Abstract
Nowadays, an eco-friendly way to satisfy the high-energy demand is by the exploitation of renewable sources. Wind energy is one of the viable sustainable sources. In particular, small-scale wind turbines are an attractive option for meeting the high demand for domestic energy consumption since exclude the installation problems of large-scale wind farms. However, appropriate wind resource, installation costs, and other factors must be taken into consideration as well. Therefore, a feasibility study for the setting up of this technology is required beforehand. This requires a decision-making problem involving complex conditions and a degree of uncertainty. It turns out that Bayesian Decision Networks are a suitable paradigm to deal with this task. In this work, we present the development of a decision-making method, built with Decision Bayesian Networks, to assess the use of small-scale wind turbines to meet the high-energy demand considering the available wind resource, installation costs, reduction in CO2 emissions and the achieved savings.
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References
López Obrador, A.M.: Presidencia de la República: Plan Nacional de Desarrollo 2019–2024. Diario Oficial de la Federación. México (2019)
Tummalaa, A., Velamati, R.K., Sinha, D.K., Indraja, V., Krishna, V.H.: A review on small-scale wind turbines. Renew. Sustain. Energy Rev. 56, 1351–1371 (2016)
Garduno, R., Borunda, M., Hernandez, M.A., Zubeldia, G.: Speed control of a wind turbine using fuzzy logic. In: Martínez-Villaseñor, L., et al. (eds.) MICAI 2019. LNAI, vol. 11835 pp. 522–536 (2019)
Predescu, M.: Economic evaluation of small wind turbines and hybrid systems for residential use. Renew. Energy Environ. Sustain. 1, 33 (2016)
Gagliano, A., Nocera, F., Patania, F., Capizzi, A.: Assessment of micro-wind turbines performance in the urban environments: an aided methodology through geographical information systems. Int. J. Energy Environ. Eng. 4, 43 (2013)
Lee, A.H.I., Chen, H.H., Kang, H.Y.: Multi-criteria decision-making on strategic selection of wind farms. Renew. Energy 34, 120–126 (2009)
Goh, H.H., Lee, S.W., Kok, B.C., Ng, S.L.: Wind farm allocation in Malaysia based on multi-criteria decision-making method. In: 2011 National Postgraduate Conference. IEEE, Kuala Lumpur (2011)
Sánchez-Lozano, J.M., García-Cascales, M.S., Lamata, M.T.: GIS-based onshore wind farm site selection using fuzzy multi-criteria decision-making methods. Evaluating the case of southeastern Spain. Appl. Energy 171, 86–102 (2016)
Haaren, R.H., Fthenakis, V.: GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): evaluating the case for New York State. Renew. Sustain. Energy Rev. 15(7), 3332–3340 (2011)
Villacreses, G., Gaona, G., Martínez, J., Jijón, D.J.: Wind farms suitability location using geographical information system (GIS), based on multi-criteria decisión making (MCDM) methods: The case of continental Ecuador. Renew. Energy 109, 275–286 (2017)
Tegou, L.I., Polatidis, H., Haralambopoulos, D.A.: Environmental management framework for wind farm siting: methodology and case study. J. Environ. Manage. 91, 2134–2147 (2010)
Carbon Trust: Small-scale wind energy. Policy insights and practical guidance. London, UK (2008)
Reuther, N., Thull, J.P.: Feasibility study of small and micro wind turbines for residential use in New Zealand. LEaP Research Report No. 30, Canterbury, New Zealand (2011)
Ugur, E., Elma, O., Selamogullari, U.S., Tanrioven, M., Uzunoglu, M.: Financial payback analysis of small wind turbines for a smart home application in Istanbul/Turkey. In: International Conference on Renewable Energy Research and Applications (ICRERA), Madrid, Spain (2013)
Bortolini, M., Gamberi, M., Graziani, A., Manzini, R., Pilati, F.: Performance and viability analysis of small wind turbines in the European Union. Renew. Energy 62, 629–639 (2014)
Olsen, T., Preus, R.: Small wind site assessment guidelines. Technical Report NREL/TP-5000–63696, Denver, USA (2015)
Abdelhady, S., Borello, D., Santori, S.: Economic feasibility of small wind turbines for domestic consumers in Egypt based on the new Feed-in Tariff. Energy Procedia 75, 664–670 (2015)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco (1988)
Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence, 2nd edn. CRC Press, Boca Raton (2011)
Sucar, L.E.: Probabilistic Graphical Models: Principles and Applications. Springer, London (2015). https://doi.org/10.1007/978-1-4471-6699-3
Howard, R.A., Matheson, J.E.: Influence diagrams. Decis. Anal. 3(2), 127–143 (2005)
International Energy Agency: IEA Wind Technology Collaboration Programme 2017 Annual Report. Olympia, USA (2018)
Comisión Reguladora de Energía: Reporte Mensual de Estadísticas del Sector Eléctrico, pp. 1–5 (2014)
Oropeza-Perez, I., Petzold-Rodriguez, A.: Analysis of the energy use in the Mexican residential sector by using two approaches regarding the behavior of the occupants. Appl. Sci. 8(11), 2136 (2018)
Acknowledgments
Monica Borunda wishes to thank CONACYT for her Catedra Research Position with ID 71557, and to INEEL for its hospitality.
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Borunda, M., Garduno, R., Nicholson, A.E., de la Cruz, J. (2019). Assessment of Small-Scale Wind Turbines to Meet High-Energy Demand in Mexico with Bayesian Decision Networks. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_40
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