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Data centers are a major topic in the energy-efficiency industry, as utilities face unprecedented grid strain and pressure to avoid charging existing customers for infrastructure costs driven by new, large energy users. In this article, we’ll discuss how New Jersey and Virginia are taking action to protect ratepayers and to tap data centers as demand-response and virtual power plant providers.
New Jersey recently passed a bill requiring the Board of Public Utilities (BPU) to investigate bring-your-own distributed capacity solutions for data centers, with robust M&V using AMI data. The bill aimed to ensure that large energy users (to be defined through this process) cover their full cost of electricity and do not shift this risk onto general ratepayers. The law requires these large energy users to couple robust AMI interval-data tracking with M&V 2.0 methodologies.M&V 20 uses high-frequency data and real-time analytics to track and verify savings continuously with robust modeling approaches (i.e., Lawrence Berkley Time-of-Week and Temperature models).
AMI data plays an important role in the bill’s requirements. First, the bill requires minimum load guarantees, evaluated using AMI data. This means data centers must pay for at least 85% of their requested capacity, whether they consume it or not. This helps cover the infrastructure costs the utility incurs to support the data center (i.e., physical substations, transformers, power lines) without passing those costs to general ratepayers.
M&V 2.0 supports the required Virtual Power Plant (VPP) and load flexibility rules. In a demand response (DR) event, the utility sends an automated message requiring a specific consumption reduction for an allotted time period. The data center would then shut down non-critical servers or switch to battery storage to reduce grid load. M&V 2.0 methodologies would then determine what the data center would have consumed had the DR message not been sent. This enables the Electric Distribution Company to credit the facility for its participation in the DR event. Alternatively, customers can supply energy to the grid when the grid is strained. M&V 2.0 methodologies can measure how much energy was supplied to the grid.
New Jersey’s approach is twofold: hold the data centers responsible for their infrastructure costs and require them to participate in demand-side management.
Northern Virginia (referred to as “data center alley”) holds the highest concentration of data centers in the US. Dominion Energy, Virginia’s largest utility, will begin deploying its Virtual Power Plant Program in 2027. Like New Jersey, the program will require AMI data and M&V 2.0 methodologies to quantify program impact.
While New Jersey’s legislation touches on VPPs as a component of its large load focus, Virginia’s legislation that led to the development of Dominion’s program is focused squarely on the establishment of VPPs and is inclusive of a wide variety of distributed energy resources for that purpose, but stops short of prescribing M&V requirements in the Act itself.
When the program was being developed, there were central disagreements on how to measure and verify load reductions. Some commenters advocated for device-level metering (i.e., BMS systems, sub-metering equipment), but the Virginia State Corporation Commission (SCC) ruled that any device-level metering be coupled with AMI interval data. This requirement allows the program to verify whether the facility’s overall power draw actually decreased.
As stated by Dominion during the adjudication of its proposed VPP program, AMI meter data combined with device-specific data feeds ensures that “settlement reflects actual net impacts on the electric grid at the point of interconnection, provides an independent data source for cross-checking performance, and maintains consistency with existing tariff structures and billing practices.”
Verify™, Power TakeOff’s M&V platform, was built specifically for AMI-based M&V 2.0 approaches. Utilities that require AMI data and M&V 2.0 to measure VPP program success can use Verify to quantify savings, collaborate in the browser-based platform, and measure program success.