通过混乱的本地搜索和粒子群优化技术优化微电网的能源管理策略
Heliyon
|September 16, 2024
概括
本研究介绍了多微电网 (MG) 系统的新能源管理策略,该策略结合了混乱局部搜索 (CLS) 和粒子群集优化 (PSO). 该方法显著降低了运营成本,并提高了微型能源网络的优化性能.
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:
- 多微电网 (MG) 系统需要有效的管理策略来最大限度地降低运营成本.
- 现有的优化方法可能会在复杂的能源系统中与局部优化作斗争.
研究的目的:
- 开发和评估多微电网系统的创新能源管理战略.
- 通过先进的优化来降低微型能源网络的运营和能源转换成本.
主要方法:
- 混沌局部搜索 (CLS) 与粒子集群优化 (PSO) 的集成,以增强全球和本地搜索功能.
- 开发一个PSO算法框架,结合混乱的本地搜索原则,以避免本地最佳.
- 综合MG系统的建模,包括光伏发电,电池存储和微型燃气轮机.
主要成果:
- 拟议的CLS-PSO算法在48.2-51.7范围内实现了最佳解决方案,超过了现有方法.
- 与第一种情景相比,经营成本下降了24.22%,初级能源转换成本下降了31.39%.
- 与情景2相比,运营和初级能源转换成本的进一步降低分别为3.08%和6.05%,实现了进一步的降低.
结论:
- 该CLS-PSO战略在优化MG能源系统方面表现出卓越的表现.
- 该方法在应用到微型能源网络时,证明了其实际相关性和有效性.
- 这项研究为优化MG能源系统提供了整体解决方案,将理论进步与实际应用联系起来.
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