A zonal congestion management using PSO and Real Coded Genetic Algorithm

In deregulated electricity market transmission congestion occurs when there is insufficient transmission capacity to simultaneously accommodate all requests for transmission service within a region. One of the most important tasks of independent system operator (ISO) is to manage congestion as it threatens system security and may cause rise in electricity price resulting in market inefficiency. In corrective action of congestion management schemes, it is crucial for ISO to select the most sensitive generators to re-schedule their real and reactive powers in congestion management. In this paper the optimal rescheduling of both real and reactive power to minimize the total congestion cost using particle swarm optimization (PSO) and real coded genetic algorithm (RCGA) is proposed. And the selection of most sensitive generators to re-dispatch both the real and reactive powers is done using transmission congestion distribution factors (TCDFs) [3, 17]. The proposed methods have been tested on a practical 75-bus Indian System.