How to cite this paper
Alvarez, G. (2022). Enhancing the large-scale electric power systems to meet future demands considering the sustainable technologies.Management Science Letters , 12(4), 331-340.
Refrences
Alvarez, G. (2022). Integrated modeling of the peer-to-peer markets in the energy industry. International Journal of Industrial Engineering Computations, 13(1), 101–118. https://doi.org/10.5267/j.ijiec.2021.7.002
Alvarez, Gonzalo. (2020a). Integrated scheduling from a diversity of sources applied to the Argentine electric power and natural gas systems. Computers & Chemical Engineering, 134, 106691.
Alvarez, Gonzalo. (2020b). Operation of pumped storage hydropower plants through optimization for power systems. Energy, 202, 117797. https://doi.org/10.1016/j.energy.2020.117797
Aquila, G., de Queiroz, A. R., Balestrassi, P. P., Rotella Junior, P., Rocha, L. C. S., Pamplona, E. O., & Nakamura, W. T. (2020). Wind energy investments facing uncertainties in the Brazilian electricity spot market: A real options approach. Sustainable Energy Technologies and Assessments, 42, 100876. https://doi.org/10.1016/j.seta.2020.100876
Arora, J. S. (2012). Multi-objective Optimum Design Concepts and Methods. In Introduction to Optimum Design (pp. 657–679). Elsevier. https://doi.org/10.1016/B978-0-12-381375-6.00017-6
Arze del Granado, F. J., Coady, D., & Gillingham, R. (2012). The Unequal Benefits of Fuel Subsidies: A Review of Evidence for Developing Countries. World Development, 40(11), 2234–2248. https://doi.org/10.1016/j.worlddev.2012.05.005
Asociación Argentina del Presupuesto. (2015). Los subsidios energéticos en Argentina. https://web.iae.org.ar/wp-content/uploads/2015/12/LOS-SUBSIDIOS-ENERG--TICOS-EN-ARGENTINA-RESUMEN-EJECUTIVO.pdf
Azam, A., Rafiq, M., Shafique, M., Zhang, H., Ateeq, M., & Yuan, J. (2021). Analyzing the relationship between economic growth and electricity consumption from renewable and non-renewable sources: Fresh evidence from newly industrialized countries. Sustainable Energy Technologies and Assessments, 44, 100991. https://doi.org/10.1016/j.seta.2021.100991
Azpiazu, D., Forcinito, K., & Schorr, M. (2001). Privatizaciones en la Argentina: renegociación permanente, consolidación de privilegios, ganancias extraordinarias y captura institucional. In Área Economía y Tecnología (p. 69). FLACSO. http://economia.flacso.org.ar/pdf/202.pdf
Bøckman, T., Fleten, S.-E., Juliussen, E., Langhammer, H. J., & Revdal, I. (2008). Investment timing and optimal capacity choice for small hydropower projects. European Journal of Operational Research, 190(1), 255–267. https://doi.org/10.1016/j.ejor.2007.05.044
Boomsma, T. K., Meade, N., & Fleten, S.-E. (2012). Renewable energy investments under different support schemes: A real options approach. European Journal of Operational Research, 220(1), 225–237. https://doi.org/10.1016/j.ejor.2012.01.017
Breen, M., Murphy, M. D., & Upton, J. (2019). Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms. Applied Energy, 242, 1697–1711. https://doi.org/10.1016/j.apenergy.2019.03.059
Bussieck, M., & Meeraus, A. (2004). General algebraic modeling system (GAMS). Applied Optimization, 88, 137–158. https://doi.org/10.1007/978-1-4613-0215-5_8
Castillo, D., & Szkolnik, M. (2019). Energía subsidiada vs. Tarifas liberadas. La disputa por los proyectos sociales, consumos, y derechos en la Argentina entre 2002 y 2019. In Observatorio de Economía Política de la Facultad de Ciencias Sociale. http://observatorioeconomiapolitica.sociales.uba.ar/wp-content/uploads/sites/251/2020/01/Subsidios-vs-tarifas-liberadas.pdf
