IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v50y2015icp1373-1382.html
   My bibliography  Save this article

Analyzing cost of grid-connection of renewable energy development in China

Author

Listed:
  • Lin, Boqiang
  • Li, Jianglong

Abstract

Renewable energy is believed to be the central issue in sustainable development. Literatures on renewables׳ costs are rather sparse, especially on costs of integration and system balancing. The objective of this paper is to fill the research gap by providing an assessment for the cost of China׳s grid-connected renewable energy development and analyze its sharing between different stakeholders. Due to China׳s pricing mechanism of renewable energy and their cost decreasing potential, the pricing model of feed-in tariff and dynamic technological learning processes are employed. In the estimation of purchasing cost, the positive bias is overcome by considering China׳s energy-saving dispatching policy. The results suggest that purchasing cost would be 32.57–40.80 billion Yuan over 2012–2020, and peak in 2017. Grid integration costs which further involve costs of grid infrastructure and system balancing are also investigated. We find that grid infrastructure will cost 27.88 billion Yuan by 2015 and soar to 45.32 billion by 2020, while system balancing will cost 31.49 billion Yuan in 2015 and 63.97 billion Yuan in 2020 among which a substantial part (over 60%) comes from electricity loss in energy transfer. The different parts of these costs are underwritten by disparate participants due to China׳s renewable energy policies and its institutional arrangement. Purchasing cost is shared by power consumers through RES; the cost of grid infrastructure is mainly covered by grid enterprises; and there is no mechanism to specify how to share the cost electricity loss during system balancing which might become a major obstacle for system balancing.

Suggested Citation

  • Lin, Boqiang & Li, Jianglong, 2015. "Analyzing cost of grid-connection of renewable energy development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1373-1382.
    • Handle: RePEc:eee:rensus:v:50:y:2015:i:c:p:1373-1382
    DOI: 10.1016/j.rser.2015.04.194
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S136403211500547X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2015.04.194?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. Nick Johnstone & Ivan Haščič & David Popp, 2010. "Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 45(1), pages 133-155, January.
    2. Arent, Douglas J. & Wise, Alison & Gelman, Rachel, 2011. "The status and prospects of renewable energy for combating global warming," Energy Economics, Elsevier, vol. 33(4), pages 584-593, July.
    3. Unido, 2013. "International Yearbook of Industrial Statistics 2013," Books, Edward Elgar Publishing, number 15223.
    4. Owen, Anthony D., 2006. "Renewable energy: Externality costs as market barriers," Energy Policy, Elsevier, vol. 34(5), pages 632-642, March.
    5. Peidong, Zhang & Yanli, Yang & jin, Shi & Yonghong, Zheng & Lisheng, Wang & Xinrong, Li, 2009. "Opportunities and challenges for renewable energy policy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 439-449, February.
    6. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    7. Yu, Xiao & Qu, Hang, 2013. "The role of China's renewable powers against climate change during the 12th Five-Year and until 2020," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 401-409.
    8. Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
    9. AfDB AfDB, . "African Statistical Journal Vol.16," African Statistical Journal, African Development Bank, number 455.
    10. Frondel, Manuel & Ritter, Nolan & Schmidt, Christoph M. & Vance, Colin, 2010. "Economic impacts from the promotion of renewable energy technologies: The German experience," Energy Policy, Elsevier, vol. 38(8), pages 4048-4056, August.
    11. 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.
    12. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    13. AfDB AfDB, . "Compendium of Statistics on Bank Group Operations 2013," Compendium of Statistics on AfDB Group Operations, African Development Bank, number 454.
    14. Capros, Pantelis & Mantzos, Leonidas & Parousos, Leonidas & Tasios, Nikolaos & Klaassen, Ger & Van Ierland, Tom, 2011. "Analysis of the EU policy package on climate change and renewables," Energy Policy, Elsevier, vol. 39(3), pages 1476-1485, March.
    15. Ming, Zeng & Song, Xue & Mingjuan, Ma & Xiaoli, Zhu, 2013. "New energy bases and sustainable development in China: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 169-185.
    16. Salim, Ruhul A. & Rafiq, Shuddhasattwa, 2012. "Why do some emerging economies proactively accelerate the adoption of renewable energy?," Energy Economics, Elsevier, vol. 34(4), pages 1051-1057.
    17. Isoard, Stephane & Soria, Antonio, 2001. "Technical change dynamics: evidence from the emerging renewable energy technologies," Energy Economics, Elsevier, vol. 23(6), pages 619-636, November.
    18. Lin, Boqiang & Wesseh, Presley K., 2013. "Valuing Chinese feed-in tariffs program for solar power generation: A real options analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 474-482.
    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. Lin, Boqiang & He, Jiaxin, 2016. "Learning curves for harnessing biomass power: What could explain the reduction of its cost during the expansion of China?," Renewable Energy, Elsevier, vol. 99(C), pages 280-288.
    2. 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).
    3. Modhurima Dey Amin & Syed Badruddoza & Jill J. McCluskey, 2021. "Does conventional energy pricing induce innovation in renewable energy? New evidence from a nonlinear approach," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(2), pages 659-679, June.
    4. Kundu, Nobinkhor, 2014. "Sustainable energy for Development: Access to finance on renewable energy and energy efficiency technologies for Bangladesh," MPRA Paper 65154, University Library of Munich, Germany, revised 20 Jun 2014.
    5. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    6. Cong, Rong-Gang & Shen, Shaochuan, 2014. "How to Develop Renewable Power in China? A Cost-Effective Perspective," MPRA Paper 112209, University Library of Munich, Germany.
    7. 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.
    8. Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).
    9. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    10. Lehmann, Paul & Gawel, Erik, 2013. "Why should support schemes for renewable electricity complement the EU emissions trading scheme?," Energy Policy, Elsevier, vol. 52(C), pages 597-607.
    11. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    12. Bongsuk Sung & Woo-Yong Song, 2017. "Does Dynamic Efficiency of Public Policy Promote Export Prformance? Evidence from Bioenergy Technology Sector," Energies, MDPI, vol. 10(12), pages 1-18, December.
    13. Paul Lehmann & Patrik Söderholm, 2018. "Can Technology-Specific Deployment Policies Be Cost-Effective? The Case of Renewable Energy Support Schemes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(2), pages 475-505, October.
    14. Omri, Emna & Chtourou, Nouri & Bazin, Damien, 2015. "Solar thermal energy for sustainable development in Tunisia: The case of the PROSOL project," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1312-1323.
    15. Grafström, Jonas & Lindman, Åsa, 2017. "Invention, innovation and diffusion in the European wind power sector," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 179-191.
    16. Pfeiffer, Birte & Mulder, Peter, 2013. "Explaining the diffusion of renewable energy technology in developing countries," Energy Economics, Elsevier, vol. 40(C), pages 285-296.
    17. Wenli Qiang & Shuwen Niu & Xiaojie Liu & Xiang Wang & Zhuo Jia & Runqi Dai, 2018. "Analysis of generation cost changes during China’s energy transition," Energy & Environment, , vol. 29(4), pages 456-472, June.
    18. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    19. Andrew Morgan & Alan Dix & Mike Phillips & Chris House, 2014. "Blue sky thinking meets green field usability: Can mobile internet software engineering bridge the rural divide?," Local Economy, London South Bank University, vol. 29(6-7), pages 750-761, September.

    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:rensus:v:50:y:2015:i:c:p:1373-1382. 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/600126/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.