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Memetic Computing
Erschienen in: Memetic Computing 2/2014

01.06.2014 | Regular research paper

Dynamic economic dispatch using Lbest-PSO with dynamically varying sub-swarms

verfasst von: Arkabandhu Chowdhury, Hamim Zafar, B. K. Panigrahi, K. R. Krishnanand, Ankita Mohapatra, Zhihua Cui

Erschienen in: Memetic Computing | Ausgabe 2/2014

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Abstract

Dynamic economic dispatch (DED) is one of the major planning problem in a power system. It is a non-linear optimization problem with various operational constraints, which includes the constraints of the generators operating characteristics and the system constraints. Its principal aim is to minimize the cost of power production of all the participating generators over a time horizon of 24 h, while satisfying the system constraints. This problem deals with non-convex characteristics if generation unit valve-point effects are taken into account. The paper intends to solve the DED problem with valve-point effects, using our modified form of Local-best variant of Particle Swarm Optimization (Lbest PSO) algorithm. We have tested our algorithm on 5-unit, 10-unit and 110-unit test system with non-smooth fuel cost functions to prove the effectiveness of the suggested method over different state of the art methods.
Vorheriger Artikel Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions
Nächster Artikel The break scheduling problem: complexity results and practical algorithms

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Literatur
1.
Hindi KS, Ab Ghani MR (1989) Multi-period economic dispatch for large scale power systems. lEE Proc Part C 136(3):130–136
2.
Granelli GP, Marannino P, Montagna M, Silvestri A (1989) Fast and efficient gradient projection algorithm for dynamic generation dispatching. IEE Proc Gener Transm Distrib 136(5):295–302CrossRef
3.
Somuah CB, Khunaizi N (1990) Application of linear programming re-dispatch technique to dynamic generation allocation. IEEE Trans Power Syst 5(1):20–26CrossRef
4.
Travers DL, Kaye RJ (1998) Dynamic dispatch by constructive dynamic programming. IEEE Trans Power Syst 13(1):72–78CrossRef
5.
Liang RH (1999) A neural-based re-dispatch approach to dynamic generation allocation. IEEE Trans Power Syst 14(4):1388–1393CrossRef
6.
Yalcinoz T, Short MJ (1998) IEEE Trans Power Syst. Neural networks approach for solving economic dispatch problem with transmission capacity constraints 13(2):307–313
7.
Panigrahi BK, Chattopadhyay PK, Chakrabarti RN, Basu M (2006) Simulated annealing technique for dynamic economic dispatch. Electr Power Comp Syst 34(5):577–586CrossRef
8.
Wong KP, Fung CC (1993) Simulated annealing based economic dispatch algorithm. IEE Proc Gener Transm Distrib 140(6):509–515CrossRef
9.
Walters DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8(3):1325–1332CrossRef
10.
Chiang C (2005) Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels. IEEE Trans Power Syst 20(4):1690–1699CrossRef
11.
Yang H, Yang P, Huang C (1996) Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions. IEEE Trans Power Syst 11(1):112–118CrossRef
12.
Ravi G, Chakrabarti R, Choudhuri S (2006) Nonconvex economic dispatch with heuristic load patterns using improved fast evolutionary program. Electr Power Comp Syst 34(1):37–45CrossRef
13.
Lin W, Cheng F, Tsay M (2002) An improved tabu search for economic dispatch with multiple minima. IEEE Trans Power Syst 17(1):108–112CrossRef
14.
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micromachine and human science, Nagoya
15.
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, pp 1942–1948
16.
Park JB, Lee KS, Shin JR, Lee KY (2005) \(A\) particle swarm optimization for economic dispatch with non-smooth cost functions. IEEE Trans Power Syst 20(1):34–42 Feb.CrossRef
17.
Ghosh A, Chowdhury A, Sinha S, Vasilakos AV, Das S (2012) A genetic Lbest particle swarm optimizer with dynamically varying subswarm topology. IEEE congress on evolutionary computation (CEC 2012), Brisbane, 978-1-4673-1508-1
