Bio-Inspired Bird Swarm Algorithm for Solving Economic Load Dispatch Problems

Authors

DOI:

https://doi.org/10.18486/ijcsnt/6.2.081

Keywords:

Economic Load Dispatch, Bird Swarm Algorithm, Metaheuristic, Swarm Intelligence, Valve Point Loading, Prohibited Operating Zones

Abstract

The power system need to be operated economically and with cost efficiency. In this paper application of new bio-inspired metaheuristic named as Bird Swarm Algorithm (BSA) has been adopted for the optimization of ELD problems in power system operation. The concept has been conceived from the flocking behavior of birds. Birds mainly have three kinds of behaviors i.e. foraging behavior, vigilance behavior and flight behavior. Therefore by the implementation of social behavior, social interaction and swarm intelligence, BSA has been formulated for economic load dispatch problems.

References

Altun H and Yalcinoz T. Implementing soft computing techniques to solve economic dispatch problem in power systems. Expert Syst Appl 2008; 35(4): 1668–1678. DOI: https://doi.org/10.1016/j.eswa.2007.08.066

Sinha N, Chakrabarti R and Chattopadhyay PK. Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput 2003; 7(1): 83–94. DOI: https://doi.org/10.1109/TEVC.2002.806788

Park JB, Lee KS, Shin JR et al. A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Trans Power Syst 2005; 20(1): 34–42. DOI: https://doi.org/10.1109/TPWRS.2004.831275

Coelho LDS, Bora TC and Mariani VC. Differential evolution based on truncated Lévy-type flights and population diversity measure to solve economic load dispatch problems. Electr Power Energy Syst 2014; 57: 178–188. DOI: https://doi.org/10.1016/j.ijepes.2013.11.024

Hemamalini S and Simon SP. Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions. Electr Power Compon Syst 2010; 38(7): 786–803. DOI: https://doi.org/10.1080/15325000903489710

Bhattacharjee K, Bhattacharya A and Dey SH. Backtracking search optimization based economic environmental power dispatch problems. Int J Electr Power Energy Syst 2015; 73: 830–842. DOI: https://doi.org/10.1016/j.ijepes.2015.06.018

Hota PK, Barisal AK and Chakrabarti R. Economic emission load dispatch through fuzzy based bacterial foraging algorithm. Int J Electr Power Energy Syst 2010; 32(7): 794–803. DOI: https://doi.org/10.1016/j.ijepes.2010.01.016

Bhattacharya A and Chattopadhyay PK. Biogeography-based optimization for different economic load dispatch problems. IEEE Trans Power Syst 2010; 25(2): 1064–1077. DOI: https://doi.org/10.1109/TPWRS.2009.2034525

Al-Betar MA, Awadallah MA, Khader AT et al. Tournament-based harmony search algorithm for non-convex economic load dispatch problem. Appl Soft Comput 2016; 47: 449–459. DOI: https://doi.org/10.1016/j.asoc.2016.05.034

Basu M. Group search optimization for combined heat and power economic dispatch. Int J Electr Power Energy Syst 2016; 78: 138–147. DOI: https://doi.org/10.1016/j.ijepes.2015.11.069

Yang XS, Hosseini SSS and Gandomi AH. Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 2012; 12(3): 1180–1186. DOI: https://doi.org/10.1016/j.asoc.2011.09.017

Wang LJ. An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems.

Mandal B, Roy PK and Mandal S. Economic load dispatch using krill herd algorithm. Electr Power Energy Syst 2014; 57: 1–10. DOI: https://doi.org/10.1016/j.ijepes.2013.11.016

Adarsh BR, Raghunathan T, Jayabarathi T et al. Economic dispatch using chaotic bat algorithm. Energy 2016; 96: 666–675. DOI: https://doi.org/10.1016/j.energy.2015.12.096

Basu M. Modified particle swarm optimization for nonconvex economic dispatch problems. Int J Electr Power Energy Syst 2015; 69: 304–312. DOI: https://doi.org/10.1016/j.ijepes.2015.01.015

Basu M. Improved differential evolution for economic dispatch. Int J Electr Power Energy Syst 2014; 63: 855–861. DOI: https://doi.org/10.1016/j.ijepes.2014.07.003

Vishwakarma KK, Dubey HM, Pandit M et al. Simulated annealing approach for solving economic load dispatch problems with valve point loading effects. Int J Eng Sci Technol 2012; 4(4): 60–72. DOI: https://doi.org/10.4314/ijest.v4i4.6

Naama B, Bouzeboudja H and Allali A. Solving the economic dispatch problem by using tabu search algorithm. Energy Procedia 2013; 36: 694–701. DOI: https://doi.org/10.1016/j.egypro.2013.07.080

Pothiya S, Ngamroo I and Kongprawechnon W. Ant colony optimisation for economic dispatch problem with non-smooth cost functions. Int J Electr Power Energy Syst 2010; 32(5): 478–487. DOI: https://doi.org/10.1016/j.ijepes.2009.09.016

