AI Concentrated Solar Aim Optimization

Optimizes heliostat aiming and field control with AI to increase receiver efficiency and reduce thermal stress.

The Problem

“Maximize CSP output via real-time heliostat aiming”

Organizations face these key challenges:

Flux hot spots and non-uniformity drive receiver tube damage risk, conservative derates, and accelerated fatigue

Static aiming tables cannot keep up with transient conditions (wind, clouds) and gradual drift (alignment, soiling), causing chronic spillage and lost thermal input

Manual tuning and calibration campaigns are time-consuming, require specialized expertise, and often produce plant-specific settings that degrade between updates

Impact When Solved

Increase absorbed receiver power and AEP by 1.5%–4.0% through dynamic aim-point optimizationReduce receiver thermal stress: 10%–25% fewer over-temperature alarms/derates and improved flux uniformityLower O&M and downtime: 5%–15% reduction in receiver-related maintenance and fewer forced outages

The Shift

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

Artificial Intelligence in Renewable Energy Optimization

This is like giving a wind farm or solar plant a very smart autopilot. It studies weather, demand, prices, and equipment behavior, then constantly tweaks how the system runs so you get more clean energy for less money and wear-and-tear.