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Modeling technological learning and its application for clean coal technologies in Japan
Applied Energy 88 (2011) 330–336
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Modeling technological learning and its application for clean coal technologies in Japan Toshihiko Nakata *, Takemi Sato, Hao Wang, Tomoya Kusunoki, Takaaki Furubayashi Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, 6-6-11-815 Aoba-Yama, Sendai 980-8579, Japan
a r t i c l e
i n f o
Article history: Received 20 August 2008 Received in revised form 24 May 2010 Accepted 26 May 2010 Available online 24 July 2010 Keywords: Energy model Technological learning Learning curve Spillover effect Clean coal technologies Carbon tax
a b s t r a c t Estimating technological progress of emerging technologies such as renewables and clean coal technologies becomes important for designing low carbon energy systems in future and drawing effective energy policies. Learning curve is an analytical approach for describing the decline rate of cost and production caused by technological progress as well as learning. In the study, a bottom-up energy-economic model including an endogenous technological learning function has been designed. The model deals with technological learning in energy conversion technologies and its spillover effect. It is applied as a feasibility study of clean coal technologies such as IGCC (Integrated Coal Gasification Combined Cycle) and IGFC (Integrated Coal Gasification Fuel Cell System) in Japan. As the results of analysis, it is found that technological progress by learning has a positive impact on the penetration of clean coal technologies in the electricity market, and the learning model has a potential for assessing upcoming technologies in future. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction For the energy policy decision and long-term energy system design or evaluation, it becomes important to consider cost decline and performance improvement through technology progress. Learning curve (experience curve) is one of the methods to quantify the technology development through learning process. A many trials and researches on the assessment of technological learning rates are still under progress [1–5]. For example, forecast of the future price of fuel cell vehicles with technological learning [6], and evaluation of the impacts of learning curve on CO2 reduction as a result of penetration of renewable energy such as solar, wind and fuel cell [7] are typical application of learning. Moreover, as well as focusing on accumulated capacity, R&D investment is also considered as a major parameter in recent researches [8]. Most of these researches are targeted on optimizing R&D investment, subsidy, and energy policy decision. Recently, technological learning analysis is comprehensively reviewed for energy demand technologies [9] and for renewable energy technologies [10]. Technology learning for fuel cells are analyzed by examining past fuel cell cost reductions for both individual manufactures and the global market [11]. In order to make concentrated solar power competitive with coal, the costs of electricity is estimated by considering induced technological learning [12]. Applying the learning curve approach to forecast technology costs involves, however, unresolved uncer* Corresponding author. Tel./fax: +81 22 795 7004. E-mail address: [email protected] (T. Nakata). 0306-2619/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2010.05.022
tainties, as we demonstrate in a case study for clean coal technologies. Zhou et al. presents a real options model incorporating policy uncertainty described by carbon price scenarios, allowing for possible technological change. This model is further used to determine the best strategy for investing in CCS technology in an uncertain environment in China and the effect of climate policy on the decision making process of investment into carbon-saving technologies [13]. A real-options algorithm has been created for optimum energy investments. A systematic impact assessment of stochastic interest and inflation rates on the analysis of energy investments is presented [14]. Evaluating the total energy system is as important as evaluating technological learning. For example, with the low-cost energy conversion technology penetrating into the market, other energy technologies would gradually be abolished. As for the system evaluation, it is necessary to consider both technology and economic sides. In the sense of systems approach, a technology is a part of the system, and the system has a requirement constrained by surrounded environment like society. Understanding this interaction among technology components is a key to set a target of R&D toward the future. However, how to model the mechanism of cost decline with time horizon is not clearly revealed by current energy-economic models. Therefore, energy-economic models in which technological progress is internalized would become an important tool to evaluate the total system and decision making in technology policy. Kannan develops the UK MARKAL energy systems model to investigate these long-term uncertainties in key
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electricity generation options [15]. A range of power sector specific parametric sensitivities have been performed to provide a systematic exploration of least-cost energy system configurations under a broad, integrated set of input assumptions. In this research, an energy-economic model including an endogenous technological learning is created in order to study the feasibility of clean coal technologies and its impacts on electric power sector. Clean coal includes several energy conversion technologies such as coal gasification, advanced combustion, and fuel cells for power generation. Most of them are still under development having some economic uncertainties in electricity markets. The study aims to give clear vision of these technology feasibilities, considering contribution to CO2 emission reduction, system cost comparison, and expected learning of technology components. 2. An energy-economic model considering technological learning In the study, based on METANet [16] modeling approach, technological learning is internalized. METANet is a partial equilibrium modeling system that allows for explicit price competition among technologies in markets by comparing capacity, supply price, quantity and system characteristics. The total annual cost includes both annual specific capital cost and annual O&M cost. By minimizing the annual cost, the energy price is shown as follows:
ðLf Af Þ ½ðPc CcÞ DCF þ Rf ¼ SCC Pc ¼
ð1Þ
SCC Rf þ Cc Lf Af DCF DCF
0
ð1 Þ
Pc: present value, DCF: discount rate, Rf: future profits, Cc: marginal costsSCC: specific capital cost, Lf: road factor, Af: the rate of operation. Strictly, the energy-economic model utilized in this research is a simulation tool rather than an optimization tool. Based on specific capital cost, O&M cost and fuel cost, current electricity generation price could be calculated as well as considering the future revenue expectation. By using the electricity generator price, the shares of different types of electricity generations could be calculated. The future revenue expectation could also be obtained in the similar way based on the current market prices and the market shares. If great future revenue is expected, the unit price of energy supply could be controlled to help the technology penetration. If specific capital cost is reduced with technological learning, the new cost will be utilized and the price of this period will be modified. By repeating the same process, the electricity generation mix of each period could be calculated. The learning curve is specified as follows: b
Cost t ¼ Cost 0 Cumulativ eProduction
ð2Þ
LR ¼ 1 2b
ð3Þ
In those equations, Cost is the cost of technology or product in period t, CumulativeProduction is accumulated production quantity (if referred to energy technologies, it means accumulated capacity). b is the inclination of learning curve. LR is learning rate which means the cost decline rate by two times increasing of accumulated production quantity. The former researches of technological learning are mostly based on the assumption that each CumulativeProduction is decided by each specific capital cost. However, if more technological details are taken into account, common component technologies exist in different energy conversion methods and potential of technological improvement or innovation should not be ignored as well. Therefore, it is possible that the technologies, which are used in different energy conversion methods, could be transferred to the
Fig. 1. Common component technologies between energy conversion technologies.
whole system. In another word, same technological learning such as knowledge or experience could be spilled out to many different sectors. A combined cycle gas turbine plant is an example. In terms of technological learning, the component technologies in gas combined cycle is gas turbine, HRSG, steam turbine, and auxiliary machine. Accessory machine includes pumps, generator, flue-gas denitration, and control system. Compared to traditional combined cycle, advanced combined cycle has more advanced gas turbine in the way to improve combustion stability and efficiency, higher firing temperature, lower emissions such as NOx, CO, and THC, and cool down the combustor wall using combustion air and/or steam (Fig. 1). In fact many R&D efforts have been done in the past two decades on the improvement of gas turbine. In this case, for energy-economic model considering technological learning, the specific capital cost of technologies is decided by the total accumulated capacity of common component technologies. Based on the mechanism, the spillover effect could be modeled, which is expressed as follows:
CmpSCC t;c
!b P AllTech CumCapt;c P ¼ CmpSCC 0;c AllTech CumCap0;c
ð4Þ
P Here, CumCap is accumulated capacity, is the total accumulated capacity of all the component technologies, CmpSCC is the specific capital cost of component technology. For the analysis, first CmpSCC is calculated and then the specific capital cost is obtained. By considering the cost weight, the total specific capital cost can be generated.
SCC t ¼
X
CmpSCC t;c CostWeightt;c
ð5Þ
c
Moreover, for applying the energy-economic model considering the technological learning, necessary data include learning rate of component technologies, cost weight, starting capacity and starting capital cost. 3. Evaluating the feasibility of introduction of clean coal technologies 3.1. Design of energy-economic model By utilizing the energy-economic model including endogenous technological learning, the feasibility of introducing clean coal
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technologies is evaluated. Clean coal technologies are already commercialized in Europe and the US. In Japan, integrated coal gasification combined cycle (IGCC) and integrated coal gasification fuel cell system (IGFC) is on their R&D process [17]. Recently, 17 IGCC plants are operated in the world, and their net electric efficiency is around 42% [18]. Future IGCC plants are expected to achieve efficiency above 50% within 10 years through R&D effort. R&D effort for IGCC is being carried out on gasification systems, gas turbines and oxygen production. Moreover, there are some R&D projects of IGFC, i.e. Eagle project in Japan and FutureGen project in the US. SOFC system of 3–10 kW has been demonstrated in 2005 in the US [19]. Target electric efficiency by 2025 is 55% in Japan [20]. In IGCC power plants, coal and air are reacted in a gasifier to produce coal gas, which is then cleaned up and burned through gas turbine, and generate electricity. After that, by HRSG, thermal energy is reused to generate electricity through steam turbine. The component technologies of IGCC are gasifier, gas turbine, and HSRG. Moreover, IGFC process is based on IGCC, and by using the H2 and CO generated from gasifier, electricity is generated by fuel cell and through combine cycle. In the research, gasifier, HSRG, and gas turbine are considered as common component technologies in IGCC, and IGFC, gas turbine, and HSRG are even considered as common technologies in all generations that uses gas combined cycles. In fact, for the different purposes the changes of temperature in turbine can cause changes of thermal efficiency, component materials, and cooling technologies. Zaporowski analyses energy-conversion processes in gas-steam power plants integrated with coal gasification considering thermal balances and mass flow [21,22]. Kim et al. examines influence of system integration options on
the performance of an integrated gasification combined cycle power plant using an optimization software Aspen [23]. They analyses the impact of the use of syngas on operating conditions of the gas turbine in an IGCC plant, and evaluate the performance of a gas turbine under operating limitations in terms of compressor surge and turbine metal temperature [24]. Although firing temperature should be considered in different operating conditions such as 1300 °C (CCGT), 1500 °C (IGCC) and 1000 °C (IGFC), the clean coal technologies in the study are considered as an integrated system therefore those details are illuminated. As the study focuses on the possibility of cost competitiveness of IGCC and IGFC in Japan, technical parameters for both coming technologies are set based on the target of R&D there. It gives at least advantages for new technologies to penetrate into the market. IGCC with the firing temperature of 1500 °C is under research and development in Japan as a next project target considering future technological change and learning [17]. It introduces air-blown gasifier, wet-type clean up to remove H2S and COS in coal gasified fuel, and high temperature gas turbine to meet higher thermal efficiency. The Japan’s energy model for the analysis is illustrated in Fig. 2. Both IGCC and IGFC are introduced as energy conversion nodes in electric power sector on lower right. Table 1 summarizes all the input parameters related to component technologies and technological learning. The specific capital cost of IGCC is decided by the product of Japanese pulverized coal power plants cost [25] with the ratio of American commercial IGCC to American pulverized coal power plants [26]. The specific capital cost of IGFC is the product of IGCC’s specific capital cost and the ratio of IGCC to IGFC [27].
Fig. 2. Japanese energy-economic model.
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T. Nakata et al. / Applied Energy 88 (2011) 330–336 Table 1 Cost and technical parameters of power generation technologies. Variable O&M cost
Efficiencyd (%, HHV)
Component technology
74.0
1.7
46
Combustion turbine-advanced HRSG Balance of plant-combined cycle
30 40 30
10 1 1
IGCC (starting year :2020)
137.6
1.2
48
Combustion turbine-advanced HRSG Gasifier Balance of plant-IGCC
15 20 41 24
10 1 10 1
IGFC (starting year :2026)
179.7c
1.2
55
Combustion turbine-advanced HRSG Gasifier Balance of plant-IGFC Fuel cell
11 15 31 18 23
10 1 10 1 10
Pulverized coal Gas boiler
115.8 79.8
2.0 1.9
41 39
– –
100 100
1 1
Power generation technology Gas combined cycle
a b c d e
Initial SCCb (US$/mmBtu/yr)
Cost weighta (%)
Learning ratea,e (%)
USDOE/EIA (2005). Nagata (2001). Longwell (1996). NEDO (2005). McDonald (2001).
