APPLICATION OF GENETIC ALGORITHMS FOR STATE ESTIMATION IN DISTRIBUTION NETWORKS CONSIDERING AGGREGATED MEASUREMENTS AND LOW VOLTAGE LOSSES

State Estimation is responsible for processing a set of redundant measurements and obtaining the most likely operating condition of power systems, being an essential tool for the success of modern automation and control functions in distribution systems. Real-time measurements are usually scarce in distribution systems, but this has been changing with the deployment of many smart meters in the power grid. However, data from smart meters must be aggregated as a single load measurement at a given medium voltage system node, without representing the low voltage network and therefore neglecting power losses at this level. This paper presents a genetic-based methodology for distribution State Estimation, using aggregated smart meter measurements and considering the power losses at the low voltage network, which are determined during the evolutionary process. Simulations with a three-phase, unbalanced 34-bus test system illustrate the proposed approach.