Whether this is feasible depends on how concentrated that 0.25% of the year is (expected to be), because that determines the size of the battery that you’d need to cover the blackout period (which I think would be unacceptable for a lot of AI customers).
If it happens in a single few days then this makes sense, buying 22GWh of batteries for a 1GW dataset is still extremely expensive (2B$ for a 20h system at 100$ / kWh plus installation, maybe too expensive for reliability for a 1GW datacenter I would expect, assuming maybe 10B revenue from the datacenter??). If it’s much less concentrated in time then a smaller battery is needed (100M$ for a 1h system at 100$/kWh), and I expect AI scalers would happily pay this for the reliability of their systems if the revenue from those datacenters
Demand response could be done by covering the data center with battery energy or not. Demand response and batteries can stack: if the grid is really stressed, a data center can both turn off and discharge its battery into the grid.
Economically, it makes sense to accept some true downtime to avoid months-long delays in data center construction. This is clearly true for training workloads which are very important but don’t have live demand. But downtime for even inference clusters is acceptable: you can reduce the compute demand by temporarily slowing down token generation, or use dynamic rate limits. And any curtailment would almost certainly be isolated to one region, so inference data centers in other places would still be operational.
In any case, the paper says the curtailments would last about two hours each:
The average duration of load curtailment (i.e., the length of time the new load is curtailed during curtailment events) would be relatively short, at 1.7 hours when average annual load curtailment is limited to 0.25%, 2.1 hours at a 0.5% limit, and 2.5 hours at a 1.0% limit
Whether this is feasible depends on how concentrated that 0.25% of the year is (expected to be), because that determines the size of the battery that you’d need to cover the blackout period (which I think would be unacceptable for a lot of AI customers).
If it happens in a single few days then this makes sense, buying 22GWh of batteries for a 1GW dataset is still extremely expensive (2B$ for a 20h system at 100$ / kWh plus installation, maybe too expensive for reliability for a 1GW datacenter I would expect, assuming maybe 10B revenue from the datacenter??). If it’s much less concentrated in time then a smaller battery is needed (100M$ for a 1h system at 100$/kWh), and I expect AI scalers would happily pay this for the reliability of their systems if the revenue from those datacenters
Demand response could be done by covering the data center with battery energy or not. Demand response and batteries can stack: if the grid is really stressed, a data center can both turn off and discharge its battery into the grid.
Economically, it makes sense to accept some true downtime to avoid months-long delays in data center construction. This is clearly true for training workloads which are very important but don’t have live demand. But downtime for even inference clusters is acceptable: you can reduce the compute demand by temporarily slowing down token generation, or use dynamic rate limits. And any curtailment would almost certainly be isolated to one region, so inference data centers in other places would still be operational.
In any case, the paper says the curtailments would last about two hours each: