Scenario analysis of CO2 emission abatement effect based on LEAP

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Applied A En F 2018 bon ms, A Applied Ennergy nergy Sympo Sympoosium osium and and Forum F Forum 20188: 8: Low Low carb carb bon cities cities an annd nd urban urban ene eneergy ergy system system ms, CUE2018-AppliedCUE2018 Energy Symposium and Forum 2018: Low carbon cities and 8, 5–7June 2 2018, Shang ghai, China CUE20188, 5–7June 2018, 2 Shang ghai, China urban energy systems, 5–7 June 2018, Shanghai, China The 15th International Symposium on District Heating and Cooling

Sc aanalysis of eemission nn abatem ment AP Sccenario cenario analysis a of CO CO22 emission e abatem ment effe effeect ect based baseddd on on LEA LEA AP Assessing the feasibility of using the heat demand-outdoor temperature function for a long-term district heat demand forecast b b

a aa

a b b Liya G Jianfe Ca aiFerrão Liyaa**a, ,,J.Guo GFournier Guo Jianfebeng eng I. Andrića,b,c*, A. Pinaa, Ca P.ai ., B. Lacarrièrec, O. Le Correc

State Power Innvestment Corporration Research Institute, I Future Science S Park, Beiiqijia, Changpingg District, Beijingg 102209, China State Power nvestment Corpor ration Research I Research Institute, Future S Science Park, Bei iqijia, Changping g District,1,Beijing g 102209, China IN+ Center forIn Technology and Policy - Instituto Superior Técnico, Av. Rovisco 1049-001 Lisbon, Portugal Insttitute of Science aInnovation, and Development t, Chinese Academ my of Sciences, N No.15 ZhongGuan nCunBeiYiTiao, H Pais District Haidian B Beijing 100190, China C b Insttitute of Science and a Development t, Recherche Chinese Academ my of Sciences, N No.15 ZhongGuan nCunBeiYiTiao, H France Haidian District B Beijing 100190, China C Veolia & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France

Absttract Absttract

S Stabilization aand and CO pp Abstract S Stabilization a reduction reduction of of the the atmos atmosspheric spheric carbo carboon on dioxide dioxide (C (C CO22)) concentr concentration ation will will be be one one of of the the prime prime chal lenges for the e energy secto or in the upco oming decade es. Carbon ca apture and sto orage (CCS) i s widely seen n challenges for thee energy sectoor in the upcooming decadees. Carbon caapture and stoorage (CCS) is widely seen n as as aa poss sibility to con tinue fossil po ower generati ion while con ntributing to CO C abatement t. In future en nergy systems s with District heating are commonly addressed in thecon literature as to oneCO most effective solutions forsystems decreasing the poss sibility to connetworks tinue fossil poower generati ion while ntributing C of22 the abatement t. In future ennergy s with high h shares of fluc ctuating renew wable energy generation, nu uclear power will w become i increasingly im mportant for power p greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat highh shares of flucctuating renew wable energy generation, nuuclear power will w become increasingly i im mportant for power p sales. Due The to the changed climate conditions power and building renovation policies, heat demand intothered future could decrease, gene ddevelopment oof of ne ns geneeration. eration. The development d o natural natural gas gas power is is con connsidered nsidered as as on on ne of of effective effectiveee ways ways to redduce duce emission emission ns and and prolonging the investment returnAs period. socio o-economic ccosts as well. A aa measure ee to establish aa climate-frien ndly energy ssystem, system, aa pow wer system res search socio o-economic osts as well. A As measure to establish climate-frien ndly energy s pow wer system res search The main scope of this paper is to assess the and feasibility of using the heatwould demand – outdoor temperature function for heat demand on ccoordinated coordinated ddevelopment development oof of dd natural gas dd be consider red. Therefor re, elec onforecast. c d o nuclear nuclear natural gas power power would consider red.The Therefor re, isfuture future elecctrical ctrical The district of at Alvalade, located and inan Lisbon (Portugal), wasfor used asbea would case study. district consisted of 665 pow wer scenarios a aimed env ironmental nd economic effects China C c considered a f full mix of en nergy pow wer scenarios a in both aimed at construction environmental anndand economic effects for China C scenarios would(low, c medium,a high) considered ffull mix of en nergy buildings that vary period typology.es Three weather andtothree district optio ons. Based o tool, this pa aper establish a bottom-u up model (L EAP-China-P Power) sim mulate n the LEAP optio ons. Based on the tool, (shallow, this paaper establishes a bottom-u up model (LEAP-China-P Power) sim mulate LEAP renovation scenarios were developed intermediate, deep). To estimate the error, obtained heat demandtovalues were diffe erent ppower power plannin ng scennarios that uld dd from 2012 to In add diffe erent electric electric p from plannin ng policy policy narios that cou cou uld be be enacted enacted from tooo 2050. 2050. adddition dition to to aa bas basseline seline compared with results a dynamic heat scen demand model, previously developed and2012 validated by theInauthors. scen nario, we des sce enario and nu Na Co ombined Cyc sce The re The results whensce only weather considered, margin of error could be acceptable for some applications scen nario, weshowed design ignthatCCS CCS enario and change nuuclear uclearis and and Naatural aturaltheGas Gas Co ombined Cyccle(N&N) cle(N&N) sceenario. enario. The reesults esults indic cate that N&N N scenario is superior to C CCS scenario. And China’s future power r planning rec commendation ns (thecate error in N&N annual demand was lower than all weather However, after introducing renovation indic that N scenario is superior to 20% C for CCS scenario. Andscenarios China’sconsidered). future power r planning reccommendation ns are are prop posed. scenarios, prop posed. the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered).

