24/7) WITCH Overall welfaremaximization of one agent per region Overall welfare maximization of one agent per region n/a Prognos 2011 Geographical dimensions of energy supply are addressed by most of the models by some approach of grid modelling. Some studies use external grid models to cope with this requirement (e.g. KEMA-grid model for the ECF-Roadmap-scenarios, DIgSILENT PowerFactory for the Greenpeace Energy[R]evolution scenarios). Applied grid models are characterized by a simplified representtation of the European transmission grid. They do not cover distribution grids. On the contrary, WEM of the IEA WEO implements annual constraints, whereas in WITCH grid issues are not addressed. Overall, it remains unclear in which way or to which degree the deployment of technologies (e.g. in the power sector) is affected by the development of grids. Energy efficiency improvements are either endogenised by the availability of energy saving supply technologies (ETP, PRIMES) or investment in R&D (WITCH), or are defined exogenously. Concerning technological change, models often use exogenous learning rates to determine technological development within the model (e.g. WITCH, McKinsey). Some models determined 13 Long Run Marginal Costs 91

114 technological development exogenously (at least for a subset of technologies, e.g. WEM using data from the WorldRES model) or use additional approaches for an endogenous calculation of technological change (e.g. learning by researching induced by investment in research in the WITCH model). Table 5-4 provides an overview about these technological issues. Table 5-4: Comparison of model function Technological framework Model Grid Energy efficiency Technological change WEM Grid-constraints via annual restrictions for RESdeployment Improvements implemented via policy variables Exogenous characteristics of technologies Technological learning taken from WorldRES model ETP MARKAL, TIMES European countries connected via transmission lines (TIMES) Optional expansion of transmission capacities Grid-expansion approximated by distance classes for RES to demand centres Alternative energy saving supply options (technologies) Technological development improves overall energy efficiency Expert s judgement or endogenous learning for characteristics of future technologies Technology constraints (max. rates for the introduction and decay of technologies) PRIMES Interconnections among European countries (one country = one node) DC-linearised power flow computation Endogenous investment in energy efficient technology options Exogenously defined or based on technologyselection decisions (learning through experience) McKinsey Power Generation Model Additive power grid model (KEMA/ICL): 9 regions, each with one demand centre and interconnections, min. system costs n/a Exogenously (expert s judgement) defined learning rates MESAP /PlaNet Additive grid modelling (Energynautics): DC load flow calculation and power flow optimisation Implications of extreme seasonal events Energy efficiency measures implemented by rising shares of energyefficient technologies Exogenously determined learning curves and growth rates of technologies WITCH Not considered in the applied model framework Improvements endogenized via investments in R&D (carbon prices induce R&D-investments) Exogenous Hicks neutral technological change Learning by doing and learning by researching Prognos 2011 Climate and energy policies are often taken into account via constraints which induce carbon prices to rise. Thus, inefficient technologies and technologies using fossil fuels loose competitiveness. Of course, such an implication is only possible in models which use an optimisation module. Other models (e.g. MESAP PlaNet) use a more direct approach by adjusting market shares for (bundles of) technologies. Emission trading is often modelled via sub-models, such as the carbon-flow sub model of the WEM. Table 5-5 provides an overview about these topics. 92

115 Table 5-5: Comparison of model function Policy measures Model Energy policy Climate policy Emission trading WEM ETP MARKAL, TIMES Policy variables feed into energy demand Existing energy policies in the ETP Baseline n.a. Carbon-flow sub-model: Emissions level and mitigation cost curve determine global equilibrium price Implemented via carbon prices Existing energy policies in the ETP Baseline PRIMES McKinsey Power Generation Model MESAP/PlaNet RES-value: constraint integrating future REStargets into individual decision making Carbon-value: constraint integrating future CO 2 -targets into individual decision making n.a. Carbon shadowprices calculated within the GE-model Market shares of technologies Market shares of technologies WITCH n.a. Emission stabilization target leads to rising carbon prices Emission trade module n.a. n.a. Global carbon market, marginal abatement costs equalized Prognos Conclusion on Models To sum-up information provided on models, the large variety of model frameworks and methodologies has to be emphasized. Important differences exist in the determination of energy supply and demand technologies (optimisation vs. exogenously determined market shares). Furthermore, significant differences exist in the geographical coverage and timeframe of the models, as well as in the modelling of grid issues and policy measures. Models make use of inputs from other scenario studies and the most important role in the provision of input data is occupied by the IEA WEO. Model frameworks are often characterised by submodels (e.g. power grids, emission trading, economy-wide effects), which are to a greater or lesser extent linked to the main model. One important issue is the distinction between models which (partly) determine energy supply via expert s judgement and models which use optimisation regimes. Moreover, various optimisation regimes may also lead to different outcomes. For example, optimisation models can be distinguished into individual optimisation (generally using relatively high and different interest rates) and optimisation by one social planner (applying relatively low discount rates). From our point of view, optimisation regimes do not take into account revenue expectations resulting from the competitiveness of power generation technologies on future energy markets. Different approaches of grid-modelling are another sensitive issue. The extent of grid modelling implemented in the model may lead to 93

116 different constraints in the deployment of technologies and different estimations of compliance costs. Most models use simplified transmission grid (sub-)models to cope with these issues. However, no information was found concerning the inclusion of distribution networks in grid modelling and the linkage of technological choices and grid development remains rather unclear. Technological change is often implemented by an approach of technological learning (learning by doing), but often restricted to emerging technologies (e.g. renewables). Energy and climate policies are often implemented via carbon prices or policy variables (in optimisation models) or through the determination of market shares for technologies. These are a few examples of relevant model properties. A more thorough analysis and understanding of the properties of different models would of course allow for further interpretations in this direction, but seems to be beyond the scope of this work. Nevertheless, it can be stated that the understanding of model functions, especially when confronted with different scenario frameworks, is crucial for the interpretation of scenario results. 94

117 6 What if : the underlying stories to make such a scenario happen 6.1 Background and objective 6.2 Technological issues In this phase of the project the underlying stories of each scenario are broken down consistently and placed into specific categories. Missing pieces of the scenario stories will be, if possible, completed by the contractor. The requirements to make the scenario materialize as well as the general implications of the scenarios are listed by categories Type of electricity and heat generation In all of the studies, a Baseline or Reference scenario is compared with scenarios which are more ambitious in reducing GHG-emissions. These ambitious scenarios mostly assume significant growth rates in renewable energy sources for power production (up to generation shares of e.g. 50% in the ETP Blue Map and 97% in the Energy [R]evolution scenario by 2050) and agree in the main technologies for renewable heat and electricity production, namely wind (onshore/offshore), biomass and solar-pv/solarthermal. The studies are more diverse regarding estimations for the shares of traditional power sources: In general, higher shares of fossil fuels are estimated in the absence of additional policies promoting RES-deployment. But some studies (like the EU DG ENV Baseline) consider the phase-out of nuclear power and high gas prices as main arguments for a sustained share of (clean) coal-fired power generation (19 % output-share of solids in 2030). Other scenarios (like WEO 450 ppm) estimate large displacements of coal-fired plants (generation-share for coal decreasing to 7-8 % in 2030). Questions on the deployment and the costs of CCS and on carbon-prices are estimated to be important factors concerning these developments (see also chapter 6.2.2). In the scenarios involving more ambitious emission-reduction policies, gas-fired power plants often have a high relevance in serving peak-loads and load-following, due to high shares of intermittent renewable sources. Nuclear power plants, if not restricted by assumptions about overall nuclear phase-out (like in the Greenpeace E[R] scenarios), are often considered as a vital option to help reducing GHG-emissions from power generation to almost zero (e.g. ETP Blue Map). Table 6-1 provides an overview about the electricity and heat generation technologies considered in the scenario studies, separated into fossil fuels, renewables for power generation and renewables for heat generation. Deployment 95

118 of nuclear power is analysed extensively in chapter 7. A graphical representation of the development of power generation technologies can be found in chapter Table 6-1: Main electricity and heat technologies available in the alternative scenarios Study and scenario WEO 450 ppm ETP Blue Map EU DG TREN Reference EU DG ENV NSAT ECF Pathways E [R] Advanced Eurelectric Power Choices FEEM- WITCH Conventional (heat and power) Supercritical coal plants from 2020 Natural gas (power 2025) Nuclear Power, Gen. III Coal-plants (CCS) Gas CHP, NGCC and NGOC (incl. CCS) Nuclear Power, Gen. III Growth of NGCC until 2015 Supercritical coal plants Nuclear Power, Gen. III New clean coal technologies by 2030 Nuclear Power, Gen. III Nuclear Power, Gen. III Equal generation shares for gas and coal (CCS) Gas (as transition fuel) Coal-fired plants with lifetimes of 20a Nuclear Power, Gen. III Peak for gas-fired power generation: 2025 Nuclear Power, Gen. III Coal IGCC-CCS Natural gas RES-E RES-H Focus Wind (offshore after 2020, onshore before 2020) and biomass Wind (first onshore, than offshore), biomass, solar PV/CSP (after 2020) Wind (offshore after 2020), biomass and solar PV Wind (30% offshore) and biomass Wind (offshore after 2020) and biomass Decentralized power generation: wind, solar PV, CSP, geothermal Wind (offshore after 2020), biomass, solar PV Wind and solar power + breakthrough techn. Biomass, solar thermal, geothermal Heat pumps, solar thermal, biomass (CHP) Biomass, heat pumps, solar Biomass/waste Biomass, heat pumps Biomass, solar thermal, geothermal Conventional biomass, solar, geothermal Breakthrough technology Wind Nuclear Natural gas Biomass Wind Nuclear Coal CCS Natural gas Nuclear Wind Oil Wind Nuclear Biomass Natural gas Wind Solar Nuclear CCS Wind Solar Geothermal Nuclear Wind CCS Natural gas Nuclear Wind Breakthrough Prognos Development in additional selected technologies The scenario studies show quite different results concerning their estimation of the deployment of technologies like CCS and innovative solutions for road transport (electric vehicles, biofuels). Some studies (e.g. EU DG ENV) mention the sensitivity of CCSdevelopment with regard to prevailing CO 2 -prices. Fundamental breakthroughs in new technologies are not taken into account in most of the scenarios analysed. Notwithstanding, FEEM explicitly 96

