Aug 1, 1999 - Western Europe including clusters of technologies ... specific investment cost of a 'learning' technology decreases as a function of cumulative ...
August 1999
ECN-RX--99-028
Modelling technological progress in a MARKAL model for Western Europe including clusters of technologies
Paper to be presented at the European IAEE/AEE Conference ‘Technological Progress and the Energy Challenge’ 30 Sep - 1 Oct 1999, Paris, France A.J. Seebregts T. Kram G.J. Schaeffer A.J.M. Bos
Abstract The paper describes experience gained from experiments with the comprehensive energy system model MARKAL for Western Europe, including endogenous technology learning based on the learning-by-doing mechanism. These experiments have confirmed the benefits expected from adoption of the mechanism. An important issue, often overlooked in simple models is the notion of interdependency between (families of) technologies, rather than considering individually learning technologies. An approach to address such clusters has been developed and tested. The results indicate that the new ‘cluster feature’ improves the internal consistency and allows for assessment of spill-over and cross-over effects, and other mechanisms identified in technology dynamics. Insights are gained in economic benefits associated with context-driven technological progress, as well as in the potential of R&D and market deployment policies.
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CONTENTS 1. INTRODUCTION
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2. THE LEARNING CURVE IN MARKAL
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3. INSIGHTS FROM THE FIRST EXPERIMENTS
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4. CLUSTERS OF TECHNOLOGIES
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5. NUMERICAL RESULTS
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6. SUMMARY AND CONCLUSIONS
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REFERENCES
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ANNEX A
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TECHNOLOGY DATA AND LEARNING PARAMETERS
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1.
INTRODUCTION
Technological change is widely recognised as a key factor in economic progress, as it enhances the productivity of factor inputs. In recent years also the notion has developed that targeted technological development is a main means to reconcile economic ambitions with ecological considerations. This raises the issue that assessments of future trajectories of for example energy systems should take into account context-specific technological progress. Rather than taking characteristics of existing and emerging technologies as a given, their development should be a function of dedicated Research, Development and Demonstration (RD&D) actions and market deployment under varying external conditions. Endogenous technological learning (ETL) has recently demonstrated to be a very promising new feature in energy system models. Examples are the MESSAGE (Messner, 1997) and GENIE (Mattsson, 1997) models. A learning curve describes the specific (investment) cost as a function of the cumulative capacity for a given technology. It reflects the fact that technology costs often decline as a result of its increasing adoption into the society due to the accumulation of knowledge (e.g. learning-by-doing and learning-by-using). In 1998, the Paul Scherrer Institute (PSI)1 and ECN carried out the first MARKAL experiments with ETL in the framework of an EU2 sponsored research project ‘TEEM’ (Energy Technology Dynamics and Advanced Energy System Modelling, see TEEM (1999). This paper summarises recent experience gained at ECN with its comprehensive energy system model MARKAL for Western Europe, expanding on the concept of technology clusters.
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The co-operation with Leonardo Barreto and Socrates Kypreos from PSI is highly acknowledged. The European Commission is acknowledged for sponsoring the ECN contribution to the TEEM project (Non Nuclear Energy Programme JOULE III, contract JOS3-CT97 0013).
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2.
