Solution of Multi Objective Environmental Economic Dispatch by Grey Wolf Optimization Algorithm
DOI:
https://doi.org/10.18201/ijisae.2019151250Keywords:
Grey wolf optimization, Economic dispatch, Environmental economic dispatch, nonconvex emission, Loss minimization.Abstract
This paper presents the recently developed Grey Wolf Optimization (GWO) algorithm, which is based on the food collecting behavior of grey wolves to determining the feasible optimal solution of the multi objective environmental economic dispatch (MOEED) problem. Nonlinear characteristics of alternators and exponential emissions and loss minimization are considered in the problem. While searching for a better solution, GWO does not require any statistics about the gradient of the objective function. The GWO algorithm effectiveness has been tested on four different systems as 6-unit (IEEE 30-bus), 10-unit, 11-unit and 14-unit (IEEE 118-bus) test systems to solve the MOEED problems. The result of the test systems shows, for practical power systems GWO as a better option to solve the MOEED problems. Both the optimality of the solution to test systems and the convergence speed of the GWO algorithm are promising.
Downloads
References
. A.Hima Bindu, Dr. M. Damodar Reddy, “Economic Load Dispatch Using Cuckoo Search Algorithm”, IJERA Vol. 3, Issue 4, Jul-Aug 2013, pp. 498- 502.
. Xin-She Yang, Amir, “Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect”, Applied Soft Computing 12 (2012) 1180–1186.
. Ching-Tzong Su, Chien-Tung Lin, “New Approach with a Hopfield Modeling Framework to Economic Dispatch”, IEEE transactions on power systems, vol. 15, no. 2, may 2000.
. Ganga Reddy Tanksala, “artificial bee colony optimization for economic load dispatch of a modern power system”, International Journal of Scientific & Engineering Research, Volume 3, Issue 1, January-2012.
. P. Subbaraj, “Enhancement of Self-adaptive real-coded genetic algorithm using Taguchi method for economic dispatch problem”, Applied Soft Computing (2011) 83–92.
. Ling Wang, Ling-po Li, “An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems”, Electrical Power and Energy Systems 44 (2013) 832–843.
. Mostafa Modiri-Delshad, Nasrudin Abd Rahim, “Solving non-convex economic dispatch problem via backtracking search algorithm”, Energy 77 (2014) 372e381.
. Dinu Calin Secui, “A new modified artificial bee colony algorithm for the economic dispatch problem”, Energy Conversion and Management 89 (2015) 43–62.
. M. S. P. Subathra, “A Hybrid With Cross-Entropy Method and Sequential Quadratic Programming to Solve Economic Load Dispatch Problem”, 1932-8184 © 2014 IEEE.
. Jiejin CAI, Qiong Li, “A hybrid CPSO–SQP method for economic dispatch considering
the valve-point effects”, Energy Conversion and Management 53 (2012) 175–181.
. Tianyu Liu, Licheng Jiao, “Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch”, Applied Soft Computing 48 (2016) 597–611.
. H. Shayeghi, A. Ghasemi, “A modified artificial bee colony based on chaos theory for solving non-convex emission/economic dispatch”, E 79 (2014) 344–354.
. Yun-Chia Liang, “A normalization method for solving the combined economic and emission dispatch problem with meta-heuristic algorithms”, Electrical Power and Energy Systems 54 (2014) 163–186.
. U. Güvenç, “Combined economic and emission dispatch solution using
gravitational search algorithm”, Scientia Iranica D (2012) 19 (6), 1754–1762.
. Samir Sayah, “Efficient hybrid optimization approach for emission constrained economic dispatch with nonsmooth cost curves”, Electrical Power and Energy Systems 56 (2014) 127–139.
. Mostafa Modiri-Delshad, Nasrudin Abd Rahim, “Multi-objective Backtracking Search Algorithm for Economic Emission Dispatch Problem”, Applied soft computing.
. L.H. Wu, “Environmental/economic power dispatch problem using multi-objective differential evolution algorithm”, EPS Research 80 (2010) 1171–1181.
. Provas Kumar Roy, “Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem”, Electrical Power and Energy Systems 53 (2013) 937–948.
. D. Nelson, “Glowworm swarm optimization algorithm with topsis for solving multiple objective environmental economic dispatch problems”, APC 23 (2014) 375–386.
. Abd Allah A. Mousa, “Hybrid ant optimization system for multiobjective economic emission load dispatch problem under fuzziness”, Swarm and Evolutionary Computation 18 (2014) 11–21.
. Dexuan Zou, Steven Li, “A new global particle swarm optimization for the economic emission dispatch with or without transmission losses”, Energy Conversion and Management 139 (2017) 45–70.
. A.Y. Abdelaziz, E.S. Ali, “Implementation of flower pollination algorithm for solving economic load dispatch and combined EED problems in power systems”, energy 101 (2016) 506-518.
. Seyedali Mirjalili, Andrew Lewis, “Grey Wolf Optimizer”, Advances in Engineering Software 69 (2014) 46–61.
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