119 takes into account two breakthrough technologies, without precisely describing the characteristics of these technologies. Table 6-2 provides a brief overview about the estimations derived in the different scenarios on these topics. Table 6-2: Development estimated in selected technologies Study and scenario WEO Ref WEO 450 ppm CCS CCS-deployment on a small scale by 2030 Significant share of CCS in coal power plants (depending on political framework) ETP Baseline Minor deployment of CCS ETP Blue Map EU DG TREN Reference EU DG ENV Baseline EU DG ENV NSAT CCS in power generation, fuel transformation and industrial production Large shares of coal plants use CCS (global: 90%) Demonstrational CCS plants, but no large deployment after 2020 Road transport EV and plug-in hybrids remain niche markets Small deployment of 2nd generation biofuels EV and plug-in hybrids 2nd gen. biofuels from 2015 Minor deployment of biofuels and EV, no deployment of hydrogen By 2050: 70% of new vehicles are electric 2nd gen. biofuels for trucks, ships, aircraft Hydrogen after 2030 Hybrid vehicles and biofuels, but no EV CCS not justified until 2030 EV: 3 % of car fleet by 2030 Biofuels (2nd generation) with a share of 9,5 % by CCS-technology mature until 2030 ECF Baseline Some CCS pilot projects ECF Pathways E[R] Ref. E[R] Advanced Eurelectric Baseline Eurelectric Power Choices FEEM - WITCH CCS for coal/gas plants beyond 2020, CCS-retrofit CCS power plants not considered CCS power plants not considered CCS: pilot projects in operation by 2020 CCS: pilot projects by 2020, rising storage and transport costs High deployment until 2030, decrease afterwards (costs increase with installed capacity) Biofuels in transportation increases towards 2030 Only current technology in road transport Mix of EV, biofuels and hydrogen: 20 % EV (2020) Small penetration of biofuels and EV by 2050 Final energy share of EV: 14 % (2030), 62 % (2050) Increasing role for hydrogen Biomass in stat. applications Electricity is a major fuel in road transport % of road transport electrified by 2050 (PIHV and EV) EV-development not considered in this study Other new power/energy technologies No breakthrough CSP in Northern Africa and Southern Europe No breakthrough n/a CSP-plants slightly develop in Europe n/a Geothermal/tidal plants: small penetration by 2030 Geothermal, tide/wave develop at a slow pace No breakthroughs Limited geothermal power No breakthroughs n/a Hydrogen for industrial heat CSP, geothermal, ocean energy No consideration of new power technologies No consideration of new power technologies Breakthrough technology substituting nuclear power Breakthrough substituting oil in the non-electric energy sector, Prognos

120 6.2.3 Energy efficiency and energy storage Efficiency considerations on the one hand affect end-user efficiency and on the other hand the energy transformation sector (mainly power generation). There is little information on the latter and if, the studies estimate improvements in the efficiency of traditional power generation technologies, but on a small scale (e.g. in the ECF Baseline efficiencies of 60 % are assumed for CCGT-plants and 50 % for coal-fired plants in 2050). Most of the studies emphasize the importance of policies concerning end-user efficiency (residential and industrial energy demand) and some studies describe measures in this field as crucial factors in the short run (2010 to 2030) to reach the emission targets set for the long run (e.g. ETP Blue Map). Proposed measures comprise the thermal integrity of buildings, heat pumps, technological development in processes of the energy-intensive industries and more efficient vehicles (see also chapter for a comparison of final energy demand and energy efficiency development, measured in final energy demand per GDP) Power system stability and grid issues Scenarios with high shares of renewable energy sources are confronted with concerns about power system stability due to the intermittent properties of some renewable (e.g. wind and solar power) technologies. Because of estimated substitutions of electricity for fossil fuels in buildings and transportation and resulting higher electricity demands, this problem is further exacerbated. These challenges are more or less addressed by all of the studies and three main approaches can be identified in the scenarios: Flexible fossil-fuelled power plants for load-following and backup capacity Transmission expansion Smart grids Special attention is generally given to gas-fired power plants, which are estimated to serve as dispatchable capacity in a range of the considered scenarios (e.g. Eurelectric Power Choices, EU DG TREN, EU DG ENV, ETP Blue Map), with far lower utilisation rates than currently observed. In addition to that, studies with ambitious emission reduction targets assume technological progress in grid technology and management and estimate that large increases in transmission capacities are needed (e.g. EU DG TREN, Energy[R]evolution, ECF). The scenarios generally expect increasing grid and back-up 98

121 costs due to the estimated expansion of transmission capacities (see chapter for a more detailed coverage of this topic). Almost all studies emphasize the relevance of technologically advanced smart grids and smart metering, especially those confronted with ambitious emission reductions (ECF Pathways, Energy[R]evolution, ETP Blue Map, Eurelectric Power Choices). However, technological characteristics, as well as cost estimations for smart grids (and distribution grids in general, see chapter 6.4) are not available in most of the scenarios. Table 6-3 provides an overview about the estimated expansion of transmission capacities in the alternative scenarios and further characteristics of future power grids. Table 6-3: Comparison of grid-development estimated in the studies Study and scenario Grid development Characteristics of future power grids Smart grids WEO 450 ppm Lower global grid development compared to the Ref. (lower power demand) n/a Smart-grids seen as important for EVdeployment ETP Blue Map n/a Crucial role for new grid Smart grids seen as an technology (e.g. HVDClines) important factor EU DG TREN Reference n/a EU DG ENV NSAT ECF Pathways E[R] advanced Eurelectric Power Choices Reinforcement of power grids needed Up to 170 GW of interregional capacity (significant expansion between FR and SP) Expansion required in the next 5-10 years 58,3 GW strengthened HVAC interconnection, 27,56 GW strengthened HVDC connections +40% interconnection capacity needed, 40 (of 241 lines overall) new transmission lines Development in grid technology and management n/a Future transmission mix: 73% AC, 27% DC, 67% overhead, 33% underground Combination of smart grids, micro grids and efficient large-scale super grids (HVDC) High importance for transmission lines from north and south to Central Europe n/a n/a n/a FEEM WITCH Fb 3.2 n/a n/a n/a Smart grids seen as a critical technology Crucial role of smart grids Crucial role for the timeline of the implementation of smart grids Prognos

122 6.3 Policy issues Emission trading Emission trading is taken into account in most of the studies, some models used even have modules which simulate a market for emission allowances (e.g. PRIMES used by the EU DG ENV, DG TREN and Eurelectric). However, the studies and scenarios differ in the design for future emission trading markets. A large consensus can be found concerning the sectors included in the market, but assumptions about the geographical coverage of emission trading are quite diverse. Some studies (e.g. EU DG ENV) do not assume an extension of the current EU-emission trading, whereas others (e.g. Power Choices, WEO 450 ppm) are expecting an expansion of the market in the range from OECD+ up to a global dimension. With the CDM-mechanism included, another possibility to enlarge the geographic coverage of the allowances market exists implicitly. The EU-scenarios (DG TREN and DG ENV) show a strong focus on this question, whereas in the other scenarios little information is given on this topic. Finally some scenarios (e.g. EU DG ENV) expect small deviations from the current status of the allocation process, assuming full auctioning in the power sector and grandfathering in the other sectors. On the contrary, some other scenarios (e.g. Power Choices, Energy[R]evolution) tend to a general full auctioning of allowances. Generally, a larger geographical coverage and the inclusion of more sectors in the emission trading system would lead to decreasing prices for certificates (see chapter for a comparison of the development of emission prices in the various scenarios). Table 6-4 provides a brief overview on topics concerning emission trading policies. 100

123 Study and scenario IEA - WEO Table 6-4: Comparison of properties of emission trading schemes considered in the studies Sectoral coverage Existing EU-ETS, including aviation Geographical coverage Auctioning or grandfathering CDM n/a n/a CDM taken into account IEA WEO n/a OECD+ from 2013 n/a CDM taken 450 ppm (not linked to major into account other economies) ETP Baseline Existing EU-ETS Existing EU-ETS Existing EU-ETS n/a ETP Blue Map n/a n/a n/a n/a EU DG TREN Existing EU-ETS n/a Reference including aviation EU DG TREN Baseline EU DG ENV Baseline EU DG ENV NSAT/NSAT- CDM ECF Baseline ECF Pathways E[R] Ref E [R] advanced Eurelectric Baseline Eurelectric Power Choices Auctioning in power sector, grandfathering for other-sectors n/a n/a Auctioning in power sector, grandfathering for other-sectors Existing EU-ETS, including aviation Existing EU-ETS, including aviation Industry, power sector, aviation Industry, power sector, aviation ETS for all major sectors after 2020 ETS for all major sectors after 2020 EU-27 EU-27 Grandfathering in all sectors Auctioning in power sector, grandfathering for other-sectors n/a n/a n/a Until 2020 OECD, from 2020 incl. developing countries Global CO 2 trading in the long term Global CO 2 trading in the long term n/a All allowances should be auctioned All allowances should be auctioned n/a n/a Full auctioning as of 2015 ETS for all major sectors after 2020 International carbon market after 2020 Full auctioning as of 2015 CDM taken into account Limited use of CDMcredits CDM not considered CDM only considered in NSAT-CDM n/a n/a n/a n/a n/a FEEM-WITCH n/a n/a n/a n/a Prognos Policies concerning renewables and energy efficiency Information on promotion policies for renewables and the improvement of end-user energy efficiency is rather diverse throughout the scenarios, depending on the models used (explicit consideration of policies or not) and the sectoral focus of the scenarios (e.g. power generation). Baseline or Reference scenarios generally include existing policy measures and assume that no additional measures are implemented. The EU Energy Policy scenarios (EU DG ENV) of Capros et.al (2008) are explicitly taking into account the question if trading of renewables obligations among EU-member countries is applied in the future policy framework. Other policy measures included in the 101

