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AI & Automation

Solar Optimization

A renewable energy team needed better field performance without blunt site interventions, balancing yield against ecological and permitting limits. Decisions were being made on fragmented calculations, so no two projects could be compared. We built a planning model that made scenarios comparable and auditable.

Snapshot

1–2 quartersmodel build and rollout

Client profile
Renewable energy project team, 30–100 people, GCC and North America portfolio
Engagement
Catalyst — core model build and rollout
Timeline
One to two quarters
Scope
Layout optimisation, shading-aware placement, scenario comparison, data model
01

The problem

The engineering team wanted better field performance without relying on blunt site interventions, which meant balancing yield goals against ecological and permitting constraints. Site decisions were being made on fragmented calculations with inconsistent documentation, so scenarios could not be compared across projects.

  • Panel placement was not consistently linked to long-horizon performance outcomes
  • Environmental constraints were handled late instead of at planning time
  • Reviewing alternatives was slow because model assumptions were not standardised
02

What we built

First one planning framework for geometry, shading constraints, and success criteria — defined once so it could be reused across sites rather than rebuilt per project.

Then optimisation workflows for panel positioning and orientation, with scenario outputs structured for the engineering and operations reviews that consume them.

Then governance checklists and documentation standards, so planning assumptions stay auditable all the way through delivery.

03

What stuck

  • Teams could evaluate trade-offs against shared scenario logic
  • Performance planning became repeatable rather than analyst-dependent
  • Reviews gained clear traceability from assumptions to decisions
04

Impact

  • Scenario comparison stopped depending on one analyst's spreadsheet
  • Constraint conflicts were caught at planning time instead of causing late redesign
  • Engineering and operations shared confidence in deployment decisions