Technologies and technology learning, contributions to IEA's Energy ...

August 2003

ECN-C--03-046

Technologies and technology learning, contributions to IEA's Energy Technology Perspectives

K.E.L. Smekens P. Lako A.J. Seebregts

Acknowledgement ECN wishes to thank IEA, and especially Dolf Gielen, for their willingness to grant this study to ECN Policy Studies. Also their contributions during the data gathering and analysis process throughout this whole study was very valuable. Furthermore, ECN wishes to thank Amit Kanudia of KanOrs for making the Western European database available for the review. The report is registered under project number 7.7427.01.01.

Abstract This report describes the technology data collection and analysis to set up a set of input data for the ETP MARKAL model of the IEA. The technology data is focussed on the setting up of learning curve parameters, using learning in clusters. The technology areas covered are electricity production, up stream oil and gas sector and CO2 capture. In addition, global but regionalised cost curves for biological CO2 storage in forestry activities are estimated and examined. Finally a comparison is made of the Western European database as set up for ETP and ECN’s own database covering the same area. The comparison looked in some key result indicators and tried to explain the differences.

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CONTENTS SUMMARY

5

1.

THE ENERGY TECHNOLOGY PERSPECTIVES (ETP) PROJECT 1.1 Decomposing and clustering 1.2 Global learning

7 7 8

2.

DATA 2.1 Upstream oil and gas 2.1.1 Introduction 2.1.2 Technologies 2.1.3 ETP data 2.2 Power production 2.2.1 Introduction 2.2.2 Technologies 2.2.3 ETP data 2.3 CO2 capture in the power sector 2.3.1 Introduction 2.3.2 Technologies 2.3.3 ETP data 2.4 CO2 capture in other sectors 2.4.1 Introduction 2.4.2 Industry 2.4.3 Conversion 2.5 Land Use, Land Use Change and Forestry (LULUCF) activities 2.5.1 Introduction 2.5.2 Processes 2.5.3 ETP data

10 10 10 10 11 13 13 13 14 25 25 25 26 30 30 30 31 33 33 33 35

3.

DATABASE REVIEW 3.1 Introduction 3.2 Quality assurance log file 3.3 Results 3.3.1 Primary energy supply 3.3.2 Final energy 3.3.3 Electricity 3.3.4 CO2 emissions

40 40 43 47 48 50 55 56

REFERENCES

58

LIST OF ABREVIATIONS

61

APPENDIX A PRODUCTION AND COST OVERVIEW UPSTREAM OIL AND GAS

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SUMMARY This report summarises the work ECN has carried out for the Energy Technology Perspectives (ETP) project of the International Energy Agency (IEA). The work has been concentrating on two major topics: data and regional database review. The basis of this exercise was the database of Western Europe as it was made available in May 2002. The structure and contents of this database has been used as guidance to determine the technology list and technology data development. For the data part, several areas have been explored in detail: the upstream oil and gas technologies, the power producing sector and the CO2 capture and sequestration technologies. The data for the technologies in these areas have been collected and reviewed and made ready for import into the regional model databases. From the start, the data structure was set up in order to be able to be used in the cluster approach for endogenous technology learning (ETL) in MARKAL. This approach has been previously developed by ECN. The learning part for the ETP model has been assumed to be global, i.e. the riding down on the learning curve (specific investment cost is dependent on the cumulative built up capacity) takes place on a single, global level. The learning components and parameters for technologies are identical for all regions, the balance of system costs are region dependent and for this latter, a common rule of thumb has been used, even if there were some, but far from complete, detailed regional data available. ETL data for technologies in the electricity producing sector, in the upstream oil and gas sector, for CO2 capture and storage has been researched. Additional cost curve data concerning the potential for CO2 storage in biological sinks (forestry activities) for different world regions is also provided. For the Western European database comparison, the ETP database has been checked against ECN’s own database for the same region. Results show considerable difference for a number of indicators such as primary energy level and mix, electricity production, final energy use and emissions. These differences can be traced back to model structure differences whereas others are data or assumptions dependent. Some indications where further analysis is needed are given.

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1.

THE ENERGY TECHNOLOGY PERSPECTIVES (ETP) PROJECT

In 2001, the International Energy Agency, IEA, launched a major modelling effort, the Energy Technology Perspectives project ETP. The objective was to complement the existing publications on the World Energy Outlook (IEA, WEO 2001) with a more technology detailed analysis. For this purpose, the IEA wanted to use as much as possible existing knowledge within its own environment and called upon ETSAP (Energy Technology Systems Analysis Programme) as one of the Implementing Agreements of the IEA. The various ETSAP partners participated on a different level to the establishment of the tool to be use for ETP. ECN participated in this project for elaboration on technology data, learning parameters and database review. This tool will be a global regionalised - 15 regions - model, based on the TIMES formulations (The Integrated MARKAL EFOM System). The regional models are built up starting from the energy balances of the IEA and using a MARKAL based technology conversion programme, developed by Haloa and KanOrs in Canada, resulting in a regional technology database which can be used in a MARKAL or in a TIMES model. For a brief description of these models see e.g. (Seebregts et al, 2001) and (Remme et al, 2001) respectively. The regional databases contain a common technology repository, with identical data like investment costs, availabilities, efficiencies, lifetimes, etc. In additional, regional specificities are taken up by using other model possibilities like constraints and ratios. The global model is complemented with a ‘technology manufacturing region’ that contains the learning part of the model. The learning in the model does not use the straightforward learning by technology, but uses a more complex and more correct approach of learning by components and clusters. The principle of learning remains, but a technology is now decomposed in its components. The latter have an individual learning curve. The components can be part of different technologies, meaning that there is spill-over of learning effects over these technologies and over the different regions. The learning in clusters’ approach has been previously developed by ECN (Seebregts et al, 1999 and 2001).

1.1

Decomposing and clustering

Decomposing and clustering is a way to enhance the learning capacity of a MARKAL type of model (technology bottom up optimisation). Using the following example, decomposing and clustering are explained. We have selected two technologies and tree components, all pure illustrative. In the rows of the table, the technologies are represented and in the columns, the components. The initial investment cost of the technology, as it used to be modelled in MARKAL before learning curve were endogenised, is given in the second column. In the column headed by Component the fraction of each component in the technology is represented. For example, technology B is a gas-fired combined cycle electricity production unit where per kWe capacity, 60% of the electricity is generated by a gas turbine and the remaining 40% by the steam turbine. This technology B is composed of 60 % of component j and 40% of component k. Each component has beside technical characteristics, also an investment cost on its own (per kWe). The summed product of the component investment costs and the fraction of each component in a technology adds up to what is called the total ETL part of the technology investment cost (ETL stands for Endogenous Technology Learning). The difference between the initial technology cost and the total ETL part is called the N-ETL part (non ETL) and comprises the non-learning part of the technology. The ratio between the ETL and N-ETL part of technologies ECN-C--03-046

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can vary substantially, depending on the choice of technologies and components. The more (well chosen) learning components are included in the model, the smaller the remaining N-ETL part will be. A cluster is defined as the group of technologies that have a learning component in common, a single technology can belong to different clusters. Table 1.1 Decomposing and clustering of technologies initial INVESTMENT cost Component i Component j Component k per unit without learning ETL part N-ETL part Technology A 1000 750 250 1 1 1500 310 1190 0.6 0.4 Technology B … … … … … … … 400 350 250 0.95 0.88 0.9

fraction of component in technology

INVESTMENT cost of component Progress Ratio PR

Of course, for each of the components all necessary parameters to establish a learning curve in the model (initial built up capacity, initial investment cost, maximal cumulative capacity, progress ratio) have to be provided. For existing components, historic data and statistics can be used to determine these values. For future components these are much more difficult to estimate. Experience from modelling learning curves (not only in a MARKAL model) has shown that the initial built up cumulative capacity is quite an important factor, since it determines how much additional capacity is needed to reach the first and also further doublings of capacity and consequently the reduction in specific investment cost. In order not to distort the speed of learning and the reduction of the investment cost by having a first doubling with only (too) little additional capacity, it is advisable to include the first commercial sized application of a future component in the initial capacity. This will ensure that future investment cost reduction will be based on similar sized applications for the capacity built up.

1.2

Global learning

For the ETP project as a whole, which will consist of 15 regional databases with partly specific and partly common technologies, the modelling of learning has been proposed as follows. Global learning is assumed, i.e. there are a number of global components with unique parameters and learning curves, but which use the capacity built up in each and all of the regions to determine the specific investment cost using the global totalled capacity. The following figure shows schematically how a multi-regional global model with learning could be set up: the regions (I, II, III, …) exchange commodities (wide arrows) and contains technologies (A, B, C, …); these technologies use components (ß, µ, …). As explained above, different technologies can use different components and belong to multiple clusters. Investment in particular regions will contribute to the global capacity built up of the components. To model this global capacity build-up, a special region is introduced where the technology components are manufactured. Investment in a technology in a particular physical region is then represented in the model as an increase of the capacity of the technology, and hence the associated components, in the manufacturing region. The increase in installed capacity results in a lowering of the specific investment costs of the component or components that constitute the technology. This lowering is communicated back to the physical region. The transfer of capacity and lower specific costs between manufacturing and physical region is depicted in the figure by the twoheaded narrow arrows between the two regions. Since the technologies are clustered, an investment in a specific technology may, through the advancement of its component(s), lead to spill-over in other technologies. Furthermore, the manufacturing region communicates the decrease of specific costs not only to the investing region, but also to the other regions in the model. Hence, the formulation with a manufacturing region facilitates the possible spill-over between regions.

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Energy Technology Manufacturing region (separate)

INV

Components Component ß (in A&B) Component µ (in B&C)

CAP

III

I Technology AI Technology BI Technology CI ...

Technology CIII ...

II

Technology AII Technology CII ... Figure 1.1 Technology manufacturing region (learning technologies) in a multi regional model structure

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2.

DATA

2.1

Upstream oil and gas

2.1.1 Introduction The upstream oil and gas sector, in short the exploration part of the fuel supply cycle, found itself high on the list of interested sectors for the ETP project. In this globalised sector many of the activities take place by multinational or joint venture companies and the sector is subject to a very high level of cost management. For this study a restriction to the offshore activities has been made, with the specific technologies used for fuel extraction. The variety and diversity of locations and fuel composition has introduced a number of advanced technologies. Unfortunately, the details on these technologies are almost always considered as confidential or are only published in specialised editions on oil and gas companies’ performance and activities. The project’s budget did not allow making use of these highly detailed figures; hence a more simple approach was followed. Moreover, next to the technology data, also data on the offshore oil and gas fields are necessary. These should not only contain information on existing areas (in production, in reserve or under development), but also give indications or estimates on areas that still have to be discovered.

2.1.2 Technologies The next figure shows a limited amount of current technologies used in the offshore area. Nowadays the exploitation technologies are often a combination of a number of technologies used in the offshore industry, like a floating production storage and offloading device (FPSO, a tanker with an extraction superstructure) or a deep-water platform in combination with sub sea systems. The deployment of the extraction technologies is obviously closely linked to the location and characteristics of the oil and gas fields. A distinction can be made between shallow, deep and ultra deep water, combined with different geological formations.

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Courtesy of the American Petroleum Institute.

Figure 2.1 Offshore oil and gas extraction technologies

2.1.3 ETP data For ETP we differentiate between three types of offshore extraction: an existing type, based on an average platform, an advanced type based on a combination of platform and sub sea systems and a future advanced type, based on a combination of a FPSO and subsea systems. The fuel supply for oil and gas in the ETP databases is provided through 3 options, namely localised reserves, reserves growth and new discoveries. Each of these options has three subdivisions, with an equal cumulative capacity but with a different cost, resulting in 9 possible supply or resource activities. The steps for the supply subdivisions have a relative cost of roughly 100, 130 and 155% respectively for the options localised reserves and reserves growth, for the option new discoveries, this is roughly 100, 120 and 140% respectively. Ideally, each of the supply options should correspond with an extraction location in order to link location and extraction technology realistically together. Unfortunately, the necessary effort to do so is quite substantial. A lot of information and estimates have to be distilled out and it is not yet incorporated in the databases. Hence, a simplified approach is used with sufficient techno-

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logical detail to model learning in the upstream oil and gas sector. The supply curves as modelled are replaced by these technological options. The following data has been assumed for the learning components in ETP: Table 2.1 Learning curve parameters for offshore extraction technologies PR Initial Investment [€98/PJ capacity/year] Platform 0.8 3.6 FPSO 0.85 2.03 Subsea 0.8 5.33

Life time [years] 30 30 30

Out of collected data, a further differentiation has been estimated regarding the output of the extraction technologies: 3 types can be distinguished 100% oil (light (OILLIG) or heavy oil (OILHEA)), 100% gas (GASNAT) and simultaneous 80% oil-20% gas (OILLIG or OILHEA GASNAT). The latter is an extraction mix that occurs quite often as can be derived from some detailed platform production information (see appendix). After defining the key components and specifying the associated parameters, we have to define the relation between components and technologies. This is done in the following cluster matrix for offshore upstream technologies. Note that as a consequence of the remarks in the previous paragraph, each technology has three possible combinations depending on the fuel output. However, because of lack of statistical or historical material, a simplification is introduced through the assumption that the costs of the combinations are assumed to be identical. Table 2.2 Cluster matrix for offshore upstream technologies Platform Component Technology Existing (3) - shallow Advanced (3) - shallow and middle depths Future advanced (3) -deep and ultra deep

FPSO

Subsea

0.8

0.6 0.2

1 0.4

The values in these matrices have been based on publicly available material, limited probably in coverage and aggregated for the Western European situation. For other model regions, like the Mexican Gulf or South East Asia, the numbers for the fuel output share and the fractions in the cluster matrix can be different. Regarding the deployment and competition between the different extraction technologies, it is necessary to estimate the initial capacity build up of each one per region, represented by a residual capacity, and to estimate the future share of each technology per supply option. As a rule of thumb, one could attribute the existing technology to Step 1, the existing and advanced to Step 2 and advanced and future advanced to Step 3 of the supply curve. For the localised reserves and reserves growth supply options, the existing and advanced technology seem to be the most likely options. For the new discoveries, the advanced and future advanced options are the ones to retain. This approach takes care of the technological evolution and of the likelihood that advanced technologies will be deployed preferentially for new exploitable or not yet discovered offshore oil and gas fields. Choice between the technologies is left to the model in order to optimise this part of the energy system. Introducing these technologies which cover all the costs after localisation and exploration of offshore oil and gas fields, means that the resources’ cost values have to be adjusted. A realistic estimate is to maintain 10% as the ‘geological’ costs involved in exploration and test drilling of possible sites.

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Table 2.3 Resource options in the ETP database Resource Natural gas (ground) - Located reserves - Step 1 Natural gas (ground) - Located reserves - Step 2 Natural gas (ground) - Located reserves - Step 3 Natural gas (ground) - Reserves growth - Step 1 Natural gas (ground) - Reserves growth - Step 2 Natural gas (ground) - Reserves growth - Step 3 Natural gas (ground) - New discovery - Step 1 Natural gas (ground) - New discovery - Step 2 Natural gas (ground) - New discovery - Step 3 Heavy oil (ground) - Located reserves - Step 1 Heavy oil (ground) - Located reserves - Step 2 Heavy oil (ground) - Located reserves - Step 3 Heavy oil (ground) - Reserves growth - Step 1 Heavy oil (ground) - Reserves growth - Step 2 Heavy oil (ground) - Reserves growth - Step 3 Heavy oil (ground) - New discovery - Step 1 Heavy oil (ground) - New discovery - Step 2 Heavy oil (ground) - New discovery - Step 3 Light oil (ground) - Located reserves - Step 1 Light oil (ground) - Located reserves - Step 2 Light oil (ground) - Located reserves - Step 3 Light oil (ground) - Reserves growth - Step 1 Light oil (ground) - Reserves growth - Step 2 Light oil (ground) - Reserves growth - Step 3 Light oil (ground) - New discovery - Step 1 Light oil (ground) - New discovery - Step 2 Light oil (ground) - New discovery - Step 3

2.2

ETP code

Original Cost [US$/GJ]

Adjusted Cost [US$/GJ]

MINGASNAT1 MINGASNAT2 MINGASNAT3 MINGASNAT4 MINGASNAT5 MINGASNAT6 MINGASNAT7 MINGASNAT8 MINGASNAT9 MINOILHEA1 MINOILHEA2 MINOILHEA3 MINOILHEA4 MINOILHEA5 MINOILHEA6 MINOILHEA7 MINOILHEA8 MINOILHEA9 MINOILLIG1 MINOILLIG2 MINOILLIG3 MINOILLIG4 MINOILLIG5 MINOILLIG6 MINOILLIG7 MINOILLIG8 MINOILLIG9

0.7393 0.9697 1.1137 0.9025 1.1713 1.3345 1.4497 1.9009 2.1889 0.9938 1.2958 1.5447 1.1548 1.5214 1.8118 1.9827 2.4406 2.8139 0.9938 1.2958 1.5447 1.1548 1.5214 1.8118 1.9827 2.4406 2.8139

0.0739 0.0970 0.1114 0.0903 0.1171 0.1335 0.1450 0.1901 0.2189 0.0994 0.1296 0.1545 0.1155 0.1521 0.1812 0.1983 0.2441 0.2814 0.0994 0.1296 0.1545 0.1155 0.1521 0.1812 0.1983 0.2441 0.2814

Power production

2.2.1 Introduction For ETP, the database already contains a number of technologies, most of them existing ones used to calibrate the model. The data collection of this part of the task by ECN concentrated on current and future technology options. The choice of technologies was made with the idea of a future decomposing and clustering in mind. The core of the work covered the establishment of the technology list and the provision of the technology data and parameters. A main source was ECN’s existing Western European MARKAL database (e.g. see Lako et al, 1998; Seebregts et al, 2000), complemented with edited data from other ETSAP partner’s databases (USDOE USA, KIER, South Korea, JAERI Japan, GERAD Canada). Further information was retrieved from various publications and reports (see references).

