Data-Driven Model Predictive Control for Hybrid Charging Stations Using Ensemble Learning

Abstract

An increased demand in electric vehicle (EV) charging facilities has necessitated intelligent energy management systems (EMSs), to control and monitor the available energy sources in these charging stations. The goal is to create a charging schedule for EVs that minimizes the operating cost of the charging station while ensuring all connected EV's charging demands. Model predictive control (MPC) has been widely used for EMS. The challenge with MPC is that a precise representation of the underlying physical system's dynamics is essential. In this study, machine learning methods are combined with conventional MPC to build a data-driven MPC (DMPC) which can adapt to the changes in the system's behavior over time. As new data become available, the data-driven model can be updated and the MPC algorithm can be reoptimized to reflect the current behavior of the system. Ensemble learning is an effective machine learning technique that increases the effectiveness and accuracy of decision making by utilizing the combined knowledge of several models. Out of the several methods available for implementing ensemble learning, adaptive random forest (ARF) algorithm with affine functions and convex optimization is selected. The results show comparable performance of DMPC with respect to MPC implemented on a well-established mathematical model of the system.