A model and an evolutionary algorithmic approach towards optimization of Electric Vehicle fleet charging

The prevalence of the Smart Grid and its capabilities, has enabled sophisticated energy management that can be realized as a multi-constraint optimization problem and tailored to the specific scenario needs. In conjunction with the increasing introduction of Electric Vehicles (EVs), energy management tools can now consider expanded conditions including grid balance, cost optimization, EV characteristics, asset utilization, operational goals etc. In this work we analyze such a scenario and demonstrate how an EV fleet charging can be optimized in a timely manner while taking into consideration local conditions e.g., individual EV needs as well as global ones e.g., grid limits and energy price. We formalize a model that reflects the EV restrictions, and use it to assess an algorithmic approach that solves this non-linear optimization problem.