深度强化学习在电力需求响应市场中的应用:负载聚合器的需求响应决策
Guangda Xu1, Shihang Song2, Yu Li1
1State Grid Jibei Electric Power Research Institute (North China Electric Power Research Institute Co., Ltd), Beijing 100045, China.
MethodsX
|December 13, 2024
概括
负载聚合器现在可以更好地指导消费者对需求响应. 新的方法准确地预测消费者行为,并动态地对其进行分类,即使数据不完整.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 可再生能源的整合需要有效的需求响应 (DR) 战略.
- 传统的方法难以应对消费者电力行为的复杂性和不完整的数据.
- 准确的预测和选择DR的消费者对于电网稳定性和效率至关重要.
研究的目的:
- 为参与需求响应市场的负载聚合器提出一个新的决策框架.
- 开发一种动态的消费者分类方法,用于预测反应行为.
- 使聚合器能够有效指导潜在的需求响应消费者,特别是中小企业.
主要方法:
- 一个深度Q网络 (DQN) 算法用于DR市场的负载聚合器决策.
- 自组织地图 (SOM) 算法用于动态消费者分类和行为预测.
- 使用不完整的消费者信息用于DR建模和处理.
主要成果:
- 提出的方法有效地根据消费者的行为动态分类消费者.
- 对于聚合器来说,DQN算法可以实现有利的需求响应决策.
- 该方法成功地运行不完整的消费者数据,克服传统方法的局限性.
结论:
- 开发的框架增强了需求响应市场的负载聚合器能力.
- 动态分类和预测建模提高了DR参与的效率.
- 这些方法提供了一个强大的解决方案,用于管理有限的消费者信息需求响应.
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