Abstract: This paper presents and utilizes an Improved Particle Swarm Optimization algorithm (IPSO) for reactive power management in restructured power systems. Reactive power procurement is modeled as a Security Constraint Optimal Power Flow (SCOPF), which incorporates a voltage stability problem. This is a major concern in power system control and operation. The model attempts to minimize the cost of reactive power procurement and energy losses as a main objective, while the technical criteria and voltage stability margin, especially, are treated as soft constraints. From a mathematical point of view, the reactive power market can be expressed as a nonlinear non-convex optimization problem with multi-local minima. In most cases, the non-convexity results in a failure of the mathematical-based optimization algorithm to _nd the global optimum. Thus, the PSO, a powerful heuristic searching algorithm, is developed and implemented to _nd the global optimum of the reactive power market objective function. The feasibility of the methodology (IPSO) is tested over an IEEE30 bus system, while the obtained simulation results are compared with the gradient-based approach, using General Algebraic Modeling System (GAMS) software, the original PSO and another evolutionary programming called a Genetic Algorithm (GA). The results demonstrate that the IPSO can converge to better feasible solutions with less iteration and can be successfully used for reactive power scheduling in deregulation environments.

Keywords: Particle Swarm Optimization algorithm (IPSO), General Algebraic Modeling System (GAMS), Genetic Algorithm (GA).