Environmental Economic Dispatch with the use of Particle Swarm Optimization Technique based on Space Reduction Strategy

Authors

  • Τ. M. Kumar Department of Electrical and Electronics Engineering, SIMATS, India
  • Ν. A. Singh Bharat Sanchar Nigam Limited, India
Volume: 9 | Issue: 5 | Pages: 4605-4611 | October 2019 | https://doi.org/10.48084/etasr.2969

Abstract

This paper introduces a professional edition of Particle Swarm Optimization (PSO) technique, intending to address the Environmental Economic Dispatch problem of thermal electric power units. Space Reduction (SR) strategy based PSO is proposed, in order to obtain the Pareto optimal solution in the prescribed search space, by enhancing the speed of the optimization process. PSO is a natural algorithm, which can be used in a wide area of engineering issues. Many papers have illustrated different techniques that solve various types of dispatch problems, with numerous pollutants as constraints. Search SR strategy is applied to PSO algorithm in order to increase the particles’ moving behavior, by using effectively the search space, and thus increasing the convergence rate, so as to attain the Pareto optimal solution. The validation of SR-PSO algorithm is demonstrated, through its application on an Indian system with 6 generators and three IEEE systems with 30, 57 and 118 buses respectively, for variable load demands. The minimum fuel cost and least emission solutions are achieved by examining various load conditions.

Keywords:

search space reduction, Particle Swarm Optimization (PSO), Environmental/Economic Dispatch (EED) problem, Pareto optimal solution

Downloads

Download data is not yet available.

References

V. K. Jadoun, N. Gupta, K. R. Niazi, A. Swarnkar, “Modulated particle swarm optimization for economic emission dispatch”, International Journal of Electrical Power and Energy Systems, Vol. 73, pp. 80-88, 2015 DOI: https://doi.org/10.1016/j.ijepes.2015.04.004

L. Wang, C. Singh, “Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm”, Electrical Power Systems Research, Vol. 77, No. 12, pp. 1654-1664, 2007 DOI: https://doi.org/10.1016/j.epsr.2006.11.012

M. A. Abido, “Environmental/Economic power dispatch using multiobjective evolutionary algorithms”, 2003 IEEE Power Engineering Society General Meeting, Toronto, Canada, July 13-17, 2003

D. Aydin, S. Ozyon, C. Yasar, T. Liao, “Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem”, International Journal of Electrical Power and Energy Systems, Vol. 54, pp. 144-153, 2014 DOI: https://doi.org/10.1016/j.ijepes.2013.06.020

P. K. Hota, A. K. Barisal, R. Chakrabarti, “Economic emission load diapatch through fuzzy based bacterial foraging algorithm”, International Journal of Electrical Power and Energy Systems, Vol. 32, No. 7, pp. 794-803, 2010 DOI: https://doi.org/10.1016/j.ijepes.2010.01.016

D. W. Gong, Y. Zhang, C. L. Qi, “Environmental/economic power dispatch using a hybrid multi-objective optimization algorithm”, International Journal of Electrical Power and Energy Systems, Vol. 32, No. 6, pp. 607-614, 2010 DOI: https://doi.org/10.1016/j.ijepes.2009.11.017

M. A. Abido, “Multiobjective particle swarm optimization for environmental/economic dispatch problem”, Electrical Power Systems Research, Vol. 79, No. 7, pp. 1105-1113, 2009 DOI: https://doi.org/10.1016/j.epsr.2009.02.005

A. Y. Abdelaziz, E. S. Ali, S. M. Abd Elazim, “Combined economic and emission dispatch solution using Flower Pollination Algorithm”, International Journal of Electrical Power and Energy Systems, Vol. 80, pp. 264-274, 2016 DOI: https://doi.org/10.1016/j.ijepes.2015.11.093

L. Benasla, A. Belmadani, M. Rahli, “Spiral Optimization Algorithm for solving Combined Economic and Emission Dispatch”, International Journal of Electrical Power and Energy Systems, Vol. 62, pp. 163-174, 2014 DOI: https://doi.org/10.1016/j.ijepes.2014.04.037

