A hybrid invasive weed optimization algorithm for the economic load dispatch problem in power systems

Zhi-xin Zheng; Jun-qing Li; Hong-yan Sang; Zhi-xin Zheng; Jun-qing Li; Hong-yan Sang

doi:10.3934/mbe.2019138

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 4: 2775-2794. doi: 10.3934/mbe.2019138

Previous Article Next Article

Research article Special Issues

A hybrid invasive weed optimization algorithm for the economic load dispatch problem in power systems

1.
College of Computer Science, Liaocheng University, Liaocheng 252059, China
2.
School of information science and engineering, Shandong Normal University, 250014, China
3.
China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189, China

Received: 27 January 2019 Accepted: 15 March 2019 Published: 29 March 2019

In this study, a hybrid invasive weed optimization (HIWO) algorithm that hybridizes the invasive weed optimization (IWO) algorithm and genetic algorithm (GA) has been proposed to solve economic dispatch (ED) problems in power systems. In the proposed algorithm, the IWO algorithm is used as the main optimizer to explore the solution space, whereas the crossover and mutation operations of the GA are developed to significantly improve the optimization ability of IWO. In addition, an effective repair method is embedded in the proposed algorithm to repair infeasible solutions by handing various practical constraints of ED problems. To verify the optimization performance of the proposed algorithm and the effectiveness of the repair method, six ED problems in the different-scale power systems were tested and compared with other algorithms proposed in the literature. The experimental results indicated that the proposed HIWO algorithm can obtain the more economical dispatch solutions, and the proposed repair method can effectively repair each infeasible dispatch solution to a feasible solution. The convergence capability, applicability and effectiveness of HIWO were also demonstrated through the comprehensive comparison results.
- economic dispatch,
- hybrid invasive weed optimization,
- crossover operation,
- mutation operation,
- power system
Citation: Zhi-xin Zheng, Jun-qing Li, Hong-yan Sang. A hybrid invasive weed optimization algorithm for the economic load dispatch problem in power systems[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2775-2794. doi: 10.3934/mbe.2019138

Related Papers:

Abstract

In this study, a hybrid invasive weed optimization (HIWO) algorithm that hybridizes the invasive weed optimization (IWO) algorithm and genetic algorithm (GA) has been proposed to solve economic dispatch (ED) problems in power systems. In the proposed algorithm, the IWO algorithm is used as the main optimizer to explore the solution space, whereas the crossover and mutation operations of the GA are developed to significantly improve the optimization ability of IWO. In addition, an effective repair method is embedded in the proposed algorithm to repair infeasible solutions by handing various practical constraints of ED problems. To verify the optimization performance of the proposed algorithm and the effectiveness of the repair method, six ED problems in the different-scale power systems were tested and compared with other algorithms proposed in the literature. The experimental results indicated that the proposed HIWO algorithm can obtain the more economical dispatch solutions, and the proposed repair method can effectively repair each infeasible dispatch solution to a feasible solution. The convergence capability, applicability and effectiveness of HIWO were also demonstrated through the comprehensive comparison results.

