Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation

Zhu, Xiangqi; Mather, Barry

doi:10.1109/tsg.2019.2940084

Title: Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation

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Abstract

This paper presents a data-driven distribution system load modeling methodology targeting quasi-static time-series (QSTS) simulation. The proposed methodology is appropriate for modeling down to the level of the customer transformer, and it has three main features: 1) both load pattern diversity and intra-second load variability are considered, 2) the load profiles can be populated for multiple nodes on a circuit in such a way that the diversity factor of the feeder can be defined and tuned, and 3) the load aggregation method can be used to populate the profiles for different nodes at various load aggregation levels. Furthermore, as the foundation of the modeling methodology, variability and diversity libraries have been established based on high-resolution load data collected on customer transformers from real utility feeders. The proposed modeling methodology has been used to build load data sets for both the IEEE 123-bus feeder model and a realistic utility feeder model. The QSTS simulation results on the two evaluation feeders have demonstrated that the load data sets established from the proposed modeling methodology can effectively capture the load impact on feeder operations. For the realistic utility feeder, the effectiveness of the proposed methodology has also been validated by comparing the voltagemore »« less

Authors:

^[1];

^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States). Power Systems Engineering Center

Publication Date:: Tue Sep 10 00:00:00 EDT 2019

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

OSTI Identifier:: 1606307

Report Number(s):: NREL/JA-5D00-73485
Journal ID: ISSN 1949-3053; MainId:21154;UUID:b8dccfde-4944-e911-9c1c-ac162d87dfe5;MainAdminID:9630

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Smart Grid

Additional Journal Information:: Journal Volume: 11; Journal Issue: 2; Journal ID: ISSN 1949-3053

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; load modeling; distribution system; diversity; quasi-static time series; QSTS; variability

Citation Formats


                    Zhu, Xiangqi, and Mather, Barry. Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation.  United States: N. p., 2019. 
Web.  doi:10.1109/tsg.2019.2940084.

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                    Zhu, Xiangqi, & Mather, Barry. Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation.  United States.  https://doi.org/10.1109/tsg.2019.2940084

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                    Zhu, Xiangqi, and Mather, Barry. Tue .  
"Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation".  United States.  https://doi.org/10.1109/tsg.2019.2940084.  https://www.osti.gov/servlets/purl/1606307.

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@article{osti_1606307,

  title        = {Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation},

  author       = {Zhu, Xiangqi and Mather, Barry},

  abstractNote = {This paper presents a data-driven distribution system load modeling methodology targeting quasi-static time-series (QSTS) simulation. The proposed methodology is appropriate for modeling down to the level of the customer transformer, and it has three main features: 1) both load pattern diversity and intra-second load variability are considered, 2) the load profiles can be populated for multiple nodes on a circuit in such a way that the diversity factor of the feeder can be defined and tuned, and 3) the load aggregation method can be used to populate the profiles for different nodes at various load aggregation levels. Furthermore, as the foundation of the modeling methodology, variability and diversity libraries have been established based on high-resolution load data collected on customer transformers from real utility feeders. The proposed modeling methodology has been used to build load data sets for both the IEEE 123-bus feeder model and a realistic utility feeder model. The QSTS simulation results on the two evaluation feeders have demonstrated that the load data sets established from the proposed modeling methodology can effectively capture the load impact on feeder operations. For the realistic utility feeder, the effectiveness of the proposed methodology has also been validated by comparing the voltage characteristics of the feeder with modeled loads and the voltage characteristics of realistic voltage data from the same feeder.},

  doi          = {10.1109/tsg.2019.2940084},

  journal      = {IEEE Transactions on Smart Grid},

  number       = 2,

  volume       = 11,

  place        = {United States},

  year         = {Tue Sep 10 00:00:00 EDT 2019},

  month        = {Tue Sep 10 00:00:00 EDT 2019}

}

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