CUSTOMER SEGMENTATION AND VIRTUAL METER DEVELOPMENT BASED ON PROBABILISTIC MODELING OF RESIDENTIAL LOAD PROFILES THROUGH AMI

随着智能技术越来越多地融入电网，来自先进计量基础设施（AMI）的好处已经引起了配电公司的注意。AMI是一种用于客户仪表和公用事业之间自动双向通信的体系结构；从电力公司的角度来看，AMI可以提供实时的电力消耗数据，这些数据可以有效地用于网络监控等多项任务。萨斯喀彻温省的配电公司Saskatoon Light & Power （SL&P）自2016年7月以来一直在运行AMI系统。由于实际系统中的仪表数量严重不足，SL&P与其他公用事业公司类似，通过聚合由配电变压器服务的负载来创建虚拟仪表数据。虽然这个过程可以帮助运营商轻松地分析电网，但它牺牲了AMI提供的有价值的信息，并将他们的应用程序限制在计费过程中。这一重要的不足可以被认为是激励本研究计划深入研究住宅负荷剖面建模的主要因素，并找到可以促进虚拟电表发展的方法。萨斯喀彻温大学和SL&P合作的长期目标是找到一个新的框架，将客户细分和虚拟计量开发过程结合在一起，使最终结果不仅可以满足运营商的期望，而且可以形成前沿知识，激励其他研究人员关注这个问题。为了实现这一最终目标，触发启动合作的短期目标是：(i)评估住宅负荷概况建模的概率方法，并选择一种可以根据实际需求解决高度易变特性问题的方法；（ii）开发一个基于机器学习技术和(i)中进行的概率建模的客户细分和虚拟电表开发框架。这项研究的成果有望成为电力系统监测、运行和控制的里程碑，并为更可靠的电网的发展做出贡献。

Along with the growing inclusion of smart technologies into the electrical power grids, benefits, which can beoriginated from advanced metering infrastructure (AMI), have grabbed noticeable attention from distributionutilities. AMI is an architecture for automated, two-way communication between customers' meters and theutility; from the utility company point of view, AMI can provide real-time data of power consumption whichcan be efficiently used for several tasks such as network monitoring. Saskatoon Light & Power (SL&P) whichis a distribution utility in Saskatchewan has been operating the AMI system since July 2016. As the number ofmeters are severely ample in practical systems, SL&P, similar to other utilities, creates virtual meter data byaggregating loads served by distribution transformers. Although this process helps the operators to analyze thegrid with ease, it sacrifices valuable information provided by the AMI and confines their applications to thebilling process. Such an important deficiency can be considered as the main factor which motivates thisresearch program to delve more into the residential load profile modeling and find approaches which can yieldto the enhanced development of virtual meters. The long-term goal of collaboration between University ofSaskatchewan and SL&P is to find a novel framework that combines customer segmentation and virtual meterdevelopment processes together so that the end result not only can meet the operators expectations, but also canform a cutting-edge knowledge motivating other researchers to focus on this problem. To achieve this ultimategoal, the short-term goals which triggers starting a collaboration in terms of an engage grant are: (i) to assesscurrent probabilistic methods for modeling residential load profiles and select a method which can address theissue of highly volatile characteristics with respect to the practical needs; and (ii) to develop a framework forcustomer segmentation and virtual meter development based on both machine learning techniques and theprobabilistic modeling conducted in (i). The outcomes of this research are expected to constitute milestones inpower system monitoring, operation and control, and contribute to the development of a more reliable grid.