This paper presents a stochastic approach to single-phase and three-phase EV charge hosting capacity for distribution networks. The method includes the two types of uncertainties, aleatory and epistemic, and is developed from an equivalent method that was applied to solar PV hosting capacity estimation. The method is applied to two existing low-voltage networks in Northern Sweden, with six and 83 customers. The lowest background voltage and highest consumption per customer are obtained from measurements. It is shown that both have a big impact on the hosting capacity. The hosting capacity also depends strongly on the charging size, within the range of charging size expected in the near future. The large range in hosting capacity found from this study—between 0% and 100% of customers can simultaneously charge their EV car—means that such hosting capacity studies are needed for each individual distribution network. The highest hosting capacity for the illustrative distribution networks was obtained for the 3.7 kW single-phase and 11 kW three-phase EV charging power.

In hot climates, such as sub-Sahara Africa, window sizing and orientation pose challenges as they add, through solar insolation, to the building cooling energy demand and thus the cause of indoor overheating risk. This risk can be reduced through passive building-design-integrated measures, e.g., optimizing the window size, orientation and solar shading strategies. Through an IDA-ICE building performance simulation tool, the current study explores the impact of window size, optimization and building-integrated PV panels as shading strategies on cooling energy demands in three cities (Niamey, Nairobi and Harare) in sub-Sahara Africa. Results show that thermal comfort and cooling energy demand are sensitive to a window-to-wall ratio (WWR) > 70%, while the need for artificial lighting is negligible for a WWR > 50%, particularly in the north for cities in the Southern hemisphere and the south in the Northern hemisphere. A WWR > 70% in the east and west should be avoided unless shading devices are incorporated. Internal blinds perform better in improving occupant thermal comfort but increase artificial lighting while integrating PV panels, as external shading overhangs reduce cooling energy but also produce energy that can be utilized for building services, such as air conditioning. In this study, the results and implications of the optimization of window size, orientation and building-integrated shading and operation are discussed.

This paper presents a stochastic approach to assessing the hosting capacity for solar PV. The method is part of the optimal techniques for the integration of renewables. There are two types of uncertainties, namely aleatory and epistemic uncertainties. The epistemic and aleatory uncertainties influence distribution networks’ hosting capacity differently. The combination of the two uncertainties influences the planning of distribution networks. The study introduces and considers the epistemic probability distribution function (PDF). DSO does take levels of risk for a parameter violation when planning. Epistemic PDF is a range of values of the planning risk margin for quantifying the hosting capacity. The planning risk acknowledges that overvoltages may occur at weaker conceivable locations in a distribution network. In the paper, it has been shown that the number of customers who will be able to connect solar PV in future is influenced by the DSO’s planning risk margin. The DSO can be stricter or less strict in planning risk margin. It has been concluded that fewer customers can connect solar PV to a distribution network when a DSO takes a stricter planning risk. Alternatively, more customers can connect solar PV units for a less strict planning risk. How stricter or less strict the DSO is with the planning risk margin determines the investment needed for mitigation measures. The mitigation measures in the future will lead to not exceeding the overvoltage limit when solar PV is connected to the weaker conceivable points of the distribution network.