124 6.4 Economic issues studies comprise national programmes for renewable electricity and heat, regulations concerning biofuels and financial incentives for investment in renewables. In the field of energy-efficiency measures, the scenarios focus on buildings (national implementation of building directives, labelling), energy applications in the residential sector (e.g. directives for boilers), processes in the energy intensive industry (sectoral agreements on best available technologies) and transportation. Some studies (Eurelectric Power Choices, EU DG ENV) explicitly assume decreasing direct incentives for renewables in the future, due to their increasing cost-competitiveness. One outcome is common to all the considered studies: If no additional policies in the fields of renewables and/or energy efficiency are implemented, the EU-27 countries will fail to reach targets on emission reduction, even in the short term up to Compliance costs and investment expenses In the studies analysed, compliance costs and grid costs/investments are displayed in relation to a Reference or Baseline scenario (vs. Ref./BL) or in relation to the base year. Furthermore, costs are shown as yearly costs or cumulated over the time horizon of the scenario. This impedes the comparison of different results. All scenarios confronted with high emission reduction requirements estimate a considerable increase in capital costs. This estimation is generally based on two effects: Higher capital intensity of renewable technologies The need for higher power grid capacity (investment in transmission capacity is estimated to be % higher in most of the alternative scenarios, compared to the Baseline or Reference scenarios). The scenario studies also agree in the estimation of lower expenses for fossil fuels due to large substitutions of renewables for fossil fuels and energy efficiency improvements. In the estimation of additional grid investments, most of the studies only provide data on additional transmission grids or do not explicitly describe if distribution grids are also covered by cost estimations. However, some studies contain information on shares of distribution and transmission grid investment needed (e.g. WEO, ETP). In the ECF Pathways, an additional cumulated CAPEX for distribution grids is estimated at bn, 102

125 although the degree to which these costs are incremental to the Reference is stated to be unclear. Results on future investment cost are also influenced by technological developments in energy transformation and end-user applications. Due to high expected growth rates in renewable power generation throughout all scenarios with large emission reduction, the development of these technologies is of special importance. A number of studies (e.g. EU DG ENV, ECF) assume considerable learning rates 14 for some renewable power generation technologies and CCS, compared to relatively modest rates of investment cost reductions for traditional power plants. For example, in the ECF scenarios, learning rates are 5% for wind offshore/onshore, 15% for solar PV and 12% for CCS, compared to yearly reductions in investment costs per capacity estimated at 0,5% for coal/gas/oil-fired plants. Overall, these effects lead to different cost results. High capital costs are assumed to be compensated by decreasing expenses for fossil fuels and influenced by technological progress, especially in the long term. Of course, the results presented here are influenced by different modelling mechanisms (e.g. costoptimization frameworks vs. accounting frameworks), framework parameters (e.g. price developments; see also chapter for a graphical representation of the development for the main energy prices) and conventions for cost-estimations. Furthermore, results are often not available for the same timeframes and geographical boundaries. Therefore, a direct comparison of results for compliance and investment costs from different scenario studies should be done with caution. Table 6-5 provides a short overview about estimations of compliance costs available in the alternative scenarios, differentiated into total costs and grid costs or investment. 14 Representing cost reductions with every doubling of accumulated installed capacity. 103

126 Table 6-5: Comparison of compliance costs and estimated grid cost and investment in selected alternative scenarios Study and scenario Estimated compliance costs/investment Estimated grid costs/investment Comments WEO 450 ppm EU-27: bn USD (vs. Ref.) cum. investment in the energy sector (incl. grid costs) Global: bn USD (20% lower vs. Ref.) cum. investment Grid investment (Ref.): 25% transmission, 75% distribution ETP Blue Map EU DG TREN Ref. OECD-Europe: add. cum. investment (energy sector) compensated by cum. fuel savings: bn USD vs bn USD (vs. BL) Around 175 bn p.a. (2030) capital and O&M costs in power generation (i.e. 51,0 /MWh) Global: bn USD (incl. smart grids) cum. gridinvestment (+50% vs. BL) EU-27: grid costs of 10,8 /MWh (2030) vs. 7,4 (2010), i.e. around 165 bn cum. grid costs Grid investment (Ref.): 30% transmission, 70% distribution No information if distribution grid incl. EU DG ENV NSAT bn cum. power sector investment (incl. transmission), +26% vs. BL 43 /MWh fixed power generation costs (2020), vs. 37 /MWh (BL) High grid and back-up costs + 25% cum. transmission grid investment (vs. BL) ECF 80% RES Lower fuel costs dominate capital cost expenses: overall -80 bn in 2020 (-205 bn in 2030) vs. Ref. Cum. add. transmission capex: bn, add. back-up capex: bn (vs. Ref.) Cum. add. distribution capex: bn Amount by which distribution costs are incremental to the Ref. is unclear E[R] advanced EU-27: 42 bn add. investment p.a., fuel savings of 62 bn p.a. ( , vs. Ref.) Costs of 209 bn p.a. for the new European supergrid Grid costs estimated externally, cost structure of grid costs not further specified Eurelectric Power Choices +2,6 /MWh electricity prices in 2020 vs. BL Electricity price: 145 /MWh (2030) Capital and O&M costs of 53,3 /MWh in 2030 Grid-costs rise from 7,3 to 12,6 /MWh (2050) Cum. grid-investment: bn (+35% vs. BL) FEEM- WITCH Fb 3.2 Global: Around 800 (2030) and (2050) bn yearly costs (i.e. 1-2,5 % of GDP) n/a Costs are measured as consumption losses compared to the Ref. Prognos Prices Fossil-fuel prices are often exogenously determined in the scenarios and not always sensitive to the scenario targets. In the EU DG ENV-scenarios, world fossil-fuel prices are determined using a separate modelling framework, whereas the ECF and Energy[R]evolution scenarios use price developments from the WEO In the WEO 2009, international fossil fuel prices are based on a top-down assessment of prices which would create enough investment to meet energy demand over the projection period (global balance of supply and demand). Therefore, fossilfuel prices in the WEO are endogenously determined and sensitive to scenario assumptions. The ETP takes up to 2030 prices from 104

127 the WEO 2009 and calculates prices for the period beyond 2030 by taking into account the long-term oil supply cost curve. Most of the studies agree on the fact that gas-prices keep their linkage with oil prices, i.e. the ratio of gas prices and oil prices remains quite constant 15. Electricity prices increase in most of the studies in the medium term (more than 25% up to 2030 compared to present values). Some studies with high emission reduction targets expect a decrease of electricity prices in the long term (up to 2050), mainly driven by lower consumption of fossil fuels in the power sector in combination with remarkable technological improvements for renewable power plants. Not all studies actually calculate electricity prices for a market environment with supply of and demand for electricity. Therefore table 6-6 and table 6-7, providing an overview on electricity prices and their main drivers, displays electricity generation costs as a proxy for electricity prices in these cases. Table 6-6: Comparison of electricity prices and generation costs in the scenarios analysed EU Trends to Update 2007 Price of Electricity Average /MWh Cost of Generation Average /MWh EU Trends to Update Baseline Price of electricity - Pre-tax Average /MWh Electricity price - After tax Average /MWh Industry /MWh Services /MWh Households /MWh EU Trends to Update Reference Price of electricity - Pre-tax Average /MWh Electricity price - After tax Average /MWh Industry /MWh Services /MWh Households /MWh Eurelectric: Power choices Price of Electricity Average /MWh Cost of Generation Average /MWh ECF Roadmap 2050 Specific generation costs 80%-RES /MWh Greenpeace/EREC - E[R]evolution Specific generation costs E[R] Ref /MWh E[R] /MWh E[R] Adv /MWh Prognos The WEO expects gas prices in the US to be partly disconnected from oil prices, due to the large indigenous gas reserves in the US 105

128 Table 6-7: Comparison of properties of electricity prices in the scenarios analysed Study and scenario WEO Ref and 450 ppm ETP BL and Blue Map EU DG TREN Reference EU DG TREN Baseline EU DG ENV Baseline EU DG ENV NSAT EU DG ENV NSAT-CDM Electricity prices/generation costs No data for Europe No data for Europe Significant increase after 2010 Significant increase after 2010 n/a Increasing prices compared to the Baseline Increasing prices compared to the Baseline, smaller than in NSAT-CDM Main drivers No data for Europe No data for Europe Increasing fuel prices, higher capital costs of RES and CCS, auctioning of CO 2 - allowances Increasing fuel prices, higher capital costs of RES and CCS, auctioning of CO 2 - allowances n/a RES targets, higher power generation costs, competition regime RES targets, higher power generation costs, competition regime, availability of CDM-credits ECF Baseline n/a Carbon prices, fossil-fuel prices, technology learning rates ECF Pathways E [R] Ref E[R] advanced Eurelectric Baseline Eurelectric Power Choices Higher LCoE than in the Ref. (short term), slightly higher LCoE by 2050 Generation costs increase up to 2020, upward tendency until 2050 Generation costs increase up to 2030 and decrease afterwards ( % 2050 compared to the Baseline) Strong increase up to 2025, stabilization afterwards Strong increase up to 2025, slight decrease afterwards Carbon prices, fossil-fuel prices, technology learning rates Fossil fuel prices, technology improvements of RES-technologies, costs for CO 2 -allowances Fossil fuel prices, technology improvements of RES-technologies, costs for CO 2 -allowances Fossil fuel prices, restructuring of the power plant fleet Fossil fuel prices, restructuring of the power plant fleet, lower fossil fuel consumption and lower demand for CO 2 -allowances) FEEM-WITCH Ref Electricity prices stay almost constant FEEM-WITCH Fb 3.2 Increase until 2015, stagnation 2015 to 2035, sharp increase after 2035 Restructuring of power generation Restructuring of power generation, increasing electricity demand Prognos Import dependency and security of supply Import dependency of the European Union is high and will increase in the absent of emission reduction measures. This is a result communicated by most of the analysed studies. For example, the WEO Reference estimates that the share of net imports for OECD Europe will go up to 91 % for oil and 83 % for natural gas. Throughout the studies, indigenous production of 106

