论文标题
机器学习灵感的分布式能源聚类
Machine-Learning Inspired Clustering of Distributed Energy Resources
论文作者
论文摘要
随着分布式能源资源（DER）在分配网格中的渗透不断增加，不再忽略它们对网格操作的影响。但是，由于它们的数量，对这些日益普遍存在的设备的个人控制仍然是一个挑战。一种解决方案是通过虚拟发电厂分组控制它们。以前的工作通常专注于最佳派遣一组固定的DERS，避开用于最佳确定设置成员身份的方法。从直觉上讲，从控制角度来看，这些固定集可能不是最佳的。在这里，我们提出了一种基于其协方差的代理来聚集DER的方法，其目的是最大程度地降低所有DER集群的最大差异。此方法不需要对蛮力技术所需的所有组合进行列举和评估。仿真结果表明，虽然最优性损失通常很小。更重要的是，与其他方法相比，计算障碍性大大提高，这需要某种形式的列举和对DER组合的评估。
With the increasing penetration of distributed energy resources (DERs) in distribution grids, their impact on grid operations can no longer be ignored. However, the individual control of these increasingly ubiquitous devices remains a challenge due to their numbers. One solution is to control them in groups via virtual power plants. Previous work has typically focused on optimally dispatching a fixed set of DERs, eschewing methods for optimally determining set membership. Intuitively, these fixed sets may not be optimal from a control perspective. Here, we propose a method to cluster the DERs based on a proxy for their covariances, with the goal of minimising the maximum variance across all DER clusters. This method does not require the enumeration and evaluation of all DER combinations, which are required for brute force techniques. Simulation results show that while there is typically a loss in optimality, it is generally small. More importantly, computational tractability is greatly improved when compared to other methods, which require some form of enumeration and evaluation of the DER combinations.
