Nombre: RAFAEL SANTOS FREIRE FERRAZ
Fecha de publicación: 06/02/2025
Junta de examinadores:
Nombre |
Papel |
|---|---|
| LUCAS FRIZERA ENCARNACAO | Examinador Interno |
Páginas
Sumario: The growing insertion of Electric Vehicles (EVs) and, consequently, a lack of planning
in the installation of Electric Vehicle Charging Stations (EVCSs) can result in negative
impacts on the electricity network. Therefore, the optimized allocation and sizing of
EVCSs are important for reducing the costs of installing and operating the stations, in
addition to the costs incurred by EV users for travel and recharging. Furthermore, the
correct determination of slow and fast charging modes, combined with the adoption
of demand response programs such as smart charging and Time of Use tariffs, are
essential for reducing the aforementioned costs. These measures also contribute to
the improvement of the voltage profile and the minimization of power losses in the
distribution system. The purpose of this thesis is to conduct the optimized planning
of public EVCSs, as well as the optimized allocation and sizing of Distributed Energy
Resources (DERs), since DERs can play an important role in reducing the negative
effects on the power grid caused by the large increase in EV demand. Therefore, this
thesis includes four approaches using the proposed methodology. In the first approach,
the methodology was tested in the IEEE 34-node test system, considering only the fast
charging mode. In the second approach, only the slow charging mode was analyzed in the
33-node test system integrated with a 25-node traffic system. The other two approaches
integrated both fast and slow charging modes into the previously mentioned traffic
systems connected to the distribution systems. In the fourth approach, the charging
values for both modes were decision variables in the problem. Additionally, a novel
methodology was introduced for the spatiotemporal distribution of EVs over 24 hours
based on Closeness Centrality from Graph Theory, considering commercial and residential
areas in the studied systems. Different multi-objective algorithms were used to solve
the presented problem, aiming to validate the methodology. It is important to highlight
that the choice of the optimized solution was determined by graphical analysis or the
Fuzzy Decision-Making Method. In all approaches, a significant reduction in the negative
impacts of EV charging demand on the distribution system was observed, including
power losses and voltage deviations. Furthermore, there was a decrease in recharging
and travel costs for EV users, as well as a minimization of the costs for the system
operator with the installation and operation of EVCSs and DERs.
