Abstract -- Simple rules, when executed by individual agents in a large group, or swarm, can lead to complex behaviors that are often difficult or impossible to predict knowing only the rules. However, aggregate behavior is not always unpredictable—even for swarm models said to be beyond analysis. For the class of swarming algorithms examined herein, we analytically identify several possible emergent behaviors and their underlying causes: clustering, drifting, and explosion. They also analyze the likelihood of these behaviors emerging from randomly selected swarm configurations and present a few examples. The analytic results are illustrated via simulation

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 haveshown 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.