A Modified SSLPS Algorithm with Logistic Pseudo-Random Sequence Generator for Improving the Performance of Neka Power Plant: A Comparative/Conceptual Analysis

A Modified SSLPS Algorithm with Logistic Pseudo-Random Sequence Generator for Improving the Performance of Neka Power Plant: A Comparative/Conceptual Analysis

Ahmad Mozaffari (Systems Design Engineering Department, University of Waterloo, Waterloo, Canada), Mehdi Emami (Department of Electrical Engineering, Babol University of Technology, Babol, Iran), Nasser L. Azad (Systems Design Engineering Department, University of Waterloo, Waterloo, Canada) and Alireza Fathi (Department of Mechanical Engineering, Babol University of Technology, Babol, Iran)
Copyright: © 2015 |Pages: 29
DOI: 10.4018/IJAEC.2015010101
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Metaheuristic techniques have successfully contributed to the development and optimization of large-scale distributed power systems. The archived literature demonstrate that the modification or tuning of the parameters of specific metaheuristics can provide powerful tools suited for optimization of power plants with different types of constraints. In spite of the high potential of metaheuristics in dealing with such systems, most of the conducted researches only address the optimization of the electrical aspects of power systems. In this research, the authors intend to attest the applicability of metaheuristics for optimizing the mechanical aspects of a real-world large-scale power plant, i.e. Neka power plant sited in Mazandaran, Iran. To do so, firstly, based on the laws of thermodynamics and the physics of the problem at hand, the authors implement a mathematical model to calculate the values of exergetic efficiency, energetic efficiency, and total cost of the Neka power plant as three main objective functions. Besides, a memetic supervised neural network and Bahadori's mathematical model are used to calculate the dynamic values of specific heat over the operating procedure of the power plant. At the second stage, a modified version of a recent spotlighted Pareto based multiobjective metaheuristic called synchronous self-learning Pareto strategy (SSLPS) is proposed. The proposed technique is based on embedding logistic chaotic map into the algorithmic architecture of SSLPS. In this context, the resulting optimizer, i.e. chaos-enhanced SSLPS (C-SSLPS), uses the response of time-discrete nonlinear logistic map to update the positions of heuristic agents over the optimization procedure. For the sake of comparison, strength Pareto evolutionary algorithm (SPEA 2), non-dominated sorting genetic algorithm (NSGA-II) and standard SSLPS are taken into account. The results of the numerical study confirm the superiority of the proposed technique as compared to the other rival optimizers. Besides, it is observed that metaheuristics can be successfully used for optimizing the mechanical/energetic parameters of Neka power plant.
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1. 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.

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