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Design and development of a software module for optimal reconfiguration of distribution network in the context of GIS and DIgSILENT software using Python programming language
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Saleh Saeidi1   , Ali Asghar Ghadimi *2 , Mohammad Reza Miveh3 |
1- Azad University of arak
2- Arak University
3- Tafresh University |
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Abstract: (327 Views) |
The configuration of distribution networks is done by changing the structure of distribution networks through opening and closing switches and connecting maneuver points in order to improve the indicators of distribution network such as reducing losses, voltage balance, and increasing penetration of distributed generation sources. With the increasing presence of distributed generation sources in distribution networks, issues such as urban development, adding point loads, creating critical conditions in distribution network, periodic repairs of network and construction of new substation in distribution network, the structure of distribution networks need more flexibility to be able to respond to these changes. In some situations, continuous changes of load and output of distributed generation sources cause the network parameters to go out of their standard range, therefore, there is a need for optimal reconfiguration of network with great importance. Dynamic reconfiguration of network is the change of structural topology of distribution network by distribution operator with changing the status of switches automatically or manually. Therefore, the aim of this paper is to design a practical software module in GIS platform to determine the optimal configuration of network at desired times of operators for improving losses. To achieve this goal, the information available in GIS is extracted by a new toolbox. Then, with the help of designing a new module, the extracted information is converted and imported to DIgSILENT software. In the next step, with the help of the new designed module, the optimal configuration of network is done. Finally, the switches determined for reconfiguration are visible by another module in GIS.
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| Keywords: Optimal reconfiguration, Loss reduction, GIS |
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Type of Study: Applicable |
Received: 2024/02/15 | Accepted: 2024/10/6 | Published: 2025/04/6
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Phung, "A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network," International Journal of Electrical Power & Energy Systems, vol. 78, pp. 801-815, 2016. [ DOI:10.1016/j.ijepes.2015.12.030] 13. [13] A. Abdelaziz, S. Mekhamer, F. Mohammed, and M. Badr, "A modified Particle Swarm Technique for distribution systems reconfiguration," Online Journal on Electronics and Electrical Engineering (OJEEE) Vol.(1)-No.(2), 2010. 14. [14] H. R. Esmaeilian and R. Fadaeinedjad, "Energy Loss Minimization in Distribution Systems Utilizing an Enhanced Reconfiguration Method Integrating Distributed Generation," IEEE Systems Journal, vol. 9, no. 4, pp. 1430-1439, 2015. [ DOI:10.1109/JSYST.2014.2341579] 15. [15] D.-L. Duan, X.-D. Ling, X.-Y. Wu, and B. Zhong, "Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm," International Journal of Electrical Power & Energy Systems, vol. 64, pp. 88-95, 2015. [ DOI:10.1016/j.ijepes.2014.07.036] 16. [16] M. Esmaeili, M. Sedighizadeh, and M. Esmaili, "Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty," Energy, vol. 103, pp. 86-99, 2016. [ DOI:10.1016/j.energy.2016.02.152] 17. [17] M. H. Kakueinejad, A. Heydari, M. Askari, and F. Keynia, "Optimal planning for the development of power system in respect to distributed generations based on the binary dragonfly algorithm," Applied Sciences, vol. 10, no. 14, p. 4795, 2020. [ DOI:10.3390/app10144795] 18. [1] P. Ushashree and K. S. Kumar, "Power System Reconfiguration in Distribution System for Loss Minimization Using Optimization Techniques: A Review," Wireless Personal Communications, vol. 128, no. 3, pp. 1907-1940, 2023. [ DOI:10.1007/s11277-022-10026-3] 19. [2] R. Fathi, B. Tousi, and S. Galvani, "Allocation