As for the thermal efficiency, the value is according to the official target by NEDO [28]. Moreover, the learning rates for the analysis are carefully chosen based on former research by US-DOE [26]. Both IGCC and IGFC will start to be introduced from the year 2020 and 2026, respectively. As an additional unit construction of oil-fired power plant is prohibited by IEA from the year 1979, therefore, oil-fired power will be gradually abolished in the electricity market. Considering Japan’s geographical situation, it is difficult to develop nuclear power plants and hydro power plants in a large scale, therefore those two types of generations are set to keep the current situation for the study. As for renewable energy, based on RPS law, renewable energy supply is taken as responsibility and in the analysis, the responsibility is assumed to continue after the year 2010. Moreover, the technological learning of petroleum, nuclear, hydro and renewable power plants are not included in the analysis. The fuel price from the year 2002 and the annual increasing rate is decided as: Petroleum, $4.0/mmBtu and 0.45%/yr, coal, $1.74/ mmBtu and 0.21%/yr and LNG, $4.12/mmBtu and 0.37%/yr [29,30]. The price demand elasticity is categorized as industry sector (0.34), commercial sector (0.23), residential sector (0.38) and transportation sector (passenger 0.23, freight 0.17) [31].
combined cycle will not lower capital cost of IGCC and IGFC in the case A2. Thus, each electricity generation technology has its own learning rate, respectively. The learning rate is derived as a weighted average of learning rates of component technologies. The value of cost weight means the weighting number of each component technology in the total generation system. Learning rates and cost weights for the analysis carefully chosen from references [26] and are summarized in Table 1. (A3) Integrated technological learning case In this case, both component technological learning and its spillover effect are considered. For example, introduction of gas combined cycle will lower capital cost of IGCC and IGFC through the capital cost reduction of component technologies such as HRSG. Each component technology has a similar learning rate as shown in Table 1. Besides those three cases, case B in which carbon tax is imposed and case C in which both petroleum and LNG price shooting up is considered, are introduced and examined in the study. 4. Results 4.1. The impact of technological learning on power generation configuration
3.2. Case design In Japan’s government strategy of green innovation, IGCC is one of the key technologies to put their growth on a ‘‘low carbon path.” Their first target is the year 2020 to reduce CO2 by 25% based on the 1990. The second target is the year 2050 to reduce CO2 by 60–80%. Three cases are designed in the study by considering possible technological learning to meet the official target in cost-effective way. The analysis starts from the year 2002 and up to the year 2040. Each period lasts for 2 years, and the analysis is performed for 19 periods covering 38 years. (A1) Excluded technological learning case In this case, specific capital cost will not be changed since technological learning is not considered. (A2) Traditional technological learning case In this case, similar way of internalizing technological learning curve of former researches is utilized. Each electricity generation method is considered independent, which means technological spillover effect does not happen. For example, introduction of gas
The changes in electric power generation for A1, A2 and A3 scenarios from the year 2002 until the year 2040 is illustrated in Fig. 3s, respectively. IGCC is coming to the electricity sector in all three cases while the penetration of IGFC in the market is only seen in A3 case. The electricity generated from IGCC in both A2 and A3 cases is more than the electricity in A1 case, replacing pulverized coal power and gas power generation. In the year 2040, the share of total clean coal technologies achieves 17% in A1 case and reaches around 35% in A2 and A3 cases, which can be seen from Fig. 4 of capital cost comparison. The reason of share changes is coming from the reduction of specific capital cost in the future. In A2 case, since the spillover effect is not considered, IGCC benefits significantly from technological learning and ensures its leading position in fossil power generation. In A3 case, the total share of IGCC and IGFC does not change in a large scale compared with the A2 case. However, IGFC shows a large growth and accounts for around 8% in the year 2040 due to spillover effect considered in A3 case. IGFC gains almost no share
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A1
A2
6
6
IGFC
IGFC
IGCC
IGCC 9
9
4
4
Pulv.Coal
Pulv.Coal
Gas.Cmb
2
Gas.Cmb
Renew
Gas.Blr Oil.Blr
2
Renew
Gas.Blr Oil.Blr
Hydro
Hydro
Nuc.Blr
Nuc.Blr
0 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038
0 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038
Year
Year
A3 6
IGFC IGCC
9
4 Pulv.Coal Gas.Cmb
2
Renew
Gas.Blr Oil.Blr Hydro Nuc.Blr
0 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038
Year Fig. 3. Electricity generation mix up to the year 2038 in A1, A2 and A3 cases.
200 180 160 140 120 100 80 60 40 20
A2-IGFC A2-IGCC A2-Pulv.Coal A2-Gas.Cmb
A3-IGFC A3-IGCC A3-Pulv.Coal A3-Gas.Cmb
0
Year Fig. 4. Change of specific capital cost up to the year 2038 in A2 and A3 cases.
due to higher capital cost in A2 case. Since both IGCC and IGFC share the same component technology in a combined cycle in A3 case, the spillover effect of technological learning leads IGFC’s specific capital cost to become lower even before the available year. 4.2. The impact of carbon taxation and fuel price up on power generation configuration The configuration of electric power generation is not only decided by technology performance but also deeply influenced by the changes in fuel price. In the following two cases, the impact of carbon tax and petroleum/gas price on clean coal technologies’ market share is analyzed. (A) Carbon tax case
It is assumed that a $20/TC [32] tax would start to be imposed from the year 2006, which has already been discussed by the Japan’s Ministry of the Environment. Tax revenue to support climate change countermeasure is not considered. Other assumptions are the same as those in A3 case. (B) High petroleum/LNG price case In this case, both the price change of petroleum and LNG is considered to keep high. The growing rate of petroleum price is assumed at 1.02%/year. The price of LNG is tightly connected with the change price of petroleum, set as 1.19%/yr. Other assumptions are the same as in A3 case. In Fig. 5, the power generation mix in the year 2030 in A3, B and C cases are illustrated. Among these three cases, shares of power generations except clean coal technologies are similar. In case B, electricity generated by IGFC is increased by 60% due to the relatively high thermal efficiency. On the other hand, electricity generated by IGCC is decreased by 30%, which results in the reduction of total clean coal technologies. Meanwhile, the pulverized coal power generation does not change in a large scale therefore the switch from traditional coal power plant to advanced coal power plant has not happened. The result could be explained that the rate of cost reduction is slower than that of coal price increasing. In case C, high price of LNG directly influences on gas power generations and leads coal-based power generations such as IGCC, IGFC and pulverized coal to increase their share significantly. Technological learning leads to the cost reduction of clean coal power plants. As the average generation costs does not change, total electricity demand keeps similar to the A3 case. From the results of the analysis, it is revealed that both carbon tax and fuel price up influence on the configuration of power generation technologies. Considering technological learning in a large scale gives more clear view of system configuration in the future.