The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the yright © EElsevierofLtd. Elsevier Al ll rights ed. Copy Copyright © 2018 2018 Ltd. All rights reserved. decrease in the number heating hoursreserve of 22-139h during the heating season (depending on the combination of weather and yright © 2018 E Elsevier Ltd. Alll rights reserve ed. Copy Selection and peer-review underreesponsibility responsibility scientific committee of the CUE2018-Applied Energy Symposium and Selec ction and peer-r review under offof thethe scientific c intercept of Applied A Energy Symposium annd Forum 2018: on Low renovation scenarios considered). On the other hand, functioncommittee increased for 7.8-12.7% per decade (depending the Selec ction and peer-r review under re esponsibility of f the scientific c committee of Applied A Energy Symposium annd Forum 2018: Low Forum 2018: Low carbon cities and urban energy systems. carbo on cities and ur rban energy syst tems, CUE2018 8. coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and carboon cities and urrban energy systtems, CUE20188. improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of * Corresponding C autthor. Tel.: +86-100-56681932; fax: +86-10-5668100 00.The 15th International Symposium on District Heating and * Corresponding C autthor. Tel.: +86-100-56681932; fax: +86-10-566810000. Cooling. E E-mail address:[email protected] E E-mail address:[email protected]

Keywords: demand; Climate 18876-6102Heat Copyri ght ©Forecast; 2018 Elsev vier Ltd.change All rightts reserved. 18876-6102 Copyright © 2018 Elsevvier Ltd. All rightts reserved. Selecction and peer-revview under responnsibility of the sccientific committeee of the Applied Energy Symposiium and Forum 2018: Low carbon n cities Selecction and peer-revview under responnsibility of the sccientific committeee of the Applied Energy Symposiium and Forum 2018: Low carbon n cities and urban u energy systeems, CUE2018. and urban u energy systeems, CUE2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the CUE2018-Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems. 10.1016/j.egypro.2018.09.101

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Cai Liya et al. / Energy Procedia 152 (2018) 965–970 Author name / Energy Procedia 00 (2018) 000–000

