在极端天气条件下,通过深度Q网络优化太阳能集成电力系统的弹性和灵活性
Da Li1, Haixing Zheng2, Tingzhe Pan3,4
1Southern Power Grid Comprehensive Energy Co., Ltd., Guangzhou, China.
Scientific reports
|October 28, 2025
概括
本研究介绍了用于优化动态电力系统的深度Q网络 (DQN). 与传统方法相比,这种强化学习方法提供了更好的适应性和弹性.
科学领域:
- 人工智能的人工智能
- 电力系统工程 电力系统工程
- 优化理论 优化理论
背景情况:
- 传统的电力系统优化方法,如混合整数线性编程 (MILP),由于依赖于预定义的模型,因此面临实时适应性的挑战.
- 动态电力系统需要适应性决策,以管理可再生能源,负载波动和电网干扰的不确定性.
研究的目的:
- 介绍一种基于强化学习的新型优化框架,使用深度Q网络 (DQN) 来用于动态电力系统环境.
- 证明框架能够根据不断变化的电网条件实时学习和优化操作的能力.
- 在灵活性,稳定性和效率方面,将拟议的DQN方法与传统优化技术进行比较.
主要方法:
- 开发一个强化学习框架,将Q学习与深度神经网络 (Deep Q-Network) 整合在一起.
- 使用DQN来实现适应性决策,以处理电力系统运行中的不确定性.
- 通过模拟动态电网条件的多个案例研究来评估框架.
主要成果:
- 与传统方法相比,基于DQN的优化框架显示出更高的灵活性和稳定性.
- 实时适应性和减少计算开销的显著改善被观察到.
- 对意外电网中断的增强弹性是关键发现.
结论:
- 强化学习,特别是DQN,为智能,自我学习的能源管理策略提供了一个强大的方法.
- 拟议的框架非常适合现代电力系统运行,增强弹性和成本效益.
- 这项研究强调了人工智能在推动动态电网管理和优化方面的潜力.
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