Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

In this paper, a computation framework for addressing combined economic and emission dispatch (CEED) problem with valve-point effects as well as stochastic wind power considering unit commitment (UC) using a hybrid approach connecting sequential quadratic programming (SQP) and particle swarm optimization (PSO) is proposed. The CEED problem aims to minimize the scheduling cost and greenhouse gases (GHGs) emission cost. Here the GHGs include carbon dioxide (CO2), nitrogen dioxide (NO2), and sulphur oxides (SOx). A dispatch model including both thermal generators and wind farms is developed. The probability of stochastic wind power based on the Weibull distribution is included in the CEED model. The model is tested on a standard system involving six thermal units and two wind farms. A set of numerical case studies are reported. The performance of the hybrid computational method is validated by comparing with other solvers on the test system.