AI Concentrated Solar Operations
Uses AI to optimize solar tower/thermal plant dispatch, thermal storage use, and operating constraints to maximize output and revenue.
The Problem
“Reduce CSP downtime and boost thermal output”
Organizations face these key challenges:
Unpredictable thermal losses from mirror soiling, misalignment, and spillage that degrade DNI-to-thermal conversion without clear early warning
Receiver and HTF constraints (temperature limits, fouling, pump issues) causing trips, derates, and conservative operating margins
Suboptimal thermal storage charge/discharge decisions under weather uncertainty leading to missed peak-price dispatch and higher cycling wear
Impact When Solved
The Shift
Human Does
- •Review every case manually
- •Handle requests one by one
- •Make decisions on each item
- •Document and track progress
Automation
- •Basic routing only
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.
Artificial Intelligence for Energy Systems
Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.