智能建筑用可再生能源和储能系统进行能源管理,采用已修改的向量算法加权平均值
Mohamed Ebeed1, Sabreen Hassan2, Salah Kamel3
1Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag, 82524, Egypt.
Scientific reports
|February 8, 2025
概括
一个新的算法MINFO通过优化各种能源和需求响应来增强智能家居能源管理. 它显著降低了电力成本和峰值与平均值的比率,提高了整体效率.
科学领域:
- 智能电网和可再生能源系统
- 人工智能和优化算法 人工智能和优化算法
- 建筑能源管理 建筑能源管理
背景情况:
- 由于集成的传统,可再生和储能系统,智能家居能源管理 (EM) 越来越复杂.
- 传统的EM方法在解决方案准确性和处理复杂的优化问题方面存在局限性.
- 在家用电器的自动化需要先进的解决方案,以灵活和高效的智能家居EM.
研究的目的:
- 为增强智能建筑能源管理提出一种新的修改过的矢量加权平均算法 (MINFO).
- 在解决方案准确性和复杂性处理方面解决现有算法的局限性.
- 优化各种能源的调度和需求侧响应,以降低成本和峰值-平均比率.
主要方法:
- 开发了MINFO算法,将精英中心心准对立基础学习 (ECQOBL) 纳入开发中.
- 集成的自适应式飞行运动 (ALFM) 提高了勘探能力.
- 优化了柴油发电机,光伏单元,电池和可变载荷的需求侧响应 (DSR) 的调度.
主要成果:
- MINFO实现了电力成本 (仅成本) 的53.20%降低.
- 观察到峰值与平均值比率 (PAR) 减少了53.19% (仅PAR).
- 与基准情况相比,综合成本和PAR降低达到了50.84%.
结论:
- MINFO在智能建筑能源管理方面取得了显著的改进.
- 该算法为复杂的能源系统提供了强大的优化解决方案.
- 在智能建筑中,MINFO有效降低了电力成本和峰值与平均值的比率.
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