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DOE OSTI · 1602674

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

Zhu, Xiangqi·Mather, Barry A
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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.

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24 POWER TRANSMISSION AND DISTRIBUTIONdata-drivendistribution systemdiversityload modelingquasi-static time series (QSTS)variability

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Zhu, Xiangqi, Mather, Barry A. 2020-01-30. Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders. https://doi.org/10.1109/pesgm40551.2019.8973929

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