IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v250y2019icp1053-1064.html
   My bibliography  Save this article

Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution

Author

Listed:
  • Tu, Qiang
  • Betz, Regina
  • Mo, Jianlei
  • Fan, Ying
  • Liu, Yu

Abstract

China has adopted an ambitious plan for wind power to achieve grid parity with the on-grid price of coal-fired power in 2020. Whether this target can be achieved is a great concern for policy makers as well as potential investors. To address this issue, we first estimate the future levelized cost of electricity (LCOE) of wind power using a learning curve method, and then determine whether grid parity can be achieved by comparing it with the on-grid price of coal-fired power. Specially, the effect of carbon pricing on the grid-parity is explored, and a sensitivity analysis on how the discount rates, learning rates, and curtailment rates affect grid parity is conducted. The learning rate of onshore wind power is estimated using a panel dataset consisting of information of 2059 onshore wind projects in China from 2006 to 2015. Based on this learning rate, the future LCOE of Chinese onshore wind power from 2016 to 2025 is calculated. The results show that the LCOE of onshore wind power decreases by 13.91% from 0.40 RMB/kWh in 2016 to 0.34 RMB/kWh in 2025. By comparing the LCOE with the on-grid price of coal-fired power, the grid parity of onshore wind power may be achieved in 2019. With the implementation of the carbon pricing policy, the grid parity will be achieved earlier. More specifically, with the carbon price reaching 10, 35, and 60 RMB/t CO2, the grid parity can be achieved in 2019, 2017, and 2016, respectively. The results of the sensitivity analysis show that in the case of high discount rates, low learning rates, high curtailment rates, high O&M cost and low capacity factor, the grid parity will be delayed, and a high carbon price will be required to achieve the grid parity.

