Determinantes del Precio de la Electricidad en Mercados No Regulados: Un Análisis Bibliométrico
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Los procesos de liberalización de los mercados eléctricos en diferentes países generaron una nueva forma de definir los precios de la electricidad, lo que ha aumentado la dificultad para modelar y analizar su dinámica. Este estudio presenta un análisis bibliométrico de los determinantes del precio de la electricidad en mercados no regulados. La investigación sistemática a través de la base de datos Scopus, para el período enero de 1979 - abril de 2021, permitió observar 636 documentos indexados. Los resultados mostraron un aumento en el número de documentos por año en las últimas dos décadas. Además, los países con mayor producción fueron China, Estados Unidos y Alemania según su número de publicaciones. Sin embargo, Estados Unidos, Canadá e Irán tuvieron el mayor impacto según la relación entre el número de documentos y el número de citas. Los principales determinantes fueron las condiciones económicas y de mercado, el clima, el funcionamiento del sistema eléctrico y la demanda de los clientes. Del mismo modo, la tendencia de investigación en los últimos años se enfoca en pronosticar el precio con mayor eficiencia.
(1) Weron R. Modeling and forecasting electricity loads and prices: a statistical approach. Chichester, England; Hoboken, NJ: John Wiley & Sons; 2006. 178 p. (Wiley finance series).
(2) Weron R, Misiorek A. Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models. Int J Forecast. 2008 Oct;24(4):744–63. https://doi.org/10.1016/j.ijforecast.2008.08.004
(3) Gil-Zapata MM, Maya-Ochoa C. Modelación de la volatilidad de los precios de la energía eléctrica en Colombia. Rev Ing Univ Medellín. 2008;(12):87–114.
(4) Castaño E, Sierra J. Sobre la existencia de una raíz unitaria en la serie de tiempo mensual del precio de la electricidad en Colombia. Lect Econ. 2012;259–91. https://revistas.udea.edu.co/index.php/lecturasdeeconomia/article/view/12817/11548
(5) Girish GP, Vijayalakshmi S. Determinants of Electricity Price in Competitive Power Market. Int J Bus Manag. 2013 Oct 15;8(21):70–5. https://doi.org/10.5539/ijbm.v8n21p70
(6) Huisman R, Mahieu R. Regime jumps in electricity prices. Energy Econ. 2003;25(5):425–434. https://doi.org/10.1016/S0140-9883(03)00041-0
(7) Yun Z, Quan Z, Caixin S, Shaolan L, Yuming L, Yang S. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans Power Syst. 2008;23(3):853–8. https://doi.org/10.1109/TPWRS.2008.922249
(8) van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010 Aug;84(2):523–38. https://doi.org/10.1007/s11192-009-0146-3
(9) Longstaff FA, Wang AW. Electricity Forward Prices: A High-Frequency Empirical Analysis. J Finance. 2004;59(4):1877–1900. Available from: http://www.jstor.org/stable/3694881
(10) Aggarwal SK, Saini LM, Kumar A. Electricity price forecasting in deregulated markets: A review and evaluation. Int J Electr Power Energy Syst. 2009 Jan;31(1):13–22. https://doi.org/10.1016/j.ijepes.2008.09.003
(11) Xie L, Zheng H, Zhang L. Electricity price sensitivity analysis based on numerical simulation. Dianli Xitong Zidonghua Automation Electr Power Syst. 2009;33(12):38–42.
