Abstract:

The authors propose a model predictive control framework for residential electric vehicle charging scheduling, addressing uncertainties often overlooked in demand-side management studies. 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 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 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.