IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v238y2019icp1179-1191.html
   My bibliography  Save this article

Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices

Author

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

Abstract

Classifying and categorizing generators according to their financial efficiency is necessary to compare the degree of competitiveness of a generating technology. The levelized cost of energy (LCoE) is arguably the most relevant index for that purpose. It relates the capital and operating expenses to the expected energy output, to encapsulate the financial efficiency into an €/MWh. It is technology-independent, meaning that it can be used for comparing different generator types in a fair basis. However, the LCoE is defined to provide a cost figure under the assumption of a fixed-tariff. It does not make any difference when comparing the performance of an intermittent generating technology in different spot markets. The LCoE does not inherently account for the variability of prices.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:1179-1191
    DOI: 10.1016/j.apenergy.2019.01.169
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919301941
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.01.169?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Vijay, Avinash & Fouquet, Nicolas & Staffell, Iain & Hawkes, Adam, 2017. "The value of electricity and reserve services in low carbon electricity systems," Applied Energy, Elsevier, vol. 201(C), pages 111-123.
    2. Pietzcker, Robert Carl & Stetter, Daniel & Manger, Susanne & Luderer, Gunnar, 2014. "Using the sun to decarbonize the power sector: The economic potential of photovoltaics and concentrating solar power," Applied Energy, Elsevier, vol. 135(C), pages 704-720.
    3. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    4. Dressler, Luisa, 2016. "Support schemes for renewable electricity in the European Union: Producer strategies and competition," Energy Economics, Elsevier, vol. 60(C), pages 186-196.
    5. Lai, Chun Sing & McCulloch, Malcolm D., 2017. "Levelized cost of electricity for solar photovoltaic and electrical energy storage," Applied Energy, Elsevier, vol. 190(C), pages 191-203.
    6. Díaz, Guzmán & Gómez-Aleixandre, Javier & Coto, José, 2016. "Wind power scenario generation through state-space specifications for uncertainty analysis of wind power plants," Applied Energy, Elsevier, vol. 162(C), pages 21-30.
    7. Brouwer, Anne Sjoerd & van den Broek, Machteld & Zappa, William & Turkenburg, Wim C. & Faaij, André, 2016. "Least-cost options for integrating intermittent renewables in low-carbon power systems," Applied Energy, Elsevier, vol. 161(C), pages 48-74.
    8. Kapsali, M. & Kaldellis, J.K. & Anagnostopoulos, J.S., 2016. "Investigating the techno-economic perspectives of high wind energy production in remote vs interconnected island networks," Applied Energy, Elsevier, vol. 173(C), pages 238-254.
    9. Muche, Thomas, 2014. "Optimal operation and forecasting policy for pump storage plants in day-ahead markets," Applied Energy, Elsevier, vol. 113(C), pages 1089-1099.
    10. Ueckerdt, Falko & Hirth, Lion & Luderer, Gunnar & Edenhofer, Ottmar, 2013. "System LCOE: What are the costs of variable renewables?," Energy, Elsevier, vol. 63(C), pages 61-75.
    11. Feng, Ju & Shen, Wen Zhong, 2017. "Design optimization of offshore wind farms with multiple types of wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 1283-1297.
    12. Ritter, Matthias & Deckert, Lars, 2017. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," Applied Energy, Elsevier, vol. 185(P2), pages 1087-1099.
    13. Petitet, Marie & Finon, Dominique & Janssen, Tanguy, 2017. "Capacity adequacy in power markets facing energy transition: A comparison of scarcity pricing and capacity mechanism," Energy Policy, Elsevier, vol. 103(C), pages 30-46.
    14. Newbery, David M., 2016. "Towards a green energy economy? The EU Energy Union’s transition to a low-carbon zero subsidy electricity system – Lessons from the UK’s Electricity Market Reform," Applied Energy, Elsevier, vol. 179(C), pages 1321-1330.
    15. Bennedsen, Mikkel, 2017. "A rough multi-factor model of electricity spot prices," Energy Economics, Elsevier, vol. 63(C), pages 301-313.
    16. Hull, John & White, Alan, 1990. "Pricing Interest-Rate-Derivative Securities," Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 573-592.
    17. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
    18. 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.
    19. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    20. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    21. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    22. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    23. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    24. Pircalabu, A. & Benth, F.E., 2017. "A regime-switching copula approach to modeling day-ahead prices in coupled electricity markets," Energy Economics, Elsevier, vol. 68(C), pages 283-302.
    25. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    26. Edenhofer, Ottmar & Hirth, Lion & Knopf, Brigitte & Pahle, Michael & Schlömer, Steffen & Schmid, Eva & Ueckerdt, Falko, 2013. "On the economics of renewable energy sources," Energy Economics, Elsevier, vol. 40(S1), pages 12-23.
    27. Fred Espen Benth & Jan Kallsen & Thilo Meyer-Brandis, 2007. "A Non-Gaussian Ornstein-Uhlenbeck Process for Electricity Spot Price Modeling and Derivatives Pricing," Applied Mathematical Finance, Taylor & Francis Journals, vol. 14(2), pages 153-169.
    28. Hirth, Lion, 2016. "The benefits of flexibility: The value of wind energy with hydropower," Applied Energy, Elsevier, vol. 181(C), pages 210-223.
    29. Wiser, Ryan & Bolinger, Mark & Heath, Garvin & Keyser, David & Lantz, Eric & Macknick, Jordan & Mai, Trieu & Millstein, Dev, 2016. "Long-term implications of sustained wind power growth in the United States: Potential benefits and secondary impacts," Applied Energy, Elsevier, vol. 179(C), pages 146-158.
    30. Hou, Peng & Enevoldsen, Peter & Hu, Weihao & Chen, Cong & Chen, Zhe, 2017. "Offshore wind farm repowering optimization," Applied Energy, Elsevier, vol. 208(C), pages 834-844.
    31. Serrano González, Javier & Trigo García, Ángel Luis & Burgos Payán, Manuel & Riquelme Santos, Jesús & González Rodríguez, Ángel Gaspar, 2017. "Optimal wind-turbine micro-siting of offshore wind farms: A grid-like layout approach," Applied Energy, Elsevier, vol. 200(C), pages 28-38.
    32. Woo, C.K. & Moore, J. & Schneiderman, B. & Ho, T. & Olson, A. & Alagappan, L. & Chawla, K. & Toyama, N. & Zarnikau, J., 2016. "Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets," Energy Policy, Elsevier, vol. 92(C), pages 299-312.
    33. del Río, Pablo & Resch, Gustav & Ortner, Andre & Liebmann, Lukas & Busch, Sebastian & Panzer, Christian, 2017. "A techno-economic analysis of EU renewable electricity policy pathways in 2030," Energy Policy, Elsevier, vol. 104(C), pages 484-493.
    34. Lantz, Eric & Mai, Trieu & Wiser, Ryan H. & Krishnan, Venkat, 2016. "Long-term implications of sustained wind power growth in the United States: Direct electric system impacts and costs," Applied Energy, Elsevier, vol. 179(C), pages 832-846.
    Full references (including those not matched with items on IDEAS)

