Received December 27, 2020, accepted January 15, 2021, date of publication January 19, 2021, date of current version January 27, 2021. Digital Object Identifier 10.1109/ACCESS.2021.3052797 Frequency Regulation of an Isolated Microgrid With Electric Vehicles and Energy Storage System Integration Using Adaptive and Model Predictive Controllers MISHKAT ULLAH JAN 1 , AI XIN 1 , (Member, IEEE), HASEEB UR REHMAN 1 , MOHAMED ABDELKARIM ABDELBAKY 1,2 , SHEERAZ IQBAL 3 , AND MUHAMMAD AURANGZEB 1 1 State Key Laboratory of Alternate Electrical Power System With Renewable Energy Source, North China Electric Power University, Beijing 102206, China 2 Department of Electrical Power and Machines Engineering, Faculty of Engineering, Cairo University, Cairo 12613, Egypt 3 Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan Corresponding author: Mohamed Abdelkarim Abdelbaky (m_abdelbaky@ncepu.edu.cn) This work was supported by the Beijing Natural Science Foundation under Grant 3182037. ABSTRACT Energy storage system (ESS) possesses tremendous potential to counter both the rapid growth of intermittent renewable energy resources (RESs) and provide frequency support to the microgrid (MG). Since the deployment of ESS has overcome the imbalance between generation and consumption, however, their massive cost, as well as degradation tendency, are the restricting considerations that demand alternative solutions to provide stable microgrid operation. To assist ESS, the electric vehicles (EVs) are incorporated into the system. EVs have been gradually commercially viable and considerable focus has been paid to vehicle-to-grid technologies. Appropriate collaboration between ESS and EVs has good capability to manage the frequency irregularities to ensure the efficient operation of the MG. This article presents a novel combination of two control techniques i.e., model predictive control (MPC) and adaptive droop control (ADC), to tackle the frequency regulation issue in the isolated MG, by effectively controlling the ESS and EVs during the large-scale integration of RESs or huge change in load demand. Firstly, the MPC regulates the ESS according to the system frequency deviation, and secondly, the ADC manages the power of EVs according to system specifications by retaining the least possible power for potential usage of EVs. Moreover, an advanced genetic algorithm is applied to tune the MPC and ADC parameters in order to achieve optimized performance. An isolated MG is modeled and verified in MATLAB/Simulink using the above-mentioned control techniques. Further, different case studies are taken into account to validate the combination of ADC and MPC for frequency regulation of an isolated MG. Additionally, the proposed MPC controller is compared with fuzzy logic proportional-integral (FPI) controller and proportional-integral (PI) controller, the MPC provides better performance results as compared with FPI and PI controllers. INDEX TERMS Electric vehicles, adaptive droop control, energy storage system, model predictive control, frequency regulation, GA optimization technique. I. INTRODUCTION In an electrical power grid, one of the biggest challenges is preserving the power balance between power supply and con- sumption. In other terms, energy production needs to be com- parable to the energy consumed. This limitation is to some The associate editor coordinating the review of this manuscript and approving it for publication was Moussa Boukhnifer . degree relaxed by the power system’s inertia. The system’s inertia is defined by a revolving mass of synchronous generat- ing units primarily used in traditional power systems [1], [2]. For instance, if a system encounters failure in one of its generating units by any accident, an imbalance of power is observed due to a substantial fall in the generation. Hence, the other generators attached to the system will try to recover the power deficit faced by the system by increasing their 14958 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 9, 2021