可扩展的强化学习用于使用图形神经网络的大规模协调电动汽车
Stavros Orfanoudakis1, Valentin Robu2,3, E Mauricio Salazar3
1Intelligent Electrical Power Grids, Delft University of Technology, Delft, The Netherlands. s.orfanoudakis@tudelft.nl.
Communications engineering
|July 2, 2025
概括
电动汽车 (EV) 的充电优化通过EV-GNN,一种新的图形神经网络 (GNN) 解决方案得到了增强. 这种方法提高了充电点运营商的强化学习 (RL) 可扩展性和效率.
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 * 人工智能
背景情况:
- * 快速采用电动汽车 (EV) 需要高效的充电基础设施和电网稳定性解决方案.
- *目前的优化方法在复杂的城市环境中面临着可扩展性挑战.
- * 对于充电点运营商 (CPO) 来说,管理电动汽车充电的不确定性至关重要.
研究的目的:
- * 推出EV-GNN,这是一个基于图表的新型解决方案,用于大规模优化电动汽车充电.
- * 提高强化学习 (RL) 算法的可扩展性和样本效率.
- * 从CPO的角度来看,解决EV行为中的不确定性.
主要方法:
- * 开发一个与RL集成的端到端图神经网络 (GNN) 架构.
- * 实施分支修剪技术以提高计算效率.
- * 展示架构的灵活性,以与各种深度RL算法集成.
主要成果:
- *与传统的RL算法相比,EV-GNN显著提高了可扩展性和样本效率.
- * 该模型有效地处理各种行动空间,包括连续,多离散和离散.
- * 实验评估证实,在小型和大型电动汽车充电场景中,性能优越.
结论:
- *EV-GNN为优化电动汽车充电基础设施提供了可扩展和高效的解决方案.
- *GNN-RL集成为高级收费管理提供了灵活的框架.
- *这种方法有助于提高电网稳定性和有效利用资源.
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