Keywords: LEAP; CCS; renewable energy; nuclear; natural gas

1. Introduction The electrical power industry is the main cause of CO2 emissions. And it is expected to the fastest growth emission source until 2050[1]. Accordingly, a reasonable long-term power development strategy is desperately needed in each country. China’s over-reliance on coal has aggravated environmental pollution in this country. The power industry is the main sector of the energy industry. In 2014, the proportion of thermal power generation to total power generation was above 74.4%, and the percentage of thermal power capacity reached 66.7% according the data presented by the China Electricity Council in 2015. Under several uncertain conditions in the coexistence of resources and technological development, choices made regarding technological policy will play a crucial role in the medium and long-term electric power development of countries[2]. This paper establishes a LEAP-China-Power model to simulate and analyze the electricity demand, the structure of the generation of electric power structure, CO2 emissions and total cost in different policy scenarios in order to evaluate the CO2 emission abatement effect of nuclear and natural gas power and provide recommendations for China’s medium and long-term electric power planning. 2. Literature review Researches on energy strategy planning and CO2 emission reduction at the global, national and regional levels have determined that clean technology choices are an important phase in the transition to renewable energy technologies in the future. Elliston et al. [3] presented simulations for 100% renewable energy systems to meet actual hourly electricity demands in the five states and one territory spanned by the Australian National Electricity Market (NEM) in 2010. Steenhof and Fulton [4] developed a scenario-based conceptual model. Mi et al. (2012)[5] developed four mitigation scenarios with identical emissions pathways. With regard to future nuclear power and renewable energy development policies, Qi Zhang et al.[6] proposed three electricity supply scenarios for 2030. Linares et al. [7] used an oligopolistic, long-term generation expansion model to examine the possible evolution of the Spanish electricity sector under different policy scenarios. Among the numerous scenario analysis tools for electrical planning, the long-range energy alternatives planning system (LEAP) has some prominent advantages. Dagher and Ruble[8] constructed scenarios based on LEAP and examined the technical, economic, and environmental implications of all scenarios. Using the LEAP model, Jun and Lee [9] studied the economic and environmental influence of renewable energies on the existing electricity generation market in South Korea. In domestic research, Cai, Wenjia et al.[10] assessed the reduction potential of CO2 emissions in China’s electricity sector and simulated different development paths. As previous studies show, compared to other models, the LEAP model includes TED, which provides extensive information describing the technical characteristics, costs and environmental impacts of a wide range of energy technologies, including existing technologies, current best practices and next generation devices. Thus, LEAP is appropriate for energy policy analysis and climate change mitigation assessment. Most medium and long-term power planning scenario analysis models are designed in terms of improving energy efficiency, adjusting industrial structure and simulating energy demands and CO2 emissions in different policy scenarios. The results emphasized mainly the importance of technological progress and investment. However, there are some defects: (1) For CO2 emissions, previous studies simply consider CO2 emission factor at the production stage. Few studies assessed the life cycle CO2 emission factor. (2) Few studies include technological progress in evaluating cost changes. The LEAP-China-Power model covers traditional pulverized coal furnace (conventional PC), supercritical (SC), ultra-supercritical (USC), integrated gasification combined cycle (IGCC), carbon capture and carbon sequestration (CCS) technologies, natural gas combined cycle (NGCC), nuclear power, wind power, hydropower, solar power, and other renewable energy power technologies. Using this model, this paper’s contributions are as follows: First, we evaluate the life cycle CO2 emission factors of various electric power technologies and set CCS carbon capture efficiency. Second, we add a learning curve to the model, which clearly reflects the role technological progress plays in the electrical power industry.



Cai Liya et al. / Energy Procedia 152 (2018) 965–970 Author name / Energy Procedia 00 (2018) 000–000

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3. LEAP-China-Power model 3.1 LEAP-China-Power model structure framework The LEAP-China-Power model will simulate China’s medium and long-term power planning from 2012 to 2050, with 2012 as the base year. First, we set parameters for the model based on national electric power planning reports, the latest statistics, and a survey of electric power technologies. The learning curve is then added to the model. Finally, electricity demands from 2012 to 2050, power structure, CO2 emissions, and total costs of different scenarios are simulated. 3.2 LEAP-China-Power methodology and calculation Calculation methods of industrial activity level, electricity demands, CO2 emissions and total cost are according to related research[11].Total cost calculation is defined as follows. TC =

n

∑ fc * Ca i

i

+ foci * Cai + Vci * Pi

(1) where TC is the total cost (US dollar) of electric industry. fci ($/kW) is the capital cost of power technology i. foci ($/kW) is the fix O&M cost of power technology i. Cai ($/kWh) is the electricity installed capacity of power technology i. Vci ($/kWh) is the variable cost of power technology i. McDonald and Schrattenholzer[12] pointed out that the per unit electricity investment change is in line with the learning curve model. This model mainly interprets the phenomenon that cost declining is caused by cumulated experience, which can be used to describe the relationship between production costs and continuous cumulative outputs[13]. The specific model is as follows: i

fc= i (k )

fc i (1) × N ( k ) - ϕ −ϕ

(2)