References

[1]	Z. X. Liang and J. D. Glover, A zoom feature for a dynamic programming solution to economic dispatch including transmission losses, IEEE T. Power Syst., 7 (1992), 544–550.
[2]	G. Xiong and D. Shi, Orthogonal learning competitive swarm optimizer for economic dispatch problems, Appl. Soft. Comput. J., 66 (2018), 134–148.
[3]	R. A. Jabr, A. H. Coonick and B. J. Cory, A homogeneous linear programming algorithm for the security constrained economic dispatch problem, IEEE T. Power Syst., 15 (2000), 930–936.
[4]	S. Muralidharan, K. Srikrishna and S. Subramanian, Self-adaptive dynamic programming technique for economic power dispatch, Int. J. Power Energy Syst., 27 (2007), 340–345.
[5]	N. Sinha, R. Chakrabarti and P. K. Chattopadhyay, evolutionary programming techniques for economic load dispatch, IEEE T. Evol. Comput., 7 (2003), 83–94.
[6]	J. Q. Li, H. Y. Sang, Y. Y. Han, et al., Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions, J. Clean. Prod., 181 (2018), 584–598.
[7]	J. Q. Li, S. C. Bai, P. Y. Duan, et al., An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system, Int. J. Prod. Res., (2019).
[8]	J. Q. Li, Q. K. Pan and K. Mao, A discrete teaching-learning-based optimisation algorithm for realistic flowshop rescheduling problems, Eng. Appl. Artif. Intell., 37 (2015), 279–292.
[9]	J. Q. Li, P. Y. Duan, H. Y. Sang, et al., An efficient optimization algorithm for resource-constrained steelmaking scheduling problems, IEEE Access, 6 (2018), 33883–33894.
[10]	J. Q. Li, P. Y. Duan, J. D. Cao, et al., A hybrid Pareto-based tabu search for the distributed flexible job shop scheduling problem with E/T criteria, IEEE Access, 6 (2018), 58883–58897.
[11]	J. Q. Li, Q. K. Pan and M. F. Tasgetiren, A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities, Appl. Math. Model., 38 (2014), 1111–1132.
[12]	J. Q. Li, Q. K. Pan and S. X. Xie, An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems, Appl. Math. Comput., 218 (2012), 9353–9371.
[13]	J. Q. Li, Q. K. Pan and K. Z. Gao, Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems, Int. J. Adv. Manuf. Technol., 55 (2011), 1159–1169.
[14]	J. Q. Li, A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in Cloud computing system, Cluster Comput., (2019), 1–24.
[15]	P. Y. Duan, J. Q. Li, Y. Wang, et al., Solving chiller loading optimization problems using an improved teaching‐learning‐based optimization algorithm, Optim, Control Appl. Met., 39 (2018), 65–77.
[16]	Z. X. Zheng, J. Q. Li and P.Y. Duan, Optimal chiller loading by improved artificial fish swarm algorithm for energy saving, Math. Comput. Simul., 155 (2019), 227–243.
[17]	Z. X. Zheng and J. Q. Li, Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption, Energy Build, 161(2018), 80–88.
[18]	S. Kumar and R. Naresh, Nonconvex economic load dispatch using an efficient real-coded genetic algorithm, Appl. Soft Comput. J., 9 (2009), 321–329.
[19]	P. Subbaraj, R. Rengaraj and S. Salivahanan, Enhancement of Self-adaptive real-coded genetic algorithm using Taguchi method for Economic dispatch problem, Appl. Soft Comput. J., 11 (2011), 83–92.
[20]	S. O. Orero and M. R. Irving, Large scale unit commitment using a hybrid genetic algorithm, Int. J. Electr. Power Energy Syst., 19 (1997), 45–55.
[21]	P. Chen and H. Chang, Large-scale economic dispatch by genetic algorithm, IEEE T. Power Syst., 10 (1995), 1919–1926.
[22]	A. I. Selvakumar, A new particle swarm optimization solution to nonconvex economic dispatch problems, IEEE T. Power Syst., 22 (2007), 42–51.
[23]	J. B. Park, Y. W. Jeong, J. R. Shin, et al., An improved particle swarm optimization for nonconvex economic dispatch problems, IEEE T. Power Syst., 25 (2010), 156–166.
[24]	Z. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE T. Power Syst., 18 (2003), 1187–1195.
[25]	K. T. Chaturvedi, M. Pandit and L. Srivastava, Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch, IEEE T. Power Syst., 23 (2008), 1079–1087.
[26]	Q. Qin, S. Cheng, X. Chu, et al., Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization, Appl. Soft Comput. J., 59 (2017), 229–242.
[27]	X. S. Yang, S. S. S. Hosseini and A. H. Gandomi, Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect, Appl. Soft Comput. J., 12 (2012), 1180–1186.
