An Optimizer-Tool-Based Improved Metaheuristic Method for Solving Security Optimal Power Flow: Interactive Power System Planning Tool

An Optimizer-Tool-Based Improved Metaheuristic Method for Solving Security Optimal Power Flow: Interactive Power System Planning Tool

Belkacem Mahdad (University of Biskra, Algeria)
Copyright: © 2018 |Pages: 36
DOI: 10.4018/978-1-5225-3935-3.ch006

Abstract

In this chapter, an interactive tool using graphic user interface (GUI) environment-based MATLAB is proposed to solve practical optimal power system planning and control. The main particularity of the proposed tool is to assist student and researchers understanding the mechanism search of new metaheuristic methods. The proposed tool allows users to interact dynamically with the program. The users (students or experts) can set parameters related to a specified metaheuristic method to clearly observe the effect of choosing parameters on the solution quality. In this chapter, a new global optimization method named grey wolf optimizer (GWO) and pattern search algorithm (PS) have been successfully applied within the interactive tool to solve the optimal power flow problem. The robustness of the two proposed metaheuristic methods is validated on many standard power system tests. The proposed interactive optimal power flow tool is expected to be a useful support for students and experts specialized in power system planning and control.
Chapter Preview
Top

Background

During the last two decades a large number of metaheuristic methods have been developed and applied to improve the solution of a variety of problems related to power system planning and control. The optimal power flow (OPF) is one of the most problems analyzed and discussed by researchers more than half a century. The main issue of OPF is to ensure delivering energy quality to customers by considering the cost and other objective functions such as, emission, voltage deviation, power loss, and voltage stability (Mahdad, 2014; Mahdad, 2015). As well indicated in many review papers, a large number of mathematical methods (Huneault, 1991; Momoh, 1993; Dommel, 1968; Alsac, 1974; Yan, 1999; Frank, 2012) have been proposed and applied to solve the standard OPF. Experience confirmed that the majority of the mathematical methods fail to achieve the near global solution when considering practical constraints related to generation units such as valve point effect and prohibited zones (Mahdad & Srairi, 2015). With the large integration of various types of FACTS devices and renewable sources characterized by their dynamic and intermittent constraints, the solution of OPF using conventional methods fail to achieve the optimal desired objective (Mahdad & Srairi, 2015). To overcome many drawbacks of the traditional optimization methods, a new category of optimization methods called metaheuristic techniques has been proposed to improve the solution quality of the OPF considering various practical operation constraints. Alsumait et al. (2010) proposed A hybrid GA–PS–SQP to solve the economic dispatch considering valve point effect, Bakirtzis et al. (2002) applied an enhanced genetic algorithm to solve the standard OPF problem, Lai et al. (1997) proposed an improved variant based genetic algorithm to solve the OPF problem under normal and contingent operation states, Attia et al. (2012) proposed an adaptive GA with adjusting population size to solve the standard OPF problem, Ghanizadeh et al. (2011) proposed a new metaheuristic method called imperialist competitive algorithm, Karaboga (2005) developed a new algorithm based on honey bee swarm for numerical optimization, Adaryani et al. (2013) proposed an artificial bee colony algorithm for solving multi-objective optimal power flow problem, Gnanambal et al. (2012) adapted and applied a hybrid differential evolution with particle swarm optimization to improve the maximum loadability limit of power system, Mahdad & Srairi (2012) proposed a hierarchical adaptive PSO for solving the multi-objective OPF considering emissions based shunt FACTS, Mahdad (2013) proposed various metaheuristic methods to solve the security OPF considering the integration of FACTS devices, Mahdad & Srairi (2013) proposed and applied the differential evolution algorithm to solve the multi objective OPF considering Wind and STATCOM, the state of the art of the majority of these methods proposed and applied to solve many problems related to power system planning and control is given in (Frank et al. (2012)).

Key Terms in this Chapter

FACTS Devices: A new technology based on power electronics elements designed to ensure flexible control of modern power system in particular at critical conditions.

Hybrid Methods: A new category of global optimization methods, based on combining various methods, designed to improve the performances of the standard metaheuristic methods.

Global Optimization Methods: A new category of optimization methods inspired from nature based on interaction between exploration and exploitation, designed to improve the solution of complex optimization problems.

Graphic User Interface: An interactive platform based on visual programming designed to analyze data with simplicity and accuracy.

Reactive Power Planning: An important sub problem of power system planning. It consists of improving security optimal power flow by optimizing specified control variables.

Complete Chapter List

Search this Book:
Reset