A biomass waste evaluation for power energy generation in Mexico based on a SWOT & Fuzzy-logic analysis

被引:1
作者
Hernandez-Escalante M. [1 ]
Martin-Del-campo C. [1 ]
机构
[1] Facultad de Ingeniería, Universidad Nacional Autónoma de México, Ciudad Universitaria, Avenida Universidad 3000, Coyoacán, Ciudad de México
关键词
Bioenergy; Fuzzy logic; Indicative planning; SWOT analysis; Waste;
D O I
10.54337/ijsepm.7073
中图分类号
学科分类号
摘要
Power energy generation in Mexico based on bioenergy is currently insignificant. However, the potential for taking advantage of biomass resources in the country is considerable. This article aims to evaluate the use of biomass waste for the Mexican energy transition in the near future. The methodology starts by identifying sites with biomass waste and establishing the conversion processes needed to produce electricity for each type of biomass. A SWOT analysis was implemented to define the criteria for evaluating all options on the same basis. The opinion of experts in energy systems was collected to assign priority to each criterion. A fuzzy-logic inference system was formulated to assess the options based on the quality of their attributes. The output obtained from the fuzzy analysis is a sustainability prioritisation of all options. We analysed a case study for the Baja California Sur (BCS) region, and the results show the prioritisation ranking of 24 alternatives regarding the sustainable use of bioenergy in the region and we made a proposal of an indicative plan to introduce bioenergy in the region from now until 2032. If the indicative plan were implemented, 61% of the power demand of BCS could be covered with bioenergy by 2032. © 2022, Aalborg University press. All rights reserved.
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页码:5 / 26
页数:21
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  • [1] United Nations Framework Convention on Climate Change, (2015)
  • [2] Ritchie H., Roser M., What Share of electricity comes from fossil fuels?, Energy.Published at Our World In Data, (2019)
  • [3] Victor N., Nichols C., Zelek C., The U.S. power sector decarbonization: Investigating technology options with MARKAL nine-region model, Energy Economics, 73, pp. 410-425, (2018)
  • [4] Mileva A., Johnston J., Nelson J. H., Kammen D. M., Power system balancing for deep decarbonization of the electricity sector, Applied Energy, 162, pp. 1001-1009, (2016)
  • [5] Rudisuli M., Romano E., Eggimann S., Patel M. K., Decarbonization strategies for Switzerland considering embedded greenhouse gas emissions in electricity imports, Energy Policy, 162, (2022)
  • [6] Connolly D., Mathiesen B. V., A technical and economic analysis of one potential pathway to a 100% renewable energy system, International Journal of Sustainable Energy Planning and Management, 1, pp. 7-28, (2014)
  • [7] Contribución Determinada a nivel Nacional: México, (2020)
  • [8] LEY GENERAL DE CAMBIO CLIMÁTICO, (2020)
  • [9] LEY DE TRANSICIÓN ENERGÉTICA TÍTULO PRIMERO Disposiciones Generales Capítulo Único Del Objeto de la Ley y Definiciones, (2015)
  • [10] Programa de Desarrollo del sistema Eléctrico nacional 2021-2035 PRODESEN 2021, (2021)