Power loss minimization of an IEEE 33 bus radial distribution grid using system reconfiguration with genetic algorithm
 
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Northern Technical University, Iraq
 
These authors had equal contribution to this work
 
 
Submission date: 2024-07-22
 
 
Final revision date: 2024-12-02
 
 
Acceptance date: 2024-12-12
 
 
Online publication date: 2025-01-19
 
 
Publication date: 2025-01-19
 
 
Corresponding author
Dalya Ayad AbdelQader   

Northern Technical University, Iraq
 
 
 
KEYWORDS
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ABSTRACT
This study tackles the issue of active and reactive power losses by reconfiguring a radial distribution network and optimizing the placement and sizing of distributed generation (DG) units and capacitors to improve network reliability and voltage management. The reconfiguration process is intricate and nonlinear, requiring the identification of the optimal radial arrangement to meet these objectives. These goals are mathematically formulated and addressed using optimization algorithms. The study explored the combined use of DG and capacitors in the reconfiguration process, finding that this integrated approach yielded better results than using either component alone. The combination of DG and capacitors notably reduced power losses and enhanced voltage profiles, underscoring the effectiveness of their joint deployment in distribution systems. Simulations were conducted on a 33-bus system, and findings were analysed across various scenarios, comparing the system's performance before and after optimization. MATLAB and ETAP were employed to obtain the results.
FUNDING
This research received no external funding.
REFERENCES (30)
1.
Rojas AL et al. Distribution network reconfiguration for voltage stability enhancement via feasibility-preserving evolutionary optimization. in 2018 IEEE Electrical Power and Energy Conference (EPEC). IEEE.2018. http://dx.doi.org/10.1109/EPEC....
 
2.
Onlam A, et al., Power loss minimization and voltage stability improvement in electrical distribution system via network reconfiguration and distributed generation placement using novel adaptive shuffled frogs leaping algorithm. Energies. 2019;12(3):553. https://doi.org/10.3390/en1203....
 
3.
Abdelaziz A, Osama RA, El-Khodary S. Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimisation algorithm. IET generation, transmission & distribution. 2012;6(2):176-187. http://dx.doi.org/10.1049/iet-....
 
4.
Chowdhury A, Koval D. Power distribution system reliability: practical methods and applications. John Wiley & Sons. 2011.
 
5.
Mendoza J, et al., Microgenetic multiobjective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network. IET Generation, Transmission & Distribution. 2009;3(9):825-840. https://doi.org/10.1049/iet-gt....
 
6.
Kayal P, Chanda C. Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement. International Journal of Electrical Power & Energy Systems. 2013;53:795-809. https://doi.org/10.1016/j.ijep....
 
7.
Imran AM, Kowsalya M. A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm. International Journal of Electrical Power & Energy Systems. 2014;62:312-322. https://doi.org/10.1016/j.ijep....
 
8.
Hung DQ, Mithulananthan N, Bansal R. Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability. Applied Energy. 2014;113:1162-1170. https://doi.org/10.1016/j.apen....
 
9.
Alkhayyat MT, Bashi SM. Reduce the impact of voltage sag with phase jumping in AC line using unified power quality conditioners UPQC and open UPQC. in 2019 2nd International Conference on Electrical, Communication, Computer, Power and Control Engineering (ICECCPCE). IEEE. 2019. https://doi.org/10.1109/ICECCP....
 
10.
Nguyen TT, Truong AV. Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm. International Journal of Electrical Power & Energy Systems. 2015;68:233-242. https://doi.org/10.1016/j.ijep....
 
11.
Mam M, Leena G, Saxena N. Distribution network reconfiguration for power loss minimization using bacterial foraging optimization algorithm. International Journal of Engineering and Manufacturing (IJEM). 2016;6(2):18-32. https://doi.org/10.23919/GCEle....
 
12.
Esmaeilian HR, Fadaeinedjad R. Distribution system efficiency improvement using network reconfiguration and capacitor allocation. International Journal of Electrical Power & Energy Systems. 2015;64:457-468. https://doi.org/10.1016/j.ijep....
 