L R =− 1 P R =− 1 2 (3) where fci(k) is the per unit electric investment cost, which is a function of the cumulative electricity installed capacity N(k). fci(1) is the initial per unit capital investment cost. -ϕ is the elasticity of cumulative electricity installed capacity to unit capital investment cost. LR is the learning rate. The declining rate of investment cost or technological progress rate PR is decided by ϕ. Learning rates of wind[14], biomass [12] , IGCC[15], CCS[16] and other power technologies are obtained from references. 4. Scenario design We incorporate relevant policies into the scenario designs according to future national mediums and long-term planning for the development of electrical power. These considerations are combined with both the current situation and future trends of electrical power development in China. The research designs include the baseline scenario, the CCS scenario and the nuclear and NGCC scenario. Economic development in the three scenarios is assumed according to the conclusions of previous scenario analysis researches to reflect China’s future economic development as mentioned in current and previous comments[11]. The population development pattern is based on national population planning. The scenario is redesigned and data in three scenarios have been updated. Baseline scenario is according to national policy and international requirements for energy saving, summarizing electricity development trends in the past, depending on present China electricity development situation. After 2020, SC and USC will become main power technologies. Large number of small thermal power will be shut down from 2020. In CCS scenario, CCS will be deployed to SC, USC, and IGCC from 2030, and capture efficiency is 85%[17]. Nuclear and NGCC(N&N) scenario will be deployed from 2015. China’s nuclear and NGCC power installed capacity will account for about 20% of China's total electricity installed capacity by 2050. We can get the accurate value by using the interp function and obtain the actual value of each power technology in three scenarios.

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5. Results of the scenario analysis 5.1 Electricity demands Based on the LEAP-China-Power model, the total demands for electricity in the three scenarios of the whole society are equal. Total demands for electricity will increase from 4976.3 billion kWh in 2012 to 15,648.6 billion kWh in 2050. The average annual growth rate will be 2.8%. The secondary industry is still a large consumer of electricity, the average annual growth rate of 2.7%. The proportion of secondary industrial demand for electricity in the whole society will reduce from 74.5% to 71.1%, which will be mainly caused by the decrease in both the proportion of industrial value-added in the GDP and the power consumption per unit GDP. The proportion of electricity consumption by the secondary industry in the whole society will decrease after 2020. The demands by the tertiary industrial sector for electricity will increase year by year Both the demands for and proportion of primary industrial electricity show downward trends. Residential electricity demands will increase rapidly. See Table 1 for more details. Table 1 Electricity demands forecasting in China (billion kWh, %) Sector Primary industry Secondary industry Industry Construction Tertiary industry Resident Urban Rural Total

2012

2015

2020

2025

2030

2035

2040

2045

2050

99.7

113.9

134.6

136.5

124.6

119.6

109.5

98.4

83.9

(2)

(1.9)

(1.7)

(1.5)

(1.2)

(1.0)

(0.8)

(0.7)

(0.5)

3672.8

4379.9

5826.4

6860.7

7641.1

8699.5

9851.7

10492.7

11123.1

(74.5)

(74.8)

(75.6)

(74.6)

(73.6)

(73.1)

(72.6)

(71.7)

(71.1)

3611.3

4309.0

5739.5

6691.0

7355.6

8303.9

9311.0

9786.1

10212.5

(73)

(73.6)

(74.4)

(72.8)

(70.8)

(69.7)

(68.6)

(66.9)

(65.3)

61.5

70.9

86.9

169.6

285.5

395.6

540.7

706.6

910.6

(1.2)

(1.2)

(1.1)

(1.8)

(2.7)

(3.3)

(4.0)

(4.8)

(5.8)

587.1

701.7

943.5

1,209.2

1,454.3

1,801.4

2,205.9

2,474.2

2,737.1

(11.9)

(12)

(12.2)

(13.2)

(14.0)

(15.1)

(16.3)

(16.9)

(17.5)

571

661.5

807.5

987.8

1167.3

1287.5

1401.8

1561.8

1704.5

(11.6)

(11.3)

