Inventarisasi Spasial Infrastruktur Jaringan Listrik untuk Optimalisasi Pelayanan Kelistrikan di Musi Rawas

Achmad Asril Asri; Oknovia Susanti

doi:10.55606/srj-yappi.v4i3.2691

Authors

Achmad Asril Asri Universitas Andalas
Oknovia Susanti Universitas Andalas

DOI:

https://doi.org/10.55606/srj-yappi.v4i3.2691

Keywords:

Asset Inventory, Electricity Distribution, Electricity Service, GIS, Spatial Database

Abstract

Electricity service quality depends not only on physical network expansion but also on the availability of accurate, traceable, and spatially integrated asset data. This study aims to formulate an infrastructure inventory plan for electricity distribution networks as a basis for optimizing electricity services in Musi Rawas Regency, Indonesia. A descriptive-technical approach was applied using administrative maps, spatial planning documents, distribution network maps, feeder single-line diagrams, January 2026 customer data, bill of quantity documents for electricity and public street lighting, and the planned network drawing for Mandi Aur Village. The results show that 14 target districts served approximately 101,932 customers, consisting of 78,564 postpaid and 23,368 prepaid customers. Tugumulyo, Megang Sakti, Muara Kelingi, and Muara Lakitan represented the largest customer concentrations and should be prioritized for detailed asset verification. The proposed inventory object includes medium-voltage lines, low-voltage lines, poles, distribution transformers, protection equipment, public street lighting, panels, and operation-worthiness documents. The inventory database should include unique asset codes, coordinates, feeder identity, specifications, physical condition, photographs, operational status, and follow-up actions. Spatial inventory is expected to improve maintenance planning, outage response, public lighting management, safety control, and evidence-based network development.

References

Aghahadi, M., Bosisio, A., Merlo, M., Berizzi, A., Pegoiani, A., & Forciniti, S. (2024). Digitalization processes in distribution grids: A comprehensive review of strategies and challenges. Applied Sciences, 14(11), 4528. https://doi.org/10.3390/app14114528

Aghazadeh Ardebili, A., Zappatore, M., Ramadan, A. I. H. A., Longo, A., & Ficarella, A. (2024). Digital Twins of smart energy systems: A systematic literature review on enablers, design, management and computational challenges. Energy Informatics, 7, 94. https://doi.org/10.1186/s42162-024-00385-5

Alharbey, R., Shafiq, A., Daud, A., Dawood, H., Bukhari, A., & Alshemaimri, B. (2024). Digital twin technology for enhanced smart grid performance: Integrating sustainability, security, and efficiency. Frontiers in Energy Research, 12, 1397748. https://doi.org/10.3389/fenrg.2024.1397748

Alquraidi, A., & Awad, M. (2024). Physical asset management for critical utilities-A systematic literature review. IEEE Access, 12, 90644-90659. https://doi.org/10.1109/ACCESS.2024.3421335

Al-Shetwi, A. Q., Atawi, I. E., El-Hameed, M. A., & Abuelrub, A. (2025). Digital twin technology for renewable energy, smart grids, energy storage and vehicle-to-grid integration: Advancements, applications, key players, challenges and future perspectives in modernising sustainable grids. IET Smart Grid, 8(1), e70026. https://doi.org/10.1049/stg2.70026

Antic, T., & Capuder, T. (2024). A geographic information system-based modelling, analysing and visualising of low voltage networks: The potential of demand time-shifting in the power quality improvement. Applied Energy, 353, 122056. https://doi.org/10.1016/j.apenergy.2023.122056

Dashti, R., & Rouhandeh, M. (2023). Power distribution system planning framework (A comprehensive review). Energy Strategy Reviews, 50, 101256. https://doi.org/10.1016/j.esr.2023.101256

De-Jesus-Grullon, R. E., Batista Jorge, R. O., Espinal Serrata, A., Bueno Diaz, J. E., Pichardo Estevez, J. J., & Guerrero-Rodriguez, N. F. (2024). Modeling and simulation of distribution networks with high renewable penetration in open-source software: QGIS and OpenDSS. Energies, 17(12), 2925. https://doi.org/10.3390/en17122925

Hedayati, M., Taghitahooneh, M., Shaghaghi, A., & Dashti, R. (2024). Influence of investment on failure rate in power distribution systems based on the value of assets. International Journal of Reliability and Safety, 18(2), 209-230. https://doi.org/10.1504/IJRS.2024.139218

Heluany, J. B., & Gkioulos, V. (2024). A review on digital twins for power generation and distribution. International Journal of Information Security, 23, 1171-1195. https://doi.org/10.1007/s10207-023-00784-x

Hua, W., Stephen, B., & Wallom, D. C. H. (2023). Digital twin based reinforcement learning for extracting network structures and load patterns in planning and operation of distribution systems. Applied Energy, 342, 121128. https://doi.org/10.1016/j.apenergy.2023.121128

Jiang, J.-L., Zhan, T.-S., & Tsai, M.-T. (2025). A web-based distribution network Geographic Information System with protective coordination functionality. Energies, 18(15), 4127. https://doi.org/10.3390/en18154127

