AI Thermal Energy Storage

Machine learning for thermal energy storage charging and dispatch

The Problem

“Optimize thermal storage dispatch amid volatile grids”

Organizations face these key challenges:

Inaccurate short-term heat demand forecasts causing over/under-charging, temperature violations, or forced peak electricity purchases

Static rule-based dispatch that ignores real-time price spikes, demand charge windows, renewable curtailment opportunities, and carbon signals

Operational complexity and constraint management (stratification, equipment limits, ramping, minimum service temperatures/pressures) leading to conservative operation and lost value

Impact When Solved

Reduce peak grid import by 10-30% via optimized TES discharge during critical hoursLower total thermal supply cost by 8-20% through co-optimized charging, dispatch, and equipment efficiencyIncrease asset utilization and reliability with anomaly detection, reducing unplanned downtime and performance losses by 20-40%

Real-World Use Cases

Data-driven optimal configuration of hybrid energy storage in park micro-energy grids

This is like designing the right mix and size of batteries for an industrial or campus-sized “mini power grid” so it can quickly ramp power up and down when needed, without overpaying for equipment or risking reliability.

End-to-End NNEmerging Standard

8.5

Energy Storage Optimization using AI

AI helps batteries work better by deciding when to store or release energy.

optimizationgrowing

7.0