Integrated off-grid hybrid renewable energy system optimization based on economic, environmental, and social indicators for sustainable development

被引:66
|
作者
Hassan, Rakibul [1 ]
Das, Barun K. [1 ]
Hasan, Mahmudul [2 ]
机构
[1] Rajshahi Univ Engn & Technol, Dept Mech Engn, Rajshahi 6204, Bangladesh
[2] Univ Sydney, Sch Chem & Biomol Engn, Sydney, NSW 2006, Australia
关键词
Hybrid renewable energy system; Optimization; Social indicators; Lifecycle emissions; MULTIOBJECTIVE GENETIC ALGORITHM; POWER-GENERATION; MICRO-HYDRO; RURAL ELECTRIFICATION; REMOTE AREA; SOLAR PV; TECHNOECONOMIC FEASIBILITY; SENSITIVITY-ANALYSIS; UTTARAKHAND STATE; OPTIMAL-DESIGN;
D O I
10.1016/j.energy.2022.123823
中图分类号
O414.1 [热力学];
学科分类号
摘要
Conventionally, modelling of hybrid renewable energy systems emphasizes on the technical, economic and environmental features of these systems and disregards their social implications. This study focuses on modelling a hybrid renewable system that is both economic, environmentally benign and at the same time socially beneficial by including a social indicator in the design criteria. A hybrid renewable energy system comprising solar photovoltaic (PV), wind turbine (WT), micro-hydro turbine (MHT), biogas generator (BG) and vanadium redox flow (VRF) battery is proposed to meet the community load demand varying in the range of 951-1526 kWh/day in a remote rural part of Bangladesh. The enormous prospect of job creation from renewable technologies and the prevailing poor status of employment sector in Bangladesh makes job creation (number of jobs) an obvious choice as the social indicator. Aside from that, cost of energy ($/kWh) and lifecycle emission (kg CO2-eq/yr) are considered as economic and environmental indicators, respectively. Six different configurations are assessed to acquire a system that can meet the demand while satisfying certain technical and reliability constraints. The multi-objective sizing optimization performed using non-dominated sorting genetic algorithm-II reveals that the optimum configuration incorporates 68 kW PV, 90 kW WT, 50 kW MHT, 50 kW BG, and 300 kWh VRF and it offers COE of 0.126 $/kWh, emits 60,116 kg CO2-eq/yr and is able to create 0.7396 jobs in the community. (C) 2022 Elsevier Ltd. All rights reserved.
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页数:17
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