Velocity feedback adaptive particle swarm optimization for optimal congestion management

Abstract EN

In a deregulated electricity market one of the most important tasks of System Operator is to manage congestion.

One of the most practical and obvious technique of congestion management (CM) is rescheduling the power outputs of generators in the system.

The optimal rescheduling of powers in a pool model is formulated as a constrained nonlinear optimization problem.

This paper proposes a novel Velocity Feedback Adaptive Particle Swarm Optimization (VFAPSO) for solving the constrained nonlinear optimization problem of the congestion management problem to minimize the Congestion Cost using the optimal rescheduling of reactive power generation of both generator and capacitor along with the rescheduling of active power.

In the proposed VFAPSO method the inertia weight is dynamically adjusted using the information of average velocity of swarm as feedback.

The proposed method deals with an adaptive strategy for tuning the parameters of Particle Swarm Optimization method based on the analysis of the dynamics of PSO.

The effectiveness of the proposed method has been tested on 39-bus New England Test system.

The simulation experiments reveal that the proposed method performs better than the Standard Particle Swarm Optimization (SPSO), Linearly Decreasing Inertia Weight PSO and other Evolutionary Algorithms such as Binary Coded Genetic Algorithm (BCGA) and Real Coded Genetic Algorithm (RCGA).