(10.5)

(10.7)

(11.2)

(10.8)

(10.3)

(10.7)

(10.9)

367.0

424.6

531.7

665.5

812.4

892.1

975.0

1078.2

1185.5

(7.5)

(7.2)

(6.9)

(7.2)

(7.8)

(7.5)

(7.2)

(7.4)

(7.6)

204.0

236.9

275.8

322.3

354.9

395.4

426.8

483.6

519.0

(4.1)

(4.0)

(3.6)

(3.5)

(3.4)

(3.3)

(3.1)

(3.3)

(3.3)

4931.0

5857.0

7712.0

9194.1

10387.3

11908.0

13568.9

14627.1

15648.6

Note: The value between parentheses is the proportion of each sector’s electricity demands.

5.2 Electrical power structure We obtain the inclusion by analysing the power structure of three policy scenarios. The installed capacity of SC, USC and IGCC in the CCS scenario are significantly reduced compared to the other scenarios. The installed capacity of SC, USC and IGCC will decrease by 1.2%, 2.2%, 2.5%, respectively, by 2030 when CCS technology is originally deployed and the three decrease rates reach 20%, 28%, 34%, respectively, in 2050. This is mainly because in CCS thermal power technology, SC, USC and IGCC are the base load power. The installed capacity of SC, USC and IGCC will inevitably decrease every year along with the increase in CCS thermal power technology. Compared to the baseline scenario, the nuclear power installed capacity in nuclear and NGCC scenario will increase sharply as the base load which will result in the decrease of other power technologies.



Cai Liya et al. / Energy Procedia 152 (2018) 965–970 Author name / Energy Procedia 00 (2018) 000–000

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5.3 CO2 emissions The LEAP-China-Power model simulates the CO2 emissions in each scenario. The CO2 emissions in the CCS scenario are the lowest, followed by those in the N&N scenario. The CO2 emissions in the baseline scenario are the highest. Compared with the baseline scenario, the cumulative reduction in CO2 emissions in the CCS scenario to 2050 is 9878.1 million tons. The CO2 emissions in the CCS scenario decrease from 2030, which is mainly because SC and USC undertake the base load when SC and USC electricity generation increases, conventional PC is eliminated. Compared to the N&N scenario, the CCS scenario has the obvious advantage of reduction in CO2 emissions. See Fig. 1. 7,500.0

Baseline Scenario CCS

Million Metric Tonnes

7,000.0

Nuclear+NGCC

6,500.0 6,000.0 5,500.0 5,000.0 4,500.0 2015

2020

2025

2030

2035

2040

2045

2050

Fig. 1. CO2 emissions trend

The CO2 emissions per power generation and per unit GDP in the CCS scenario are the lowest of the three scenarios at only 0.32 kg/kWh and 0.18 metric tons/thousand U.S. dollars, respectively, to 2050, which will decrease by 14% compared to the baseline scenario. The CO2 emissions per power generation and per unit GDP in the N&N scenario are 0.34 kg/kWh and 0.19 metric tons/thousand U.S. dollars, which will decrease by 7.3% compared to the baseline scenario. 5.4 Total cost analysis Based on the electrical power structure of the three scenarios, we analyze total electricity generation costs in the LEAP-China-Power model. The total cost of nuclear and NGCC scenario policy is lower than CCS scenario, which is superior to CCS scenario. See Table 2. Table 2 Total cost of different scenarios (Unit: Billion U.S. dollars) Scenarios

2015

2020

2025

2030

2035

2040

2045

2050

Baseline Scenario

150.3

227.9

295.8

353.8

448.2

543.7

609.3

670.1

CCS

150.3

227.9

296.6

354.9

454.6

554.8

624.5

688.9

Nuclear+NGCC

150.3

227.9

297.8

354.7

450.5

544.9

610.9

671.8

6. Conclusions In this paper, the environmental and economic impacts of renewable electrical power energy, nuclear energy, and new thermal power technologies were evaluated from the perspective of the life cycle using the LEA-China-Power model. Learning curves are involved in the model, which better reflect the role technological progress plays in the

