Xinda Zheng

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.