129 fossil fuels is expected to decrease in the future (mainly due to international competition) with the exception of the ECF scenarios, which suggests the possibility of an expanding lignite production in Eastern Europe. All of the studies estimate higher shares of renewables for the alternative scenarios and therefore expect import dependency to be reduced. However, some ambitious scenarios (e.g. WEO 450 ppm) expect an increase in gas-imports for the EU-27 in the medium term due to low indigenous production and high demand if natural gas is used as a transition fuel. Furthermore, some studies expect imports of electricity from concentrated solar power (CSP) plants in North-Africa to be a possible option for Europe (e.g. ETP Blue Map, Energy[R]evolution). The implementation of this option is nonetheless estimated with caution and no study anticipates importation of solar power to be the primal solution for Europe. Other studies also emphasize the need to import a considerable share of biomass used in Europe (e.g. Energy[R]evolution, ETP Blue Map). Besides the development of security of supply for electricity, security of supply of energy resources in general is also an issue addressed in the studies. All scenarios expect reserves of natural gas to be sufficient to meet future demands, whereas unconventional oil reserves are expected to be deployed in some scenarios (e.g. EU DG ENV and ETP Baseline). Concentration of countries exporting fossil fuels is also an issue taken into consideration by some studies (e.g. ETP Baseline) Labour 6.5 Environmental issues Only small information concerning tendencies and effects on the labour market was found in the studies. However, in some scenarios (ECF Pathways, Energy[R]evolution), sectoral shifts on the labour market from traditional energy sectors (e.g. fossil fuels) to sectors linked to renewable installations and decarbonisation measures are expected. The magnitude of these effects is estimated at to additional green jobs (by 2020, compared to recent numbers, mainly in construction and mechanical engineering) vs. around jobs at stake in conventional fuels sectors (by 2020) in the ECF Pathways. In the Energy[R]evolution advanced scenario up to additional jobs (compared to recent numbers) in the renewable power sector are estimated by Throughout most of the studies, only scarce information is found whether effects from climate change are implemented endogenously in the scenarios. Some scenarios (e.g. EU DG TREN Baseline) state that degree days are kept constant at a level 107

130 6.6 Social issues corresponding to a base year (2000 in the EU DG TREN Baseline). It can be assumed that most of the studies do not take into account adverse effects of climate change on the economy in the models used. One exception can be found in the FEEMscenarios (WITCH-model). This model incorporates an integrated assessment module which is able to take into account a dynamic linkage of climate change and economic activity. The ambitious CO 2 -reduction scenarios in some cases include emission reduction targets which are set in regard to the possibility to limit climate change at a level of 2 C (e.g. WEO 450 ppm and ETP Blue Map). For example, the WEO 450 ppm scenario estimates that with the implemented emission reduction a probability of 50% to limit climate change at a level von 2 C is reached, compared to an estimated global increase in temperature of 6 C in the WEO Reference. In addition, some scenarios (e.g. Eurelectric Power Choices, ECF Pathways) expect overall lower local pollution levels in the case of ambitious CO 2 -reductions. Considered pollutants comprise SO 2, NO x, carbon black and heavy metals. However, no information was found concerning the geographical distribution of these pollutants. In the scenarios analyzed, no information on fundamental changes in the behavioural patterns of the economic agents was found. Some studies (e.g. Eurelectric, EU DG ENV and DG TREN, FEEM) apply microeconomic decisions of economic agents concerning demand for energy related products and investment in energy supply equipment. These scenarios partly take into account different levels of risk-awareness of agents (higher levels for individuals than for enterprises, reflected by high discount rates for individuals), lack of information, market barriers for new technologies and rebound-effects in energy-efficiency investments. Investment decisions are modelled under full information and perfect foresight assumptions. The most important effects in the social structure of the society implemented by the scenarios are changes in the population. Throughout the studies, a slight increase for the population of the EU is expected in the medium term (dynamic immigration and ageing society), with the tendency to a stabilization of population in the long term. Some studies also assume a significant decrease in the size of households, e.g. from ca. 2,5 down to 2,1 persons per household by 2030 in the EU DG ENV scenarios. 108

131 7 Implications for nuclear power 7.1 Introduction 7.2 Nuclear development In the last part of the study, the role of nuclear power in the scenarios is analysed apart. Can specific conditions or implications for nuclear power be derived from the scenarios? And what can finally be said about the assumptions or stories of the scenarios in relation to the development and role of nuclear power? Figure 7-1 and figure 7-2 display nuclear capacity and generation development estimated in the scenario studies. Nuclear generation and capacities tend to be highest in scenarios which have to reach ambitious emission targets. The main reason for this is the loss of market shares for fossil-fuel fired power plants (except CCS-plants) due to high carbon prices or other climate policy measures. Two main exceptions can be identified from the figures below: First, in the WITCH model, deployment of nuclear power plants is significant in the Fb 3p2 scenario, but in the Reference as well. This can be explained by the exclusion of exogenous constraints for the development of nuclear power, relatively low investment costs (see chapter 7.5) and decreasing interest rates estimated by this study (which generally favour more capital intensive investment). Furthermore, long planning and construction time seems to be of no concern in this study. Second, the Energy[R]evolution scenarios (advanced and basic) assume a general phase-out of nuclear power and are therefore committed to reach their goals with low and decreasing shares of nuclear power. In all other alternative scenarios (except for the ECF 80% RES case with exogenous market shares), nuclear power increases in the long term, but reaches levels clearly below the results of WITCH FB 3p2 and Ref in 2030/2050. On the other hand, development paths for nuclear power capacity and generation in the Reference or Baseline scenarios show a slight decrease in the next years (which can also be observed in the alternative scenarios and seems to be caused by small nuclear capacity buildup in recent years and few projects for the near future) and a slight increase or stabilization afterwards. Additionally, the sharp increase in nuclear capacity/generation estimated in the EU HOG BL scenario indicates the possible relevance of oil and gas prices for nuclear development. Therefore, decreasing oil, gas and coal prices (due to low oil, gas and coal demand) could work in the opposite direction of 109

132 increasing carbon prices, concerning the competitiveness of nuclear vs. gas and coal power plants. Figure 7-1: Development of European nuclear power generation, in TWh (for comparison also the worldwide development based on WEO 2009) TWh Eurostat WEO Ref WEO 450ppm WEO Ref OECD Eur ETP BL OECD Eur ETP Blue OECD Eur EU Trends 2007 EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv PLANETS WI Ref* PLANETS WI FB 3p2* ECF Ref ECF 80% RES ECF 60% RES ECF 40% RES WEO Ref World WEO 450ppm World Prognos

133 Figure 7-2: Development of European nuclear capacities, in GW GW WEO Ref WEO 450ppm WEO Ref OECD Eur ETP BL OECD Eur ETP Blue OECD Eur EU Trends 2007 EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh E [R] Ref E [R] E [R] Adv ECF Ref ECF 80% RES ECF 60% RES ECF 40% RES PLANETS WI Ref* PLANETS WI FB 3p2* WEO Ref World WEO 450ppm World Prognos 2011 When comparing European development of nuclear power plants with the worldwide development (by using the WEO 2009), it can be seen that the European share of worldwide nuclear power generation is large in 2010 but continuously diminishes until 2030 (see figure 7-3). In other words, main parts of the growth of nuclear power generation occur outside Europe. China and the USA show significant shares and growth rates of nuclear power generation, even in the Reference scenarios. Overall, global nuclear power generation increases in both scenarios, in contrast to decreasing nuclear generation in the Reference scenario for Europe. 111

134 Figure 7-3: Development of global nuclear power generation, in TWh TWh Ref ppm 2030 Ref ppm US EU Japan China Russia India Other Prognos 2011 Table 7-1 provides additional information about main assumptions leading to the development paths for nuclear power shown above (e.g. nuclear phase-out policies) as well as additional determinants and outcomes of overall nuclear development. In general the scenarios stick to the phase out-policies as implemented presently. Some sensitivities are worked out: namely total nuclear phase out in the E[R]-scenarios and Eurelectric Power Choices (as separate sensitivity). Whereas most studies show results of nuclear development for Europe only, the EU DG ENV and DG TREN scenarios implement assumptions about the nuclear option on the country-level. There is a range of countries which are assumed not to take into consideration nuclear power plants (AT, CY, DK, EE, GR, IE, LV, LU, MT, PT). Other countries are assumed to develop nuclear energy (IT and PL) and a range of countries are assumed to have the possibility to further invest in nuclear power (BG, CZ, FR, FI, HU, LT, RO, SK, SI, SP, UK). Lifetime extensions to 60 years are assumed to take place in SE. Additionally, closure of existing plants based on fixed schedules is taken into account in BG, LT and SK and phase-out is assumed for BE and DE. 112

135 Table 7-1: Nuclear development, comparison of scenarios Study and scenario Phase-out policies Overall nuclear development Others WEO Reference Phase-out policies in some member states Significant decline of nuclear power capacity Renewed interest in nuclear power may change the outcome WEO 450 ppm n/a Capacity increase compared to present (mainly ) Electricity from new plants cost between $55 and $80/MWh; ETP Baseline Phase-out strategies considered Generation share falls from 26 to 17 % ETP Blue Map n/a Generation share increase slightly Limitations of max. 30 GW annual global addition EU DG TREN Reference EU DG ENV Baseline Phase-out based on the lifetime of plants: BE, GE Phase-out in some member states (BE, GE, SE) Generation remains roughly stable Generation share declines from 30% in 2006 to 20% in 2030 EU DG ENV NSAT EU DG ENV NSAT-CDM Phase-out in some member states (BE, GE, SE) Phase-out in some member states (BE, GE, SE) ECF Baseline n/a n/a 21% of generation in 2030 ECF Pathways n/a 10 to 40% by 2050 in the generation fuelmix E[R] Ref. n/a n/a E [R] advanced and basic Eurelectric Baseline Eurelectric Power Choices General phase-out of nuclear power Phase-out in GE and BE remains Phase-out in BE and GE (Sensitivity: no phase-out) Minor development of nuclear power until 2020 Roughly stable Minor development of generation until 2030 nuclear power until 2020 Constant decreasing share 30% increase of generation (by 2050) 42% increase of generation (by 2050) FEEM n/a Significant increase of generation 7.3 Fuel cycle issues new power plants per decade Most new nuclear capacity is completed until 2025 Prognos 2011 In this matter uranium resources, fuel assembly and waste management are key elements. The world's present identified resources (reasonably assured resources and inferred resources) economically recoverable at a price of US$ 130/kg, will, according to NEA/IAEA, last for some years, given current consumption. Though there is at present a gap between uranium supply and demand, which is filled by secondary sources (e.g. Uranium 113

