# AI Data Center

Power swings at AI data centers make grid-import smoothing hard, and with limited battery power and energy, forecast errors of similar size can lead to very different scheduling costs. BS-DFL writes the battery scheduling objective into training: a constraint-aware neural optimization layer first learns schedules without optimal-solution labels, then is frozen so scheduling costs and ramping risk train the demand predictor, and deployment needs no optimization solver. Over a 10,000-minute simulation using loads derived from measured AI workloads, BS-DFL cut the mean absolute minute-to-minute grid-import change by 71.70% and total scheduling cost by 18.01% relative to an MSE-trained predictor with a Gurobi solver, at an average decision time of 18.77 ms.

Power swings at AI data centers make grid-import smoothing hard, and with limited battery power and energy, forecast errors of similar size can lead to very different scheduling costs. BS-DFL writes the battery scheduling objective into training: a constraint-aware neural optimization layer first learns schedules without optimal-solution labels, then is frozen so scheduling costs and ramping risk train the demand predictor, and deployment needs no optimization solver. Over a 10,000-minute simulation using loads derived from measured AI workloads, BS-DFL cut the mean absolute minute-to-minute grid-import change by 71.70% and total scheduling cost by 18.01% relative to an MSE-trained predictor with a Gurobi solver, at an average decision time of 18.77 ms.

AI data center siting, power procurement and cooling are still settled once by static single-point scoring on day-zero numbers, though water, power and carbon drift over a 10- to 20-year asset life and commitments bind for years. The build-out becomes a tiered Markov decision process: strategic commitments occur only in discrete windows, carry switching costs and take effect after source-dependent lead times, dispatch uses only capacity that exists, and a preference-conditioned multi-objective agent returns a policy family steerable at inference time across cost, carbon, water and reliability. On real site data, preference conditioning gives a confirmed, steerable trade-off on both objective pairs across three commitment regimes, and lock-in covers the frontier more evenly.

AI data center siting, power procurement and cooling are still settled once by static single-point scoring on day-zero numbers, though water, power and carbon drift over a 10- to 20-year asset life and commitments bind for years. The build-out becomes a tiered Markov decision process: strategic commitments occur only in discrete windows, carry switching costs and take effect after source-dependent lead times, dispatch uses only capacity that exists, and a preference-conditioned multi-objective agent returns a policy family steerable at inference time across cost, carbon, water and reliability. On real site data, preference conditioning gives a confirmed, steerable trade-off on both objective pairs across three commitment regimes, and lock-in covers the frontier more evenly.

Across five AI data centers with equal completed work and network resources, folding optical circuit switching into joint compute-electricity-network scheduling cut real-time grid purchase cost by 12.50% and raised local renewable utilization by 2.724 percentage points versus fixed optical operation.

Across five AI data centers with equal completed work and network resources, folding optical circuit switching into joint compute-electricity-network scheduling cut real-time grid purchase cost by 12.50% and raised local renewable utilization by 2.724 percentage points versus fixed optical operation.

In a 2048-GPU, renewable-direct-supply AI data center, embedding checkpoint waiting and feasible 3D-parallel GPU modes of LLM pre-training into day-ahead dispatch cuts operating cost by 20.02% and renewable curtailment from 1.077 to 0.014 MWh relative to fixed continuous training.

In a 2048-GPU, renewable-direct-supply AI data center, embedding checkpoint waiting and feasible 3D-parallel GPU modes of LLM pre-training into day-ahead dispatch cuts operating cost by 20.02% and renewable curtailment from 1.077 to 0.014 MWh relative to fixed continuous training.