Uncertainties in model predictive control for decentralized autonomous demand side management of electric vehicles

Optimal scheduling of electric vehicle charging is a non -trivial problem associated with multiple sources of uncertainties. These uncertainties are often neglected in demand -side management studies assuming perfect predictions, which in practice is unrealistic. In this paper, we propose a model predictive control framework for scheduling the charging of residential electric vehicles to account for the uncertainties associated. The evaluations are performed considering a decentralized demand -side management algorithm proposed in the literature, which aims to exploit the flexibility of electric vehicles to fill the valleys in the demand curve in order to flatten the aggregated load. The performance of the method is evaluated in response to the uncertainty in the non -elastic load, aggregated electric vehicle demand, and electric vehicle user behavior. The results show that the variance in the demand is reduced by a factor of 4.8 in the proposed model predictive -based method in the presence of all three uncertainties considered relative to the uncontrolled charging. Under perfect prediction, the reduction is a factor of 7.5, thereby indicating that the method is a viable solution against uncertainties. Moreover, the study provides an overview of the degree of overestimations in the desired outcomes realized under the assumptions of perfect predictions for the different uncertain parameters, demonstrating that the most significant impact arises from uncertainty in mobility usage.