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Techno-economic and environmental assessment of a hybrid renewable energy system using multi-objective genetic algorithm: A case study for remote Island in Bangladesh
Energy Conversion and Management ( IF 8.208 ) Pub Date : 2021-01-13 , DOI: 10.1016/j.enconman.2020.113823
Barun K. Das; Rakibul Hassan; Mohammad Shahed H.K. Tushar; Forhad Zaman; Mahmudul Hasan; Pronob Das

Renewable hybrid energy systems are well-proven to be capable of supplying reliable power in the remote areas, where grid extension is not viable due to geographical constraints, but not absolutely emissions free. The present study investigates a hybrid energy system that entails photovoltaic module, wind turbine, biogas generator, and vanadium redox flow battery for supplying stable power to a remote Island, Saint Martin, Bangladesh. Two well-known multi-objective optimisation techniques such as non-dominated sorting genetic algorithm II and infeasibility driven evolutionary algorithm are applied to size the hybrid system components based on the cost of energy ($/kWh) and life cycle emissions (kg CO2-eq/yr) under a certain reliability. In addition, a fuzzy decision-making technique is applied to find the optimal solution. A comparative analysis of using single objective function is compared with the multi-objective one. In addition, results from the non-dominated sorting genetic algorithm II optimisation technique is compared with the widely utilized software hybrid optimisation of multiple energy resources tool and the infeasibility driven evolutionary algorithm. Although the cost of energy is relatively comparable between the objective functions considered, the multi-objective approach provides better environmental benefits than the single objective optimisation system. The analyzed results also indicate that the intelligent techniques are the superior to the hybrid optimisation of multiple energy resources software tool in terms of costs and environmental point of view. Furthermore, the unit electricity cost of the proposed hybrid system configuration is comparable with the grid electricity supply at the loss of power supply probability of over 8% with significantly lower life cycle emissions.



中文翻译:

基于多目标遗传算法的混合可再生能源系统的技术经济与环境评估:以孟加拉国偏远岛屿为例

事实证明,可再生混合能源系统能够在偏远地区提供可靠的电力,在偏远地区,由于地理限制,电网扩展不可行,但并非绝对没有排放。本研究调查了一种混合能源系统,该系统包含光伏模块,风力涡轮机,沼气发电机和钒氧化还原液流电池,用于为孟加拉国圣马丁岛的偏远岛提供稳定的电力。基于能量成本($ / kWh)和生命周期排放量(kg CO 2),应用了两种著名的多目标优化技术(例如非支配排序遗传算法II和不可行驱动的进化算法)来确定混合系统组件的大小。-eq / yr)。另外,应用模糊决策技术来找到最优解。比较了使用单目标函数和多目标函数的比较分析。此外,将非支配排序遗传算法II优化技术的结果与广泛使用的多种能源工具的软件混合优化和不可行驱动的进化算法进行了比较。尽管在考虑的目标函数之间能源成本相对可比,但是多目标方法比单目标优化系统提供了更好的环境效益。分析结果还表明,就成本和环境角度而言,智能技术优于多种能源软件工具的混合优化。此外,所提出的混合动力系统配置的单位电力成本可与电网电力供应相媲美,而电力供应损失的可能性超过8%,并且生命周期排放显着降低。

更新日期:2021-01-13
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