This paper presents a transient analysis of the trans quasi-Z-source inverter (trans-qZSI) topology operating in discontinuous conduction mode (DCM). Usually only the continuous conduction mode (CCM) is considered and high current operating points are assumed in the literature. Thus the main contributions of this article are a clear definition of the limits of the DCM and the development of a small-signal model that takes into account the dynamics introduced by the particular Z network contained in the trans-qZSI converter, which allows the behaviour of the system working in DCM to be analysed under different current demand circumstances. Moreover, the obtained small-signal model allows the expressions of the control-to-capacitor transfer function to be derived and used together with any inverter control strategy. For trans-qZSI topology performance analysis and model validation, computer simulation results are presented, including frequency domain analysis and small-signal/PSIM comparisons. Lastly, experimental validation is presented for the time and frequency domains.

This paper proposes a genetic algorithm-based method for sizing the energy storage system (ESS) in microgrids. The main goal of the proposed method is to find the energy and power capacities of the storage system that minimizes the operating cost of the microgrid. The energy management strategy (EMS) used in this paper is based on a fuzzy expert system which is responsible for setting the power output of the ESS. The design of the EMS is carried out by means of a genetic algorithm that is used to set the fuzzy rules and membership functions of the expert system. Given that the size of the storage system has a major influence on the energy management strategy, in this paper the EMS and ESS capacities are jointly optimized. In addition, the proposed method uses an aging model to predict the lifetime of the ESS. In this way it is possible to determine the cost associated with energy storage in a more precise manner. The unit commitment problem, which is crucial for the proper operation of the microgrid, has been also considered in the present work. The suggested sizing methodology has been validated in two case studies.

This paper presents an algorithm for reducing the operating cost of microgrids. The proposed algorithm determines the day-ahead microgrid scheduling and builds a fuzzy expert system to control the power output of the storage system. To perform such tasks, two genetic algorithms were employed. One of them generates the microgrid scheduling and determines the fuzzy rules of the expert system, whereas the other is used to tune the membership functions. In this way it is possible to optimize the expert system according to load demand, wind power availability and electricity prices. Simulations were carried out in a microgrid comprising a diesel generator, a microturbine, a fuel cell, a wind turbine and a battery. Both interconnected and island operation modes were considered. Simulation results verify the effectiveness of the proposed algorithm.