Sensing Cloud Optimization applied to a non-convex constrained economical dispatch

In this paper it is intended to solve an Economical Dispatch (ED) problem with a new tool, named Sensing Cloud Optimization (SCO). It is a technique based on clouds of particles which allow a dynamic change in search space. It has appropriate heuristic characteristic to solve not convex, not differentiable and highly constrained optimisation problems. It is provided with a statistical analysis which determines the cloud's dimension with dynamic adjustments in search space in order to accelerate the convergence and to avoid to get trapped in local minima. Two case studies are presented in which SCO demonstrated good performances reaching lower cost values where compared with other techniques.