Suggested Citation

  • Tu, Qiang & Betz, Regina & Mo, Jianlei & Fan, Ying & Liu, Yu, 2019. "Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution," Applied Energy, Elsevier, vol. 250(C), pages 1053-1064.
  • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1053-1064
    DOI: 10.1016/j.apenergy.2019.05.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919308967
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.05.039?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hayashi, Daisuke & Huenteler, Joern & Lewis, Joanna I., 2018. "Gone with the wind: A learning curve analysis of China's wind power industry," Energy Policy, Elsevier, vol. 120(C), pages 38-51.
    2. Yao, Xilong & Liu, Yang & Qu, Shiyou, 2015. "When will wind energy achieve grid parity in China? – Connecting technological learning and climate finance," Applied Energy, Elsevier, vol. 160(C), pages 697-704.
    3. Ouyang, Xiaoling & Lin, Boqiang, 2014. "Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China," Energy Policy, Elsevier, vol. 70(C), pages 64-73.
    4. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    5. Fan, Jing-Li & Wei, Shijie & Yang, Lin & Wang, Hang & Zhong, Ping & Zhang, Xian, 2019. "Comparison of the LCOE between coal-fired power plants with CCS and main low-carbon generation technologies: Evidence from China," Energy, Elsevier, vol. 176(C), pages 143-155.
    6. Han, Jingyi & Mol, Arthur P.J. & Lu, Yonglong & Zhang, Lei, 2009. "Onshore wind power development in China: Challenges behind a successful story," Energy Policy, Elsevier, vol. 37(8), pages 2941-2951, August.
    7. Yang, Mian & Yang, Fuxia & Sun, Chuanwang, 2018. "Factor market distortion correction, resource reallocation and potential productivity gains: An empirical study on China's heavy industry sector," Energy Economics, Elsevier, vol. 69(C), pages 270-279.
    8. Yang Liu, 2015. "CDM and national policy: Synergy or conflict? Evidence from the wind power sector in China," Climate Policy, Taylor & Francis Journals, vol. 15(6), pages 767-783, November.
    9. Yang, Mian & Ma, Tiemeng & Sun, Chuanwang, 2018. "Evaluating the impact of urban traffic investment on SO2 emissions in China cities," Energy Policy, Elsevier, vol. 113(C), pages 20-27.
    10. Lam, Long T. & Branstetter, Lee & Azevedo, Inês M.L., 2017. "China's wind industry: Leading in deployment, lagging in innovation," Energy Policy, Elsevier, vol. 106(C), pages 588-599.
    11. Yu, Yang & Li, Hong & Che, Yuyuan & Zheng, Qiongjie, 2017. "The price evolution of wind turbines in China: A study based on the modified multi-factor learning curve," Renewable Energy, Elsevier, vol. 103(C), pages 522-536.
    12. Tian Tang & David Popp, 2016. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(1), pages 195-222, January.
    13. Duan, Hongbo & Mo, Jianlei & Fan, Ying & Wang, Shouyang, 2018. "Achieving China's energy and climate policy targets in 2030 under multiple uncertainties," LSE Research Online Documents on Economics 86481, London School of Economics and Political Science, LSE Library.
    14. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    15. Caralis, George & Diakoulaki, Danae & Yang, Peijin & Gao, Zhiqiu & Zervos, Arthouros & Rados, Kostas, 2014. "Profitability of wind energy investments in China using a Monte Carlo approach for the treatment of uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 224-236.
    16. Qiu, Yueming & Anadon, Laura D., 2012. "The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization," Energy Economics, Elsevier, vol. 34(3), pages 772-785.
    17. Lewis, Joanna I., 2010. "The evolving role of carbon finance in promoting renewable energy development in China," Energy Policy, Elsevier, vol. 38(6), pages 2875-2886, June.
    18. Li, Canbing & Shi, Haiqing & Cao, Yijia & Wang, Jianhui & Kuang, Yonghong & Tan, Yi & Wei, Jing, 2015. "Comprehensive review of renewable energy curtailment and avoidance: A specific example in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1067-1079.
    19. Xiliang Zhang & Shiyan Chang & Molin Huo & Ruoshui Wang, 2009. "China's wind industry: policy lessons for domestic government interventions and international support," Climate Policy, Taylor & Francis Journals, vol. 9(5), pages 553-564, September.
    20. Gu Choi, Dong & Yong Park, Sang & Park, Nyun-Bae & Chul Hong, Jong, 2015. "Is the concept of ‘grid parity’ defined appropriately to evaluate the cost-competitiveness of renewable energy technologies?," Energy Policy, Elsevier, vol. 86(C), pages 718-728.
    21. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    22. Jacques, David A. & Guan, Dabo & Geng, Yong & Xue, Bing & Wang, Xiaoguang, 2013. "Inter-provincial clean development mechanism in China: A case study of the solar PV sector," Energy Policy, Elsevier, vol. 57(C), pages 454-461.
    23. Ding, Yi & Shao, Changzheng & Yan, Jinyue & Song, Yonghua & Zhang, Chi & Guo, Chuangxin, 2018. "Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China," Applied Energy, Elsevier, vol. 228(C), pages 426-436.
    24. Duan, Hongbo & Mo, Jianlei & Fan, Ying & Wang, Shouyang, 2018. "Achieving China's energy and climate policy targets in 2030 under multiple uncertainties," Energy Economics, Elsevier, vol. 70(C), pages 45-60.
