Wind-thermal dynamic economic emission dispatch with a hybrid multi-objective algorithm based on wind speed statistical analysis

Based on the analysis on the uncertain nature of wind power output, the Weibull distribution parameters of regional wind speed at different periods are calculated to obtain the probability density function (PDF) and the cumulative distribution function (CDF) of the wind power. The spinning reserve (...

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Veröffentlicht in:	IET generation, transmission & distribution Jg. 12; H. 17; S. 3972 - 3984
Hauptverfasser:	Liu, Gang, Zhu, Yong Li, Jiang, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 30.09.2018
Schlagworte:	CDF cumulative distribution function differential evolution dynamic economic emission dispatch evolutionary computation hybrid multiobjective algorithm Pareto dominance theory Pareto optimisation particle swarm optimisation particle swarm optimisation algorithm performance indicators power generation dispatch power generation economics probability density function PSO algorithm regional wind speed Research Article spinning reserve requirements SR requirements statistical analysis Weibull distribution Weibull distribution parameters wind power wind power incorporation wind power output wind power plants wind speed statistical analysis wind‐thermal dynamic economic emission dispatch Weibull distribution parameters spinning reserve requirements particle swarm optimisation CDF dynamic economic emission dispatch PSO algorithm Pareto dominance theory regional wind speed power generation economics wind power incorporation wind-thermal dynamic economic emission dispatch Weibull distribution wind power plants probability density function Pareto optimisation particle swarm optimisation algorithm wind power output SR requirements differential evolution evolutionary computation power generation dispatch wind power performance indicators hybrid multiobjective algorithm cumulative distribution function statistical analysis wind speed statistical analysis
ISSN:	1751-8687, 1751-8695
Online-Zugang:	Volltext
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