Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

TL;DR

This paper introduces an automated method to improve distribution system models by integrating AMI data and ACOPF, enhancing accuracy in systems with high DER penetration, verified through real feeder data and comparison with OpenDSS.

Contribution

The paper presents a novel, automated approach that combines AMI data, ACOPF, and unmetered load estimation to enhance distribution system modeling accuracy.

Findings

Improved model accuracy verified against OpenDSS simulations.

Effective integration of AMI data with ACOPF for system enhancement.

Automated process reduces manual modeling efforts.

Abstract

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs). This paper presents a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements. The method is demonstrated using an actual feeder from an electrical utility with high penetration of DERs. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, and data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. Additionally, the method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. Furthermore, the accuracy of the method developed is verified by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification automatically.