Voltage Drop and Power Loss Mitigation on SGN-14 via SGN-15 Feeder Design in Distribution System ULP Magelang

Abstract

Feeder SGN-14 of PT PLN (Persero) ULP Magelang operates under overload conditions, significantly degrading voltage quality and increasing technical losses. PLN (Perusahaan Listrik Negara) is Indonesia’s State Electricity Company, while ULP (Unit Layanan Pelanggan) refers to a customer service unit. This study designs Feeder SGN-15 as a 20 kV load-splitting feeder supplied from Sanggrahan Substation and terminating near KH. Maksum Street (Tempuran). The feeder is 20.7 km long and routed close to the load centre to reduce line losses. Network performance is assessed using ETAP load-flow simulations and independent GNU Octave calculations of voltage profile, current, and power/energy losses, referenced to SPLN T6.001:2013 with a 10% voltage-drop limit. The proposed feeder uses 8,152 m of insulated MVTIC and 12,584 m of AAAC conductors, supported by 238 concrete poles, together with required switching devices, line accessories, and four CSP transformers. After reconfiguration, the maximum voltage drops on SGN-14 decreases from 12.82% to 6.5%, while SGN-15 operates at about 4.95%, ensuring all buses comply with SPLN T6.001:2013. Technical losses on SGN-14 fall from 388.711 to 112.337 (W/kWh), and SGN-15 contributes 81.130 (W/kWh), giving total post-reconfiguration losses of 195.467 (W/kWh). The reduction in energy-loss cost yields an estimated saving of Rp228.82 million per month, lowering losses from Rp460.32 million/month to Rp231.44 million/month. Unlike studies that optimize only switch states or voltage-regulator placement, this work shows that adding a new 20 kV feeder can jointly improve voltages, reduce losses, and deliver tangible benefits for the distribution utility.

Keywords

Voltage drop; power loss; power distribution system; optimization; distribution network reconfiguration.

References

F. O. Akpojedje and E. A. Ogujor. “Optimal network reconfiguration of electric power distribution system for power loss reduction and voltage profile improvement.” Journal of Energy Technology and Environment. vol. 3. no. 1. 2021. https://doi.org/10.37933/nipes.e/3.1.2021.6

T. Jayabarathi. T. Raghunathan. N. Mithulananthan. S. H. C. Cherukuri. and G. L. Sai. “Enhancement of distribution system performance with reconfiguration. distributed generation and capacitor bank deployment.” Heliyon. vol. 10. no. 7. Art. no. e26343. 2024. https://doi.org/10.1016/j.heliyon.2024.e26343

N. Gholizadeh and P. Musilek. “A generalized deep reinforcement learning model for distribution network reconfiguration with power flow-based action-space sampling.” Energies. vol. 17. no. 20. Art. no. 5187. 2024. https://doi.org/10.3390/en17205187

Z. Hu. H. Zhu. and C. Deng. “Distribution network reconfiguration optimization method based on undirected-graph isolation group detection and the whale optimization algorithm.” AIMS Energy. vol. 12. no. 2. pp. 484–504. 2024. https://doi.org/10.3934/energy.2024023

L. A. Gallego Pareja. J. M. López-Lezama. and O. Gómez Carmona. “Optimal feeder reconfiguration and placement of voltage regulators in electrical distribution networks using a linear mathematical model.” Sustainability. vol. 15. no. 1. Art. no. 854. 2023. https://doi.org/10.3390/su15010854

B. Alemayehu. S. Mishra. G. G. Tejani. and S. Tripathi. “Network reconfiguration and DG based compensation of Wolaita Sodo distribution system by using particle swarm optimisation.” PLOS One. vol. 20. no. 10. Art. no. e0335512. 2025. https://doi.org/10.1371/journal.pone.0335512

J. Tian et al.. “Distributed optimization and scheduling strategy for source-load-storage distribution grid based on alliance chain.” AIMS Energy. vol. 12. no. 5. pp. 946–967. 2024. https://doi.org/10.3934/energy.2024044

A. Gill. P. Singh. J. H. Jobanputra. and M. L. Kolhe. “Placement analysis of combined renewable and conventional distributed energy resources within a radial distribution network.” AIMS Energy. vol. 10. no. 6. pp. 1216–1229. 2022. https://doi.org/10.3934/energy.2022057

