Optimizing Rural Electricity Distribution with Minimum Spanning Tree and Mixed Integer Programming: Evidence from Sungai Mengkuang Village, Indonesia

The increasing demand for PLN electricity connections in line with population growth and rural development necessitates efficient and standardized network planning. This study aims to design a two-stage optimization algorithm that ensures effective customer-to-pole electricity connections, utilizing the Minimum Spanning Tree (MST) and distance-based optimization methods. A case study was conducted in Sungai Mengkuang Village, Bungo Regency, involving 186 customers and 27 PLN electricity poles. The spatial data, comprising latitude and longitude coordinates, were collected using the ARCGIS application. The first optimization stage grouped customers with a maximum of five series connections, in compliance with the SLP (Standard Layak Pelanggan) regulation. The second stage connected these groups to the nearest poles while maintaining a five-connection limit per pole. The algorithm applied a modified Haversine formula to calculate cable distances. The results revealed an optimal configuration with a minimum cable length of approximately 1.028 kilometers and an equitable distribution of connections across poles. The model also identified underutilized poles, suggesting potential for future expansion. This study demonstrates that integrating MST with Mixed Integer Programming (MIP) and spatial data can yield a replicable and efficient model for electricity distribution in rural settings. The approach respects regulatory constraints and minimizes infrastructure costs. For further research, incorporating load balancing, terrain constraints, and dynamic demand forecasting is recommended to enhance model applicability under real-world conditions. This model supports PLN’s rural electrification strategy while aligning with sustainable development goals.