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A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making

机译:热电联产经济调度的两阶段方法:多目标优化与综合决策相结合

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摘要

To address the problem of combined heat and power economic emission dispatch (CHPEED), a two-stage approach is proposed by combining multi-objective optimization (MOO) with integrated decision making (IDM). First, a practical CHPEED model is built by taking into account power transmission losses and the valve-point loading effects. To solve this model, a two-stage methodology is thereafter proposed. The first stage of this approach relies on the use of a powerful multi-objective evolutionary algorithm, called theta-dominance based evolutionary algorithm (theta-DEA), to find multiple Pareto-optimal solutions of the model. Through fuzzy c-means (FCM) clustering, the second stage separates the obtained Pareto-optimal solutions into different clusters and thereupon identifies the best compromise solutions (BCSs) by assessing the relative projections of the solutions belonging to the same cluster using grey relation projection (GRP). The novelty of this work is in the incorporation of an IDM technique FCM-GRP into CHPEED to automatically determine the BCSs that represent decision makers' different, even conflicting, preferences. The simulation results on three test cases with varied complexity levels verify the effectiveness and superiority of the proposed approach. (C) 2018 Elsevier Ltd. All rights reserved.
机译:为了解决热电联产调度(CHPEED)的问题,提出了一种将多目标优化(MOO)与集成决策(IDM)相结合的两阶段方法。首先,通过考虑动力传输损耗和阀点负载效应来构建实用的CHPEED模型。为了解决该模型,此后提出了两阶段方法。此方法的第一阶段依赖于使用强大的多目标进化算法(称为基于theta优势的进化算法(theta-DEA))来找到模型的多个Pareto最优解。通过模糊c均值(FCM)聚类,第二阶段将获得的Pareto最优解分成不同的聚类,然后通过使用灰色关联投影评估属于同一聚类的解的相对投影来确定最佳折衷解(BCS)。 (GRP)。这项工作的新颖之处在于将IDM技术FCM-GRP整合到CHPEED中,以自动确定代表决策者不同甚至冲突的偏好的BCS。在具有不同复杂度级别的三个测试用例上的仿真结果证明了该方法的有效性和优越性。 (C)2018 Elsevier Ltd.保留所有权利。

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