    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. Zerrahn, Alexander, 2017. "Wind Power and Externalities," Ecological Economics, Elsevier, vol. 141(C), pages 245-260.
    2. Su, Yufei & Kern, Jordan D. & Characklis, Gregory W., 2017. "The impact of wind power growth and hydrological uncertainty on financial losses from oversupply events in hydropower-dominated systems," Applied Energy, Elsevier, vol. 194(C), pages 172-183.
    3. 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.
    4. Romeiro, Diogo Lisbona & Almeida, Edmar Luiz Fagundes de & Losekann, Luciano, 2020. "Systemic value of electricity sources – What we can learn from the Brazilian experience?," Energy Policy, Elsevier, vol. 138(C).
    5. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    6. Alexandra G. Papadopoulou & George Vasileiou & Alexandros Flamos, 2020. "A Comparison of Dispatchable RES Technoeconomics: Is There a Niche for Concentrated Solar Power?," Energies, MDPI, vol. 13(18), pages 1-22, September.
    7. 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.
    8. Kruyt, Bert & Lehning, Michael & Kahl, Annelen, 2017. "Potential contributions of wind power to a stable and highly renewable Swiss power supply," Applied Energy, Elsevier, vol. 192(C), pages 1-11.
    9. 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.
    10. Duy Nguyen, 2018. "A hybrid Markov chain-tree valuation framework for stochastic volatility jump diffusion models," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 1-30, December.
    11. Panagiotis Christodoulou & Nils Detering & Thilo Meyer-Brandis, 2018. "Local Risk-Minimization With Multiple Assets Under Illiquidity With Applications In Energy Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-44, June.
    12. Duffie, Darrell, 2003. "Intertemporal asset pricing theory," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 11, pages 639-742, Elsevier.
    13. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    14. Sinn, Hans-Werner, 2017. "Buffering volatility: A study on the limits of Germany's energy revolution," European Economic Review, Elsevier, vol. 99(C), pages 130-150.
    15. Marco Di Francesco & Roberta Simonella, 2023. "A stochastic Asset Liability Management model for life insurance companies," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(1), pages 61-94, March.
    16. Anders B. Trolle & Eduardo S. Schwartz, 2009. "A General Stochastic Volatility Model for the Pricing of Interest Rate Derivatives," Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 2007-2057, May.
    17. Philip Tafarte & Marcus Eichhorn & Daniela Thrän, 2019. "Capacity Expansion Pathways for a Wind and Solar Based Power Supply and the Impact of Advanced Technology—A Case Study for Germany," Energies, MDPI, vol. 12(2), pages 1-23, January.
    18. Nemat Safarov & Colin Atkinson, 2017. "Natural Gas-Fired Power Plants Valuation And Optimization Under Lévy Copulas And Regime Switching," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 1-38, February.
    19. Francisco Blasques & Siem Jan Koopman & Karim Moussa, 2023. "Extremum Monte Carlo Filters: Real-Time Signal Extraction via Simulation and Regression," Tinbergen Institute Discussion Papers 23-016/III, Tinbergen Institute.
    20. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).

    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:238:y:2019:i:c:p:1179-1191. 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.