Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders

Research output: Contribution to conferencePaperpeer-review

4 Scopus Citations

Abstract

This paper presents a data-driven load modeling methodology for distribution system quasi-static time-series (QSTS) simulation considering both diversity and variability characteristics of distribution loads. Based on our previous work in [1]-[2], a variability library and diversity library have been established based on the realistic high-resolution data collected from actual utility feeders. Given the load profile for the start-of-circuit load of a feeder, the loads on the feeder nodes can be modeled with both diversity and variability instead of being directly scaled from the substation load profile according to the distribution allocation factors. With diversified load models, the load-induced impact on the feeder operation characteristics, such as voltage ramp and regulator operations, can be better considered in QSTS simulation. The proposed modeling methodology has been tested on both the IEEE 123-bus feeder and an actual utility feeder model, and the simulation results have demonstrated the merits of deploying the proposed load modeling methodology.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - Aug 2019
Event2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States
Duration: 4 Aug 20198 Aug 2019

Conference

Conference2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Country/TerritoryUnited States
CityAtlanta
Period4/08/198/08/19

Bibliographical note

See NREL/CP-5D00-73146 for preprint

NREL Publication Number

  • NREL/CP-5D00-76220

Keywords

  • data-driven
  • distribution system
  • diversity
  • load modeling
  • quasi-static time series (QSTS)
  • variability

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