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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.12, No.3, Jun. 2020

Single and Multi-Area Optimal Dispatch by Modified Salp Swarm Algorithm

Full Text (PDF, 1841KB), PP.18-26


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Author(s)

Deepak Kumar Sharma, Hari Mohan Dubey, Manjaree Pandit

Index Terms

Salp swarm algorithm;leader and followers;benchmark functions;multi-area economic dispatch

Abstract

This paper presents modified salp swarm algorithm (MSSA) for solution of power system scheduling problems with diverse complexity level. Salp swarm algorithm (SSA) is a recently proposed efficient nature inspired (NI) optimization method inspired by foraging behaviour of salps found in deep ocean. SSA sometimes suffers to stagnation at local minima, to overcome this problem and enhancing searching capability by both exploration and exploitation MSSA is proposed in this paper. MSSA applied and tested on two types of problems. Type one is having five benchmark functions of diverse nature, whereas type two is related with real world problem of power system scheduling of a standard IEEE 114 bus system with 54 thermal units for (i) single area system, (ii) two area system and (iii) three area system. Finally Outcome of simulation results are validated with reported results by other method available in literature. 

Cite This Paper

Deepak Kumar Sharma, Hari Mohan Dubey, Manjaree Pandit, "Single and Multi-Area Optimal Dispatch by Modified Salp Swarm Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.12, No.3, pp.18-26, 2020. DOI: 10.5815/ijisa.2020.03.03

Reference

[1]A. K. Kar, Bio Inspired Computing - A Review of Algorithms and Scope of Applications, Expert Systems with Applications, 2016, 59: 20-32.

[2]A. A. B. Baqais, A Multi-view Comparison of Various Metaheuristic and Soft Computing Algorithms, I.J. Mathematical Sciences and Computing, 2017, l.3 (4):8-19. 

[3]S. Roy, S. Biswas, S. S. Chaudhuri, Nature-Inspired Swarm Intelligence and Its Applications, I.J. Modern Education and Computer Science, 2014, 12: 55-65.

[4]Z. Qu, Q.Yang, Improved Particle Swarm Optimization for Constrained Optimization, I.J. Education and Management Engineering, 2012, 2: 21-28.

[5]R. C. Bansal, Optimization Methods for Electric Power Systems: An Overview, I. J. Emerging Electric Power Systems, 2005, 2(1):1-23.

[6]H.M. Dubey, M. Pandit, B. K. Panigrahi, An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch, Swarm and Evolutionary Computation, 2018, 38: 12-34.

[7]AJ Wood, BF Wollenberg, Power generation operation and control, 2nd ed, Newyork, Wiley, 1996.

[8]B. H. Chowdhury, S, Rahrnan, A Review of recent advances in economic dispatch, IEEE Trans. On Power Systems, 1990, 5(4):1248 - 1259.

[9]N.Sinha, R. Chakrabarti, P. K. Chattopadhyay, Evolutionary Programming Techniques for Economic Load Dispatch, IEEE Trans Evol. Comput. 2003, 7(1):83-93.

[10]N. Noman, H. Iba, Differential evolution for economic load dispatch problems, Electric Power Systems Research, 2008, 78(8):1322-1331.

[11]M. Fesanghary, M.M. Ardehali, A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem, Energy, 2009, 34:757-766.

[12]A. Bhattacharya, P. K. Chattopadhyay, Biogeography-Based Optimization for Different Economic Load Dispatch Problems, IEEE Trans. Power Systems, 2010,25(2):1064-1077.

[13]B. Mahdad, K. Srairi, Solving Practical Economic Dispatch Problems Using Improved Artificial Bee Colony Method, I.J. Intelligent Systems and Applications, 2014, 07: 36-43.

[14]F. Mohammadi, H.Abdi, A modified crow search algorithm (MCSA) for solving economic load dispatch problem, Applied Soft Computing,2018, 71: 51–65.

[15]Hardiansyah, A Novel Hybrid PSO-GSA Method for Non-convex Economic Dispatch Problems, I.J. Information Engineering and Electronic Business, 2013, 5: 1-9. 

[16]H.M. Dubey, M. Pandit, B.K. Panigrahi, A Biologically Inspired Modified Flower pollination algorithm for solving economic load dispatch problems in Modern Power system, Cogn Comp.2015,7(5):594-608.

[17]G. Abbas, J. Gu, U. Farooq,A. Raza, M.U. Asad,  M. E. El-Hawary, Solution of an Economic Dispatch Problem Through Particle Swarm Optimization: A Detailed Survey – Part II, IEEE access,2017,5:24426-24445.

