An Efficient Stud Krill Herd Framework for Solving Non-Convex Economic Dispatch Problem
The problem of economic dispatch (ED) is the basic problem of power framework, its main goal is to find the most favorable generation dispatch to generate each unit, reduce the whole power generation cost, and meet all system limitations. A heuristic algorithm, recently developed called Stud Krill Herd (SKH), has been employed in this paper to treat non-convex ED problems. The proposed KH has been modified using Stud selection and crossover (SSC) operator, to enhance the solution quality and avoid local optima. We are demonstrated SKH effects in two case study systems composed of 13-unit and 40-unit test systems to verify its performance and applicability in solving the ED problems. In the above systems, SKH can successfully obtain the best fuel generator and distribute the load requirements for the online generators. The results showed that the use of the proposed SKH method could reduce the total cost of generation and optimize the fulfillment of the load requirements.
[1] Wood A. J. and Wollenberg B. F., 1996. Power Generation Operation and Control. Wiley, New York, 2nd ed.
[2] Niknam T, Mojarrad HD, Meymand HZ. A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects. Energy Convers Manag 2011;52(4):1800-9.
[3] Adarsh BR, Raghunathan T, Jayabarathi T, Yang X. Economic dispatch using chaotic bat algorithm. Energy 2016; 96:666-75.
[4] Jayabarathi T, Raghunathan T, Adarsh BR, Ponnuthurai Nagaratnam Suganthan. Economic dispatch using hybrid grey wolf optimizer. Energy 2016;111: 630-41.
[5] Cai J, Li Q, Li L, Peng H, Yang Y. A hybrid CPSO-SQP method for economic dispatch considering the valve-point effects. Energy Convers Manag 2012;53: 175-81.
[6] Jeddi B, Vahidinasab V. A modified harmony search method for environmental/economic load dispatch of real-world power systems. Energy Convers Manag 2014;78(2):661-75.
[7] Wang Y, Zhou J, Lu Y, Qin H, Wang Y. Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valvepoint effects. Expert Syst Appl 2011;38:14231-7.
[8] Wang G-G, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363-370. doi:10.1016/j.neucom.2013.08.031
[9] A. H. Gandomi, A. H. Alavi, Krill herd: a new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numer. Simulat. 17 (12) (2012) 4831–4845.
[10] Wong, K., Fung, C.: Simulated annealing based economic dispatch algorithm. In: IEE Proceedings C (Generation, Transmission and Distribution) 1993, pp. 509-515. IET.
[11] He, D., Wang, F., Mao, Z.: A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect. International journal of electrical power & energy systems 30(1), 31-38 (2008).
[12] C. L. Chiang, Genetic-based algorithm for power economic load dispatch, IET Gener. Transm. Distrib., vol. 1, n. 2, March 2007, pp. 261-269.
[13] Özyön, S., Aydin, D.: Incremental artificial bee colony with local search to economic dispatch problem with ramp rate limits and prohibited operating zones. Energy Conversion and Management 65, 397-407 (2013).
[14] C. L. Chen, Non-convex economic dispatch: A direct search approach, Energy Conversion and Management, vol. 48, 2007, pp. 219-225.
[15] A. Y. Abdelaziz, E. S. Ali, and S. M. Abd Elazim, 'Combined Economic and Emission Dispatch Solution Using Flower Pollination Algorithm', IJEPES, Vol. 80, September 2016, pp.264-274.
[16] Hosseinnezhad V, Rafiee M, Ahmadian M, Ameli MT. Species-based quantum particle swarm optimization for economic load dispatch. Int J Electr Power & Energy Syst;63(1):311-22 (2014).
[17] Secui DC. A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Convers Manag 2015;89(1):43e62.
[18] Elsayed WT, El-Saadany EF. A fully decentralized approach for solving the economic dispatch problem. Power Syst IEEE Trans 2014. http://dx.doi.org/ 10.1109/TPWRS.2014.2360369.
[19] Selvakumar AI, Thanushkodi K. Optimization using civilized swarm: solution to economic dispatch with multiple minima. Electr Power Syst Res 2009;79(1):8-16.
[20] A. I. Selvakumar, K. Thanushkodi, Anti-predatory particle swarm optimization: solution to nonconvex economic dispatch problems, Electr. Power Syst. Res. 78, 2–10 (2008).
[21] B. K. Panigrahi, V. R. Pandi, Bacterial foraging optimization: Nelder–Mead hybrid algorithm for economic load dispatch, IET Gener. Transm. Distrib. 2 (4) (2008) 556–565.
[22] Victoire, T. A. A., Jeyakumar, A. E.: Hybrid PSO–SQP for economic dispatch with valve-point effect. Electric Power Systems Research 71(1), 51-59 (2004).
[23] Bhattacharya, A., Chattopadhyay, P. K.: Biogeography-based optimization for different economic load dispatch problems. Power Systems, IEEE Transactions on 25(2), 1064-1077 (2010).
[1] Wood A. J. and Wollenberg B. F., 1996. Power Generation Operation and Control. Wiley, New York, 2nd ed.
[2] Niknam T, Mojarrad HD, Meymand HZ. A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects. Energy Convers Manag 2011;52(4):1800-9.
