Methodology for close to real time profiling of aggregated demand using data streams from smart meters

This paper discusses potential improvement in accuracy of estimation of load profiles at substation/aggregation point if the demand data is collected directly from smart meters rather than from balancing meters at bulk supply points. It proposes a bottom-up approach for development of daily load curves for domestic load sector by aggregating data coming as real-time data series from smart meters. In order to illustrate the concepts an assumption is made that all the smart meters in an area have the ability to measure instantaneous real power demand of each individual appliance. Following this, a probabilistic bottom-up approach is applied to generate reactive power demand at the point of aggregation. It is further assumed that the collected data streams have different sampling steps and that there are some missing data in recorded data streams. Different data conditioning methods are used to investigate the accuracy of demand aggregation at different aggregation levels not only in terms of total demand but also in terms of demand categories and controllable and uncontrollable demand.