IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v145y2015icp223-233.html
   My bibliography  Save this article

Valuation of wind power distributed generation by using Longstaff–Schwartz option pricing method

Author

Listed:
  • Díaz, Guzmán
  • Moreno, Blanca
  • Coto, José
  • Gómez-Aleixandre, Javier

Abstract

In the context of decaying capital cost and uncertain revenues, prospective valuation of a wind power distributed generation (DG) project is difficult. The conventional net present value (NPV) presents a static picture that does not account for the value of waiting for better market conditions to proceed with a DG investment. On the contrary, real options (RO) analysis does account for the managerial flexibility to switch between options over the investment horizon.

Suggested Citation

  • Díaz, Guzmán & Moreno, Blanca & Coto, José & Gómez-Aleixandre, Javier, 2015. "Valuation of wind power distributed generation by using Longstaff–Schwartz option pricing method," Applied Energy, Elsevier, vol. 145(C), pages 223-233.
  • Handle: RePEc:eee:appene:v:145:y:2015:i:c:p:223-233
    DOI: 10.1016/j.apenergy.2015.02.046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915002238
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2015.02.046?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    3. Venetsanos, Konstantinos & Angelopoulou, Penelope & Tsoutsos, Theocharis, 2002. "Renewable energy sources project appraisal under uncertainty: the case of wind energy exploitation within a changing energy market environment," Energy Policy, Elsevier, vol. 30(4), pages 293-307, March.
    4. McKenna, R. & Hollnaicher, S. & Fichtner, W., 2014. "Cost-potential curves for onshore wind energy: A high-resolution analysis for Germany," Applied Energy, Elsevier, vol. 115(C), pages 103-115.
    5. Kroniger, Daniel & Madlener, Reinhard, 2014. "Hydrogen storage for wind parks: A real options evaluation for an optimal investment in more flexibility," Applied Energy, Elsevier, vol. 136(C), pages 931-946.
    6. Ibenholt, Karin, 2002. "Explaining learning curves for wind power," Energy Policy, Elsevier, vol. 30(13), pages 1181-1189, October.
    7. Grothe, Oliver & Schnieders, Julius, 2011. "Spatial dependence in wind and optimal wind power allocation: A copula-based analysis," Energy Policy, Elsevier, vol. 39(9), pages 4742-4754, September.
    8. 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.
    9. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    10. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    11. Johnson, Jeremiah X. & De Kleine, Robert & Keoleian, Gregory A., 2014. "Assessment of energy storage for transmission-constrained wind," Applied Energy, Elsevier, vol. 124(C), pages 377-388.
    12. Mason, James E. & Archer, Cristina L., 2012. "Baseload electricity from wind via compressed air energy storage (CAES)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1099-1109.
    13. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    14. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    15. Delucchi, Mark A. & Jacobson, Mark Z., 2011. "Providing all global energy with wind, water, and solar power, Part II: Reliability, system and transmission costs, and policies," Energy Policy, Elsevier, vol. 39(3), pages 1170-1190, March.
    16. Panda, R.K. & Sarkar, T.K. & Bhattacharya, A.K., 1990. "Stochastic study of the wind-energy potential of India," Energy, Elsevier, vol. 15(10), pages 921-930.
    17. Martin Barlow & Yuri Gusev & Manpo Lai, 2004. "Calibration Of Multifactor Models In Electricity Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 101-120.
    18. Levitt, Andrew C. & Kempton, Willett & Smith, Aaron P. & Musial, Walt & Firestone, Jeremy, 2011. "Pricing offshore wind power," Energy Policy, Elsevier, vol. 39(10), pages 6408-6421, October.
    19. Monjas-Barroso, Manuel & Balibrea-Iniesta, José, 2013. "Valuation of projects for power generation with renewable energy: A comparative study based on real regulatory options," Energy Policy, Elsevier, vol. 55(C), pages 335-352.
    20. Welch, Jonathan B. & Venkateswaran, Anand, 2009. "The dual sustainability of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1121-1126, June.
    21. Hull, John & White, Alan, 1990. "Pricing Interest-Rate-Derivative Securities," The Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 573-592.
    22. Lee, Shun-Chung & Shih, Li-Hsing, 2010. "Renewable energy policy evaluation using real option model -- The case of Taiwan," Energy Economics, Elsevier, vol. 32(Supplemen), pages 67-78, September.
    23. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    24. 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.
    25. Broadie, Mark & Glasserman, Paul, 1997. "Pricing American-style securities using simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1323-1352, June.
    26. de Jong, Piet & Penzer, Jeremy, 2004. "The ARMA model in state space form," Statistics & Probability Letters, Elsevier, vol. 70(1), pages 119-125, October.
