智能家居能源管理和电力交易优化使用增强的鱼鱼食优化优化
Heba Youssef1, Salah Kamel2, Mohamed H Hassan1,3
1Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswân, 81542, Egypt.
Scientific reports
|December 13, 2023
概括
本研究介绍了使用需求响应和先进算法的家庭优化能源管理计划. 这种方法有效地降低了电力成本和峰值使用量,同时最大限度地提高了可再生能源的利.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 住宅能源消耗对整体需求产生重大影响.
- 越来越多的人口增加了电网的压力.
- 需求响应程序使用户能够提高电网可靠性.
研究的目的:
- 为了降低住宅电力成本和峰值与平均值的比率.
- 为了尽量减少从电网中进口的电力.
- 通过利用可再生能源和能源交易,最大限度地提高利.
主要方法:
- 开发并验证了长期记忆鱼捕食优化 (LMMRFO) 算法.
- 将LMMRFO与基准函数上的其他优化技术进行比较.
- 应用LMMRFO优化智能家居能源管理,包括微电网运营 (太阳能,风能) 和能源存储.
主要成果:
- 在优化任务中,LMMRFO表现出卓越的性能.
- 拟议的智能家居能源管理系统有效地平衡了成本,需求和本地发电.
- 案例研究证实了电力成本和峰值-平均比率的显著降低,以及利最大化.
结论:
- LMMRFO算法是优化能源管理的强大工具.
- 拟议的需求响应战略提高了智能家居的能源效率和利能力.
- 该系统有效地整合了可再生能源和储能,以确保电网稳定性.
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