Värdkapaciteten för elbilsladdning i lokalnätet; Utvärdering av Monte Carlo-metod i PSS®SINCAL

Sammanfattning: The presence of electric vehicles (EVs) in the passenger car fleet has increased at a rapid rate and the development is predicted to continue in the same direction. The charging of these vehicles predominantely takes place in the owners’ homes and thus increases the households’ demand for electricity - both in terms of the amount of energy and the size of power peaks. The task of planning and dimensioning the electrical grids that households are connected to is thus becoming more difficult. Partly because traditional methods for estimating the load are at risk of no longer being representative when consumption takes new shapes, but also by reason of the uncertainty that electric car charging entails regarding when and where it will take place. As a result, there is a need for tools that enable estimation of the impact that charging has on the distribution grid. In this master thesis work, a method for estimation and prediction of the impact of EV-charging on the local electric grid has been studied. The method was based on the network calculation software PSS®SINCAL from Siemens, and makes use of a Monte carlo method of simulation to find the hosting capacity of a local grid. Through repeated simualtions of the grid model with stochastic placement and choice of charging power; the method aimed to mirror the situations which could arise on the grid, as an increasing number of households therein acquire electric vehicles. The result which the method yielded was the hosting capacity - formulated in terms of the percentage of households which can simultaneously charge EVs without the grid deviating from acceptable limits of voltage and component loadning - as well as information about voltage levels in specific nodes and loading of specific components in the grid. These results were found to be somewhat difficult to interpret but, if done with care, could provide useful insights about the grid. Especially in the form of identification of components at risk of overloading, which could enable the DSO to target their network reinforcement-efforts. The method was evaluated through a case study of a real grid located in the city of Rättvik in Sweden. By application of the method, two of the three low voltage areas constituting this grid were found to have a limited capacity for EV charging at about 30%, with overloading of the transformer being the limiting factor. The remaining low voltage grid and the medium voltage area connecting the three, were not found to be limited.