L. H. Wu, Y. N. Wang, X. F. Yuvan, S. W. Zhou, “Environmental/economic power dispatch problem using multi-objective differential evolution algorithm”, Electrical Power Systems Research, Vol. 80, No. 9, pp. 1171-1181, 2010 DOI: https://doi.org/10.1016/j.epsr.2010.03.010

L. Wang, C. Singh, “Stochastic economic emission load dispatch through a modified particle swarm optimization algorithm”, Electrical Power Systems Research, Vol. 78, pp. 1466-1476, 2008 DOI: https://doi.org/10.1016/j.epsr.2008.01.012

M. A. Abido, “A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch”, International Journal of Electrical Power and Energy Systems, Vol. 25, No. 2, pp. 97-105, 2003 DOI: https://doi.org/10.1016/S0142-0615(02)00027-3

A. Y. Abdelaziz, E. S. Ali, S. M. Abd Elazim,“Flower pollination algorithm to solve combined economic and emission dispatch problems”, Engineering Science and Technology, an International Journal, Vol. 19, No. 2, pp. 980-990, 2016 DOI: https://doi.org/10.1016/j.jestch.2015.11.005

F. Chen, G. H. Huang, Y. R. Fan, R. F. Liao, “A nonlinear fractional programming approach for environmental-economic power dispatch”, International Journal of Electrical Power and Energy Systems, Vol. 78, pp. 463-469, 2016 DOI: https://doi.org/10.1016/j.ijepes.2015.11.118

S. Dhanalakshmi, S. Kannan, K. Mahadevan, S. Baskar, “Application of modified NSGA-II algorithm to Combined Economic and Emission Dispatch problem”, International Journal of Electrical Power and Energy Systems, Vol. 33, No. 9, pp. 992-1002, 2011 DOI: https://doi.org/10.1016/j.ijepes.2011.01.014

A. A. Abou El Ela, M. A. Abido, S. R. Spea, “Differential evolution algorithm for emission constrained economic power dispatch problem”, Electric Power Systems Research, Vol. 80, No. 10, pp. 1286-1292, 2010 DOI: https://doi.org/10.1016/j.epsr.2010.04.011

T. Niknam, H. D. Mojarrad, B. B. Firouzi, “A new optimization algorithm for multi-objective Economic/Emission Dispatch”, International Journal of Electrical Power and Energy Systems, Vol. 46, pp. 283-293, 2013 DOI: https://doi.org/10.1016/j.ijepes.2012.10.001

Y. Zhang, D. W. Gong, Z. Ding, “A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch”, Information Sciences, Vol. 192, pp. 213-227, 2012 DOI: https://doi.org/10.1016/j.ins.2011.06.004

M. Modiri-Delshad, N. Abd Rahim, “Multi-objective backtracking search algorithm for economic emission dispatch problem”, Applied Soft Computing, Vol. 40, pp. 476-494, 2016 DOI: https://doi.org/10.1016/j.asoc.2015.11.020

M. Basu, “Economic environmental dispatch using multi-objective differential evolution”, Applied Soft Computing, Vol. 11, No. 2, pp. 2845-2853, 2011 DOI: https://doi.org/10.1016/j.asoc.2010.11.014

S. P. Karthikeyan, K. Palanichami, C. Rani, I. J. Raglend, D. P. Kothari, “Security Constrained Unit Commitment Problem with Operational, Power Flow and Environmental Constraints”, WSEAS Transactions on Power Systems, Vol.4, pp. 53-66, 2009

B. Hadji, B. Mahdad, K. Srairi, N. Mancer, “Multi-objective PSO-TVAC for Environmental/Economic Dispatch Problem”, Energy Procedia, Vol. 74, pp. 102-111, 2015 DOI: https://doi.org/10.1016/j.egypro.2015.07.529

J. Cai, X. Ma, Q. Li, L. Li, H. Peng, “A multi-objective chaotic ant swarm optimization for environmental/economic dispatch”, International Journal of Electrical Power and Energy Systems, Vol. 32, No. 5, pp. 337-344, 2010 DOI: https://doi.org/10.1016/j.ijepes.2010.01.006

