Hierarchical Economic Model Predictive Control Approach for a Building Energy Management System With Scenario-Driven EV Charging

To handle the increasing number of electric vehicles (EVs) and their effect on the infrastructure, intelligent and coordinated charge management is necessary. In this paper, this problem is considered in the context of a commercial building energy management system (EMS) with vehicle-to-grid capable employee EV charging stations (EVCSs). We propose a hierarchical economic model predictive control (EMPC) scheme for the operation of the EMS with EV charge management. An aggregator plans the operation of the EMS and an aggregated perspective of the EVCSs. A distributor then allots the aggregated charge power to the individual EVCSs. Both layers employ EMPC to jointly consider the objectives of monetary costs, building temperature comfort, EV charge satisfaction and battery degradation. For the predictions of EV usage behavior, scenario generation based on usage data and user input is employed. The main contributions of this paper are the symmetric consideration of EV charging objectives using EMPC in both levels of the hierarchy, as well as the introduction of a scalable and adaptable averaged scenario generation approach.

Automated summary

In this paper, a hierarchical economic model predictive control scheme is proposed for EV charge management in a commercial building energy management system. The scheme considers symmetrical charging objectives and introduces a scalable and adaptable scenario generation approach.