Cayir Ervural, B., Evren, R., & Delen, D. (2018). A multi-objective decision-making approach for sustainable energy investment planning. Renewable Energy, 126, 387–402. https://doi.org/10.1016/j.renene.2018.03.051
Chen, W., Chen, J., & Ma, Y. (2021). Renewable energy investment and carbon emissions under cap-and-trade mechanisms. Journal of Cleaner Production, 278, 123341. https://doi.org/10.1016/j.jclepro.2020.123341
Comisión Nacional de Energía. (2020). Informe de costos de tecnologías de generación. https://www.cne.cl/wp-content/uploads/2020/03/ICTG-Marzo-2020.pdf
Esmaili, M., Amjady, N., & Shayanfar, H. A. (2011). Multi-objective congestion management by modified augmented ε-constraint method. Applied Energy, 88(3), 755–766. https://doi.org/10.1016/j.apenergy.2010.09.014
Fazlollahi, S., Mandel, P., Becker, G., & Maréchal, F. (2012). Methods for multi-objective investment and operating optimization of complex energy systems. Energy, 45(1), 12–22. https://doi.org/10.1016/j.energy.2012.02.046
Groisman, F. (2012). Salario mínimo y empleo en Argentina. REV. DE ECONOMÍA POLÍTICA DE BS. AS, 11, 9–47. http://157.92.136.232/index.php/REPBA/article/view/407
Guzowski, C. (2011). Tarifa Social: En los sectores de infraestructura en la Argentina. Estudios Economicos, 28(56). http://bibliotecadigital.uns.edu.ar/scielo.php?script=sci_arttext&pid=S2525-12952011001100004&lng=en&nrm=iso
Heinen, S., Burke, D., & O’Malley, M. (2016). Electricity, gas, heat integration via residential hybrid heating technologies – An investment model assessment. Energy, 109, 906–919. https://doi.org/10.1016/j.energy.2016.04.126
Ji, X., Li, G., & Wang, Z. (2017). Allocation of emission permits for China’s power plants: A systemic Pareto optimal method. Applied Energy. https://doi.org/10.1016/j.apenergy.2017.07.033
Krey V., O. et al. (2014). Annex II: Metrics & Methodology. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_annex-iii.pdf#page=7
Lin, B., & Wesseh, P. K. (2013). Valuing Chinese feed-in tariffs program for solar power generation: A real options analysis. Renewable and Sustainable Energy Reviews, 28, 474–482. https://doi.org/10.1016/j.rser.2013.08.043
Lopetegui, G. (2019). Energía en Argentina y sus perspectivas. https://ucema.edu.ar/conferencias/download/2019/08.14ER.pdf
Martínez, P., & Eliceche, A. M. (2009). Multi Objective Optimization Using Life Cycle Environmental Impact and Cost in the Operation of Utility Plants (pp. 1869–1874). https://doi.org/10.1016/S1570-7946(09)70702-3
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455–465. https://doi.org/10.1016/j.amc.2009.03.037
Nugent, D., & Sovacool, B. K. (2014). Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey. Energy Policy, 65, 229–244. https://doi.org/10.1016/j.enpol.2013.10.048
Osorio Muriel, A. F., Brailsford, S., & Smith, H. (2014). A bi-objective optimization model for technology selection and donor’s assignment in the blood supply chain. Sistemas y Telemática, 12(30), 9. https://doi.org/10.18046/syt.v12i30.1854
Overbye, T. J., Cheng, X., & Sun, Y. (2004). A comparison of the AC and DC power flow models for LMP calculations. Proceedings of the 37th Annual Hawaii International Conference on System Sciences, 9. https://doi.org/10.1109/HICSS.2004.1265164
Pereira, L., & Posen, I. D. (2020). Lifecycle greenhouse gas emissions from electricity in the province of Ontario at different temporal resolutions. Journal of Cleaner Production, 270, 122514. https://doi.org/10.1016/j.jclepro.2020.122514
Rabobank. (2013). The Argentine Crisis 2001/2002. In Rabobank - Economic Report.
