A Novel Hybrid Method based on Krill Herd and Cuckoo Search for Optimal Power Flow Problem

Full Text (PDF, 811KB), PP.1-17

Views: 0 Downloads: 0

Author(s)

Aboubakr Khelifi 1,* Bachir Bentouati 1 Saliha Chettih 1 Ragab A. El-Sehiemy 2

1. Electrical Engineering Department, LMSF Laboratory, Amar Telidji University of Laghouat, Algeria

2. Electrical Engineering Department, Faculty of Engineering-Kafrelsheikh University, Egypt

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.09.01

Received: 24 Oct. 2018 / Revised: 19 Jun. 2019 / Accepted: 7 Aug. 2019 / Published: 8 Sep. 2019

Index Terms

Cuckoo search algorithm (CS), krill herd algorithm (KHA), optimal power flow (OPF), voltage stability (VS), valve-point effect, emission reduction

Abstract

Solving the Optimal power flow (OPF) problem is an urgent task for power system operators. It aims at finding the control variables’ optimal scheduling subjected to several operational constraints to achieve certain economic, technical and environmental benefits. The OPF problem is mathematically expressed as a nonlinear optimization problem with contradictory objectives and subordinated to both constraints of equality and inequality. In this work, a new hybrid optimization technique, that integrates the merits of cuckoo search (CS) optimizer, is proposed to ameliorate the krill herd algorithm (KHA)'s poor efficiency. The proposed hybrid CS-KHA has been expanded for solving for single and multi-objective frameworks of the OPF problem through 11 case studies. The studied cases reflect various economic, technical and environmental requirements. These cases involve the following objectives: minimization of non- smooth generating fuel cost with valve-point loading effects, emission reduction, voltage stability enhancement and voltage profile improvement. The CS-KHA presents krill updating (KU) and krill abandoning (KA) operator derived from cuckoo search (CS) amid the procedure when the krill updating in order to extraordinarily improve its adequacy and dependability managing OPF problem. The viability of these improvements is examined on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test system. The experimental results prove the greatest ability of the proposed hybrid meta-heuristic CS-KHA compared to other famous methods.

Cite This Paper

Aboubakr Khelifi, Bachir Bentouati, Saliha Chettih, Ragab A. El-Sehiemy, "A Novel Hybrid Method based on Krill Herd and Cuckoo Search for Optimal Power Flow Problem", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.9, pp. 1-17, 2019. DOI: 10.5815/ijigsp.2019.09.01

Reference

[1]Shaheen, A.M., El-Sehiemy, R.A., Farrag, S.M., "Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm," IET Generation, Transmission and Distribution 2016, 10(7), pp. 1634-1647

[2]Huneault M, Galiana FD. A survey of the optimal power flow literature. IEEE Trans Power Syst 1991; 6(2):762–70.

[3]Chowdhury B, Rahman S. A review of recent advances in economic dispatch. IEEE Trans Power Syst 1990; 5 (4): 1248–59.

[4]T. Niknam, M. Rasoul Narimani, M. Jabbari, A.R. Malekpour, A modified shuffle frog leaping algorithm for multi-objective optimal power flow, Energy 36 (2011) 6420–6432, 

[5]Roa-Sepulveda C, Pavez-Lazo B. A solution to the optimal power flow using simulated annealing. Int J Electri Power Energy Syst 2003; 25:47–57.

[6]L.L. Lai & J.T. Ma. Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Electrical Power and Energy Systems, 19, 287-292, 1997.

[7]S.R. Paranjothi, K. Anburaja, Optimal power flow using refined genetic algorithm, Electr. Power Components Syst. 30 (2002) 1055–1063.

[8]Shaheen, A.M., Farrag, S.M., El-Sehiemy, R.A., "MOPF solution methodology,", IET Generation, Transmission and Distribution 2017 11(2), pp. 570-581

[9]M. Abido, Optimal power flow using Tabu search algorithm, Electric Power Syst. Res. 30 (2002) 469–483.

[10]Ghasemi M, Ghavidel S, Ghanbarian MM, Massrur HR, Gharibzadeh M. Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: a comparative study. Inf Sci (Ny) 2014; 281:225–47.

[11]M. A. Abido, Optimal power flow using particle swarm optimization, Electrical Power and Energy Systems, 24, 563-571, 2002.

[12]A. Ramesh Kumar & L. Premalatha, Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization, Electrical Power and Energy Systems, 73,393–399, 2015.

[13]P.K. Roy, S.P. Ghoshal, S.S. Thakur, Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function, Expert Syst. Appl. 37 (2010) 8221–8228. 

[14]Bhattacharya, A., & Chattopadhyay, P. K. (2011). Application of biogeography-based optimization to solve different optimal power flow problems. IET generation, transmission & distribution, 5(1), 70-80.