THE LEARNING CURVE IN MARKAL
Typically, energy scenarios analysed with energy system models assume that characteristics of technologies (cost, performance and emission indicators) can change over time. This can be seen as a reflection on technology dynamics (learning), but with a few exceptions the trends are exogenous - a function of time, for instance - to the energy system analysis model. Hence the characteristics do not respond to changing circumstances during the time horizon of the modelling exercise. Recent experiments with the relatively small-scale energy system models GENIE (Mattsson, 1997, 1998) and the reduced version of MESSAGE (Messner, 1997) have shown that formulations with endogenous learning are feasible and lead to insights not directly obtainable from conventional models. The two models both adopt a learning or experience curve approach: the specific investment cost of a ‘learning’ technology decreases as a function of cumulative capacity (‘learning-by-doing’ mechanism). This reflects the fact that technologies may experience declining costs as a result of its increasing adoption into the society due to the accumulation of knowledge. The learning curve represents the compounded effect of learning-by-doing and learning-by-using (Dutton and Thomas, 1984; Grübler, 1998), but also a number of other technical, economical, environmental and social factors. The cumulative capacity is used as a measure of the knowledge accumulation for the technology. A learning curve can be expressed as:
SC (C ) = SC 0 (C / C 0) − b
(Eq. 2.1)
Where: SC : Specific cost as function of C C : Cumulative capacity b : Learning index (constant) C0 : Initial cumulative capacity (at t = 0) SC0 : Initial specific cost (at t = 0) The learning index b can be used to calculate the progress ratio (pr): pr = 2-b. The progress ratio expresses the rate at which the cost declines each time the cumulative production doubles. E.g., a progress ratio of 0.8 means that the costs per unit of newly installed capacity decrease by 20% each time the cumulative installed capacity is doubled. The progress ratio (and b) thus constitutes a key factor for technological progress because it determines the speed of learning for the technology. One example is given in Figure 2.1 and concerns fuel cell costs as a function of time (derived from Schaeffer, 1998). Combined with a (crude) estimation of the cumulative capacity of all kinds of demonstration fuel cells during the same years (also based on Schaeffer, 1998), the progress ratio for the period 1962-1997 can be estimated at 0.66. This low value reflects the high R&D intensity of fuel cell development. It also illustrates that a low progress ratio is not necessarily favourable for the technology, as it is in this case achieved by producing very little capacity and spending much R&D on the technology. Continuing on the same track, it may well take several more decades to arrive at a commercially viable product. The only commercial manufacturer of fuel cells claims to currently achieve a progress ratio in its manufacturing of fuel cells of 75% (Whitaker, 1998). However, it is not very plausible that, as markets for fuel cells will be developed, the progress ratio will remain on the low historical values of 0.66 or 0.75. An estimate in line with this insight (Thomas et al., 1998) leads to a progress ratio of 0.82.
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Figure 2.1 Historical specific costs of fuel cells (Schaeffer, 1998) As another example, Figure 2.2 displays the cost of photovoltaic cells as a function of cumulative installed capacity. The progress ratio derived from this figure equals 0.81. Clearly, estimating progress ratios is far from straightforward. For more details on how to obtain or estimate progress ratios see Chapter 3 of Seebregts et al. (1998). [ECU/kW] 18000 16000
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Figure 2.2 Development of investment costs photovoltaic cells world wide (Bos et al., 1999) The ‘standard’ MARKAL is a widely applied bottom-up, linear programming (LP) model (IEAETSAP, 1999) developed by the Energy Technology Systems Analysis Programme (ETSAP) of the International Energy Agency (IEA)3. The learning curve concept as formulated above introduces a non-linear relation between model variables. Moreover, the resulting problem is nonconvex and constitutes a problem for current NLP solving algorithms (Mattson, 1997). An obvious solution is to approximate the non-linear with mixed-integer (MIP) relations, as demonstrated earlier in MESSAGE (Messner, 1997) and GENIE model experiments (Mattsson, 1997). The MIP feature approximates the non-convex objective function by piece-wise linear functions, and with use of a branch-and-bound algorithm a unique optimal solution can be found. The Paul Scherrer Institute (PSI) developed basic source code to incorporate technology learning in MARKAL (Kypreos and Barreto, 1998b). ECN then incorporated, tested, and slightly adapted the PSI formulation in the most recent MARKAL version (Seebregts et al., 1998). 3
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In addition to this ‘standard’ MARKAL LP model, the MARKAL family of models includes MARKALMACRO (non-linear programming, NLP, a combination with the general economics of MACRO, a long-term neoclassical growth model), MARKAL-MICRO (NLP), and MARKAL-ED (LP), combinations with partial equilibrium models allowing energy demands to be responsive to prices.
