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

#### 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

#### Full Text:

PDF#### 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

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

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

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

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

M. A. Abido, “Multiobjective particle swarm optimization for environmental/economic dispatch problem”, Electrical Power Systems Research, Vol. 79, No. 7, pp. 1105-1113, 2009

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

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

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

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

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

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

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

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

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

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

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

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

M. Basu, “Economic environmental dispatch using multi-objective differential evolution”, Applied Soft Computing, Vol. 11, No. 2, pp. 2845-2853, 2011

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

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

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

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

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

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

B. R. Adarsh, T. Raghunathan, T. Jayabarathi, X. S. Yang, “Economic dispatch using chaotic bat algorithm”, Energy, Vol. 96, pp. 666–675, 2016

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

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

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

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

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

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

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

eISSN: 1792-8036 pISSN: 2241-4487