The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field.

This paper presents a contribution related to the control of nonlinear variable-speed marine current turbine (MCT) without pitch operating below the rated marine current speed. Given that the operation of the MCT can be divided into several operating zones on the basis of the marine current speed, the system control objectives are different for each zone. To deal with this issue, we develop a new control approach based on a linear quadratic regulator with variable generator torque. Our proposed approach enables the optimization of the rotational speed of the turbine, which maximizes the power extracted by the MCT and minimizes the transient loads on the drivetrain. The novelty of our study is the use of a real profile of marine current speed from the northern coasts of Morocco. The simulation results obtained using MATLAB Simulink indicate the effectiveness and robustness of the proposed control approach on the electrical and mechanical parameters with the variations of marine current speed.

This paper presents a study of Proton Exchange Membrane (PEM) system which is coupled directly to a PV array with a power of 10KW in order to produce hydrogen. For that, we propose a method for determining the different parameters that will allow us to simulate the electrical behavior of the PEM electrolyzer system. We also establish the equations required to predict the quantities of hydrogen produced by the electrolyzer system. Our proposed contribution is modeled and simulated by using Matlab/Simulink environment.