Electric vehicles (EVs) and solar photovoltaic (PV) systems pose challenges to existing power grids, which were often not designed to accommodate such large loads or local generation. With growing adoption of these technologies, hosting capacity (HC) studies are essential for understanding their impact and supporting future planning. However, combined HC assessments of EVs and PVs remain limited compared to studies treating the technologies separately, particularly when accounting for spatial uncertainties, temporal correlations, varying implementation levels, and different charging strategies. This study addresses this gap by applying a broadly applicable method for calculating the HC of EVs and PV systems in a Swedish residential grid. A combined timeseries and stochastic approach was used across multiple scenarios, supported by geographic information systembased (GIS-based) PV potential analysis. The analysis is based on real-world data and performed as a case study, providing new insights into joint EV-PV integration. Results show that spot price-based charging resulted in the most frequent violations. Evenly distributed charging was the most grid-friendly strategy in winter, while PV generation caused significant violations in summer regardless of charging strategy. Under the even charging strategy, the average number of unique buses with voltage violations during summer was 11-63 times higher than during winter. This is because the strategy uses low power, limiting the impact of EV charging, while high uncontrolled PV production in summer becomes the main driver of voltage problems. The findings highlight the need for strategic charging strategies and additional measures to manage the grid impact of high PV implementation.

The rapid growth of electric vehicles (EVs) is placing new demands on residential distribution networks. Smart charging and Vehicle-to-Grid (V2G) technologies offer potential solutions for mitigating the large peak load and enhancing the grid hosting capacity (HC), yet their effectiveness across varying EV penetration levels remains underexplored. Therefore, this study evaluates how EV penetration levels affect the effectiveness of these two technologies. Furthermore, to help improve the grid performances, this study also pioneeringly proposes two hypothetical pricing settings—diverse electricity buying prices and reduced electricity selling prices—and evaluates their performances by comparing with existing price settings. Using real-world network and EV charging data from Sweden, we assess peak load and HC under different scenarios of power flow direction, charging controls, and electricity prices strategies across eight EV penetration levels. Results reveal that coordinated charging, especially with V2G, more effectively reduces peak loads compared to individual controls. However, V2G, if not regulated well, can increase peak loads at high EV penetration levels. Diversified electricity buying prices help lower aggregated peak loads but are less effective in enhancing local HC due to peak load shifting rather than peak load reduction. Additionally, high electricity selling prices benefit at low EV penetration but become less effective as penetration grows. The findings suggest that electricity pricing strategies and charging controls should adapt dynamically to the level of EV penetration. These insights provide critical guidance to policymakers, distribution system operators, and aggregators in designing adaptive pricing and control strategies to integrate EVs without overburdening the grid.

Electric vehicles (EVs) are increasing in popularity and play an important role in decarbonizing the transport sector. However, a growing EV fleet can cause problems for power grids as the grids are not initially designed for EV charging. The potential of a power grid to accommodate EV loads can be assessed through hosting capacity (HC) analysis. The HC is grid specific and varies, therefore it is necessary to conduct analysis that reflects local conditions and covers uncertainties and correlations over time.

This theses aims to investigate the HC for EVs in existing residential power grids, and to gain a better understanding of how it varies based on how the EVs are implemented and charged. The work is in collaboration with a distribution system operator (DSO) and is based on two case studies using real-life data reflecting conditions in Swedish grids. Combinations of different HC assessment methods have been used and the HC is evaluated based on cable loading, transformer loading and voltage deviation. Additionally, the study investigated three distinct charging strategies: charging on arrival, evenly spread charging over whole connection period, and charging at the lowest spot price. 

The results show that decisions on acceptable voltage deviation limit can have a large influence on the HC as well as the charging strategy used. A charging strategy based on energy prices resulted in the lowest HC, as numerous EVs charging simultaneously caused high power peaks during low spot price periods. Charging on arrival was the second worst strategy, as the peak power coincided with household demand. The best strategy was to evenly spread out the charging, resulting in fewer violations for 100% EV implementation compared to the other two strategies for 25% EV implementation. 

The findings underscore the necessity for coordinated charging controls for EV fleets or diversified power tariffs to balance power on a large scale in order to use the grids efficiently.

The deployment of electric vehicles (EVs) is growing significantly in recent years. The increasing EV charging loads pose great stress on power grids in Sweden, as many existing power grids are not designed to host such large shares of new electric loads. Hence, studies investigating the impact of EV charging are needed. This study conducts a case study based on an existing Swedish residential power grid using real-life EV charging data to estimate the local grid hosting capacity (HC) for EVs. A combined time-series and stochastic HC assessment method is used with voltage deviation, cable loading and transformer loading as the performance indices. Uncertainty in EV charging locations and individual charging behaviour have been considered via Monte Carlo simulations. The power grid HC is analysed and compared under three charging strategies and four EV penetration levels. Study results show that a charging strategy based on low electricity prices gave lower HC due to simultaneous EV loads compared to the other two strategies: charging directly after plugging in the EV and an even charging load through the plug-in session. This implies the need for coordinated charging controls of EV fleets or diversified power tariffs to balance power on a large scale.

Hosting capacity (HC) is described as the maximum amount of new production or consumption that can be added to the grid without causing a violation. In this case study, a deterministic approach is used to investigate the HC of electric vehicle (EV) charging in a low-voltage grid, containing 13 detached single-family houses. It investigates how different parameters affect the HC, and what is causing the violation in the grid. Two different performance indices (PI) are used in the study: power cable overloading and voltage drop. The local grid is simulated for one year for four cases and the HC is derived for these. The cases are distinguished by two different violation thresholds for the voltage drop and two different implementation orders of the location of the charging. The results show that the HC of the grid is 6-11 EVs charging simultaneously. The difference in HC is primarily due to variation in the baseload through the year and location of charging. The cable between the substation and the first cable cabinet was the major contributor to the fault, and the PI causing the violation differed depending on what case was used.