Identifying ZIP Coefficients of Aggregated Residential Load Model Using AMI Data

摘要

Aggregated load modeling is an important daily practice in utility companies, but existing statistics- or measurement-based methods suffer from various limitations. This work proposes an effective approach to identify ZIP coefficients of the aggregated residential load model using advanced measurement infrastructure (AMI) data. A non-intrusive load monitoring (NILM) based algorithm is developed to disaggregate household power consumption into appliance-level activities. Such appliance activities are then associated with ZIP attributes and aggregated to household and feeder level. Therefore, the equivalent ZIP coefficients are obtained without the dependence of other information. The effectiveness of the proposed approach is verified using a real-world smart meter data set and the IEEE 34-bus feeder test case.

樊海锋

硕士研究生

于鹤洋

博士研究生

于鹤洋，博士研究生在读，研究方向为电力系统灵活资源的深度感知和聚合调控、人工智能与物联网在电力系统中的应用。

耿光超

副教授 | 博士生导师

耿光超，工学博士，浙江大学电气工程学院副教授、博士生导师，电机工程学系副主任，电力系统自动化所副所长，电气工程学院特聘助理，IEEE高级会员。