2.2.2 Technologies The technologies cover the whole of the power production, ordered by fuel consumption. However it has to be noticed that bi- or multi-fuel applications have not been included at this stage. A number of examples could be: co-firing of solid biomass in coal power plants, gas-coal bifuel power plants, recovery gas (cokes oven and blast furnace) plants, etc. Also hydrogen based power plant technology is underrepresented at this stage. For technology options available in the future, an approach directed more towards using generic types rather than very detailed tech-

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nologies has been followed. Where possible combined heat and power plants (CHP) have been included separately, however no heating plants are included. A variation of technologies by performance within a region, especially solar or hydro-based ones, has not been made. More analysis is required to determine the seasonal availability of such technologies. This latter can become important when the list of learning components (see further) is extended to include more mature technologies like boilers, steam turbines, etc.

2.2.3 ETP data For each technology the following parameters are provided in the next table: • The descriptive name of the technology. • Specific investment cost in US$/kWelectric for the start year mentioned. • The fixed operational and maintenance cost* in US$/kWelectric. • The variable operational and maintenance cost* in US$/kWh. • The efficiency, expressed in percent net electric yield, for CHP both the electric yield (suffix e) and the total (electricity + heat) (suffix t) are given. • The capacity factor, the yearly fraction that the technology is available, due to maintenance and outage or seasonal availability and intermittence. • The life in years. • The start year the technology becomes available for the model. • The peak contribution, the fraction with which the technology can contribute to the satisfaction of the peak electricity demand, is a measure for the flexibility and momentary availability of the technology. *

where ‘nd’ means that data is missing.

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36 42.75

45 57.5

1237 1620

10000 4860 2160 3150

7200

2070

1710

PV off-grid PV grid-connected Solar thermal power Solar tower

Fusion

Fission: Light Water Reactor Fission: Gas Cooled Reactor

27

1300

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35 75

1180 2500

Natural gas SOFC Decentral Natural gas SOFC CHP Fuel Cell CHP

14.5

3000

PEM fuel cell

28 15 30 32 37.5 30

1260 1354 1850 1900 2000 1200

Mini hydro Medium Hydro Large hydro Pumped storage Tidal power Geothermal

315

45 18 27 47

25 22 81

810 900 1200

FIXOM [$/kWe]

Wind onshore large Wind onshore small Wind onshore large with storage Wind near-shore (shallow) Wind offshore

Investment cost [$/kWe]

0.003

0.005 0.008

0.0055

nd

nd

nd

nd nd nd nd

nd nd

nd nd nd

VAROM [$/kWh]

0.63 0.55 electric 0.69 total 0.45 electric 0.80 total

0.50

0.80

0.35

0.33

Efficiency [%]

Table 2.4 Technical and economical data for electricity production technologies

0.75

0.75 0.85

0.9

0.466 0.5 0.392 0.8 0.23 0.7

0.85

0.85

0.675

0.2 0.15 0.25 0.6

0.34 0.375

0.24 0.24 0.36

Capacity factor

25

25 25

30

40 50 60 60 60 30

30

40

30

30 25 30 30

25 25

25 25 25

Life

2020

2010 2010

2010

2000 2000 2000 2000 2000 2000

2010

2000

2040

2000 2000 2000 2010

2000 2000

2000 2000 2010

Start year

1

1 0.1

1

0.3 0.9 0.6 1 0.15 0.9

1

1

1

0 0.075 0.15 0.4

0.13 0.15

0.1 0.1 0.25

Peak contribution

15

7 30

35 37 37 27 35 28

510

900

590

1300

540

362 825 1220

1125 1250 1260 900

1025 1315

Gas turbine CHP

Gas CC SOFC CHP

Gas CC CHP

Diesel engine Oil-fired conventional Oil IGCC

Coal-fired conventional Coal supercritical Coal ultra supercritical Fluidised Bed Combustion (FBC) Pressurised FBC Integrated Gasification Combined Cycle (IGCC)

16

8

510

2.4 20 30

11

55

20

12 21 10 8

800 390 810 510 510

Micro gas turbine Gas turbine, large Gas-fired conventional Gas Combined Cycle (CC) Gas Combined Cycle (CC) 2010 Gas Combined Cycle (CC) 2020 Gas Combined Cycle (CC) 2030 Gas engine CHP

25

FIXOM [$/kWe]

700

Investment cost [$/kWe]

Gas engine

Table 2.4 continued

0.002 0.008

0.002 0.002 0.002 0.002

0.001 0.001 0.002

0.003

0.005

0.0025

0.0025

0.002

0.002

0.0025 0.002 0.002 0.002

0.001

VAROM [$/kWh]

0.42 0.43

0.38 0.42 0.44 0.37

0.38 0.45 0.47

0.36 electric 0.885 total 0.36 electric 0.80 total 0.55 electric 0.80 total 0.43 electric 0.80 total

0.61

0.59

0.30 0.33 0.47 0.55 0.57

0.36

Efficiency [%]

0.80 0.80

0.75 0.75 0.75 0.80

0.68 0.75 0.75

0.8

0.8

0.8

0.45

0.8

0.8

0.9 0.9 0.85 0.8 0.8

0.65

Capacity factor

30 30

30 30 30 30

15 30 25

25

25

25

15

25

25

10 25 30 25 25

15

Life

2000 2000

2000 2005 2005 2000

2000 2000 2005

2000

2020

2000

2000

2030

2020

2005 2000 2000 2000 2010

2000

Start year

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1 1

1 1 1 1

0.9 1 1

1

0.8

1

1

1

1

1 1 1 1 1

1

Peak contribution

22

20

50 50

25 52

1315

1315

1790

1100

1850

1710

2250

7000 1600 2700 1900 640 2050

2960

1300

1430

FBC CHP

IGCC CHP

IGCC SOFC CHP

Solid waste incineration Biomass fired conventional Stirling engine with gasifier Biomass IGCC Bio oil (HTU) CC Biomass gasification CHP

Tomlinson +Bark boiler

Indirect Black Liquor GCC + bark boiler Ind Bl Liq GCC + bark GCC

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nd = data is missing

25

1315

Integrated Gasification Combined Cycle (IGCC) 2010 Integrated Gasification Combined Cycle (IGCC) 2020 Integrated Gasification Combined Cycle (IGCC) 2030 IGCC Solid Oxide Fuel Cell (SOFC) Steam turbine Coal CHP

62

30 200

67 43

54

36

110

FIXOM [$/kWe]

Investment cost [$/kWe]

Table 2.4 continued

0.003

0.003

0.006

0.01 0.002 0.004 0.015 0.015 0.02

0.015

0.01

0.0025

0.001

0.01

0.005

0.006

0.007

VAROM [$/kWh]

0.08 electric 0.48 total 0.18 electric 0.53 total 0.17 electric 0.37 total

0.25 0.38 0.20 0.35 0.52 0.40 electric 0.70 total

0.35 electric 0.80 total 0.25 electric 0.80 total 0.40 electric 0.75 total 0.45 electric 0.75 total

0.55

0.52

0.49

0.45

Efficiency [%]

0.75

0.75

0.75

0.75 0.75 0.6 0.75 0.75 0.75

0.75

0.75

0.80

0.85

0.75

0.80

0.80

0.80

Capacity factor

25

25

25

30 30 15 25 25 25

25

25

30

25

25

30

30

30

Life

2000

2000

2000

2000 2000 2005 2010 2005 2010

2020

2010

2000

2000

2020

2000

2000

2000

Start year

0.8

0.8

0.8

1 1 1 1 1 1

1

1

1

1

1

1

1

1

17

Peak contribution

The capacity factors quoted here, are technical availabilities. In reality yearly availability is very much dependent on the used dispatching system, e.g. in a liberalised market numbers are different than in a monopolistic or state controlled market. Latest figures for 1999 from VDEW, Germany (Ensoc, 2001) give the following: Nuclear: 7599 h/a or 0.867; lignite 7100 h/a or 0.811; hydro 5994 h/a or 0.684; coal 4645 h/a or 0.530 and gas 2260 h/a or 0.258. Wind achieved 1624 h/a or 0.185. In this first stage of the research for ETP, only a limited number of learning components will be included. The limit is not based on methodological grounds. It is rather based on practical reasons, the main one being the unknown complexity of the combination of a multi-region TIMES model with endogenous learning. To ensure an acceptable computational time, also in view of the number of scenarios what will be considered, the present number of components is chosen. The components are assumed to learn globally, and follow a single experience curve and hence share the same specific investment cost over all regions. The emphasis was put on renewable options with large potentials (Solar PV and wind turbine), promising fossil fuel technology options (gasifier and gas-based fuel cells) and some more classic and maturing technologies but still with a large world wide potential (gas turbines & combined cycle recovery boiler). No further disaggregation than to component level has been made, e.g. a wind turbine is not further split up into rotor, generator, transmission, tower, grid connection, etc. This level would require a much more detailed model than at this stage is developed. Other technologies, which were included in the SAPIENT project from which the learning data were retrieved (de Feber et al, 2002), are mentioned, but not used. The matrix of technologies (rows) and learning components (columns) is represented in the next table.

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Micro gas turbine Gas turbine, large Gas-fired conventional Gas Combined Cycle (CC) (all years)

Wind onshore large Wind onshore small Wind onshore large with storage Wind near-shore (shallow) Wind offshore PV off-grid PV grid-connected Solar thermal power Solar tower Fusion Fission: Light Water Reactor Fission: Gas Cooled Reactor Mini hydro Hydro medium Large hydro Pumped storage Tidal power Geothermal PEM fuel cell IGCC Solid Oxide Fuel Cell (SOFC) Natural gas SOFC Decentral nat gas SOFC CHP Fuel Cell CHP Gas engine

Technology

0.9

0.82 2000

PR

Start year

1 1

800

4000

Investment cost [$/ kWe]

1 1 1 1 1

2000

Wind turbine

Solar PV

Component

0.8 1 0.6

1 0.6

2010

0.82

1325

Fuel cell

Table 2.5 Cluster matrix learning components and technologies

1

2000

0.9

640

Gasifier

0.67

0.33

0.2

0.2 1 1

0.08

0.2

2000

0.95

450

CC boiler

0.12

0.2

2000

0.87

390

Gas turbine

1 0.33

0.2

0.08

1 0.2

1 1 1

1

2000

Steam turbine

1

2000

Boiler

1 1

2000

Nuclear reactor

1 1 1 1

2000

Hydro turbine

19

1 1 2 3

4 1 4 0

1 1 1 1 1 1 1 1 0 1 2 2 1 1 1 1 0 0 2 5

Number of components per technology

20

Total number that component occurs

Gas engine CHP Gas turbine CHP Gas CC SOFC CHP Gas CC CHP Diesel engine Oil-fired conventional Oil IGCC Coal-fired conventional Coal supercritical Coal ultra supercritical Fluidised Bed Combustion (FBC) Pressurised FBC Integrated Gasification Combined Cycle (IGCC) (all years) Steam turbine Coal CHP FBC CHP IGCC CHP IGCC SOFC CHP Solid waste incineration Biomass fired conventional Stirling engine Biomass IGCC bio oil CC Biomass gasification CHP Tomlinson +Bark boiler Indirect Black Liquor GCC + bark boiler Ind Black Liquor GCC + bark GCC

Technology

Table 2.5 continued

Component

2

Solar PV

5

Wind turbine

7

0.6

0.8

Fuel cell

0.6 0.2

1 1

18

0.6

1 10

0.6

0.8

1

0.6 0.67 0.6

0.6

1

1 1

0.6

1 0.12 0.67

Gas turbine

1

Gasifier

15

0.4

0.4

0.4 0.33 0.4

0.4 0.2

0.4

0.4

0.08 0.33

CC boiler

31

0.4

0.4

0.4 0.33 0.4

1 1 0.4 0.2 1 1

1 0.4 1 1 1 1 1 0.4

0.08 0.33

Steam turbine

9

1 0.2

1 1

1 1 1

1

Boiler

2

Nuclear reactor

4

1 1 4 5 2 2 1 4 3 4 1 5

0 1 4 3 0 2 4 2 2 2 1 1 4

ECN-C--03-046

4

Hydro turbine

Technologies which do not have a component attributed are either mature technologies with a small learning potential, e.g. engine technology, or are so specific that a separate set of components has to be included to cover this technology, e.g. tidal or geothermal or solar thermal power. For these latter, an approach in which these technologies are considered to learn as single technology, i.e. without components, can be an intermediate solution. The combination of the components and cluster matrix and the technology list results in the following cost table in which the investment costs for the ETL and non ETL part (N-ETL) for each technology are represented, expressed in US$/kWelectric. The N-ETL part can be considered as the investment cost remaining after all the learning component costs have been subtracted from the technology investment cost of the first year available. This remaining cost is regionally dependent, and assumed constant over time. For the regional differentiation, only the N-ETL part is considered, resulting in a smaller difference than the case where the overall investment cost (ETL+N-ETL) would be made regional dependent. The use of the global learning approach does not allow at this moment to use regional differentiated costs in the experience curve. In addition, for the final data set used in the model, the same approach as suggested in (Manne, 2002) can be used, i.e. to assume a constant improvement of a certain percentage, e.g. 0.5% per year for the N-ETL investment cost. For the N-ETL part a relative cost percentage for each region has been used. The original numbers had the USA set on 100%, for the values in the table a conversion has been made where the WEU number has been set on 100%. Both percentages are given in the table. The N-ETL part takes care of regional differences in investment costs for each technology. As can be deduced from the table, the share of ETL and N-ETL over the technologies can vary enormously, from 0% to 100% for both e.g. a large gas turbine has 100% ETL, a solar thermal system has 100% N-ETL. Of course, the more learning components are introduced, the more the N-ETL part will decrease in favour of a larger ETL part of the investment cost. Only for an ETL cost of non-zero, values have been filled in; rows that are blank mean a zero ETL cost for that specific technology. The ETL costs for the components were based on work by ECN for the TEEM and Sapient projects (Seebregts et al, 1998, 1999).