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INSIGHTS FROM THE FIRST EXPERIMENTS
The first practical experience from MARKAL with endogenous technology learning was gained from a small-scale application: a simple MARKAL model for the global electricity sector (Kypreos and Barreto, 1998a), and from a large-scale example covering Western Europe (Seebregts et al., 1998). It was shown that even a detailed, full-scale MARKAL model with endogenous learning is able to generate globally optimal solutions efficiently. A second important finding is that the model outcomes (e.g., prospects of technologies) can differ drastically once ETL is adopted. The insights and results of these first MARKAL experiments have been used to compare the experience obtained by two other models used within TEEM, the ERIS prototype and MESSAGE (Seebregts et al., 1999). From a methodological point of view, all these teams agreed on the added value of adopting the endogenous learning mechanism. Still, a number of issues needed resolution and further research to benefit fully from ETL or to apply ETL as standard practice. The three most important issues are: 1. How to deal with uncertainty with respect to learning? Not only the (historic) progress ratio itself is uncertain, but it is also uncertain if it will retain the same level over the entire trajectory considered or if it might rise or decline after an initial period of learning e.g. once a certain capacity threshold is exceeded. 2. Can we develop formal, quantitative models to link RD&D measures directly to learning parameters, notably the progress ratio, and can such models be supported by reliable input data? The few experiments so far indicate that the new models enable at least a qualitative assessment of the effect of RD&D policy instruments on technological development. For a more quantitative assessment, additional modelling work and establishment of reliable input data are required. 3. How to model interdependencies between technologies that share common key components? This last issue has been dealt with as part of more recent ECN experiments expanding on the concept of technology clusters. It is the topic of the remaining part of this paper.
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4.
CLUSTERS OF TECHNOLOGIES
A ‘cluster of technologies’ is defined as a group of technologies sharing a common essential component. This component, which can be a technology in itself, is called the ‘key technology’ and is selected as the learning component in each of the technologies in the cluster. Examples of key technologies are gas turbines, fuel cells, photo-voltaic (PV) modules, wind turbines, burners and boilers. The vast majority of the about 500 technologies defined in the MARKAL technology database for Western Europe rely on some 20 of such key technologies (Seebregts et al., 1998). To implement the concept of clusters the following approach has been followed: (1) Identify the clusters and key technologies from the technology database. (2) Review the characteristics of the technologies in each cluster. (3) Add the common component as key technology to the technology database. (4) Add equations coupling the key with the technologies in the corresponding cluster. (5) Assign to the key technology the learning parameters. (6) Calibrate all learning parameters so that they are in line with the current available cost and capacity data. (7) Adjust the characteristics of the remaining parts of the technologies in the corresponding cluster. (8) Adjust bounds or other parameters of the key technologies or of the technologies in the clusters when deemed necessary. The technology database used for this study was developed in a recent long-term scenario study (Lako et al., 1998). This database, originally covering the period 1990-2100 but truncated to 2050 for the learning experiments), includes over 400 demand technology applications and approximately 100 supply technologies. Some of the energy technologies considered are well known, e.g. proven coal and gas-fired power technologies. A lot, however, are in various stages of development, demonstration, or (early) deployment, e.g. wind energy, biomass power, PV, and others. The approach outlined above has been applied to five clusters: wind turbines (WT), solar PV modules (PV), fuel cells (FC), gasifiers (GF), and gas turbines (GT). These clusters include relatively innovative technologies (e.g. solar PV and fuel cells) with a large learning potential, but also more mature technologies (e.g. gas turbines). Table A.1 in the Annex shows the technologies that form the five clusters. Table A.2 in the same Annex presents the learning parameters used.
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5.
NUMERICAL RESULTS
The concept of ‘learning’ clusters of technologies is illustrated by the results outlined below; more details are reported in (TEEM, 1999). The results depart from the technology database and a scenario developed in an earlier scenario study (Lako et al., 1998). The Market Drive scenario used here assumes a world, in which market mechanisms are seen as most appropriate to generate prosperity and handle complexity under uncertainty, like long-term ecological concerns. The Market Drive scenario with a time horizon until 2050 is the Base case for the new MARKAL runs with ETL including clusters of technologies. Departing from this Base case, a case with a CO2 emission constraint has been investigated (‘CO2’). This involves an extrapolation of the Kyoto target for the EU, that implies an annual emission limit equal to 92% (hence, -8%) of the 1990 level for the 2010 period. Thereafter the annual emission limits are linearly extrapolated to meet 84% (-16%) in 2030 and 76% (-24%) by 2050. The most interesting cases are summarised here. These cases show the effect of: (1) endogenous technology learning compared to exogenous learning, (2) clusters of technologies. Other interesting phenomena, e.g. mechanisms such as path-dependency and lock-out, the influential role of inputs like progress ratios, user-defined bounds on capacity, market penetration constraints, are reported in Seebregts et al. (1998) and TEEM (1999).
