Three-phase state estimation in the medium-voltage network with aggregated smart meter data

In distribution networks, the lack of measurement data is usually thought to be an inevitable bottleneck of conventional grid operation and planning. Recently, the availability of smart meters in the distribution network has provided an opportunity to improve the network observability. In medium-voltage (MV) distribution networks, there is an increasing demand to use aggregated smart meter data for the state estimation, instead of adopting pseudo-measurements with a low level of accuracy. However, the performance of an estimator requires good knowledge of the available measurements, in terms of both expected values and associated uncertainties. Therefore, this paper intends to firstly pave a new way of utilizing smart meter data gathered from the low-voltage (LV) feeders in a concrete and reliable manner. For the purpose of state estimation in MV distribution networks, smart meter data is to be processed through three steps: phase identification, data aggregation and uncertainty evaluation. The feasibility of the proposed method is verified on the IEEE European LV Test Feeder with a set of real-world smart meter data. Afterwards, the influence of the aggregated smart meter data on the three-phase state estimation are investigated on the modified IEEE 13-node test system and IEEE 34-node test system. Simulation results show that the effect of aggregated smart meter data on the accuracy of state estimators is dependent on both the accuracy level of the aggregated data and the measurement configuration in the network. Furthermore, the use of aggregated smart meter data is shown to be able to provide improved state estimation.