机器学习优化用于在可再生微电网中充电混合动力电动汽车
1Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo, Egypt, 2033, Sheraton. eng_marwa@aast.edu.
Scientific reports
|June 17, 2024
概括
本研究引入了用于可再生微电网能源管理的机器学习方法,优化混合动力电动汽车的充电,以降低成本和提高可靠性. 智能充电策略被证明比协调充电更具成本效益.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 可再生微电网对于提高电力系统安全性,可靠性和电力质量至关重要.
- 在微电网中整合太阳能和风能能源有助于减少温室气体排放.
- 管理能源需求,特别是来自混合动力电动汽车 (HEV) 的能源需求,是微电网运营的一个关键挑战.
研究的目的:
- 为可再生微电网提出基于机器学习的能源管理系统.
- 模拟和管理混合动力电动汽车 (HEV) 充电需求的影响.
- 通过协调和智能充电策略,优化微电网运行.
主要方法:
- 利用高斯过程 (GP) 来建模HEV充电需求.
- 开发了一种新的优化方法,灵感来源于Krill Herd Algorithm (KHA) 进行能源管理.
- 在KHA中实现了自适应性修改,用于定制解决方案.
- 在IEEE微电网上模拟了拟议的方法.
主要成果:
- 实现了低的平均绝对百分比误差 (MAPE) 1.02381,用于预测总HEV充电需求.
- 证明了协调和智能充电场景的效率.
- 与协调性充电相比,智能充电策略可以降低微电网运营成本.
结论:
- 拟议的机器学习方法有效地管理可重组结构的可再生微电网中的能源.
- 与协调式充电相比,智能HEV充电提供了显著的运营成本节省.
- 整合GP和KHA为微电网能源管理挑战提供了强大的解决方案.
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