136 7.4 Market issues stocks), new supply capacities are required. In the meantime the price may be volatile, although fuel elements are normally bought with a long term contract and can also be stored over a longer period. Furthermore the relevance of the costs of fuel supply is marginal for the overall generation costs. As for waste management, spent fuel could be reprocessed (closed fuel cycle) or stored temporarily. By now, only a small share of the spent fuel is processed, partly because of economic reasons. Some studies do respond to these questions, e.g. ECF and the ETP Blue Map assume that with the estimated nuclear development, no scarcity of uranium results. Additionally, the WEO 450 ppm scenario states, that investment in uranium mining has to rise considerably to meet the fuel needs of new power plants. 7.5 Financial / investment issues Today s market for nuclear power plant manufacturers is an oligopoly. There are 11 manufacturers world-wide and most of them are specialized in one reactor type. Because of the relatively few new built plants in the last two decades their capacities are restricted, although they are currently increasing again. The same holds true for the component suppliers. The capacity shortage at Japan Steel Works (JSW), the only business company capable of producing large single-piece pressure vessels, is for example evident (Prognos, 2009). Both the WEO 450 ppm and the ETP Blue Map scenarios expect, that significant increases of capacity in construction and operation are needed and that manufacturers of nuclear equipment face a real challenge with assumed build-up rates of nuclear capacity. There is also a clear need for new qualified personnel, not only for future-built capacities, but also to replace the relatively aged employees in the existing plants (Prognos, 2009). These issues are not addressed in the analysed scenario studies. Past investment costs for nuclear power plants varied widely depending on time of construction, region, reactor-type etc. For new plants, few data is available, as, especially in the EU-27, only a few have been built in the last decades and only a few are being built at present. For all power plants, developments of manufacturing markets (capacities) as well as of raw material markets (e.g. steel) have led to increasing prices. Several aspects have to be taken into account when financing a new nuclear power plant: state regulations, availability of state guarantees, amount of equity capital, conditions for acquiring loan 114

137 capital (international capital market), profitability compared to other power plant types, amount of insurance costs for final storage of nuclear waste etc. (De)regulation is both a market and a financial issue. In liberalised electricity markets nuclear power has to compete with other technologies on a free market, not by full costs, but by marginal costs (see merit order above), which may lead to price risks. Another question is whether (wholesale) price increases can be passed on to consumers. Nuclear power plants are much more capital intensive than fossil-fuelled power plants (like gas or coal plants). Therefore, estimations concerning investment costs (and possible technological learning) are one of the most important factors influencing the competitiveness of nuclear power plants. Such differences in investment costs have important implications in financing new nuclear power plants and the competitiveness of nuclear power vs. other generation options in electricity markets. The significant deployment of nuclear power plants in the ETP Blue Map scenario, and especially in the WITCH scenarios (see figure 7-1 and figure 7-2 above) can partly be explained by low investment costs. Figure 7-4 displays nuclear investments costs from today up to Of course, estimations for 2050 are uncertain for several reasons already mentioned and should be interpreted with caution. Considering the estimated development paths for nuclear power (in the alternative scenarios) the most relevant cost assumptions are those for the period For 2030, cost estimations range between slightly below USD 2008 /kw and USD 2008 /kw. Such differences in investment costs have important implications in financing new nuclear power plants and the competitiveness of nuclear power vs. other generation options in electricity markets. The significant deployment of nuclear power plants in the ETP Blue Map scenario, and especially in the WITCH scenarios (see figure 7-1 and figure 7-2 above) can partly be explained by low investment costs. 115

138 Figure 7-4: Development of investment costs for nuclear power plants, in USD 2008 /kw USD 2008 / kw status quo status quo refers to different years 16 Prognos 2011 In addition to the investment cost data presented above, the Eurelectric study estimates nuclear investment cost to be increasingly dependent on economic conditions in the future and therefore expects them to increase non-linearly. Furthermore, the WEO 450 ppm scenario states that high up-front costs could induce risks for investors and prevent investment in nuclear power plants, therefore governments may need to mitigate these risks. In the long run, it is stated, a well functioning carbon-market should lead to enough incentives for investment in nuclear power and nuclear power plants should gain a strong competitive position against fossil-fuel fired power plants. Throughout the studies, no assumptions about lifetime-extension of existing nuclear power plants are formulated, with the exception of Swedish power plants in the Eurelectric scenarios. In the Eurelectric, EU DG ENV and ETP scenarios, lifetime extensions throughout other member states are explicitly not taken into account. There are no new nuclear designs taken into consideration for new built power plants in most of the analysed studies. Scenarios expect future power plants up to 2050 to be currently available generation III reactors or III+ reactors in the long term. One exception can be found in the ETP 2010, which assumes that 16 Earliest year for which data available (ca ) 116

139 7.6 Safety issues Generation IV reactors are built and operated from However, they are not expected to gain large shares by Outstanding issues A continuous issue of nuclear power is operational safety. A steady increase of safety measures and regulations has been realised over the years. Passive safety measures have been and are being intensified. However, the question of nuclear safety is still under discussion in the public. Neither of these issues is addressed directly in the scenario studies. Of course, through the application of new nuclear designs, nuclear safety can be estimated to be further improved implicitly. These are generally reflected in the investment costs. Two further issues, incessantly on the agenda, are final storage of nuclear waste and proliferation risks. At present, research is being conducted worldwide to identify possible storage locations and first sites have been selected (e.g. Sweden). Though implementation of the solution for waste management (geological disposal) has started in some countries, final storage of nuclear waste has not yet taken place. Some scenarios also address these questions. The Eurelectric scenarios assume that nuclear waste management is manageable until 2050, with additional energy consumption for nuclear fuel and waste treatment considered in the scenarios. The FEEM scenarios take into account waste management and proliferation costs through the inclusion of an additional O&M burden in the model (initially set at 1 musd/kwh, which is the charge paid for the US depository at Yucca Mountain and assumed to grow linearly with the quantity of nuclear power generated. This value is also applied for Europe. Concerning proliferation risks, a number of international agreements, such as the Non-Proliferation Treaty, have been signed. No additional information to this topic has been found in the studies (so far). Information concerning costs for waste disposal, storage and proliferation prevention (as well as costs for decommissioning) is often not explicitly mentioned in the scenario studies. These costs may though be covered by fuel and investment costs implemented in the studies. 117

140 7.8 Conclusion To sum-up information on the consideration of nuclear power plants in the scenario studies analyse, the following can be noticed: Scenario studies which contain ambitious emission reduction targets generally estimate larger deployment of nuclear power plants compared to the Reference or Baseline scenarios, unless the scenarios exogenously assume general phase-out of nuclear power. Results mainly differ due to assumptions on investment costs, interest rates and exogenous constraints (determination of markets shares, maximum capacity build-up rates). Information on issues other than investment costs and capacity/generation development are rather spare. Factors which could be crucial for the development of nuclear power, like safety issues, fuel cycle issues, acceptance and future design of energy and financial markets, are not analysed in depth throughout the scenario studies. 118

141 8 Comparison of the scenario studies: overview In the previous chapters quantitative and qualitative information of each scenario has been listed for a number of issues. In this chapter the findings are summarized for selected issues by scenario, to display the main conformities and differences among the scenarios (see table 8-1). This evaluation is restricted to variables for which a comparison is straightforward (e.g. comparison of compliance cost estimations is not reflected for this reason). The arrows in table 8-1 reflect tendencies in the development of variables (for a specific scenario) up to 2050 and in relation to the development estimated in other scenarios. For studies with a shorter timeframe (up to 2030) the development until 2030 is evaluated. Development in the period estimated in the FEEM-WITCH model is not taken into account in this comparison. The studies are quite consistent in the estimation of GPDdevelopment (per capita) and the increase of electricity demand. Final energy demand stabilises or decreases in the alternative scenarios, therefore energy efficiency (measured in EEV per GDP) decreases. Total emissions are merely stabilised (in the Baseline or Reference scenarios) or reduced (in the alternative scenarios). Future power mix is marked by an increase of renewables, whereas the role of nuclear and CCS is diverse. The latter is mainly due to assumptions and bounds set to these technologies. Grid investment (taking into account estimations on financial investment as well as capacity additions) tends to increase, especially in the alternative scenarios. In general, fuel prices tend to increase. Competiveness of (conventional) power plants is, besides fuel prices, also influenced by the development CO 2 -certificate prices. These vary among the scenario, which is, besides other factors, a result of different geographical coverage of emission trading. 119

142 Table 8-1: Tendencies in the development of main variables in the scenario studies Study Timeframe up to year x Model type BU / TD Main target GHG or RES GDP p.c. Oil price Gas price Coal price CO 2 - price Model mechanism Opt or Sim ETScoverage Final energy demand Power demand Energy efficiency EEV per GDP Nuclear generation RES RES- (focus) CCS TWh TWh TWh EV CO 2 - emissions Gridinvestment Add. grids WEO Ref 2030 WEO 450ppm 2030 BU (add. TD) BU (add. TD) Opt + Sim Opt + Sim ETP BL 2050 BU Opt ETP Blue 2050 BU Opt EU DG TREN Base EU DG TREN Ref EU DG ENV BL EU DG ENV NSAT EU NSAT- CDM BU/TD mixed BU/TD mixed BU/TD mixed BU/TD mixed BU/TD mixed Opt Opt Opt Opt Opt target -80% GHG (2050) targets -74% GHG (2050) targets OECD+ (2013), CDM Wind (offshore after 2020), solar, bio (by 2030) EU-27 Wind, solar OECD+ OME EU-27, CDM EU-27, CDM Wind, bio, solar (PV/CSP after 2020) Wind, solar, bio Wind (offshore after 2020), solar, bio EU-27 Wind, bio, solar targets EU-27 Wind, bio, solar EU-27, Wind, bio, CDM solar targets ECF-Ref 2050 ECF 80% RES ECF 60% RES BU with add. TD BU with add. TD BU with add. TD Sim Sim Sim targets -80% GHG (2050) -80% GHG (2050) OECD (2020) Wind Wind OECD (offshore (2020) after 2020), OECD (2020) bio, heat pump Wind, bio, heat pump (after 2020) (after 2020) 120

