International Electrical Engineering Journal (IEEJ) Vol. (2016) No. 1, pp. 2136-2147 ISSN 2078-2365 http://www.ieejournal.com/ 2136 Fatma et. al., Solution of Economic Load Dispatch using Recent Swarm-based Meta-heuristic Algorithms: A Survey Abstract- Economic load dispatch (ELD) in the operation of electric power system is an essential task, since it is required to determine the optimal output of electricity generating facilities, supplying the power to meet load demand at minimum cost while satisfying transmission and operational constraints. Several techniques were applied to solve the economic load dispatch problem, both conventional and intelligent methods. Recently, researchers are paying more attention to intelligent techniques such as Swarm- based algorithms and their development in order to be used to successfully solve complicated real life optimization problems. This paper presents a survey on the novel modifications applied to swarm- based algorithms used in solving ELD problems and its variants. Swarm optimization algorithms used in this paper are: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA), Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search Algorithm (CSA), Bat Algorithm (BA) and Grey Wolf Optimization (GWO). Keywords: Economic load dispatch (ELD), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA), Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search Algorithm (CSA), Bat Algorithm (BA) and Grey Wolf Optimization (GWO) I. INTRODUCTION Due to the increase in power demand and continuous rise in fuel costs in the recent years, decreasing the cost of operating and generating electrical power has become a necessity. The main objective of ELD is to meet load demand and reduce total operating costs while satisfying operational constraints of the generation resources available. The variants of the ELD problem include: Combined Heat and Power Economic Dispatch, Emission/Environmental Economic Dispatch and Dynamic Economic Dispatch. In practical, multiple fuel options, valve loading effect, security constraints, Prohibited Operating Zones and Ramp Rate Limit Constraints should be considered in solving the ELD problem [1, 2]. Many researchers have proposed and developed many techniques to solve the ELD problem. Conventional methods like Lambda iteration method and Newton’s method are fast and reliable yet have limitations in finding global optimum. To overcome such limitations, intelligent meta-heuristics methods have been developed. These state of the art algorithms could be categorized based on their inspiration into: Swarm Intelligence (SI), defined as “The emergent collective intelligence of groups of simple agents” by Bonabeau et al, [3] has drawn the attention of many researchers in different fields. SI is based on the mimicking of social behavior exhibited in nature such as: foraging of bees, bird flocking, nest building, fish schooling, hunting and microbial intelligence. The two principles in swarm intelligence are: 1- Self-organization which is based on: activity amplification/ balancing by positive/ negative feedback, random fluctuations and multiple interactions. 2- Stimulation by work which is based on: work being independent on specific individuals and division of labor amongst individuals. • Evolutionary Programming • Genetic Algorithm • Differential Evolution 1) Evolutionary • Particle Swarm Optimization • Ant Colony Optimization • Firefly Algorithm 2) Swarm based • Big Bang Big Crunch • Gravitational Search Algorithm • Simulated Annealing 3) Physics and chemistry based • Flower Pollination Algorithm • Invasive Weed Optimization 4) Nature based Solution of Economic Load Dispatch using Recent Swarm-based Meta-heuristic Algorithms: A Survey Fatma Sayed Moustafa*, N. M. Badra*, Almoataz Y. Abdelaziz** *Department of Engineering Physics and Mathematics, Faculty of Engineering, Ain Shams University, Cairo, Egypt **Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt
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International Electrical Engineering Journal (IEEJ)
Vol. (2016) No. 1, pp. 2136-2147
ISSN 2078-2365
http://www.ieejournal.com/
2136 Fatma et. al., Solution of Economic Load Dispatch using Recent Swarm-based Meta-heuristic Algorithms: A Survey
Abstract- Economic load dispatch (ELD) in the operation of
electric power system is an essential task, since it is required to
determine the optimal output of electricity generating facilities,
supplying the power to meet load demand at minimum cost
while satisfying transmission and operational constraints.
Several techniques were applied to solve the economic load
dispatch problem, both conventional and intelligent methods.
Recently, researchers are paying more attention to intelligent
techniques such as Swarm- based algorithms and their
development in order to be used to successfully solve
complicated real life optimization problems. This paper
presents a survey on the novel modifications applied to swarm-
based algorithms used in solving ELD problems and its
variants. Swarm optimization algorithms used in this paper
are: Ant Colony Optimization (ACO), Particle Swarm
International Electrical Engineering Journal (IEEJ)
Vol. (2016) No. 1, pp. 2136-2147
ISSN 2078-2365
http://www.ieejournal.com/
2146 Fatma et. al., Solution of Economic Load Dispatch using Recent Swarm-based Meta-heuristic Algorithms: A Survey
REFERENCES
[1] Liu, D., and Cai, Y. (2005). Taguchi method for solving the economic dispatch problem with nonsmooth cost functions. Power Systems, IEEE Transactions on, 20(4), 2006-2014.