335
Electricity generation (10 9 mmBtu/yr)
T. Nakata et al. / Applied Energy 88 (2011) 330–336
6
1400 IGFC IGCC
1200 Fuel cost
1000
Operating cost
4
800
Pulv.Coal
Capital cost
600 Gas.Cmb
2
Oil.Blr
Gas.Blr
Renew
Hydro
400 200
Tax Revenue
0
Nuc.Blr
A1
0
Reference (A3)
Carbon tax (B)
High Oil/LNG price (C)
A2
A3
B
C
-200 Fig. 7. Details of system cost in A1, A2, A3, B and C cases.
Fig. 5. Electricity generation mix change when carbon tax and high fuel price are considered.
5. Discussions
4.3. CO2 emission For evaluating the changes in CO2 emission, one more case D is introduced and the comparison between A3 case and D case is taken into account. In the case D, both IGCC and IGFC are not supposed to be introduced into electric power sector and the technological learning only happens in gas combined cycle, gas boiler and pulverized coal power plants. Fig. 6 illustrates the changes in CO2 emission from electric power sector in A3 and D cases. In the year 2040, CO2 from A3 case reaches 23.9 mmTC (million tones of carbon) more than that from D case. Although clean coal technologies are more efficient than traditional coal-based generations, CO2 emission factor of CCTs is still larger than that of natural gas. For example, the CO2 emission factor is 0.037 TC/mmBtu (0.12 TC/MWh) in gas boiler, 0.031 TC/ mmBtu (0.11 TC/MWh) in gas combined cycle, 0.054 TC/mmBtu (0.18 TC/MWh) in IGCC and 0.047 TC/mmBtu (0.16 TC/MWh) in IGFC. Therefore, since in A3 case clean coal technologies replaces not only replace pulverized coal power plants but also gas boiler and gas combined cycle power plants, the total emission of CO2 from electric power sector increases as a result. 4.4. System cost The total cost and tax is shown in Fig. 7. Cost includes specific capital cost, O&M cost, and fuel cost. In case B, where carbon tax is imposed, with the price rising and demand decreasing, the total cost becomes lower. The tax revenue illustrated in Fig. 7 does not include the cost generated on the process of tax imposing. Moreover, case C case shows the highest total cost in all five cases.
250 200 150 100 2
Reference (A3) (A3)
50
Non CCT (D) Non-CCT (D)
0 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038
Year Fig. 6. Change of CO2 emission in the electric power sector in A3 and D cases.
5.1. The feasibility of clean coal technologies If the specific capital cost of IGCC achieves the commercial level in international markets, IGCC would gain enough economic competitiveness and be considered as one of the basic electricity generation types in Japan’s electric power sector. IGFC, according to the result of A3, is also worth being developed, because which has enough potential to generate electricity in a cost-effective way in future. However, if considering technological learning, the key precondition is whether IGCC technology is mature or not. Moreover, if the price of petroleum and LNG turns to become higher while that of coal stays in the same level, clean coal technologies will become more preferable. On the other hand, clean coal technologies are not always preferable solutions when CO2 emission is considered. Although clean coal technologies are supposed to meet both efficiency and environment requirement, as shown in Section 4.3, the CO2 emission from the total electric power sector would increase. Imposing carbon tax will not only restrain the penetration of IGCC but also slow lower the speed of technological learning process therefore the replacement of traditional coal-based power plants would probably be slow downed. Moreover, considering the CO2 emission reduction, besides R&D support, focusing on the replacement of traditional coal-based technologies is also important according to analytical results.
5.2. Applicability of the study In the analysis, the penetration of clean coal technologies varies when technological learning or component technologies are considered. For IGFC, if component technologies and the spillover effect of technological learning are considered, it gains much market competitiveness. For those technologies that do not have potential for technology innovation, the ones that are based on the improvement of traditional technologies or the ones that share some component technologies of new technologies, taking technological learning and its spillover effect is necessary. For example, in the transportation sector recently, besides traditional automobiles that utilize internal-combustion engine, hybrid cars or fuel cell cars are also greatly being developed. In that field, common component technologies partially exist such as internal-combustion engine, fuel cell, motor, and body. Moreover, for research areas such as forecasting the future development like fuel cell stack, it is also possible to apply the result of the study.
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5.3. Future work The technological learning effect is limited on accumulated production domestically in the analysis. On the other hand, in different countries, the capacity structure, generation cost and competition varies therefore technological learning condition also varies. In the study, the capacity in the model is limited only in Japan. Of course, technologies that are greatly developed world wide such as IGCC should never be constrained in one country. The research development of IGCC abroad would no doubt be a key factor to influence Japan’s situation. As common component technologies for all the clean coal technologies such as gas turbine, the equipment with different characteristics exist widely, how to model the technological learning considering equipment diversity is one of the future tasks. In the study, several measurements considering component technologies are suggested. However, in reality, it is difficult to obtain exact data on technological learning. Using the historical trend data has sometimes problems that former trend could not always be the same in future, therefore uncertainty should be well discussed. Furthermore, this research does not include innovative break through caused by new technologies that never exist before. The mechanism of technology development is complicated and it is not easy to consider all the factors that influence the process. In the future work, wider and deeper empirical research and the development of technology progress theory become important. 6. Conclusions In this study, an energy-economic model that considers the learning rates of component technologies and their spillover effects are created. The model is then applied in order to study the feasibility of clean coal technologies. Following results could be revealed according to the analysis. (1) It has great impacts on the penetration of clean coal technologies that component technologies and technological learning are considered. The future penetration of IGCC and IGFC can be obviously analyzed if spillover effects among component technologies are taken into account. (2) Carbon tax would reduce the market share of clean coal technologies. Changes in fuel price will influence the speed of technological learning as well as configuration of electricity generation. (3) CO2 emission from electric power sector would increase with the penetration of clean coal technologies. The result of the study could be applied for not only clean coal technologies but also other upcoming technologies that are expected to come into the market in the future. Acknowledgement We appreciate useful comments by anonymous reviewers. References [1] McDonald A, Schrattenholzer J. Learning rates for energy technologies. Energy Policy 2001;29:255–61.