18.
Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation (CEC 1999), Piscataway, pp 1931–1938
19.
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2002), Honolulu
20.
Suganthan PN (1999) Particle swarm optimizer with neighborhood operator. In: Proceedings of the IEEE congress on evolutionary computation (CEC 1999), Piscataway, pp 1958–1962
21.
Hu X, Eberhart RC (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, Hawaii
22.
Mendes R, Kennedy J, Neves J (June 2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210
23.
Blackwell TM, Branke J (2004) Multi-swarm optimization in dynamic environments. LNCS No. 3005. In: Proceedings of applications of evolutionary computing: EvoWorkshops 2004: EvoBIO. EvoCOMNET.EvoHOT. EvoISAP. EvoMUSART and EvoSTOC, Coimbra, pp 489–500
24.
Løvbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimiser with breeding and subpopulations. In: Proceedings of the genetic and evolutionary computation conference, GECCO 2001
25.
Attaviriyanupap P, Kita H, Tanaka E, Hasegawa J (2002) A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Trans Power Syst 17(2):411–416CrossRef
26.
Panigrahi BK, Pandi V Ravikumar, Das Sanjoy (2008) Adaptive particle swarm optimization approach for static and dynamic economic load dispatch. Energy Convers Manage 49:1407–1415CrossRef
27.
Balamurugan R, Subramanian S (2008) Differential evolution-based dynamic economic dispatch of generating units with valve-point effects. Electr Power Comp Syst 36:828–843CrossRef
28.
Yuan X, Wang L, Yuan Y, Zhang Y, Cao B, Yang B (2008) A modified differential evolution approach for dynamic economic dispatch with valve-point effects. Energy Convers Manage 49(12):3447–3453
29.
Yuan X, Wang L, Yuan Y, Zhang Y, Yuan Y (2009) A hybrid differential evolution method for dynamic economic dispatch with valve-point effects. Expert Syst Appl 36(2):4042–4048CrossRef
30.
Victoire T, Jeyakumar A (2005) Deterministically guided PSO for dynamic dispatch considering valve-point effect. Electr Power Syst Res 73(3):313–322CrossRef
31.
Victoire TAA, Jeyakumar AE (August 2005) Reserve constrained dynamic dispatch of units with valve-point effects. IEEE Trans Power Syst 20(3):1273–1282
32.
Basu M (2009) Hybridization of artificial immune systems and sequential quadratic programming for dynamic economic dispatch. Electr Power Comp Syst 37(9):1036–1045CrossRef
33.
Hemamalini S, Simon SP (2011) Dynamic economic dispatch using artificial immune system for units with valve-point effect. Int J Electr Power Energy Syst 33:868–874CrossRef
34.
Hemamalini S, Simon S (2011) Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect. Eur Trans Electr Power 21:70–81
35.
Pandi VR (2011) Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm. Expert Syst Appl 38:8509e14
36.
Dakuo H, Dong G, Wang F, Mao Z (2011) Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms. Energy Convers Manage 52:1026e32
37.
Yuan X, Su A, Yuan Y, Nie H, Wang L (2009) An improved pso for dynamic load dispatch of generators with valve-point effects. Energy 34:67e74CrossRef
38.
Lu Y, Zhou J, Qin H, Wang Y, Zhang Y (2011) Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects. Eng Appl Artif Intell 24:378e87
39.
Lu Y, Zhou J, Qin H, Li Y, Zhang Y (2010) An adaptive hybrid differential evolution algorithm for dynamic economic dispatch with valve-point effects. Expert Syst Appl 37:4842e9
40.
Orero SO, Irving MR (1997) Large scale unit commitment using a hybrid genetic algorithm. Electr Power Energy Syst 19(1):45–55CrossRef
Metadaten
Titel
Dynamic economic dispatch using Lbest-PSO with dynamically varying sub-swarms
verfasst von
Arkabandhu Chowdhury
Hamim Zafar
B. K. Panigrahi
K. R. Krishnanand
Ankita Mohapatra
Zhihua Cui
Publikationsdatum
01.06.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 2/2014
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-013-0127-1

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