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

Abstract

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.

Authors:
ORCiD logo; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability; China Scholarship Council
OSTI Identifier:
1490180
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Energy (Oxford)
Additional Journal Information:
Journal Volume: 162; Journal Issue: C; Journal ID: ISSN 0360-5442
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
cogeneration; economic emission dispatch; grey relational projection; integrated decision making; integrated energy system; multi-objective optimization; q-dominance based evolutionary algorithm; two-stage approach; valve-point loading effects

Citation Formats

Li, Yang, Wang, Jinlong, Zhao, Dongbo, Li, Guoqing, and Chen, Chen. A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making. United States: N. p., 2018. Web. doi:10.1016/j.energy.2018.07.200.
Li, Yang, Wang, Jinlong, Zhao, Dongbo, Li, Guoqing, & Chen, Chen. A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making. United States. doi:10.1016/j.energy.2018.07.200.
Li, Yang, Wang, Jinlong, Zhao, Dongbo, Li, Guoqing, and Chen, Chen. Thu . "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making". United States. doi:10.1016/j.energy.2018.07.200.
@article{osti_1490180,
title = {A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making},
author = {Li, Yang and Wang, Jinlong and Zhao, Dongbo and Li, Guoqing and Chen, Chen},
abstractNote = {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.},
doi = {10.1016/j.energy.2018.07.200},
journal = {Energy (Oxford)},
issn = {0360-5442},
number = C,
volume = 162,
place = {United States},
year = {2018},
month = {11}
}