一个混合蝶算法在微电网的最佳经济运行中
Guohao Sun1, Sen Yang1, Shouming Zhang1
1School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
一个新的混合蝶算法 (HBOA) 优化了微电网 (MG) 运营,大大降低了成本. 这种先进的方法提高了岛屿和电网连接的MG系统的效率.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 优化算法 优化算法
背景情况:
- 微电网 (MG) 系统正在迅速发展,因为可再生能源的整合增加.
- 最佳的经济运行是有效管理MG系统的关键挑战.
- 降低整体发电成本是可持续的MG运营的主要目标.
研究的目的:
- 为微电网的经济运行提出一种新的优化算法.
- 提高MG系统优化算法的准确性和融合速度.
- 为了降低各种运营模式中的微电网的运营成本.
主要方法:
- 开发一个混合蝶算法 (HBOA),结合自适应切换值.
- 实施多样性学习机制,以改善人口信息交换.
- 利用精英指导的指导策略来加速算法融合.
- 使用10个标准测试函数和微电网系统的模拟实验进行验证.
主要成果:
- 与测试函数的其他方法相比,HBOA算法证明了优越的优化准确性和更快的融合.
- 在岛屿微电网模式下,HBOA实现了高达11.7%的运营成本削减.
- 在连接到电网的微电网模式下,HBOA实现了高达17.7%的运营成本降低.
结论:
- 拟议的HBOA在解决微电网中最佳经济运行问题方面是有效和优越的.
- 在岛屿和电网连接的微电网配置中,HBOA提供了显著的成本节约.
- 算法的自适应机制和指导策略有助于提高其性能.
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