TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Information and Optimization Sciences
Open Access Research Article

An optimal power flow problem incorporating stochastic wind and solar power through the modified competitive swarm optimization algorithm

* ,

* Corresponding author · click or hover a name for details

pp. 545–560Vol. 45Issue 2March 2024DOI: 10.47974/JIOS-1592XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1592
Pages:
545–560

Abstract

Increasing power demand day by day is the primary concern for power system practitioners and researchers. Generating a large amount of power through thermal power plants is neither economical nor environmentally friendly. The cost of fuel (Coal) is growing gradually as the coal reserves are depleting exponentially. Moreover, the government is imposing strict environmental guidelines on power-generating utilities. All these circumstances restrict the generation of electricity through fossil fuel-based plants. It is of most importance to develop for an alternative to fulfill consumers’ economic and clean energy demand. Based on it, this paper explains minimization fuel cost along with emission, power loss and voltage deviation. Modified competitive swarm optimization (MCSO) algorithm is used to determine optimization. The results obtained from the modified competitive swarm optimization algorithm are compared with the competitive swarm optimization (CSO) algorithm and successive history-based adaptive differential evolution (SHADE-SF) algorithm to verify the efficiency of modified competitive swarm optimization. The work is executed on a standard IEEE 30 bus system, and MATLAB is used as a simulation platform.

Keywords

Fuel costEmissionPower lossVoltage deviationOptimizationHybrid power system

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] Key World Energy Statistics, International Energy Agency (2019).
[2] A. Kadir, Y. Volkan, Differential search algorithm for solving multi-objective optimal power flow problem, Int. J. Electr. Power Energy Syst. 79, 1–10 (2016).
[3] B. Kar, K.K. Mandal, D. Pal, N. Chakraborty, combined economic and emission dispatch by ANN with backprop algorithm using variant learning rate & momentum coefficient, in: 2005 International Power Engineering Conference, pp. 1–235 (2005).
[4] K. Chandrasekaran, S.P. Simon, NP. Padhy, Cuckoo search algorithm for emission reliable economic multi-objective dispatch problem, IETE J Res. 6,0 128–138 (2014).
[5] B. Ramesh, V. Chandra, V.C. Reddy, Application of bat algorithm for combined economic load and emission dispatch, J Electr Eng. 13, 214-219 (2013).
[6] J.P. Zhan, Q.H. Wu, C.X. Guo, XX. Zhou, Fast lambda-iteration method for economic dispatch, IEEE Trans Power Syst. 29, 990-991 (2014).
[7] K. Chandrasekaran, S.P. Simon, Firefly algorithm for reliable/emission/economic dispatch multi objective problem, Int Rev Electr Eng-Iree. 7, 3414–3425 (2012).
[8] A.Y. Abdelaziz, E.S. Ali, SM. Abd Elazim, Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems, Energy. 101, 506–518 (2016).    
[9] A.A. Abou El Ela, M.A. Abido, S.R. Spea, Differential evolution algorithm for emission constrained economic power dispatch problem, Electr Power Syst Res, 80, 1286–1292 (2010).
[10] U. Güvenç, Y. Sönmez, S. Duman, N. Yörükeren, combined economic and emission dispatch solution using gravitational search algorithm, Sci Iran, 19, 1754-1762 (2012).
[11] A. Bhattacharya, P.K. Chattopadhyay, Application of Biogeography-based Optimization for Solving Multi-objective Economic Emission Load Dispatch Problems, Electr Power Compon Syst, 38, 340–365 (2010).
[12] I. Karakonstantis, A. Vlachos, Ant colony optimization for continuous domains applied to emission and economic dispatch problems, J Inform Optim Sci. 36, 23–42 (2015).
[13] A. Panda, M. Tripathy, Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm, Energy. 93, 816–827 (2015).
[14] J.P. Zhan, Q.H. Wu, C.X. Guo, XX. Zhou, Fast lambda-iteration method for economic dispatch, IEEE Trans Power Syst. 29, 990-991 (2014).
[15] P.K. Singhal, R. Naresh, V. Sharma, N. Goutham Kumar, Enhanced lambda iteration algorithm for the solution of large-scale economic dispatch problem, In: Recent Adv Innov Eng (ICRAIE), IEEE, pp : 1–6 (2014).
[16] R. Cheng, Y. Jin, A competitive swarm optimizer for large scale optimization problems. IEEE Trans. 45 (2), 191–204 (2014).
[17] P. Mohapatra, K.N. Das, S. Roy, R. Kumar, A. Kumar, CSO Technique for Solving the Economic Dispatch Problem Considering the Environmental Constraints, Asian Journal of Water, Environment and Pollution. 16(2), 43-50 (2019).

Views: 142Downloads: 81Citations: 0