多代理 DDPG 基于 IIoT 智能电网的多设备充电安排
Haiyong Zeng1,2, Yuanyan Huang1, Kaijie Zhan1
1Guangxi Key Laboratory of Braininspired Computing and Intelligent Chips, School of Electronic and Information Engineering/School of Integrated Circuits, Guangxi Normal University, Guilin 541001, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种新的多代理深度决定性政策梯度 (MADDPG) 算法,用于电动汽车 (EV) 充电安排. 新方法显著降低了电动汽车充电成本,并提高了智能电网的稳定性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 智能电网技术 智能电网技术
背景情况:
- 电动汽车 (EV) 和工业物联网 (IIoT) 智能电网的广泛采用需要协调的充电策略.
- 传统算法在多设备环境中面临着可扩展性挑战,在连续控制场景中面临着局限性.
研究的目的:
- 为电动汽车提出使用多代理深度决定性政策梯度 (MADDPG) 的动态充电调度算法.
- 优化连续动作空间中的多个电动汽车的充电和放电策略,降低成本和平衡电网负载.
主要方法:
- 开发了一个基于MADDPG的算法,集成实时电价,电池状态和传感器数据.
- 员工协调多代理学习,以动态优化电动汽车的充电和放电.
- 利用车辆到电网 (V2G) 技术,以适应价格波动和用户需求.
主要成果:
- 与基线方法相比,在30天的评估期内,收费成本减少了41.12%.
- 证明有效适应电价波动和用户需求变化.
- 通过优化充电时间分配,展示了增强的电网稳定性.
结论:
- 拟议的MADDPG算法为IIoT智能电网中的动态电动汽车充电安排提供了一个可扩展和有效的解决方案.
- 使用V2G技术协调的多代理学习显著提高了经济效率和电网稳定性.
- 这种方法解决了传统方法在管理复杂,连续控制场景方面的局限性.
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