4.7 Article

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

Journal

ENERGY POLICY
Volume 97, Issue -, Pages 181-192

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.enpol.2016.07.028

Keywords

Feed-in tariff; Solar photovoltaic power generation; Carbon emission trading scheme; Real options; Uncertainty

Funding

  1. National Natural Science Foundation of China [71573121, 71573119, 71573186]
  2. Jiangsu Natural Science Foundation for Distinguished Young Scholars [BK20140038]
  3. Jiangsu 333 Programme Research Project [BRA2015332]
  4. NUAA Fundamental Research Fund [NE2013104, NJ20150034]
  5. MOE Project of Humanities and Social Sciences [15YJC630048]

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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/IcWh. 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. (C) 2016 Elsevier Ltd. All rights reserved.

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