对于多个相互连接的微电网,在考虑负载管理的电网连接和自主运行模式期间的能源管理系统
H K Shaker1, H E Keshta2, Magdi A Mosa3
1Faculty of Engineering, Helwan University, Helwan, Egypt. heshamkhalaf20@gmail.com.
Scientific reports
|October 17, 2024
概括
这项研究将人工智能 (AI) 与分布式能源 (DER) 整合起来,以优化微电网 (MG) 运营. 一个先进的算法在联网模式下降了1.6%的运营成本,在岛屿模式下降了0.47%.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 电力系统在性能和效率方面面临着挑战.
- 分布式能源资源 (DER) 的整合提供了一个解决方案.
- 微电网 (MGs) 可以在电网连接和自主模式下运行,需要复杂的管理.
研究的目的:
- 为了提高电力系统的电网性能和效率.
- 开发一种基于人工智能 (AI) 的方法,用于链接微电网中的经济调度和负载管理.
- 为了降低微电网的运营成本.
主要方法:
- 一种前一天排程方法,用于MG中能源的最佳设置点.
- 一种负载管理方法,涉及转移可控负载.
- 使用基于一对一的优化器 (OOBO),这是一个先进的元启发式方法,用于优化.
主要成果:
- 拟议的方法允许在连接的微电网中进行经济的分配.
- 实施了优化的调度和负载转移策略.
- 每日运营成本在电网连接模式下降1.6%,在岛屿运营模式下降0.47%.
结论:
- 基于人工智能的能源管理策略提高了系统性能和效率.
- OOBO算法有效地处理能源调度和负载转移的复杂优化挑战.
- 该研究提出了一种可行和有效的方法来管理与DERs连接的微电网.
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