Coordinated load management of building clusters and electric vehicles charging: An economic model predictive control investigation in demand response

This paper introduces a probabilistic load coordination approach to optimize the combined management of home-charged electric vehicles and space heating demand. Reduced-order resistance–capacitance models are applied for building thermal simulations, while support vector machine models predict baseline electric loads. Monte Carlo simulations are used to estimate arrival times and remaining charge of electric vehicles, assessing the advantages of Level 1 and Level 2 infrastructure for one-way and two-way residential charging stations. The Individual Stress Level, a novel metric for supervisor coordination within an economic model predictive control framework, is introduced. The methodology is tested on ten homes managed by an energy aggregator in Québec, Canada. Results show monodirectional electric vehicle charging not disrupting grid stability under static-price tariffs. However, time-of-use pricing structures increase average demand by 19–29% and peak capacity by 3–18%. Bidirectional scenarios indicate a 15.5% increase in maximum demand and a 17.5% rise in normal capacity. The combined building energy flexibility index indicates reductions of 74–140% for morning events and 54–98% for evening events. A sensitivity analysis highlights the role of demand charges in the Individual Stress Level activation, showing reduced price sensitivity for monodirectional setups compared to bidirectional configurations.