ECN-C--03-046

21

90 82 800 8 800 82 800 327 800 358 800 671 4000 4909 4000 704 1767 2577 5891

1694

1399 1031 1108 1514 1555 1636 982 1325 1374

110 100

800 10 800 100 800

400 800

437 800 820 4000 6000 4000 860

2160

3150

7200

2070

1710

1260

1354

1850

1900

2000

1200 1325 1675

Basic Relative % Used relative %

Wind onshore large ETL N-ETL Wind onshore small ETL N-ETL Wind onshore large ETL with storage N-ETL Wind near-shore ETL (shallow) N-ETL Wind offshore ETL N-ETL ETL PV off-grid N-ETL PV grid-connected ETL N-ETL Solar thermal power ETL N-ETL ETL Solar tower N-ETL ETL Fusion N-ETL Fission: Light Water ETL Reactor N-ETL Fission: Gas Cooled ETL Reactor N-ETL Mini hydro ETL N-ETL ETL Hydro medium N-ETL ETL Large hydro N-ETL Pumped storage ETL N-ETL ETL Tidal power N-ETL ETL Geothermal N-ETL PEM fuel cell ETL N-ETL

22

China

WEU

1364 1325 1910

2273

2159

2102

1539

1432

1943

2352

8182

3580

2455

497 800 932 4000 6818 4000 977

455 800

800 11 800 114 800

125 114

Australia

1527 1325 2127

2545

2418

2355

1724

1604

2176

2635

9164

4009

2749

557 800 1044 4000 7636 4000 1095

509 800

800 13 800 127 800

140 127

Japan

1091 1325 1524

1818

1727

1682

1231

1145

1555

1882

6545

2864

1964

398 800 745 4000 5455 4000 782

364 800

800 9 800 91 800

100 91

US

Table 2.6 Investment cost split in learning and non learning part

1091 1325 1524

1818

1727

1682

1231

1145

1555

1882

6545

2864

1964

398 800 745 4000 5455 4000 782

364 800

800 9 800 91 800

100 91

Korea

1091 1325 1524

1818

1727

1682

1231

1145

1555

1882

6545

2864

1964

398 800 745 4000 5455 4000 782

364 800

800 9 800 91 800

100 91

Canada

1200 1325 1675

2000

1900

1850

1354

1260

1710

2070

7200

3150

2160

438 800 820 4000 6000 4000 860

400 800

800 10 800 100 800

110 100

EEU

982 1325 1374

1636

1555

1514

1108

1031

1399

1694

5891

2577

1767

358 800 671 4000 4909 4000 704

327 800

800 8 800 82 800

90 82

India

1364 1325 1910

2273

2159

2102

1539

1432

1943

2352

8182

3580

2455

497 800 932 4000 6818 4000 977

455 800

800 11 800 114 800

125 114

rest of Asia

1364 1325 1910

2273

2159

2102

1539

1432

1943

2352

8182

3580

2455

497 800 932 4000 6818 4000 977

455 800

800 11 800 114 800

125 114

FSU

1364 1325 1910

2273

2159

2102

1539

1432

1943

2352

8182

3580

2455

497 800 932 4000 6818 4000 977

455 800

800 11 800 114 800

125 114

Africa

1091 1325 1524

1818

1727

1682

1231

1145

1555

1882

6545

2864

1964

398 800 745 4000 5455 4000 782

364 800

800 9 800 91 800

100 91

Mexico

ECN-C--03-046

1364 1325 1910

2273

2159

2102

1539

1432

1943

2352

8182

3580

2455

497 800 932 4000 6818 4000 977

455 800

800 11 800 114 800

125 114

Middle East

1364 1325 1910

2273

2159

2102

1539

1432

1943

2352

8182

3580

2455

497 800 932 4000 6818 4000 977

455 800

800 11 800 114 800

125 114

Latin America

190 1142 37 1325

1175 963 337

N-ETL ETL N-ETL ETL

N-ETL ETL N-ETL ETL N-ETL ETL N-ETL ETL N-ETL ETL

ECN-C--03-046

663 410 82 736 390 164 1142 129 410 107 296

675 1054 136

920 1023

1031

100

900 390 200 1142 157 410 130

362

825 1054 166

1125

1250

1260

573 390 335 390 0

964 963 276

155 1142 31 1325

1600

China

810 410

700 390 410 390 0

1600

WEU

ETL

N-ETL Gas Combined Cycle ETL (CC) (all years) N-ETL Gas engine CHP ETL N-ETL ETL Gas turbine CHP N-ETL Gas CC SOFC CHP ETL N-ETL Gas CC CHP ETL N-ETL ETL Diesel engine N-ETL ETL Oil-fired conventional N-ETL ETL Oil IGCC N-ETL Coal-fired ETL conventional N-ETL Coal supercritical ETL N-ETL ETL Coal ultra supercritical N-ETL

Gas-fired conventional

Gas turbine, large

Micro gas turbine

Gas engine

Fuel Cell CHP

Decentral nat gas SOFC CHP

Natural gas SOFC

IGCC Solid Oxide Fuel Cell (SOFC)

Table 2.6 continued

1432

1420

1278

938 1054 189

412

1023 390 227 1142 179 410 148

114

920 410

795 390 466 390 0

1340 963 384

216 1142 42 1325

1600

Australia

1604

1591

1432

1050 1054 211

461

1145 390 255 1142 200 410 165

127

1031 410

891 390 522 390 0

1492 963 428

242 1142 47 1325

1600

Japan

1145

1136

1023

750 1054 151

329

818 390 182 1142 143 410 118

91

736 410

636 390 373 390 0

1069 963 307

173 1142 34 1325

1600

US

1145

1136

1023

750 1054 151

329

818 390 182 1142 143 410 118

91

736 410

636 390 373 390 0

1069 963 307

173 1142 34 1325

1600

Korea

1145

1136

1023

750 1054 151

329

818 390 182 1142 143 410 118

91

736 410

636 390 373 390 0

1069 963 307

173 1142 34 1325

1600

Canada

1260

1250

1125

825 1054 166

362

900 390 200 1142 157 410 130

100

810 410

700 390 410 390 0

1175 963 337

190 1142 37 1325

1600

EEU

1031

1023

920

675 1054 136

296

736 390 164 1142 129 410 107

82

663 410

573 390 335 390 0

964 963 276

155 1142 31 1325

1600

India

1432

1420

1278

938 1054 189

412

1023 390 227 1142 179 410 148

114

920 410

795 390 466 390 0

1340 963 384

216 1142 42 1325

1600

rest of Asia

1432

1420

1278

938 1054 189

412

1023 390 227 1142 179 410 148

114

920 410

795 390 466 390 0

1340 963 384

216 1142 42 1325

1600

FSU

1432

1420

1278

938 1054 189

412

1023 390 227 1142 179 410 148

114

920 410

795 390 466 390 0

1340 963 384

216 1142 42 1325

1600

Africa

1432

1420

1278

938 1054 189

412

1023 390 227 1142 179 410 148

114

920 410

795 390 466 390 0

1340 963 384

216 1142 42 1325

1600

Middle East

23

1145

1136

1023

750 1054 151

329

818 390 182 1142 143 410 118

91

736 410

636 390 373 390 0

1069 963 307

173 1142 34 1325

1600

Mexico

1432

1420

1278

938 1054 189

412

1023 390 227 1142 179 410 148

114

920 410

795 390 466 390 0

1340 963 384

216 1142 42 1325

1600

Latin America

N-ETL ETL N-ETL ETL

N-ETL ETL

24

N-ETL Steam turbine Coal ETL CHP N-ETL ETL FBC CHP N-ETL IGCC CHP ETL N-ETL IGCC SOFC CHP ETL N-ETL ETL Solid waste incineration N-ETL ETL Biomass fired conventional N-ETL ETL Stirling engine N-ETL Biomass IGCC ETL N-ETL ETL bio oil CC N-ETL Biomass gasification ETL CHP N-ETL ETL Tomlinson +Bark boiler N-ETL ETL Indirect Black Liquor GCC + bark boiler N-ETL Ind Bl Liq GCC + ETL bark GCC N-ETL

Integrated Gasification Combined Cycle (IGCC) (all years)

Pressurised FBC

Fluidised Bed Combustion (FBC)

Table 2.6 continued

214

900 1514 1054 537 1603 532

5727

1309 640 1685 1054 695 450 189 1054 817

2427 926

307 1054 308

1100

1850 1054 656 1603 650

7000

1600 640 2060 1054 850 450 230 1054

996

2960 926

374 1054

376

841 1054

738

1031

China

261

1025 1054

900

1260

WEU

429

426 1054

3374 926

1135

1818 640 2341 1054 966 450 262 1054

7955

2102 1054 745 1603 739

1250

297

1169 1054

1026

1432

Australia

478

475 1054

3759 926

1265

2036 640 2622 1054 1082 450 292 1054

8909

2355 1054 835 1603 827

1400

332

1302 1054

1143

1604

Japan

342

340 1054

2694 926

906

1455 640 1873 1054 773 450 209 1054

6364

1682 1054 596 1603 591

1000

237

933 1054

819

1145

US

342

340 1054

2694 926

906

1455 640 1873 1054 773 450 209 1054

6364

1682 1054 596 1603 591

1000

237

933 1054

819

1145

Korea

342

340 1054

2694 926

906

1455 640 1873 1054 773 450 209 1054

6364

1682 1054 596 1603 591

1000

237

933 1054

819

1145

Canada

376

374 1054

2960 926

996

1600 640 2060 1054 850 450 230 1054

7000

1850 1054 656 1603 650

1100

261

1025 1054

900

1260

EEU

308

307 1054

2427 926

817

1309 640 1685 1054 695 450 189 1054

5727

1514 1054 537 1603 532

900

214

841 1054

738

1031

India

429

426 1054

3374 926

1135

1818 640 2341 1054 966 450 262 1054

7955

2102 1054 745 1603 739

1250

297

1169 1054

1026

1432

rest of Asia

429

426 1054

3374 926

1135

1818 640 2341 1054 966 450 262 1054

7955

2102 1054 745 1603 739

1250

297

1169 1054

1026

1432

FSU

429

426 1054

3374 926

1135

1818 640 2341 1054 966 450 262 1054

7955

2102 1054 745 1603 739

1250

297

1169 1054

1026

1432

Africa

342

340 1054

2694 926

906

1455 640 1873 1054 773 450 209 1054

6364

1682 1054 596 1603 591

1000

237

933 1054

819

1145

Mexico

ECN-C--03-046

429

426 1054

3374 926

1135

1818 640 2341 1054 966 450 262 1054

7955

2102 1054 745 1603 739

1250

297

1169 1054

1026

1432

Middle East

429

426 1054

3374 926

1135

1818 640 2341 1054 966 450 262 1054

7955

2102 1054 745 1603 739

1250

297

1169 1054

1026

1432

Latin America

2.3

CO2 capture in the power sector

2.3.1 Introduction CO2 capture and sequestration emerged as an important feature to be included in the ETP model and scenario calculations. At ECN, there were some rough modelling data available for capture options in the electricity production, in industry and in fuel conversion. They were modelled as CO2 removals from the input energy carriers in specific technologies able to include capture (IGCC, NGCC, H2 production, etc.). This approach was not sufficient to include CO2 capture and removal in the ETP model and therefore a more technology detailed approach has been chosen, closely linked to the list of electricity producing technologies. Data on CO2 capture is scarce, as there only exist few well-documented plants and plans. Therefore some very straightforward assumptions have been made, mainly based on expert views, in order to set up a working set of data (e.g. Herzog et al, 2001, IEA GHG, 2001).

2.3.2 Technologies The IEA Greenhouse Gas R&D Implementing Agreement has performed a lot of research on CO2 capture technologies and options for the electricity-producing sector. This input formed the basis of the data that was developed for ETP, together with work by Howard Herzog, MIT. Both researches foresee CO2 capture mainly from fossil fuel based electricity production, in particular natural and coal based. For each of the fuels several options to capture and remove the carbon from the fuel exist. They can be subdivided in a flue gas removal option or a pre-combustion type of removal where the carbon and hydrogen are separated before combustion and conversion to electricity.

Figure 2.2 Flue gas CO2 capture using absorption technology

ECN-C--03-046

25

Figure 2.3 Cryogenic capture in an oxycycle technology

2.3.3 ETP data The following types and technologies are retained for the ETP model: Coal-based: • Integrated coal Gasification Combined Cycle or IGCC with flue gas capture. • IGCC with input fuel capture. • Conventional advanced coal with flue gas capture. • Retrofit of existing conventional coal with flue gas capture. • IGCC Solid Oxide Fuel Cell (SOFC) with capture. Gas-based: • Natural gas Combined Cycle (NGCC) with flue gas capture. • Fuel cell with input gas capture. • Oxycycle with flue gas capture. • Natural gas high temperature turbine CHP with capture. IGCC with flue gas capture: The final flue gas is decarbonised using a combination of gas separation membranes and gas absorption membranes (with monoethanolamine or MEA). Absorption and adsorption options for flue gas decarbonisation are not particular well suited for IGCC, as the costs increase too much to be competitive. IGCC with input fuel capture: This technology offers decarbonisation of the fuel before combustion in a gas turbine. The synthesis gas (syngas) obtained after the gasifier is shifted to CO2 and H2. Because the syngas is under pressure, it is possible to use physical solvents that need less regeneration energy than chemical solvents. The absorption solvent is called Selexol. Desorption of CO2 is followed by compression and drying. Conventional advanced coal with flue gas capture and Retrofit of existing power plants: This plant decarbonises the flue gases by chemical absorption using amine after the desulphurisation unit before the stack. NGCC with flue gas capture The decarbonisation takes place after the heat recovery part and is performed with amine absorption.

26

ECN-C--03-046

This technology combines a natural gas combined cycle with a flue gas decarbonisation using cryogenic temperature to condense and separate CO2. Moreover this technology uses oxygen as combustion medium in stead of air. About 80% of this oxygen is recycled through the cryogenic unit. Table 2.7 Technical and economical data for electricity production technologies with CO2 capture Investment FIXOM [cost $/kWe] [$/kWe] IGCC with flue gas capture

2700

IGCC with input fuel capture (Selexol)

1730

Conventional advanced coal with flue gas capture

2040

Retrofit of existing conventional coal with flue gas capture

28

VAROM [$/kWh]

Efficiency [%]

Capacity Life Start year Peak CO2 removed [%] factor contribution

0.012

0.39

0.80

30

2010

1

85

0.011

0.36 (2010)

0.80

30

2010

1

88

0.80

30

2010

1

90

0.80

30

2010

1

90

0.48 (2040) 0.007

0.35 (2000) 0.40 (2020)

817

0.002

-9% (2000) -4% (2020)

IGCC SOFC with capture

2500

100

0.02

0.51

0.75

25

2020

1

95

NGCC with flue gas capture

1120

10

0.005

0.47 (2010)

0.75

25

2010

1

89

Nat gas Fuel cell with capture

1500

75

0.015

0.60

0.75

25

2010

1

95

Industrial HT gas turbine CHP with CO2 capture

1185

22

0.08

0.35 electric

0.85

25

2010

0.3

90

0.52 (2030)

0.80 total

The learning components for capture are the following, flue gas capture coal and gas based and input capture coal based. They are an aggregate of the different capture options (membranes, MEA absorption, pressure or temperature swing adsorption). The components are kept at this aggregate level because the uncertainty in data and the model database structure makes it very hard to distinguish between the different options. The progress ratios are derived from a few data sets available that present a future outlook on investment costs of power plants with CO2 capture (Herzog et al, 2002). Table 2.8 Learning curve parameters PR Flue gas capture coal based Flue gas capture gas based Input fuel capture coal based

ECN-C--03-046

0.8 0.75 0.70

Initial Investment [€/kWe] 817 595 430

Life time [years] 30 30 30

Start year 2010 2010 2010

27

28

IGCC with flue gas capture IGCC with input fuel capture (Selexol) Conventional advanced coal with flue gas capture Retrofit of existing conventional coal with flue gas capture IGCC SOFC with capture NGCC with flue gas capture Nat gas Fuel cell with capture Industrial HT gas turbine CHP with CO2 capture

Technology 0.6 0.6

0.2 0.67 0.12 1

1

0.2 0.33 0.08

0.6 0.8

0.2 0.33 0.08

1

1

1

1

0.4 0.4

1

0.4 0.4

1

1

1

ECN-C--03-046

Gas turbine Steam turbine Fuel cell CC boiler Flue gas capture Flue gas capture Input fuel capture Oxycycle (SOFC) coal-based gas-based coal-based cryogenic unit

1 1

Component Gasifier

Table 2.9 Summarising cluster matrix

829 1484

246 817

1223 817

0 2198

302 1005

100 1142

357 985

200

N-ETL ETL

N-ETL ETL

N-ETL ETL

N-ETL ETL

N-ETL ETL

N-ETL ETL

N-ETL ETL

N-ETL

IGCC with flue gas capture

ECN-C--03-046

Industrial HT gas turbine CHP with CO2 capture

Nat gas Fuel cell with capture

NGCC with flue gas capture

IGCC SOFC with capture

Retrofit of existing conventional coal with flue gas capture

Conventional advanced coal with flue gas capture

IGCC with input fuel capture (Selexol)

100

1871

ETL

Used relative %

WEU

164

292 985

82 1142

247 1005

0 2198

1001 817

201 817

678 1484

1871

82

China

227

406 985

114 1142

343 1005

0 2198

1390 817

280 817

942 1484

1871

114

Australia

255

454 985

127 1142

384 1005

0 2198

1557 817

313 817

1055 1484

1871

127

Japan

182

325 985

91 1142

275 1005

0 2198

1112 817

224 817

754 1484

1871

91

US

Table 2.10 Investment cost split in learning and non learning part

182

325 985

91 1142

275 1005

0 2198

1112 817

224 817

754 1484

1871

91

Korea

182

325 985

91 1142

275 1005

0 2198

1112 817

224 817

754 1484

1871

91

Canada

200

357 985

100 1142

302 1005

0 2198

1223 817

246 817

829 1484

1871

100

EEU

164

292 985

82 1142

247 1005

0 2198

1001 817

201 817

678 1484

1871

82

India

227

406 985

114 1142

343 1005

0 2198

1390 817

280 817

942 1484

1871

114

rest of Asia

227

406 985

114 1142

343 1005

0 2198

1390 817

280 817

942 1484

1871

114

FSU

227

406 985

114 1142

343 1005

0 2198

1390 817

280 817

942 1484

1871

114

Africa

227

406 985

114 1142

343 1005

0 2198

1390 817

280 817

942 1484

1871

114

Middle East

182

325 985

91 1142

275 1005

0 2198

1112 817

224 817

754 1484

1871

91

Mexico

29

227

406 985

114 1142

343 1005

0 2198

1390 817

280 817

942 1484

1871

114

Latin America

2.4

CO2 capture in other sectors

2.4.1 Introduction Not only in the power generation there are relative large and condensable flows of CO2, also in certain industrial and fuel conversion processes, CO2 is released in large and concentrated amounts, but not necessary from fuel combustion like in the power sector. These emissions also are available for sequestration. For ETP we limit the data to the technologies already incorporated in the Western European MARKAL database of ECN. Much more detail has to be put into the ETP database to cover the industrial processes and their alternatives and substitution possibilities.

2.4.2 Industry Table 2.11 Technical and economical data for ammonia production

INVESTMENT FIXOM VAROM AF INPUT Fuel

[PJ/Mton]

START YEAR Life CO2 produced CO2 removed

[years] [Mton/Mton] [Mton/Mton]

Ammonia production conventional

Ammonia production advanced

330 4 6 0.95 1 27.3

370 4 6 0.95 0.9 24

2020 25 1.5315 1.5

2020 25 1.3464 1.3

[$/ton] [$/ton] [$/ton] electricity gas

Table 2.12 Technical and economical data for blast furnace Blast furnace with coal injection Input

[GJ/t]

coal 15 cokes 5 electricity 0.55 fuel oil 0.1 bfg 1.9 [$/ton] 150 [ton/ton] 1.508 [ton/ton] 0.077

Output INV CO2 net CO2 process Total CO2 [ton/ton]

30

2000

1.585

2020 Blast furnace with coal injection and CO2 removal H2 prod

2010

2020

5 Input 6.6 0.55 0.1 1.9 150 0.744 Output 0.077

15 5 1.16 0.1 1.77 1.42 0 1.62

5 6.6 0.71 0.1 1.56 1.25 0 1.62

240 1.968 0.077 2.045 1.869

240 1.204 0.077 1.281 1.143

0.821 INV CO2 net CO2 process Total CO2 CO2 capture

[GJ/t]

[$/ton] [ton/ton] [ton/ton] [ton/ton] [ton/ton]

coal cokes electricity fuel oil LTS HTS bfg h2

ECN-C--03-046

Table 2.13 Technical and economical data for CCF and COREX CCF Input

output INV CO2 net CO2 process Total CO2

[GJ/t]

coal cokes electricity fuel oil bfg

[$/ton] [ton/ton] [ton/ton] [ton/ton]

2000

COREX

20.1 0 0.5 0 4 200 0.921 0.077 0.998

input

2010

COREX with CO2 removal

20.1 0 1.06 0 0 5.67 245 1.889 0.077 1.966 1.795

input

CCF with CO2 removal input

[GJ/t]

output INV CO2 net CO2 process Total CO2 CO2 capture

coal cokes electricity fuel oil bfg h2

[$/ton] [ton/ton] [ton/ton] [ton/ton] [ton/ton]

output INV CO2 net CO2 process Total CO2

output INV CO2 net CO2 process Total CO2 CO2 capture

2000 [GJ/t]coal 29 cokes 0 electricity 0 fuel oil 0 bfg 0 [$/ton] 200 [ton/ton] 2.726 [ton/ton] 0.15 [ton/ton] 2.876 2010

[GJ/t]coal 29 cokes 0 electricity 1.53 fuel oil 0 bfg 0 h2 9.07 [$/ton] 270 [ton/ton] 2.726 [ton/ton] 0.15 [ton/ton] 2.876 [ton/ton] 2.590

Table 2.14 Technical and economical data for DRI DRI input

INV CO2 net CO2 process Total CO2

[GJ/t]

[$/ton] [ton/ton] [ton/ton] [ton/ton]

coal cokes electricity nat. gas

2000

2020

DRI with CO2 removal

0 0 0.7 11 100 0.617 0.077 0.694

0 0 0.5 10 100 0.561 0.077 0.638

input

INV CO2 net CO2 process Total CO2 CO2 capture

[GJ/t]coal cokes electricity nat gas [$/ton] [ton/ton] [ton/ton] [ton/ton] [ton/ton]

2010

2020

0 0 0.90 11 101 0.6171 0.077 0.694 0.586

0 0 0.68 10 101 0.561 0.077 0.638 0.533

2.4.3 Conversion Table 2.15 Technical and economical data for H2 production technologies Electrolysis, general, no distinction for wind or solar hydrogen production INV [$/GJ] 30 FIXOM [$/GJ] 0.95 VAROM [$/GJ] 0 AF 0.85 INP [PJ/PJ] electricity OUTP [PJ/PJ] H2 Start year 2000 [Mton/PJ] 0 CO2 reduced life 30

ECN-C--03-046

1 0.8

31

Table 2.15 continued Natural gas to H2 INV [$/GJ] FIXOM [$/GJ] VAROM [$/GJ] AF INP [PJ/PJ] OUTP [PJ/PJ] Start year [Mton/PJ] CO2 reduced life Natural gas to H2 with CO2 removal INV [$/GJ] FIXOM [$/GJ] VAROM [$/GJ] AF INP [PJ/PJ] OUTP Start year CO2 reduced life Coal to H2 INV FIXOM VAROM AF INP OUTP Start year CO2 reduced life Coal to H2 with CO2 removal INV FIXOM VAROM AF INP OUTP Start year CO2 reduced life

32

10 0.56 0 0.95 nat. gas H2 2000 0 20 12.5 0.56 0 0.95 nat. gas electricity H2 2020

[PJ/PJ] [Mton/PJ]

[$/GJ] [$/GJ] [$/GJ]

[$/GJ] [$/GJ] [$/GJ] [PJ/PJ] [PJ/PJ] [Mton/PJ]

1 0.017 0.81

-0.055 20 33.5 1.5 0.2 0.95

[PJ/PJ] [PJ/PJ] [Mton/PJ]

1 0.81

Coal H2 2000

1 0.63

0 20 36 1.74 0.22 0.95 Coal electricity H2 2020 -0.0893 20

1 0.017 0.63

ECN-C--03-046

2.5

Land Use, Land Use Change and Forestry (LULUCF) activities

2.5.1 Introduction During the discussions held at several meetings between the IEA and the contributors to the ETP process, introduction and elaboration of CO2 storage in the global model came forward as a priority issue. ECN agreed to take the lead in determining the possibilities of the storage by LULUCF at this stage in the model development. As there is at this moment still a large amount of uncertainty surrounding CO2 storage by LULUCF, especially in modelling exercises, the following should be regarded as preliminary. In ETP, there can only be a simplified representation of CO2 removal by LULUCF because the model structure does only contain the energy system, a full material and land area system is not included. This means that for the model there is no competition on available land between e.g. agriculture, forestry and buildings/construction. This leads to a modest model representation of LULUCF taking into account regional differences in uptake potential, but with abstraction of the physical processes causing uptake or release of CO2.

2.5.2 Processes The numbers provided here for the ETP undertaking are derived from a study performed by RFF, “Estimating Carbon Supply Curves for Global Forests and Other Land Uses” (Sedjo et al, 2001). This study gives a regionalised overview of added carbon stock (cumulative carbon supply curves) under several C prices. This analysis results in this global evolution of the carbon stock supply curve, expressed as CO2: 3400 3300

Gton CO2

3200

price 4 price 3 price 2 price 1 base

3100 3000 2900 2800 2700 2600 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Figure 2.4 Global CO2 stock supply curve in [Gton CO2] For ETP, an approach in which each region is provided with four price categories of carbon uptake, is chosen. The division into four categories results from the analysis of all the scenario results, where all supply curves were compared and combined by region to establish regional supply curves that take into account different levels of storage at different price levels. Proposed is to model CO2 storage by region as an activity, which can take up a maximum amount of CO2 at a specific price level, both variable over time.

ECN-C--03-046

33

In the base case, the CO2 stock is reduced globally by 107 Gton CO2 by 2100. The C-price scenarios introduce additional CO2 stocks over time. As ETP only runs till 2050, further analysis will be reduced to this period, although extension in time is possible at a later stage. In the study, 9 regions are distinguished, compared to the 15 of ETP. Further disaggregation rules to cover each ETP region still have to be worked out. A simplified way would be to split up the results given here by area (surface) for the ETP regions that are comprised in a mentioned region. Table 2.16 Annual Additional CO2 stock in [Gton CO2] by region and for each price category1 2000 2010 2020 2030 2040 2050

2000 2010 2020 2030 2040 2050

2000 2010 2020 2030 2040 2050

2000 2010 2020 2030 2040 2050

cost

EU

NA

SA

FSU

CHIN

IND

OCE

ASPA

AFR

total

5.45 5.45 5.45 5.45 5.45 5.45

0.00 0.40 0.92 1.10 1.28 2.20

0.00 1.47 2.93 3.67 3.67 5.50

-0.37 7.33 7.33 7.33 11.00 11.00

-0.29 1.47 2.20 2.20 2.57 3.67

0.00 0.73 0.73 1.47 1.47 1.47

0.00 0.07 0.07 0.07 0.18 0.18

0.00 0.37 0.37 0.37 0.37 0.73

-0.11 4.95 7.33 7.33 7.33 11.00

-0.07 3.67 3.67 3.67 7.33 11.00

-0.84 20.46 25.56 27.21 35.20 46.75

cost

EU

NA

SA

FSU

CHIN

IND

OCE

ASPA

AFR

total

13.64 13.64 13.64 13.64 19.09

0.95 0.92 1.65 2.38 3.12

0.73 1.83 4.40 5.87 9.17

3.67 5.50 9.17 15.40 25.67

6.60 6.97 5.13 6.97 8.98

0.37 0.37 2.20 5.50 2.57

0.11 0.11 0.20 0.26 0.37

0.26 0.37 0.44 1.10 1.10

1.83 5.50 11.00 20.17 29.33

3.67 4.77 7.33 10.63 16.50

0.00 18.19 26.33 41.53 68.27 96.80

cost

EU

NA

SA

FSU

CHIN

IND

OCE

ASPA

AFR

total

24.55 24.55 27.27 27.27 27.27

0.26 1.83 2.46 2.24 2.02

4.77 7.70 13.57 12.47 12.83

5.50 15.40 23.83 24.93 22.00

2.20 1.47 4.03 2.57 2.38

1.47 2.57 3.48 4.03 5.87

0.00 0.37 0.42 0.42 0.55

0.11 0.73 1.72 1.28 1.83

3.67 7.70 13.20 12.83 12.83

1.47 11.37 18.33 21.63 20.17

0.00 19.43 49.13 81.05 82.41 80.48

cost

EU

NA

SA

FSU

CHIN

IND

OCE

ASPA

AFR

total

37.09 47.73

1.80 3.23

6.97 12.47

3.67 7.33

3.30 5.13

2.57 7.33

0.37 0.59

1.28 1.83

13.20 18.33

5.50 11.00

0.00 0.00 0.00 0.00 38.65 67.25

Total possible cumulative additional CO2 stock in [Gton CO2] 2000 2010 2020 2030 2040 2050 1

EU

NA

SA

FSU

CHIN

IND

OCE

ASPA

AFR

total

0.00 1.61 3.67 5.21 7.70 10.56

0.00 6.97 12.47 21.63 28.97 39.97

-0.37 16.50 28.23 40.33 55.00 66.00

-0.29 10.27 10.63 11.37 15.40 20.17

0.00 2.57 3.67 7.15 13.57 17.23

0.00 0.18 0.55 0.70 1.23 1.69

0.00 0.73 1.47 2.53 4.03 5.50

-0.11 10.45 20.53 31.53 53.53 71.50

-0.07 8.80 19.80 29.33 45.10 58.67

-0.84 58.08 101.02 149.78 224.53 291.28

Each table contains the additional stock above the previous price level, the cost is expressed in US$/ton CO2

34

ECN-C--03-046

2.5.3 ETP data For ETP, this carbon stock has to be converted firstly to annual stock increase and secondly to an annual carbon uptake rate. Based on experiences with the ANSWER version of the model, cumulative CO2 uptakes cannot be handled directly. Indeed for CO2 uptake in LULUCF, an annual uptake is not sufficient; uptakes are sequestered for long time in LULUCF products (forests, soils, etc.). This means that the (additional to a base case) CO2 stock in a certain year is composed of CO2 stored in previous periods and the CO2 stored in that particular year. For the ETP modelling effort, which runs up to 2050, we assume that the carbon is not released and stored perpetually in the system. This approach is not as such valid for runs with a longer time horizon, further research and analysis is needed. As can be seen from the table, some regions see a decline of added CO2 stock between two periods, meaning that there is a release. At this moment such effects, even if they may be of considerable impact, will be levelled out. One way to deal with CO2 storage model wise is to create for each year and cost, a process that can take up the annual amount of CO2 per period as determined above. This may be too cumbersome to do so, hence a simplified approach is taken in which per region the incremental storage is determined and an annual CO2 uptake is derived from this. This leads to the above-mentioned cumulative CO2 stocks per year. A zeroing of negative increments was also done. This incremental storage can be translated into a maximal annual CO2 uptake. However, this assumes that the model will follow this path anyway, if the model decides only to store carbon later on, it cannot use unused storage capacity from previous periods, model experiments will have to show whether this happens. In this case the modelling approach will have to be adjusted to become more flexible and to incorporate a transfer of unused storage capacity over consecutive periods. Table 2.17 Incremental CO2 storage in [Gton] between periods with zeroing of negatives 1

2000 2010 2020 2030 2040 2050 Cum total

2000 2010 2020 2030 2040 2050 Cum total

Cost [$/ton CO2]

EU

NA

SA

FSU

CHIN

IND

OCE

ASPA

AFR

total

5.45 5.45 5.45 5.45 5.45 5.45

0.00 0.40 0.51 0.18 0.18 0.92 2.20

0.00 1.47 1.47 0.73 0.00 1.83 5.50

0.00 7.70 0.00 0.00 3.67 0.00 11.37

0.00 1.76 0.73 0.00 0.37 1.10 3.96

0.00 0.73 0.00 0.73 0.00 0.00 1.47

0.00 0.07 0.00 0.00 0.11 0.00 0.18

0.00 0.37 0.00 0.00 0.00 0.37 0.73

0.00 5.06 2.38 0.00 0.00 3.67 11.11

0.00 3.74 0.00 0.00 3.67 3.67 11.07

0.00 21.30 5.10 1.65 7.99 11.55 47.59

Cost [$/ton CO2]

