Proton Exchange Membrane Fuel cells (PEMFC) are used in many engineering applications as a power source. The device has a mathematical model by which the output voltage-current (V-I) characteristics can be estimated at various operating conditions. However, this model comprises several nonlinear coupled parameters, and that makes the identification problem as challenging task. Owing to that, some advanced techniques been employed to extract the optimal constants of a PEMFC including meta-heuristic optimization algorithms. At this point, this paper deals with a novel swarm-intelligence optimization method named Whale Optimization Algorithm for the purpose of extracting the exact parameters of the PEMFC. This investigation is conducted to examine the effectiveness of the method in providing better results compared to other methods presented in the literature. A commercial PEMFC from Heliocentris with rated power = 40 W is employed to conduct a series of experiments in the laboratory. Moreover, to confirm the effectuality of the method, the results are compared with Particle-Swarm-Optimization (PSO) algorithm. The proposed method showed a significant enhancement in terms of accuracy and convergence speed compared to PSO, where, the error between the predicted and real data was negligibly small (Mean Absolute Error = 0.0726 V).
Recently, the reduction of electricity costs in residential areas has become one of the major research fields. A comprehensive power management system is required to lower the cost of both power generation and consumption. Moreover, the world's energy consumption is continuously increasing. This rise is the consequence of perpetual birth rates and the expansion of factories, which have both significantly increased carbon dioxide emissions and global warming. In order to address these difficulties, hybrid renewable energy systems have evolved in an important way since the development of renewable energy sources. Two meta-heuristic approaches are applied in this paper: the first one is the particle swarm optimization (PSO), which is inspired by the social behavior of bird swarms, and the second one is the whale optimization algorithm (WOA) which is inspired by humpback whale hunting behavior, to tackle the main issue in this work which is decreasing the overall electricity cost of a residential home. The residential home considered in this work consists of two renewable energy sources: a solar panel and the wind turbine, and a power storage system based on battery. This residential home is connected to the main grid through a bidirectional inverter. Furthermore, a comparative optimization study was suggested where we propose two different residential load demands. The results showed the best decrease in the total electricity cost and the best optimal solution by employing the whale optimization approach in both proposed cases.
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