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Optimal feed-in tariff for solar photovoltaic power generation in China: A real options analysis

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  • Zhang, M.M.
  • Zhou, D.Q.
  • Zhou, P.
  • Liu, G.Q.

Abstract

The feed-in tariff policy is widely used to promote the development of renewable energy. China also adopts feed-in tariff policy to attract greater investment in solar photovoltaic power generation. This study employs real options method to assess the optimal levels of feed-in tariffs in 30 provinces of China. The uncertainties in CO2 price and investment cost are considered. A method that integrates the backward dynamic programming algorithm and Least-Squares Monte Carlo method is used to solve the model. The results demonstrate that the feed-in tariffs of 30 provinces range from 0.68 RMB/kWh to 1.71 RMB/kWh, and the average level is 1.01 RMB/kWh. On this basis, we find that the levels of sub-regional feed-in tariff announced in 2013 are no longer appropriate and should be adjusted as soon as possible. We have also identified the implications of technological progress and carbon emission trading schemes, as well as the importance of strengthening electricity transmission. It has been suggested that the Chinese government takes diverse measures, including increasing research and development investment, establishing and improving a nationwide carbon emission trading scheme and accelerating the construction of electricity-transmission infrastructure, to reduce the required feed-in tariff and promote the development of solar photovoltaic power generation.

Suggested Citation

  • Zhang, M.M. & Zhou, D.Q. & Zhou, P. & Liu, G.Q., 2016. "Optimal feed-in tariff for solar photovoltaic power generation in China: A real options analysis," Energy Policy, Elsevier, vol. 97(C), pages 181-192.
  • Handle: RePEc:eee:enepol:v:97:y:2016:i:c:p:181-192
    DOI: 10.1016/j.enpol.2016.07.028
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    1. Klein, Martin & Deissenroth, Marc, 2017. "When do households invest in solar photovoltaics? An application of prospect theory," Energy Policy, Elsevier, vol. 109(C), pages 270-278.
    2. Barbara Glensk & Reinhard Madlener, 2019. "Energiewende @ Risk: On the Continuation of Renewable Power Generation at the End of Public Policy Support," Energies, MDPI, Open Access Journal, vol. 12(19), pages 1-25, September.
    3. Yu Zeng & Weidong Chen, 2019. "The Determination of Concession Period for Build-Operate-Transfer Solar Photovoltaic Power Project under Policy Incentives: A Case Study of China," Energies, MDPI, Open Access Journal, vol. 12(18), pages 1-23, September.
    4. 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.
    5. Ozcan, Mustafa, 2018. "The role of renewables in increasing Turkey's self-sufficiency in electrical energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2629-2639.
    6. Yiqing Li & Weiguo Yang & Lixin Tian & Jie Yang, 2018. "An Evaluation of Investment in a PV Power Generation Project in the Gobi Desert Using a Real Options Model," Energies, MDPI, Open Access Journal, vol. 11(1), pages 1-16, January.
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    13. Lu, Yuehong & Zhang, Xiao-Ping & Li, Jianing & Huang, Zhijia & Wang, Changlong & Luo, Liang, 2019. "Design of a reward-penalty cost for the promotion of net-zero energy buildings," Energy, Elsevier, vol. 180(C), pages 36-49.
    14. Yu-zhuo, Zhang & Xin-gang, Zhao & Ling-zhi, Ren & Ji, Liang & Ping-kuo, Liu, 2017. "The development of China's biomass power industry under feed-in tariff and renewable portfolio standard: A system dynamics analysis," Energy, Elsevier, vol. 139(C), pages 947-961.
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