Z. Zhang. W. Du. Y. Lu. G. Xu. and Y. Zhao. “Two-stage robust distribution network reconfiguration against failures of lines and renewable generations.” IEEE Access. vol. 10. pp. 108614–108624. 2022. https://doi.org/10.1109/ACCESS.2022.3213803

J. Lei et al.. “Economic dispatch of distribution network with dispersed wind power considering network reconfiguration.” Frontiers in Energy Research. vol. 10. Art. no. 942350. 2022. https://doi.org/10.3389/fenrg.2022.942350

S. Malekshah. A. Rasouli. Y. Malekshah. A. Ramezani. and A. Malekshah. “Reliability driven distribution power network dynamic reconfiguration in presence of distributed generation by the deep reinforcement learning method.” Alexandria Engineering Journal. vol. 61. no. 8. pp. 6541–6556. 2022. https://doi.org/10.1016/j.aej.2021.12.012

Y. Xu. M. Zhao. H. Wu. S. Xiang. and Y. Yuan. “Coordination of network reconfiguration and mobile energy storage system fleets to facilitate active distribution network restoration under forecast uncertainty.” Frontiers in Energy Research. vol. 10. Art. no. 1024282. 2023. https://doi.org/10.3389/fenrg.2022.1024282

J. Muñoz. L. Tipán. C. Cuji. and M. Jaramillo. “Resilient distribution system reconfiguration based on genetic algorithms considering load margin and contingencies.” Energies. vol. 18. no. 11. Art. no. 2889. 2025. https://doi.org/10.3390/en18112889

H. Lotfi. M. E. Hajiabadi. and H. Parsadust. “Power distribution network reconfiguration techniques: A thorough review.” Sustainability. vol. 16. no. 23. Art. no. 10307. 2024. https://doi.org/10.3390/su162310307

Q. Liu. L. Zhang. X. Ji. and H. Shi. “Dynamic reconfiguration of distribution network considering the uncertainty of distributed generation and loads.” Frontiers in Energy Research. vol. 11. Art. no. 1279579. 2023. https://doi.org/10.3389/fenrg.2023.1279579

O. M. Neda et al.. “Distribution system reconfiguration for enhancing techno-economic advantages using a novel technique: A case study of Al-Fuhood substation. Iraq.” Progress in Engineering Science. vol. 2. no. 3. Art. no. 100124. 2025. https://doi.org/10.1016/j.pes.2025.100124

F. Bohigas-Daranas. O. Gomis-Bellmunt. and E. Prieto-Araujo. “Open-source implementation of distribution network reconfiguration methods: Analysis and comparison.” arXiv preprint arXiv:2511.22957. 2025. Available: https://arxiv.org/abs/2511.22957

A. Owosuhi. Y. Hamam. and J. Munda. “A new framework for active loss reduction and voltage profile enhancement in a distributed generation-dominated radial distribution network.” Applied Sciences. vol. 14. no. 3. Art. no. 1077. 2024. https://doi.org/10.3390/app14031077

Z. Pei. J. Chen. Z. Zhang. W. Liu. X. Yan. and Y. Jin. “Opinions on dynamic topology reconfiguration of distribution networks for PV hosting capacity enhancement.” Frontiers in Energy Research. vol. 12. Art. no. 1512790. 2024. https://doi.org/10.3389/fenrg.2024.1512790

X. Li. M. Li. M. Yu. and Q. Fan. “Fault reconfiguration in distribution networks based on improved discrete multimodal multi-objective particle swarm optimization algorithm.” Biomimetics. vol. 8. no. 5. Art. no. 431. 2023. https://doi.org/10.3390/biomimetics8050431

Y. Yu. M. Yang. Y. Zhang. P. Ye. X. Ji. and J. Li. “Fast reconfiguration method of low carbon distribution network based on convolutional neural network.” Frontiers in Energy Research. vol. 11. Art. no. 1102949. 2023. https://doi.org/10.3389/fenrg.2023.1102949

http://dx.doi.org/10.28989/avitec.v8i1.3686

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