Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, BE1410, Brunei Darussalam.
Environmental Studies, Faculty of Arts and Social Sciences, Universiti Brunei Darussalam, Jalan Tungku Link, BE1410, Brunei Darussalam.
J Environ Manage. 2020 Apr 15;260:109978. doi: 10.1016/j.jenvman.2019.109978. Epub 2020 Jan 22.
This is an evidence from a high-income economy in Southeast Asia and a support for scientific planning of the energy sector in ensuring air pollution and climate change mitigation. A comparative analysis of the energy options for electricity generation in the nation was made considering availability, cost and greenhouse gases emission - CO, NO and CH, using a two-stage method comprising multi-objective optimization and TOPSIS. The renewable (RE) and non-renewable energy (NRE) options available were assessed through the lifecycle approach to determine the lifecycle greenhouse gas emission (LCGHG) and levelized cost of energy (LCOE) per MWh of electricity. Considering historical electricity consumption, annual GDP and population growth from 1965, energy consumption for the year 2035 was forecasted using support vector machine regressor in Weka. Future plans in energy diversification pathways were examined through various scenario multi-objective optimizations with a constraint on resource availability and energy target using genetic algorithm in MATLAB. The outputs were ranked using TOPSIS method. Results showed that greenhouse gases emission could be reduced by 10.3 percent compared to business as usual scenario while the energy mix could attain 10 percent renewable energy in the grid at a relatively lower generation cost.
这是东南亚高收入经济体的证据,为确保减少空气污染和气候变化的能源部门的科学规划提供了支持。考虑到供应、成本和温室气体排放——CO、NO 和 CH,采用包含多目标优化和 TOPSIS 的两阶段方法,对全国发电的能源选择进行了比较分析。通过生命周期方法评估可再生能源 (RE) 和不可再生能源 (NRE) 选择,以确定每兆瓦时电力的生命周期温室气体排放 (LCGHG) 和能源平准化成本 (LCOE)。根据 1965 年以来的历史用电量、年 GDP 和人口增长率,使用 WEKA 中的支持向量机回归器预测 2035 年的能源消耗。使用 MATLAB 中的遗传算法,在资源可用性和能源目标的约束下,通过各种情景的多目标优化来检查未来的能源多样化途径。使用 TOPSIS 方法对结果进行排名。结果表明,与照常营业情景相比,温室气体排放量可减少 10.3%,而在相对较低的发电成本下,能源组合可在电网中达到 10%的可再生能源。