Cai J, Ma X, Li Q et al. A multiobjective chaotic ant swarm optimization for environmental/economic dispatch. Int J Electr Power Energy Syst; 32: 337–344. DOI: https://doi.org/10.1016/j.ijepes.2010.01.006

Secui DC. A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Convers Manag 2015; 89: 43–62. DOI: https://doi.org/10.1016/j.enconman.2014.09.034

Dubey HM, Pandit M and Panigrahi BK. A biologically inspired modified flower pollination algorithm for solving economic dispatch problems in modern power systems. Cogn Comput 2015; 7(5): 594–608. DOI: https://doi.org/10.1007/s12559-015-9324-1

Serapião ABS. Cuckoo search for solving economic dispatch load problem. Intell Control Autom 2013; 04(04): 385–390. DOI: https://doi.org/10.4236/ica.2013.44046

Nguyen TT and Vo DN. The application of one rank cuckoo search algorithm for solving economic load dispatch problems. Appl Soft Comput 2015; 37: 763–773. DOI: https://doi.org/10.1016/j.asoc.2015.09.010

Basu M. Kinetic gas molecule optimization for nonconvex economic dispatch problem. Int J Electr Power Energy Syst 2016; 80: 325–332. DOI: https://doi.org/10.1016/j.ijepes.2016.02.005

Pradhan M, Roy PK and Pal T. Grey wolf optimization applied to economic load dispatch problems. Int J Electr Power Energy Syst 2016; 83: 325–334. DOI: https://doi.org/10.1016/j.ijepes.2016.04.034

Yu JJQ and Li VOK. A social spider algorithm for solving the non-convex economic load dispatch problem. Neurocomputing 2016; 171: 955–965. DOI: https://doi.org/10.1016/j.neucom.2015.07.037

Neto JXV, Reynoso-Meza G, Ruppel TH et al. Solving non-smooth economic dispatch by a new combination of continuous GRASP algorithm and differential evolution. Int J Electr Power Energy Syst 2017; 84: 13–24. DOI: https://doi.org/10.1016/j.ijepes.2016.04.012

Sayah S and Hamouda A. A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems. Appl Soft Comput 2013; 13(4): 1608–1619. DOI: https://doi.org/10.1016/j.asoc.2012.12.014

Elattar EE. A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem. Int J Electr Power Energy Syst 2015; 69: 18–26. DOI: https://doi.org/10.1016/j.ijepes.2014.12.091

Dubey HM, Pandit M, Panigrahi BK et al. Economic load dispatch by hybrid swarm intelligence based gravitational search algorithm. Int J Intell Syst Appl 2013; 5(8): 21. DOI: https://doi.org/10.5815/ijisa.2013.08.03

Duman S, Yorukeren N and Altas IH. A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect. Int J Electr Power Energy Syst 2015; 64: 121–135. DOI: https://doi.org/10.1016/j.ijepes.2014.07.031

Cai J, Li Q, Li L et al. A hybrid CPSO–SQP method for economic dispatch considering the valve-point effects. Energy Convers Manag 2012; 53(1): 175–181. DOI: https://doi.org/10.1016/j.enconman.2011.08.023

Lee FN and Breipohl AM. Reserve constrained economic dispatch with prohibited operating zones. IEEE Trans Power Syst 1993; 8(1): 246–254. DOI: https://doi.org/10.1109/59.221233

Meng XB, Gao XZ, Lu L et al. A new bio-inspired optimisation algorithm: Bird swarm algorithm. J Exp Theor Artif Intell 2016; 28(4): 673–687. DOI: https://doi.org/10.1080/0952813X.2015.1042530

Gaing ZL. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 2003; 18(3): 1187–1195. DOI: https://doi.org/10.1109/TPWRS.2003.814889

Elsayed WT and El-Saadany EF. A fully decentralized approach for solving the economic dispatch problem. IEEE Trans Power Syst 2015; 30(4): 2179–2189. DOI: https://doi.org/10.1109/TPWRS.2014.2360369

Sun J, Palade V, Wu XJ et al. Solving the power economic dispatch problem with generator constraints by random drift particle swarm optimization. IEEE Trans Ind Inform 2014; 10(1): 222–232. DOI: https://doi.org/10.1109/TII.2013.2267392

Lohokare MR, Panigrahi BK, Pattnaik SS et al. Neighborhood search-driven accelerated biogeography-based optimization for optimal load dispatch. IEEE Trans Syst Man Cybern Part C Appl Rev 2012; 42(5): 641–652. DOI: https://doi.org/10.1109/TSMCC.2012.2190401

Selvakumar AI and Thanushkodi K. A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans Power Syst 2007; 22(1): 42–51. DOI: https://doi.org/10.1109/TPWRS.2006.889132

Park JB, Jeong YW, Shin JR et al. An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE Trans Power Syst 2010; 25(1): 156–166. DOI: https://doi.org/10.1109/TPWRS.2009.2030293

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Published

2017-08-31

How to Cite

Bio-Inspired Bird Swarm Algorithm for Solving Economic Load Dispatch Problems. (2017). International Journal of Communication Systems and Network Technologies, 6(2), 66-84. https://doi.org/10.18486/ijcsnt/6.2.081