Different energy sources and converters need to be integrated with each other for extended usage of alternative energy, in order to meet sustained load demands during various weather conditions. The objective of this paper is to associate photovoltaic generators, fuel cells, and electrolysers. Here, to sustain the power demand and solve the energy storage problem, electrical energy can be stored in the form of hydrogen. By using an electrolyser, hydrogen can be generated and stored for future use. The hydrogen produced by the electrolyser using PV power is used in the FC system and acts as an energy buffer. Thus, the effects of reduction and even the absence of the available power from the PV system can be easily tackled. Modeling and simulations are performed using MATLAB/Simulink and SimPowerSystems packages and results are presented to verify the effectiveness of the proposed system.
Purpose -This paper aims to focus on the trade-off between losses and converter cost. Design/methodology/approach -The continual development of power electronic converters, for a wide range of applications such as renewable energy systems (interfacing photovoltaic panels via power converters), is characterized by the requirements for higher efficiency and lower production costs. To achieve such challenging objectives, a computer-aided design optimization based on genetic algorithms is developed in Matlab environment. The elitist non-dominated sorting genetic algorithm is used to perform search and optimization, whereas averaged models are used to estimate power losses in different semiconductors devices. The design problem requires minimizing the losses and cost of the boost converter under electrical constraints. The optimization variables are, as for them, the switching frequency, the boost inductor, the DC capacitor and the types of semiconductor devices (IGBT and MOSFET). It should be pointed out that boost topology is considered in this paper but the proposed methodology is easily applicable to other topologies. Findings -The results show that such design methodology for DC-DC converters presents several advantages. In particular, it proposes to the designer a set of solutions -as an alternative of a single one -so that the authors can choose a posteriori the adequate solution for the application under consideration. This then allows the possibility of finding the best design among all the available choices. Furthermore, the design values for the selected solution were obtainable components. Originality/value -The authors focus on the general aspect of the discrete optimization approach proposed here. It can also be used by power electronics designers with the help of additional constraints in accordance with their specific applications. Furthermore, the use of such non-ideal average models with the multi-objective optimization is the original contribution of the paper and it has not been suggested so far.
Purpose -The purpose of this paper is to develop a simulation tool which permits reducing the cost of long time-range simulation of complex converters and running at high frequency. Design/methodology/approach -A different method is used to represent a simplified converter but the adopted technique uses the average representation of the cell converter. Findings -The paper shows that the use of averaged representation of the pulse width modulation switch in multilevel converters is staying applied. The main advantage of the proposed averaged model is its simplified representation when only electrical behaviour is considered.Research limitations/implications -The analytical algorithm of the averaged model can be introduced in different simulator as it has a description language, enabling study of the Compatibilité Electromagnétique and electrothermal phenomena. Originality/value -This paper presents an averaged model of the multilevel converter which can be implemented in any simulator as it has a description language.
The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA’s simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA.
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