适应性层次学习,用于不确定性意识的分布式能源资源规划.
Yue Xiang1, Lingtao Li2, Yu Lu3
1College of Electrical Engineering, Sichuan University, Chengdu, China. xiang@scu.edu.cn.
Communications engineering
|January 23, 2026
概括
本研究介绍了一种适应性等级学习框架,用于规划分布式能源 (DERs),以管理不确定性. 它优化了DERs的使用.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能在能源中的作用
背景情况:
- 越来越多的分布式能源 (DER) 引入了复杂的,多来源的不确定性到发电网.
- 传统的规划方法与来自第三方运营商对安全约束的部分可观测性所带来的隐含不确定性作斗争.
- 现有的方法通常依赖于模型简化和预定义的场景,限制了适应性.
研究的目的:
- 开发一个数据驱动的框架,共同优化DER的位置,能力和运营战略.
- 为了使隐含约束的自主学习,而不需要完整的模型知识.
- 解决分布网络规划中的多源不确定性所带来的挑战.
主要方法:
- 提出了一个自适应的等级学习架构.
- 一个双层的Stackelberg结构集成蒙特卡罗树搜索 (上层) 规划方案的生成.
- 多代理强化学习 (较低级别) 通过实时数据从部分可观测的实时数据中学习操作政策.
主要成果:
- 该框架表明,与传统方法相比,投资成本较低.
- 实现了更快的溶解时间,同时保持电压稳定性.
- 卓越的可扩展性和适应隐性不确定性的能力在基准和大规模系统上得到了验证.
结论:
- 拟议的自适应层次学习框架有效地管理了分布网络规划中的不确定性.
- 数据驱动的优化和来自实时数据的学习比基于场景的方法具有显著的优势.
- 该方法为集成DERs提供了强大的和高效的解决方案.
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