- Siemens invested in Emerald AI to enable dynamic shifting of AI workloads in time and location to align with grid conditions.
- Fluence's grid‑scale battery storage shapes load and ramp rates to accelerate grid connection, enabling deployment in months instead of years of grid upgrades.
- Siemens and PhysicsX apply physics‑AI to reduce multi‑physics simulations from days to under a second for real‑time thermal prediction and faster power‑infrastructure design.
Overview
Siemens Smart Infrastructure expanded its data center ecosystem via a strategic investment in Emerald AI, integration of Fluence grid‑scale energy storage, and a collaboration with PhysicsX to better align AI compute demand with grid capacity.
Emerald AI
Emerald AI enables AI workloads to shift in time and location to match grid conditions, coordinating workload scheduling with onsite energy dispatch to smooth peaks, enable faster and larger grid connections, and add compute‑layer flexibility for IT/OT convergence.
Fluence energy storage
Fluence’s battery solutions shape load and ramp rates to make large AI demand more predictable for utilities, accelerating grid connection timelines (deployments in months versus years of grid upgrades) and providing dispatchable onsite power during build‑outs, capacity shortfalls or outages while supporting power quality and scalable capacity.
PhysicsX modeling
PhysicsX applies physics‑based AI trained on multi‑physics simulation data to predict thermal behavior in complex busway systems in real time, cutting simulation runtimes from days to under a second to enable faster design iteration and the basis for predictive facility monitoring.
Impact
Combined, these capabilities coordinate AI workload orchestration, grid‑integrated energy systems and AI‑optimized physical infrastructure to help data centers connect to the grid faster, scale efficiently and operate reliably under power constraints.
