Otimização multiobjetivo para a alocação e dimensionamento de geradores distribuídos e estações de carregamento lento e rápido considerando programas de resposta à demanda

• Ano de defesa: 2025
• Autor(a) principal: Ferraz, Rafael Santos Freire
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Doutorado em Engenharia Elétrica; Centro Tecnológico; UFES; Programa de Pós-Graduação em Engenharia Elétrica
• Palavras-chave em Português: Algoritmo multiobjetivo; Estação de carregamento; Geração distribuída; Método de decisão fuzzy; Planejamento otimizado; Programa de resposta à demanda; Demand response program; Distributed generation; Charging station; Fuzzy decision-making method; Multi-objective algorithm; Optimized planning; Engenharia Elétrica
• Link de acesso: http://repositorio.ufes.br/handle/10/18364

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. In the third approach, the planning of charging stations considered the integration of both fast and slow charging modes into the traffic systems interconnected with the distribution systems. Finally, in the fourth approach, in addition to this integration, the charging prices for both modes were treated as decision variables in the problem, also incorporating the perspective of the system operator. 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