Rasanen, T. A., Varis, O., Scherer, L., & Kummu, M. (2018). Greenhouse gas emissions of hydropower in the Mekong River Basin. Environmental Research Letters. https://doi.org/10.1088/1748-9326/aaa817
Reinhart, Carmen and Savastano, M. (2003). The Realities of Modern Hyperinflation. Finance and Development, 40(2), 20–23. https://mpra.ub.uni-muenchen.de/id/eprint/7578
Secretaría de Energía de la República Argentina. (2021). Distribución de Energía Eléctrica Facturada y Cantidad de Usuarios por tipo y por jurisdicción provincial. http://www.energia.gov.ar/contenidos/verpagina.php?idpagina=4021
Sovacool, B. K. (2008). Valuing the greenhouse gas emissions from nuclear power: A critical survey. Energy Policy, 36(8), 2950–2963. https://doi.org/10.1016/j.enpol.2008.04.017
Stott, B., Jardim, J., & Alsac, O. (2009). DC Power Flow Revisited. IEEE Transactions on Power Systems, 24(3), 1290–1300. https://doi.org/10.1109/TPWRS.2009.2021235
Weisser, D. (2007). A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies. Energy, 32(9), 1543–1559. https://doi.org/10.1016/j.energy.2007.01.008
Yang, M., Blyth, W., Bradley, R., Bunn, D., Clarke, C., & Wilson, T. (2008). Evaluating the power investment options with uncertainty in climate policy. Energy Economics, 30(4), 1933–1950. https://doi.org/10.1016/j.eneco.2007.06.004
Zhang, M., Zhou, P., & Zhou, D. (2016). A real options model for renewable energy investment with application to solar photovoltaic power generation in China. Energy Economics, 59, 213–226. https://doi.org/10.1016/j.eneco.2016.07.028
Zykina, A. V. (2004). A Lexicographic Optimization Algorithm. Automation and Remote Control, 65(3), 363–368.
Alvarez, Gonzalo. (2020a). Integrated scheduling from a diversity of sources applied to the Argentine electric power and natural gas systems. Computers & Chemical Engineering, 134, 106691.
Alvarez, Gonzalo. (2020b). Operation of pumped storage hydropower plants through optimization for power systems. Energy, 202, 117797. https://doi.org/10.1016/j.energy.2020.117797
Aquila, G., de Queiroz, A. R., Balestrassi, P. P., Rotella Junior, P., Rocha, L. C. S., Pamplona, E. O., & Nakamura, W. T. (2020). Wind energy investments facing uncertainties in the Brazilian electricity spot market: A real options approach. Sustainable Energy Technologies and Assessments, 42, 100876. https://doi.org/10.1016/j.seta.2020.100876
Arora, J. S. (2012). Multi-objective Optimum Design Concepts and Methods. In Introduction to Optimum Design (pp. 657–679). Elsevier. https://doi.org/10.1016/B978-0-12-381375-6.00017-6
Arze del Granado, F. J., Coady, D., & Gillingham, R. (2012). The Unequal Benefits of Fuel Subsidies: A Review of Evidence for Developing Countries. World Development, 40(11), 2234–2248. https://doi.org/10.1016/j.worlddev.2012.05.005
Asociación Argentina del Presupuesto. (2015). Los subsidios energéticos en Argentina. https://web.iae.org.ar/wp-content/uploads/2015/12/LOS-SUBSIDIOS-ENERG--TICOS-EN-ARGENTINA-RESUMEN-EJECUTIVO.pdf
Azam, A., Rafiq, M., Shafique, M., Zhang, H., Ateeq, M., & Yuan, J. (2021). Analyzing the relationship between economic growth and electricity consumption from renewable and non-renewable sources: Fresh evidence from newly industrialized countries. Sustainable Energy Technologies and Assessments, 44, 100991. https://doi.org/10.1016/j.seta.2021.100991
