Impact of the line resistance statistical distribution on a Probabilistic Load Flow computation

The structure of the Low Voltage distribution networks is not always accurately known by the Distribution System Operators, especially with the significant meteorological variations observed in recent years and the growth of decentralized PV production sources. In this paper a probabilistic Load Flow algorithm has been developed for radial Low Voltage network considering the line resistance distribution as an uncertainty depending on the temperature. The single-phase network model is therefore associated to the temperature variation in the network deployment area. The Load demand and the PV production generally used in classical Load Flow calculation are computed using Smart Meter data with a quarter of an hour resolution time. Both power values are considered to be time varying. Either the resistance value or the network to client exchanged power are randomly selected at each iteration using a Monte Carlo method. Both annual and seasonal dependencies of the line resistance have been implemented in the developed Probabilistic Load Flow. The simulation results have shown that integrating the resistance distribution in a seasonal probabilistic tool can impact the collected reliability indices up to 10.4% depending on the season. In a context of upgrading the Low Voltage electrical network knowledge by the Distribution System Operator, and with an accordance to the requirements of the EN50160 standard, this tool can be presented as an efficient algorithm for quantifying the impact of the line resistance statistical distribution on a Probabilistic Load Flow computation.