分布式多代理增强学习,以实现微电网的多目标最佳调度.
Xiaowen Wang1, Shuai Liu1, Qianwen Xu2
1School of Control Science and Engineering, Shandong University, Jinan, 250012, China.
ISA transactions
|January 29, 2025
概括
本研究介绍了用于微电网调度的分布式多代理强化学习 (MARL) 算法. 这种新的方法优化了经济和环境目标,同时减少了资源需求和保护隐私.
科学领域:
- 电力系统工程 电力系统工程
- 人工智能的人工智能
- 控制理论 控制理论
背景情况:
- 分布式微电网对于可靠和经济的电力系统运行至关重要.
- 协调的经济和环境目标对于微电网至关重要.
- 现有的方法在处理持续的状态变化和资源限制方面面临挑战.
研究的目的:
- 开发一个分布式的多代理强化学习 (MARL) 算法,用于微电网的最佳调度.
- 解决连续状态和功率值的多目标优化问题.
- 为了减少计算和通信资源的要求,同时确保代理人的隐私.
主要方法:
- 一个分布式的多代理强化学习 (MARL) 算法,使用演员关键架构.
- 学习子任务的多个批评者,并使用仅邻近的信息来调度策略.
- 使用线性函数近似来保证算法收.
主要成果:
- 拟议的算法有效地处理微电网的多目标最佳调度.
- 实现了计算和通信资源需求的显著减少.
- 在信息交互期间,成功保护了代理人的隐私.
结论:
- 开发的MARL算法证明了在微电网中实现多目标最佳调度的有效性.
- 该方法为分布式微电网管理提供了一个可扩展和保护隐私的解决方案.
- 模拟结果验证了算法的性能和实际适用性.
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