143 ECF 40% RES 2050 BU with add. TD Sim -80% GHG (2050) OECD (2020) E[R] Ref 2050 BU Sim Global (long term) basic E[R] 2050 BU Sim advanced E[R] Eurelectric PowCh 2050 BU Sim 2050 BU/TD mixed Opt WITCH Ref 2100 TD Opt WITCH FB TD Opt -80% CO 2 (2050) -95% CO 2 (2050) -75% GHG (2050) ca. 500 ppm CO 2 Global (long term) Global (long term) Global (2020) Global (2012) Wind, bio, heat pump (after 2020) Wind, solar, geo Wind, solar, geo Wind (offshore after 2020), bio, solar PV (after 2020) Wind, solar Wind, solar Legend: add.: additional, BU: bottom-up, TD: top-down, Opt: optimisation, Sim: Simulation, EV: electric vehicles, bio: biomass, geo: geothermal OME: other major economies, CDM: Clean Development Mechansim, ETS: Emission Trading System, Arrows describe tendencies in the development of variables over the timeframe applied in the scenario studies. Developments are evaluated in comparison with developments in other scenario studies (e.g. a sharp increase in one variable means that the variable increases relatively sharp compared to the development in other scenario studies). (by (by 2030) 2030) Prognos 2011 Sharp increase Moderate increase Almost stable or small increase/decrease Moderate decrease Sharp decrease No information available or no development estimated in the scenario studies 121

144 9 Interpretation of results Overall, the following conclusions can be drawn: Scenarios are marked by their GHG-targets. Demand Without new policy measures, energy demand will increase due to GDP growth (around 1 to 2 % per year). Electrification occurs in (almost) all scenarios. Electricity is estimated to gain shares in final energy demand, especially in scenarios confronted with ambitious GHG-targets (mainly as a substitute for fossil fuels) reaching shares of 30 to 40 % (i.e. an increase in final electricity demand of around 1 % per year). In road transport, the use of hybrid cars and electric vehicles is generally increasing towards Nuclear Power The role of nuclear power is generally an outcome of the bounds and investment cost assumptions set by the scenario developers. Without (major) restrictions, nuclear power tends to expand in Europe (reaching shares of 35 to 45 % of electricity generation) and especially worldwide. Nuclear power is estimated to gain higher importance in scenarios with ambitious GHG-targets (corresponding to an increase of around 30 % to 100 % of the currently installed capacity), unless the development is restricted exogenously. Lifetime-extension of nuclear power plants is not (explicitly) assumed in the scenario studies (except for some individual countries). Scenarios expect future nuclear power plants up to 2050 to be dominated by Generation III or III+ reactors. Detailed information about the framework in which nuclear power can develop is not given by the studies. Some studies address the importance or are aware of relevant market issues, like investment in uranium mining, capacity needs in construction and operation and final storage solutions. Renewables Absolute and relative increase of RES in the power sector (reaching shares of 40 % in the baseline scenarios, compared to up to 100 % of electricity generation in 2050 in the alternative scenarios). 122

145 Investment costs for RES decrease significantly in the scenarios, especially in the long term. Wind power, solar heat and solar PV as well as biomass show the main contributions in the deployment of RES Fossil fuel plants CCS plays an increasing role in several scenarios (from 2030, reaching shares of up to 30 % of electricity generation in 2050) and can be seen as the second main power generation technology (beneath nuclear power) to provide baseload power. In scenarios applying optimisation models, development of carbon prices is found to be crucial for the emergence of CCS. Special attention is generally given to gas-fired power plants, which are estimated to serve as dispatchable capacity in a range of the considered scenarios, but with far lower utilisation rates than currently observed. Models used The models used are characterized by complex interrelationships between and inside energy sectors, individual behaviour, policies and the whole economy. Though, the detailedness of the sectors and energy carriers varies among the models as well as the way the models are functioning and which issues are emphasized (e.g. optimisation regimes, compliance costs, use of grid sub-models). This hampers the comparison of the studies outcomes. Models used for the scenario studies can be mainly grouped into optimisation models and simulation models which use exogenously defined market shares. Optimisation models can be further split into models which apply optimisation for individual agents (stable behaviour presumed) and models applying general optimisation frameworks. If optimisation is applied, deployment of different energy supply and demand technologies is mainly affected by cost assumptions and exogenously (e.g. fossil fuel prices) or endogenously determined prices (e.g. carbon prices) and their impacts. Costs Compliance costs for emission reduction tend to be high, mainly driven by increasing capital costs (e.g. capital-intensive power generation technologies and grid costs), but decrease in 123

146 the long term due to lower fossil-fuel consumption and technological improvements in renewable technologies. Electricity prices are estimated to increase in most of the studies in the medium term (up to 2030). Some studies with high emission reduction targets expect a decrease of electricity prices in the long term (up to 2050), mainly due to the decrease of fossil-fired generation shares and technological development in renewable power generation. Comparison of compliance costs between the scenario studies is hindered by different frameworks and conventions in the quantification of costs and a lack of information on these issues. Grids Studies with ambitious emission reduction targets assume technological progress in grid technology and management and estimate that large increases in transmission capacities are needed. Transmission extension leads to additional capital costs for the power sector (e.g. cumulated grid investment of up to bn needed until 2050 in the eurelectric Power Choices scenario). Almost all studies emphasize the relevance of technologically advanced smart grids and smart metering frameworks, especially those confronted with ambitious emission reduction (although, details on the technology of choice are not determined). The linkage of grid (sub-)models and the development of generation technologies remains rather unclear in the studies analysed. Implications for distribution networks are not addressed in detail by most of the studies. Import dependency and security of supply Import dependency of the European Union is high and will increase in the absent of emission reduction measures. All of the studies agree that higher emission reduction needs result in higher shares of renewables and therefore reduce import dependency compared to scenarios with lower emission reduction. Security of resources is presumed in several studies, security of supply for electricity is assumed to be equivalent to current levels in some scenarios (if information is available at all). 124

147 Appendix A.1 Literature BNL (2004). MARKAL Modelling Environment and Regionalization. Chip Friley, Brookhaven National Laboratory, July 15, 2004 Bosetti et.al (2009). The 2008 WITCH Model: New Model Features and Baseline. Nota di lavoro , Fondazione Eni Enrico Mattei, Milano. Capros et al. (2008). Model-based Analysis of the 2008 EU Policy Package on Climate Change and renewables. By P. Capros, L. Mantzos, V. Papandreou, N. Tasios, E3MLab/NTUA. Report to the European Commission - DG ENV, Brussels, Belgium. Connolly, D. (2010). Mesap PlaNet.David Connolly, University of Limerick DBT (2009), Future energy systems in Europe. Danish Board of Technology (DBT), Copenhagen, Denmark. For the EUROPEAN PARLIAMENT, Science and Technology Options Assessment, Brussels, Belgium DLR (2005). Concentrating Solar Power for the Mediterranean Region Final Report. German Aerospace Center, Institute of Technical Thermodynamics (Section Systems Analysis and Technology Assessment), Stuttgart Dupuy, A., Hicks Neutral Technical Change Revisited: CES Production Function and Information of General Order. Topics in Macroeconomics, Vol. 6, Issue 2, Article 2, The Berkeley Electronic Press. E3M-Lab (2007). The PRIMES Energy Model. Energy- Economy-Environment Modelling Laboratory, National Technical University of Athens, E3M Lab, November 2007, Brussels E3M-Lab (2010). PRIMES Model Versions used for the 2007 Scenarios for the European Commission including new submodels recently added. E3M-Lab of ICCS/NTUA, Athens ECF (2010). Roadmap 2050; A practical guide to a prosperous, low-carbon Europe. European Climate Foundation, The Hague, The Netherlands ECN (2005). The next 50 years: Four European energy futures. Bruggink, J.C.C., Energy research Centre of the Netherlands (ECN), Petten/Amsterdam, the Netherlands. 125

148 EEA (2005). European environment outlook. European Environment Agency, Copenhagen, Denmark, EEA Report No 4/2005. ISBN EEA (2005). The European Environment state and Outlook European Environment Agency, Copenhagen, Denmark. ISBN EIA/DOE (2010). International Energy Outlook Energy Information Administration, U.S. Department of Energy, Washington DC, USA. EIA/DOE (2008), International Energy Outlook 2008, Energy Information Administration - U.S. Department of Energy. Energy Watch Group (2008). Renewable Energy Outlook 2030; Energy Watch Group Global Renewable Energy Scenarios. Lehmann, H.; Peter, S., ISUSI & WCRE, Bonn, Germany. Energy Watch Group / Ludwig-Boelkow-Foundation, Berlin, Germany EREC (2010). RE-thinking 2050; A 100% Renewable Energy Vision for the European Union. European Renewable Energy Council, Brussels, Belgium EREC (2005). Renewable Energy Scenario to 2040; Half of the Global Energy Supply from renewables in European Renewable Energy Council, Brussels, Belgium EU (1994) in Rienstra, S.A. (1998). Options and Barriers for Sustainable Transport Policies; a scenario approach. Vrije Universiteit van Amsterdam; Faculteit der Economische Wetenschappen en Economotrie. Ph.D-Thesis. ISBN x. EU (2011). Daten zu EU Trends to Interne Dokumente (nicht veröffentlicht). EU DG Research (2006). World Energy Technology Outlook WETO H2. European Commission, Directorate-General for Research,Directorate Energy, Brussels, Belgium EU DG TREN (2006). European Energy and Transport; Scenarios on energy efficiency and renewables. European Commission; Directorate-General for Energy and Transport, Brussels, Belgium. ISBN EU DG TREN (2008). European Energy and Transport; TRENDS TO UPDATE European Commission, DG TREN, Brussels, Belgium. ISBN

