Real time Energy Management System of a photovoltaic based e-vehicle charging station using Explicit Model Predictive Control accounting for uncertainties

Abstract

This paper proposes an Explicit Model Predictive Control (eMPC) for the energy management of an e-vehicle charging station fueled by a photovoltaic plant (PV), a battery energy storage system (BESS), and the electrical grid. The method computes offline an explicit solution of the MPC, which is stored and then used for real time control. Multiparametric programming is used to calculate the explicit solution by mapping the MPC laws as a function of uncertain parameters. In this paper, the uncertainties introduced into the multiparametric programming are the photovoltaic power production, the electricity price, the battery’s state of charge, and the electric vehicle power consumption. Moreover, the environmental impact of the charging station operation is considered through the C O 2 emissions level. The explicit solution is computed for a specific range of uncertain parameters. Then, during the real-time control, their current values are measured to evaluate the control laws saved beforehand. The proposed approach, consisting of an offline MPC-based determination of the control laws followed by their online use, reduces the computational burden without affecting the MPC performance. Numerical simulations and experimental results confirm the eMPC’s performance for the proposed application. • EV charging station based on PV/BESS uses eMPC control as its EMS. • PV and EV power, CO 2 , price, and BESS’s SoC are included as uncertainties. • eMPC creates offline the control laws as function of the uncertain parameters. • The online evaluation of the control laws takes less than one second.