Optimization of Photovoltaic Cable Tray Layout

Optimizing PV cable tray layouts involves combining graph-theoretic modeling, clustering algorithms, MILP formulations, and CAD-based automation to minimize cable length, reduce losses, and improve voltage consistency.Graph-Theoretic and MILP ApproachesLarge solar farms can be modeled as layered graphs, where PV strings are nodes and cables are edges with different capacities and costs. The goal is to find a cost-optimal cable layout that respects cable and node capacities, which is an NP-hard problem for large instances . Mixed-Integer Linear Programming (MILP) formulations can be used to select cable types and routing paths, providing a baseline for optimization . For very large farms, heuristic algorithms often outperform MILP in runtime while still producing near-optimal solutions .Clustering and Resource Matching AlgorithmsFor low-carbon and efficient PV layouts, clustering algorithms like the improved KICCA can group PV modules to optimize cable paths. This method uses ordered initialization, extended Manhattan distance matrices, and breadth-first neighbor search to improve clustering accuracy . Coupled with Simulated Annealing-Tabu Search (SA-TS) for resource matching, this approach efficiently schedules cable routing, reduces voltage differences, and shortens cable lengths by up to 3–4% compared to manual layouts .Practical CAD-Based ToolsSoftware like Virto.CAD automates cable tray design for rooftop and large-scale PV projects. It allows engineers to define 3D cable tray paths, account for height differences, roof edges, and obstacles, and automatically calculate string lengths and cable counts . This reduces manual errors, shortens design cycles, and ensures efficient routing of AC and DC cables to inverters and combiner boxes.Layout ConsiderationsDistrict Partitioning: Divide the PV plant into districts with a single inverter per district to optimize cable lengths and maintenance access .PV Array Placement: Optimize row spacing and inclination angles based on latitude to maximize sunlight absorption and minimize shading .Cable Type Selection: Choose cables based on current capacity, cost, and voltage drop constraints .Voltage Consistency: Ensure minimal voltage drop across the network by optimizing cable paths and lengths .Recommended Optimization WorkflowModel the PV farm as a graph with nodes representing PV strings and edges representing potential cable paths.Cluster PV modules using improved KICCA or similar algorithms to reduce cable routing complexity.Apply MILP or heuristic algorithms to select cable types and routing paths while minimizing cost and losses.Use CAD-based tools to visualize and finalize cable tray layouts in 3D, accounting for physical constraints.Validate voltage drop and cable lengths to ensure compliance with electrical standards and efficiency goals. By integrating these methods, engineers can achieve optimized PV cable tray layouts that reduce costs, improve efficiency, and support low-carbon energy objectives.