这项研究检查了Buea住宅应用的自主混合可再生能源系统(HRES)的最佳尺寸,位于喀麦隆西南部地区。两个混合动力系统,光伏电池和光伏电池柴油,已经进行了评估,以确定哪个是更好的选择。这项研究的目的是提出一个可靠的,低成本电源作为布埃亚不可靠和高度不稳定电网的替代方案。提议的HRES的决策标准是能源成本(COE),而系统的可靠性约束是电源损失概率(LPSP)。小龙虾优化算法(COA)用于优化所提出的HRES的组件尺寸,并将结果与鲸鱼优化算法(WOA)获得的结果进行了对比,正弦余弦算法(SCA),和蝗虫优化算法(GOA)。利用MATLAB软件对组件进行建模,标准,和约束这个单目标优化问题。对小于1%的LPSP进行仿真后获得的结果表明,COA算法优于其他三种技术,无论配置如何。的确,使用COA算法获得的COE为0.06%,0.12%,比WOA提供的COE低1%,SCA,和GOA算法,分别,对于PV电池配置。同样,对于PV-电池-柴油配置,使用COA算法获得的COE为0.065%,0.13%,比WOA提供的COE低0.39%,SCA,和GOA算法,分别。对两种配置获得的结果的比较分析表明,与PV电池配置相比,PV电池柴油配置的COE降低了4.32%。最后,在PV-电池-柴油配置中评估了LPSP降低对COE的影响。由于柴油发电机的标称容量,LPSP的减少导致COE的增加。
This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system\'s dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.