AI-Powered cable route optimization for renewable energy infrastructure
The Context
Spreadsheets and static GIS files weren’t built for multi-variable optimization. Every new renewable site meant redrawing cable routes through terrain full of obstacles — while engineers balanced cable capacity, feeder limits, and cost variations across cable types. The client needed a system that could think through the trade-offs at the pace the business needed to move.
Key Challenges
- Manual engineering complexity — Every project meant rebuilding the same calculations, extending timelines and pulling senior engineers into non-strategic work.
- Cost & capacity constraints — Cable types, feeder capacities, and cost variations had to be optimized together; getting one wrong propagated through the rest of the plan.
- No unified planning interface — Inputs were fragmented across spreadsheets, static map files, and disconnected tools.
- Limited collaboration visibility — Stakeholders needed interactive outputs they could explore; static PDFs and CSVs slowed approvals.
- Obstacle-rich terrain — Routes had to account for 8 distinct obstacle categories — water bodies, villages, residential zones, forest areas, industrial zones, hills, railways, and roads & highways. The combinatorial complexity was the real problem.
Our Approach
Kansoft designed an AI-driven geospatial intelligence platform — combining optimization algorithms, terrain analytics, and engineering configuration under one interface, so planners could work end-to-end without exporting to other tools.
- AI Optimization Engine — Automated route calculations that take terrain and obstacle constraints as inputs and return the optimal cable path — no manual redraw per revision.
- Geospatial Intelligence Layer — Map-based visualization and in-place editing of terrain, obstacles, and the proposed route in one view.
- Scenario Modeling Framework — Capacity, cable-type, and cost simulations compared side by side — moving the cost conversation upstream into engineering.
- Workflow Automation — Export and notification automation: KMZ outputs land where downstream tools expect them; reviewers get the interactive map link without a follow-up email.
What we built — six modules
- Project Management & Secure Access — Authentication, a centralized multi-project dashboard, and project-level segregation.
- Structured Data Input Framework — Downloadable templates, CSV ingestion for turbines/substations/geolocation, and validation at upload.
- Interactive Obstacle Mapping — Editable obstacle layers on a single map surface, feeding straight into the optimization engine.
- AI-Powered Route Optimization — Optimal cable routes given terrain, capacity, and cost constraints — re-running in seconds when an input changes.
- Engineering Configuration Controls — Turbines-per-cable, substation/feeder capacity, and cable-type/cost adjustments that flow through to the optimization run.
- Export & Notification Automation — KMZ outputs, automated email notifications bundling the map link, and an audit trail of who saw which route version.
Before & After
| Metric | Before | After |
|---|---|---|
| Route planning | Manual & spreadsheet-driven | AI-powered automation |
| Obstacle handling | Static analysis | Dynamic geospatial modeling |
| Cost visibility | Limited | Configurable simulations |
| Decision speed | Slow | Accelerated |
| Collaboration | Fragmented | Unified digital platform |
Business Impact
- Reduced manual route-planning effort across every new project.
- Improved infrastructure deployment efficiency end to end.
- Enabled cost-aware engineering decisions earlier in the cycle.
- Higher planning accuracy in obstacle-rich environments.
- Accelerated approvals through interactive visualization stakeholders could explore themselves.
Client Overview
A leading infrastructure developer in the energy sector, focused on delivering scalable transmission solutions for large-scale renewable energy projects. As renewable installations expanded, planning cable infrastructure between turbines and substations grew more complex with every new site — routes had to cross water bodies, villages, forests, and existing rail and road networks while balancing cable capacity, feeder limits, and cost across multiple cable types.
- Industry Energy & Utilities
- Technology Stack Interactive GIS Web App, AI Optimization Engine, MongoDB Cloud, KMZ Export, SMTP Automation
- Services Used Data, AI & analytics, Geospatial Intelligence, Custom Platform Build
Questions about this engagement
What does the optimisation engine route around?
Eight distinct obstacle categories: water bodies, villages, residential zones, forest areas, industrial zones, hills, railways, and roads and highways. The combinatorial complexity of handling them together was the core problem.
How quickly does a route update when an input changes?
The engine re-runs in seconds, so a changed turbine position, feeder capacity or cable type does not mean redrawing the route by hand.
Can cost be compared before a route is committed?
Yes. The scenario modelling framework compares capacity, cable type and cost side by side, which moves the cost conversation upstream into engineering rather than leaving it to procurement.
How do results reach the people who have to approve them?
Routes export as KMZ files for downstream tools, and reviewers receive an automated notification carrying an interactive map link. Who saw which route version is recorded in an audit trail.