使用云模型和改进的华卢斯优化器对中国地区的混合能源系统的随机大小和能源管理
Wenjun Liao1, Qing Xiong2,3,4, Zilong Chen1
1Control and Safety Key Laboratory of Sichuan Province, School of Automobile and Transportation, Xihua University, Chengdu, 610039, Sichuan, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一个新的框架,用于优化混合可再生能源系统 (HRES),使用云模型和改进的Walrus Optimizer (IWO). IWO方法有效地将能源成本降至最低,并在不确定的条件下确保可靠性.
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
- 能源管理 能源管理
背景情况:
- 混合可再生能源系统 (HRES) 对可持续能源至关重要,但面临着来自发电和负载不确定性的挑战.
- 精确的尺寸和能源管理对于最大限度地降低成本和确保HRES可靠性至关重要.
- 现有的优化方法可能无法充分解决随机能量环境的复杂性.
研究的目的:
- 开发一个随机智能框架,用于测量和管理混合可再生能源系统 (PV/风/FC).
- 在不确定的条件下,尽量减少能源成本 (COE),同时满足能源损失概率 (LOEP).
- 评估改进的海优化器 (IWO) 在优化HRES设计方面的有效性.
主要方法:
- 一个新的随机智能框架,集成云模型用于不确定性量化.
- 应用一个改进的Walrus Optimizer (IWO) 带有逐块线性混沌地图,以获得最佳的组件大小.
- 使用确定性和随机性场景与四个中国城市的真实气象数据进行评估.
主要成果:
- 太阳能/风能/制冷系统配置表现出卓越的性能,在决定性情景中实现了最低的COE和LOEP.
- 在COE,可靠性,收性和稳定性方面,IWO算法超过了传统方法 (WO,PSO,MRFO,GWO).
- 随机分析显示,由于不确定性,COE增加,云模型提供了对不确定性分布影响的见解.
结论:
- 拟议的随机智能框架提供了一个比确定性模型更全面和可靠的HRES设计方法.
- IWO算法是优化HRES组件大小和能源管理在不确定性条件下的有效工具.
- 云模型集成可以增强对可再生能源系统不确定性的理解和管理.
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