VAr planning using genetic algorithm and linear programming - Mantovani, J. R S [UNESP]; Modesto, S. A G [UNESP]; Garcia, A. V. [UNESP] (Institution of Engineering and Technology (IET), 2001)
A combined methodology consisting of successive linear programming (SLP) and a simple genetic algorithm (SGA) solves the reactive planning problem. The problem is divided into operating and planning subproblems; the operating subproblem, which is a nonlinear, ill-conditioned and nonconvex problem, consists of determining the voltage control and the adjustment of reactive sources. The planning subproblem consists of obtaining the optimal reactive source expansion considering operational, economical and physical characteristics of the system. SLP solves the optimal reactive dispatch problem related to real variables, while SGA is used to determine the necessary adjustments of both the binary and discrete variables existing in the modelling problem. Once the set of candidate busbars has been defined, the program implemented gives the location and size of the reactive sources needed, if any, to maintain the operating and security constraints.
Keywords: Busbars; Electric transformers; Function evaluation; Genetic algorithms; Linear programming; Linearization; Mathematical models; Matrix algebra; Reactive power; Voltage control; Optimal reactive dispatch problem; Optimal reactive source expansion; Reactive planning; Successive linear programming; Electric power systems