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Performance assessment and optimization of thermal power plants by DEA BCC and multivariate analysis

机译:DEA BCC和多元分析法对火电厂进行绩效评估和优化

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摘要

This study presents an integrated Data Envelopement Analysis (DEA) methodology using Banker Charnes Cooper (BCC) input oriented model for assessment and optimization of conventional thermal power plants (gas, steam and combined cycles). Installed capacity, fuel consumption, labor cost, internal power, forced outage hours and operating hours are used as input parameters whereas total power generation is used as output parameter. Moreover, 40 power plants in Iran were used as decision-making units and DEA-BCC model was used to assess their efficiency and rank during 1997-2000. Principal Component Analysis (PCA) and Numerical Taxonomy (NT) together with Spearman correlation technique were used to verify and validate the findings of DEA-BCC approach. In addition, all regional power plants have been ranked, assessed and optimized in comparison with all thermal power plants.
机译:这项研究提出了一种使用Banker Charnes Cooper(BCC)输入导向模型的集成数据包络分析(DEA)方法,用于评估和优化常规火力发电厂(燃气,蒸汽和联合循环)。装机容量,燃料消耗,人工成本,内部功率,强制停机时间和运行时间用作输入参数,而总发电量用作输出参数。此外,伊朗的40个发电厂被用作决策单位,DEA-BCC模型被用来评估其效率和等级(1997-2000年)。使用主成分分析(PCA)和数值分类法(NT)以及Spearman相关技术来验证和验证DEA-BCC方法的发现。此外,与所有火力发电厂相比,所有区域性火力发电厂都得到了排名,评估和优化。

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