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Title: Zigzag search for multi-objective optimization considering generation cost and emission

Abstract

The zigzag search algorithm has been applied in engineering fields, such as oil well placement, with satisfactory results. Here, the zigzag search algorithm is introduced, modified with enhancement, and effectively applied to solve an economic emission dispatch problem and to demonstrate its practicability in power systems. The problem is formulated as a non-linear multi-objective optimization model taking energy constraints, generation limits, and transmission constraints into consideration. A set of non-dominant solutions can be obtained to form the Pareto front. Case studies are carried out with the IEEE 30-bus system and IEEE 118-bus system. The results indicate that the proposed zigzag search algorithms have the ability to deal with relevant power system problems. Comparisons are made with algorithms which have been widely used in literatures, such as the genetic algorithm (GA) and particle swarm optimization (PSO). Finally, this demonstrates that the zigzag search is easy to implement and is superior to other multi-objective (MO) techniques in both accuracy and efficiency.

Authors:
 [1]; ORCiD logo [1];  [2]; ORCiD logo [3]
  1. Univ. of Tennessee, Knoxville, TN (United States)
  2. Univ. of La Verne, CA (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1561677
Alternate Identifier(s):
OSTI ID: 1564549
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 255; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Zigzag search algorithm; Economic emission dispatch; Multi-objective optimization; Non-dominated sorting genetic algorithm; Particle swarm optimization

Citation Formats

Zhang, Qiwei, Li, Fangxing, Wang, Honggang, and Xue, Yaosuo. Zigzag search for multi-objective optimization considering generation cost and emission. United States: N. p., 2019. Web. doi:10.1016/j.apenergy.2019.113814.
Zhang, Qiwei, Li, Fangxing, Wang, Honggang, & Xue, Yaosuo. Zigzag search for multi-objective optimization considering generation cost and emission. United States. doi:10.1016/j.apenergy.2019.113814.
Zhang, Qiwei, Li, Fangxing, Wang, Honggang, and Xue, Yaosuo. Tue . "Zigzag search for multi-objective optimization considering generation cost and emission". United States. doi:10.1016/j.apenergy.2019.113814.
@article{osti_1561677,
title = {Zigzag search for multi-objective optimization considering generation cost and emission},
author = {Zhang, Qiwei and Li, Fangxing and Wang, Honggang and Xue, Yaosuo},
abstractNote = {The zigzag search algorithm has been applied in engineering fields, such as oil well placement, with satisfactory results. Here, the zigzag search algorithm is introduced, modified with enhancement, and effectively applied to solve an economic emission dispatch problem and to demonstrate its practicability in power systems. The problem is formulated as a non-linear multi-objective optimization model taking energy constraints, generation limits, and transmission constraints into consideration. A set of non-dominant solutions can be obtained to form the Pareto front. Case studies are carried out with the IEEE 30-bus system and IEEE 118-bus system. The results indicate that the proposed zigzag search algorithms have the ability to deal with relevant power system problems. Comparisons are made with algorithms which have been widely used in literatures, such as the genetic algorithm (GA) and particle swarm optimization (PSO). Finally, this demonstrates that the zigzag search is easy to implement and is superior to other multi-objective (MO) techniques in both accuracy and efficiency.},
doi = {10.1016/j.apenergy.2019.113814},
journal = {Applied Energy},
number = C,
volume = 255,
place = {United States},
year = {2019},
month = {9}
}

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This content will become publicly available on September 10, 2020
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