A Regression-based Voltage Estimation Method for Distribution Volt-Var Control with Limited Data

TL;DR

This paper introduces a regression-based approach for estimating voltages and sensitivities in distribution systems with limited data, enabling effective volt-var control without full system visibility.

Contribution

It proposes a novel regression-based estimator that uses limited data to accurately predict voltages and sensitivities for volt-var control in distribution networks.

Findings

Estimator achieves accurate voltage predictions with limited data

Enables effective volt-var control in unbalanced rural feeders

Comparable control performance to full system visibility scenarios

Abstract

This paper presents a regression-based method for estimating voltages and voltage sensitivities for volt-var control on distribution circuits with limited data. The estimator uses power flow results for representative load and PV output scenarios as training data. Using linear regressions on power flow results, the voltages at critical nodes are calculated online based on power measurements at the feeder head and at each PV plant. The voltage sensitivity to changes in reactive power injection by each PV plant is also found online using regressions on power flow results. The estimator thus provides the estimated critical voltages and their sensitivities to each possible control action. The estimator is tested in conjunction with a volt-var optimization on real, unbalanced rural distribution feeder. The optimal control actions and voltage results using the estimator are compared to the optimal results assuming full visibility of the distribution system. Results show that the estimator can estimate voltages and sensitivities with adequate accuracy for successful centralized volt-var control.