Gómez-Ríos, María del Carmen and Juárez-Luna, David (2018): Costo de generación eléctrica incorporando externalidades ambientales: Mezcla óptima de tecnologías de carga base.
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Abstract
This paper aims to calculate the Total Levelized Cost of Generation with Externalities (CTNGE, in Spanish) of three baseload technologies: coal thermoelectric, combined cycle and nuclear power plant. Monte Carlo simulation is used to estimate the CTNGE probability densities. The portfolio theory is used to find the mix of technologies that provides the least risky CTNGE and with the lowest average. We find that the nuclear power plant has the lowest CTNGE. The coal-fired thermoelectric plant is the technology with the largest and riskiest CTNGE. The analysis suggests that, when generating electricity, it is convenient to leave out the coal-fired thermoelectric plant and focus on two technologies: combined cycle and nuclear power plant, assigning a higher participation to the latter. One limitation of the work is that the probability densities of the CTNGE estimated through the Monte Carlo simulation depend on the data used. The present analysis suggests that the CTNGE can be significantly modified by including the cost of CO2.
Item Type: | MPRA Paper |
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Original Title: | Costo de generación eléctrica incorporando externalidades ambientales: Mezcla óptima de tecnologías de carga base |
English Title: | Cost of electric generation accounting for environmental externalities: Optimal mix of baseload technologies |
Language: | Spanish |
Keywords: | CO2 Emissions, Generation, Electricity, Levelized Cost. |
Subjects: | D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q40 - General Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q53 - Air Pollution ; Water Pollution ; Noise ; Hazardous Waste ; Solid Waste ; Recycling |
Item ID: | 89717 |
Depositing User: | Dr. David Juárez-Luna |
Date Deposited: | 27 Oct 2018 19:13 |
Last Modified: | 26 Sep 2019 11:27 |
References: | 1. Annual Energy Outlook. (2017) [AEO (2017)]. Editado por la U.S. Energy Information Administration. 2. Awerbuch, S., Berger, M. (2003). Applying portfolio Theory to EU Electricity Planning and Policy-Making. IEA/EET Working Paper. IEA. Paris. 3. CENACE, Centro Nacional de Control de Energía. Secretaría de Energía. https://www.gob.mx/cenace [Consultado el 15 de Octubre de 2017]. 4. Comisión Federal de Electricidad. (2014). Costos y Parámetros de Referencia para la Formulación de Proyectos de Inversión del Sector Eléctrico (COPAR). Subdirección de Programación, Comisión Federal de Electricidad. 5. Dixit, A. y Pindyck, R. S. (1994). Investment under uncertainty. Princeton University. 6. Eschenbach, T. G. (2006). Technical note: Constructing tornado diagrams with spreadsheets. The Engineering Economists 51:195–204. 7. Forbes C., Evans, M., Hastings, N., and Peacock, B. (2011). Statistical Distributions, 4th ed. New York: Wiley. 8. Freund, J. E., Miller, I, y Miller, M. (2000). Estadística matemática con aplicaciones. Pearson educación. 9. Gómez-Ríos, M. d. (2016). Aplicación de modelos estocásticos en centrales nucleares generadoras de energía eléctrica para detectar el impacto que tiene la volatilidad de los mercados financieros en los costos nivelados de generación. En C. IMEF, Tópicos actuales de Finanzas. pp 220 - 260. 10. Gómez-Ríos, M.-d.-C. (2008). La Energía Nuclear: una alternativa de generación de energía eléctrica de carga base en México. Tesis de Doctorado, México, Universidad Anáhuac México, campus Norte. 11. Hrafnkelsson, B., Oddsson, V., and R. Unnthorsson. (2016). "A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation." Energies 9, no. 4: 286. 