[18]R.R. Shoults, S.K. Chang, S. Helmick, W.M. Grady, A practical approach to unit commitment, economic dispatch and savings allocation for multiple-area pool operation with import/export constraints, IEEE Trans. Power Appar. Syst., 1980, PAS-99, (2): 625–635.

[19]Z. Ouyang, S.M. Shahidehpour, Heuristic multi-area unit commitment with economic dispatch,  IEE Proceedings C – Gener. Trans. Distri., 1991, 138(3):242 – 252. 

[20]D. Streiffert, Multi-Area Economic Dispatch with Tie Line Constraints, IEEE Trans. Power Systems, 1995, 10(4): 1946-1951.

[21]V. R. Pandi, B. K. Panigrahi, M. K. Mallick, Improved Harmony Search for Economic Power Dispatch, IEEE 9th Inter Conf on Hybrid Intelligent Systems, (HIS-2009), DOI: 10.1109/HIS.2009.294.

[22]M. Basu, Artificial bee colony optimization for multi-area economic dispatch, Energy, 2013, 49: 181-187.

[23]S. Vijayaraj, R. K. Santhi, Multi-Area Economic Dispatch Using Flower Pollination Algorithm, IEEE Inter Conf. ICEEOT2016, DOI: 10.1109/ICEEOT.2016.7755541.

[24]T. Jayabarathi, G. Sadasivam, V. Ramachandran, Evolutionary Programming-Based Multiarea Economic Dispatch with Tie Line Constraints, Electric Machines & Power Systems, 2000,28(12): 1165-1176. 

[25]M. Basu, Fast Convergence Evolutionary Programming for Multi-area Economic Dispatch, Electric Power Components and Systems, 2017. DOI: 10.1080/15325008.2017.1376234.

[26]L. Lakshminarasimman, M. Siva, R. Balamurugan, Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem, I. J. Computer Applications ,2017,167(5):19-27.

[27]M. Basu, Teaching learning-based optimization algorithm for multi-area economic dispatch, Energy, 2014, 68:21-28.

[28]J.K. Pattanaik, M. Basu, D.P. Dash, Review on application and comparison of metaheurisctic techniques to multi-area economic dispatch problem, protection and control of modern power systems,2017 2:17, https: //doi.org/ 10.1186 / s41601-017-0049-x.

[29]D. C. Secui, The chaotic global best artificial bee colony algorithm for the multi-area economic/emission dispatch, Energy, 2015, 93(2): 2518-2545.

[30]J. Q. Li, Q. K. Pan, P. Y. Duan, H. Y. Sang, and K. Z. Gao, Solving multi-area environmental/economic dispatch by Pareto-based chemical-reaction optimization algorithm, IEEE/CAA Journal of Automatica Sinica, 2017:1−11. DOI: 10.1109/JAS.2017.7510454

[31]D. C. Secui, Large-scale multi-area economic/emission dispatch based on a new symbiotic organisms search algorithm, Energy conver. Manag. 2017, 154:.203-223.

[32]H. Narimani, S.E. Razavi, A. Azizivahed, E. Naderi, M. Fathi,M. H. Ataei, M. R. Narimani, A multi-objective framework for multi-area economic emission dispatch, Energy,2018, 154:126-142.

[33]C. L. Chen, Optimal generation and reserve dispatch in a multi area competitive market using a hybrid direct search method, Energy Conver. Manag. 2005, 46: 2856-2872.

[34]M. Ghasemi, J.Aghaei, E. Akbari, S. Ghavidel, L. Li, A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems, Energy ,2016, 107: 182-195.

[35]M. Pandit, L.Srivastava, M. Sharma, Performance comparison of enhanced PSO and DE variants for dynamic energy/reserve scheduling in multi-zone electricity market, Applied Soft Computing, 2015,37:619-631.

[36]C. L. Chen, Z.Y. Chen, T.Y. Lee, Multi-area economic generation and reserve dispatch considering large-scale integration of wind power, I. J. Electrical Power & Energy Systems,2014,55:171-178.

[37]M. Doostizadeh, F. Aminifar, H. Lesani, H. Ghasemi, Multi-area market clearing in wind-integrated interconnected power systems: A fast parallel decentralized method, Energy Conversion and Management, 2016, 113:131–142.

[38]Z. Li, M. Shahidehpour, W.Wu, B.Zeng, B. Zhang, W. Zheng, Decentralized Multiarea Robust Generation Unit and Tie-Line Scheduling under Wind Power Uncertainty,  IEEE Trans. on Sustainable Energy,2015,6(4): 1377-1388.

[39]S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, S. M. Mirjalili, Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software, 2017, 114: 163-191.

[40]V.N. Dieu, P. Schegner, Real Power Dispatch on Large Scale Power Systems by Augmented Lagrange Hopfield Network, I. J. Energy Optimization and Engineering, 2012,1(1):19-38.