一个HLBDA,GA和COA,以实现分布式能源的最佳运行
Bilal Naji Alhasnawi1, Sabah Mohammed Mlkat Almutoki2, Hayder Khenyab Hashim3
1Department of Fuel and Energy Techniques Engineering, Petroleum and Energy Engineering Technical College, Al-Furat Al-Awsat Technical University, Kufa, Iraq.
PloS one
|January 30, 2026
概括
本研究介绍了使用超级学习二元龙算法 (HLBDA) 的混合可再生能源发电厂的能源管理系统. 该系统优化了经济效益,同时降低了微电网中的运营成本和污染气体排放.
科学领域:
- 能源系统工程 能源系统工程
- 环境科学 环境科学
- 计算智能是一种计算智能.
背景情况:
- 可再生能源对环境可持续性至关重要,但面临的挑战是微电网的间歇性.
- 将储能与可再生能源相结合是一个可行的策略,可以增强微电网的经济效益.
- 混合可再生能源发电厂需要复杂的能源管理系统来平衡供需.
研究的目的:
- 开发混合可再生能源发电厂的能源管理系统.
- 为了优化经济效益,最大限度地降低运营成本,并减少污染性气体排放.
- 解决微电网内可再生能源的间歇性和不规则性问题.
主要方法:
- 使用超级学习二元龙算法 (HLBDA) 制定一个优化问题的方法.
- 在混合系统中包括燃料电池,风力轮机,太阳能电池,电池储能和微型轮机.
- 与遗传算法 (GA) 和鱼优化算法 (COA) 等现有方法进行比较.
- 使用随机框架来处理不确定的参数以实现最佳运行.
主要成果:
- 拟议的HLBDA方法显示,系统总体成本和污染气体排放大幅减少.
- 与GA相比,HLBDA实现了12.4%的成本节约,而COA显示了3.24%的改善.
- HLBDA记录了最高的减排率,为9.54%,而COA显示2.40%的改善.
结论:
- 由HLBDA优化开发的能源管理系统,在微电网的成本和减排方面提供了显著的优势.
- 在微电网内实现最佳运行点方面,HLBDA方法被证明是有效的,即使具有不确定的参数.
- 该研究强调了先进的优化算法的潜力,以提高混合可再生能源系统的经济和环境性能.
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