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

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)).