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Title: Distribution system model calibration with big data from AMI and PV inverters

Efficient management and coordination of distributed energy resources with advanced automation schemes requires accurate distribution system modeling and monitoring. Big data from smart meters and photovoltaic (PV) micro-inverters can be leveraged to calibrate existing utility models. This paper presents computationally efficient distribution system parameter estimation algorithms to improve the accuracy of existing utility feeder radial secondary circuit model parameters. The method is demonstrated using a real utility feeder model with advanced metering infrastructure (AMI) and PV micro-inverters, along with alternative parameter estimation approaches that can be used to improve secondary circuit models when limited measurement data is available. Lastly, the parameter estimation accuracy is demonstrated for both a three-phase test circuit with typical secondary circuit topologies and single-phase secondary circuits in a real mixed-phase test system.
ORCiD logo [1] ;  [2] ;  [2] ;  [1]
  1. Georgia Institute of Technology, Atlanta, GA (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 1949-3053; 603404
Grant/Contract Number:
Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Name: IEEE Transactions on Smart Grid; Journal ID: ISSN 1949-3053
Institute of Electrical and Electronics Engineers (IEEE)
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
24 POWER TRANSMISSION AND DISTRIBUTION; load modeling; parameter estimation; power distribution; power system modeling; power system measurements; power system simulation; regression analysis; smart grids
OSTI Identifier: