Graph-Based Optimization of Distribution Networks Using Minimum Spanning Tree Algorithms - Muhung Anggarawan; Hadi Permana; Fatia Amalia Maresti (MALCOM: Indonesian Journal of Machine Learning and Computer Science, 2026)
Urban electric power distribution networks must operate efficiently while supporting sustainability and green economy objectives. This study analyzes the optimization of an urban power distribution network using graph theory approaches, specifically Minimum Spanning Tree methods. The electrical network is modeled as an undirected weighted graph, where substations are represented as nodes and cable connections as edges with distance-based weights. Kruskal and Prim algorithms are applied to determine the optimal network configuration that minimizes total cable length while maintaining full connectivity. A case study of an existing urban distribution network consisting of 229 substations is used to evaluate the proposed approach. The results show that the optimized network configuration reduces total cable length from 61,474.23 meters to 49,391.44 meters a 19.66% reduction (12,082.79 meters saved) leading to improved material efficiency and lower infrastructure costs. Both algorithms produce identical optimal results, confirming their reliability for practical network planning. The findings demonstrate that graph-based optimization techniques can provide effective decision support for designing more efficient and environmentally responsible power distribution systems. This research highlights the potential of mathematical and computational methods for sustainable infrastructure development and for implementing a green economy in urban energy systems.