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Real Option Valuation of an Emerging Renewable Technology Design in Wave Energy Conversion

Author

Listed:
  • James A. DiLellio

    (Graziadio Business School, Pepperdine University, Malibu, CA 90263, USA)

  • John C. Butler

    (McCombs School of Business, University of Texas at Austin, Austin, TX 78705, USA)

  • Igor Rizaev

    (Visual Information Laboratory, University of Bristol, Bristol BS8 1QU, UK)

  • Wanan Sheng

    (Department of Aerospace and Mechanical Engineering, South East Technological University, R93 V960 Carlow, Ireland)

  • George Aggidis

    (School of Engineering, Lancaster University, Lancaster LA1 4YW, UK)

Abstract

The untapped potential of wave energy offers another alternative to diversifying renewable energy sources and addressing climate change by reducing CO 2 emissions. However, development costs to mature the technology remain significant hurdles to adoption at scale and the technology often must compete against other marine energy renewables such as offshore wind. Here, we conduct a real option valuation that includes the uncertain market price of wholesale electricity and managerial flexibility expressed in determining future optimal decisions. We demonstrate the probability that the project’s embedded compound real option value can turn a negative net present value wave energy project to a positive expected value. This change in investment decision uses decision tree analysis, where real options are developed as decision nodes, and models the uncertainty as a risk-neutral stochastic process using chance nodes. We also show how our results are analogous to a financial out-of-the-money call option. Our results highlight the distribution of outcomes and the benefit of a staged long-term investment in wave energy systems to better understand and manage project risk, recognizing that these probabilistic results are subject to the ongoing evolution of wholesale electricity prices and the stochastic process models used here to capture their future dynamics. Lastly, we show that the near-term optimal decision is to continue to fund ongoing development of a reference architecture to a higher technology readiness level to maintain the long-term option to deploy such a renewable energy system through private investment or private–public partnerships.

Suggested Citation

  • James A. DiLellio & John C. Butler & Igor Rizaev & Wanan Sheng & George Aggidis, 2025. "Real Option Valuation of an Emerging Renewable Technology Design in Wave Energy Conversion," Econometrics, MDPI, vol. 13(1), pages 1-18, March.
  • Handle: RePEc:gam:jecnmx:v:13:y:2025:i:1:p:11-:d:1605157
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    References listed on IDEAS

    as
    1. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474.
    2. Guanche, R. & de Andrés, A.D. & Simal, P.D. & Vidal, C. & Losada, I.J., 2014. "Uncertainty analysis of wave energy farms financial indicators," Renewable Energy, Elsevier, vol. 68(C), pages 570-580.
    3. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
    4. Hahn, Warren J. & DiLellio, James A. & Dyer, James S., 2014. "What do market-calibrated stochastic processes indicate about the long-term price of crude oil?," Energy Economics, Elsevier, vol. 44(C), pages 212-221.
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