智能充电优化器:智能电动汽车充电和放电
Archana Y Chaudhari1, Prashant B Koli2, Surbhi D Pagar3
1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
MethodsX
|August 25, 2025
概括
电动汽车可以给电网带来压力. 这项研究介绍了两种智能充电策略,LSTM-ILP和Q-learning,以有效地管理电动汽车的充电和放电,降低电网负载和用户成本.
科学领域:
- 电气工程
- 可持续能源系统
- 人工智能
背景情况:
- 电动汽车的广泛采用对于低碳经济至关重要,但由于高电耗,对电网可靠性构成风险.
- 电网变压器的过载是一个与电动汽车快速发展相关的重大问题.
- 有效的充电和放电调度策略对于减轻电动汽车对电网的负面影响至关重要.
研究的目的:
- 探索和评估两个智能策略来安排电动汽车的充电和放电,以减少电网变压器的过载.
- 通过峰值削减和谷填充,最大限度地降低电网负载的峰值与平均值的比率.
- 降低用户的电动汽车充电成本,同时确保满足用户的移动需求.
主要方法:
- 将长短期内存 (LSTM) 与整数线性编程 (ILP) 结合起来,以优化充电和放电时间表,专注于尽量减少延迟.
- 应用Q学习,一种强化学习技术,以根据电荷状态和电网需求确定最佳的电动汽车充电/放电动作.
- 使用智能调度来管理电动汽车与电气系统的整合.
主要成果:
- 无论是LSTM-ILP还是Q-learning策略,都成功地降低了电网负载的峰值与平均值的比率.
- 实施的战略有效地减少了电动汽车充电需求对电网的影响.
- 这项研究证实智能调度能够平衡电网稳定性和用户收费需求.
结论:
- 智能充电和放电调度策略,如LSTM-ILP和Q-learning,在管理电动汽车对电网的影响方面是有效的.
- 通过优化电动汽车的整合,这些方法有助于实现更可靠和可持续的能源未来.
- 这项研究提供了一种可行的方法,可以最大限度地减少电网压力,同时降低电动汽车用户的充电成本.
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