[28]	K. Bhattacharjee, A. Bhattacharya and S. H. N. Dey, Oppositional real coded chemical reaction optimization for different economic dispatch problems, Int. J. Electr. Power Energy Syst., 55 (2014), 378–391.
[29]	A. S. Reddy and K. Vaisakh, Shuffled differential evolution for large scale economic dispatch, Electr. Power Syst. Res., 96 (2013), 237–245.
[30]	D. Zou, S. Li, G. G. Wang, et al., An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects, Appl. Energy, 181 (2016), 375–390.
[31]	B. R. Adarsh, T. Raghunathan, T. Jayabarathi, et al., Economic dispatch using chaotic bat algorithm, Energy, 96 (2016), 666–675.
[32]	A. K. Barisal and R. C. Prusty, Large scale economic dispatch of power systems using oppositional invasive weed optimization, Appl. Soft Comput. J., 29 (2015), 122–137.
[33]	S. Banerjee, D. Maity and C. K. Chanda, Teaching learning based optimization for economic load dispatch problem considering valve point loading effect, Int. J. Electr. Power Energy Syst., 73 (2015), 456–464.
[34]	M. A. Al-Betar, M. A. Awadallah, A. T. Khader, et al., Tournament-based harmony search algorithm for non-convex economic load dispatch problem, Appl. Soft Comput. J., 47 (2016), 449–459.
[35]	M. Pradhan, P. K. Roy and T. Pal, Grey wolf optimization applied to economic load dispatch problems, Int. J. Electr. Power Energy Syst., 83 (2016), 325–334.
[36]	T. Jayabarathi, T. Raghunathan, B. R. Adarsh, et al., Economic dispatch using hybrid grey wolf optimizer, Energy, 111 (2016), 630–641.
[37]	M. Kumar and J. S. Dhillon, Hybrid artificial algae algorithm for economic load dispatch, Appl. Soft Comput., 71 (2018), 89–109.
[38]	M. Modiri-Delshad and N. A. Rahim, Solving non-convex economic dispatch problem via backtracking search algorithm, Energy, 77 (2014), 372–381.
[39]	J. J. Q. Yu and V. O. K. Li, A social spider algorithm for solving the non-convex economic load dispatch problem, Neurocomputing, 171 (2016), 955–965.
[40]	A. I. Selvakumar and K. Thanushkodi, Optimization using civilized swarm: Solution to economic dispatch with multiple minima, Electr. Power Syst. Res., 79 (2009), 8–16.
[41]	M. Basu, Kinetic gas molecule optimization for nonconvex economic dispatch problem, Int. J. Electr. Power Energy Syst., 80 (2016), 325–332.
[42]	J. Cai, Q. Li, L. Li, et al., A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects, Energy, 38 (2012), 346–353.
[43]	J. S. Alsumait, J. K. Sykulski and A. K. Al-Othman, A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems, Appl. Energy, 87 (2010), 1773–1781.
[44]	J. Cai, Q. Li, L. Li, et al., A hybrid CPSO-SQP method for economic dispatch considering the valve-point effects, Energy Convers. Manag., 53 (2012), 175–181.
[45]	S. Sayah and A. Hamouda, A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems, Appl. Soft Comput. J., 13 (2013), 1608–1619.
[46]	A. R. Mehrabian and C. Lucas, A novel numerical optimization algorithm inspired from weed colonization, Ecol. Inform., 1 (2006), 355–366.
[47]	T. A. A. Victoire and A. E. Jeyakumar, Reserve constrained dynamic dispatch of units with valve-point effects, IEEE T. Power Syst., 20 (2005), 1273–1282.
[48]	T. Niknam and F. Golestaneh, Enhanced adaptive particle swarm optimisation algorithm for dynamic economic dispatch of units considering valve-point effects and ramp rates, IET Gener. Transm. Distrib., 6 (2012), 424–435.
[49]	M. A. Elhameed and A. A. El-Fergany, Water cycle algorithm-based economic dispatcher for sequential and simultaneous objectives including practical constraints, Appl. Soft Comput. J., 58 (2017), 145–154.
[50]	E. Afzalan and M. Joorabian, An improved cuckoo search algorithm for power economic load dispatch, Int. Trans. Electr. Energy Syst., 25 (2015), 958–975.
[51]	Y. Labbi, D. B. Attous, H. A. Gabbar, et al., A new rooted tree optimization algorithm for economic dispatch with valve-point effect, Int. J. Electr. Power Energy Syst., 79 (2016), 298–311.
[52]	N. Ghorbani and E. Babaei, Exchange market algorithm for economic load dispatch, Int. J. Electr. Power Energy Syst., 75 (2016), 19–27.

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)