The effect of endogenous technology learning The first experiments with ETL in the Western European MARKAL model have already shown that the new feature really makes a difference (Seebregts et al., 1998, 1999). More recently, runs were made to mimic the situation without endogenous learning. By setting the progress ratios to 1.0, the costs of the key technologies cannot change from their initial level. The CO2 reduction cost is interesting to evaluate in this respect. As can be observed from Figure 5.1, the overall costs for CO2 reduction are lower with learning (ETL) than without (NETL). [ECU95/tCO2] 250 ETL NETL
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Figure 5.1 Marginal cost of CO2 reduction, with endogenous technology learning (ETL) and without it (NETL) The marginal cost figures show that ETL tends to induce early action (in 2010 the marginal cost is higher for ETL). The benefits of these early investments arise in later periods, in which the marginal cost is substantially lower.
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The impact of clustering technologies Figure 5.2 shows the installed capacity of the five clusters. In the Base case only the gas turbine and gasifier key technologies are widely applied, mainly as component of coal-based integrated gasification combined cycle plants (see Figure 5.3). Fuel cells and solar PV do not enter the market and wind turbines play only a marginal role. As a consequence gas turbine costs are reduced by 40% compared to the 1999 level (dropping to 230 ECU95/kW in 2050) and the gasifier costs drop 60 % (to 340 ECU/kW in 2050). The CO2 reduction target has a big impact on the market penetration and cost development of some key technologies. Compared with the Base case, gasifiers and gas turbines are used less (see Figure 5.2, right). They are still used, but now in different applications than in the Base case: e.g. in natural gas-fired combined cycles and in biomass gasification plants (see also Figure 5.3) Thus, these two emerge as robust key technologies. Wind turbines, especially offshore, become the most interesting option to reduce CO2 emissions (see also Figure 5.2, right). As a consequence of the massive investment in wind turbines, their costs are reduced by 50% compared to 1999 levels and reach 400 ECU95/kW in 2050 (see Figure 5.4). Note, however, that this only applies to the wind turbine component: additionally, 170 (onshore) to 900 (offshore) ECU95/kW are required for the tower, foundation and electrical infrastructure. Fuel cell and solar PV technologies are still not attractive. [GW]
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Figure 5.2 Installed capacity of the three key technologies, Base (left) and CO2 (right), stacked area chart The results discussed above have already shown some important impacts of clustering technologies. The results of a few variants illustrate the interaction between two different market areas of one key technology, and the advantages of modelling this interaction through the concept of key and clusters of technologies.
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Figure 5.3 Installed capacity of cost-effective gas turbine technologies, Base (left) and CO2 (right) [ECU95/kW] 1200 Gasifier 1000
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Figure 5.4 Cost development of key technologies, CO2 reduction case Fuel cells (FC) can be used as supply option for power and heat, but also in vehicles. In the cases described above, the transport applications were not included in the FC cluster. So, their original, exogenous cost profile was retained and experience from the stationary application was not transferred to the transport application, or vice versa. The combined learning effect of both stationary and transport FC applications, however, did lead to market penetration. The most attractive applications for fuel cells to reduce CO2 appear to be in the transportation sector. Several advanced vehicle types equipped with fuel cells enter the market, starting with the most attractive market segment (see Figure 5.5). The big transport markets opening up induce considerable reduction in FC costs.
