智能家居能源管理:考虑电能储存和可再生能源资源的最佳多目标设备调度模型
Moslem Dehghani1, Seyyed Mohammad Bornapour1
1Electrical Engineering Department, Faculty of Engineering, Yasouj University, Yasouj, Iran.
Heliyon
|February 24, 2025
概括
智能家居 (SHs) 可以通过新的多目标优化方法来优化能源使用并降低成本. 这种方法通过管理家庭能源和需求响应计划,尽量减少每日电费和峰值与平均比率 (PAR).
科学领域:
- 电气工程 电气工程
- 优化算法 优化算法
- 智能电网技术 智能电网技术
背景情况:
- 智能家居 (SH) 越来越多地集成到配送系统中.
- 微电网调度对于管理SH负载和减少峰值需求至关重要.
- 需求响应 (DR) 程序和使用时间关税是现代能源管理的关键组成部分.
研究的目的:
- 为智能家居能源管理 (SHEM) 和 DR 计划提出一个多目标优化方法.
- 为了最大限度地降低每日电费和峰值与平均比率 (PAR) 的同时.
- 将灵活的和固定的家用电器与可再生能源和储能系统相结合.
主要方法:
- 开发了SHEM的数学公式,考虑了使用时间关税和能源交易.
- 包括光伏和风力轮机系统与电能存储 (EES) 系统.
- 利用改进的基于生物地理的优化算法 (IBBO) 来解决多目标问题.
主要成果:
- 在各种场景中降低了PAR和运营成本,包括不同的可再生能源容量和充电/放电率.
- 证明了将多余电力出售回电网的能力.
- 与灰狼优化器 (GWO) 和鱼优化算法 (WOA) 相比,IBBO显示出更高的性能和稳定性,标准偏差和平均目标函数值较低.
结论:
- 拟议的SHEM计划有效地降低了每日电力成本和PAR.
- IBBO算法是解决智能家居能源管理中复杂的多目标优化问题的强大而优质的方法.
- 可再生能源和EES系统的整合提高了峰值负载性能和电网互动.
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