Alocação ótima de estações de recargas rápidas em rodovias considerando critérios de diversas naturezas

• Ano de defesa: 2021
• Autor(a) principal: Lucca, Tiago Guterres
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia Elétrica; UFSM; Programa de Pós-Graduação em Engenharia Elétrica; Centro de Tecnologia
• Palavras-chave em Português: Algoritmo genético; Alocação; Estação de recarga rápida; Rodovias; Veículos elétricos; Genetic algorithm; Allocation; Fast charging station; Highways; Electric vehicles; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.ufsm.br/handle/1/29157

The growth of global concern with environmental issues is directly linked to the need to decarbonise the vehicle fleet in order to achieve the objectives set out in the Paris Agreement. The advance of the circulation of Electric Vehicles (VEs) is taking place all over the world, in Brazil even more conservatively, since just in 2018 a Normative Resolution (RN) No. 819 was established to guide the procedures and conditions for commercial and financial exploitation of recharge activities. Conducting studies on charging stations, such as technical and economic impacts, is of paramount importance to encourage the sale of EVs. This methodology aims to define the best allocation for Rapid Recharge Stations (ERRs) on roads that do not yet have this type of infrastructure. For this, the research considers four factors to determine the restriction MDERR (Maximum Distance between two ERRs): autonomy of EVs marketed in the study country, driver's reach anxiety, thermal comfort (air conditioning) and a safety margin, called of unscheduled stops factor. In the matter of choosing the location to install the charging infrastructure, stopping points on the route are analyzed, named after Candidate Establishments (ECs), which can be hotels, motels, restaurants, stops, gas stations, markets, shopping malls, stores and etc. For each of the ECs, scores are determined for three variables: daily passenger vehicle flow, population of the nearby city and level of service that the location provides. With the help of MATLAB software, an optimization with Genetic Algorithm (AG) is elaborated to maximize the score of the chosen CEs within the imposed restrictions. Different scenarios were used to analyze the impact of the weight of each variable in defining the chosen establishments. It is concluded that the variable level of service has a higher impact compared to the flow of vehicles and population as it presents greater variety in neighboring CE. The algorithm points out the Selected Establishments (EE) and the Optional Establishments (EO) that could be chosen without impacting the overall score and the MDERR restriction.