Abstract:
The subject of electric mobility has aroused great interest among researchers
in recent years, being a actual topic under discussion as Electric Vehicles (EV) reach
greater market share. Electric mobility, which is linked to the energy transition,
experiences a transformation of concepts that starts with the consumption of fossil
fuels and ends with zero carbon emissions. These changes correspond to the need
for cleaner, decentralized, digitized energy delivery systems and services, as well
as greater electrification of modes of transportation. In this context, this thesis
proposes and implements a novel methodology for calculating the hosting capacity
of EVs in low and medium voltage distribution systems through a case study.
The carrying capacity of EVs is evaluated in the literature using load flow
processing, which uses several criteria and methods for evaluation. On the one hand,
deterministic methods produce a single number for EV hosting capability and ignore
the uncertainties inherent in the allocation process. Stochastic methods, on the other
hand, generate a probability distribution of hosting capabilities. The suggestion of
a new methodology that combines deterministic and stochastic methods, takes into
account several operational criteria, and can be used in low and medium voltage
applications becomes more suitable and innovative.
The results indicate that the distribution system’s EV hosting capacity
varies with sector of the evaluated network. Loading can increase and alter the
load curve considerably in the secondary distribution system’s downstream of the
transformer, causing undervoltage and overload in the grid. The charging rate of
transformers with EV input, on the other hand, can change the primary distribution
system’s hosting capacity.
