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

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  • 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
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    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.
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