Energy Analytics

Data Center Demand Response | Participate, Power, Profit

byElliot Ryland

Network infrastructure expansion will lag load growth, driven primarily by the rapid expansion of data centers to support artificial intelligence. Data hub developers are looking to gain a first-to-market advantage and are eager to find ways to connect to the grid as soon as possible. Demand response (DR) – a voluntary reduction in power consumption during peak hours – is one method to ensure that new loads can be integrated into grids despite current capacity constraints. Enverus Intelligence® Research’s (EIR) new report measures the efficacy of DR for rapidly connecting new loads to the grid.

Using hourly operating data from the EIA, EIR estimates that PJM and MISO have the greatest ability to accommodate new loads with DR. At only 100 hours of DR, PJM and MISO can add 11.9 GW and 8.2 GW, respectively. CAISO would see the greatest utilization of idle resources, with the added capacity at 100 hours (5.8 GW) making up 14% of peak net load. These values increase proportionally with the number of DR hours. Although promising, large loads with high-reliability requirements, such as data centers, may lack the capability to reduce their power consumption without expanded backup generation. Furthermore, increasing loads without expanded infrastructure may lead to higher wholesale power prices.

Research Highlights:

Enverus Intelligence® | Research, Inc. (EIR) is a subsidiary of Enverus that publishes energy-sector research focused on the oil, natural gas, power and renewable industries. EIR publishes reports including asset and company valuations, resource assessments, technical evaluations and macro-economic forecasts, and helps make intelligent connections for energy industry participants, service companies and capital providers worldwide. See additional disclosures here. 

Picture of Elliot Ryland

Elliot Ryland

Elliot joined the Enverus Intelligence Research (EIR) team in March 2025. He has his Masters of Public Policy from the University of Calgary's School of Public Policy. He has extensive experience in the public sector, having previously worked with Alberta Wildfire, Environment and Climate Change Canada, and the Canada Energy Regulator. Elliot is interested in all things related to data modelling, with his current work focusing on power system optimization models.

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