### Eagle Strategy based Crow Search Algorithm for solving Unit Commitment Problem

#### Abstract

Eagle strategy is a two-stage optimization strategy, which is inspired by the observation of the hunting behavior of eagles in nature. In this two-stage strategy, the first stage explores the search space globally by using a Levy flight; if it finds a promising solution, then an intensive local search is employed using a more efficient local optimizer, such as hillclimbing and the downhill simplex method. Then, the two-stage process starts again with new global exploration, followed by a local search in a new region. One of the remarkable advantages of such a combina-tion is to use a balanced tradeoff between global search (which is generally slow) and a rapid local search. The crow search algorithm (CSA) is a recently developed metaheuristic search algorithm inspired by the intelligent behavior of crows.This research article integrates the crow search algorithm as a local optimizer of Eagle strategy to solve unit commitment (UC) problem. The Unit commitment problem (UCP) is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. There are many constraints in unit commitment problem such as spinning reserve, minimum up/down, crew, must run and fuel constraints. The proposed strategy ES-CSA is tested on 10 to 100 unit systems with a 24-h scheduling horizon. The effectiveness of the proposed strategy is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by reported numerical results, it has been found that proposed strategy yields global results for the solution of the unit commitment problem.

#### Keywords

#### Full Text:

PDF#### References

A. J. Wood and B. F. Wollenberg, Power generation, operation, and control. John Wiley & Sons, 2012.

R. Burns and C. Gibson, “Optimization of priority lists for a unit commitment program,” in IEEE Transactions on Power Apparatus and Systems, vol. 94, no. 6. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 345 E 47TH ST, NEW YORK, NY 10017-2394, 1975, pp. 1917–1917.

G. B. Sheble, “Solution of the unit commitment problem by the method of unit periods,” IEEE Transactions on Power Systems, vol. 5, no. 1, pp. 257–260, 1990.

C. Tseng, “On power system generation unit commitment problems.” 1998.

W. L. Snyder, H. D. Powell, and J. C. Rayburn, “Dynamic programming approach to unit commitment,” IEEE Transactions on Power Systems, vol. 2, no. 2, pp. 339–348, 1987.

Z. Ouyang and S. Shahidehpour, “An intelligent dynamic programming for unit commitment application,” IEEE Transactions on Power Systems, vol. 6, no. 3, pp. 1203–1209, 1991.

F. Zhuang and F. D. Galiana, “Towards a more rigorous and practical unit commitment by lagrangian relaxation,” IEEE Transactions on Power Systems, vol. 3, no. 2, pp. 763–773, 1988.

A. I. Cohen and M. Yoshimura, “A branch-and-bound algorithm for unit commitment,” IEEE Transactions on Power Apparatus and Systems, no. 2, pp. 444–451, 1983.

J. A. Muckstadt and R. C. Wilson, “An application of mixed-integer programming duality to scheduling thermal generating systems,” IEEE Transactions on Power Apparatus and Systems, no. 12, 1968.

S. A. Kazarlis, A. Bakirtzis, and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE transactions on power systems, vol. 11, no. 1, pp. 83–92, 1996

K. Juste, H. Kita, E. Tanaka, and J. Hasegawa, “An evolutionary pro-gramming solution to the unit commitment problem,” IEEE Transactions on Power Systems, vol. 14, no. 4, pp. 1452–1459, 1999.

F. Zhuang and F. Galiana, “Unit commitment by simulated annealing,” IEEE Transactions on Power Systems, vol. 5, no. 1, pp. 311–318, 1990.

B. Zhao, C. Guo, B. Bai, and Y. Cao, “An improved particle swarm optimization algorithm for unit commitment,” International Journal of Electrical Power & Energy Systems, vol. 28, no. 7, pp. 482–490, 2006.

X.-S. Yang and S. Deb, “Eagle strategy using levy´ walk and firefly algorithms for stochastic optimization,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Springer, 2010, pp. 101– 111.

X.-S. Yang, S. Deb, and X. He, “Eagle strategy with flower algorithm,” in Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on. IEEE, 2013, pp. 1213–1217.

X.-S. Yang and S. Deb, “Two-stage eagle strategy with differential evolution,” International Journal of Bio-Inspired Computation, vol. 4, no. 1, pp. 1–5, 2012.

A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm,” Computers & Structures, vol. 169, pp. 1–12, 2016.

Y.-W. Jeong, W.-N. Lee, H.-H. Kim, J.-B. Park, and J.-R. Shin, “Thermal unit commitment using binary differential evolution,” Journal of Elec-trical Engineering and Technology, vol. 4, no. 3, pp. 323–329, 2009.