of renewable resources with radial distribution network reconfiguration using improved salp swarm algorithm," Applied Soft Computing, vol. 132, p. 109828, 2023. [ DOI:10.1016/j.asoc.2022.109828] 20. [3] S. Mishra, D. Das, and S. Paul, "A comprehensive review on power distribution network reconfiguration," Energy Systems, vol. 8, pp. 227-284, 2017. [ DOI:10.1007/s12667-016-0195-7] 21. [4] M. Mahdavi, H. H. Alhelou, N. D. Hatziargyriou, and F. Jurado, "Reconfiguration of electric power distribution systems: Comprehensive review and classification," IEEE Access, vol. 9, pp. 118502-118527, 2021. [ DOI:10.1109/ACCESS.2021.3107475] 22. [5] A. Mishra, M. Tripathy, and P. Ray, "A survey on different techniques for distribution network reconfiguration," Journal of Engineering Research, 2023. 23. [6] A. Zidan, R. S. Al-Abri, and E. F. El-Saadany, "Load model effect on distributed generation allocation and feeders' reconfiguration in unbalanced distribution systems," in GCC Conference and Exhibition (GCCCE), 2015 IEEE 8th, 2015, pp. 1-5: IEEE. [ DOI:10.1109/IEEEGCC.2015.7060021] 24. [7] H. Zhai, M. Yang, B. Chen, and N. Kang, "Dynamic reconfiguration of three-phase unbalanced distribution networks," International Journal of Electrical Power & Energy Systems, vol. 99, pp. 1-10, 2018. [ DOI:10.1016/j.ijepes.2017.12.027] 25. [8] S. R. Tuladhar, J. G. Singh, and W. Ongsakul, "Multi-objective approach for distribution network reconfiguration with optimal DG power factor using NSPSO," IET Generation, Transmission & Distribution, vol. 10, no. 12, pp. 2842-2851, 2016. [ DOI:10.1049/iet-gtd.2015.0587] 26. [9] A. M. Tahboub, V. R. Pandi, and H. Zeineldin, "Distribution system reconfiguration for annual energy loss reduction considering variable distributed generation profiles," IEEE Transactions on power delivery, vol. 30, no. 4, pp. 1677-1685, 2015. [ DOI:10.1109/TPWRD.2015.2424916] 27. [10] J. Shukla, B. Das, and V. Pant, "Stability constrained optimal distribution system reconfiguration considering uncertainties in correlated loads and distributed generations," International Journal of Electrical Power & Energy Systems, vol. 99, pp. 121-133, 2018. [ DOI:10.1016/j.ijepes.2018.01.010] 28. [11] N. G. Paterakis et al., "Multi-objective reconfiguration of radial distribution systems using reliability indices," IEEE Transactions on Power Systems, vol. 31, no. 2, pp. 1048-1062, 2016. [ DOI:10.1109/TPWRS.2015.2425801] 29. [12] T. T. Nguyen, A. V. Truong, and T. A. Phung, "A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network," International Journal of Electrical Power & Energy Systems, vol. 78, pp. 801-815, 2016. [ DOI:10.1016/j.ijepes.2015.12.030] 30. [13] A. Abdelaziz, S. Mekhamer, F. Mohammed, and M. Badr, "A modified Particle Swarm Technique for distribution systems reconfiguration," Online Journal on Electronics and Electrical Engineering (OJEEE) Vol.(1)-No.(2), 2010. 31. [14] H. R. Esmaeilian and R. Fadaeinedjad, "Energy Loss Minimization in Distribution Systems Utilizing an Enhanced Reconfiguration Method Integrating Distributed Generation," IEEE Systems Journal, vol. 9, no. 4, pp. 1430-1439, 2015. [ DOI:10.1109/JSYST.2014.2341579] 32. [15] D.-L. Duan, X.-D. Ling, X.-Y. Wu, and B. Zhong, "Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm," International Journal of Electrical Power & Energy Systems, vol. 64, pp. 88-95, 2015. [ DOI:10.1016/j.ijepes.2014.07.036] 33. [16] M. Esmaeili, M. Sedighizadeh, and M. Esmaili, "Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty," Energy, vol. 103, pp. 86-99, 2016. [ DOI:10.1016/j.energy.2016.02.152] 34. [17] M. H. Kakueinejad, A. Heydari, M. Askari, and F. Keynia, "Optimal planning for the development of power system in respect to distributed generations based on the binary dragonfly algorithm," Applied Sciences, vol. 10, no. 14, p. 4795, 2020. [ DOI:10.3390/app10144795] |
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Saeidi S, Ghadimi A A, Miveh M R. Design and development of a software module for optimal reconfiguration of distribution network in the context of GIS and DIgSILENT software using Python programming language. ieijqp 2024; 13 (1) URL: http://ieijqp.ir/article-1-986-en.html
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