149 EU DG TREN (2010). European Energy and Transport; TRENDS TO UPDATE European Commission, DG TREN, Brussels, Belgium. ISBN Eurelectric (2009). Power Choices: Pathways to Carbon- Neutral Electricity in Europe by Union of the Electricity Industry - EURELECTRIC, Brussels, Belgium. Eurelectric (2007). The Role of Electricity; A New Path to Secure, Competitive Energy in a Carbon-Constrained World. Union of the Electricity Industry - EURELECTRIC, Brussels, Belgium. Eurostat (2011). BIP-Deflator (teina110), ExxonMobil (2009). Outlook for Energy; A View to ExxonMobil, Corporate Headquarters, Texas, USA. ExxonMobil (2009). Energieprognose ; Schwerpunkt: Erdgas umweltschonende Energie für Deutschland. ExxonMobil Central Europe Holding GmbH, Hamburg, Germany FEEM (2010). PLANETS: Probabilistic Long-term Assessment of New Energy Technology Scenarios. Fondazione Eni Enrico Mattei (FEEM) et al., Milan, Italy. FEEM (2011). The WITCH Model, [ ] Gielen D. & Taylor M. (2007). Modelling industrial energy use: The IEAs Energy Technology Perspectives. Energy Economics, Vol. 29, , Elsevier Greenpeace/EREC (2008). Energy [r]evolution; A Sustainable Global Energy Outlook. Greenpeace International, Amsterdam, the Netherlands & European Renewable Energy Council (EREC), Brussels, Belgium. ISBN ' Greenpeace/EREC (2010a). Energy [r]evolution, EU Energy Roadmaps Compared. Tobias Boßmann, Frauke Thies. Greenpeace/EREC (2010b). Energy [r]evolution; A Sustainable World Energy Outlook. Greenpeace International, Amsterdam, the Netherlands & European Renewable Energy Council (EREC), Brussels, Belgium. ISBN

150 Greenpeace/EREC (2010c). Energy [r]evolution; Towards a fully renewable energy supply in the EU 27. Greenpeace International, Amsterdam, the Netherlands & European Renewable Energy Council (EREC), Brussels, Belgium. Additional information provided by Dr. T. Pregger, German Aerospace Center (DLR), S. Teske, Greenpeace International. Greenpeace/EREC (2010d). [r]enewables 24/7 Infrastructure needed to save the Climate. Greenpeace International, Amsterdam, the Netherlands & European Renewable Energy Council (EREC), Brussels, Belgium. IAEA (2009). Energy, Electricity and Nuclear Power Estimates for the Period up to International Atomic Energy Agency, Vienna, Austria. ISBN ' IEA (2008a). Energy Technology Perspectives 2008; Scenarios & Strategies to International Energy Agency, Paris, France. ISBN IEA (2008b), World Energy Outlook 2008, International Energy Agency, Paris. IEA (2009a). International Energy Outlook International Energy Agency, Organisation for Economic Co-operation and Development, Paris, France. ISBN: ' IEA (2009b). World Energy Model methodology and assumptions. International Energy Agency, Paris. IEA (2010a). Energy Technology Perspectives 2010; Scenarios & Strategies to International Energy Agency, Paris, France. ISBN , to be released' IEA (2010b). World Energy Model Methodology and Assumptions, International Energy Agency, Paris IHS (2008). European Energy and Environmental Outlook. IHS Global Insight, London, United Kingdom. Johnson (2004). An EPA Overview: Energy Technology Assessment and Regional MARKAL Modeling Initiatives. Timothy Johnson, National Risk Management Research Laboratory, Office of Research and Development, U.S. EPA Loulou et.al. (2005). Documentation for the TIMES Model Part I, Energy Technology Systems Analysis Programme, International Energy Agency, Paris NEA (2010). Technology Roadmap; Nuclear Energy. Nuclear Energy Agency, Organisation for Economic Co-operation and Development, Paris, France. 128

151 NEA (2008). Nuclear Energy Outlook Nuclear Energy Agency, Organisation for Economic Co-operation and Development, Paris, France. NEA No ISBN NTUA/Prof. P. Capros (2000). The PRIMES Energy System Model; Summary Description. National technical university of Athens, Prof. P. Capros. European Commission Joule-III Programme. Ogilvy, J. & Schwartz, P. (1995), Rehearsing the future through scenario planning. In: Proceedings of Profutures Workshop "Scenario Building. Convergences and Differences." Sevilla (Spain). European Commission. Pp Öko-Institute (2006). The Vision Scenario for the European Union. Matthes, F. et al., Öko-Institut e.v., Berlin, Germany. In Cooperation with Poutrel, S. (ICE). Project sponsored by Greens/EFA Group in the European Parliament, Brussels, Belgium Prognos (2007). The Future Role of Coal in Europe. Prognos AG, Berlin/Basle. On behalf of Euracoal, Brussels, Belgium. Prognos (2010). Prognos World Report Industrial Countries Facts, Figures and Forecasts. PWC (2006). The World in 2050; Implications of global growth for carbon emissions and climate policy. Hawksworth, J., PriceWaterhouseCoopers LLP, United Kingdom. Rienstra, S.A. (1998), Options and Barriers for Sustainable Transport Policies; a scenario approach. Vrije Universiteit van Amsterdam; Faculteit der Economische Wetenschappen en Economotrie. ISBN x Rits, V. (2003), Exploring the diffusion of fuel-cell cars in China; a scenario approach, Eindhoven University of Technology, the Netherlands & Paul Scherrer Institute, Villigen PSI, Switzerland. Schaeffer, G.J. (1998), Fuel Cells for the future; A contribution to technology forecasting from a technology dynamics perspective. University of Twente. ISBN Schlenzig, C (1998). PlaNet - Ein entscheidungsunterstützendes System für die Energie- und Umweltplanung (PlaNet - a decision support system for energy and environmental planning). Ph.D. Thesis, Institut für Energiewirtschaft und Rationelle Energieanwendung, Universität Stuttgart, Stuttgart, Germany. 129

152 Shell (2008). Shell energy scenarios to th edition. Shell International BV, The Hague, The Netherlands. US Gov (2009). Budget of the United States Government: Historical Tables Fiscal Year 2009, Table 10.1 Gross Domestic Product and Deflators Used in the Historical Tables: WEC (2007). Deciding the Future: Energy Policy Scenarios to World Energy Council, London, United Kingdom. ISBN: X 130

153 A.2 Abbreviations and acronyms BL BU CCGT CCS CDM CHP CO 2 CSP DC DG EC ECF ECN EEA e.g. EIA ENEF EREC etc. ETS baseline bottom-up combined cycle gas turbine carbon capture storage Clean Development Mechanism combined heat and power carbon dioxide concentrating solar power direct current Directorate-General European Commission European Climate Foundation Energy Research Centre of the Netherlands European Environment Agency for example Energy Information Administration European Nuclear Energy Forum European Renewable Energy Council et cetera Emission Trading System/Scheme EU DG TREN European Commission Transport and Energy EU DG ENV FEEM GDP GHG European Commission Environment Fondazione Eni Enrico Mattei gross domectic product greenhouse gas 131

154 IAEA IEA IGCC MS n.a. / n/a NEA NGCC NGOC NO X NTUA OECD OECD+ OME ppp PV PWC R&D RES TD vs. WEC US$/USD International Atomic Energy Agency International Energy Agency integrated gasification combined cycle member states not available Nuclear Energy Agency natural gas combined cycle natural gas open cycle nitrogen oxide National Technical University of Athens Organisation for Economic Co-operation and Development countries which are member of EU and/or OECD Other Major Economies (Brazil, Russia, South Africa and the countries of the Middle East) Purchasing Power Parity photovoltaics PricewaterhouseCoopers research and development Renewable Energy System/Source top-down versus World Energy Council United States Dollar 132

155 Country codes Code Country Code Country Code Country AT Austria FI Finland NL Netherlands BE Belgium FR France PL Poland BG Bulgaria GR Greece PT Portugal CY Cyprus HU Hungary RO Romania CZ Czech Republic IE Ireland SE Sweden DE / GE Germany IT Italy SI Slovenia DK Denmark LT Lithuania SK Slovakia EE Estonia LV Latvia UK United Kingdom ES Spain LU Luxembourg US United States EU European Union MT Malta 133

156 A.3 Conversion factors SI prefixes Prefix (symbol): Factor: Label: Factor: Nano (n) 10-9 Mega (M) 10 6 Micro (μ) 10-6 Giga (G) 10 9 Milli (m) 10-3 Tera (T) Kilo (k) 10 3 Peta (P) Energy units conversion From: \ To: TJ Mtoe MBtu GWh TJ x Mtoe x x MBtu x x x10-4 GWh x Energy density and cubic measures Fuel: Energy density: Cubic measure: Coal GJ/t 1 cubic m 3 Lignite 8.45 GJ/t 1 barrel 159 liter Oil 6.12 GJ/barrel 1 US gallon liter Gas kj/m 3 1 UK gallon liter 134

157 A.4 Conversions of prices, handling of differing base years in index diagrams For enabling a comparison of prices and GDP, monetary values have been converted into real US-dollars with base year 2008 (prices) and real Euro with base year 2008 (CO 2 -certificates and investment costs). If only base year differs from target unit, statistical data for deflators have been used (US Gov, 2009 for USD, Eurostat 2011 for EUR). In WEO and ETP real exchange rates are set to remain constant over projection period. Conversion factors for Energy [R]evolution have been taken from a comparison of Greenpeace (Greenpeace/EREC, 2010a). Roadmap 2050 gives information on nominal exchange rates which have been used together with deflators for US and EU, taken from Prognos World Report 2010 (Prognos, 2010), in order to calculate real exchange rates. For conversion of energy prices of EU ENV and FEEM-WITCH real exchange rates of Prognos World Report have been used. In order to compare the developments of GDP, final energy productivity (final energy demand / GDP) indexed developments have been represented from 2007 onwards. For EU DG TREN 2007 and EU DG ENV, which have been published before the financial crises and which provide data for 2005 and 2010, linear interpolation has been used to set a value for For studies published after the financial crisis, statistical data for nominal development of GDP, and deflation has been used (Prognos 2010), to determine values for 2007 backwards from values given in the studies 2010 (ECF, FEEM-WITCH, Power Choices, EU DG TREN 2009). 135

158 A.5 Detailed data sheets A.5.1 Investment costs of power plants Table A-1: General information of investment costs of power plants study region remarks ETP USA WEO 450ppm global (range due to regional differences) EU Trends 2009 EU Eurelectric EU E[R]ev probably EU ECF EU including interest during construction FEEM-WITCH global average Prognos 2011 Table A-2: Investment costs of nuclear and fossil power plants, in USD 2008/kW technology study status quo min max min max min max gas+ccs ECF EU Trends ETP gas E[R]ev FEEM-WITCH 880 Eurelectric ECF EU Trends ETP lignite + CCS EU Trends lignite E[R]ev EU Trends coal + CCS FEEM-WITCH Eurelectric ECF EU Trends ETP coal E[R]ev FEEM-WITCH Eurelectric ECF EU Trends ETP nuclear FEEM-WITCH ECF EU Trends ETP WEO 450ppm Prognos

