Optimizing the deployment of fast-charging stations for electric vehicles: A mixed-integer nonlinear programming approach with particle swarm optimization

Environmental concerns have increasingly drawn attention to electric vehicles (EVs) over recent decades. The setting up of fast-charging stations requires careful consideration of details, including the precise location and size of the charging stations. Additionally, it is essential to persuade individuals to fund the installation of outlets for charging and create favorable prerequisites for investors to profit from their investments. In this article, the problem of planning fast-charging stations is represented as a nonlinear programming using integers. In this model, the target functions of the allocation business and the charging station owner are evaluated independently. The position and dimensions of charging stations, as well as the cost of electricity exchange between the distribution company and the charging station, are determined in such a way that the goal is accomplished of both entities is enhanced. In this approach, the theory of queues and traffic assignment models according to user equilibrium are employed to ascertain the magnitude of charging stations. In this paper, an improved Particle Swarm Optimization algorithm with variable coefficients is defined, which enhances local search capability and provides superior exploration ability.