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Taking advantage of this development in the shared key technology, eventually stationary applications in the FC cluster also unfold. It must be noted that careful tuning of the model is crucial to ensure credible behaviour of such complex clustering mechanisms. In the example, transport applications remain favourite: of the total cumulative FC capacity of 625 GW in 2050, a mere 5 GW are invested in stationary applications. [PJ/yr] 180
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Figure 5.5 Market penetration of fuel cell transport technologies, CO2 case, with both stationary and transport applications in fuel cell cluster
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SUMMARY AND CONCLUSIONS
The learning-by-doing mechanism has been successfully incorporated and tested in the largescale MARKAL application for Western Europe. The main advantage of modelling technology learning endogenously (ETL) is the enhanced consistency compared to the commonly applied exogenous cost projections. In the new ETL formulation, technology cost development is fully consistent with its uptake on the market. In addition, the necessary technology maturing costs are factored into the overall benefit/cost assessment. From the model user’s point of view, awareness of the ‘learning-by-doing’ mechanism tends to foster model input improvements (e.g. technology characterisation), even if the model is operated in a non-learning mode. Incorporating the learning-by-doing concept makes an important difference. A comparison with the original models with exogenous cost projections (either constant cost over time or assuming a regular decline over time) shows that the resulting technology prospects differ substantially. The experiments quantify the benefits of investing early in emerging technologies that are not competitive at the moment of their deployment. They also show that the long-term impact of policy instruments, such as CO2 taxes or emission limits and RD&D instruments, on technological development can be assessed adequately Recent results confirm that spill-over and cross-over effects can be captured by the feature of ‘clusters of technologies’. For example, if the notion that technologies do not learn on their own but essentially in clusters was not taken into account, the technological progress would be underestimated, and hence, costs would be overestimated. One of the specific advantages is that experience gained in different markets can yield mutual benefit. The results of the Post-Kyoto CO2 reduction analyses indicate that the estimated cost of CO2 reduction will be decreased if endogenous technology learning is adopted. Policy measures aiming at CO2 emission reduction appear to have a clear and often decisive positive impact on the prospect of clean technologies by providing an incentive to trigger market deployment. This phenomenon underlines their important role in guiding technology development towards more sustainable directions. Future research will aim to combine the endogenous learning feature with well-established methods to encompass uncertainty in technology rich models, inter alia to explore the role of R&D in hedging strategies and the option value of technological progress.
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REFERENCES Bos, A.J.M., P. Lako, T.J. de Lange, G.J. Schaeffer (1999). Toekomstige energietechnologieën? - Technologiekarakteristieken van PV-systemen, windturbines, brandstofcellen, de biomassavergasser en de HTR (in Dutch). Report ECN, Petten, The Netherlands, 1999. (To be published) Dutton, J. M., and A. Thomas (1984). Treating Progress Functions as a Managerial Opportunity, Academy of Management Review. Vol. 9, No. 2, pp. 235-247. Grübler A. (1998). Technology and Global Change. Cambridge University Press, Cambridge, UK. IEA-ETSAP (1999). The MARKAL energy system model. http://www.ecn.nl/unit_bs/etsap/markal/main.html, and Frequently Asked Questions MARKAL. http://www.ecn.nl/unit_bs/etsap/markal/faq.html, February 1999. Kypreos, S. and L. Barreto (1998a). A Simple Global Electricity MARKAL Model with Endogenous Learning. Presented at the Joint ALEP/ETSAP Workshop, Antalya, Turkey, 26-28 October 1998. Kypreos, S. and L. Barreto (1998b): Mixed Integer Programming Formulation for Experience Curves in MARKAL - PSI Version. PSI, Villigen, Switzerland. Lako, P., J.R. Ybema, A.J. Seebregts (1998): The long term potential of fusion power in Western Europe - MARKAL scenarios until 2100. Report ECN-C--98-071, ECN, Petten, The Netherlands. Mattson, N. (1997). Internalizing technological development in energy system models. Thesis for the degree of licentiate in engineering, Chalmers University of Technology, report ISRN CTH-EST-R--97/3-SE, Göteborg, Sweden. Mattson, N. (1998). GENIE: an Energy System Model with Uncertain Learning. Proceedings of the IEA ETSAP/Annex VI 5th Workshop, Berlin, Germany, 5-7 May 1998, ECN, Petten, The Netherlands. Messner, S. (1997). Endogenised technological learning in an energy systems model. J. Evol Econ, 7: 291-313. Neij, L. (1997). Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology. Energy Policy, Vol. 23, No. 13, pp. 1099-1107. Schaeffer, G.J. (1998). Fuel Cells for the Future; A Contribution to Technology Forecasting from a Technology Dynamics Perspective. Ph.D. Thesis, Petten, 1998. Seebregts A.J., T. Kram, G.J. Schaeffer, A. Stoffer (1998). Endogenous Technological Learning: Experiments with MARKAL - Contribution to task 2.3 of the Project TEEM of EC-JOULE-III, report ECN-C--98-064, ECN, Petten, The Netherlands. Seebregts A.J., T. Kram, G.J. Schaeffer, A. Stoffer, S. Kypreos, L. Barreto, S. Messner, L. Schrattenholzer (1999). Endogenous technological change in energy system models. Synthesis of experience with ERIS, MARKAL, and MESSAGE’. Contribution to the TEEM Project of Joule III, report ECN-C--99-025, ECN, Petten, The Netherlands. TEEM, Final Report (1999). In preparation by the TEEM Consortium. Thomas, C.E., B.D. James, F.D. Lomax (1998). Market Penetration Scenarios for Fuel Cell Vehicles, in International Journal of Hydrogen Energy, Vol. 23, No. 10, 1998, pp. 949966. Whitaker, R. (1998). Investment in volume building: the ‘virtuous cycle’ in PAFC, in Journal of Power Sources, Vol. 71, Elsevier, Lausanne, 1998, pp.71-74.
