一个高效的能源管理方案,使用基于规则的群体智能方法,通过太阳能电池-超电容器混合能源系统支持脉冲负载
Muhammad Shahid Wasim1, Muhammad Amjad1, Muhammad Abbas Abbasi1
1Faculty of Engineering, The Islamia University of Bahawalpur, Bahawalpur, Punjab, 63100, Pakistan.
Scientific reports
|February 17, 2024
概括
本研究介绍了一种基于规则的虫优化算法,用于太阳能混合能源系统. 它有效地管理来自光伏阵列和存储的电力,以满足脉冲负载,在稳定性和能量捕获方面表现优于其他方法.
科学领域:
- 可再生能源系统可再生能源系统
- 优化算法 优化算法
- 电力电子 电力电子 电力电子
背景情况:
- 太阳能光伏 (PV) 系统需要有效的能源管理,以提供稳定的电力,特别是在波动的负载下.
- 混合式储能系统,结合电池和超级电容器,提供更高的性能,但需要复杂的控制策略.
- 脉冲负载 (PL) 需求对能源管理方案 (EMS) 构成重大挑战,因为功率的快速变化.
研究的目的:
- 为太阳能电池-超级电容器混合系统开发和评估一种新的能源管理方案 (EMS).
- 优化光伏 (PV) 阵列,电池 (BB) 和超级电容器 (UC) 的功率分配,以实现高效的脉冲负载 (PL) 需求.
- 通过最大限度地利用光伏能源的同时,最大限度地减少功率激增和振荡来提高混合系统的性能.
主要方法:
- 基于规则的草优化算法 (RB-GOA) 的实施,用于动态电力份额分配.
- 定义一组IF-THEN规则,以限制PV,BB和UC的搜索空间.
- 与其他群体智能技术 (SIT) 进行比较分析,包括Cuckoo Search Algorithm (CSA),灰狼优化 (GWO) 和Salp Swarm Algorithm (SSA).
主要成果:
- 在降低功率激增 (降低高达26%) 和减轻振荡 (比CSA/GWO快两倍) 方面,RB-GOA表现出卓越的性能.
- 在处理可变光伏功率和脉冲负载条件方面,观察到显著的改进,与同行相比,功率激增降低了多达9倍.
- 在各种天气条件下,MPP跟踪速度提高了29%至61%,表现出强大的性能.
结论:
- 拟议的基于RB-GOA的EMS对于面临脉冲负载的太阳能混合动力储能系统非常有效.
- 该算法显著提高了系统稳定性,减少了功率波动,并最大限度地提高了可再生能源采集的效率.
- 在混合可再生能源系统中,RB-GOA为管理复杂的能源需求提供了对现有SIT的优质替代方案.
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