Corrective action planning considering FACTS allocation and optimal load shedding using bacterial foraging oriented by particle swarm optimization algorithm
Reactive power planning (RPP) involves optimal allocation and determination of the type and size of new reactive power (VAR) supplies to satisfy voltage constraints during normal and contingency states. The RPP issue is in fact an optimization of large scale mixed integer nonlinear programming problem, so it is proper to use an evolutionary algorithm to solve the problem. In this paper, in order to solve the RPP problem for corrective action of power systems, the bacterial foraging (BF) oriented by particle swarm optimization (PSO) algorithm (BF-PSO) is proposed. In the algorithm, the VAR control has been carried out by using flexible AC transmission systems (FACTS) devices, in order to minimize the installation costs of these devices. In order to determine the saving rate in the costs, corrective control is also performed by the utilization of load shedding algorithm. The IEEE 57-Bus system, is used to test the proposed method. The simulation results of the proposed algorithm are compared with PSO and genetic algorithms (GA) to show the efficiency of this method in the RPP problem.
load shedding reactive power planning facts devices bf algorithm pso algorithm