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Counterfactual Comparisons of Investment Options for Wind Power and Agricultural Production in the United States: Lessons from Northern Ohio

Author

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  • Ribeiro Scarcioffolo, Alexandre

    (West Virginia University)

  • Finotti Cordeiro Perobelli, Fernanda

    (Universidade Federal de Juiz de Fora)

  • Baumgratz Chimeli, Ariaster

    (Departamento de Economia, Universidade de São Paulo)

Abstract

We analyze potential efficiency gains in wind power projects by comparing counterfactual investment decisions in two different scenarios under a real options framework. The first scenario is a standard wind power investment, where the investor rents the land from local farms. In the second scenario, the wind power investor buys the land and commercializes both electricity and crop production, thus shortening the revenue risk through the diversification. Both scenarios have a waiting option, with the wholesale prices leading the installation decision. We model the electricity price as a mean reverting process with jumps and with different jumping probabilities for the different seasons of the year. Corn prices follow a mean reverting process. The waiting flexibility was modeled as a bundle of European options. The results indicate that the waiting option is exercised in 100% of our simulations in both scenarios, suggesting the still important role of government policies to stimulate wind power. More importantly, in more than 90% of the simulations, the second scenario brought value to the investment. Furthermore, net present values are more sensitive to reductions in capital costs than electricity prices. These results can form the basis for more effective policies for the wind power sector.

Suggested Citation

  • Ribeiro Scarcioffolo, Alexandre & Finotti Cordeiro Perobelli, Fernanda & Baumgratz Chimeli, Ariaster, 2018. "Counterfactual Comparisons of Investment Options for Wind Power and Agricultural Production in the United States: Lessons from Northern Ohio," TD NEREUS 1-2018, Núcleo de Economia Regional e Urbana da Universidade de São Paulo (NEREUS).
    • Handle: RePEc:ris:nereus:2018_001
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    1. Weron, R & Bierbrauer, M & Trück, S, 2004. "Modeling electricity prices: jump diffusion and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 39-48.
    2. Luis M. Abadie & José M. Chamorro, 2014. "Valuation of Wind Energy Projects: A Real Options Approach," Energies, MDPI, vol. 7(5), pages 1-38, May.
    3. Yang, Ming & Blyth, William & Bradley, Richard & Bunn, Derek & Clarke, Charlie & Wilson, Tom, 2008. "Evaluating the power investment options with uncertainty in climate policy," Energy Economics, Elsevier, vol. 30(4), pages 1933-1950, July.
    4. Hitaj, Claudia, 2013. "Wind power development in the United States," Journal of Environmental Economics and Management, Elsevier, vol. 65(3), pages 394-410.
    5. De Jong Cyriel, 2006. "The Nature of Power Spikes: A Regime-Switch Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-28, September.
    6. Wickart, Marcel & Madlener, Reinhard, 2007. "Optimal technology choice and investment timing: A stochastic model of industrial cogeneration vs. heat-only production," Energy Economics, Elsevier, vol. 29(4), pages 934-952, July.
    7. de Jong, C.M. & Huisman, R., 2002. "Option Formulas for Mean-Reverting Power Prices with Spikes," ERIM Report Series Research in Management ERS-2002-96-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    9. Womeldorf, Carole A. & Chimeli, Ariaster B., 2014. "A computational fluid dynamics approach to wind prospecting: Lessons from the U.S. Appalachian region," Energy Policy, Elsevier, vol. 73(C), pages 645-653.
    10. Schwartz, Eduardo, 1998. "Valuing long-term commodity assets," Journal of Energy Finance & Development, Elsevier, vol. 3(2), pages 85-99.
    11. Bolinger, Mark & Wiser, Ryan, 2006. "A comparative analysis of business structures suitable for farmer-owned wind power projects in the United States," Energy Policy, Elsevier, vol. 34(14), pages 1750-1761, September.
    12. Möst, Dominik & Keles, Dogan, 2010. "A survey of stochastic modelling approaches for liberalised electricity markets," European Journal of Operational Research, Elsevier, vol. 207(2), pages 543-556, December.
    13. Fuss, Sabine & Szolgayova, Jana & Obersteiner, Michael & Gusti, Mykola, 2008. "Investment under market and climate policy uncertainty," Applied Energy, Elsevier, vol. 85(8), pages 708-721, August.
    14. Ben Hambly & Sam Howison & Tino Kluge, 2009. "Modelling spikes and pricing swing options in electricity markets," Quantitative Finance, Taylor & Francis Journals, vol. 9(8), pages 937-949.
    15. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
    16. Robert S. Pindyck, 1999. "The Long-Run Evolutions of Energy Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-27.
    17. Eduardo S. Schwartz, 1998. "Valuing Long-Term Commodity Assets," Financial Management, Financial Management Association, vol. 27(1), Spring.
    18. Bastian-Pinto, Carlos & Brando, Luiz & Hahn, Warren J., 2009. "Flexibility as a source of value in the production of alternative fuels: The ethanol case," Energy Economics, Elsevier, vol. 31(3), pages 411-422, May.
    19. repec:dau:papers:123456789/1433 is not listed on IDEAS
    20. Laurikka, Harri, 2006. "Option value of gasification technology within an emissions trading scheme," Energy Policy, Elsevier, vol. 34(18), pages 3916-3928, December.
    21. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    22. Hélyette Geman & Andrea Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1225-1262, May.
    23. Higgs, Helen & Worthington, Andrew, 2008. "Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market," Energy Economics, Elsevier, vol. 30(6), pages 3172-3185, November.
    24. Blyth, William & Bradley, Richard & Bunn, Derek & Clarke, Charlie & Wilson, Tom & Yang, Ming, 2007. "Investment risks under uncertain climate change policy," Energy Policy, Elsevier, vol. 35(11), pages 5766-5773, November.
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    Cited by:

    1. Scarcioffolo, Alexandre R. & Etienne, Xiaoli, 2021. "Testing directional predictability between energy prices: A quantile-based analysis," Resources Policy, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    wind energy; corn; real option framework; investment decision;
    All these keywords.

    JEL classification:

    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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