使用人工蜂群算法在各种负载和太阳辐射条件下在微电网中分布式发电资源的最佳能源管理
Tareq Hamadneh1, Omar Alsayyed2, Belal Batiha3
1Department of Mathematics, Al Zaytoonah University of Jordan, Amman, 11733, Jordan.
Scientific reports
|August 24, 2025
概括
这项研究使用人工蜂群算法来实现混合微电网的最佳能源管理. 与其他生物启发方法相比,它有效地降低了运营成本.
科学领域:
- 电气工程
- 可再生能源系统
- 优化技术
背景情况:
- 有效的能源管理对于微型电网至关重要,它会影响成本,排放,电力质量和可靠性.
- 复杂的微电网系统需要强大的优化方法,能够处理非线性,多目标的问题.
- 超听觉算法为微电网能源管理提供灵活可靠的解决方案.
研究的目的:
- 在混合微电网中应用人工蜂群 (ABC) 算法以实现最佳的能源管理.
- 在动态条件下评估ABC算法的性能,以尽量减少运营成本.
- 将ABC算法的效率与其他已知的生物灵感优化技术进行比较.
主要方法:
- 混合微电网系统的建模,包括光伏电池板,风力轮机,燃料电池,微型轮机和电池储能.
- 实施人工蜂群算法进行能源管理.
- 在四个不同太阳辐射的运行场景中进行性能评估.
主要成果:
- 人工蜂群算法在最大限度地降低运营成本方面表现出卓越的表现.
- 与基因算法,粒子群优化和修改后的蝙蝠算法相比,观察到持续的成本降低.
- 该算法在动态操作条件和不同太阳辐射下被证明是有效的.
结论:
- 人工蜂群算法是实时微电网能源管理的强大而高效的工具.
- 这种方法为优化混合微电网运营提供了显著的优势.
- 这些发现支持采用ABC来提高微电网性能和节省成本.
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