Development of a New Unit Commitment Method with Quantum Predator Prey Brain Storm Optimization

Abstract

This paper proposes a new method for unit commitment (UC) with Quantum Predator Prey Brain Storm Optimization (QPPBSO). The UC problems may be expressed as a mixed integer nonlinear programing problem in which binary variables mean on/off conditions of units and continuous ones imply their output. Recently, Evolutionary Computation (EC) has been applied to the UC problems due to the existence of indifferentiable cost functions such as large-scale steam turbine units, etc. However, there is still room for improvement in EC because the UC problems have high nonlinear features. This paper focuses on the integration of EC with Quantum Computing (QC) that is promising in power systems. Specifically, this paper combines QC with Predator Prey Brain Storm Optimization (PPBSO) of high performance EC. The effectiveness of the proposed method is demonstrated in the New England 39-node system.