Ant colony optimisation for economic dispatch problem with non-smooth cost functions

被引：177

作者：

Pothiya, Saravuth ^{[2
]}

Ngamroo, Issarachai ^{[1
]}

Kongprawechnon, Waree ^{[2
]}

机构：

[1] King Mongkuts Inst Technol Ladkrabang, Fac Engn, Ctr Excellence Innovat Energy Syst, Bangkok 10520, Thailand

[2] Thammasat Univ, Sch Commun Instrumentat & Control, Sirindhorn Int Inst Technol, Pathum Thani, Thailand

来源：

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS | 2010年 / 32卷 / 05期

关键词：

Ant colony optimisation; Economic dispatch problem; Genetic algorithm; Particle swarm optimisation; Tabu search; GENETIC ALGORITHM; TABU SEARCH; UNITS;

D O I：

10.1016/j.ijepes.2009.09.016

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a novel and efficient optimisation approach based on the ant colony optimisation (ACO) for solving the economic dispatch (ED) problem with non-smooth cost functions. In order to improve the performance of ACO algorithm, three additional techniques, i.e. priority list, variable reduction, and zoom feature are presented. To show its efficiency and effectiveness, the proposed ACO is applied to two types of ED problems with non-smooth cost functions. Firstly, the ED problem with valve-point loading effects consists of 13 and 40 generating units. Secondly, the ED problem considering the multiple fuels consists of 10 units. Additionally, the results of the proposed ACO are compared with those of the conventional heuristic approaches. The experimental results show that the proposed ACO approach is comparatively capable of obtaining higher quality solution and faster computational time. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：478 / 487

页数：10

共 50 条

[41] Smooth minimization of non-smooth functions
Yu. Nesterov
Mathematical Programming, 2005, 103 : 127 - 152
[42] Solution of Non-Smooth Economic Dispatch Using Interactive Grouped Adaptive Bat Algorithm: Solving Practical Economic Dispatch
Mahdad, Belkacem
INTERNATIONAL JOURNAL OF ENERGY OPTIMIZATION AND ENGINEERING, 2019, 8 (01) : 88 - 114
[43] Evolutionary programming-based goal-attainment method for economic emission load dispatch with non-smooth fuel cost and emission level functions
Basu, M.
Journal of the Institution of Engineers (India): Electrical Engineering Division, 2005, 86 (SEPT.): : 95 - 99
[44] Non-smooth/non-convex economic dispatch by a novel hybrid differential evolution algorithm
Wang, S.-K.
Chiou, J.-P.
Liu, C.-W.
IET GENERATION TRANSMISSION & DISTRIBUTION, 2007, 1 (05) : 793 - 803
[45] Economic dispatch with non-smooth objectives - Part II: Dimensional steepest decline method
College of Electrical Engineering, Zhejiang University, Hangzhou, China
不详
L69 3GJ, United Kingdom
不详
不详
IEEE Trans Power Syst, 2 (722-733):
[46] Non-smooth economic dispatch computation by fuzzy and self adaptive particle swarm optimization
Niknam, Taher
Mojarrad, Hasan Doagou
Meymand, Hamed Zeinoddini
APPLIED SOFT COMPUTING, 2011, 11 (02) : 2805 - 2817
[47] Economic Dispatch With Non-Smooth Objectives-Part I: Local Minimum Analysis
Zhan, Junpeng
Wu, Q. H.
Guo, Chuangxin
Zhou, Xiaoxin
IEEE TRANSACTIONS ON POWER SYSTEMS, 2015, 30 (02) : 710 - 721
[48] An efficient hybrid MPSO-GA algorithm for solving non-smooth/non-convex economic dispatch problem with practical constraints
Barati, Hassan
Sadeghi, Mohammad
AIN SHAMS ENGINEERING JOURNAL, 2018, 9 (04) : 1279 - 1287
[49] Robust eigenstructure clustering by non-smooth optimisation
Minh Ngoc Dao
Noll, Dominikus
Apkarian, Pierre
INTERNATIONAL JOURNAL OF CONTROL, 2015, 88 (08) : 1441 - 1455
[50] TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions
Vemula, Anirudh
Bagnell, J. Andrew
2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2020, : 4157 - 4163

← 1 2 3 4 5 →