Quantifying Performance of Distribution System State Estimators in Supporting Advanced Applications

Abstract

A common challenge forward-looking utilities are facing when deploying advanced applications that facilitate voltage optimization and service restoration is to provide adequate sensor data for a Distribution System State Estimator (DSSE) so that it provides sufficiently accurate estimates of the system states to enable these applications in an operational environment. We developed a stochastic method that informs telemetry and operational forecasting requirements by quantifying the DSSE performance in supporting advanced applications. The performance metric used is the α risk, which is the likelihood of a DSSE giving a false positive when determining if voltage and loading constraints are met. We applied this method to six real-world industrial/commercial/residential distribution circuits and evaluated α risk improvements provided by circuit-level sensors and operational forecasting. The results show that a combination of sensor deployment schemes was needed to reduce the α risk for undervoltage to effectively zero. Also, sensors deployed at large loads significantly reduce c risks on industrial/commercial circuits while operational forecasting consistently reduces α risks on all circuits. The practical method does not require advanced mathematics and can be readily used by utilities to inform grid modernization investments in sensor technologies so that advanced applications can be executed optimally and violation-free.

Keywords

• Distribution management system
• Power system restoration
• Sensor placement
• Situational awareness
• Smart grid
• State estimation