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Modeling technological learning and its application for clean coal technologies in Japan Toshihiko Nakata *, Takemi Sato, Hao Wang, Tomoya Kusunoki, Takaaki Furubayashi Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, 6-6-11-815 Aoba-Yama, Sendai 980-8579, Japan
a r t i c l e
i n f o
Article history: Received 20 August 2008 Received in revised form 24 May 2010 Accepted 26 May 2010 Available online 24 July 2010 Keywords: Energy model Technological learning Learning curve Spillover effect Clean coal technologies Carbon tax
a b s t r a c t Estimating technological progress of emerging technologies such as renewables and clean coal technologies becomes important for designing low carbon energy systems in future and drawing effective energy policies. Learning curve is an analytical approach for describing the decline rate of cost and production caused by technological progress as well as learning. In the study, a bottom-up energy-economic model including an endogenous technological learning function has been designed. The model deals with technological learning in energy conversion technologies and its spillover effect. It is applied as a feasibility study of clean coal technologies such as IGCC (Integrated Coal Gasification Combined Cycle) and IGFC (Integrated Coal Gasification Fuel Cell System) in Japan. As the results of analysis, it is found that technological progress by learning has a positive impact on the penetration of clean coal technologies in the electricity market, and the learning model has a potential for assessing upcoming technologies in future. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction For the energy policy decision and long-term energy system design or evaluation, it becomes important to consider cost decline and performance improvement through technology progress. Learning curve (experience curve) is one of the methods to quantify the technology development through learning process. A many trials and researches on the assessment of technological learning rates are still under progress [1–5]. For example, forecast of the future price of fuel cell vehicles with technological learning [6], and evaluation of the impacts of learning curve on CO2 reduction as a result of penetration of renewable energy such as solar, wind and fuel cell [7] are typical application of learning. Moreover, as well as focusing on accumulated capacity, R&D investment is also considered as a major parameter in recent researches [8]. Most of these researches are targeted on optimizing R&D investment, subsidy, and energy policy decision. Recently, technological learning analysis is comprehensively reviewed for energy demand technologies [9] and for renewable energy technologies [10]. Technology learning for fuel cells are analyzed by examining past fuel cell cost reductions for both individual manufactures and the global market [11]. In order to make concentrated solar power competitive with coal, the costs of electricity is estimated by considering induced technological learning [12]. Applying the learning curve approach to forecast technology costs involves, however, unresolved uncer* Corresponding author. Tel./fax: +81 22 795 7004. E-mail address: [email protected] (T. Nakata). 0306-2619/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2010.05.022
tainties, as we demonstrate in a case study for clean coal technologies. Zhou et al. presents a real options model incorporating policy uncertainty described by carbon price scenarios, allowing for possible technological change. This model is further used to determine the best strategy for investing in CCS technology in an uncertain environment in China and the effect of climate policy on the decision making process of investment into carbon-saving technologies [13]. A real-options algorithm has been created for optimum energy investments. A systematic impact assessment of stochastic interest and inflation rates on the analysis of energy investments is presented [14]. Evaluating the total energy system is as important as evaluating technological learning. For example, with the low-cost energy conversion technology penetrating into the market, other energy technologies would gradually be abolished. As for the system evaluation, it is necessary to consider both technology and economic sides. In the sense of systems approach, a technology is a part of the system, and the system has a requirement constrained by surrounded environment like society. Understanding this interaction among technology components is a key to set a target of R&D toward the future. However, how to model the mechanism of cost decline with time horizon is not clearly revealed by current energy-economic models. Therefore, energy-economic models in which technological progress is internalized would become an important tool to evaluate the total system and decision making in technology policy. Kannan develops the UK MARKAL energy systems model to investigate these long-term uncertainties in key
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electricity generation options [15]. A range of power sector specific parametric sensitivities have been performed to provide a systematic exploration of least-cost energy system configurations under a broad, integrated set of input assumptions. In this research, an energy-economic model including an endogenous technological learning is created in order to study the feasibility of clean coal technologies and its impacts on electric power sector. Clean coal includes several energy conversion technologies such as coal gasification, advanced combustion, and fuel cells for power generation. Most of them are still under development having some economic uncertainties in electricity markets. The study aims to give clear vision of these technology feasibilities, considering contribution to CO2 emission reduction, system cost comparison, and expected learning of technology components. 2. An energy-economic model considering technological learning In the study, based on METANet [16] modeling approach, technological learning is internalized. METANet is a partial equilibrium modeling system that allows for explicit price competition among technologies in markets by comparing capacity, supply price, quantity and system characteristics. The total annual cost includes both annual specific capital cost and annual O&M cost. By minimizing the annual cost, the energy price is shown as follows:
ðLf Af Þ ½ðPc CcÞ DCF þ Rf ¼ SCC Pc ¼
ð1Þ
SCC Rf þ Cc Lf Af DCF DCF
0
ð1 Þ
Pc: present value, DCF: discount rate, Rf: future profits, Cc: marginal costsSCC: specific capital cost, Lf: road factor, Af: the rate of operation. Strictly, the energy-economic model utilized in this research is a simulation tool rather than an optimization tool. Based on specific capital cost, O&M cost and fuel cost, current electricity generation price could be calculated as well as considering the future revenue expectation. By using the electricity generator price, the shares of different types of electricity generations could be calculated. The future revenue expectation could also be obtained in the similar way based on the current market prices and the market shares. If great future revenue is expected, the unit price of energy supply could be controlled to help the technology penetration. If specific capital cost is reduced with technological learning, the new cost will be utilized and the price of this period will be modified. By repeating the same process, the electricity generation mix of each period could be calculated. The learning curve is specified as follows: b
Cost t ¼ Cost 0 Cumulativ eProduction
ð2Þ
LR ¼ 1 2b
ð3Þ
In those equations, Cost is the cost of technology or product in period t, CumulativeProduction is accumulated production quantity (if referred to energy technologies, it means accumulated capacity). b is the inclination of learning curve. LR is learning rate which means the cost decline rate by two times increasing of accumulated production quantity. The former researches of technological learning are mostly based on the assumption that each CumulativeProduction is decided by each specific capital cost. However, if more technological details are taken into account, common component technologies exist in different energy conversion methods and potential of technological improvement or innovation should not be ignored as well. Therefore, it is possible that the technologies, which are used in different energy conversion methods, could be transferred to the
Fig. 1. Common component technologies between energy conversion technologies.