Azpiazu, D., Forcinito, K., & Schorr, M. (2001). Privatizaciones en la Argentina: renegociación permanente, consolidación de privilegios, ganancias extraordinarias y captura institucional. In Área Economía y Tecnología (p. 69). FLACSO. http://economia.flacso.org.ar/pdf/202.pdf
Bøckman, T., Fleten, S.-E., Juliussen, E., Langhammer, H. J., & Revdal, I. (2008). Investment timing and optimal capacity choice for small hydropower projects. European Journal of Operational Research, 190(1), 255–267. https://doi.org/10.1016/j.ejor.2007.05.044
Boomsma, T. K., Meade, N., & Fleten, S.-E. (2012). Renewable energy investments under different support schemes: A real options approach. European Journal of Operational Research, 220(1), 225–237. https://doi.org/10.1016/j.ejor.2012.01.017
Breen, M., Murphy, M. D., & Upton, J. (2019). Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms. Applied Energy, 242, 1697–1711. https://doi.org/10.1016/j.apenergy.2019.03.059
Bussieck, M., & Meeraus, A. (2004). General algebraic modeling system (GAMS). Applied Optimization, 88, 137–158. https://doi.org/10.1007/978-1-4613-0215-5_8
Castillo, D., & Szkolnik, M. (2019). Energía subsidiada vs. Tarifas liberadas. La disputa por los proyectos sociales, consumos, y derechos en la Argentina entre 2002 y 2019. In Observatorio de Economía Política de la Facultad de Ciencias Sociale. http://observatorioeconomiapolitica.sociales.uba.ar/wp-content/uploads/sites/251/2020/01/Subsidios-vs-tarifas-liberadas.pdf
Cayir Ervural, B., Evren, R., & Delen, D. (2018). A multi-objective decision-making approach for sustainable energy investment planning. Renewable Energy, 126, 387–402. https://doi.org/10.1016/j.renene.2018.03.051
Chen, W., Chen, J., & Ma, Y. (2021). Renewable energy investment and carbon emissions under cap-and-trade mechanisms. Journal of Cleaner Production, 278, 123341. https://doi.org/10.1016/j.jclepro.2020.123341
Comisión Nacional de Energía. (2020). Informe de costos de tecnologías de generación. https://www.cne.cl/wp-content/uploads/2020/03/ICTG-Marzo-2020.pdf
Esmaili, M., Amjady, N., & Shayanfar, H. A. (2011). Multi-objective congestion management by modified augmented ε-constraint method. Applied Energy, 88(3), 755–766. https://doi.org/10.1016/j.apenergy.2010.09.014
Fazlollahi, S., Mandel, P., Becker, G., & Maréchal, F. (2012). Methods for multi-objective investment and operating optimization of complex energy systems. Energy, 45(1), 12–22. https://doi.org/10.1016/j.energy.2012.02.046
Groisman, F. (2012). Salario mínimo y empleo en Argentina. REV. DE ECONOMÍA POLÍTICA DE BS. AS, 11, 9–47. http://157.92.136.232/index.php/REPBA/article/view/407
Guzowski, C. (2011). Tarifa Social: En los sectores de infraestructura en la Argentina. Estudios Economicos, 28(56). http://bibliotecadigital.uns.edu.ar/scielo.php?script=sci_arttext&pid=S2525-12952011001100004&lng=en&nrm=iso
Heinen, S., Burke, D., & O’Malley, M. (2016). Electricity, gas, heat integration via residential hybrid heating technologies – An investment model assessment. Energy, 109, 906–919. https://doi.org/10.1016/j.energy.2016.04.126
Ji, X., Li, G., & Wang, Z. (2017). Allocation of emission permits for China’s power plants: A systemic Pareto optimal method. Applied Energy. https://doi.org/10.1016/j.apenergy.2017.07.033
Krey V., O. et al. (2014). Annex II: Metrics & Methodology. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_annex-iii.pdf#page=7
