推广的 Osprey Optimizer:解决了电动汽车透的ORPD问题
Ziang Liu1, Xiangzhou Jian2, Touseef Sadiq3
1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pennsylvania, 15213, USA.
Scientific reports
|November 14, 2024
概括
一个新的促销 Osprey 优化器 (POO) 有效地将电动汽车 (EV) 集成到最佳的反应动力调度中. 这种方法显著减少了电力系统中的功率损失和电压偏差.
科学领域:
- 电气工程 电气工程
- 优化算法 优化算法
- 电力系统 电力系统
背景情况:
- 电动汽车 (EV) 的整合为最佳反应功率调度 (ORPD) 提出了挑战.
- 现有的元启发算法可能会与电网中电动汽车集成的复杂性作斗争.
研究的目的:
- 引入一个新的优化技术,用于ORPD问题与EV集成.
- 通过尽量减少损失和电压偏差来提高电力系统的效率和稳定性.
主要方法:
- 一个修改后的元启发算法,促进鱼优化器 (POO),灵感来自鱼狩猎行为.
- 在各种电动汽车透场景下,将POO模型应用于IEEE118总线和IEEE57总线系统.
主要成果:
- 该POO算法实现了有效功率损失 (高达22.2%) 和电压偏差 (高达20.6%) 的显著降低.
- 与现有的元启发式技术相比,在最大限度地减少功耗损失和电压偏差方面表现出卓越的性能.
- 显示的具体减少: 57 辆公共汽车系统 (1.93 兆瓦损耗, 0.027 p.u. 电压偏差) 和118总线系统 (3.53MW损耗,0.043 p.u. 的电压偏差).
结论:
- POO算法是优化ORPD与EV集成的高效工具.
- POO为提高电力系统性能和稳定性提供了一个有前途的解决方案.
- 在管理电动汽车集成方面,电力系统运营商和规划人员的潜在好处.
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