The mass adoption of electric vehicles (EVs) is transforming the automotive sector, driven by environmental concerns and technological advancements. Governments and companies are investing in the expansion of charging networks, focusing on fast charging to meet the growing demand. Developing a robust infrastructure of charging stations is essential to eliminate “range anxiety” and encourage the transition to EVs. Fast charging is crucial for the success of vehicle electrification. It allows batteries to be charged much more quickly than conventional charging, increasing convenience for users and improving the overall user experience. As more fast-charging stations are installed, consumer confidence in EVs grows, paving the way for a more sustainable future. With a well-distributed fast-charging network, EVs become a practical alternative to fossil fuel-powered vehicles, accelerating the transition to greener mobility. However, fast charging of EVs can cause technical impacts on medium voltage networks. The high current demand can result in voltage drops, especially in areas with weaker distribution infrastructure. Transformers can be overloaded, reducing their lifespan and increasing the risk of failures. Excessive heating of conductors due to high current can also cause losses and damage cables. These challenges highlight the need for proper planning and investments in electrical infrastructure to support the increase in fast charging. A probabilistic analysis of the impact of fast charging on medium voltage networks is crucial. Energy demand varies throughout the year due to seasonal factors, such as the use of air conditioning in summer and heaters in winter. Fast charging adds a considerable load to the network, which can coincide with these demand peaks, exacerbating management challenges. The installation of multiple charging points can cause voltage fluctuations and overloads. Probabilistic analysis helps predict these impacts and develop mitigation strategies by simulating charging scenarios and user behaviors. This allows for more precise infrastructure planning, including network reinforcements and improvements to ensure supply reliability. This thesis proposes a probabilistic methodology to evaluate the impact of fast charging of electric vehicles on medium voltage distribution networks, considering voltage drops, network element loading, and regulator tap changes. Using the Power Factory software by DIgSILENT©, a real feeder in Brazil is simulated, analyzing different case studies. Three fast charging stations (FCS) are connected, each with six charging points of 100 kVA, totaling 600 kVA per EP. The charging profile of the EPs is programmed with stochastic variables. Finally, a Volt/Var control strategy is presented to mitigate the impact on voltage drops and regulator tap changes, allowing reactive power injection without the need for communication between charging points.
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