[2] A. Y. Abdelaziz, S. F. Mekhamer, M. A. L. Badr and M. Z. Kamh, ‘Economic Dispatch Using an Enhanced Hopfield Neural Network’, Electric Power Components and Systems Journal, Vol. 36, No. 7, July 2008, pp. 719-732.
[3] Bonabeau E, Dorigo M, Theraulaz G. Swarm Intelligence: From Natural to Artificial Systems. Journal of Artificial Societies and Social Simulation. 1999; 4: 320.
[4] M. Dorigo and L. M. Gambardella 1992, Ant algorithms for discrete optimization, Artificial Life, Vol. 5 (2), pp. 137–172.
[5] Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A Comprehensive Review of Swarm Optimization Algorithms. PLoS ONE 10(5): e0122827. doi:10.1371/journal.pone.0122827
[6] Ioannis Karakonstantis & Aristidis Vlachos (2015) Ant Colony Optimization for Continuous Domains applied to Emission and Economic Dispatch Problems, Journal of Information and Optimization Sciences, 36:1-2, 23–42 DOI: 10.1080/02522667.2014.932094
[7] Secui D. C. (2015), A method based on the ant colony optimization algorithm for dynamic economic dispatch with valve-point effects, Int. Trans. Electr. Energ. Syst., 25, 262–287, doi: 10.1002/etep.1841
[8] Aristidis Vlachos , Isidoros Petikas & Simos Kyriakides (2011) A Continuous Ant Colony (C-ANT) algorithm solving the Economic Load Dispatch (ELD) Problem, Journal of Information and Optimization Sciences, 32:1, 1-13, DOI: 10.1080/02522667.2011.10700039
[9] Rahmat, N. A., Musirin, I., & Abidin, A. F. (2014). Differential Evolution Immunized Ant Colony Optimization (DEIANT) Technique in Solving Weighted Economic Load Dispatch Problem. Asian Bulletin of Engineering Science and Technology, 1(1), 17-26.
[10] Eberhart, R. C., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science (Vol. 1, pp. 39-43).
[11] Hosseini, H. , Shahbazian, M. , & Takassi, M. A. (2014). The Design of Robust Soft Sensor Using ANFIS Network. Journal of Instrumentation Technology, 2(1), 9-16.
[12] Lin, J., Chen, C. L., Tsai, S. F., & Yuan, C. (2015). New intelligent particle swarm optimization algorithm for solving economic dispatch with valve-point effects. Journal of Marine Science and Technology, 23(1), 44-53.
[13] Basu, M. (2015). Modified particle swarm optimization for nonconvex economic dispatch problems. International Journal of Electrical Power & Energy Systems, 69, 304-312.
[14] Duman, S., Yorukeren, N., & Altas, I. H. (2015). A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect. International Journal of Electrical Power & Energy Systems, 64, 121-135.
[15] Jadoun, V. K., Gupta, N., Niazi, K. R., & Swarnkar, A. (2015). Modulated particle swarm optimization for economic emission dispatch. International Journal of Electrical Power & Energy Systems, 73, 80-88.
[16] Yu, Z., & Zhou, F. (2015). Chaotic Iteration Particle Swarm Optimization Algorithm Based on Economic Load Dispatch. In Intelligent Computing Theories and Methodologies (pp. 567-575). Springer International Publishing.
[17] Prabakaran, S., Senthilkumar, V., & Baskar, G. Economic Dispatch Using Hybrid Particle Swarm Optimization with
Prohibited Operating Zones and Ramp Rate Limit Constraints. J Electr Eng Technol.10(4): 1441-1452
[18] Yousefi, N. (2015). Solving nonconvex economic load dispatch problem using particle swarm optimization with time varying acceleration coefficients. John Wiley & Sons, Ltd, Complexity, http://dx.doi.org/10.1002/cplx.21689
[19] Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. Control Systems, IEEE, 22(3), 52-67.
[20] Das, S., Biswas, A., Dasgupta, S., & Abraham, A. (2009). Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. InFoundations of Computational Intelligence Volume 3 (pp. 23-55). Springer Berlin Heidelberg.
[21] Elattar, E. E. (2015). A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem. International Journal of Electrical Power & Energy Systems, 69, 18-26.
