Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/14466
ชื่อเรื่อง: Lagrangian relaxation combined with differential evolution algorithm for unit commitment problem
ผู้แต่ง: Sum-Im T.
Keywords: Algorithms
Ant colony optimization
Computer programming
Constraint theory
Factory automation
Genetic algorithms
Lagrange multipliers
Optimization
Scheduling
Thermoelectric power plants
Ant colony search algorithms
Differential evolution algorithms
LaGrangian relaxation
Lagrangian relaxations
Optimization capabilities
Power generation scheduling
Unit commitment problem
Unit-commitment
Evolutionary algorithms
วันที่เผยแพร่: 2014
บทคัดย่อ: In this paper, a technique of combining Lagrangian relaxation (LR) with a differential evolution algorithm (DEA) method (LR-DEA) is proposed for solving unit commitment (UC) problem of thermal power plants. The merits of DEA method are parallel search and optimization capabilities. The unit commitment problem is formulated as the minimization of a performance index, which is sum of objectives (fuel cost, start-up cost) and several equality and inequality constraints (power balance, generator limits, spinning reserve, minimum up/down time). The efficiency and effectiveness of the proposed technique is initially demonstrated via the analysis of 10-unit test system. A detailed comparative study among the conventional LR, genetic algorithm (GA), evolutionary programming (EP), a hybrid of Lagrangian relaxation and genetic algorithm (LRGA), ant colony search algorithm (ACSA), and the proposed method is presented. From the experimental results, the proposed method has high accuracy of solution achievement, stable convergence characteristics, simple implementation and satisfactory computational time. © 2014 IEEE.
URI: https://ir.swu.ac.th/jspui/handle/123456789/14466
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946688588&doi=10.1109%2fETFA.2014.7005111&partnerID=40&md5=14bffea72d3bac3213b033c414fa8e7f
Appears in Collections:Scopus 1983-2021

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