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ANNEX A TECHNOLOGY DATA AND LEARNING PARAMETERS This Annex provides more details on the data used for the results reported in Section 5 of this paper. Table A.1 shows the technologies as identified for the five selected clusters. Note that technologies can belong to more than one cluster. Table A.1 Technologies in the five clusters Description of technology
in cluster(s)
START (if not 1990)
Integrated Coal gasification power plant Integrated Coal Gasification Fuel Cell plant Gas turbine peaking plant Existing gas Combined Cycle power plant New gas Combined Cycle power plant Combined cycle Fuel Cell power plant Existing gas turbine CHP plant Existing Combined Cycle CHP plant Gas Fuel Cell plant (total energy for H, C and A) Biomass gasification: small industrial CHP Biomass gasification: Combined Cycle power plant Biomass gasification: ISTIG+reheat Solar PV in Northern Europe Solar PV roofs southern ESP, IT, GR Solar PV in Central Europe Solar PV roofs/barren land cent. ESP, IT Solar PV: import from North Africa Large onshore wind turbine – inland Large onshore wind turbine – shore Off-shore wind turbine – near shore Off-shore wind turbine – off shore Fuel Cell car with modified frame (MF) and regenerative braking (RB) Fuel Cell car with modified frame (MF) Fuel Cell van with modified frame (MF) and regenerative braking (RB) Fuel Cell van with modified frame (MF) Fuel Cell Truck with modified frame (MF) and regenerative braking (RB) Fuel Cell Truck with modified frame (MF) Fuel Cell bus with modified frame (MF) and regenerative braking (RB)
GF GT GF GT FC GT GT GT GT FC GT GT FC GF GT GF GT GF GT PV PV PV PV PV WT WT WT WT FC FC FC FC FC FC FC
2000 2020
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Table A.2 shows the relevant data for the five key technologies. The original technology database formed the basis to derive the initial costs, the initial cumulative capacity and the maximum cumulative capacity of the key technologies. Note from Table A.2 that the ‘floor-cost as percentage of initial cost’ is a good measure for the innovative nature and hence learning potential of a technology. In addition, the ‘maximum number of doublings’ in cumulative capacity is a measure of the future market potential. Both measures are a function of the four basic learning parameters: progress ratio, initial costs, the initial cumulative capacity, and the maximum cumulative capacity. Table A.2 Learning parameters of key technologies Key Description
Progress Initial cost Cost 1999 Initial Maximum Implied floor-cost2) maximum 1) ratio 1990 cumulative cumulative ‘floor-costs’ as percent- number of age of initial doublings (calibrated) capacity capacity [GW] [GW] [ECU95/kW] [-] cost [%] [ECU95/kW] [ECU95/kW] [-]
FC Fuel Cell 0.82 2000 2000 0.08 1000 134 6.7 13.6 GF GasiFier 0.90 968 840 0.653) 1000 317 32.8 10.6 GT Gas Turbine 0.88 490 380 16.2 1000 229 46.8 6.0 PV Solar PV-modules 0.82 6341 4000 0.1 6004) 525 8.3 12.6 WT Wind turbine 0.90 1390 800 0.147 1000 379 27.3 12.7 Notes: (1) Progress ratios have been estimated mainly on the basis of (Seebregts et al., 1998). It should be noted that these progress ratios can be rather uncertain, as was shown in (Seebregts et al., 1999) by comparison of values used in three different models. (2) The implied floor-cost are the costs when the maximum cumulative capacity is reached in 2050. There may still be a cost portion (not learning endogenously) in the original technologies, i.e. that portion either remains constant or decreases exogenous. (3) 1999 value, used to calibrate initial cost 1990. (4) The maximum cumulative capacity for solar PV, 600 GW, was based on a combination of upper investment bounds of all solar PV technologies in the original Market Drive scenario. For the other key technologies an arbitrary value of 1000 GW was chosen, which turned out well.
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