改进的混沌虫优化器及其对HRES技术经济评估的应用
Min Zhang1, Heng Lyu2,3, Hengran Bian4
1The School of Artificial Intelligence, Neijiang Normal University, Neijiang, 641000, Si chuan, China.
Heliyon
|February 1, 2024
概括
改进的混沌虫优化器有效地降低了综合可再生能源系统的成本,在效率和精度方面超过了标准技术,以实现最佳的技术经济评估.
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
- 技术经济分析 技术经济分析
背景情况:
- 全球转向可再生和清洁能源,原因是化石燃料的消费量增加和减少.
- 集成的绿色电力系统 (太阳能,风能,燃料电池) 提高了效率和输出,减少了存储需求.
- 需要先进的优化技术,以便对这些复杂的系统进行有效的技术经济评估.
研究的目的:
- 引入和评估一个改进的基于混沌的优化器 (ICGO),用于对集成绿色电力系统的技术经济评估.
- 提高可再生能源系统优化的性能,精度和稳定性.
- 为了证明ICGO在为最大效率的系统设备分配最佳评级方面的能力.
主要方法:
- 混沌理论与虫优化技术的整合,开发了混乱的虫优化器 (ICGO).
- 应用ICGO模型,对一个由太阳能,风能和燃料电池发电源组成的综合系统进行技术经济评估.
- 使用四个基准任务来评估ICGO的性能评估,以评估其精度和稳定性.
主要成果:
- ICGO算法实现了最低最低的净当前成本 (NPC) 274.541E4美元和最高最高的NPC 311.94E4美元.
- ICGO算法的平均NPC (289.176E4美元) 与其他检查的算法相比具有竞争力.
- 与标准优化技术相比,ICGO表现出更高的效率和精度,有效地处理多个目标,约束和变量.
结论:
- 开发的混沌虫优化器是混合可再生能源系统 (HRES) 的高效技术.
- 在降低可再生能源系统的总成本方面,ICGO显著优于传统的优化方法.
- 该算法在性能降低最小的情况下表现出稳健性,使其适合于可再生能源领域的复杂,多目标的优化任务.
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