[22] Li, M. S., Hu, Y., & Zhang, X. (2015). Stochastic Economic Dispatch Using Bacterial Swarm Algorithm. International Conference on Power Electronics and Energy Engineering (PEEE 2015)
[23] Eusuff, M.M. and Lansey, K.E., Optimization of water distribution network design using the shuffled frog leaping algorithm (SFLA). J. Water Resources Planning Mgmt, Am. Soc. Civ. Engrs, 2003, 129(3), 210–225.
[24] Afzalan, E., Taghikhani, M. A., & Sedighizadeh, M. (2012). Optimal placement and sizing of dg in radial distribution networks using sfla.International Journal of Energy Engineering, 2(3), 73-77.
[25] Karimzadeh, M. K. (2013). Improved Shuffled Frog Leaping Algorithm for the Combined Heat and Power Economic Dispatch. Volume 2 March 2013.
[26] Narimani, M. R. (2011). A new modified shuffle frog leaping algorithm for non-smooth economic dispatch. World Applied Sciences Journal, 12(6), 803-814.
[27] P. Roy, et al., Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect, Appl. Soft Comput. J. (2013),http://dx.doi.org/10.1016/j.asoc.2013.07.006
[28] Y.N.Vijayakumar, Dr. Sivanagaraju. (2015) Non-Convex Economic Dispatch by Using optimization Techniques. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 4 (2)
[29] Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
[30] Sharma, T. K. (2012). Improved Local Search in Artificial Bee Colony using Golden Section Search. Journal of Engineering, 1(1), 14-19
[31] Secui, D. C. (2015). A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Conversion and Management, 89, 43-62.
[32] Afandi, A. N., & Miyauchi, H. (2014). Improved artificial bee colony algorithm considering harvest season for computing economic dispatch on power system. IEEJ Transactions on Electrical and Electronic Engineering, 9(3), 251-257.
[33] Arunachalam, S., Saranya, R., & Sangeetha, N. (2013). Hybrid Artificial Bee Colony Algorithm and Simulated Annealing Algorithm for Combined Economic and Emission Dispatch Including Valve Point Effect. In Swarm, Evolutionary, and Memetic Computing (pp. 354-365). Springer International Publishing.
International Electrical Engineering Journal (IEEJ)
Vol. (2016) No. 1, pp. 2136-2147
ISSN 2078-2365
http://www.ieejournal.com/
2147 Fatma et. al., Solution of Economic Load Dispatch using Recent Swarm-based Meta-heuristic Algorithms: A Survey
[34] Shayeghi, H., & Ghasemi, A. (2014). A modified artificial bee colony based on chaos theory for solving non-convex emission/economic dispatch. Energy Conversion and Management, 79, 344-354.
[35] Rathinaraj, m. s., and Prakash, p. (2015). Optimization of economic load dispatch problem using artificial bee colony algorithm with dynamic population size. Optimization, 1(4), 13-19.
[36] Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.
[37] A. Y. Abdelaziz, S. F. Mekhamer, M.A.L. Badr, M. A. Algabalawy, 'The Firefly Meta-Heuristic Algorithms: Developments and Applications', International Electrical Engineering Journal (IEEJ), Vol. 6, No. 7, September 2015, pp. 1945-1952.
[38] Solano-Aragón, C., & Castillo, O. (2015). Optimization of Benchmark Mathematical Functions Using the Firefly Algorithm with Dynamic Parameters. In Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics (pp. 81-89). Springer International Publishing.
[40] Jalili, A., Noruzi, A., Yazdani, M., & Mirzayi, M. Solving Economic Load Dispatch With Valve Point Effect Based On Firefly Algorithm. V18(1)
[41] Malini, T. Firefly Algorithm for Solving Economic and Environmental Dispatch considering Security constraint. Special Issue on International Conference on Synergistic Evolutions in Engineering (ICSEE) – 2015.
[42] Maidl, G., de Lucena, D. S., & dos Santos Coelho, L. (2013). Economic dispatch optimization of thermal units based on a modified firefly algorithm. 22nd International Congress of Mechanical Engineering (COBEM 2013)
[43] Loona, M. M., Mehta, M. S., & Prashar, M. S. (2014). A Hybrid Firefly-DE Algorithm For Economic Load Dispatch. International Journal of Research in Advent Technology, 2(8).
[44] Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. InNature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (pp. 210-214). IEEE.
[45] Sharma, R., & Sharma, R. (2015). Improved General Self Cuckoo Search based Routing Protocol for Wireless Sensor Networks. International Journal of Computer Applications, 122(4).
