دانلود کتاب طراحی بهینه سازی سیستم های انرژی تجدیدپذیر با استفاده از الگوریتم های بهینه سازی پیشرفته

Global warming is a significant concern that raises a need for cleaner production of energy. Renewable energy resources are significant sources of contribution to such clean energy demands. Taking advantage of these renewable energy sources provides significant opportunities for handling energy-related problems. In the last few decades, researchers have focused on renewable energy resources like solar energy, bioenergy, wave energy, ocean thermal energy, tidal energy, geothermal energy, and wind energy. This has resulted in the development of new techniques and tools that could harvest energy from renewable energy sources. However, to meet energy demands and reduce investment, a rigorous study of energy extraction systems is required. Identifying, analyzing, and optimizing the effect of various parameters of a renewable energy system contribute significantly to assessing the system performance. Furthermore, it is always not preferable to present the optimum system parameters considering only a single objective as these systems have multiple objectives such as power output, system efficiency, investment cost, and economic and ecological factors. Hence, researchers have developed various optimization models of these systems and presented optimum system parameters through single- and multi-objective optimization using advanced optimization algorithms. All evolutionary computation and swarm intelligence-based optimization algorithms are population-based algorithms and have control parameters such as population size, crossover probability, mutation probability, scaling factor, inertia weight, social and cognitive parameters, among others. Appropriate adjustment of the control parameters dictates the algorithm convergence toward the global optimum. Inappropriate adjustment of the control parameters leads to premature convergence and increased computational efforts. Also, selecting an appropriate population size for different optimization applications is a tedious job. Additionally, in multi-objective optimization, selecting the most suitable solution from non-dominated solution set is difficult. In this book, recently developed Jaya and Rao (Rao-1, Rao-2, and Rao-3) algorithms are described for single- and multi-objective optimization of selected renewable energy systems. In addition, variants of the Jaya and Rao algorithms are presented to show the improvement in performances. Furthermore, variants of the Jaya algorithm, namely multi-team perturbation guiding Jaya (MTPG-Jaya) algorithm and adaptive multi-team perturbation guiding Jaya (AMTPG-Jaya) algorithm, and variants of the Rao algorithms, namely elitistRao (ERao-1, ERao-2, and ERao-3) algorithms and self-adaptive population Rao (SAP-Rao) algorithm, are demonstrated for optimization of the selected renewable energy systems. These algorithms have no algorithm-specific parameters and require only the common control parameters. Additionally, the applicability of multi-attribute decision-making methods in multiobjective optimization problems is discussed, and a decision-making procedure is recommended based on average rank to identify the best solution in a Pareto-front.