Some data centers proposed for construction may not materialize, as utilities become more discerning.
## Managing 'Phantom Data Centers': Utilities and Grid Operators Take Action
In an effort to address the issue of 'phantom data centers' and their impact on forecasts and long-term planning, utilities and grid operators across the United States are implementing various strategies.
### Demand Management Programs
One such strategy is the implementation of demand management programs. For instance, Texas Senate Bill 6 requires data centers to disclose energy usage and applications, aiming to eliminate phantom load requests by making developers register backup generators and plan for infrastructure upgrades. This approach ensures that real projects are financially committed and planned for, reducing speculative load submissions.
### Infrastructure Cost-Sharing
Large energy users, like data centers, are also being required to fund infrastructure upgrades and shoulder grid connection costs, ensuring they are not subsidized by residential rates. This approach helps manage speculative applications that might inflate load forecasts.
### Load Interconnection Processes Standardization
Encouraging standardized load interconnection processes can help reduce uncertainty associated with load growth. This includes clearly defined queue protocols and robust financial readiness criteria to eliminate non-viable projects from the queue.
### Onsite Generation and Co-located Power Plants
Utilities are promoting strategies where data centers generate or co-locate their own power, reducing reliance on external grid capacity and mitigating the impact of phantom loads.
### Transparent Disclosure Requirements
Policies like California's AB 222 and Texas Senate Bill 6 require data centers to report their energy usage, enhancing transparency and helping utilities better predict and plan for actual demand.
### Grid Planning Reforms
Encouraging reforms in grid planning to incorporate more flexible and adaptive strategies can help manage the risks associated with speculative projects and ensure that resource allocation is more efficient.
In addition to these strategies, utilities are taking different approaches to derating proposed data center loads, requiring developers to meet commercial readiness tests, pay phased fees that increase as projects progress, and include a mechanism for removing nonviable projects from the queue.
For example, Rappahannock's proposal requires new data centers to put up collateral, cover some infrastructure upgrades, pay up to 100% of contracted load, and deal directly with special-purpose subsidiaries to protect existing customers.
Experts predict wild divergence in near-term AI power demand forecasts, and utilities are seeking help from state policymakers to implement these strategies effectively.
Meanwhile, Great River Energy, which shares its home state with Xcel Energy, is using a $812 million federal grant to procure nearly 1.3 GW of renewable power to serve new load in the coming years. Great River Energy also bills petitioners for the staff work related to vetting large-load requests.
In the Minneapolis-St. Paul metro area, Xcel and Great River Energy member cooperatives have drawn proposals for at least 11 data center campuses since 2020. Of these proposals, none of the utilities expected an actual five-year share above 35% of peak load. As of earlier this year, only Meta's data center had begun construction.
These strategies aim to diminish the impact of phantom data centers on utility forecasting and planning processes, ensuring a more efficient and sustainable energy future.
Data centers, operating within the technology industry, are revealing their energy usage and applications as part of demand management programs, reducing speculative load submissions and ensuring genuine projects are financially committed and planned (Demand Management Programs). Moreover, these large energy consumers, such as data centers, are contributing towards infrastructure costs to avoid being subsidized by residential rates, thereby helping manage speculative applications and inflated load forecasts (Infrastructure Cost-Sharing).