S. Patra, S. Goswami, and B. Goswami, “A binary differential evolution algorithm for transmission and voltage constrained unit commitment,” in Power System Technology and IEEE Power India Conference, 2008. POWERCON 2008. Joint International Conference on. IEEE, 2008, pp. 1–8.

S. Patra, S. Goswami, and B. Goswami, “Differential evolution algorithm for solving unit commitment with ramp constraints,” Electric power components and systems, vol. 36, no. 8, pp. 771–787, 2008.

A. S¸. Uyar, B. Turkay,¨ and A. Keles¸, “A novel differential evolution application to short-term electrical power generation scheduling,” Inter-national Journal of Electrical Power & Energy Systems, vol. 33, no. 6, pp. 1236–1242, 2011

X. Yuan, A. Su, H. Nie, Y. Yuan, and L. Wang, “Application of enhanced discrete differential evolution approach to unit commitment problem,” Energy Conversion and Management, vol. 50, no. 9, pp. 2449–2456, 2009.

X. Yuan, A. Su, H. Nie, Y. Yuan, and L. Wang, “Unit commitment problem using enhanced particle swarm optimization algorithm,” Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol. 15, no. 1, pp. 139–148, 2011.

A. Askarzadeh, “Electrical power generation by an optimised au-tonomous pv/wind/tidal/battery system,” IET Renewable Power Gener-ation, 2016.

D. Oliva, S. Hinojosa, E. Cuevas, G. Pajares, O. Avalos, and J. Galvez,´ “Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm,” Expert Systems with Applications, vol. 79, pp. 164–180, 2017.

C.-P. Cheng, C.-W. Liu, and C.-C. Liu, “Unit commitment by lagrangian relaxation and genetic algorithms,” IEEE transactions on power systems, vol. 15, no. 2, pp. 707–714, 2000.

W. Ongsakul and N. Petcharaks, “Unit commitment by enhanced adaptive lagrangian relaxation,” IEEE Transactions on Power Systems, vol. 19, no. 1, pp. 620–628, 2004.

T. Senjyu, H. Yamashiro, K. Uezato, and T. Funabashi, “A unit commit-ment problem by using genetic algorithm based on unit characteristic classification,” in Power Engineering Society Winter Meeting, 2002. IEEE, vol. 1. IEEE, 2002, pp. 58–63.

I. G. Damousis, A. G. Bakirtzis, and P. S. Dokopoulos, “A solution to the unit-commitment problem using integer-coded genetic algorithm,” IEEE Transactions on Power systems, vol. 19, no. 2, pp. 1165–1172, 2004.

D. Srinivasan and J. Chazelas, “A priority list-based evolutionary algo-rithm to solve large scale unit commitment problem,” in Power System Technology, 2004. PowerCon 2004. 2004 International Conference on, vol. 2. IEEE, 2004, pp. 1746–1751.

I. C. Silva, S. Carneiro, E. J. de Oliveira, J. Pereira, P. A. Garcia, and A. L. Marcato, “A lagrangian multiplier based sensitive index to determine the unit commitment of thermal units,” International Journal of Electrical Power & Energy Systems, vol. 30, no. 9, pp. 504–510, 2008.

K. Chandram, N. Subrahmanyam, and M. Sydulu, “Unit commitment by improved pre-prepared power demand table and muller method,” International Journal of Electrical Power & Energy Systems, vol. 33, no. 1, pp. 106–114, 2011.

Y.-W. Jeong, J.-B. Park, S.-H. Jang, and K. Y. Lee, “A new quantum-inspired binary pso: application to unit commitment problems for power systems,” IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1486– 1495, 2010.

T. Lau, C. Chung, K. Wong, T. Chung, and S. Ho, “Quantum-inspired evolutionary algorithm approach for unit commitment,” IEEE Transac-tions on Power Systems, vol. 24, no. 3, pp. 1503–1512, 2009.

C. Chung, H. Yu, and K. P. Wong, “An advanced quantum-inspired evolutionary algorithm for unit commitment,” IEEE Transactions on Power Systems, vol. 26, no. 2, pp. 847–854, 2011.

J. Ebrahimi, S. H. Hosseinian, and G. B. Gharehpetian, “Unit com-mitment problem solution using shuffled frog leaping algorithm,” IEEE Transactions on Power Systems, vol. 26, no. 2, pp. 573–581, 2011.

M. M. Hadji and B. Vahidi, “A solution to the unit commitment problem using imperialistic competition algorithm,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 117–124, 2012.

DOI: http://doi.org/10.11591/ijeecs.v12.i1.pp%25p

### Refbacks

- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.