ADAPTIVE FUZZY PARTICLE SWARM OPTIMIZATION BASED CONGESTION MANAGEMENT USING OPTIMAL RESCHEDULING OF ACTIVE AND REACTIVE POWER
Main Article Content
In a deregulated electricity market one of the most important tasks of System Operator is to manage congestion. Therefore, investigation of techniques for congestion free-wheeling power is of paramount interest. One of the most practical and obvious technique of congestion management is rescheduling the power outputs of generators in the system. In the present paper, the optimal rescheduling of reactive power generation of both generator and capacitor along with the rescheduling of active power is considered to relieve congestion. The optimal rescheduling of powers in a pool model is formulated as a nonlinear optimization problem. This paper proposes Adaptive Fuzzy Particle Swarm Optimization based Optimal Power Flow for solving the non linear optimization problem to minimize the Congestion Cost. For the better performance of Particle Swarm Optimization, in the proposed method, the inertia weight is dynamically adjusted using fuzzy IF/THEN rules to increase the balance between global and local searching abilities. The effectiveness of the proposed method has been tested on a 75-bus Indian Practical System and 39-bus New England system. The simulation experiments reveal that AFPSO performs better than other Evolutionary Algorithms such as Particle Swarm Optimization, Real and Binary Coded Genetic Algorithm and Conventional Optimization methods.