Evaluation of AMI and SCADA Data Synergy for Distribution Feeder Modeling

Abstract

Distribution system states have become more dynamic due to the integration of intermittent distributed generation/load and bi-directional power flows. The availability of close to real-time distribution system models will help the controls and customer participations in the active distribution networks. A framework using on-line and off-line computing power and data in existing distribution management system to work together and build distribution feeder models for operation purposes is proposed. Simulations and evaluation results of the synergy of big heterogeneous topological and measurement data from systems that have different data formats, polling cycles, and accuracy to establish immediate past and future feeder models are presented. Archived customer meter interval and supervisory control and data acquisition (SCADA) data with the same time stamp are used to build quasi dynamic models off-line, and to forecast the states and measurements at the feeder buses. The results are then used in the on-line quasi dynamic state estimations (DySEs). The state tracking capability of two DySE techniques in conjunction with a static state estimator under various data accuracy assumptions is evaluated. Numerical results demonstrate the benefit of data synergy in the improvement of system operation models and network efficiency.