Predictive Control of Adaptive Micro-Grid Energy Management System Considering Electric Vehicles Integration

Abstract

This paper addresses the problems of control and energy management in micro-grid with the incorporation of renewable energy generation, hybrid storage technologies, and the integration of the electric vehicles (EVs) with vehicle to grid (V2G) technology. The adaptive model predictive control (AMPC) technique is used to optimize the charge/discharge of the EVs in a receding horizon manner in order to reduce operational cost in a renewable energy-based micro-grid. V2G systems integration can be a crucial element in the assurance of network reliability against variability in loads. In this context, the paper presents an AMPC algorithm for the optimization of a micro-grid coupled with a V2G system consisting of six electric vehicle charging stations. The proposed algorithm effectively manages the use of renewable energy sources, vehicles charge, energy storage units, and the purchase and sale of electric power to the external network. Two scenarios are investigated in this paper to examine the performance of the proposed controller to manage the renewable energy sources in the micro-grid system. The first case uses a load shifting mechanism to solve the charge management problem during a known interval of parking time. The second case introduces the EVs with V2G capabilities when connected with the micro-grid. In this case, the vehicle battery collaborates with the ESS of the micro-grid to maximize costs benefits and mitigate the intermittency of renewable generation. Furthermore, other benefits of V2G concepts, such as voltage and frequency control for the micro-grid stability, are investigated. Therefore, it is evident from the obtained results that the proposed control algorithm was able to effectively manage the renewable energy sources, energy storage units, vehicles charge, and the purchase and sale of electric power with the grid. Keywords: Adaptive model predictive control, Energy management system, Electric vehicles, Vehicle to grid technology, Grid reliability, Load shifting, Optimization problem and MATLAB/Simulink.