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Applying real options analysis to assess cleaner energy development strategies

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  • Cheng, Ching-Tsung
  • Lo, Shang-Lien
  • Lin, Tyrone T.

Abstract

The energy industry, accounts for the largest portion of CO2 emissions, is facing the issue of compliance with the national clean energy policy. The methodology for evaluating the energy mix policy is crucial because of the characteristics of lead time embedded with the power generation facilities investment and the uncertainty of future electricity demand. In this paper, a modified binomial model based on sequential compound options, which may account for the lead time and uncertainty as a whole is established, and a numerical example on evaluating the optional strategies and the strategic value of the cleaner energy policy is also presented. It is found that the optimal decision at some nodes in the binomial tree is path dependent, which is different from the standard sequential compound option model with lead time or time lag concept. The proposed modified binomial sequential compound real options model can be generalized and extensively applied to solve the general decision problems that deal with the long lead time of many government policies as well as capital intensive investments.

Suggested Citation

  • Cheng, Ching-Tsung & Lo, Shang-Lien & Lin, Tyrone T., 2011. "Applying real options analysis to assess cleaner energy development strategies," Energy Policy, Elsevier, vol. 39(10), pages 5929-5938, October.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:10:p:5929-5938
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    Cited by:

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    2. Morgan Bazilian & Debabrata Chattopadhyay, 2015. "Considering Power System Planning in Fragile and Conflict States," Cambridge Working Papers in Economics 1530, Faculty of Economics, University of Cambridge.
    3. Schachter, Jonathan A. & Mancarella, Pierluigi & Moriarty, John & Shaw, Rita, 2016. "Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation," Energy Policy, Elsevier, vol. 97(C), pages 439-449.
    4. Chen, Siyuan & Zhang, Qi & Li, Hailong & Mclellan, Benjamin & Zhang, Tiantian & Tan, Zhizhou, 2019. "Investment decision on shallow geothermal heating & cooling based on compound options model: A case study of China," Applied Energy, Elsevier, vol. 254(C).
    5. Mei, Bin & Wetzstein, Michael, 2017. "Burning wood pellets for US electricity generation? A regime switching analysis," Energy Economics, Elsevier, vol. 65(C), pages 434-441.
    6. Gonzalez, Asa O. & Karali, Berna & Wetzstein, Michael E., 2012. "A public policy aid for bioenergy investment: Case study of failed plants," Energy Policy, Elsevier, vol. 51(C), pages 465-473.
    7. Dalbem, Marta Corrêa & Brandão, Luiz Eduardo Teixeira & Gomes, Leonardo Lima, 2014. "Can the regulated market help foster a free market for wind energy in Brazil?," Energy Policy, Elsevier, vol. 66(C), pages 303-311.
    8. Lee, Hyounkyu & Park, Taeil & Kim, Byungil & Kim, Kyeongseok & Kim, Hyoungkwan, 2013. "A real option-based model for promoting sustainable energy projects under the clean development mechanism," Energy Policy, Elsevier, vol. 54(C), pages 360-368.
    9. Chuan-Chuan Ko & Tyrone T. Lin & Fu-Min Zeng & Chien-Yu Liu, 2018. "Optimum Technology Product Life Cycle Technology Innovation Investment-Using Compound Binomial Options," Risks, MDPI, vol. 6(3), pages 1-14, September.

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