whole system. In another word, same technological learning such as knowledge or experience could be spilled out to many different sectors. A combined cycle gas turbine plant is an example. In terms of technological learning, the component technologies in gas combined cycle is gas turbine, HRSG, steam turbine, and auxiliary machine. Accessory machine includes pumps, generator, flue-gas denitration, and control system. Compared to traditional combined cycle, advanced combined cycle has more advanced gas turbine in the way to improve combustion stability and efficiency, higher firing temperature, lower emissions such as NOx, CO, and THC, and cool down the combustor wall using combustion air and/or steam (Fig. 1). In fact many R&D efforts have been done in the past two decades on the improvement of gas turbine. In this case, for energy-economic model considering technological learning, the specific capital cost of technologies is decided by the total accumulated capacity of common component technologies. Based on the mechanism, the spillover effect could be modeled, which is expressed as follows:
CmpSCC t;c
!b P AllTech CumCapt;c P ¼ CmpSCC 0;c AllTech CumCap0;c
ð4Þ
P Here, CumCap is accumulated capacity, is the total accumulated capacity of all the component technologies, CmpSCC is the specific capital cost of component technology. For the analysis, first CmpSCC is calculated and then the specific capital cost is obtained. By considering the cost weight, the total specific capital cost can be generated.
SCC t ¼
X
CmpSCC t;c CostWeightt;c
ð5Þ
c
Moreover, for applying the energy-economic model considering the technological learning, necessary data include learning rate of component technologies, cost weight, starting capacity and starting capital cost. 3. Evaluating the feasibility of introduction of clean coal technologies 3.1. Design of energy-economic model By utilizing the energy-economic model including endogenous technological learning, the feasibility of introducing clean coal
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technologies is evaluated. Clean coal technologies are already commercialized in Europe and the US. In Japan, integrated coal gasification combined cycle (IGCC) and integrated coal gasification fuel cell system (IGFC) is on their R&D process [17]. Recently, 17 IGCC plants are operated in the world, and their net electric efficiency is around 42% [18]. Future IGCC plants are expected to achieve efficiency above 50% within 10 years through R&D effort. R&D effort for IGCC is being carried out on gasification systems, gas turbines and oxygen production. Moreover, there are some R&D projects of IGFC, i.e. Eagle project in Japan and FutureGen project in the US. SOFC system of 3–10 kW has been demonstrated in 2005 in the US [19]. Target electric efficiency by 2025 is 55% in Japan [20]. In IGCC power plants, coal and air are reacted in a gasifier to produce coal gas, which is then cleaned up and burned through gas turbine, and generate electricity. After that, by HRSG, thermal energy is reused to generate electricity through steam turbine. The component technologies of IGCC are gasifier, gas turbine, and HSRG. Moreover, IGFC process is based on IGCC, and by using the H2 and CO generated from gasifier, electricity is generated by fuel cell and through combine cycle. In the research, gasifier, HSRG, and gas turbine are considered as common component technologies in IGCC, and IGFC, gas turbine, and HSRG are even considered as common technologies in all generations that uses gas combined cycles. In fact, for the different purposes the changes of temperature in turbine can cause changes of thermal efficiency, component materials, and cooling technologies. Zaporowski analyses energy-conversion processes in gas-steam power plants integrated with coal gasification considering thermal balances and mass flow [21,22]. Kim et al. examines influence of system integration options on
the performance of an integrated gasification combined cycle power plant using an optimization software Aspen [23]. They analyses the impact of the use of syngas on operating conditions of the gas turbine in an IGCC plant, and evaluate the performance of a gas turbine under operating limitations in terms of compressor surge and turbine metal temperature [24]. Although firing temperature should be considered in different operating conditions such as 1300 °C (CCGT), 1500 °C (IGCC) and 1000 °C (IGFC), the clean coal technologies in the study are considered as an integrated system therefore those details are illuminated. As the study focuses on the possibility of cost competitiveness of IGCC and IGFC in Japan, technical parameters for both coming technologies are set based on the target of R&D there. It gives at least advantages for new technologies to penetrate into the market. IGCC with the firing temperature of 1500 °C is under research and development in Japan as a next project target considering future technological change and learning [17]. It introduces air-blown gasifier, wet-type clean up to remove H2S and COS in coal gasified fuel, and high temperature gas turbine to meet higher thermal efficiency. The Japan’s energy model for the analysis is illustrated in Fig. 2. Both IGCC and IGFC are introduced as energy conversion nodes in electric power sector on lower right. Table 1 summarizes all the input parameters related to component technologies and technological learning. The specific capital cost of IGCC is decided by the product of Japanese pulverized coal power plants cost [25] with the ratio of American commercial IGCC to American pulverized coal power plants [26]. The specific capital cost of IGFC is the product of IGCC’s specific capital cost and the ratio of IGCC to IGFC [27].
Fig. 2. Japanese energy-economic model.
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T. Nakata et al. / Applied Energy 88 (2011) 330–336 Table 1 Cost and technical parameters of power generation technologies. Variable O&M cost
Efficiencyd (%, HHV)
Component technology
74.0
1.7
46
Combustion turbine-advanced HRSG Balance of plant-combined cycle
30 40 30
10 1 1
IGCC (starting year :2020)
137.6
1.2
48
Combustion turbine-advanced HRSG Gasifier Balance of plant-IGCC
15 20 41 24
10 1 10 1
IGFC (starting year :2026)
179.7c
1.2
55
Combustion turbine-advanced HRSG Gasifier Balance of plant-IGFC Fuel cell
11 15 31 18 23
10 1 10 1 10
Pulverized coal Gas boiler
115.8 79.8
2.0 1.9
41 39
– –
100 100
1 1
Power generation technology Gas combined cycle
a b c d e
Initial SCCb (US$/mmBtu/yr)
Cost weighta (%)
Learning ratea,e (%)
USDOE/EIA (2005). Nagata (2001). Longwell (1996). NEDO (2005). McDonald (2001).
