Wu Dianfa, Wang Ningling, Yang Zhiping, Li Chengzhou, Yang Yongping
National Research Center for Thermal Power Engineering and Technology, North China Electric Power University, Changping District, Beijing 102206, China.
Entropy (Basel). 2018 Mar 23;20(4):215. doi: 10.3390/e20040215.
In recent years, coal-fired power plants contribute the biggest part of power generation in China. Challenges of energy conservation and emission reduction of the coal-fired power plant encountering with a rapid growth due to the rising proportion of renewable energy generation in total power generation. Energy saving power generation dispatch (ESPGD) based on power units sorting technology is a promising approach to meet the challenge. Therefore, it is crucial to establish a reasonable and feasible multi-index comprehensive evaluation (MICE) framework for assessing the performance of coal-fired power units accessed by the power grid. In this paper, a hierarchical multiple criteria evaluation system was established. Except for the typical economic and environmental indices, the evaluation system considering operational flexibility and power quality indices either. A hybrid comprehensive evaluation model was proposed to assess the unit operational performance. The model is an integration of grey relational analysis (GRA) with analytic hierarchy process (AHP) and a novel entropy-based method (abbreviate as BECC) which integrates bootstrap method and correlation coefficient (CC) into entropy principle to get the objective weight of indices. Then a case study on seven typical 600 megawatts coal-fired power units was carried out to illustrate the proposed evaluation model, and a weight sensitivity analysis was developed in addition. The results of the case study shows that unit 4 has the power generating priority over the rest ones, and unit 2 ranks last, with the lowest grey relational degree. The weight sensitivity analysis shows that the environmental factor has the biggest sensitivity coefficient. And the validation analysis of the developed BECC weight method shows that it is feasible for the MICE model, and it is stable with an ignorable uncertainty caused by the stochastic factor in the bootstrapping process. The elaborate analysis of the result reveals that it is feasible to rank power units with the proposed evaluation model. Furthermore, it is beneficial to synthesize the updated multiple criteria in optimizing the power generating priority of coal-fired power units.
近年来，火力发电厂在中国发电中占比最大。随着可再生能源发电量在总发电量中的占比不断上升，火力发电厂的节能减排面临巨大挑战。基于机组排序技术的节能发电调度是应对这一挑战的一种有前景的方法。因此，建立一个合理可行的多指标综合评价（MICE）框架对于评估接入电网的火力发电机组性能至关重要。本文建立了一个层次多准则评价体系。除了典型的经济和环境指标外，该评价体系还考虑了运行灵活性和电能质量指标。提出了一种混合综合评价模型来评估机组运行性能。该模型是灰色关联分析（GRA）与层次分析法（AHP）以及一种新颖的基于熵的方法（简称为BECC）的集成，该方法将自助法和相关系数（CC）集成到熵原理中以获得指标的客观权重。然后对7台典型的600兆瓦火力发电机组进行了案例研究，以说明所提出的评价模型，并进行了权重敏感性分析。案例研究结果表明，4号机组的发电优先级高于其他机组，2号机组排名最后，灰色关联度最低。权重敏感性分析表明，环境因素的敏感性系数最大。对所开发的BECC权重法的验证分析表明，它对于MICE模型是可行的，并且在自助过程中由随机因素引起的不确定性可忽略不计，具有稳定性。对结果的详细分析表明，用所提出的评价模型对机组进行排序是可行的。此外，在优化火力发电机组发电优先级时综合更新的多准则是有益的。
