A variable bus voltage DC microgrid (MG) is simulated in Simulink for optimization purposes. It is initially controlled with a Voltage Event Control (VEC) algorithm supplemented with a State of Charge Event Control (SOCEC) algorithm. This control determines the power generated/consumed by each element of the MG based on bus voltage and battery State of Charge (SOC) values. Two supplementary strategies are proposed and evaluated to improve the DC-DC converters’ efficiency. First, bus voltage optimization control: a centralized Energy Management System (EMS) manages the battery power in order to make the bus voltage follow the optimal voltage reference. Second, online optimization of switching frequency: local drivers operate each converter at its optimal switching frequency. The two proposed optimization strategies have been verified in the simulations.

Purpose -The purpose of this paper is to develop and test a prototype of the dual stator-winding induction generator (DWIG) and its dynamic model to verify the validity of this machine design as variable speed generator for renewable energy systems. Design/methodology/approach -Implementation and laboratory test of a DWIG prototype. Analysis and simulation of the developed DWIG model. Findings -The proposed DWIG makes a better use of energy than a squirrel cage induction generator (SCIG) in variable speed applications. The performance of DWIG with a bidirectional converter is very similar to those described in other studies with brushless doubly-fed induction generators.Research limitations/implications -The results can be used to test different control techniques and to analyse the dynamic performance of DWIG-converter system in variable load and speed conditions. Originality/value -The stator winding design does not involve a significant increase in the complexity of the machine assembly and cost of this induction machine.

This work suggests an integration procedure for a ESS (Energy Storage System) control algorithm into optimization control strategies minimizing cost functions for a microgrid system. This approach is based on a modification of the optimization strategy for adding absorption and flotation stages after each bulk charge to preserve the battery lifetime. These stages are computed out of the optimization program to reduce both computation complexity and convergence problems. Simulation results have confirmed the feasibility of this procedure at expenses of only a slight cost function increase, which can be assumed to preserve the battery lifetime.Peer ReviewedPostprint (published version

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