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

Journal Article · · IEEE Transactions on Smart Grid

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.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1606307
Report Number(s):
NREL/JA--5D00-73485; MainId:21154; UUID:b8dccfde-4944-e911-9c1c-ac162d87dfe5; MainAdminID:9630
Journal Information:
IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 2 Vol. 11; ISSN 1949-3053
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English