A New Hybrid Technique of Cuckoo Search and Harmony Search for Solving Non-smooth Optimal Power Flow Framework

A New Hybrid Technique of Cuckoo Search and Harmony Search for Solving Non-smooth Optimal Power Flow Framework

Aboubakr Khelifi* Saliha Chettih Bachir Bentouati

University of Ammar Teledji Laghouat, Electrical Engineering Department, Laghouat, Algeria

Corresponding Author Email: 
15 March 2018
| |
5 June 2018
| | Citation



In order to improve the search capability of the existing Cuckoo Search (CS) algorithm, an enhanced robust technique is proposed in this paper, called hybrid Cuckoo Search and Harmony Search (CSHS). In CSHS technique, HS incorporates the mutation operator into the Cuckoo Search technique. The proposed technique is applied to solve the highly nonlinear and non-convex optimal power flow (OPF) problem. In this paper, OPF is mathematically formulated as nonlinear multi-objective optimization problem. The developed formulation minimizes simultaneously the conflicting objectives of fuel cost, valve-point effect, emission reduction, voltage profile improvement and voltage stability enhancement subject to system equality and inequality constraints. OPF problem is solved using the proposed CSHS algorithm and tested on standard IEEE 30-bus and IEEE 57-bus with different case studies. The results obtained are compared with the reported literature. The results demonstrate that the proposed algorithm outperforms the original CS and HS and other algorithms available in the literature.


Cuckoo Search, harmony search, optimal power flow, emission, constraints

1. Introduction
2. Optimal Power Flow (OPF)
3. Harmony Search
4. Cuckoo Search
5. Hybrid Harmony Search and Cuckoo Search
6. Application and Results
7. Conclusion

[1] Cain M, O’Neill R, Castillo A. (2012). History of optimal power flow and formulations. FERC Staff Tech Pap, pp. 1–36.

[2] Hinojosa VH, Araya R. (2013). Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems. Appl. Soft Comput. J 13: 3839–3852. https://doi.org/10.1016/j.asoc.2013.05.005

[3] Ghasemi M, Ghavidel S, Akbari E, Vahed AA. (2014). Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos. Energy 73: 340–353. https://doi.org/10.1016/j.energy.2014.06.026

[4] Abaali H, Lamchich MT, Raoufi M. (2007). Average current mode to control the three phase shunt active power filters under distorted and unbalanced Voltage conditions. AMSE Journals, Series 2A 80(2): 68-81. 

[5] Ambriz-Perez H, Acha E, Fuerte-Esquivel CR, De La Torre A. (1998). Incorporation of a UPFC model in an optimal power flow using Newton’s method. IEEE Proc Gener Transm Distrib 145: 336e44. https://doi.org/10.1049/ip-gtd:19981944

[6] Yan X, Quintana VH. (1999). Improving an interior-point-based OPF by dynamic adjustments of step sizes and tolerances. IEEE Trans. Power Syst 14: 709–717. https://doi.org/10.1109/59.761902

[7] Al-Muhawesh TA, Qamber IS. (2008). The established megawatt linear programming-based optimal power flow model applied to the real power 56-bus system in eastern province of Saudi Arabia. Energy 33: 12–21.

[8] Frank S, Steponavice I. (2012). Optimal power flow: A bibliographic survey I. Formulations and Deterministic Methods 3(3): 221–58.

[9] Chettih S, Khiat M, Chaker A. (2009). Var-voltage control by particle swarm optimization (PSO) method-application in the western algerian transmission system. AMSE Journals, Series Modeling A 82(2): 65-79. 

[10] Ben Attous D, Labb Y. (2010). Particle swarm optimisation based optimal power flow for units with non-smooth fuel cost functions. AMSE Journals, Series Modelling A 83(3): 24-37.

[11] Benhamida F, Bendaoud A. (2009). A new formulation of dynamic economic dispatch using a hopfield neural network. AMSE Journals, Series Modelling A 82(2): 33-47. 

[12] Bentouati B, Chettih S, El Sehiemy RA, Wang GG. (2017). Elephant herding optimization for solving non-convex optimal power flow problem. Journal of Electrical and Electronics Engineering 10(1): 1-6.

[13] Bentouati B et al. (2016). Optimal power flow using the moth flam optimizer: A case study of the algerian power system. TELKOMINIKA (1): 3. http://doi.org/10.11591/ijeecs.v1.i3.pp431-445

[14]  Bhattacharya A, Chattopadhyay P. (2011). Application of bio-geography-based optimization to solve different optimal power flow problems. IET Gener Transm Distrib 5(1): 70. https://doi.org/10.1049/iet-gtd.2010.0237

[15] Abaci K, Yamacli V. (2016). Differential search algorithm for solving multi-objective optimal power flow problem. Int. J. Electr. Power Energy Syst. 79: 1–10. https://doi.org/10.1016/j.ijepes.2015.12.021

[16] Bentouati B, et al. (2016). A solution to the optimal power flow using multi-verse optimizer. J. Electrical Systems 12-4 pp. 716-733,

[17] Roy PK, Paul C. (2015). Optimal power flow using krill herd algorithm. Int. Trans. Electr. Energy Syst 25(8): 1397–1419. https://doi.org/10.1002/etep.1888

[18] Deb XYS. (2013). Cuckoo search: recent advances and applications. https://doi.org/10.1007/s00521-013-1367-1

[19] Geem ZW, Kim JH, Logan than GV. (2001). A new heuristic optimization algorithm: Harmony search. Simulation 76(2): 60–68. https://doi.org/10.1177/003754970107600201

[20] Chaib AE, Bouchekara HREH, Mehasni R, Abido MA. (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.

[21] Abou El Ela AA, Abido MA. (2010). Optimal power flow using differential evolution algorithm. Electr. Power Syst. Res 80 (7): 878–885.

[22] Duman S. (2016). Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Comput. Appl. 28(11): 3571-3585.

[23] Niknam T, Narimani MR, Jabbari M, Malekpour AR. (2011). A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy 36(11): 6420–32. https://doi.org/10.1016/j.energy.2011.09.027

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

[25] Bouchekara HREH, Chaib AE, Abido MA, El-Sehiemy RA. (2016). Optimal power flow using an Improved Colliding Bodies Optimization algorithm. Applied Soft Computing 42: 119-131.

[26] Mahdad B, Srairi K. (2016). Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm. Applied Soft Computing 46: 501-522.

[27] Ramesh Kumar A, Premalatha L. (2015). Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. Electrical Power and Energy Systems 73: 393–399. https://doi.org/10.1016/j.ijepes.2015.05.011

[28] Roy R, Jadhav HT. (2015). 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.

[29] Mohamed AAA, Mohamed YS, El-Gaafary AAM, Hemeida AM. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research 142: 190–206.

[30] Ghasemi M, Ghavidel S, Ghanbarian M. (2015). Multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm. Information Sciences.  294: 286-304. https://doi.org/10.1016/j.ins.2014.09.051

[31] El-Fergany AA, Hasanien HM. (2015). Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms. Electric Power Components and Systems 43: 1548–1559. https://doi.org/10.1080/15325008.2015.1041625

[32] Kessel P, Glavitsch H. (1986). Estimating the voltage stability of a power system. IEEE Trans Power Deliv 1: 346–54. https://doi.org/10.1109/TPWRD.1986.4308013

[33] Zimmerman RD, Murillo-Sánchez CE, Thomas RJ. Matpower http://www.pserc.cornell.edu/matpower