(12) Felten B. An integrated model of coupled heat and power sectors for large-scale energy system analyses. Appl Energy. 2020;266. https://doi.org/10.1016/j.apenergy.2020.114521
(13) Askari MT, Ab Kadir MZA, Hizam H, Jasni J. A new comprehensive model to simulate the restructured power market for seasonal price signals by considering on the wind resources. J Renew Sustain Energy. 2014;6(2):023104. https://doi.org/10.1063/1.4869141
(14) Motamedi Sedeh O, Ostadi B. Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price. Energy Policy. 2020;145:111740. https://doi.org/10.1016/j.enpol.2020.111740
(15) Krečar N, Gubina AF. Risk mitigation in the electricity market driven by new renewable energy sources. Wiley Interdiscip Rev Energy Environ. 2020;9(1):e362. https://doi.org/10.1002/wene.362
(16) Joskow PL, Kahn E. A quantitative analysis of pricing behavior in California’s wholesale electricity market during summer 2000. Energy J. 2002;23(4):1–35. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol23-No4-1
(17) Brouwer AS, van den Broek M, Zappa W, Turkenburg WC, Faaij A. Least-cost options for integrating intermittent renewables in low-carbon power systems. Appl Energy. 2016;161:48–74. https://doi.org/10.1016/j.apenergy.2015.09.090
(18) Jurasz J, Kies A, Zajac P. Synergetic operation of photovoltaic and hydro power stations on a day-ahead energy market. Energy. 2020;212:118686. https://doi.org/10.1016/j.energy.2020.118686
(19) Hellwig M, Schober D, Woll O. Measuring market integration and estimating policy impacts on the Swiss electricity market. Energy Econ. 2020;86:104637. https://doi.org/10.1016/j.eneco.2019.104637
(20) Keles D, Scelle J, Paraschiv F, Fichtner W. Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl Energy. 2016;162:218–30. https://doi.org/10.1016/j.apenergy.2015.09.087
(21) Rafiei M, Niknam T, Khooban M-H. Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network. IEEE Trans Ind Inform. 2017;13(1):71–9. https://doi.org/10.1109/TII.2016.2585378
(22) Bento PMR, Pombo JAN, Calado MRA, Mariano SJPS. A bat optimized neural network and wavelet transform approach for short-term price forecasting. Appl Energy. 2018;210:88–97. https://doi.org/10.1016/j.apenergy.2017.10.058
(23) Mujeeb S, Javaid N, Ilahi M, Wadud Z, Ishmanov F, Afzal MK. Deep long short-term memory: A new price and load forecasting scheme for big data in smart cities. Sustain. 2019;11(4):987. https://doi.org/10.3390/su11040987
(24) Kath C. Modeling intraday markets under the new advances of the cross-border Intraday Project (XBID): Evidence from the German intraday market. Energies. 2019;12(22):4339. https://doi.org/10.3390/en12224339
(25) Demir S, Mincev K, Kok K, Paterakis NG. Introducing technical indicators to electricity price forecasting: A feature engineering study for linear, ensemble, and deep machine learning models. Appl Sci. 2020;10(1): 255. https://doi.org/10.3390/app10010255
(26) Karakatsani NV, Bunn DW. Intra-day and regime-switching dynamics in electricity price formation. Energy Econ. 2008;30(4):1776–97. https://doi.org/10.1016/j.eneco.2008.02.004
(27) Forbes KF, Zampelli EM. Do day-ahead electricity prices reflect economic fundamentals? Evidence from the California ISO. Energy J. 2014;35(3):129–44. https://doi.org/10.5547/01956574.35.3.6
(28) Tranberg B, Hansen RT, Catania L. Managing volumetric risk of long-term power purchase agreements. Energy Econ. 2020;85:104567. https://doi.org/10.1016/j.eneco.2019.104567
(29) Hinderks WJ, Wagner A. Factor models in the German electricity market: Stylized facts, seasonality, and calibration. Energy Econ. 2020;85:104351. https://doi.org/10.1016/j.eneco.2019.03.024
(30) Mosquera-López S, Uribe JM, Manotas-Duque DF. Nonlinear empirical pricing in electricity markets using fundamental weather factors. Energy. 2017;139:594–605. https://doi.org/10.1016/j.energy.2017.07.181
