A novel PSO based OPF for zonal congestion management using optimal real and reactive power dispatch

In a competitive electricity market, he most important tasks of congestion management requires the Independent System Operator (ISO) to identify and relieve 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 optimal real and reactive powers in congestion management. As the real and reactive power dispatches play 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 to manage congestion. The re-dispatch of transactions for congestion management in a pool model is formulated as a Nonlinear Programming (NLP). This paper proposed Fitness Distance Ratio Particle Swarm Optimization based Optimal Power Flow (FDRPSO-OPF) to solve the NLP. The Conventional PSO (CPSO) oscillate in damped sinusoidal waves until they converge to points in between their previous pBest and gBest positions which prevent the particles from effective search for the global optimum. To overcome this problem the FDRPSO has been used in this paper. The proposed method has been tested on a practical 75-bus Indian System and 39-bus New England System for single line congestion case and the results are compared with Conventional PSO (CPSO), Binary Coded Genetic Algorithm (BCGA) and Real Coded Genetic Algorithm (RCGA) based OPF methods.