[46] Tran, C. D., Dao, T. T., Vo, V. S., & Nguyen, T. T. (2015). Economic Load Dispatch with Multiple Fuel Options and Valve Point Effect Using Cuckoo Search Algorithm with Different Distributions. International Journal of Hybrid Information Technology, 8(1), 305-316.
[47] K. Chandrasekaran, Sishaj P. Simon & Narayana Prasad Padhy (2014) Cuckoo Search Algorithm for Emission Reliable Economic Multi-objective Dispatch Problem, IETE Journal of Research, 60:2, 128-138
[48] Thao, N. T. P., & Thang, N. T. (2014). Environmental Economic Load Dispatch with Quadratic Fuel Cost Function Using Cuckoo Search Algorithm.International Journal of u-and e-Service, Science and Technology, 7(2), 199-210.
[49] Afzalan, E., and Joorabian, M. (2015), An improved cuckoo search algorithm for power economic load dispatch. Int. Trans. Electr. Energ. Syst., 25, 958–975. doi: 10.1002/etep.1878.
[50] Yang, X. S., & Hossein Gandomi, A. (2012). Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29(5), 464-483.
[51] Ochoa, A., Margain, L., Arreola, J., De Luna, A., Garcia, G., Soto, E., ... & Scarandangotti, V. (2013, December). Improved solution based on Bat Algorithm to Vehicle Routing Problem in a Caravan Range Community. InHybrid Intelligent Systems (HIS), 2013 13th International Conference on (pp. 18-22). IEEE.
[52] Hosseini, S. S. S., Yang, X. S., Gandomi, A. H., & Nemati, A. (2015). Solutions of non-smooth economic dispatch problems by swarm intelligence. In Adaptation and Hybridization in Computational Intelligence (pp. 129-146). Springer International Publishing.
[53] Dao, T. K., Pan, T. S., and Chu, S. C. (2015). Evolved Bat Algorithm for Solving the Economic Load Dispatch Problem. In Genetic and Evolutionary Computing (pp. 109-119). Springer International Publishing.
[54] Reddy, P. S. K., Kumar, P. A., and Vaibhav, G. N. S. (2015). Application of BAT Algorithm for Optimal Power Dispatch. Int J Innov Res Adv Eng 2(2):113–119
[55] Mirjalili, S., Mirjalili, S. M., and Lewis, A. (2014). Grey wolf optimizer.Advances in Engineering Software, 69, 46-61.
[56] Dr.Sudhir Sharma,Shivani Mehta, Nitish Chopra. (2015).Economic Load Dispatch Using Grey Wolf Optimization. Int. Journal of Engineering Research and Applications ,5,(4) 28-132
[57] Wong, L. I., Sulaiman, M. H., and Mohamed, M. R. (2015, August). Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer. In Applied Mechanics and Materials (Vol. 785, pp. 511-515). Trans Tech Publications.
[58] Rama Prabha, D., Krishna Prasad Raju, A., Saikumar, S., Mageshvaran, R., and Narendiranath Babu, T. (2013, March). Application of Bacterial Foraging and Firefly Optimization Algorithm to Economic Load Dispatch Including Valve Point Loading. In Circuits, Power and Computing Technologies (ICCPCT), 2013 International Conference on (pp. 99-106). IEEE.
[59] Saber, A. Y., & Venayagamoorthy, G. K. (2008, September). Economic load dispatch using bacterial foraging technique with particle swarm optimization biased evolution. In Swarm Intelligence Symposium, 2008. SIS 2008. IEEE(pp. 1-8). IEEE.
[60] Gerhardt, E., & Gomes, H. M. (2012, July). Artificial bee colony (ABC) algorithm for engineering optimization problems. In International Conference on Engineering Optimization (pp. 1-11).
[61] Selvi, V., & Umarani, D. R. (2010). Comparative analysis of ant colony and particle swarm optimization techniques. International Journal of Computer Applications (0975–8887), 5(4).
[62] Pal, S. K., Rai, C. S., & Singh, A. P. (2012). Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. International Journal of Intelligent Systems and Applications (IJISA), 4(10), 50.
[63] Yang, X. S., & He, X. (2013). Bat algorithm: literature review and applications. International Journal of Bio-Inspired Computation, 5(3), 141-149.
[64] Yang, X. S., & Deb, S. (2010). Engineering optimisation by cuckoo search.International Journal of Mathematical Modelling and Numerical Optimisation,1(4), 330-343.