EU

NA

SA

FSU

CHIN

IND

OCE

ASPA

AFR

total

0.00 13.64 13.64 13.64 13.64 19.09

0.00 0.95 0.00 0.73 0.73 0.73 3.15

0.00 0.73 1.10 2.57 1.47 3.30 9.17

0.00 3.67 1.83 3.67 6.23 10.27 25.67

0.00 6.60 0.37 0.00 1.83 2.02 10.82

0.00 0.37 0.00 1.83 3.30 0.00 5.50

0.00 0.11 0.00 0.09 0.06 0.11 0.37

0.00 0.26 0.11 0.07 0.66 0.00 1.10

0.00 1.83 3.67 5.50 9.17 9.17 29.33

0.00 3.67 1.10 2.57 3.30 5.87 16.50

0.00 18.19 8.18 17.03 26.75 31.46 101.60

ECN-C--03-046

35

Table 2.17 continued Cost [$/ton CO2] 2000 2010 2020 2030 2040 2050 Cum total

2000 2010 2020 2030 2040 2050 Cum total 1

EU

NA

SA

0.00 0.26 1.58 0.62 0.00 0.00 2.46

0.00 4.77 2.93 5.87 0.00 0.37 13.93

0.00 5.50 9.90 8.43 1.10 0.00 24.93

Cost [$/ton CO2]

EU

NA

SA

FSU

CHIN

IND

OCE

0.00 0.00 0.00 0.00 37.09 47.73

0.00 0.00 0.00 0.00 1.80 1.43 3.23

0.00 0.00 0.00 0.00 6.97 5.50 12.47

0.00 0.00 0.00 0.00 3.67 3.67 7.33

0.00 0.00 0.00 0.00 3.30 1.83 5.13

0.00 0.00 0.00 0.00 2.57 4.77 7.33

0.00 0.00 0.00 0.00 0.37 0.22 0.59

0.00 0.00 0.00 0.00 1.28 0.55 1.83

0.00 24.55 24.55 27.27 27.27 27.27

FSU 0.00 2.20 0.00 2.57 0.00 0.00 4.77

CHIN 0.00 1.47 1.10 0.92 0.55 1.83 5.87

IND 0.00 0.00 0.37 0.06 0.00 0.13 0.55

OCE 0.00 0.11 0.62 0.99 0.00 0.55 2.27

ASPA

AFR

total

0.00 1.47 9.90 6.97 3.30 0.00 21.63

0.00 19.43 30.43 31.92 4.95 2.88 89.61

ASPA

AFR

total

0.00 0.00 0.00 0.00 13.20 5.13 18.33

0.00 0.00 0.00 0.00 5.50 5.50 11.00

0.00 0.00 0.00 0.00 38.65 28.60 67.25

0.00 3.67 4.03 5.50 0.00 0.00 13.20

Italic underlined numbers where applied

The total difference between this, with zeroing of negative increments, and the original data, lies in the order of 5%. The maximum annual uptake (Mton CO2/year) per price level is as shown in the following tables, a zero value means that the CO2 stock does not increase during that period. The numbers take into account all effects underlying the original RFF study and some mathematical corrections for the ETP modelling, especially the split of the regions EU and North America in Eastern and Central Europe (EEU) and Western Europe (WEU) and in the USA and Canada respectively. A rule using land area has been used to estimate the potential additional CO2 uptake, for the EEU in addition the FSU uptake ratio has been used. For Korea, Japan and the Middle East, it is assumed that there is no possibility for CO2 uptake in LULUCF at this stage. Their potential is set to zero. Table 2.18 Maximum annual CO2 uptake in Mton/year (cost is expressed in $/ton CO2)1 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

36

Cost EEU

WEU

USA Canada

5.45 0.00 5.45 5.72 5.45 11.44 5.45 8.10 5.45 4.77 5.45 2.38 5.45 0.00 5.45 1.19 5.45 2.38 5.45 4.77 5.45 7.15

0.00 14.45 28.89 37.73 46.57 32.45 18.33 17.14 15.95 50.23 84.52

0.00 35.52 71.04 71.04 71.04 53.28 35.52 17.76 0.00 44.40 88.80

0.00 37.81 75.63 75.63 75.63 56.72 37.81 18.91 0.00 47.27 94.53

SA

FSU CHIN IND OCE ASPA

AFR

total

0.00 0.00 0.00 0.00 0.00 0.00 0.00 385.00 88.00 36.67 3.67 18.33 253.00 187.00 770.00 176.00 73.33 7.33 36.67 506.00 374.00 385.00 124.67 36.67 3.67 18.33 372.17 187.00 0.00 73.33 0.00 0.00 0.00 238.33 0.00 0.00 36.67 36.67 0.00 0.00 119.17 0.00 0.00 0.00 73.33 0.00 0.00 0.00 0.00 183.33 18.33 36.67 5.50 0.00 0.00 183.33 366.67 36.67 0.00 11.00 0.00 0.00 366.67 183.33 73.33 0.00 5.50 18.33 183.33 366.67 0.00 110.00 0.00 0.00 36.67 366.67 366.67

0.00 1065.17 2130.33 1320.00 509.67 337.33 165.00 482.17 799.33 977.17 1155.00

ECN-C--03-046

Table 2.18 continued 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Cost EEU

WEU

13.64 0.00 13.64 21.45 13.64 42.90 13.64 22.64 13.64 2.38 13.64 1.19 13.64 0.00 13.64 5.96 13.64 11.92 16.36 12.51 19.09 13.11

0.00 26.22 52.43 25.03 0.00 35.48 73.33 67.38 61.42 60.82 60.23

Cost EEU

WEU

1

0.00 17.76 35.52 44.40 53.28 88.80 124.32 97.68 71.04 115.44 159.84

SA

0.00 0.00 18.91 183.33 37.81 366.67 47.27 275.00 56.72 183.33 94.53 275.00 132.34 366.67 103.98 495.00 75.63 623.33 122.89 825.00 170.161026.67

USA Canada

SA

24.55 0.00 0.00 0.00 0.00 0.00 24.55 7.15 5.68 115.44 122.89 275.00 24.55 14.30 11.37 230.89 245.78 550.00 24.55 7.15 84.52 186.48 198.52 770.00 24.55 0.00 157.67 142.08 151.25 990.00 25.91 8.34 101.66 213.13 226.88 916.67 27.27 16.68 45.65 284.17 302.50 843.33 27.27 8.34 22.83 142.08 151.25 476.67 27.27 0.00 0.00 0.00 0.00 110.00 27.27 0.00 0.00 8.88 9.45 55.00 27.27 0.00 0.00 17.76 18.91 0.00 Cost EEU WEU

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

USA Canada

37.09 0.00 37.09 0.00 37.09 0.00 37.09 0.00 37.09 0.00 37.09 0.00 37.09 0.00 37.09 10.73 37.09 21.45 42.41 16.68 47.73 11.92

0.00 0.00 0.00 0.00 0.00 0.00 0.00 79.11 158.22 144.65 131.08

USA Canada 0.00 0.00 0.00 0.00 0.00 0.00 0.00 168.72 337.45 301.93 266.41

0.00 0.00 0.00 0.00 0.00 0.00 0.00 179.61 359.22 321.41 283.59

SA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 183.33 366.67 366.67 366.67

FSU CHIN IND OCE ASPA

AFR

total

0.00 91.67 183.33 275.00 366.67 458.33 550.00 733.33 916.67 916.67 916.67

0.00 183.33 366.67 238.33 110.00 183.33 256.67 293.33 330.00 458.33 586.67

0.00 909.33 1818.67 1318.17 817.67 1260.42 1703.17 2189.00 2674.83 2910.42 3146.00

FSU CHIN IND OCE ASPA

AFR

total

0.00 183.33 366.67 385.00 403.33 476.67 550.00 275.00 0.00 0.00 0.00

0.00 73.33 146.67 568.33 990.00 843.33 696.67 513.33 330.00 165.00 0.00

0.00 971.67 1943.33 2493.33 3043.33 3117.58 3191.83 1843.42 495.00 391.42 287.83

FSU CHIN IND OCE ASPA

AFR

total

0.00 0.00 0.00 0.00 0.00 0.00 0.00 275.00 550.00 550.00 550.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 1932.33 3864.67 3362.33 2860.00

0.00 0.00 330.00 18.33 660.00 36.67 348.33 18.33 36.67 0.00 18.33 91.67 0.00 183.33 91.67 256.67 183.33 330.00 192.50 165.00 201.67 0.00

0.00 0.00 110.00 73.33 220.00 146.67 110.00 128.33 0.00 110.00 128.33 100.83 256.67 91.67 128.33 73.33 0.00 55.00 0.00 119.17 0.00 183.33

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 165.00 128.33 330.00 256.67 256.67 366.67 183.33 476.67

0.00 5.50 11.00 5.50 0.00 4.58 9.17 7.33 5.50 8.25 11.00

0.00 0.00 0.00 18.33 36.67 21.08 5.50 2.75 0.00 6.42 12.83

0.00 12.83 25.67 18.33 11.00 9.17 7.33 36.67 66.00 33.00 0.00

0.00 5.50 11.00 36.67 62.33 80.67 99.00 49.50 0.00 27.50 55.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 18.33 64.17 660.00 36.67 128.33 1320.00 29.33 91.67 916.67 22.00 55.00 513.33

A zero means no net uptake this year/period, the stock remains unchanged

The figures show the annual uptake of CO2 for each region and for the 4 different price levels, in clockwise order starting for the upper left graph. It can be noticed that the two lower price levels see a peak round 2010 in uptake and a dip round 2020-2030. The two higher levels switch around 2030-2035.

ECN-C--03-046

37

4000

AFR

4000

AFR

3500

ASPA

3500

ASPA

3000

OCE

3000

OCE

IND

2500

CHIN

2000

FSU

1500

Canada

500

USA

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

CHIN

2000

FSU

1500

SA

1000

IND

2500

SA

1000

Canada

500

WEU

USA

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

EEU

WEU EEU

4000

AFR

4000

AFR

3500

ASPA

3500

ASPA

3000

OCE

3000

OCE

IND

2500

CHIN

2000

FSU

1500

Canada

500

USA

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

CHIN

2000

FSU

1500

SA

1000

IND

2500

SA

1000

Canada

500

WEU

USA

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

EEU

WEU EEU

Figure 2.5 Annual CO2 uptake per region for the 4 price levels [Mton/year] The next graphs show the total annual uptake by region and the net annual uptake, taking into account the following annual baseline uptake/emissions. A positive sign means uptake, a negative means an emission. Globally the LULUCF activities emits more than 1 Gton CO2 per year, concentrated in South America (SA), Asian Pacific (ASPA) and Africa (AFR) and some in the Former Soviet Union (FSU) and Eastern Europe (EEU). CO2 uptake occurs in Oceanea (OCE), the USA and Canada, Western Europe (WEU) and to a lesser extent in India (IND) and China (CHIN). The net uptake is actually an emission in the first period, only after 2005 the CO2 stock decrease is reversed and a positive uptake occurs. Table 2.19 Baseline emissions (-) and uptake(+) per region EEU WEU USA Canada SA [Mton CO2/year]

-1

93

25

26

-521

FSU CHIN IND OCE ASPA AFR

Total

-22

-1071

7

11

84

-334

-440

8000

7000 AFR ASPA

6000

OCE IND

Mton CO2/year

5000

CHIN FSU

4000

SA Canada USA

3000

WEU EEU 2000

1000

0 2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Figure 2.6 Global annual CO2 uptake per region [Mton/year]

38

ECN-C--03-046

8000 7000 AFR

6000

ASPA

Mton CO2/year

5000

OCE IND

4000

CHIN

3000

FSU SA

2000

Canada

1000

USA WEU

0

EEU

-1000 -2000 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Figure 2.7 Net CO2 annual uptake by region [Mton CO2/year] The following graph shows what the maximum attainable additional stock of CO2 can be for each region and the global total, a differentiation by price level is also made per region. The regions with the largest potential are in order of magnitude: SA (South America) and ASPA (Asian pacific), AFR (Africa), USA and Canada, FSU (Former Soviet Union), CHIN (China), WEU (Western Europe), OCE (Oceanea) and IND (India) and Eastern Europe (EEU). The total global CO2 stock can increase with less than 50 000 Mton in 2010 to over 280 000 Mton in 2050. The net CO2 stock increase is 60 Gton less due to the emissions in the baseline. The additional stock at the highest CO2 price (37-48 US$/ton CO2) remains limited, a saturation effect occurs. Most CO2 is stored in the medium price range: 13-28 US$/ton CO2. 300000

250000

AFR ASPA

Cumulative Mton CO2

OCE 200000

IND CHIN FSU

150000

SA Canada USA

100000

WEU EEU

50000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Figure 2.8 Evolution of global CO2 stock increase above the baseline [Mton CO2]

ECN-C--03-046

39

3.

DATABASE REVIEW

3.1

Introduction

This review is based on the database made available around the spring 2002 meeting of ETSAP. An error concerning emission accounting (GHG) has been solved during that workshop under inspiration from Gary Goldstein. The following sequence was used to import the data in Answer 3.5.13: • Create new database RMarkal with start year 2000, 5 year periods and 11 periods. • Import the unit file delivered with the data. • Import the data. This was the message received upon importing the unit file. Record 28 has error: Duplicate value in index, primary key, or relationship. Changes were unsuccessful. PJ/a Petajoules/annum Record 73 has error: Can't add or change record. Referential integrity rules require a related record in table 'tblUnits'. TCAP PJ/a Record 116 has error: Can't add or change record. Referential integrity rules require a related record in table 'tblUnitGroups'. T DMD TCAP PJ/a Record 120 has error: Can't add or change record. Referential integrity rules require a related record in table 'tblUnitGroups'. T PRE TCAP PJ/a In edit database ‘BASE’, this has been changed from PJa to PJ/a, before importing the data. Afterwards the data have been imported successfully. The corrections on the emission accounting refer to an error in the ENV_GWP parameter: the order of both emissions should be reversed in the table definition. In the original database total and additional emission were interchanged. In the corrected version this is repaired, such that the totalling emissions comes first, followed by the emission that should be added, and finally the scale (weight) factor is given. This change ensures correct accounting for totalled emissions like GHG and TOTCO2. The changes are illustrated in the tables below. It should be noted that CH4 emissions are not accounted for in total GHG, they require a factor of 0.000021, too small for Answer.

