Optimal Planning of Battery-Powered Electric Vehicle Charging Station Networks

This paper develops a novel, two-stage optimization framework for the planning of electric vehicles charging stations powered by batteries. Genetic algorithm is used at the first stage to minimize the EV transportation energy losses, which selects the best charging station locations in the case of Corpus Christi, TX where a population of ~10,000 electric vehicles is assumed. At the second stage, linear programming is employed to minimize the costs of investment, operations, and maintenance of the charging facilities which results in the optimum numbers of slow and fast charging facilities required to meet charging demands. With the design, this paper also evaluates the daily energy consumption and annual revenue at each charging station. The work will have impacts on future designs of green transportation infrastructure.