Including synchronized and non-synchronized measurements with different sample rates in distribution system state estimation

Abstract

The distribution system (DS) was considered a predictable, collectively managed system that needed little real-time interventions unless under emergency situations. As more distributed energy resources (DER), such as photovoltaics (PV), electric vehicles (EV), and distributed generators, are integrated, and actively participate in demand side management programs, the intermittent bus net-loads would cause power flows and feeder voltage profiles in the distribution network to become more diversified and unpredictable. To maintain the service quality, there is an urgent need for active monitoring of the grid in real-time and intervention by the operator when necessary.Complex interactions among different functions in modern distribution networks have significantly changed the feeder load profiles, network configuration, and operation practice. To mitigate possible impacts of DER on the network security and power quality, smart grid initiatives have been deployed, which created new sources of data. Data gathered promptly at various information systems from intelligent electronic devices (IEDs), automated feeder switches and voltage regulators, smart inverters of DER and phasor measurement units (PMUs) provide an opportunity to enhance system situation awareness [1-6]. Through active monitoring systems, operators are not just seeking to improve network reliability and efficiency but also maximize utilization of existing assets to accommodate DER integrations without compromising established operational restrictions [7-10].A distribution system state estimation (DSSE)-based real-time network model is an essential instrument in the control and protection of distribution networks to meet the changes in technology, environment, and commerce. However, due to economic and technical limitations, measurements cannot be independently utilized to estimate the complete DS states. Synergy among all types of sensor data can refine and achieve network models for analyses more promptly when needed. This chapter introduces techniques suitable for DSSE in presence of non-synchronized measurements with different sampling rates.