40

ECN-C--03-046

Old table: BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE BASE

ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 1.0000 1.0000 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 1.0000 1.0000 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 1.0000 1.0000 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 1.0000 1.0000 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 1.0000 1.0000 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 1.0000 1.0000 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 1.0000 1.0000 ENV_GWP 0.0003 0.0003

ECN-C--03-046

4 0.0000 4 0.0010 4 1.0000 4 0.0003 4 0.0000 4 0.0010 4 1.0000 4 0.0003 4 0.0000 4 0.0010 4 1.0000 4 0.0003 4 0.0000 4 0.0010 4 1.0000 4 0.0003 4 0.0000 4 0.0010 4 1.0000 4 0.0003 4 0.0000 4 0.0010 4 1.0000 4 0.0003 4 0.0000 4 0.0010 4 1.0000 4 0.0003

0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 0.0003

AGRCH4N 0.0000 0.0000 AGRCO2N 0.0010 0.0010 AGRCO2N 1.0000 1.0000 AGRN2ON 0.0003 0.0003 COMCH4N 0.0000 0.0000 COMCO2N 0.0010 0.0010 COMCO2N 1.0000 1.0000 COMN2ON 0.0003 0.0003 ELCCH4N 0.0000 0.0000 ELCCO2N 0.0010 0.0010 ELCCO2N 1.0000 1.0000 ELCN2ON 0.0003 0.0003 INDCH4N GHG 0.0000 0.0000 INDCO2N GHG 0.0010 0.0010 INDCO2N TOTCO2 1.0000 1.0000 INDN2ON GHG 0.0003 0.0003 RESCH4N GHG 0.0000 0.0000 RESCO2N GHG 0.0010 0.0010 RESCO2N TOTCO2 1.0000 1.0000 RESN2ON 0.0003 0.0003 TRACH4N 0.0000 0.0000 TRACO2N 0.0010 0.0010 TRACO2N 1.0000 1.0000 TRAN2ON 0.0003 0.0003 UPSCH4N GHG 0.0000 0.0000 UPSCO2N GHG 0.0010 0.0010 UPSCO2N TOTCO2 1.0000 1.0000 UPSN2ON 0.0003 0.0003

GHG 0.0000 GHG 0.0010 TOTCO2 1.0000 GHG 0.0003 GHG 0.0000 GHG 0.0010 TOTCO2 1.0000 GHG 0.0003 GHG 0.0000 GHG 0.0010 TOTCO2 1.0000 GHG 0.0003 0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 GHG 0.0003 GHG 0.0000 GHG 0.0010 TOTCO2 1.0000 GHG 0.0003 0.0000 0.0010 1.0000 GHG 0.0003

0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 0.0003 0.0000 0.0010 1.0000 0.0003 0.0000

0.0000

0.0000

0.0000

0.0010

0.0010

0.0010

1.0000

1.0000

1.0000

0.0003

0.0003

0.0003

0.0000

0.0000

0.0000

0.0010

0.0010

0.0010

1.0000

1.0000

1.0000

0.0003

0.0003

0.0003

0.0000

0.0000

0.0000

0.0010

0.0010

0.0010

1.0000

1.0000

1.0000

0.0003

0.0003

0.0003

0.0000

0.0000

0.0000

0.0010

0.0010

0.0010

0.0010

1.0000

1.0000

1.0000

1.0000

0.0003

0.0003

0.0003

0.0003

0.0000

0.0000

0.0000

0.0000

0.0010

0.0010

0.0010

0.0010

1.0000

1.0000

1.0000

1.0000

0.0003 0.0000 0.0010 1.0000 0.0003 0.0000

0.0003

0.0003

0.0003

0.0000

0.0000

0.0000

0.0010

0.0010

0.0010

1.0000

1.0000

1.0000

0.0003

0.0003

0.0003

0.0000

0.0000

0.0000

0.0010

0.0010

0.0010

0.0010

1.0000

1.0000

1.0000

1.0000

0.0003

0.0003

0.0003

0.0003

41

New table: CORRECT 0.0000 CORRECT 0.0010 CORRECT 0.0003 CORRECT 0.0000 CORRECT 0.0010 CORRECT 0.0003 CORRECT 0.0000 CORRECT 0.0010 CORRECT 0.0003 CORRECT 0.0000 CORRECT 0.0010 CORRECT 0.0003 CORRECT 0.0000 CORRECT 0.0010 CORRECT 0.0003 CORRECT 0.0000 CORRECT 0.0010 CORRECT 0.0003 CORRECT 0.0000 CORRECT 0.0010 CORRECT 0.0003 CORRECT 1.0000 CORRECT 1.0000 CORRECT 1.0000 CORRECT 1.0000 CORRECT 1.0000 CORRECT 1.0000 CORRECT 1.0000

ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 0.0003 0.0003 ENV_GWP 0.0000 0.0000 ENV_GWP 0.0010 0.0010 ENV_GWP 0.0003 0.0003 ENV_GWP 1.0000 1.0000 ENV_GWP 1.0000 1.0000 ENV_GWP 1.0000 1.0000 ENV_GWP 1.0000 1.0000 ENV_GWP 1.0000 1.0000 ENV_GWP 1.0000 1.0000 ENV_GWP 1.0000 1.0000

4 0.0000 4 0.0010 4 0.0003 4 0.0000 4 0.0010 4 0.0003 4 0.0000 4 0.0010 4 0.0003 4 0.0000 4 0.0010 4 0.0003 4 0.0000 4 0.0010 4 0.0003 4 0.0000 4 0.0010 4 0.0003 4 0.0000 4 0.0010 4 0.0003 4 1.0000 4 1.0000 4 1.0000 4 1.0000 4 1.0000 4 1.0000 4 1.0000

0.0000 0.0010 0.0003 0.0000 0.0010 0.0003 0.0000 0.0010 0.0003 0.0000 0.0010 0.0003 0.0000 0.0010 0.0003 0.0000 0.0010 0.0003 0.0000 0.0010 0.0003 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

GHG 0.0000 GHG 0.0010 GHG 0.0003 GHG 0.0000 GHG 0.0010 GHG 0.0003 GHG 0.0000 GHG 0.0010 GHG 0.0003 GHG 0.0000 GHG 0.0010 GHG 0.0003 GHG 0.0000 GHG 0.0010 GHG 0.0003 GHG 0.0000 GHG 0.0010 GHG 0.0003 GHG 0.0000 GHG 0.0010 GHG 0.0003 TOTCO2 1.0000 TOTCO2 1.0000 TOTCO2 1.0000 TOTCO2 1.0000 TOTCO2 1.0000 TOTCO2 1.0000 TOTCO2 1.0000

AGRCH4N 0.0000 0.0000 AGRCO2N 0.0010 0.0010 AGRN2ON 0.0003 0.0003 COMCH4N 0.0000 0.0000 COMCO2N 0.0010 0.0010 COMN2ON 0.0003 0.0003 ELCCH4N 0.0000 0.0000 ELCCO2N 0.0010 0.0010 ELCN2ON 0.0003 0.0003 INDCH4N 0.0000 0.0000 INDCO2N 0.0010 0.0010 INDN2ON 0.0003 0.0003 RESCH4N 0.0000 0.0000 RESCO2N 0.0010 0.0010 RESN2ON 0.0003 0.0003 TRACH4N 0.0000 0.0000 TRACO2N 0.0010 0.0010 TRAN2ON 0.0003 0.0003 UPSCH4N 0.0000 0.0000 UPSCO2N 0.0010 0.0010 UPSN2ON 0.0003 0.0003 AGRCO2N 1.0000 1.0000 COMCO2N 1.0000 1.0000 ELCCO2N 1.0000 1.0000 INDCO2N 1.0000 1.0000 RESCO2N 1.0000 1.0000 TRACO2N 1.0000 1.0000 UPSCO2N 1.0000 1.0000

0.0000 0.0010 0.0003 0.0000 0.0010 0.0003 0.0000 0.0010 0.0003 0.0000

0.0000

0.0000

0.0010

0.0010

0.0003

0.0003

0.0000

0.0000

0.0010

0.0010

0.0003

0.0003

0.0000

0.0000

0.0010

0.0010

0.0003

0.0003

0.0000

0.0000

0.0010

0.0010

0.0010

0.0003

0.0003

0.0003

0.0000

0.0000

0.0000

0.0010

0.0010

0.0010

0.0003 0.0000 0.0010 0.0003 0.0000

0.0003

0.0003

0.0000

0.0000

0.0010

0.0010

0.0003

0.0003

0.0000

0.0000

0.0010

0.0010

0.0010

0.0003 1.0000 1.0000 1.0000 1.0000

0.0003

0.0003

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000 1.0000

1.0000

1.0000

1.0000

1.0000

The review will focus on energy consumption (totals and patterns) and mix of power production technologies. For a comparison the existing Western Europe model of ECN without alterations will be used. By this is meant that what is usually referred to as the reference case will be used. This reference case is the most recent model in which no policy measures or constraints are included (see for example de Feber et al, 2002).

42

ECN-C--03-046

3.2

Quality assurance log file

The first item that has been looked into is the generated log file that contains information on the database contents (structure and data). The file is represented in its entirety. Proposed actions are inserted in bold next to or immediately following the item. ***** QUALITY ASSURANCE LOG ***** *** CON Sub-sets Consistency Checks * WARNING - HDE Hydro-electric Plant : EGHDD100 Is not in REN * WARNING - HDE Hydro-electric Plant : EGHDR100 Is not in REN * WARNING - HDE Hydro-electric Plant : EHYDD105 Is not in REN Definition problem between technologies and energy carriers *** CON Fuel Classification * WARNING - FOSsil Conversion Technology : CHPGBIO100 Using Non-fossil Energy Carrier : ELCBIO * WARNING - FOSsil Conversion Technology : CHPGGEO100 Using Non-fossil Energy Carrier : ELCGEO * WARNING - FOSsil Conversion Technology : EBIOM105 Using Non-fossil Energy Carrier : ELCBIO * WARNING - FOSsil Conversion Technology : EGBIO100 Using Non-fossil Energy Carrier : ELCBIO * WARNING - FOSsil Conversion Technology : EGEOT105 Using Non-fossil Energy Carrier : ELCGEO * WARNING - FOSsil Conversion Technology : EGGEO100 Using Non-fossil Energy Carrier : ELCGEO * WARNING - FOSsil Conversion Technology : EGHDD100 Using Non-fossil Energy Carrier : ELCHYD * WARNING - FOSsil Conversion Technology : EGHDR100 Using Non-fossil Energy Carrier : ELCHYD * WARNING - FOSsil Conversion Technology : EGSOL100 Using Non-fossil Energy Carrier : ELCSOL * WARNING - FOSsil Conversion Technology : EGTID100 Using Non-fossil Energy Carrier : ELCTDL * WARNING - FOSsil Conversion Technology : EGWIN100 Using Non-fossil Energy Carrier : ELCWIN * WARNING - FOSsil Conversion Technology : EHYDD105 Using Non-fossil Energy Carrier : ELCHYD * WARNING - FOSsil Conversion Technology : EMSWG105 Using Non-fossil Energy Carrier : ELCBIO * WARNING - FOSsil Conversion Technology : ESOPV105 Using Non-fossil Energy Carrier : ELCSOL * WARNING - FOSsil Conversion Technology : ESOTH105 Using Non-fossil Energy Carrier : ELCSOL * WARNING - FOSsil Conversion Technology : EWIND105 Using Non-fossil Energy Carrier : ELCWIN * WARNING - FOSsil Conversion Technology : HETBIO105 Using Non-fossil Energy Carrier : ELCBIO * WARNING - FOSsil Conversion Technology : HETGBIO00 Using Non-fossil Energy Carrier : ELCBIO * WARNING - FOSsil Conversion Technology : HETGEO105

ECN-C--03-046

43

Using Non-fossil Energy Carrier : ELCGEO * WARNING - FOSsil Conversion Technology : HETGGEO00 Using Non-fossil Energy Carrier : ELCGEO * WARNING - FOSsil Conversion Technology : HETGSOL00 Using Non-fossil Energy Carrier : ELCSOL Definition problem between technologies and energy carriers *** Illegal Consumption of Electricity/Heat * WARNING - Convension Technology : CHPGBIO100 Consuming Electricity : ELCELC * WARNING - Convension Technology : CHPGCOA100 Consuming Electricity : ELCELC * WARNING - Convension Technology : CHPGGAS100 Consuming Electricity : ELCELC * WARNING - Convension Technology : CHPGGEO100 Consuming Electricity : ELCELC * WARNING - Convension Technology : CHPGOIL100 Consuming Electricity : ELCELC * WARNING - Convension Technology : EGCOA100 Consuming Electricity : ELCELC * WARNING - Convension Technology : EGGAS100 Consuming Electricity : ELCELC * WARNING - Convension Technology : EGGEO100 Consuming Electricity : ELCELC * WARNING - Convension Technology : EGOIL100 Consuming Electricity : ELCELC Probably ELCELC is meant to be the own use of power plants, perhaps it could be done as adjustment of the plant efficiency * WARNING - Process Technology : UCOAPRDB00 Consuming Heat : UPSHET * WARNING - Process Technology : UCOAPRDH00 Consuming Heat : UPSHET * WARNING - Process Technology : UTRFCKOV00 Consuming Heat : UPSHET * WARNING - Process Technology : UTRFLXREF0 Consuming Heat : UPSHET This relates to the fact that in MARKAL processes can NOT consume LTH energy carriers and this is ignored in the results, no Heat is consumed (should be about 5PJ in 1990) *** Production/Use of Energy Carriers+Material * WARNING - Energy Carrier/Material : BIOCHR Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : BIOETH Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : BIOMET Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : COABCOLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : COAGSC Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : COAHCOLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : DUMM2T

44

ECN-C--03-046

Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : ELCCGO Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : GASETHLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : GASLPGLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : GASNGALOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : IELP Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : IENM Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : INDCRD Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : INDETH Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : INDGAS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : INDOXY Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILASPLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILAVGLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILCRHLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILCRLLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILDSTLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILFEELOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILGSLLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILHFOLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILJTGLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILJTKLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILKERLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILLUBLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILNAPLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILNCRLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILNSPLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILPTCLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : OILWAXLOS Is not consumed by any Resource or Technology

ECN-C--03-046

45

* WARNING - Energy Carrier/Material : OILWSPLOS Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : TRACOA Is not consumed by any Resource or Technology * WARNING - Energy Carrier/Material : UPSREN Is not consumed by any Resource or Technology This can be ignored, these default items do not occur in the WEU region. *** Inconsistent or Unlimited Resource Options * WARNING - For SRCENCP : MINGEO0 No Table or Cost/Bound/Cum/Growth Entry Found * WARNING - For SRCENCP : MINHYD0 No Table or Cost/Bound/Cum/Growth Entry Found * WARNING - For SRCENCP : MINSOL0 No Table or Cost/Bound/Cum/Growth Entry Found * WARNING - For SRCENCP : MINTDL0 No Table or Cost/Bound/Cum/Growth Entry Found * WARNING - For SRCENCP : MINWIN0 No Table or Cost/Bound/Cum/Growth Entry Found * WARNING - For SRCENCP : EXPGHGPMTY No Table or Cost/Bound/Cum/Growth Entry Found * WARNING - For SRCENCP : EXPGHGPMTF No Table or Cost/Bound/Cum/Growth Entry Found * WARNING - For SRCENCP : IMPGHGPMTY No Table or Cost/Bound/Cum/Growth Entry Found * WARNING - For SRCENCP : IMPGHGPMTF No Table or Cost/Bound/Cum/Growth Entry Found Can be ignored *** Some Electric/Heat CONSTANT Entries Missing * REMINDER - No ETRANINV/OM or EDISTINV/OM for Electric Grid : ELC * REMINDER - No ETRANINV/OM or EDISTINV/OM for Electric Grid : AGRELC * REMINDER - No ETRANINV/OM or EDISTINV/OM for Electric Grid : COMELC * REMINDER - No ETRANINV/OM or EDISTINV/OM for Electric Grid : ELCELC * REMINDER - No ETRANINV/OM or EDISTINV/OM for Electric Grid : INDELC * REMINDER - No ETRANINV/OM or EDISTINV/OM for Electric Grid : RESELC * REMINDER - No ETRANINV/OM or EDISTINV/OM for Electric Grid : TRAELC * REMINDER - No ETRANINV/OM or EDISTINV/OM for Electric Grid : UPSELC * WARNING - No HRESERV for Heat Grid : LTH * REMINDER - No DTRANINV/OM for Heat Grid : LTH * REMINDER - No DTRANINV/OM for Heat Grid : AGRHET * REMINDER - No DTRANINV/OM for Heat Grid : COMHET * REMINDER - No DTRANINV/OM for Heat Grid : HET * REMINDER - No DTRANINV/OM for Heat Grid : INDHET * REMINDER - No DTRANINV/OM for Heat Grid : RESHET * REMINDER - No DTRANINV/OM for Heat Grid : UPSHET Can be ignored *** No Conversion Technologies Contributing to Peak * WARNING - For Heat Grid : LTH Can be ignored *** Every Technology has an Input * WARNING - Process Technology : UTRFLIQU00 Does not consume any Energy Carrier/Material

46

ECN-C--03-046

* WARNING - Process Technology : UTRFNSPC00 Does not consume any Energy Carrier/Material * WARNING - Demand Device : RRRRHW000 Does not consume any Energy Carrier/Material Can be ignored *** Every Process has an Output * WARNING - Process Technology : IMLPELH005 Does not produce any Energy Carrier/Material * WARNING - Process Technology : UGASFLAG00 Does not produce any Energy Carrier/Material * WARNING - Process Technology : UOILFLAH00 Does not produce any Energy Carrier/Material * WARNING - Process Technology : UOILFLAL00 Does not produce any Energy Carrier/Material * WARNING - Process Technology : UOILFLAS00 Does not produce any Energy Carrier/Material Can be ignored *** DELIV Cost Consistency Check * WARNING - Process Technology : ISNFCOA000 Does not consume Energy Carrier : ISNF * WARNING - Process Technology : ISNFDST000 Does not consume Energy Carrier : ISNF * WARNING - Process Technology : ISNFHFO000 Does not consume Energy Carrier : ISNF * WARNING - Process Technology : ISNFNGA000 Does not consume Energy Carrier : ISNF Should be checked why this appears, ISNF is defined as OUTPUT of these technologies and can not have a DELIV attributed to it (even if value is zero in the database) *** Illegal OUT(DM) Specification * WARNING - Sum of DM output fractions Not = 1 for DMD : IOOIHET000 * WARNING - Sum of DM output fractions Not = 1 for DMD : IPNMHET000 * WARNING - Sum of DM output fractions Not = 1 for DMD : ISLPHET000 * WARNING - Sum of DM output fractions Not = 1 for DMD : IOOIHET005 * WARNING - Sum of DM output fractions Not = 1 for DMD : IPNMHET005 * WARNING - Sum of DM output fractions Not = 1 for DMD : ISLPHET005 OUTDM should be corrected to 1, this means fuel in- and output should be adjusted accordingly. DMD’s can have auxiliary output but have to satisfy one or more DM’s (but total should be 1 and not 0.001 as in the database)

3.3

Results

The review of the results will not focus on details or numbers, an approach giving a direct overview of the largest differences is chosen, meaning that there will be ample use of figures and less of tables.

