改进了Lyrebird优化,用于多个微电网的分区化和分布式发电的成本高效调度.
Karthik Nagarajan1, Arul Rajagopalan2, Mohit Bajaj3
1Department of Electrical and Electronics Engg, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu, India.
Scientific reports
|May 19, 2025
概括
本研究介绍了一种改进的Lyrebird优化算法 (ILOA),用于优化多微电网,降低能源成本和功率损失. 国际劳工协会提高了电力系统的效率和可靠性,超过了现有的算法.
科学领域:
- 电气工程 电气工程
- 优化算法 优化算法
- 智能电网技术 智能电网技术
背景情况:
- 能源需求的增加和电网效率低下需要先进的解决方案.
- 有分布式发电 (DG) 的微电网 (MG) 提供了更好的可靠性和灵活性.
- 当前的优化方法在平衡成本,损失和可靠性方面面临挑战.
研究的目的:
- 引入改进的Lyrebird优化算法 (ILOA) 以实现多微电网系统的最佳分区和调度.
- 为了最大限度地降低发电成本和主动功率损耗,同时确保系统可靠性.
- 在单一和多目标场景中对标准算法进行ILOA绩效评估.
主要方法:
- 通过整合Levy Flight来增强本地搜索和全球搜索的混乱正弦图来开发ILOA.
- 应用国际劳工协会对一个修改后的33辆公共汽车配送系统进行了应用,该系统分为三个微电网.
- 在单一目标 (成本/损失最小化) 和多目标优化方面的评估绩效,包括可靠性约束 (能源可靠性指数 - EIR).
主要成果:
- 国际劳工组织在单一目标 (例如,每小时19254.64美元的成本,0.7118千瓦的损失) 和多目标优化 (例如,每小时89792.18美元的成本,10.26千瓦的损失) 方面取得了卓越的结果.
- 与莱尔伯德优化算法 (LOA),贾亚算法 (JAYA) 和遗传算法 (GA) 相比,在运营成本和功耗损失方面显著降低.
- 纳入EIR约束进一步验证了ILOA在最大限度地减少功耗损失方面的稳定性和效率.
结论:
- 国际劳工协会 (ILOA) 是一个高效和可靠的算法,用于多微电网分区和分布式发电调度.
- 该算法显示了现实世界智能电网应用的巨大潜力,包括动态经济调度和需求响应.
- 国际劳工组织提供了改进的勘探开发平衡和全球搜索能力,超越了传统的优化技术.
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