Optimizing Power Cable Routing using AI

Abstract

Planning underground transmission cable routes is traditionally a labor-intensive and expert-driven task involving multiple, often conflicting, objectives. To meet increasing energy demands, scalable and intelligent methods are needed to enhance efficiency while accounting for diverse spatial constraints. This study investigated how AI-driven optimization algorithms can improve underground cable routing by integrating them with Geographic Information Systems (GIS) data. Key geospatial constraints, such as soil type, land use, and vegetation, were identified and systematically encoded into a unified cost surface using the Analytic Hierarchy Process (AHP). Three algorithmic approaches were implemented and evaluated: Dijkstra’s algorithm (including single- and bi-objective variants), the A* algorithm with weighted heuristics, and a bi-objective Ant Colony Optimization (ACO) algorithm. These were assessed across synthetic and real-world environments, including the planned Gotland link in Sweden. While each model exhibited distinct strengths and limitations, all produced competitive and adaptable routes compared to manual planning, effectively balancing traversal cost and distance, and identifying Paretooptimal solutions that highlighted strategically important areas. This suggests that the integration of AI and GIS has strong potential to automate and improve cable routing processes. At the same time, the study underscores the importance of tuning both model architecture and environmental representation to maximize real-world applicability, revealing that model performance is not only algorithm-dependent but also highly sensitive to the spatial structure and scaling of the input data.