ECN-C--03-046

47

3.3.1 Primary energy supply 2000 results The following table gives the values fro the primary energy use for 2000 (middle of 5 (ETP) or 10 (ECN) year period). While for the ETP model this is the starting year and hence the numbers are calibrated on IEA statistics, this is not entirely the case for ECN’s WEU model. In this model 2000 is a result year and not yet calibrated, while the 1990 10 year period is. Hence the outcome of the 2000 period is already partial determined by the calibration of the 1990 period. For the non carbon fuels (hydro, solar, wind and geothermal), the same conversion factor (of the ETP model, namely 2.8244) has been used to calculate the primary energy equivalent of these fuels. Table 3.1 Primary energy summary for 2000 ETP [PJ] Solid Liquid Gas Hydro Solar Wind Geothermal Nuclear Waste Biomass Total

2000 10652.4 25795.1 11719.7 4751.22 17.27 116.68 158.93 8620.98 395.15 1715.17 63942.5

ECN 2000 11416.0 28219.7 12200.0 4668.8 14.5 156.4 36.1 7908.3 1568.5 5415.3 71603.4

Percentual difference ECN/ETP [%] 7.17 9.40 4.10 -1.74 -16.20 34.03 -77.31 -8.27 296.93 215.73 11.98

Results are different for all fuels, the largest relative difference is found in waste and biomass, the smallest in hydro, nuclear and all the fossil fuels. Assuming identical values for waste and biomass in both models, the totals only differ 4.4%.

Trend 2000-2050 The following figure represents the results of a BAU run with both models. As can been seen the results differ completely. Implicit and explicit scenario assumptions (discount and growth rates, autonomous improvements, …) partially cause this difference. Average annual growth in primary energy is 1.16% for ETP and 0.47% for ECN. 120000

120000

100000

100000

biomass

biomass waste

waste 80000

nuclear

80000

nuclear geothermal wind

60000

wind solar

PJ

PJ

geothermal 60000

solar hydro gas liquid

hydro 40000

gas

40000

liquid 20000

solid

solid 20000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

ETP Figure 3.1 Primary energy

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

ECN

Especially the increase in gas use is absent in the ECN model, also liquid fuels do not increase, on the contrary they decrease (as primary energy). Wind energy on the other hand is not appear-

48

ECN-C--03-046

ing in the ETP model, what is not quite in line with the current situation and plans in Western Europe. For wind, the following maximum capacity projection is used in the ECN model. The 2000 data are based on current installed capacities. Table 3.2 Potential wind capacity 2000 2010 [GWel] Wind onshore 13 72 Wind offshore 0.5 6.5

2020 103 56

2030 123 103

2040 130 135

2050 136 148

For hydro, the following potentials are used for low and high head dams and pumped storage together: Table 3.3 Potential hydro capacity 2000 2010 [GWel] Hydro maximum 176.0 201.9 Hydro minimum 171.5 184.7

2020 226.7 187.1

2030 249.6 188.5

2040 271.0 188.9

2050 289.1 188.9

As alternative ETP scenarios, one where the ADRATIO data are all set to zero after 2005 (a free model), except for maximum hydro, is also included. In this scenario the following adjusted global parameters have been included as well: Table 3.4 General system parameters Parameter Original ETP value DISCOUNT 0.1 STARTYEARS 2 QHR(Z)(Y) I-D 0.1667 QHR(Z)(Y) I-N 0.1667 QHR(Z)(Y) S-D 0.1667 QHR(Z)(Y) S-N 0.1667 QHR(Z)(Y) W-D 0.1667 QHR(Z)(Y) W-N 0.1667

Proposed value 0.02 2.5 0.3330 0.1670 0.1670 0.0830 0.1670 0.0830

This results in a different energy mix, where the growth rate remains as high as in the reference ETP run. After a dip in 2005-2010, primary energy use increases as before. Coal remains dominant and replaces gas. Liquid and biomass use decreases. Geothermal increases due use in the commercial and residential sectors and reaches in 2010 its maximum level. It remains on this level for the rest of the modelling time, so probably some constraints in these sectors or for this application have to be maintained.

ECN-C--03-046

49

120000

biomass

100000

waste nuclear

80000

PJ

geothermal wind

60000

solar hy dro

40000

gas liquid

20000

solid

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Figure 3.2 Primary energy in ETP adjusted

3.3.2 Final energy The final energy is more difficult to compare for a number of reasons: • The definition of the different end use (final energy use) sectors is not identical between both models. • The definition of energy carries may be different. • The modelling of the end-use part of the energy system is different, e.g. more processes (PRC) and intermediate energy/material carriers are used in the ECN model. Nevertheless an attempt to compare both results is made in which the common elements are treated in such a way that they represent as much as possible the same sectors. A certain level of aggregation was necessary; hence not all the details are maintained. Table 3.5 Comparison of final energy by sector ETP 2500

2000

ECN 1200

biomass Liquified Petroleum Gas Low-temperature Heat

500

Gas for agricultural sector

Gas for agricultural sector Residual fuel oil for agriculture

1000

Low -temperature Heat

800

Electricity Gas oil for agricultural sector

Residual fuel oil for agriculture PJ

Agriculture

PJ

1500

Liquified Petroleum Gas

1000

600

Electricity Gas oil for agricultural sector

400

Coal for agricultural sector

200

Coal for agricultural sector 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Gasoline for agricultural sector

Gasoline for agricultural sector 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: although the initial energy mix is similar, the projections differ largely. Such a growth in energy use in agriculture, resulting in a doubling by 2035, is hard to imagine. A more modest growth of 1.1% annually which is offset by efficiency improvements may be more realistic.

50

ECN-C--03-046

Table 3.5 continued ETP

ECN 14000

6000

Other electricty 5000

Gas oil for comm ercial sector Gas for comm ercial sector

3000

Oth er electricty Oth er energy in com m ercia l s ector Low-tem pera tu re Heat

10000 8000

Gas oil for com m ercia l sector Gas for com m ercia l s ector

PJ

PJ

4000

Commercial heating and other

12000

Other energy in comm ercial sector Low-temperature Heat

6000

Electricity

Electricity

2000

1000

Energy saving

4000

Energy savin g

Coal for comm ercial sector

2000

Coal for com m ercial sector

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: very large differences here both in level and mix, and this time a higher growth in the ECN model than the in ETP one. More gas and less heat used in the ETP model. There is no fuel switch away from liquids in ETP. 7000

10000 9000

Wood for households

Wood for households 6000

7000

Residential heating

PJ

6000

Petroleum products for households

5000

Petroleum products for households

Low -temperature Heat

4000

Low -temperature Heat

5000 4000 3000 2000

Total energy heat in residential sector

Total energy heat in residential sector

Gas for households

PJ

8000

Gas for households 3000 Electricity

Electricity 2000

Energy saving

Energy saving

1000 Coal for households

1000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Coal for households

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: reasonable similar figures for 2000 in both cases, but further development goes in opposite directions, a steady growth in ETP with little switch between fuels, in the ECN model a steady decrease and a major switch from oil to gas between 2020-2030. 1800

1200

Wood for households

Wood for households

1600

1200

Petroleum products for households

1000

Low -temperature Heat

800

Residential hot water

600

Gas for households

1000

Total energy heat in residential sector Petroleum products for households

800

Low -temperature Heat PJ

Total energy heat in residential sector

PJ

1400

Electricity

600 Gas for households Electricity

400

400 Energy saving

Energy saving 200

200

Coal for households

Coal for households 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: again a higher level for ETP in 2000 but the shares are similar, trend later on goes in opposite directions. Heat is absent as fuel in the ETP model. 400

700

350

600

Wood for households

300

500

PJ

Residential cooking

Gas for households

200

Electricity

150

Petroleum products for households Gas for households

300

Electricity Coal for households

200

Coal for households

100

Wood for households

400 PJ

Petroleum products for households

250

100

50

0 2000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

2005 2010 2015 2020 2025

2030

2035 2040 2045 2050

Comments: a higher level for ECN and also liquid and biomass fuels included, although the latter fade out over time. Gas is in both the major energy carrier but grows more in ECN. 3000

2500

2500

2000

Other fossil 2000

Refrigerators and freezers-GAS Washing machines

1500

1500

Refrigerators and freezers Lighting

Residential other uses (ELC except if mentioned)

Other new electric appliances 1000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Tumble driers

Lighting

1000

Other existing electric appliances Dishw ashers

500

Refrigerators and freezers-GAS Washing machines

Refrigerators and freezers

PJ

PJ

Tumble driers

500

Other new electric appliances Other existing electric appliances Dishwashers

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: major differences here, probably caused by other meaning and definition of categories. ECN shows a increasing and stabilising trend whereas ETP slightly increases over time.

ECN-C--03-046

51

Table 3.5 continued ETP

ECN

3000

1200

2500

1000 800

2000 1500

proces heat

PJ

PJ

others

Pulp and paper

steam

600

electricity

electricity 1000

400

500

200 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: starting level is more or less the same but the largest difference is seen in the trend: rising in ETP, declining in ECN, especially in steam use. A doubling of energy use by 2050 seems very unlikely in this sector. 2000

180

1800

160

1600

140

1200

others

1000

proces heat

800

electricity

120 other

100

PJ

Non ferro (only Aluminium for ECN)

PJ

1400

gas

80

600

60

400

40

200

electricity

20

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: again diverting graphs, in ECN major technology changes and improvements take place in the Aluminium sector, something completely absent in ETP. 4000

900

3500

800 700

3000

600 proces heat

1500

electricity

PJ

others

2000

PJ

Non metal minerals (only cement and bricks for ECN)

2500

other

500

gas

400

electricity

300

1000

200

500

100

0

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: very difficult to judge because it is not clear what is included in ETP. Again doubling of energy use in ETP. 6000

2500

5000

2000

4000

electricity

Chemicals

other

PJ

PJ

proces heat

proces heat

1500

others 3000

gas 1000

electricity

2000 1000 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

500

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: very difficult to judge because it is not clear what is included in ETP. More than tripling of energy use in ETP, rather unlikely seen the efforts in this sector to reduce energy intensity.

52

ECN-C--03-046

Table 3.5 continued ETP

ECN

1200

1800 1600

1000

1400

800

1200

600

others

PJ

Iron and steel

PJ

proces heat

1000

others electricity

800

electricity 400

600 400

200

200 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: steam is not accounted for in the same way in both models, in ECN fuel inputs are implicitly used for steam generation within the processes and not delivered from outside the sector as in ETP. Taking into account efficiencies for steam generation, both models are in the same order of magnitude, show similar fuel shares and show a likewise trend: declining over time. 10000

14000

9000

12000

8000

10000

6000

7000

proces heat

6000

others

5000

others

4000

electrcity

PJ

Other industries

PJ

8000

electricity

4000

3000

2000

2000

proces heat

1000

0

0

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: apart from level, the tripling of energy use in ETP is different than the limited growth in ECN. 16,000

30000

14,000

25000

12,000

20000

10,000

PJ

Total Industry

15000

others electricity

PJ

proces heat

8,000 6,000

10000

proces heat others electricity

4,000

5000

2,000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: taking abstraction from the influences by steam production vs. fossil fuel use, both models act differently and this for the 2000 level and certainly for the trend, ECN remains more or less stable, ETP increases 2.5 times. 7000

10000 9000

6000

8000 7000

others

4000

gas

6000

gas

Cars

LPG

PJ

others

PJ

5000

5000

3000

diesel

4000

2000

gasoline

3000

LPG diesel gasoline

2000

1000 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

1000 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: ECN has a higher level (75% more than ETP) and almost exclusive gasoline. Consumption stabilises after 2020 whereas for ETP it continues to grow.

ECN-C--03-046

53

Table 3.5 continued ETP

ECN

300

300

250

250 others

200

others

200

gas

LPG

150

PJ

Bus

PJ

gas

LPG

150

diesel

diesel 100

gasoline

100

gasoline

50

50

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: very similar, except trend and introduction of gasoline in ETP. 3500

1600 1400

3000

1200

2500 others

1500

PJ

PJ

Vans

LPG

others

1000

gas

2000

gas LPG

800

diesel

diesel

600

gasoline 1000

gasoline

400

500

200 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: level in ETP is 2 times higher than ECN, which lacks non gasoline fuels in the results, the growth is also higher for ECN. 9000

4000

8000

3500

7000

others

5000

gas LPG

4000

diesel

3000

gasoline

2500 PJ

others

PJ

Trucks

3000

6000

gas LPG

2000

diesel

1500

gasoline 1000

2000

500

1000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: consumption in ETP is at the same level as cars, ECN is only half of ETP. Coincidentally both show a peak and dip around the same time (2015-2020) 9000

10000

8000

9000 8000

7000 6000

4000 3000

PJ

PJ

Other

marine diesel

aviation

6000

aviation 5000

bunkers

7000

bunkers

marine dies el

5000

rail electric

4000

rails diesel

3000

rail electric rails dies el

2000

2000

1000

1000

0

0

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: both level and rate are in line, although there must be a difference in understanding of domestic and international aviation. ECN sees also a growth in rail and navigation fuel use. 25000

30000 25000

15000

20000

5000 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

gas

gas LPG diesel

10000

others

others 15000

LPG

PJ

Total transport (incl. Bunkers)

PJ

20000

diesel 10000

gasoline

gasoline 5000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Comment: total energy consumption does not differ much and remains almost identical till 2015-2020, after that ETP foresees more growth than ECN. ECN has more gasoline than diesel vis-à-vis ETP, but mix remains stable. The difference in fuel definition and classification may also cause some of the differences.

54

ECN-C--03-046

3.3.3 Electricity Another item to look into is the electricity generation and capacity in both models. Although the definition and naming of power plants is different, results by input fuel type will be compared.

Production For both models the same scale has been used as well as a similar colour-coding scheme to enhance comparability. 5000

5000

4500

4500

4000

solar wind hydro

2500

gas

2000

solar wind

2500

hydro gas

2000

liquid

liquid

1500

solid nuclear

1000

1500

solid

1000

nuclear

500

500 0 2000

biomass

3000 TWh

3000

others

3500

biomass

3500

TWh

4000

others

0

2005

2010

2015

2020

2025

2030

2035

2040

2045

2000

2050

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

ECN

ETP Figure 3.3 Electricity generation by fuel use

Again numbers for 2000 match very well, for later years some very important differences can be noticed: • The growth in ETP (1.1% annually) is quite larger than in ECN (0.7%). • Nuclear fluctuates more in ECN. • Solid fuel powered plants phase out significantly in ETP, whereas in ECN they increase their production after 2020. • Liquid disappears in ETP but stays (although small) in ECN. • Gas becomes the preponderant fuel in ETP, in ECN it remains small. • Hydro is constant in ECN, in ETP there is some room for increase. • Renewables do not appear in ETP, in ECN, wind and biomass have a share in the production.