12. ICF Consulting Canada Inc. (2017). Long-Term Carbon Price Forecast Report. May 31, 2017. https://www.oeb.ca/sites/default/files/uploads/OEB-LTCPF-Report-20170531.pdf [Consultado el 15 de marzo de 2018]. 13. Jansen, J.C., Beurskens, L.W.M., and Tilburg, X.V. (2006). Application of portfolio analysis to the Dutch generating mix. ECN report C-05-100. Energy research council of Netherlands. 14. Karkhov, A. (2002). Economic evaluation of bids for nuclear power plants. Atomnaya Tekhnika za Rubezhom, 23 - 26. 15. Khindanova, I. (2013). A Monte Carlo Model of a Wind Power Generation Investment. The Journal of Applied Business and Economics, 15(1), 94. 16. Kienzle F., and G. Andersson (2008). “Efficient multi-energy generation portfolios for the future,” 4th Annu. Carnegie Mellon Conf. Elect. Ind. pp. 1–18. 17. Mas-Colell, A., Whinston, M. D. y Green, J. R. (1995). Microeconomic theory. Oxford University Press. 18. Markowitz H. M. (1952), “Portfolio Selection”, Journal of Finance, Vol. 7, pp 77-91. 19. Nuclear Energy Agency (NEA) e International Energy Agency (IEA). (2015). Projected Costs of Generating Electricity. 20. Organisation for Economic Co-operation and Development. (2017). The arrangement for officially supported export credits. 21. Organisation for Economic Co-operation and Development. (2017). CO2 emissions from fuel combustion: overview (2017 edition). International Energy Agency. 22. Organisation for Economic Co-operation and Development. (2001). International Emission Trading from Concept to Reality. International Energy Agency. 23. Organisation for Economic Co-operation and Development. (2014). International Energy Agency 2013 Annual Report. International Energy Agency. 24. Organisation for Economic Co-operation and Development. (2003). Nuclear Electricity Generation: What are the external costs? NEA 4372. 25. Organisation for Economic Co-operation and Development. (2005). Act locally, trade globally. Emissions trading for climate policy. International Energy Agency. 26. Secretaría de Hacienda y Crédito Público, (2017). Informe Semanal del Vocero. 27. Ramirez, J. R., Alonso, G., Perry, R. y Ortiz, J. (2006). Assessment of MOX fuel assembly design for a BWR mixed reload. Nuclear Technology. Vol. 156. 28. Rode, D., Fishbeck, P., and Dean, S. (2001), “Monte Carlo Methods for Appraisal and valuation: A Case Study of a Nuclear Power Plant”, Journal of Structured and Project Finance, 7:3. p. 38-48. 29. Roques, F. A., Newbery, D. M., and Nuttall, W. J. (2008), “Fuel mix diversification incentives in liberalized electricity markets: A Mean–Variance Portfolio theory approach”, Energy Economics, Volume 30, Issue 4: 1831-1849. 30. Roques, F. (2006). Power generation investments in liberalised markets: methodologies to capture risk, flexibility, and portfolio diversity. Économies et Sociétés, 40(10/11), 1563. 31. Roques, F. A., W. J. Nuttall, D. M. Newbery, R. de Neufville, S. Connors (2006) “Nuclear Power: a Hedge against Uncertain Gas and Carbon Prices?” The Energy Journal, 27 (4): 1-24 32. Ross, S., (1999). Simulación. Prentice Hall. 33. Spadaro J. V., Langlois L. y Hamilton B. (2000) “Assessing the difference”. IAEA Bull. 2000;42 (2):19–24 34. Vithayasrichareon, P., MacGill, I.F., and Wen, F.S., (2010a). Electricity generation portfolio evaluation for highly uncertain and carbon constrained future electricity industries. IEEE Power and Energy Society General Meeting. 35. Vithayasrichareon, P., MacGill, I.F., and Wen, F., (2010b). Electricity Generation Portfolio Analysis for Coal, Gas and Nuclear Plant under Future Uncertainties. 4th IASTED Asian Conference on Power and Energy Systems. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/89717 |