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Optimal Power Flow Problem Solution Based on Hybrid Firefly Krill Herd Method

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Abstract:

Optimal Power Flow (OPF) problem is one of the most important and widely studied nonlinear optimization problems in power system operation. This study presents the implementation of a new technology based on the hybrid Firefly and krill herd method (FKH), which has been provided and used for OPF problems in power systems. In FKH, an improved formulation of the attractiveness and adjustment of light intensity operator initially employed in FA, named attractiveness and light intensity the update operator (ALIU), is inserted into the KH approach as a local search perform. The FKH is prove with the solving of the OPF problem for various types of single-objective and multi-objective functions such as generation cost, reduced emission, active power losses and voltage deviation which are optimized simultaneously on exam system, viz the IEEE-30 Bus test system, which is used to test and confirm the efficiency of the proposed FKH technique. By comparing with several optimization techniques, the results produced by using the recommended FKH technique are provided in detail. The results obtained in this study appear that the FKH technique can be efficiency used to solve the non-linear and non-convex problems and high performance compared with other optimization methods in the literature. This study can achieve a minimum objective by finding the optimum setting for system control variables.

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International Journal of Engineering Research in Africa Vol. 44

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Periodical:

International Journal of Engineering Research in Africa (Volume 44)

Pages:

213-228

DOI:

https://doi.org/10.4028/www.scientific.net/JERA.44.213

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Online since:

August 2019

Authors:

Aboubakr Khelifi*, Bentouati Bachir, Chettih Saliha

Keywords:

Active Power Losses, Emission, Firefly (FA), Krill Herd (KH), Optimal Power Flow (OPF)

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© 2019 Trans Tech Publications Ltd. All Rights Reserved

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[1] Momoh JA, Zhu JZ. Improved interior point method for OPF problems. IEEE Trans Power Syst 1999; 14(3):1114–20.

DOI: 10.1109/59.780938

[2] B. Bentouati, S. Chettih, Djekidel, R. and El-sehiemy, R. A. An Efficient Chaotic Cuckoo Search Framework for Solving non-Convex Optimal Power Flow Problem, International Journal of Engineering Research in Africa. 33, 84–99 (2017).

DOI: 10.4028/www.scientific.net/jera.33.84

[3] B. Bentouati, S. Chettih, and El-sehiemy, R. A. A Chaotic Krill Herd Algorithm for Optimal Solution of the Economic Dispatch Problem, International Journal of Engineering Research in Africa. 31, 155–168 (2017).

DOI: 10.4028/www.scientific.net/jera.31.155

[4] B. Bentouati. L. Chaib and S. Chettih, A hybrid whale algorithm and pattern search technique for optimal power flow problem, 8th International Conference on Modelling, Identification and Control (ICMIC), Algiérs, (IEEE), pp.1048-1053 (2016).

DOI: 10.1109/icmic.2016.7804267

[5] P.K. Roy, C. Paul, Optimal power flow using krill herd algorithm, Int. Trans. Electr. Energy Syst. 25 (8) (2015) 1397–1419.

DOI: 10.1002/etep.1888

[6] M.R. Adaryani, A. Karami, Artificial bee colony algorithm for solving multi-objective optimal power flow problem, Int. J. Electr. Power Energy Syst.53 (2013) 219–230.

DOI: 10.1016/j.ijepes.2013.04.021

[7] H.R.E.H. Bouchekara, A.E. Chaib, M.A. Abido, R.A. El-Sehiemy, Optimal power flow using an improved colliding bodies optimization algorithm, Appl. Soft Comput. 42 (2016) 119–131.

DOI: 10.1016/j.asoc.2016.01.041

[8] K. Abaci, V. Yamacli, Differential search algorithm for solving multi-objective optimal power flow problem, Int. J. Electr. Power Energy Syst. 79 (2016) 1–10.

DOI: 10.1016/j.ijepes.2015.12.021

[9] A.M. Shaheen, R.A. El-Sehiemy, S.M. Farrag, Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm, IETGener. Transm. Distrib. (2016).

DOI: 10.1049/iet-gtd.2015.0892

[10] M. Ghasemi, S. Ghavidel, M. Gitizadeh, E. Akbari, An improved teaching-learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow, Int. J. Electr. Power Energy Syst.65 (2015) 375–384.

DOI: 10.1016/j.ijepes.2014.10.027

[11] T. Niknam, M.R. Narimani, R. Azizipanah-Abarghooee, A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect, Energy Convers. Manage. 58 (2012) 197–206.