Capacity Also in installed capacity there are major differences. This is already apparent in the 2000 level of the total installed capacity, which is about 70 GW lower in ECN than in ETP. The large capacity of liquid units, mainly the existing 2000 capacity remaining and hardly any new investment, in ETP does not occur in ECN, on the other hand wind and biomass do occur in ECN. 1000

1000

900

900

800

800

others

others 700

500

hydro gas

400

liquid solid

300

600 TWh

w ind GW

solar

solar

600

biomass

700

biomass

w ind

500

hydro gas

400

liquid 300

solid

200

nuclear

nuclear 200

100

100 0 2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

0 2000

ETP Figure 3.4 Electricity generating capacity by fuel use

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

ECN

From both production and capacity, the average annual utilisation can be calculated as indication of activity per power plant type. Where in ECN the total annual utilisation remains around ECN-C--03-046

55

50% from all power plants together, in ETP it increases continuously from 42 to 55%. Both the magnitude and the development of the utilisation of the various technologies are considerably different between the two models. The range of utilisation differs from 0% to 82% for ETP, while for ECN it varies between 19% and 90%. The differences in technology characterisation, e.g. availability and load pattern (base load or peak load, combined heat and power cycles), explains somewhat the difference, scenario assumptions and demand structure and level contribute too. 100%

100%

90%

90% nuclear

80%

gas

60%

hydro

50%

wind

40%

solar biomass

30%

annual utilisation rate

annual utilisation rate

liquid

others 20%

nuclear

80%

solid 70%

solid

70%

liquid gas

60%

hydro

50%

wind

40%

solar biomass

30%

others

20%

total park

total park

10%

10%

0%

0%

2000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

ECN ETP Figure 3.5 Average electricity generating utilisation rate by fuel use The marginal cost of electricity (the most important energy carrier) is also compared to investigate of both models are within the same order of magnitude and have the same trend. 4.00

3.00

3.50

2.50

3.00 annaul

1.50

peak

1.00

€ct/kWh

€ct/kWh

2.00

2.50 annual

2.00

peak

1.50 1.00

0.50

0.50

0.00 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

ETP Figure 3.6 Marginal cost of electricity

0.00 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

ECN

Both models give a similar trend: a rise earlier on and stabilisation afterwards, although the level is higher in ECN, both starting and final level are about 1 to 1.50 €ct/kWh higher. In ETP there is no peak marginal for electricity.

3.3.4 CO2 emissions If the total CO2 emitted by fuel combustion in the system is compared, what immediately strikes is that the accounting in the ETP model is far from complete. The figure below compares three total CO2 emissions: the summed sectoral CO2 in ETP as provided (tot CO2), the total primary CO2 by accounting import, mining and exports in ETP (primary CO2) and the total CO2 form ECN (ECN CO2). For the primary CO2 accounting, the default IPCC emission factors have been used and put into the ETP model as separate case. As can be seen, primary and ECN CO2 are the same for 2000, the differences in energy use and mix make that they divert later in time. This is in line with the comparative primary energy use in 2000 for both models and in line with the emission inventories for the Climate Change Convention (www.unfccc.de). ETP’s reported total CO2 emissions however are almost only half of the real emissions linked to the primary energy use and need further work to complete.

56

ECN-C--03-046

8000 7000

Mton CO2

6000 5000

tot CO2

4000

Primary CO2 ECN CO2

3000 2000 1000 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Figure 3.7 Total CO2 emissions Both models give also sectoral emissions, but as already can be seen from the totals, they can not be compared because the ETP emission accounting is not complete. 4500

4500

4000

4000

conevrsion

2500

transport industry

2000 1500 1000 500 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

ETP Figure 3.8 Sectoral CO2 emissions

3500

power

3000

commercial residential agriculture

Mton CO2

Mton CO2

3500

power

3000

conevrsion

2500

transport industry

2000

commercial residential

1500

agriculture

1000 500 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

ECN

A comparison of CO2 reduction cost curves was intended but not done because of the large deficiencies in ETP’s emission accounting. This would lead to different emission reduction cost curves for both models and give basis to incomparable conclusions.

ECN-C--03-046

57

REFERENCES Audus H. (2001): An overview of climate change and CO2 abatement by capture and storage, AUBE. Aznar C. et .al (1999): The role of carbon sequestration in a global future, Minisymposium on CO2 capture and storage, Chalmers, 1999. Bigg, S. (2002): Sequestering carbon from power plants, the jury is still out, 2002. Bonefeld et al (2001): Status of Horns Rev offshore project. EWEC 2001, Copenhagen, 2001, pp 32-34. BP, Offshore activities, Internet: www.BP.com. BWEA (2000): Prospects for Offshore Wind Energy, 2000. Caddet Renewable Energy Newsletter, March 2001. CETS (1995): Coal: Energy for the Future, Commission on Engineering and Technical Systems, National Academic press, Washington D.C. Coal Research Forum, Internet: http://www.coalresearchforum.org/index.html. David J. (2000): Economic evaluation of leading technology options for sequestration of CO2, MIT 2000. David J., H. Herzog (1999): The cost of carbon capture, MIT. Delft University Wind Energy Research Institute (2001): Offshore wind energy ready to power a sustainable Europe, December 2001. Dresdner Kleinwort Wasserstein (2001): Power Generation in the 21st Century, 2001. ECN (2002): MARKAL Database Western Europe, 2002. ECN (2002): REBUS database, 2002 ENSOC weekly, September 2001. Feber, M. de, A.J. Seebregts, K.E.L. Smekens (2002): Learning in clusters-Methodological issues and Lock-out effects, IEW-EMF, 2002. GERAD (1995): MARKAL Databases Canadian Provinces, 1995. Gonzalvez C.J. (2001): Wind energy in Spain. EWEC 2001, Copenhagen, 2001, pp 79-82. Greenpeace (1999): Wind power in Denmark. Technologies, policies and results. September 1999. Greenpeace (2000): Wind force 10, a blueprint to achieve 10% of the world’s electricity from wind power by 2020, edition 2000. Greenpeace/Deutsches Windenergie Institut (2000): North Sea offshore wind - A power house for Europe. October 2000, pp 80-81. Greenpeace/EPIA (2001): Solar Generation, September 2001. Hamacher and Bradshaw (2001): Fusion as a Future Power Source: recent achievements and prospects, 2001. Harmon, C. (2000): Experience curves of photovoltaic technology. IIASA, IR-00-014, March 2000.

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Hassan, Garrad and Partners Ltd (2000): The potential of wind energy to reduce carbon dioxide emissions. IEA Greenhouse Gas R&D Programme. Report Number PH3/24, October 2000. Herzog, H. (1999): The economics of CO2 separation and capture, MIT. Herzog, H. et al (1997): CO2 capture, re use and storage technologies for mitigating global climate change, DOE Order No DE-ASF22-96PC01257, 1997. Hilten, O. van, T. Gerlagh (2000): Bedrijfseconomische en beleidsmatige evaluatie van elektriciteit- en warmteopwekking uit afval en biomassa, ECN, 2000. IEA (2001): Trends in Photovoltaic Applications, PVPS T1 -10:2001. IEA Coal research, the Clean Coal Center, Internet: www.iea-coal.org.uk/iea1.htm. IEA GHG R&D, Carbon capture from power stations, Internet: www.ieagreen.org.uk/capt1. JAERI (1995): MARKAL database Japan, 1995. Kerr, H. (2001): CO2 capture and storage JIP technical program overview, EU NGO workshop, 2001. KIER (1995): MARKAL Database Korea,1995. Knight, S. (2001): German offshore plans in costs trap. Windpower Monthly, September 2001, pp 44-47. Kooijman et al (2001): Cost and potential of offshore wind energy in the Dutch part of the North Sea. EWEC 2001, Copenhagen, 2001, pp 218-221 Lako P. (2002): Learning and diffusion for wind and solar power technologies. ECN, Petten, ECN-C--02-001. Lako P. (2002): Options for CO2 sequestration and enhanced fuel supply, ECN, VLEEM monograph, 2002. Lako, P., J.R. Ybema and A.J. Seebregts (1998): The long term potential of Fusion power in Europe, ECN, 1998. Lew, Williams, Shaoxiong, Shihui (1996): Industrial Scale Wind power in China, Princeton University, 1996. Lindeberg, E. (1999): Future large-scale use of fossil energy will require CO2 sequestering and disposal, Minisymposium on CO2 capture and storage, Chalmers, 1999. Lyngfelt, A. and B. Leckner (1999): Technologies fro CO2 separation, Chalmers, Minisymposium on CO2 capture and storage, Chalmers, 1999. McDermott Technology Inc., Internet: www.mtiresearch.com/pubs.html#Index of Topics. MIT (2001): Proceedings of Workshop on carbon sequestration science, 2001. Mitsubishi Power Systems, Internet: www.mhi.co.jp/indexe.html. National Renewable Energy Laboratory (1995): Compendium of Renewable Energy Programs and Projects in Asia Pacific Economic Cooperation (APEC) Member Economies, United States Export Council for Renewable Energy, 1995. Noord, M. de (1999): Large-scale offshore wind energy. ECN, Petten. ECN-I--99-043. Offshore Wind Energy (1998): Border Wind, 1998. Oil On Line, Internet: www.oilonline.com. Reisinger and Dulle (2001): Distributed Generation versus Central generation, VLEEM monograph, 2001.

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Renewable Energy World (2001): September-October 2001, 33. Ruether J. et al (2002): Prospects for early deployment of power plants employing carbon capture, Electric Utilities Environmental Conference, 2002. Sedjo R. et al (2001) Estimating Carbon Supply Curves for Global Forests and Other Land Uses, RFF discussion paper 01-19, April 2001 Seebregts A.J. et al (1999), Modelling Technological Progress in a MARKAL Model for Western Europe Including Clusters of Technologies, ECN, Petten, ECN-RX-99-028 Seebregts A.J., Kram T., Schaeffer G.J., Stoffer A. (1998), Endogenous technological learning: Experiments with MARKAL, ECN, Petten, ECN-C-98-064 Shell (2001): Energy Needs, Choices and Possibilities, scenarios to 2050, 2001. Shell, Offshore activities, Internet: www.shell.com. Smekens, K.E.L. (2002): Multi sectoral learning in the WEU model, IEW-EMF workshop 2002. Sorensen H.C., Experience from the establishment of Middelgrunden 40 MW offshore wind farm. EWEC 2001, Copenhagen, 2001, pp 551-543. Statoil, Offshore activities, Internet: www.statoil.com. The American Petroleum Institute, Offshore activities, Internet: www.api.org. The World Commission on Dams (2000): Dams and development: a new financial framework for decision-making, November 2000. United States General Accounting Office (2001): Licensing hydropower projects. GAO-01-499, May 2001. US DOE (1997): MARKAL database USA, 1997. US DOE (1999): Market based advanced coal power systems, Final report, May 1999, Internet: www.fe.doe.gov/coal_power/special_rpts/market_systems/market_sys.shtml. US DOE (2002): Carbon sequestration technology roadmap, 2002. US Energy Information Administration (2001): International Energy Outlook 2001. Hydroelectricity and other renewable resources. 2001, pp 97-117. W/E adviseurs duurzaam bouwen (2000): Corporaties en het dilemma van zonnestroom. October 2000, p. 43. Western Power (2001): The Denham Wind Diesel System, 2001. Zongxin, DeLaquil, Larson, Wenying and Pengfei (2001): Future Implications of China's Energy technology Choices, WGEST CCICED, 2001.

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LIST OF ABREVIATIONS AF AFR ASPA BFG CC CCF CHP ECN DRI EEU ETL ETP ETSAP FBC FC FIXOM FPSO FSU HTS HTU IEA IGCC INV LTS LULUCF MARKAL MEA NA N-ETL NGCC OCE PEM PV SA SOFC TIMES VAROM WEU

ECN-C--03-046

Annual availability factor Africa Asian Pacific Blast Furnace Gas Combined Cycle Cyclone Convertor Furnace Combined Heat and Power Energy Research Center of the Netherlands Direct Reduction Iron Eastern Europe Endogenised Technology Learning Energy Technology Perspectives Energy Technology System Analysis Programme Fluidised Bed Combustion Fuel cell FIXed Operation and Maintenance costs Floating Production, Storage and Offloading Former Soviet Union High Temperature Steam Hydro Thermal Upgrading International Energy Agency Integrated Coal gasification Combined Cycle Investment cost Low Temperature Steam Land Use, Land Use Change and Forestry MARKet ALlocation MonoEthanolAmine North America Non Endogenised Technology Learning part Natural Gas Combined Cycle Oceania Proton Exchange Membrane PhotoVoltaic South America Sold Oxides Fuel Cell The Integrated MARKAL EFOM System VARiable Operation and Maintenance costs Western Europe

61

0.073 0.066 0.283 0.060 0.022

Seabeds

GULFAKS

Verlefik

Trol

62

Sleipner

0.026

Armada

GBS

11.037

platform with CO2 recovery and H2 PP

Grane field 0.215

16.000

platform

22.600

100.000

2.750

13.000

0.011 0.017

12.735

12.000

L9

6.000

0.200

0.220

platform + FPSO

ship

Morne

0.050

Argard

platform + FPSO

Yme

0.200 0.200

Gas [106 m3/d]

0.250

ultradeep+FPSO FPSO

Girassol

Oil [106 barrel/d]

Heidrum

Type

Name

Oil and gas platforms

0.090

0.065

21300

54000

8600

54000

3900 3900

685

1800-2200

100-500

41400

33000

8500

2100

2700 700

Condensate [106 barrel/d] [cost in 106 units]

Output

NRK

NRK

NRK

NRK

NRK NRK

US$

NLG

NRK

NRK

NRK1994

NRK

US$ US$

46.117

125.774

593.231

153.024 138.351

54.502

450.688

419.245

524.056

461.169

104.811

419.245 419.245

Oil

311.680

1379.116

37.926

179.285

0.156 0.234

175.630

152.213

220.659

165.494

82.747

Gas

Output in [PJ/y]

APPENDIX A PRODUCTION AND COST OVERVIEW UPSTREAM OIL AND GAS

132.146

95.439

5.76

4.55

6.30

8.39

3.06 3.38

2.98

2.99

0.20

7.30

6.53

2.30

2.40

6.44 1.67

[PJ/y]

INV cost

ECN-C--03-046

Condensate

seabed

platform

platform seabed platform seabed

minimum platform

combined platform

platform

minimum platform

platform

combined platform

FPSO

platform

gbs

platform

combined platform

StatfjordSygna

Amethyst

Andrew Cyrus Arbroath Arkwright

Bessemer

Bruce

Camelot

Davy

Everest

Forties

Foinaven

Hutton

Harding

Hyde

Indefatigable

ECN-C--03-046

Type

Name

Oil and gas platforms

0.092

0.087

0.120

0.500

0.008

0.067

0.080 0.012 0.036 0.008

0.400

Oil [106 barrel/d]

19.103

1.132

3.113

5.660

2.547

5.943

21.508

2.207

0.991

5.009

20.700

Gas [106 m3/d] 0.060

473

40

400

780

750

2000

928

75

86

1600

63

290 75 265 55

295

1400

20000

Condensate [106 barrel/d] [cost in 106 units]

Output

Pounds 1971

Pounds 1993

pounds 1996

pounds 1978

Pounds 1995

Pounds 1975

Pounds 1991

Pounds 1995

Pounds 1990

Pounds 1994

Pounds 1994

Pounds 1991 Pounds 1994 Pounds 1991 Pounds 1996

Pounds 1991

NRK

NRK

192.853

181.323

251.547

1048.112

16.770

140.447

167.698 25.155 75.464 16.770

838.490

Oil

263.446

15.612

42.932

78.058

35.126

81.961

296.620

30.443

13.660

69.081

285.477

Gas

Output in [PJ/y]

88.097

Condensate

4.58

4.30

3.58

6.71

4.43

3.72

15.12

3.72

1.62

6.15

3.48

2.47 5.01 5.43 5.66

6.60

0.20

6.42

[PJ/y]

INV cost

63

combined platform

platform

platform seabed

platform

Leman

Lomond

Magnus

Montrose

platform

Newsham

64

platform

own use

fpso

Thistle

Shiehallion

Ravesnpurns and combined platforms Cleeton Ravenspur north gbs

Type

Name

Oil and gas platforms

0.152

25500 t diesel

0.150

0.028

0.156 0.017

Oil [106 barrel/d]

0.708

45 106 m3 gas

25

845

600

11.150

204

1100 40

364

760

0.012

0.006

1190

Condensate [106 barrel/d] [cost in 106 units]

28.866

1.698

5.660

44.205

Gas [106 m3/d]

Output

Pounds 1996

Pounds 1978

Pounds 1990

Pounds 1988

Pounds 1976

Pounds 1983 Pounds

Pounds 1991

Pounds 1975

318.626

314.434

58.694

327.011 35.636

Oil

9.757

153.774

398.096

23.417

78.058

609.633

Gas

Output in [PJ/y]

17.619

8.810

4.42

4.26

6.03

3.01

5.84

5.16 1.73

6.47

3.81

[PJ/y]

ECN-C--03-046

Condensate

INV cost