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Cai Liya et al. / Energy Procedia 152 (2018) 965–970 Author name / Energy Procedia 00 (2018) 000–000

electrical power sector. Based on this model, this paper analyzed and compared the electric power structure, CO2 emissions, and total cost with the premise that electricity demands in the three scenarios were equal. Moreover, the most controversial technologies, CCS, nuclear power and NGCC, were included in the model. The results of the analysis of the three scenarios indicate the following: China’s demands for electricity will increase gradually from 2012 to 2050. The proportion of demands for electricity by the secondary industry is declining. The rate of demand for electricity by the tertiary industry, in the total demand, shows an upward trend in that industrialization is the dominant force of urbanization. With the rapid pace of urbanization, labour will transfer from the primary industry to the secondary and tertiary industries. The development of tertiary industry can also promote urbanization. There are two difficulties for large scale development of nuclear power. First, China have very limited suitable sites for nuclear plants. Thus the potential nuclear power sites in China should be exclusive to nuclear plant. The other hand, if nuclear power projects can be implemented smoothly largely depending on the public support. The only ways to improve the public acceptance of nuclear power are to develop the technologies to enhance the safety of nuclear power, and to improve the management levels of nuclear power. Acknowledgements The author would like to be grateful to Prof. Guo.We also would like to thank a number of people of Strategy and Economy Department, State Power Investment Corporation Research Institue, for their assistance in providing data and suggestions. And we appreciate anonymous reviewers whose valuable comments and criticism helped to improve the presentation of the paper. References [1] IEA. World Energy Outlook 2009[M]. Paris: International Energy Agency, 2009. [2] Yu, G., Sun, W., Cui, j. New energy electric power generation technology[M]. Beijing: China Electric Power Press, 2009. [3] Elliston, B., Diesendorf, M., MacGill, I. Simulations of scenarios with 100% renewable electricity in the Australian National Electricity Market[J]. Energy Policy, 2012, 45(0): 606-613. [4] Steenhof, P. A., Fulton, W. Scenario development in China's electricity sector[J]. Technological Forecasting and Social Change, 2007, 74(6): 779-797. [5] Mi, R., Ahammad, H., Hitchins, N., et al. Development and deployment of clean electricity technologies in Asia: A multi-scenario analysis using GTEM[J]. Energy Economics, 2012. [6] Zhang, Q., Ishihara, K. N., McLellan, B. C., et al. Scenario analysis on future electricity supply and demand in Japan[J]. Energy, 2012, 38(1): 376-385. [7] Linares, P., Santos, F. J., Pérez-Arriaga, I. J. Scenarios for the evolution of the Spanish electricity sector: Is it on the right path towards sustainability?[J]. Energy Policy, 2008, 36(11): 4057-4068. [8] Dagher, L., Ruble, I. Modeling Lebanon’s electricity sector: Alternative scenarios and their implications[J]. Energy, 2011, 36(7): 4315-4326. [9] Jun, S., Lee, S., Park, J.-W., et al. The assessment of renewable energy planning on CO2 abatement in South Korea[J]. Renewable Energy, 2010, 35: 471-477. [10] Cai, W., Wang, C., Wang, K., et al. Scenario analysis on CO2 emissions reduction potential in China's electricity sector[J]. Energy Policy, 2007, 35(12): 6445-6456. [11] Cai, L., Guo, J., Zhu, L. China's Future Power Structure Analysis Based on LEAP[J]. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2013, 35(22): 2113-2122. [12] McDonald, A., Schrattenholzer, L. Learning rates for energy technologies[J]. Energy Policy, 2001, 29(4): 255-261. [13] Kumbaroğlu, G., Karali, N., Arkan, Y. CO2, GDP and RET: An aggregate economic equilibrium analysis for Turkey[J]. Energy Policy, 2008, 36(7): 2694-2708. [14] Xu, L., Lin, L. Study on the Cost Trends of Wind Power in China Based on the Learning Curve[J]. Electric Power Science and Engineering, 2008, 24(3): 1-4. [15] Jiang, K. Energy and Emission Scenario up to 2050 for China[M]. Beijing: Energy Research Institute, 2009. [16] Lohwasser, R., Madlener, R. Relating R&D and Investment Policies to CCS Market Diffusion Through Two-Factor Learning[M]. German: Aachen University, 2010. [17] IEA. CCS roamap for China[M]. Paris: International Energy Agency, 2010.