13.
Alonso F, Oliveira DQ, De Souza AZ. Artificial immune systems optimization approach for multiobjective distribution system reconfiguration. IEEE Transactions on Power Systems. 2014;30(2): 840-847. https://doi.org/10.1109/TPWRS.....
 
14.
Carreno EM, Romero R, Padilha-Feltrin A. An efficient codification to solve distribution network reconfiguration for loss reduction problem. IEEE Transactions on Power Systems. 2008;23(4):1542-1551. https://doi.org/10.1109/TPWRS.....
 
15.
Abdelaziz M. Distribution network reconfiguration using a genetic algorithm with varying population size. Electric Power Systems Research. 2017;142: 9-11. https://doi.org/10.1016/j.epsr....
 
16.
Bernardon DP, Garcia VJ, Ferreira ASQ. Canha LN. Multicriteria Distribution Network Reconfiguration Considering Subtransmission Analysis, in IEEE Transactions on Power Delivery. 2010;25(4):2684-2691. https://doi.org/10.1109/TPWRD.....
 
17.
Gupta N, Swarnkar A, Niazi K. Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms. International Journal of Electrical Power & Energy System. 2014;54:664-671. https://doi.org/10.1016/j.ijep....
 
18.
Suliman MY, Al-Khayyat MT. Power flow control in parallel transmission lines based on UPFC. Bulletin of Electrical Engineering and Informatics. 2020;9(5): 1755-1765. https://doi.org/ 10.1109/ICRERA49962.2020.9242790.
 
19.
Rangavalli V, Komma Lavanya D. Analysis of IEEE 33, 34 and 69 Bus Systems using Gauss Seidel. International Journal for Research in Applied Science and Engineering Technology. 2022;10(6):3867-3871. https://doi.org/10.22214/ijras....
 
20.
Abou El Ela A, Abido M,Spea S. Optimal power flow using differential evolution algorithm. Electric Power Systems Research. 2010;80(7):878-885. https://doi.org/10.1016/j.epsr....
 
21.
Srinivas C, et al., Minimization of power loss in distribution system by tap changing transformer using PSO algorithm. IJEER. 2022;10(4):. 1135-1139. https://doi.org/10.1109/TPWRS.....
 
22.
Eldurssi AM, O'Connell RM. A fast nondominated sorting guided genetic algorithm for multi-objective power distribution system reconfiguration problem. IEEE Transactions on Power Systems. 2014;30(2): 593-601. https://doi.org/10.1109/TPWRS.....
 
23.
Jordehi AR. DG allocation and reconfiguration in distribution systems by metaheuristic optimisation algorithms: a comparative analysis. in 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). IEEE. 2018. https://doi.org/10.1109/ISGTEu....
 
24.
Mtonga T, Kaberere KK, Irungu GK. Optimal network reconfiguration for real power losses reduction in the IEEE 33-bus radial distribution system. Authorea Preprints. 2023. https://doi.org/10.1109/JIOT.2....
 
25.
Li M, He P, Zhao L. Dynamic load balancing applying water-filling approach in smart grid systems. IEEE Internet of Things Journal. 2017;4(1):247-257. https://doi.org/10.1109/JIOT.2....
 
26.
Bhumkittipich K, Phuangpornpitak W. Optimal placement and sizing of distributed generation for power loss reduction using particle swarm optimization. Energy Procedia. 2013;34:307-317. https://doi.org/10.1016/j.egyp....
 
27.
Niazi, G, Lalwani M. PSO based optimal distributed generation placement and sizing in power distribution networks: A comprehensive review. in 2017 International Conference on Computer, Communications and Electronics (Comptelix). IEEE. 2017. https://doi.org/10.1109/COMPTE....
 
28.
Pandey D, Bhadoriya JS. Optimal placement & sizing of distributed generation (DG) to minimize active power loss using particle swarm optimization (PSO). International Journal Of Scientific & Technology Research. 2014;3(17). https://doi.org/10.1016/j.egyp....
 
29.
Yousefi M, et al., Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN. International Journal of Energy and Environment. 2013:4.
 
30.
Nayak MR. Optimal feeder reconfiguration of distribution system with distributed generation units using hc-aco. International Journal on Electrical Engineering & Informatics. 2014;6(1). https://doi.org/10.15676/ijeei....
 
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