Lin, B., & Wesseh, P. K. (2013). Valuing Chinese feed-in tariffs program for solar power generation: A real options analysis. Renewable and Sustainable Energy Reviews, 28, 474–482. https://doi.org/10.1016/j.rser.2013.08.043
Lopetegui, G. (2019). Energía en Argentina y sus perspectivas. https://ucema.edu.ar/conferencias/download/2019/08.14ER.pdf
Martínez, P., & Eliceche, A. M. (2009). Multi Objective Optimization Using Life Cycle Environmental Impact and Cost in the Operation of Utility Plants (pp. 1869–1874). https://doi.org/10.1016/S1570-7946(09)70702-3
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455–465. https://doi.org/10.1016/j.amc.2009.03.037
Nugent, D., & Sovacool, B. K. (2014). Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey. Energy Policy, 65, 229–244. https://doi.org/10.1016/j.enpol.2013.10.048
Osorio Muriel, A. F., Brailsford, S., & Smith, H. (2014). A bi-objective optimization model for technology selection and donor’s assignment in the blood supply chain. Sistemas y Telemática, 12(30), 9. https://doi.org/10.18046/syt.v12i30.1854
Overbye, T. J., Cheng, X., & Sun, Y. (2004). A comparison of the AC and DC power flow models for LMP calculations. Proceedings of the 37th Annual Hawaii International Conference on System Sciences, 9. https://doi.org/10.1109/HICSS.2004.1265164
Pereira, L., & Posen, I. D. (2020). Lifecycle greenhouse gas emissions from electricity in the province of Ontario at different temporal resolutions. Journal of Cleaner Production, 270, 122514. https://doi.org/10.1016/j.jclepro.2020.122514
Rabobank. (2013). The Argentine Crisis 2001/2002. In Rabobank - Economic Report.
Rasanen, T. A., Varis, O., Scherer, L., & Kummu, M. (2018). Greenhouse gas emissions of hydropower in the Mekong River Basin. Environmental Research Letters. https://doi.org/10.1088/1748-9326/aaa817
Reinhart, Carmen and Savastano, M. (2003). The Realities of Modern Hyperinflation. Finance and Development, 40(2), 20–23. https://mpra.ub.uni-muenchen.de/id/eprint/7578
Secretaría de Energía de la República Argentina. (2021). Distribución de Energía Eléctrica Facturada y Cantidad de Usuarios por tipo y por jurisdicción provincial. http://www.energia.gov.ar/contenidos/verpagina.php?idpagina=4021
Sovacool, B. K. (2008). Valuing the greenhouse gas emissions from nuclear power: A critical survey. Energy Policy, 36(8), 2950–2963. https://doi.org/10.1016/j.enpol.2008.04.017
Stott, B., Jardim, J., & Alsac, O. (2009). DC Power Flow Revisited. IEEE Transactions on Power Systems, 24(3), 1290–1300. https://doi.org/10.1109/TPWRS.2009.2021235
Weisser, D. (2007). A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies. Energy, 32(9), 1543–1559. https://doi.org/10.1016/j.energy.2007.01.008
Yang, M., Blyth, W., Bradley, R., Bunn, D., Clarke, C., & Wilson, T. (2008). Evaluating the power investment options with uncertainty in climate policy. Energy Economics, 30(4), 1933–1950. https://doi.org/10.1016/j.eneco.2007.06.004
Zhang, M., Zhou, P., & Zhou, D. (2016). A real options model for renewable energy investment with application to solar photovoltaic power generation in China. Energy Economics, 59, 213–226. https://doi.org/10.1016/j.eneco.2016.07.028
Zykina, A. V. (2004). A Lexicographic Optimization Algorithm. Automation and Remote Control, 65(3), 363–368.