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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.

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Frequency Regulation of an Isolated Microgrid With Electric Vehicles and Energy Storage System Integration Using Adaptive and Model Predictive Controllers