(31) Blazsek S, Hernández H. Analysis of electricity prices for Central American countries using dynamic conditional score models. Empir Econ. 2018;55(4):1807–48. https://doi.org/10.1007/s00181-017-1341-3
(32) Mosquera-López S, Nursimulu A. Drivers of electricity price dynamics: Comparative analysis of spot and futures markets. Energy Policy. 2019;126:76–87. https://doi.org/10.1016/j.enpol.2018.11.020
(33) Yan D, Kong Y, Ren X, Shi Y, Chiang S. The determinants of urban sustainability in Chinese resource-based cities: A panel quantile regression approach. Sci Total Environ. 2019;686:1210–9. https://doi.org/10.1016/j.scitotenv.2019.05.386
(34) Díaz G, Coto J, Gómez-Aleixandre J. Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression. Appl Energy. 2019 Apr;239:610–25. https://doi.org/10.1016/j.apenergy.2019.01.213
(35) Zhao W, Yan H, He W. Equilibrium Model of Electricity Market Based on Non-Cooperative Game of Wind Farms, Thermal Power Plants and Power Grid Company. Dianwang JishuPower Syst Technol. 2018;42(1):103–9. https://doi.org/10.13335/j.1000-3673.pst.2017.1337
(36) Pakrooh P, Nematian J, Pishbahar E, Hayati B. Reforming energy prices to achieve sustainable energy consumption in the agriculture sector of Iran’s provinces: Using game approach. J Clean Prod. 2021;293:126146. https://doi.org/10.1016/j.jclepro.2021.126146
(37) Lu Q, Lü S, Leng Y. A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response. Energy. 2019;175:456–70. https://doi.org/10.1016/j.energy.2019.03.079
(38) Penkovskii AV, Stennikov VA, Khamisov OV. Optimum load distribution between heat sources based on the Cournot model. Therm Eng Engl Transl Tepl. 2015;62(8):598–606. https://doi.org/10.1134/S0040601515080054
(39) Koschker S, Möst D. Perfect competition vs. strategic behaviour models to derive electricity prices and the influence of renewables on market power. Spectr. 2016;38(3):661–86. https://doi.org/10.1007/s00291-015-0415-x
(40) Liu G, Zhao JH, Wen F, Yin X, Dong ZY. Option-game-based method for generation investment analysis considering uncertain carbon reduction policy in the electricity market. IET Gener Transm Distrib. 2010;4(8):917–27. https://doi.org/10.1049/iet-gtd.2009.0439
(41) Zhao Y, Yu Y, Liu H. Transmission constrained market power analysis based on LSFE. Dianli Xitong ZidonghuaAutomation Electr Power Syst. 2003;27(13):30-35+49.
(42) Kirschen DS, Strbac G, Cumperayot P, De Mendes DP. Factoring the elasticity of demand in electricity prices. IEEE Trans Power Syst. 2000;15(2):612–7. https://doi.org/10.1109/59.867149
(43) Deng S, Oren S. Electricity derivatives and risk management. Energy. 2006 May;31(6–7):940–53. https://doi.org/10.1016/j.energy.2005.02.015
(44) Mandal P, Senjyu T, Urasaki N, Funabashi T, Srivastava AK. A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method. IEEE Trans Power Syst. 2007 Nov;22(4):2058–65. https://doi.org/10.1109/TPWRS.2007.907386
(45) Rodriguez CP, Anders GJ. Energy Price Forecasting in the Ontario Competitive Power System Market. IEEE Trans Power Syst. 2004 Feb;19(1):366–74. https://doi.org/10.1109/TPWRS.2003.821470
(46) Bojnec S, Papler D. Deregulation of electricity market and drivers of demand for electrical energy in industry. Manag Prod Eng Rev. 2016;7(3):4–10. https://doi.org/10.1515/mper-2016-0021
(47) García-Álvarez MT, Cabeza-García L, Soares I. An assessment of supply-side and demand-side policies in EU-28 household electricity prices. Int J Sustain Energy Plan Manag. 2020;26(Special Issue):5–18. https://doi.org/10.5278/ijsepm.3417
(48) Westgaard S, Fleten S-E, Negash A, Botterud A, Bogaard K, Verling TH. Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market. Energy. 2021;214:118796. https://doi.org/10.1016/j.energy.2020.118796
(49) Afanasyev DO, Fedorova EA, Gilenko EV. The fundamental drivers of electricity price: a multi-scale adaptive regression analysis. Empir Econ. 2020;60(4):1913–1938. https://doi.org/10.1007/s00181-020-01825-3
