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Management of public charge stations for electric vehicles by using index of individualized performance

Grant number: 19/19282-2
Support type:Scholarships in Brazil - Master
Effective date (Start): December 01, 2019
Effective date (End): November 30, 2021
Field of knowledge:Engineering - Electrical Engineering - Power Systems
Principal researcher:Walmir de Freitas Filho
Grantee:Gustavo Caetano Marçal
Home Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:16/08645-9 - Interdisciplinary research activities in electric smart grids, AP.TEM

Abstract

Due to the growing environmental concerns, discussions about greenhouse gas emissions and sustainable development have increased. In these discussions, different solutions were presented to reduce these emissions, with the electrification of transportation among them. The result of this large-scale investment on electric mobility can be measured by the continuous increase in the number of electric vehicles in the last decade, highlighting the 2018 year when the fleet surpassed 5.1 million units. Consequently, it is crucial to invest in the operation, maintenance and expansion of charging infrastructure, because charging stations are high-power and energy demanding, especially fast-charging stations, resulting in technical and economic impacts in electric systems of utility and charging station. Considering the aforementioned, this project proposes the characterization and analysis of the public charging infrastructure to perform the integrated management of the energetic resources during the present and future operation based in a technical-financial analysis via individualized performance indices. Two types of solutions will be investigated: installation of complementary equipment, such as photovoltaic generators, electric batteries and additional charging points; and management of demand and contracts. Concerning the methodologies, the stochastic charging behavior will be modeled through Monte Carlo-based scenarios, accounting for uncertainties inherent of the problem to obtain cost-benefit solutions (in opposition to worst-case scenarios). (AU)

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