## 1 Introduction  
Microgrids and smart grids are the new concepts which have been brought about due to high penetration of renewable energy systems, distributed generation (DG) units, and storage systems. The environmental issues and economical, social, and political interests make the role of microgrid even more important. Nowadays, the use of DG units and microgrid systems is mainly limited to their operation in the grid-connected mode. In the grid-connected mode, a microgrid including its loads and DG units is connected to the main grid at the point of common coupling (PCC). In this case, the main grid provides fixed voltage and frequency for the microgrid and each DG unit regulates its power components based on the conventional dq-current control strategy. The set points for the dq-current controllers are determined such that the maximum power generated by the distributed energy sources is achieved. Autonomous (islanded) operation of a microgrid, however, is not as straightforward as its operation in the grid-connected mode, _i.e._, control and power management of an islanded microgrid is very difficult due to the stochastic nature of the renewable energy sources that act as the prime movers for the DG units. An islanded microgrid is a low inertia system whose dynamics highly vary due to the load perturbations. Therefore, the reliable and robust operation of a microgrid in the autonomous mode requires sophisticated control strategies which should guarantee desirable performance criteria and secure operation in a wide range of operating conditions. Time-domain simulation has a vital role in the design, development, implementation and verification cycles of controllers platforms. During early design stages, offline simulation softwares are used for verification and performance evaluation of the design concepts. Thereafter, the implementation stage begins where the control algorithm is realized on a physical platform. A hardware-in-the-loop (HIL) arrangement of a physical controller platform, in association with a real-time simulator, is needed to safely and thoroughly verify the design integrity and evaluate its functions and performance [1; 2; 3; 4]. This type of HIL testing, involving the exchange of waveforms between the simulator and the controller platform at the signal level with no real power exchange, is called controller hardware-in-the-loop (CHIL) testing. The existing real-time simulators utilize parallel processing, where multiprocessors, either General Purpose Processors (GPP) or Digital Signal Processors (DSP), or computer clusters are utilized [5; 6; 7; 8; 9; 10; 11; 12], to achieve the required real-time simulation performance. The system to be simulated is divided into zones, and the mathematical model of each zone is assigned to a given processor. A master processor (i) controls the communication and (ii) maintains synchronism among the other processors. In such paradigm, the minimum simulation timestep is dictated by the speed and number of processors, the synchronization and communication overhead time among the processors, and the efficiency of the software code. However, existing real-time simulators have limitations on (i) the minimum timestep size which is roughly between 10 and 80 \(\mu\)s, (ii) the frequency bandwidth of the simulation results [13] which is approximately up to 10 kHz, and (iii) the accuracy of the adopted models. The relatively large simulation timestep of existing simulators hinders the simulation of high-frequency converters and generally compromises the accuracy of HIL testing since switching events imposed by the controller within one timestep cannot be accounted for by the simulator until the subsequent timestep. Moreover, increasing the frequency bandwidth of the simulation results inherently requires not only the use of a smaller simulation timestep but also the use of more accurate models. However, improving the accuracy based on adopting more elaborate models increases the computational time and violation to real-time constraints occurs.