(50) Carmona R, Coulon M, Schwarz D. Electricity price modeling and asset valuation: A multi-fuel structural approach. Math Financ Econ. 2013;7(2):167–202. https://doi.org/10.1007/s11579-012-0091-4
(51) Hakala K, Addor N, Gobbe T, Ruffieux J, Seibert J. Risks and opportunities for a Swiss hydroelectricity company in a changing climate. Hydrol Earth Syst Sci. 2020;24(7):3815–33. https://doi.org/10.5194/hess-24-3815-2020
(52) Loukatou A, Johnson P, Howell S, Duck P. Optimal valuation of wind energy projects co-located with battery storage. Appl Energy. 2021;283:116247. https://doi.org/10.1016/j.apenergy.2020.116247
(53) Mikayilov JI, Darandary A, Alyamani R, Hasanov FJ, Alatawi H. Regional heterogeneous drivers of electricity demand in Saudi Arabia: Modeling regional residential electricity demand. Energy Policy. 2020;146:111796. https://doi.org/10.1016/j.enpol.2020.111796
(54) Dehning P, Blume S, Dér A, Flick D, Herrmann C, Thiede S. Load profile analysis for reducing energy demands of production systems in non-production times. Appl Energy. 2019;237:117–30. https://doi.org/10.1016/j.apenergy.2019.01.047
(55) Ergemen YE, Haldrup N, Rodríguez-Caballero CV. Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads. Energy Econ. 2016;60:79–96. https://doi.org/10.1016/j.eneco.2016.09.008
(56) Blumsack S. Impacts of the retirement of the Beaver Valley and Three Mile Island nuclear power plants on capacity and energy prices in Pennsylvania. Electr J. 2018;31(6):57–64. https://doi.org/10.1016/j.tej.2018.07.004
(57) Cardell JB. Marginal loss pricing for hours with transmission congestion. IEEE Trans Power Syst. 2007;22(4):1466–74. https://doi.org/10.1109/TPWRS.2007.907123
(58) Garcia M, Baldick R. Approximating Economic Dispatch by Linearizing Transmission Losses. IEEE Trans Power Syst. 2020;35(2):1009–22. https://doi.org/10.1109/TPWRS.2019.2941906
(59) Asija D, Soni KM, Sinha SK, Yadav VK. Congestion management through determination of congestion zones in price-based electricity markets. J Adv Res Dyn Control Syst. 2018;10(7):95–102.
(60) Zarnikau J, Tsai CH, Woo CK. Determinants of the wholesale prices of energy and ancillary services in the U.S. Midcontinent electricity market. Energy. 2020;195: 117051. https://doi.org/10.1016/j.energy.2020.117051
(61) Zhang F, Wen F, Ngan HW, Yu CW, Chung CY, Wong KP. A multi-round negotiation model for bilateral contracting considering spot price risks in electricity market environment. Dianli Xitong Zidonghua Automation Electr Power Syst. 2010;34(22):29–35.
(62) Ballester C, Furió D. Impact of wind electricity forecasts on bidding strategies. Sustain. 2017;9(8):1318. https://doi.org/10.3390/su9081318
(63) Zhang ZJ, Nair N-CC. Economic and pricing signals in electricity distribution systems: A bibliographic survey. In: 2012 IEEE International Conference on Power System Technology (POWERCON). Auckland: IEEE; 2012 [cited 2021 Jul 1]. p. 1–6. https://doi.org/10.1109/PowerCon.2012.6401470
(64) Ferreira ÂP, Ramos JG, Fernandes PO. A linear regression pattern for electricity price forecasting in the Iberian electricity market. Rev Fac Ing. 2019;(93):117–27. https://doi.org/10.17533/udea.redin.20190522
(65) Sadeghi S, Jahangir H, Vatandoust B, Golkar MA, Ahmadian A, Elkamel A. Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach. Int J Electr Power Energy Syst. 2021;127:106646. https://doi.org/10.1016/j.ijepes.2020.106646
(66) Mashlakov A, Kuronen T, Lensu L, Kaarna A, Honkapuro S. Assessing the performance of deep learning models for multivariate probabilistic energy forecasting. Appl Energy. 2021;285:116405. https://doi.org/10.1016/j.apenergy.2020.116405
(67) Ciarreta A, Lagullón M, Zarraga A. Modelación de los precios en el mercado eléctrico español. Cuad Econ. 2011;30:227–50.