159 Table A-3: Investment costs of renewable power plants, in USD 2008/kW technology study status quo min max min max min max pumped storage EU Trends tide and Wave E [R] Adv E[R]ev EU Trends ETP WEO 450ppm Geothermal E [R] Adv E[R]ev ECF EU Trends ETP WEO 450ppm biomass E[R]ev ECF EU Trends ETP WEO 450ppm solar csp E [R] Adv E[R]ev ECF EU Trends ETP WEO 450ppm solar PV E [R] Adv E[R]ev ECF EU Trends ETP WEO 450ppm wind offshore E[R]ev ECF EU Trends ETP WEO 450ppm wind onshore E [R] Adv E[R]ev ECF EU Trends ETP WEO 450ppm hydro E[R]ev FEEM-WITCH ECF EU Trends ETP WEO 450ppm Prognos

160 A.5.2 Total final consumption Table A-4: Total final consumption in 2020, in PJ 2020 total coal oil gas electricity heat biomass other res WEO Ref OECD Eur WEO Ref WEO 450ppm E [R] Ref E [R] E [R] Adv EU Trends EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh PLANETS WI Ref* PLANETS WI FB 3p2** Table A-5: Total final consumption in 2030, in PJ Prognos total coal oil gas electricity heat biomass other res WEO Ref OECD Eur WEO Ref WEO 450ppm ETP BL OECD Eur ETP blue OECD Eur E [R] Ref E [R] E [R] Adv EU Trends EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh PLANETS WI Ref* PLANETS WI FB 3p2** Prognos

161 Table A-6: Total final consumption in 2050, in PJ 2050 total coal oil gas electricity heat biomass other res ETP BL OECD Eur ETP blue OECD Eur E [R] Ref E [R] E [R] Adv Eur PowCh PLANETS WI Ref* PLANETS WI FB 3p2** Prognos

162 A.5.2 Power generation capacities 2030 WEO Ref WEO 450ppm ETP BL Table A-7: Power generation capacities in 2030, in GW ETP blue E [R] Ref E [R] E [R] Adv ECF Ref ECF 80% RES ECF 60% RES nuclear fossil coal lignite gas oil fossil ccs coal ccs gas ccs renewables hydro biomass wind wind onshore wind offshore solar solar pv solar csp other res ECF 40% RES EU Trends 2007 Table A-8: Power generation capacities in 2050, in GW EU RSAT EU RSAT CDM EU NSAT EU NSAT CDM EU CES EU CES CDM EU HOG BL EU HOG CES EU Trends 2009 BL EU Trends 2009 Ref Eur PowCh Prognos ETP BL ETP blue E [R] Ref E [R] nuclear fossil 224 coal lignite gas oil ccs coal ccs lignite ccs gas ccs renewables hydro biomass wind wind onshore wind offshore solar solar pv solar csp other res Prognos 2011 E [R] Adv ECF Ref ECF 80% RES ECF 60% RES ECF 40% RES Eur PowCh 140

163 A.5.3 Full load hours 2030 WEO Ref WEO 450ppm Table A-9: Full load hours in 2030 ETP BL ETP blue E [R] Ref E [R] nuclear fossil coal lignite gas oil ccs coal ccs lignite ccs gas ccs hydro biomass wind wind onshore wind offshore solar solar pv solar csp geothermal tidal and wave E [R] Adv ECF Ref Table A-10: Full load hours in 2050 ECF 80% RES ECF 60% RES ECF 40% RES EU Trends 2007 Eur PowCh Planets Prognos ETP BL ETP blue E [R] Ref E [R] E [R] Adv ECF Ref ECF 80% RES ECF 60% RES ECF 40% RES Eur PowCh Planets nuclear fossil coal lignite gas oil ccs coal ccs lignite ccs gas ccs hydro biomass wind wind onshore wind offshore solar solar pv solar csp geothermal tidal and wave Prognos

164 A.6 Approach (extended version) A.6.1 Working program / project steps The working program of the project consists of 4 project steps: e) Selection of scenario studies by specified criteria f) Identification of key factors and driving forces in the scenario studies g) What if. : The underlying stories and premises to make such a scenario happen h) Conditions and implications for nuclear power Figure A-1: Project steps A.6.2 Phase A: Selection of scenario studies by specified criteria Prognos 2009 Background and objective A variety of institutions, companies and universities set up midand long-term energy scenarios to reduce decision makers uncertainty and risk, or to at least teach them how to deal with these uncertainties. The purpose and the type of scenario vary in each case. In the first phase of the project out of the numerous existing scenario studies, the energy scenarios to be analyzed will be selected by multiple criteria detailed below. 142

165 Method and approach In general, scenarios serve different functions (or combinations of), which can be described as: 17 a signalling function a communication and learning function a legitimation function an exploring and explaining function a demonstration function There are also different kinds of scenarios. These can be distinguished by: 18 length (time horizon) direction of forecasting (forecasting vs. backcasting) qualitative vs. quantitative descriptive vs. normative level of aggregation (international, national, sector) and level of exploration (exploratory vs. explicatory). The energy scenarios to be selected have to fulfil several requirements (also according to the objective set by the contracting body). These criteria include: a minimum time horizon until 2030 the geographical coverage of the EU-27 (or Europe). quantifiability coverage of the electricity sector Further criteria for selection could be: type of model used (bottom-up vs. top-down). level of detail (first rating by Prognos) amount of sectors covered 17 See Rienstra (1998) 18 See Rits (2003) 143

166 being up to date (recently published / when possible, not older than 2007) international relevance (first rating by Prognos) The group of selected studies should also cover the main interest groups and stakeholders by whom or for whom the scenarios are set up. One or more of the following groups could or should be covered: business corporations or industry associations research institutes or universities international organisations governmental organisations non-governmental organizations While listing the scenario studies to be analysed in depth, the purpose and function of each scenario (study) are indicated as well. The chosen scenarios are selected in close cooperation with and have to be approved by the ENEF Competitiveness Subgroup. Outcome At the end of the first work package (about) eight comparable scenario studies are selected on the basis of the criteria catalogue set up. The selection took place in close cooperation with the ENEF Competitiveness Subgroup. A.6.3 Phase B: Identifying key factors and driving forces in the scenario studies Background and objective To set up and work out scenarios, different steps are generally followed. The general structure of the scenario methodology for model-based energy scenarios is: 19 a) Topic and aim b) Basic and future analysis c) Designing scenarios d) Analysis of model outcomes and working out scenarios 19 See Rits ( 2003), Ogilvy & Schwartz (1995), Schaeffer (1998) 144

167 e) Implications In part a) topic and aim the purpose and the framework of the scenarios are set. These aspects are basically covered by phase A of the study. In the steps b) basic and future analysis as well as c) designing scenarios the assumptions (e.g. main features of a technology and the trends of the technology development) as well the varying parameters (e.g. fuel price) to build scenarios are identified. In phase B of the research such assumptions (key factors in the micro-environment) and parameter variations (driving forces in the macro-environment) are systematically identified and listed. The analysis of the (different) outcomes of the scenario studies will be used to identify the strength of parameter inputs. The outcomes of the questionnaires of the ENEF Competiveness Subgroup are evaluated, discussed and used in this part of the study. Method and approach A wide range of input parameters are generally necessary to run the energy model. In figure A-2 these parameters are grouped into three categories: Socio-economic assumptions such as GDP, population and prices Technology assumptions such as efficiency, lifetime and capacity factor Policy assumptions such as renewable, efficiency or GHG-goals For most of the input parameters past, present and future data have to be set. Some of them are independent of the chosen scenario. Moreover, various input parameters are correlated to each other, such as population and GDP, so that these have to match. These parameters can vary significantly among the scenarios. When different scenarios are built within one study, in general two or three parameters are varied. These could be identified as the driving forces. Note that depending on the purpose of the scenario and the model used, a parameter can be in some cases an input and in other cases an output (e.g. GDP as output of economic top-down models). The (amount of) parameters also vary among the scenarios, depending on their level of detail and whether or not some aspects are demarcated, such as external costs. Besides, the (non-)availability of (valid) data is a relevant issue. 145

168 For every scenario to be analysed the main assumptions will be listed and set in contrast to each other (compare part indicators later on). Figure A-2: Possible input parameter * * Lists are not exhaustive Prognos 2009 The selection and the value of the input parameters, the parameter variation as well as the model used, determine the outcomes of the scenarios. Energy scenarios generally show the effect on e.g.: Primary energy supply Final energy consumption Electricity generation Installed capacity Emissions Investments (Consumer) Prices Economic structure and effects (GDP) Next to the input parameters, for every chosen scenario the main outcomes will be listed and set in contrast to each other (compare part indicators later on). The outcomes of the questionnaires of the ENEF Competitiveness Subgroup are being integrated in this part of the study. 146

169 Figure A-3: Output * * List is not exhaustive / depending on aim, model etc. Prognos 2009 To identify the relationship between model input and output and the differences between the models and scenarios, correlation factors or indicators can be used, such as: Unit per GDP Unit per capita % of total Index to base year or base unit etc. For this research these could be: Energy-intensity (per GDP / per capita) CO 2 -intensity (per GDP / per capita) Share of electricity Share of nuclear power (generation / installed capacity).etc. 147

170 A set of relevant indicators as well as correlation-factors (e.g. correlation GDP to primary energy supply) will be compiled. These factors will be crystallized during the research. Figure A-4: Indicators * * List is not exhaustive Prognos 2009 As mentioned before, the type of model used plays an important role in the scenario results. A distinction can be made between top-down- and bottom-up-models. Energy scenarios, such as those of the IEA and the EIA, use bottom-up-models. A second aspect of the model is how they optimize: economic equilibrium (generally top-down-models) least-costs least emissions etc. An important matter when optimizing models are the bounds to be set, for example the deployment rate of a technology (and with new technologies also the model starting year of these technologies). To analyse the power supply sector (nuclear development), it is important to understand the way new power capacities, especially nuclear ones, could emerge or are being implemented in the model outcomes. These could be set exogenously (fix) or free in the model (with or without max. or min. bounds). The market allocation in the (electricity) model is therefore to be analysed in particular. 148