Optimal Power Flow Using Genetic Algorithm: Parametric Studies for Selection of Control and State Variables

Abstract

The load flow solution using optimal power flow algorithm is gaining the importance in open market for operating the electrical network in optimal way. The optimal power flow is a power flow problem in which certain controllable variables are adjusted to minimize the objective function while satisfying the constraints on the physical state variables and operating limits. Many attempts were made through various algorithmic steps to obtain the global solution quickly using conventional and evolutionary methods. Evolutionary methods like Genetic Algorithm with its own advantages finds its own utility in optimal power flow solutions. Genetic Algorithm is simple to implement but has global convergence difficulties with slow convergence rate for optimal power flow problems. This paper presents three algorithms with an effect of selection of control variables on the convergence of OPF. Different sets of control variables are used to detect their usefulness in the OPF solutions. Statistical parameter based study is also provided to visualize the effect of selection of control variables on OPF convergence with solution time and improved value. Extensive study is provided on IEEE 30 bus system to draw certain important conclusions.