    27. Oliver Grothe & Julius Schnieders, 2011. "Spatial Dependence in Wind and Optimal Wind Power Allocation: A Copula Based Analysis," EWI Working Papers 2011-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    28. repec:cdl:anderf:qt43n1k4jb is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Work, James & Hauer, Grant & Luckert, M.K. (Marty), 2018. "What ethanol prices would induce growers to switch from agriculture to poplar in Alberta? A multiple options approach," Journal of Forest Economics, Elsevier, vol. 33(C), pages 51-62.
    2. Maeda, Mansaku & Watts, David, 2019. "The unnoticed impact of long-term cost information on wind farms’ economic value in the USA. – A real option analysis," Applied Energy, Elsevier, vol. 241(C), pages 540-547.
    3. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices," Applied Energy, Elsevier, vol. 238(C), pages 1179-1191.
    4. Zhang, Chunyu & Wang, Qi & Wang, Jianhui & Korpås, Magnus & Pinson, Pierre & Østergaard, Jacob & Khodayar, Mohammad E., 2016. "Trading strategies for distribution company with stochastic distributed energy resources," Applied Energy, Elsevier, vol. 177(C), pages 625-635.
    5. Àlex Alonso-Travesset & Diederik Coppitters & Helena Martín & Jordi de la Hoz, 2023. "Economic and Regulatory Uncertainty in Renewable Energy System Design: A Review," Energies, MDPI, vol. 16(2), pages 1-30, January.
    6. Kitzing, Lena & Juul, Nina & Drud, Michael & Boomsma, Trine Krogh, 2017. "A real options approach to analyse wind energy investments under different support schemes," Applied Energy, Elsevier, vol. 188(C), pages 83-96.
    7. Didier Nibbering & Coos van Buuren & Wei Wei, 2021. "Real Options Valuation of Wind Energy Based on the Empirical Production Uncertainty," Monash Econometrics and Business Statistics Working Papers 19/21, Monash University, Department of Econometrics and Business Statistics.
    8. Díaz, Guzmán & Moreno, Blanca, 2016. "Valuation under uncertain energy prices and load demands of micro-CHP plants supplemented by optimally switched thermal energy storage," Applied Energy, Elsevier, vol. 177(C), pages 553-569.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    2. Didier Nibbering & Coos van Buuren & Wei Wei, 2021. "Real Options Valuation of Wind Energy Based on the Empirical Production Uncertainty," Monash Econometrics and Business Statistics Working Papers 19/21, Monash University, Department of Econometrics and Business Statistics.
    3. Maeda, Mansaku & Watts, David, 2019. "The unnoticed impact of long-term cost information on wind farms’ economic value in the USA. – A real option analysis," Applied Energy, Elsevier, vol. 241(C), pages 540-547.
    4. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
    5. Grafström, Jonas & Lindman, Åsa, 2017. "Invention, innovation and diffusion in the European wind power sector," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 179-191.
    6. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    7. Zhang, Mingming & Zhou, Dequn & Zhou, Peng, 2014. "A real option model for renewable energy policy evaluation with application to solar PV power generation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 944-955.
    8. Lu, Ze-Yu & Li, Wen-Hua & Xie, Bai-Chen & Shang, Li-Feng, 2015. "Study on China’s wind power development path—Based on the target for 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 197-208.
    9. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    10. Andreas Gerster, 2016. "Negative price spikes at power markets: the role of energy policy," Journal of Regulatory Economics, Springer, vol. 50(3), pages 271-289, December.
    11. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    12. Moon, Yongma & Baran, Mesut, 2018. "Economic analysis of a residential PV system from the timing perspective: A real option model," Renewable Energy, Elsevier, vol. 125(C), pages 783-795.
    13. Zhang, M.M. & Wang, Qunwei & Zhou, Dequn & Ding, H., 2019. "Evaluating uncertain investment decisions in low-carbon transition toward renewable energy," Applied Energy, Elsevier, vol. 240(C), pages 1049-1060.
    14. Bello, S. & Reiner, 2024. "Experience Curves for Electrolysis Technologies," Cambridge Working Papers in Economics 2476, Faculty of Economics, University of Cambridge.
    15. Williams, Eric & Hittinger, Eric & Carvalho, Rexon & Williams, Ryan, 2017. "Wind power costs expected to decrease due to technological progress," Energy Policy, Elsevier, vol. 106(C), pages 427-435.
    16. Vasileios PAPADIMITRIOU & Serafeim POLYZOS & Dimitrios TSIOTAS, 2023. "Renewable Energy Project Appraisal Using The Real Options Methodology," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 85-96, June.
    17. Eickholt, Mathias & Entrop, Oliver & Wilkens, Marco, 2014. "Individual investors and suboptimal early exercises in the fixed-income market," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe 14, University of Passau, Faculty of Business and Economics.
    18. Emanuele Fabbiani & Andrea Marziali & Giuseppe De Nicolao, 2018. "Fast calibration of two-factor models for energy option pricing," Papers 1809.03941, arXiv.org, revised Dec 2020.
    19. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    20. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2015. "Bending the learning curve," Energy Economics, Elsevier, vol. 52(S1), pages 86-99.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:145:y:2015:i:c:p:223-233. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.