Joobeni, A. Y., Naghavi, M., & Dashti, R. (2026). Economic sustainability of urban power distribution asset management: A framework for stability planning in metropolitan grids. Results in Engineering, 29, 108583. https://doi.org/10.1016/j.rineng.2025.108583

Kabir, M. R., Halder, D., & Ray, S. (2024). Digital Twins for IoT-driven energy systems: A survey. IEEE Access, 12, 177123-177143. https://doi.org/10.1109/ACCESS.2024.3506660

Lee, S., Seon, J., Hwang, B., Kim, S., Sun, Y., & Kim, J. (2024). Recent trends and issues of energy management systems using machine learning. Energies, 17(3), 624. https://doi.org/10.3390/en17030624

Malek, A. F., Mokhlis, H., Mansor, N. N., Jamian, J. J., Wang, L., & Muhammad, M. A. (2023). Power distribution system outage management using improved resilience metrics for smart grid applications. Energies, 16(9), 3953. https://doi.org/10.3390/en16093953

Malik, M. T., Obaid, H. M., & Rasool, H. F. (2026). Integrating digital twin and sensor technologies for future-ready smart grids. Discover Electronics, 3, 42. https://doi.org/10.1007/s44291-026-00196-w

McGarry, C., Anderson, A. C., Elders, I., & Galloway, S. (2023). A scalable geospatial data-driven localization approach for modelling of low voltage distribution networks and low carbon technology impact assessment. IEEE Access, 11, 64567-64585. https://doi.org/10.1109/ACCESS.2023.3288811

Mchirgui, N., Quadar, N., Kraiem, H., & Lakhssassi, A. (2024). The applications and challenges of digital twin technology in smart grids: A comprehensive review. Applied Sciences, 14(23), 10933. https://doi.org/10.3390/app142310933

Mohanty, A., Ramasamy, A. K., Verayiah, R., Bastia, S., Dash, S. S., Cuce, E., Khan, T. M. Y., & Soudagar, M. E. M. (2024). Power system resilience and strategies for a sustainable infrastructure: A review. Alexandria Engineering Journal, 105, 261-279. https://doi.org/10.1016/j.aej.2024.06.092

Monaco, R., Bergaentzle, C., Leiva Vilaplana, J. A., Ackom, E., & Nielsen, P. S. (2024). Digitalization of power distribution grids: Barrier analysis, ranking and policy recommendations. Energy Policy, 188, 114083. https://doi.org/10.1016/j.enpol.2024.114083

Mortensen, L. K., Sundsgaard, K., Shaker, H. R., Hansen, J. Z., & Yang, G. (2024). Designing digitally enabled proactive maintenance systems in power distribution grids: A scoping literature review. Energy Reports, 12, 1-21. https://doi.org/10.1016/j.egyr.2024.08.044

Paul, S., Poudyal, A., Poudel, S., Dubey, A., & Wang, Z. (2024). Resilience assessment and planning in power distribution systems: Past and future considerations. Renewable and Sustainable Energy Reviews, 189(Part B), 113991. https://doi.org/10.1016/j.rser.2023.113991

Peng, Y., Zhao, F., Zhou, K., Yu, X., Jin, Q., Li, R., & Shuai, Z. (2025). Review of digital twin technology in low-voltage distribution area and the implementation path based on the 6C development goals. Energies, 18(17), 4459. https://doi.org/10.3390/en18174459

Pragestu, S., & Astarani, J. (2025). Pengembangan Sistem Informasi Geografis Penerangan Jalan Umum Kota Pontianak dengan integrasi Telegram Bot API. Jurnal Teknik Informatika dan Teknologi Informasi, 5(3), 01-17. https://doi.org/10.55606/jutiti.v5i3.6068

Rajora, G. L., Sanz-Bobi, M. A., Bertling Tjernberg, L., & Urrea Cabus, J. E. (2024). A review of asset management using artificial intelligence-based machine learning models: Applications for the electric power and energy system. IET Generation, Transmission & Distribution, 18(12), 2155-2170. https://doi.org/10.1049/gtd2.13183

Santos, A. S., Faria, L. T., Lopes, M. L. M., & Minussi, C. R. (2023). Power distribution systems vulnerability by regions caused by electrical discharges. Energies, 16(23), 7790. https://doi.org/10.3390/en16237790

Taghitahooneh, M. T., Shaghaghi, A., Rezaei, V., Zahedi, R., & Dashti, R. (2026). Optimization of maintenance planning in power distribution systems using a discrete-time Markov chain model: Asset analysis and resource allocation. Energy Strategy Reviews, 65, 102193. https://doi.org/10.1016/j.esr.2026.102193

Wardhana, Y. M. A. (2024). The provision of public street lighting based on risk mitigation for energy efficiency and environmental protection. Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram, 12(1), 148-159. https://doi.org/10.33394/j-ps.v12i1.10519

Zhan, T.-S., Su, C.-L., Lee, Y.-D., Jiang, J.-L., & Yu, J.-T. (2023). Adaptive OCR coordination in distribution system with distributed energy resources contribution. AIMS Energy, 11(6), 1278-1305. https://doi.org/10.3934/energy.2023058