通过优化独立的混合太阳能-柴油-电池系统,提高住宅能源的获取能力,在喀麦隆的布埃亚 (Buea)
Isaac Amoussou1, Eriisa Yiga Paddy2, Takele Ferede Agajie2,3
1Department of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, P.O. Box 63, Buea, Cameroon. amoussouisaac67@gmail.com.
Scientific reports
|July 5, 2024
概括
这项研究优化了喀麦隆布埃亚的混合可再生能源系统,发现光伏电池柴油系统最具成本效益. 鱼优化算法 (COA) 证明在调整这些系统的大小方面优越,确保可靠,负担得起的电力.
科学领域:
- 可再生能源系统可再生能源系统
- 优化算法 优化算法
- 电力系统工程 电力系统工程
背景情况:
- 喀麦隆布埃亚 (Buea) 的住宅电力供应受到不可靠和不稳定的影响.
- 需要可靠,低成本的替代电源.
- 混合可再生能源系统 (HRES) 是一个潜在的解决方案.
研究的目的:
- 为了确定一个自主HRES在Buea的住宅使用的最佳尺寸.
- 为了比较光伏电池和光伏电池柴油配置的成本效益和可靠性.
- 为了确定HRES组件大小的最有效的优化算法.
主要方法:
- 评估了两个HRES配置:光伏电池和光伏电池-柴油.
- 使用鱼优化算法 (COA) 来进行组件大小测量.
- 将COA与鱼优化算法 (WOA),正弦共弦算法 (SCA) 和虫优化算法 (GOA) 进行比较.
- 在MATLAB中建模系统组件,能源成本 (COE) 和电源损失概率 (LPSP).
主要成果:
- 在这两种配置中,COA算法在优化HRES组件方面表现出卓越的性能.
- 与光伏电池配置相比,光伏电池-柴油配置的COE降低了4.32%.
- 在两种配置中,COA的COE比WOA,SCA和GOA低,节省了高达1%的成本.
结论:
- 太阳能电池柴油HRES是布埃亚住宅应用中最具成本效益的解决方案.
- 鱼优化算法 (COA) 在优化HRES设计方面非常有效.
- 在光伏-电池-柴油系统中减少LPSP可以增加由于柴油发电机容量的COE.
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