[15]El-Sehiemy, R.A., Shafiq, M.B., Azmy, A.M., "Multi-     phase search optimization algorithm for constrained optimal power flow problem," International Journal of Bio-Inspired Computation, 2014, 6(4), pp. 275-289.

[16]R. Roy & 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.

[17]A.R. Bhowmik, A.K. Chakraborty, Solution of optimal power flow using non dominated sorting multi objective opposition based gravitational search algorithm, Int. J. Electr. Power Energy Syst. 64 (2015) 1237–1250.

[18]K. Ayan, U. Kılıc¸, B. Baraklı, Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow, Int. J. Electr. Power Energy Syst. 64 (2015) 136–147. 

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

[20]Bouchekara HREH. Optimal power flow using black-hole-based optimization approach. Appl Soft Comput J 2014; 24:879–88.

[21]Bouchekara HREH, Abido Ma, Boucherma M. Optimal power flow using teaching-learning-based optimization technique. Electr Power Syst Res 2014; 114:49–59.

[22]Attia, A.-F., El Sehiemy, R.A., Hasanien, H.M., "Optimal    power flow solution in power systems using a novel Sine-Cosine algorithm," International Journal of Electrical Power and Energy Systems, 2018, 99, pp. 331-343

[23]Daryani N, Hagh MT, Teimourzadeh S. Adaptive group search optimization algorithm for multi-objective optimal power flow problem. Appl Soft Comput 2016; 38:1012–24.

[24]A. Khelifi, S. Chettih, B. Bentouati, A new hybrid algorithm of particle swarm optimizer with grey wolves’ optimizer for solving optimal power flow problem, Leonardo Electronic J. of P. & Technologies. 2018, 249-270.

[25]M. AlRashidi, M. El-Hawary, Applications of computational intelligence techniques for solving the revived optimal power flow problem, Electr. Power Syst. Res. 79 (2009) 694–702. 

[26]A.H. Gandomi, A.H. Alavi, Krill herd: a new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numer. Simulat. 17 (2012) 4831–4845.

[27]Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H, Chaotic krill herd algorithm. Inf Sci 274 (2014) 17-34.

[28]Bentouati, B., Chettih, S., El-Sehiemy, R., "A chaotic firefly algorithm framework for non-convex economic dispatch problem," Electrotehnica, Electronica, Automatica (EEA), 2018, 66(1), pp. 172-179

[29]Xin-She Yang, Flower Pollination Algorithm for Global Optimization. In: Unconventional Computation and Natural Computation, Vol. 7445, Springer Lecture Notes in Computer Science, Berlin, Heidelberg, 2012, pp. 240–249.

[30]Ghasemia M, Taghizadeh M, Ghavidel S, Abbasian A. Colonial competitive differential evolution: an experimental study for optimal economic load dispatch. Appl Soft Comput 2016; 40:342e63.

[31]B. Mandal, P. Kumar Roy, Multi-objective optimal power flow using quasi-oppositional teaching–learning-based optimization, Appl. Soft Comput. 21(2014) 590–606.

[32]Bouchekara, H. R. E. H., Chaib, A. E., Abido, M. A., & El-Sehiemy, R. A. (2016). Optimal power flow using an Improved Colliding Bodies Optimization algorithm. Applied Soft Computing, 42, 119-131.

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

[34]Bachir Bentouati et al, Optimal Power Flow using the Moth Flam Optimizer: A case study of the Algerian power system, TELKOMINIKA,.1, pp. 3. 2016.

[35]Bentouati, B., Chettih, S., El Sehiemy, R., Wang, G.-G. , "Elephant herding optimization for solving non-convex optimal power flow problem," Journal of Electrical and Electronics Engineering, 201710(1), pp. 31-36 

[36]Bentouati, B., Chettih, S., Djekidel, R., 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, 2017, 33, pp. 84-99 

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

[38]Wang G, Gandomi AH, Alavi AH. An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 2014; 38(9-10):2454–62.

[39]El-Hosseini, M.A., El-Sehiemy, R.A., Haikal, A.Y., "Multi-objective optimization algorithm for secure economical/emission dispatch problems," Journal of Engineering and Applied Science, 2014 61(1), pp. 83-103

[40]Biswas, P. P., Suganthan, P. N., & Amaratunga, G. A. (2017). Optimal power flow solutions incorporating stochastic wind and solar power. Energy Conversion and Management, 148, 1194-1207.

[41]H. R. E. H. Bouchekara, Chaib, A. E., & 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).

[42]Kessel, P., & Glavitsch, H. (1986). Estimating the voltage stability of a power system. IEEE Transactions on Power Delivery, 1(3), 346-354.

[43]R.D. Zimmerman, C.E. Murillo-Sánchez, R.J. Thomas, Matpower (Available at :) http:// www. pserc. cornell. edu/ matpower.