Article Preview
Top1. Introduction
In the recent decades, a remarkable amount of attention has been absorbed towards using intelligent and soft computational tools for analyzing, controlling and optimization of large-scale power systems (Mozaffari et al., 2012a; Mozaffari et al., 2013a; Toffolo and Lazzareto, 2002). This is because of the stringent global regulations and provisions exerted by governments which oblige the industrialists and engineers to improve the performance of power systems to avoid energy dissipations and also provide monetary providences. Obviously, to do so, engineers should seek for powerful mathematical software and packages which are not only capable of complying with the abovementioned objectives, but also can provide them with some insights into the main parameters affecting the performance of power systems (Bejan et al., 1996).
In practice, it has been demonstrated that considering exergetic and economic laws can provide us with efficient tools for analyzing the main characteristics of power plants (Gorji and Mozaffari, 2013; Ebadi and Gorji, 2005). Having such a physics-based model lets us use a proper metaheuristic for optimization of the main characteristics of power plants. In this context, in a previous paper by the authors’ research group, the concepts of availability of energy (exergy), thermoeconomics (exergoeconomic), and energetic efficiency have been used for deriving a relatively accurate mathematical model to simulate the main mechanical objective functions of Damavand power plant (Gorji and Mozaffari, 2013; Mozaffari et al., 2012a; Goudarzi et al., 2014).
In that study, a comprehensive review has been carried out on the application of metaheuristic algorithms for optimizing the main operating parameters of power plants (Mozaffari et al., 2012a; Samadian et al., 2013). Based on the findings of that research paper, the authors observed that by selecting a proper version of metaheuristic optimizers, we can significantly improve the performance of large scale power plants. However, the following questions remained unsolved:
- 1.
Which types of metaheuristic techniques can result in the most optimum solution in the case of power plant optimization?
- 2.
How can we increase the reality of the obtained solutions from the thermodynamical point of view?
- 3.
Is it possible to modify the performance of metaheuristics for power plant optimization without increasing their computational complexity?
As it can be seen, according to the above dilemmas, searching for metaheuristics which can be a fit answer to the above questions is still an open field of investigation. Following the main stream of our previous papers, in this research, we intend to perform a comparative/innovative investigation to answer the above open questions.
In this context, firstly, by providing concise literature reviews, the authors try to provide the potential answers to the above questions. Then, based on the concepts of metaheuristic computing and the existing performance evaluation metrics (especially computational complexity metric), we justify which of the potential alternatives can be of use for complying with the requirements of the mentioned obstacles.