(68) Weron R. Electricity price forecasting: A review of the state-of-the-art with a look into the future. Int J Forecast. 2014 Oct;30(4):1030–81. https://doi.org/10.1016/j.ijforecast.2014.08.008
(69) Nowotarski J, Weron R. Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renew Sustain Energy Rev. 2018 Jan;81:1548–68. https://doi.org/10.1016/j.rser.2017.05.234
(70) Mosquera-López S, Manotas-Duque DF, Uribe JM. Risk asymmetries in hydrothermal power generation markets. Electr Power Syst Res. 2017 Jun;147:154–64. https://doi.org/10.1016/j.epsr.2017.02.032
(71) Lisi F, Shah I. Forecasting next-day electricity demand and prices based on functional models. Energy Syst. 2020;11(4):947–79. https://doi.org/10.1007/s12667-019-00356-w
(72) Kabak M, Tasdemir T. Electricity Day-Ahead Market Price Forecasting by Using Artificial Neural Networks: An Application for Turkey. Arab J Sci Eng. 2020;45(3):2317–26. https://doi.org/10.1007/s13369-020-04349-1
(73) Jahangir H, Tayarani H, Baghali S, Ahmadian A, Elkamel A, Golkar MA, et al. A Novel Electricity Price Forecasting Approach Based on Dimension Reduction Strategy and Rough Artificial Neural Networks. IEEE Trans Ind Inform. 2020;16(4):2369–81. https://doi.org/10.1109/TII.2019.2933009
(74) Heydari A, Majidi Nezhad M, Pirshayan E, Astiaso Garcia D, Keynia F, De Santoli L. Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Appl Energy. 2020;277:115503. https://doi.org/10.1016/j.apenergy.2020.115503
(75) Zhang Y, Li C, Li L. Wavelet transform and Kernel-based extreme learning machine for electricity price forecasting. Energy Syst. 2018;9(1):113–34. https://doi.org/10.1007/s12667-016-0227-3
(76) Chen Y, Li M, Yang Y, Li C, Li Y, Li L. A hybrid model for electricity price forecasting based on least square support vector machines with combined kernel. J Renew Sustain Energy. 2018;10(5): 055502. https://doi.org/10.1063/1.5045172
(77) Boltörk E, Oztayşi B. Risk assessment in electricity market integrating value-At-risk approach and forecasting techniques. Int J Ind Eng Theory Appl Pract. 2018;25(4):424–40.
(78) Zolotova IY, Dvorkin VV. Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks. Stud Russ Econ Dev. 2017;28(6):608–15. https://doi.org/10.1134/S1075700717060144
(79) BP. Statistical Review of World Energy 2020 [Internet]. London, United Kingdom; 2019. Report No.: 69th. Available from: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2020-full-report.pdf
(80) Nagayama H. Effects of regulatory reforms in the electricity supply industry on electricity prices in developing countries. Energy Policy. 2007 Jun;35(6):3440–62. https://doi.org/10.1016/j.enpol.2006.12.018
(81) Samudio-Carter C, Vargas A, Albarracín-Sánchez R, Lin J. Mitigation of price spike in unit commitment: A probabilistic approach. Energy Econ. 2019 May;80:1041–9. https://doi.org/10.1016/j.eneco.2019.01.029
(82) Vaca J, Núñez G, Kido A. Análisis multisectorial del incremento de precios de la electricidad en la economía de México. Probl Desarro Rev Latinoam Econ. 2019 Jan 8;50(196):167–89. https://doi.org/10.22201/iiec.20078951e.2019.196.64775
(83) Enríquez A, Ramírez JC, Rosellón J. Costos de generación, inversión y precios del sector eléctrico en México. Investig Económica. 2019;78(309):58. https://doi.org/10.22201/fe.01851667p.2019.309.70119
