Robust Power Scheduling for Smart Charging of Electric Vehicles

The increasing penetration of electric vehicles (EVs) in the mobility market and their significant impact on the power grids calls for new and smart approaches to the management of the charging process for these vehicles, aimed at maximizing the efficiency while respecting power budgets and minimizing costs. In this paper, we propose a robust model for optimal scheduling the charge power at sockets for a large EVs parking facility. We start by developing a nominal linear programming (LP) model for the smart charging problem and then observe that, in practice, key quantities such as electricity prices and the vehicles' energy demand are subject to uncertainty. We hence formulate a robust LP version of the problem, which provides charging plans that are resilient to uncertainties. The effectiveness of such model is analyzed by means of a posteriori evaluations, where we test the candidate plan against scenarios and realizations of the uncertain data, using performance metrics such as the regret that allow for a fair comparison between different solutions (e.g., robust and nominal). The robust optimization model indeed handles uncertainties without drastically compromising performance, and offers a promising approach when deployed in real time by means of a receding horizon scheme.