A Novel Population Optimizer for Unit Scheduling Problems in Power Systems

The unit commitment (UC) problem is the first step in power system optimal scheduling and system planning. However, the UC problem is a mixed integer optimization problem, which usually has the characteristics of high dimension, non-convex and nonlinear. Plug-in electric vehicles (PEVs) integration into the grid can help improve stability and flexibility of the grid. However, Large-scale PEVs charging demand may put pressure on the grid and may lead to grid overloads. Recently, a competitive swarm optimizer (CSO) is proposed to settle optimization problems, which is considerably challenging in evolutionary computation. In this paper, a binary competitive swarm optimizer (BCSO) is proposed to tackle UC problems integration with PEVs. Finally, comparison experiments on economic problems with dimensionality increasing from 10 to 100 units, which confirm the competitive performance of the proposed optimizer.