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How to design a dynamic feed-in tariffs mechanism for renewables – a real options approach

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  • Li Li
  • Junqi Liu
  • Lei Zhu
  • Xiao-Bing Zhang

Abstract

Feed-in tariffs (FITs) are among the most favoured policies with which to drive the deployment of renewable energy. This paper offers insights into quantifying dynamic FITs to realise the expected installed capacity target with minimum policy cost under uncertainties of renewable intermittence and technology learning. We incorporate real options and use stochastic dynamic programming to model the strategic behaviour between policy-maker and investor and extend the one-time investment decision described by Farrell et al. [2017. ‘Specifying an Efficient Renewable Energy Feed-in Tariff.’ The Energy Journal 38: 53–75] to multiple-period decisions. An approach that combines binary tree scenario generation and a least squares Monte Carlo method is used to numerically identify the optimal FITs plan in practice. China’s offshore wind power investment is used as a case study to investigate the relationships among the optimal dynamic FITs level, the total policy cost, the expected capacity target, and the learning effect. The simulation results demonstrate that our proposed dynamic FITs can track the changes in technology learning well and that they can avoid the inefficiency of fixed FITs in stimulating technology adoption in the initial periods, along with overpayment by the policy-maker.

Suggested Citation

  • Li Li & Junqi Liu & Lei Zhu & Xiao-Bing Zhang, 2020. "How to design a dynamic feed-in tariffs mechanism for renewables – a real options approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4352-4366, July.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:14:p:4352-4366
    DOI: 10.1080/00207543.2019.1652776
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    Cited by:

    1. Lin, Boqiang & Xie, Yongjing, 2023. "The impact of government subsidies on capacity utilization in the Chinese renewable energy industry: Does technological innovation matter?," Applied Energy, Elsevier, vol. 352(C).
    2. Miraj Ahmed Bhuiyan & Jaehyung An & Alexey Mikhaylov & Nikita Moiseev & Mir Sayed Shah Danish, 2021. "Renewable Energy Deployment and COVID-19 Measures for Sustainable Development," Sustainability, MDPI, vol. 13(8), pages 1-15, April.

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