An Efficient Greedy Algorithm for Real-World Large-Scale Electric Vehicle Charging

Abstract

The increasing use of electric vehicles amplifies the demand for affordable charging infrastructure. By smart charging applications, operators of large-scale facilities of AC chargers can save costs on installation and lighten the load on distribution grids by avoiding high peaks and unbalanced loads. In the present paper, we consider the problem of phase-balancing in the context of non-ideal charging characteristics: some electric vehicles represent unbalanced loads to the grid, and some react to inputs in an unexpected nonlinear fashion. Furthermore, users expect a fair distribution of the limited charging power. In this light, we formally characterize fairness, choose to control load in real time and model smart charging as a time-discrete knapsack problem. In order to guarantee phase symmetry and increase charging efficiency, we develop a real current measurement filter and use it to solve the problem using a branch-and-bound algorithm and to approximate solutions with a greedy algorithm. We compare these solutions in representative simulations based on real charging data. Additionally, we evaluate the greedy algorithm on real charging infrastructure with up to 100 charging points. We conclude from the results that the greedy algorithm using measurements of charging behavior guarantees capacity and symmetry constraints and demonstrates comparatively adequate fair charging efficiency and applicability to computation on resource-constrained hardware.