(84) Abos H, Ave M, Martínez-Ortiz H. A case study of a procedure to optimize the renewable energy coverage in isolated systems: an astronomical center in the North of Chile. Energy Sustain Soc. 2017;7(1). https://doi.org/10.1186/s13705-017-0109-0
(85) Bustos-Salvagno J. Bidding behavior in the Chilean electricity market. Energy Econ. 2015;51:288–99. https://doi.org/10.1016/j.eneco.2015.07.003
(86) Sikora I, Campos Abad JA, Bustos Salvagno J. Determinantes del precio spot eléctrico en el Sistema Interconectado Central de Chile. Rev Análisis Económico. 2017 Oct;32(2):3–38. https://doi.org/10.4067/S0718-88702017000200003
(87) Barria C, Rudnick H. Investment under Uncertainty in Power Generation: Integrated Electricity Prices Modeling and Real Options Approach. IEEE Lat Am Trans. 2011 Sep;9(5):785–92. https://doi.org/10.1109/TLA.2011.6030990
(88) Lucio NR, Lamas WDQ, De Camargo JR. Strategic energy management in the primary aluminium industry: Self-generation as a competitive factor. Energy Policy. 2013;59:182–8. https://doi.org/10.1016/j.enpol.2013.03.021
(89) Vahl FP, Rüther R, Casarotto Filho N. The influence of distributed generation penetration levels on energy markets. Energy Policy. 2013;62:226–35. https://doi.org/10.1016/j.enpol.2013.06.108
(90) Velásquez JD, Dyner I, Souza RC. Modelado del precio spot de la electricidad en Brasil usando una red neuronal autorregresiva. Ingeniare Rev Chil Ing. 2008 Dec;16(3):394–403. http://dx.doi.org/10.4067/S0718-33052008000300002
(91) Xavier EM, Pereira GM, Friedrich LR, Schneider LC, Danesi LC, Borchardt M. Requirements to Leverage the Electricity Distributors’ Sales and Revenues in the Brazilian Free Market. IEEE Lat Am Trans. 2016 Oct;14(10):4293–303. https://doi.org/10.1109/TLA.2016.7786308
(92) Rego EE, de Oliveira Ribeiro C, do Valle Costa OL, Ho LL. Thermoelectric dispatch: From utopian planning to reality. Energy Policy. 2017;106:266–77. https://doi.org/10.1016/j.enpol.2017.03.065
(93) Barrientos J, Rodas E, Velilla E. Modelo para el pronóstico del precio de la energía eléctrica en Colombia. Lect Econ. 2012;91–127.
(94) Díaz-Contreras JA, Macías-Villalba GI, Luna-González E. Estrategia de cobertura con productos derivados para el mercado energético colombiano. Estud Gerenciales. 2014 Jan;30(130):55–64. https://doi.org/10.1016/j.estger.2014.02.008
(95) Lira F, Muñoz C, Núñez F, Cipriano A. Short-term forecasting of electricity prices in the Colombian electricity market. IET Gener Transm Distrib. 2009 Nov 1;3(11):980–6. https://doi.org/10.1049/iet-gtd.2009.0218
(96) Medina S, Moreno J. Risk evaluation in Colombian electricity market using fuzzy logic. Energy Econ. 2007;29(5):999–1009. https://doi.org/10.1016/j.eneco.2006.02.008
(97) Quintero-Quintero M del C, Isaza-Cuervo F. Dependencia hidrológica y regulatoria en la formación de precio de la energía en un sistema hidrodominado: caso sistema eléctrico colombiano. Rev Ing Univ Medellín. 2013;12(22):85–96. https://doi.org/10.22395/rium.v12n22a7
- Diego Fernando Manotas Duque, Pablo César Manyoma, La Evaluación de Proyectos de Inversión Mediante Opciones Reales: Aspectos Conceptuales , Ingeniería y Competitividad: Vol. 3 Núm. 1 (2001)
- Diego Fernando Manotas Duque, Seleccion optima de proyectos economicos bajo incertidumbre: ilustracion de una compañia de servicios publicos , Ingeniería y Competitividad: Vol. 11 Núm. 2 (2009)
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