Optimal real and reactive power dispatch for zonal congestion management problem for multi congestion case using Adaptive Fuzzy PSO

In a deregulated electricity market 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 optimally. As the reactive power plays a vital role to relieve the congestion at low congestion cost, in this paper, the reactive support of generators, in addition to the rescheduling of real power generation, has been considered. The optimal re-dispatch of transactions for congestion management in a pool model is formulated as a nonlinear programming (NLP). The adaptive fuzzy particle swarm optimization based optimal power flow (AFPSO-OPF) is introduced first time in this paper for Congestion Management problem or multi congestion case to solve the NLP. In this method, the inertia weight is dynamically adjusted using fuzzy IF/THEN rules to increase the balance between global and local searching abilities. To minimize the number of readjustments for the congestion management, this paper has used the method of selection of generators from the most sensitive cluster/zone using two distribution factors, viz. Real and Reactive Power Transmission Congestion Distribution Factors (PTCDFs and QTCDFs). The proposed method has been tested on a practical 75-bus Indian System for multi line congestion case and the results are compared with the Conventional Particle Swarm Optimization (CPSO), Real Coded Genetic Algorithm (RCGA) and Binary Coded Genetic Algorithm (BCGA) based OPFs.