As for the thermal efficiency, the value is according to the official target by NEDO [28]. Moreover, the learning rates for the analysis are carefully chosen based on former research by US-DOE [26]. Both IGCC and IGFC will start to be introduced from the year 2020 and 2026, respectively. As an additional unit construction of oil-fired power plant is prohibited by IEA from the year 1979, therefore, oil-fired power will be gradually abolished in the electricity market. Considering Japan’s geographical situation, it is difficult to develop nuclear power plants and hydro power plants in a large scale, therefore those two types of generations are set to keep the current situation for the study. As for renewable energy, based on RPS law, renewable energy supply is taken as responsibility and in the analysis, the responsibility is assumed to continue after the year 2010. Moreover, the technological learning of petroleum, nuclear, hydro and renewable power plants are not included in the analysis. The fuel price from the year 2002 and the annual increasing rate is decided as: Petroleum, $4.0/mmBtu and 0.45%/yr, coal, $1.74/ mmBtu and 0.21%/yr and LNG, $4.12/mmBtu and 0.37%/yr [29,30]. The price demand elasticity is categorized as industry sector (0.34), commercial sector (0.23), residential sector (0.38) and transportation sector (passenger 0.23, freight 0.17) [31].
combined cycle will not lower capital cost of IGCC and IGFC in the case A2. Thus, each electricity generation technology has its own learning rate, respectively. The learning rate is derived as a weighted average of learning rates of component technologies. The value of cost weight means the weighting number of each component technology in the total generation system. Learning rates and cost weights for the analysis carefully chosen from references [26] and are summarized in Table 1. (A3) Integrated technological learning case In this case, both component technological learning and its spillover effect are considered. For example, introduction of gas combined cycle will lower capital cost of IGCC and IGFC through the capital cost reduction of component technologies such as HRSG. Each component technology has a similar learning rate as shown in Table 1. Besides those three cases, case B in which carbon tax is imposed and case C in which both petroleum and LNG price shooting up is considered, are introduced and examined in the study. 4. Results 4.1. The impact of technological learning on power generation configuration
3.2. Case design In Japan’s government strategy of green innovation, IGCC is one of the key technologies to put their growth on a ‘‘low carbon path.” Their first target is the year 2020 to reduce CO2 by 25% based on the 1990. The second target is the year 2050 to reduce CO2 by 60–80%. Three cases are designed in the study by considering possible technological learning to meet the official target in cost-effective way. The analysis starts from the year 2002 and up to the year 2040. Each period lasts for 2 years, and the analysis is performed for 19 periods covering 38 years. (A1) Excluded technological learning case In this case, specific capital cost will not be changed since technological learning is not considered. (A2) Traditional technological learning case In this case, similar way of internalizing technological learning curve of former researches is utilized. Each electricity generation method is considered independent, which means technological spillover effect does not happen. For example, introduction of gas
The changes in electric power generation for A1, A2 and A3 scenarios from the year 2002 until the year 2040 is illustrated in Fig. 3s, respectively. IGCC is coming to the electricity sector in all three cases while the penetration of IGFC in the market is only seen in A3 case. The electricity generated from IGCC in both A2 and A3 cases is more than the electricity in A1 case, replacing pulverized coal power and gas power generation. In the year 2040, the share of total clean coal technologies achieves 17% in A1 case and reaches around 35% in A2 and A3 cases, which can be seen from Fig. 4 of capital cost comparison. The reason of share changes is coming from the reduction of specific capital cost in the future. In A2 case, since the spillover effect is not considered, IGCC benefits significantly from technological learning and ensures its leading position in fossil power generation. In A3 case, the total share of IGCC and IGFC does not change in a large scale compared with the A2 case. However, IGFC shows a large growth and accounts for around 8% in the year 2040 due to spillover effect considered in A3 case. IGFC gains almost no share
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A1
A2
6
6
IGFC
IGFC
IGCC
IGCC 9
9
4
4
Pulv.Coal
Pulv.Coal
Gas.Cmb
2
Gas.Cmb
Renew
Gas.Blr Oil.Blr
2
Renew
Gas.Blr Oil.Blr
Hydro
Hydro
Nuc.Blr
Nuc.Blr
0 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038
0 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038
Year
Year
A3 6
IGFC IGCC
9
4 Pulv.Coal Gas.Cmb
2
Renew
Gas.Blr Oil.Blr Hydro Nuc.Blr
0 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038
Year Fig. 3. Electricity generation mix up to the year 2038 in A1, A2 and A3 cases.
200 180 160 140 120 100 80 60 40 20
A2-IGFC A2-IGCC A2-Pulv.Coal A2-Gas.Cmb
A3-IGFC A3-IGCC A3-Pulv.Coal A3-Gas.Cmb
0
Year Fig. 4. Change of specific capital cost up to the year 2038 in A2 and A3 cases.
due to higher capital cost in A2 case. Since both IGCC and IGFC share the same component technology in a combined cycle in A3 case, the spillover effect of technological learning leads IGFC’s specific capital cost to become lower even before the available year. 4.2. The impact of carbon taxation and fuel price up on power generation configuration The configuration of electric power generation is not only decided by technology performance but also deeply influenced by the changes in fuel price. In the following two cases, the impact of carbon tax and petroleum/gas price on clean coal technologies’ market share is analyzed. (A) Carbon tax case
It is assumed that a $20/TC [32] tax would start to be imposed from the year 2006, which has already been discussed by the Japan’s Ministry of the Environment. Tax revenue to support climate change countermeasure is not considered. Other assumptions are the same as those in A3 case. (B) High petroleum/LNG price case In this case, both the price change of petroleum and LNG is considered to keep high. The growing rate of petroleum price is assumed at 1.02%/year. The price of LNG is tightly connected with the change price of petroleum, set as 1.19%/yr. Other assumptions are the same as in A3 case. In Fig. 5, the power generation mix in the year 2030 in A3, B and C cases are illustrated. Among these three cases, shares of power generations except clean coal technologies are similar. In case B, electricity generated by IGFC is increased by 60% due to the relatively high thermal efficiency. On the other hand, electricity generated by IGCC is decreased by 30%, which results in the reduction of total clean coal technologies. Meanwhile, the pulverized coal power generation does not change in a large scale therefore the switch from traditional coal power plant to advanced coal power plant has not happened. The result could be explained that the rate of cost reduction is slower than that of coal price increasing. In case C, high price of LNG directly influences on gas power generations and leads coal-based power generations such as IGCC, IGFC and pulverized coal to increase their share significantly. Technological learning leads to the cost reduction of clean coal power plants. As the average generation costs does not change, total electricity demand keeps similar to the A3 case. From the results of the analysis, it is revealed that both carbon tax and fuel price up influence on the configuration of power generation technologies. Considering technological learning in a large scale gives more clear view of system configuration in the future.
