This paper presents an approach to estimate the hosting capacity for distribution networks considering the impact of PV penetration at different voltage levels. The estimation and the method were selected such that the results were most suitable for distribution system planning. A time-series based method was used as it covers significant aspects needed for prioritising network reinforcement. The MV background voltage was modelled varying in time, assuming the same penetration level in the other LV networks supplied by the same MV system. The hosting capacity is defined as the maximum acceptable PV size per customer for a given PV penetration. Based on the different possible combinations of PV location, the probability of overvoltage and overloading is used as a performance index. The planning risk is used as a limit for the performance criterion. The method can be automated for a large number of networks due to using an IEC 61970-based input format. It also enables linking DSO network models to customer smart metre databases. The severity and risks of limit violations are analysed with different metrics from the time-series simulations. The change in background voltage with increasing penetration is shown to impact the results significantly. When considering it, the estimated hosting capacity was reduced by 32 %, on average.

About 15 000-customers are connected to the individual secondary distribution networks supplied through 48-medium voltage 10 kV radial feeders. The hosting capacity assessment uses the end-customer voltage magnitude rise and transformer thermal overload. The hosting capacity is estimated by applying the “stochastic mixed aleatory-epistemic method” to determine the voltage magnitude rise and load flow with solar PV. The minimum power consumption is compared with the solar PV power infeed through the individual transformers. The hosting capacity estimation is done for three-phase connected solar PV sizes from 3 to 18 kW. At moderate PV penetration (25%–50%), the results showed that overvoltage would limit the hosting capacity more often than overload, but it becomes an issue only for LV networks studied with more than 8-customers. Considering all LV networks, most of the customers could install 6 kWp. Even when installing PV systems of 18 kWp (about twice the average size today and about the maximum area of a typical residential roof), two-thirds of houses would not need an upgrade to withstand SS-EN 50160 voltage limits. The latter customers can connect solar PV units with 18 kWp size without overvoltage or overload issues.

This paper proposes a deterministic and stochastic approach to quantify the hosting capacity that is often limited by the loadability limit of the cable cabinet or transformers due to customers with solar photovoltaics (PV) units. Distribution networks from two areas in Sweden supplying 309 MV/LV distribution transformers with 12,000 customers downstream have been studied. Using a deterministic model, a method is proposed and applied to assess the cable overvoltage against the loadability while considering the voltage rise margin. In addition, measurements have been applied to the methods and the loadability limits assessed. Illustrations for the concepts and important results for the guide to DSOs decision-making guide has been obtained. It is shown in the paper the hosting capacity anticipated at the end of a distribution network cable with a particular size is determined more often by the loadability at larger voltage rise margin and by overvoltage at smaller voltage rise margins. The results obtained for the data used show that the overload limit is exceeded more often for transformers than for cable supplying the cable cabinets at smaller solar PV sizes. For larger solar PV sizes, the feeder cable loadability limit is likely to be exceeded first before that of the transformers. The stochastic approach applied to the yields a small probability to exceed the hosting capacity and depend on the two epistemic uncertainties.