DOI: 10.1016/j.enconman.2012.01.017

[12] M.R. Narimani, R. Azizipanah-Abarghooee, B.Zoghdar-Moghadam-Shahrekohne, K. Gholami, A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type, Energy 49 (2013)119–136.

DOI: 10.1016/j.energy.2012.09.031

[13] Rao SB, Vaisakh K. Application of ACSA for solving multi-objective optimal power flow problem with load uncertainty. IEEE international conference on emerging trends in computing, communication and nanotechnology; 2013. P.764–71.

DOI: 10.1109/ice-ccn.2013.6528607

[14] Hazra J, Sinha AK. Environmental constrained economic dispatch using bacterial foraging optimization. IEEE Power India Conf 2008:1–6.

DOI: 10.1109/icpst.2008.4745330

[15] M. Ghasemi, S. Ghavidel, S. Rahmani, A. Roosta, H. Falah, A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions, Eng. Appl. Artif. Intell. 29 (2014) 54–69.

DOI: 10.1016/j.engappai.2013.11.003

[16] Mohamed, A. A. A., Mohamed, Y. S., El-Gaafary, A. A., and Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142, 190-206.

DOI: 10.1016/j.epsr.2016.09.025

[17] Chaib, A. E., Bouchekara, H. R. E. H., Mehasni, R., and Abido, M. A. (2016). Optimal power flow with emission and non - smooth cost functions using backtracking search optimization algorithm. International Journal of Electrical Power and Energy Systems, 81, 64-77.

DOI: 10.1016/j.ijepes.2016.02.004

[18] A. Bhattacharya and PK. Chattopadhyay, Application of biogeography-based optimization to solve different optimal power flow problems, IET Generation, Transmission and Distribution, 5(1), 70–80, (2011).

DOI: 10.1049/iet-gtd.2010.0237

[19] R. Roy and H.T. Jadhav, Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm, Electrical Power and Energy Systems, 64, 562–578, (2015).

DOI: 10.1016/j.ijepes.2014.07.010

[20] Gandomi, A. H., and Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845.

DOI: 10.1016/j.cnsns.2012.05.010

[21] Yang, X. S. (2010b). Firefly algorithm, stochastic test functions and design optimization. International Journal of Bio-inspired Computation, 2(2), 78–84.

DOI: 10.1504/IJBIC.2010.032124

[22] Yang, X.-S., Sadat Hosseini, S. S., and Gandomi, A. H. (2012). Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing, 12(3), 1180–1186.

DOI: 10.1016/j.asoc.2011.09.017

[23] Wang G-G, Gandomi, A. H., Alavi, A. H. and, Yong, (2014) A Hybrid Meta-Heuristic Method Based on Firefly Algorithm and Krill Herd.

[24] Basu, M., Multi-objective optimal power flow with FACTS,, Energy Convers. Manag, Vol. 52, pp. (2011): 903–910.

DOI: 10.1016/j.enconman.2010.08.017

[25] H. R. E. H. Bouchekara, Chaib, A. E., and Abido, M. A. Optimal power flow using GA with a new multi-parent crossover considering: prohibited zones, valve-point effect, multi-fuels and emission. Electr Eng (2016).

DOI: 10.1007/s00202-016-0488-9

[26] L. Dilip, R. Bhesdadiya, P. Jangir, Optimal Power Flow Problem Solution Using Multi-objective Grey Wolf Optimizer Algorithm, Springer Nature Singapore, Pte. Ltd. (2018).

DOI: 10.1007/978-981-10-5523-2_18

[27] M. Ghasemi, S. Ghavidel, M.M. Ghanbarian, M. Gitizadeh, A.A. Vahed. Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm. Energy (2014) 1-14.

DOI: 10.1016/j.energy.2014.10.007

[28] G. Chen, X. Yi, Z. Zhang, H. Wang. Applications of multi-objective dimension-based firefly algorithm to optimize the power losses, emission, and cost in power systems. Applied Soft Computing. (2018).

DOI: 10.1016/j.asoc.2018.04.006

[29] Kumar, A. R., and Premalatha, L. (2015). Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. International Journal of Electrical Power and Energy Systems, 73, 393-399.

DOI: 10.1016/j.ijepes.2015.05.011

[30] Partha P. Biswas, P.N. Suganthan, R. Mallipeddi, Gehan A.J. Amaratunga, Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques, Elsevier, Engineering Applications of Artificial Intelligence 68 (2018), p.81–100.

DOI: 10.1016/j.engappai.2017.10.019

[31] Bachir Bentouati, Saliha Chettih, Pradeep Jangir, Indrajit N. Trivedi, A solution to the optimal power flow using multi-verse optimizer, Journal of Electrical Systems, 2016, pp.716-733.