Dynamic security border identification using enhanced particle swarm optimization

Abstract

The ongoing deregulation of the energy market increases the need to operate modern power systems close to the security border. This requires enhanced methods for the vulnerability border tracking. The high-dimensional nature of power systems' operating space makes this difficult. However, new multiagent search techniques such as particle swarm optimization have shown great promise in handling high-dimensional nonlinear problems. This paper investigates the use of a new variation of particle swarm optimization to identify points on the security border of the power system, thereby identifying a vulnerability margin metric for the operating point.