In this paper, a computation framework for\r\naddressing combined economic and emission dispatch\r\n(CEED) problem with valve-point effects as well as stochastic\r\nwind power considering unit commitment (UC)\r\nusing a hybrid approach connecting sequential quadratic\r\nprogramming (SQP) and particle swarm optimization\r\n(PSO) is proposed. The CEED problem aims to minimize\r\nthe scheduling cost and greenhouse gases (GHGs) emission\r\ncost. Here the GHGs include carbon dioxide (CO2), nitrogen\r\ndioxide (NO2), and sulphur oxides (SOx). A dispatch\r\nmodel including both thermal generators and wind farms is\r\ndeveloped. The probability of stochastic wind power based\r\non the Weibull distribution is included in the CEED model.\r\nThe model is tested on a standard system involving six\r\nthermal units and two wind farms. A set of numerical case\r\nstudies are reported. The performance of the hybrid computational\r\nmethod is validated by comparing with other\r\nsolvers on the test system.
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