Combined economic and emission dispatch using improved particle swarm optimization
Corressponding author's email:
trungthangttt@gmail.comKeywords:
Improved Particle Swarm optimization, transmission power losses, economic load dispatch, emission dispatch, combined economic and emission dispatchAbstract
The paper presents the application of improved Particle Swarm optimization algorithm (IPSO) for solving combined economic and emission load dispatch (CEED) problems where transmission power losses are considered. The method is developed by modifying the several modifications on the conventional Particle Swarm optimization (CPSO) is aiming to improve the performance of the original one. In the IPSO, one best local particle has been used to generate new solution instead of using the global best paritical like the conventional method. IPSO is tested on two different systems with the transmission power losses. The performance of IPSO is evaluated by comparing obtained results with other existing algorithms available in the study. As a result, it can be concluded that the applied method outperforms others and are very strong for solving the CEED problem.
Downloads: 0
References
D. Kin, M. E. El-Hawary and F. El-Hawary, Optimal Environmental Dispatching of Electric Power Systems via an Improved Hopfield Neural Network Model,IEEE Trans. on Power Sysfems, vol. 10, pp. 1559-1565, 1995.
C. A. Roa-Sepulveda, E. R. Salazar-Nova, E. Gracia-Caroca, U. Knight and A. Coonick, Environmental Economic Dispatch via Hopfield Neural Network and Taboo Search,UPEC’96 Universities Power Engineering Conference, Greece, Crete, pp. 1001-1004, 1996.
Y. H. Song, G. S. Wang, P. Y. Wang and A. T. Johns, Environmental/econoimic dispatch using fuzzy logic controlled genetic algorithm,IEE Proc-Gener. Transm. Distrib, vol. 44, pp. 377-382, 1997.
C. S. Harry, Rughooputh and R. T. F. A. King, Environmental/Economic Dispatch of Thermal Units using an Elitist Multiobjective Evolutionary Algorithm, Industrial Technology 2003 IEEE International Conference in Maribor, Slovenia 1, pp.48-53, 2003.
K. K. Mandal and N. Chakraborty, Effect of Control Parameters on Differential Evolution based Combined Economic Emission Dispatch with Valve-Point Loading and Transmission Loss,International Journal of Emerging Electric Power Systems , vol. 9, pp.1-18, 2008.
A. L. Devi and O. V. Krishna, Combined economic and emission dispatch using evolutionary algorithms-a case study,ARPN J Eng Appl Sci, vol. 3, no. 6, pp.28–35, 2008.
P. K. Roy, S. P. Ghoshal and S. S. Thakur, Combined economic and emission dispatch problems using biogeography-based optimization, Electr Eng , vol. 92, pp.173–184, 2010.
M. Basu, Economic environmental dispatch using multi-objective differential evolution,Applied Soft Computing , vol. 11, pp.2845–2853., 2011.
Elaiwa, X. Xiab and A.M. Shehata, Hybrid DE-SQP and hybrid PSO-SQP methods for solving dynamic economic emission dispatch problem with valve-point effects,Electric Power Systems Research, vol. 103, pp.192–200, 2013.
H. Hamedi, Solving the combined economic load and emission dispatch problems using new heuristic algorithm, Electrical Power and Energy Systems, vol. 46, pp.10–16, 2013.
E. D. Manteaw and N. A. Odero, Combined economic and emission dispatch solution using ABC_PSO hybrid algorithm with valve point loading effect,Int J Sci Res Publ, no. 12, pp.1-9, 2012.
R. Zhang, J. Zhou, L. Mo, S. Ouyang and X. Liao, Economic environmental dispatch using an enhanced multi-objective cultural algorithm,Electr Power Syst Res, vol. 99, pp.18-29, 2012.
N. T. P. Thao and N. T. Thang, Environmental Economic Load Dispatch with Quadratic Fuel Cost Function Using Cuckoo Search Algorithm, International Journal of u- and e- Service,Science and Technology , vol. 7, no. 2, pp.199-210, 2014.
N. T. Thang, Economic emission load dispatch with multiple fuel options using Hopfiled Lagrange Network, International Journal of Advanced Science and Technology, vol. 57, pp.9-24, 2013.
Abdelaziz, E. Ali and S. A. Elazim, Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems,Energy , vol. 101, pp.506-518, 2016.
J. Kennedy and R. Eberhart, Particle swarm optimization,Proc. IEEE Int Conf Neural Networks, pp.1942–8, 1995.
H.Y.Shi and C. R. Eberhar, A modified particle swarm optimizer,IEEE Intl. Conf. on Evolutionary Computation, AK, Anchorage, pp.69-73, 1998.
C. R. Eberhart and H. Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization,Proc. of the IEEE Congress on Evolutionary Computation, USA, no. 1, pp.84–88, 2000.
P. S. Kulkarni, A. G. Kothari and D. P. Kothari, Combined economic and emission dispatch using improved back-propagation neural network,Electric Power Comp Syst, vol. 28, pp.31–44, 2000.
Downloads
Published
How to Cite
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © JTE.