L. Bayon, J. M. Grau, M. .M. Ruiz, P. M. Suarez, “The exact solution of the environmental/economic dispatch problem”, IEEE Transactions on Power Systems, Vol 27, No. 2, pp. 723-731, 2012 DOI: https://doi.org/10.1109/TPWRS.2011.2179952

B. Y. Qu, Y. S. Zhu, Y. C. Jiao, M. Y. Wu, P. N. Suganthan, J. J. Liang, “A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems”, Swarm and Evolutionary Computation, Vol. 38, pp. 1-11, 2018 DOI: https://doi.org/10.1016/j.swevo.2017.06.002

W. T. Elsayed, Y. G. Hegazy, M. S. El-bages, F. M. Bendary, “Improved random drift particle swarm optimization with self-adaptive mechanism for solving the power economic dispatch problem”, IEEE Transactions on Industrial Informatics, Vol. 13, No. 3, pp. 1017–1026, 2017 DOI: https://doi.org/10.1109/TII.2017.2695122

Q. Qin, S. Cheng, X. Chu, X. Lei, Y. Shi, “Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization”, Applied Soft Computing, Vol. 59, pp. 229–242, 2017 DOI: https://doi.org/10.1016/j.asoc.2017.05.034

B. R. Adarsh, T. Raghunathan, T. Jayabarathi, X. S. Yang, “Economic dispatch using chaotic bat algorithm”, Energy, Vol. 96, pp. 666–675, 2016 DOI: https://doi.org/10.1016/j.energy.2015.12.096

D. Zou, S. Li, G. G. Wang, Z. Li, H. Ouyang, “An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects”, Applied Energy, Vol. 181, pp. 375–390, 2016 DOI: https://doi.org/10.1016/j.apenergy.2016.08.067

M. P. Wachowiak, M. C. Timson, D. J. Du Val, “Adaptive particle swarm optimization with heterogeneous multicore parallelism and GPU acceleration”, IEEE Transactions on Parallel Distributed Systems, Vol. 28, No. 10, pp. 2784–2793, 2017 DOI: https://doi.org/10.1109/TPDS.2017.2687461

Y V. K. Reddy, M D. Reddy, “Solution of Multi Objective Environmental Economic Dispatch by Grey Wolf Optimization Algorithm”, International Journal of Intelligent Systems and Applications, Vol. 7, No. 1, pp. 34-41, 2019 DOI: https://doi.org/10.18201/ijisae.2019151250

M. Jevtic, N. Jovanovic, J. Radosavljevic, D. Klimenta, “Moth swarm algorithm for solving combined economic and emission dispatch problem”, Elektronika ir Elektrotechnika, Vol. 23, No. 5, pp. 21-28, 2017 DOI: https://doi.org/10.5755/j01.eie.23.5.19267

H. Wang, J. H. Yi, “An improved optimization method based on krill herd and artificial bee colony with information exchange”, Memetic Computing, Vol. 10, No. 2, pp. 177-198, 2018 DOI: https://doi.org/10.1007/s12293-017-0241-6

M. Neyestani, M. Hatami, S. Hesari, “Combined heat and power economic dispatch problem using advanced modified particle swarm optimization”, Journal of Renewable and Sustainable Energy, Vol. 11, No. 1, 2019 DOI: https://doi.org/10.1063/1.5048833

X. Chen, B. Xu, C. Mei, Y. Ding, K. Li, “Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation”, Applied Energy, Vol. 212, pp. 1578–1588, 2018 DOI: https://doi.org/10.1016/j.apenergy.2017.12.115

Downloads

How to Cite

[1]
Kumar Τ. M. and Singh Ν. A., “Environmental Economic Dispatch with the use of Particle Swarm Optimization Technique based on Space Reduction Strategy”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 5, pp. 4605–4611, Oct. 2019.

Metrics

Abstract Views: 360
PDF Downloads: 336

Metrics Information
Bookmark and Share