用预测智能和基于学习的定价来进行车辆到电网能源管理的层次融合框架
V Nandagopal1, K B Bhaskar2, Sudhakaran Periakaruppan3
1Faculty of Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, India.
Scientific reports
|January 22, 2026
概括
将电动汽车 (EV) 与车辆到电网 (V2G) 技术相结合,可以提高电网稳定性. 这项研究提出了一种分层融合方法,将AI,游戏理论和强化学习结合起来,以实现最佳的V2G能源管理.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 能源系统 能源系统
背景情况:
- 电网稳定性和能源效率是关键的挑战.
- 电动汽车 (EV) 通过车辆到电网 (V2G) 技术提供了一个潜在的解决方案.
- 根据驾驶员需求和电网需求优化电动汽车的充电和放电是复杂的.
研究的目的:
- 为了比较和整合用于V2G系统优化的先进方法.
- 为V2G控制开发一个层次化的协作融合方法.
- 提高V2G系统的能源管理,经济可行性和可持续性.
主要方法:
- 人工智能 (AI) 用于预测性收费和需求预测.
- 动态定价策略的游戏理论.
- 强化学习 (RL) 用于自适应实时控制.
- 一个分层的融合模型,将人工智能驱动的价格信号与RL控制集成在一起.
主要成果:
- 拟议的层次融合方法使V2G系统中的战略,财务和运营决策得以一致.
- 基于人工智能的预测为游戏理论定价提供了信息,这限制了RL的控制政策.
- RL提供适应性,实时的最佳充电和放电决策.
- 模拟数据验证了这些方法在实际应用中的有效性.
结论:
- 结合人工智能,游戏理论和RL,为V2G优化提供了一个强大的解决方案.
- 综合方法改善了能源管理,减少了电网拥堵,并提高了经济效益.
- 这项研究表明了一条通往更高效和可持续的V2G集成的道路.
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