Genetic algorithms as an optimization approach for managing electric vehicles charging in the smart grid
Sergiy Korotunov, Galyna Tabunshchyk, Vyacheslav Okhmak
Abstract
Usage of the genetic algorithms for solving electric vehicles optimization problem in the scope of smart grid is an extremely actual problem nowadays. Electric vehicles are modern and promising alternative to conventional vehicles. They are characterized by lower operation cost and environmentfriendly ability to use renewable sources of energy. Smart grids can be used in order to avoid undesirable impact of electric vehicles. Such grids require optimization and correct scheduling to handle growing number of electricity consumers. This can be achieved with implementation of specifically designed genetic algorithms. The goal of the paper is to select optimal method and propose it for using for optimization of the digital twin of the electric vehicles charging infrastructure. As a result of paper such method is proposed. Moreover, as a scientific novelty, genetic algorithm functions are compared and analyzed applying to the problem in consideration.