    25. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    26. Saed Alizamir & Francis de Véricourt & Peng Sun, 2016. "Efficient Feed-In-Tariff Policies for Renewable Energy Technologies," Operations Research, INFORMS, vol. 64(1), pages 52-66, February.
    27. Zhang, Da & Chai, Qimin & Zhang, Xiliang & He, Jiankun & Yue, Li & Dong, Xiufen & Wu, Shu, 2012. "Economical assessment of large-scale photovoltaic power development in China," Energy, Elsevier, vol. 40(1), pages 370-375.
    28. Pei, Wei & Chen, Yanning & Sheng, Kun & Deng, Wei & Du, Yan & Qi, Zhiping & Kong, Li, 2015. "Temporal-spatial analysis and improvement measures of Chinese power system for wind power curtailment problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 148-168.
    29. Zifa Liu & Wenhua Zhang & Changhong Zhao & Jiahai Yuan, 2015. "The Economics of Wind Power in China and Policy Implications," Energies, MDPI, vol. 8(2), pages 1-18, February.
    30. Lesser, Jonathan A. & Su, Xuejuan, 2008. "Design of an economically efficient feed-in tariff structure for renewable energy development," Energy Policy, Elsevier, vol. 36(3), pages 981-990, March.
    31. Liu, Hailiang & Andresen, Gorm Bruun & Greiner, Martin, 2018. "Cost-optimal design of a simplified highly renewable Chinese electricity network," Energy, Elsevier, vol. 147(C), pages 534-546.
    32. Amedeo Argentiero, Tarek Atalla, Simona Bigerna, Silvia Micheli, and Paolo Polinori, 2017. "Comparing Renewable Energy Policies in EU-15, U.S. and China: A Bayesian DSGE Model," The Energy Journal, International Association for Energy Economics, vol. 0(KAPSARC S).
    33. Esteban, Miguel & Leary, David, 2012. "Current developments and future prospects of offshore wind and ocean energy," Applied Energy, Elsevier, vol. 90(1), pages 128-136.
    34. Li, Yi & Wu, Xiao-Peng & Li, Qiu-Sheng & Tee, Kong Fah, 2018. "Assessment of onshore wind energy potential under different geographical climate conditions in China," Energy, Elsevier, vol. 152(C), pages 498-511.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tu, Qiang & Mo, Jianlei & Betz, Regina & Cui, Lianbiao & Fan, Ying & Liu, Yu, 2020. "Achieving grid parity of solar PV power in China- The role of Tradable Green Certificate," Energy Policy, Elsevier, vol. 144(C).
    2. Tu, Qiang & Betz, Regina & Mo, Jianlei & Fan, Ying, 2019. "The profitability of onshore wind and solar PV power projects in China - A comparative study," Energy Policy, Elsevier, vol. 132(C), pages 404-417.
    3. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    4. Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
    5. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    6. Chen, Hao & Gao, Xin-Ya & Liu, Jian-Yu & Zhang, Qian & Yu, Shiwei & Kang, Jia-Ning & Yan, Rui & Wei, Yi-Ming, 2020. "The grid parity analysis of onshore wind power in China: A system cost perspective," Renewable Energy, Elsevier, vol. 148(C), pages 22-30.
    7. Tian Tang & David Popp, 2014. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," NBER Working Papers 19921, National Bureau of Economic Research, Inc.
    8. Tian Tang & David Popp, 2014. "The Learning Process and Technological Change in Wind Power: Evidence from China's CDM Wind Projects," CESifo Working Paper Series 4705, CESifo.
    9. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    10. Dali T. Laxton, 2019. "Innovations in the Wind Energy Sector," CERGE-EI Working Papers wp647, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    11. Lam, Long T. & Branstetter, Lee & Azevedo, Inês M.L., 2017. "China's wind industry: Leading in deployment, lagging in innovation," Energy Policy, Elsevier, vol. 106(C), pages 588-599.
    12. Tang, Tian, 2018. "Explaining technological change in the US wind industry: Energy policies, technological learning, and collaboration," Energy Policy, Elsevier, vol. 120(C), pages 197-212.
    13. Ding, Hao & Zhou, Dequn & Zhou, P., 2020. "Optimal policy supports for renewable energy technology development: A dynamic programming model," Energy Economics, Elsevier, vol. 92(C).
    14. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    15. Hayashi, Daisuke & Huenteler, Joern & Lewis, Joanna I., 2018. "Gone with the wind: A learning curve analysis of China's wind power industry," Energy Policy, Elsevier, vol. 120(C), pages 38-51.
    16. Mo, Jian-Lei & Agnolucci, Paolo & Jiang, Mao-Rong & Fan, Ying, 2016. "The impact of Chinese carbon emission trading scheme (ETS) on low carbon energy (LCE) investment," Energy Policy, Elsevier, vol. 89(C), pages 271-283.
    17. He, Zhengxia & Cao, Changshuai & Kuai, Leyi & Zhou, Yanqing & Wang, Jianming, 2022. "Impact of policies on wind power innovation at different income levels: Regional differences in China based on dynamic panel estimation," Technology in Society, Elsevier, vol. 71(C).
    18. Fan, Xiao-chao & Wang, Wei-qing, 2016. "Spatial patterns and influencing factors of China׳s wind turbine manufacturing industry: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 482-496.
    19. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    20. Beck, Marisa & Rivers, Nicholas & Wigle, Randall, 2018. "How do learning externalities influence the evaluation of Ontario's renewables support policies?," Energy Policy, Elsevier, vol. 117(C), pages 86-99.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1053-1064. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.