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Electricity generation (10 9 mmBtu/yr)
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6
1400 IGFC IGCC
1200 Fuel cost
1000
Operating cost
4
800
Pulv.Coal
Capital cost
600 Gas.Cmb
2
Oil.Blr
Gas.Blr
Renew
Hydro
400 200
Tax Revenue
0
Nuc.Blr
A1
0
Reference (A3)
Carbon tax (B)
High Oil/LNG price (C)
A2
A3
B
C
-200 Fig. 7. Details of system cost in A1, A2, A3, B and C cases.
Fig. 5. Electricity generation mix change when carbon tax and high fuel price are considered.
5. Discussions
4.3. CO2 emission For evaluating the changes in CO2 emission, one more case D is introduced and the comparison between A3 case and D case is taken into account. In the case D, both IGCC and IGFC are not supposed to be introduced into electric power sector and the technological learning only happens in gas combined cycle, gas boiler and pulverized coal power plants. Fig. 6 illustrates the changes in CO2 emission from electric power sector in A3 and D cases. In the year 2040, CO2 from A3 case reaches 23.9 mmTC (million tones of carbon) more than that from D case. Although clean coal technologies are more efficient than traditional coal-based generations, CO2 emission factor of CCTs is still larger than that of natural gas. For example, the CO2 emission factor is 0.037 TC/mmBtu (0.12 TC/MWh) in gas boiler, 0.031 TC/ mmBtu (0.11 TC/MWh) in gas combined cycle, 0.054 TC/mmBtu (0.18 TC/MWh) in IGCC and 0.047 TC/mmBtu (0.16 TC/MWh) in IGFC. Therefore, since in A3 case clean coal technologies replaces not only replace pulverized coal power plants but also gas boiler and gas combined cycle power plants, the total emission of CO2 from electric power sector increases as a result. 4.4. System cost The total cost and tax is shown in Fig. 7. Cost includes specific capital cost, O&M cost, and fuel cost. In case B, where carbon tax is imposed, with the price rising and demand decreasing, the total cost becomes lower. The tax revenue illustrated in Fig. 7 does not include the cost generated on the process of tax imposing. Moreover, case C case shows the highest total cost in all five cases.
250 200 150 100 2
Reference (A3) (A3)
50
Non CCT (D) Non-CCT (D)
0 2002 2006 2010 2014 2018 2022 2026 2030 2034 2038
Year Fig. 6. Change of CO2 emission in the electric power sector in A3 and D cases.
5.1. The feasibility of clean coal technologies If the specific capital cost of IGCC achieves the commercial level in international markets, IGCC would gain enough economic competitiveness and be considered as one of the basic electricity generation types in Japan’s electric power sector. IGFC, according to the result of A3, is also worth being developed, because which has enough potential to generate electricity in a cost-effective way in future. However, if considering technological learning, the key precondition is whether IGCC technology is mature or not. Moreover, if the price of petroleum and LNG turns to become higher while that of coal stays in the same level, clean coal technologies will become more preferable. On the other hand, clean coal technologies are not always preferable solutions when CO2 emission is considered. Although clean coal technologies are supposed to meet both efficiency and environment requirement, as shown in Section 4.3, the CO2 emission from the total electric power sector would increase. Imposing carbon tax will not only restrain the penetration of IGCC but also slow lower the speed of technological learning process therefore the replacement of traditional coal-based power plants would probably be slow downed. Moreover, considering the CO2 emission reduction, besides R&D support, focusing on the replacement of traditional coal-based technologies is also important according to analytical results.
5.2. Applicability of the study In the analysis, the penetration of clean coal technologies varies when technological learning or component technologies are considered. For IGFC, if component technologies and the spillover effect of technological learning are considered, it gains much market competitiveness. For those technologies that do not have potential for technology innovation, the ones that are based on the improvement of traditional technologies or the ones that share some component technologies of new technologies, taking technological learning and its spillover effect is necessary. For example, in the transportation sector recently, besides traditional automobiles that utilize internal-combustion engine, hybrid cars or fuel cell cars are also greatly being developed. In that field, common component technologies partially exist such as internal-combustion engine, fuel cell, motor, and body. Moreover, for research areas such as forecasting the future development like fuel cell stack, it is also possible to apply the result of the study.
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5.3. Future work The technological learning effect is limited on accumulated production domestically in the analysis. On the other hand, in different countries, the capacity structure, generation cost and competition varies therefore technological learning condition also varies. In the study, the capacity in the model is limited only in Japan. Of course, technologies that are greatly developed world wide such as IGCC should never be constrained in one country. The research development of IGCC abroad would no doubt be a key factor to influence Japan’s situation. As common component technologies for all the clean coal technologies such as gas turbine, the equipment with different characteristics exist widely, how to model the technological learning considering equipment diversity is one of the future tasks. In the study, several measurements considering component technologies are suggested. However, in reality, it is difficult to obtain exact data on technological learning. Using the historical trend data has sometimes problems that former trend could not always be the same in future, therefore uncertainty should be well discussed. Furthermore, this research does not include innovative break through caused by new technologies that never exist before. The mechanism of technology development is complicated and it is not easy to consider all the factors that influence the process. In the future work, wider and deeper empirical research and the development of technology progress theory become important. 6. Conclusions In this study, an energy-economic model that considers the learning rates of component technologies and their spillover effects are created. The model is then applied in order to study the feasibility of clean coal technologies. Following results could be revealed according to the analysis. (1) It has great impacts on the penetration of clean coal technologies that component technologies and technological learning are considered. The future penetration of IGCC and IGFC can be obviously analyzed if spillover effects among component technologies are taken into account. (2) Carbon tax would reduce the market share of clean coal technologies. Changes in fuel price will influence the speed of technological learning as well as configuration of electricity generation. (3) CO2 emission from electric power sector would increase with the penetration of clean coal technologies. The result of the study could be applied for not only clean coal technologies but also other upcoming technologies that are expected to come into the market in the future. Acknowledgement We appreciate useful comments by anonymous reviewers. References [1] McDonald A, Schrattenholzer J. Learning rates for energy technologies. Energy Policy 2001;29:255–61.
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