[3] Adarsh BR, Raghunathan T, Jayabarathi T, Yang X. Economic dispatch using chaotic bat algorithm. Energy 2016; 96:666-75.
[4] Jayabarathi T, Raghunathan T, Adarsh BR, Ponnuthurai Nagaratnam Suganthan. Economic dispatch using hybrid grey wolf optimizer. Energy 2016;111: 630-41.
[5] Cai J, Li Q, Li L, Peng H, Yang Y. A hybrid CPSO-SQP method for economic dispatch considering the valve-point effects. Energy Convers Manag 2012;53: 175-81.
[6] Jeddi B, Vahidinasab V. A modified harmony search method for environmental/economic load dispatch of real-world power systems. Energy Convers Manag 2014;78(2):661-75.
[7] Wang Y, Zhou J, Lu Y, Qin H, Wang Y. Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valvepoint effects. Expert Syst Appl 2011;38:14231-7.
[8] Wang G-G, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363-370. doi:10.1016/j.neucom.2013.08.031
[9] A. H. Gandomi, A. H. Alavi, Krill herd: a new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numer. Simulat. 17 (12) (2012) 4831–4845.
[10] Wong, K., Fung, C.: Simulated annealing based economic dispatch algorithm. In: IEE Proceedings C (Generation, Transmission and Distribution) 1993, pp. 509-515. IET.
[11] He, D., Wang, F., Mao, Z.: A hybrid genetic algorithm approach based on differential evolution for economic dispatch with valve-point effect. International journal of electrical power & energy systems 30(1), 31-38 (2008).
[12] C. L. Chiang, Genetic-based algorithm for power economic load dispatch, IET Gener. Transm. Distrib., vol. 1, n. 2, March 2007, pp. 261-269.
[13] Özyön, S., Aydin, D.: Incremental artificial bee colony with local search to economic dispatch problem with ramp rate limits and prohibited operating zones. Energy Conversion and Management 65, 397-407 (2013).
[14] C. L. Chen, Non-convex economic dispatch: A direct search approach, Energy Conversion and Management, vol. 48, 2007, pp. 219-225.
[15] A. Y. Abdelaziz, E. S. Ali, and S. M. Abd Elazim, 'Combined Economic and Emission Dispatch Solution Using Flower Pollination Algorithm', IJEPES, Vol. 80, September 2016, pp.264-274.
[16] Hosseinnezhad V, Rafiee M, Ahmadian M, Ameli MT. Species-based quantum particle swarm optimization for economic load dispatch. Int J Electr Power & Energy Syst;63(1):311-22 (2014).
[17] Secui DC. A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Convers Manag 2015;89(1):43e62.
[18] Elsayed WT, El-Saadany EF. A fully decentralized approach for solving the economic dispatch problem. Power Syst IEEE Trans 2014. http://dx.doi.org/ 10.1109/TPWRS.2014.2360369.
[19] Selvakumar AI, Thanushkodi K. Optimization using civilized swarm: solution to economic dispatch with multiple minima. Electr Power Syst Res 2009;79(1):8-16.
[20] A. I. Selvakumar, K. Thanushkodi, Anti-predatory particle swarm optimization: solution to nonconvex economic dispatch problems, Electr. Power Syst. Res. 78, 2–10 (2008).
[21] B. K. Panigrahi, V. R. Pandi, Bacterial foraging optimization: Nelder–Mead hybrid algorithm for economic load dispatch, IET Gener. Transm. Distrib. 2 (4) (2008) 556–565.
[22] Victoire, T. A. A., Jeyakumar, A. E.: Hybrid PSO–SQP for economic dispatch with valve-point effect. Electric Power Systems Research 71(1), 51-59 (2004).
[23] Bhattacharya, A., Chattopadhyay, P. K.: Biogeography-based optimization for different economic load dispatch problems. Power Systems, IEEE Transactions on 25(2), 1064-1077 (2010).
@article{"International Journal of Electrical, Electronic and Communication Sciences:76690", author = "Bachir Bentouati and Lakhdar Chaib and Saliha Chettih and Gai-Ge Wang", title = "An Efficient Stud Krill Herd Framework for Solving Non-Convex Economic Dispatch Problem", abstract = "The problem of economic dispatch (ED) is the basic problem of power framework, its main goal is to find the most favorable generation dispatch to generate each unit, reduce the whole power generation cost, and meet all system limitations. A heuristic algorithm, recently developed called Stud Krill Herd (SKH), has been employed in this paper to treat non-convex ED problems. The proposed KH has been modified using Stud selection and crossover (SSC) operator, to enhance the solution quality and avoid local optima. We are demonstrated SKH effects in two case study systems composed of 13-unit and 40-unit test systems to verify its performance and applicability in solving the ED problems. In the above systems, SKH can successfully obtain the best fuel generator and distribute the load requirements for the online generators. The results showed that the use of the proposed SKH method could reduce the total cost of generation and optimize the fulfillment of the load requirements.
", keywords = "Stud Krill Herd, economic dispatch, crossover, stud selection, valve-point effect.", volume = "11", number = "12", pages = "1197-6", }
