Curtailment Reduction and Transmission Congestion Analysis of ERCOT through Data Center Load

The exponential growth of the data center industry, driven by the growing demands of AI [1], cloud computing, and internet services, is projected to significantly increase the data center loads from 8.5 GW to 14.3 GW by 2028 as per Electric Reliability Council of Texas (ERCOT) Large Load Interconnection Status Update [2]. Meanwhile, U.S. decarbonization targets are pushing for higher renewable energy penetration in ERCOT. This has led to the curtailment of renewable energy [3] due to transmission congestion or limited demand for the generated electricity. This paper proposes a strategic approach in which data center loads are optimally sited in four zones of ERCOT with surplus renewable energy generation but constrained transmission infrastructure [4]. This approach enables the use of excess renewable energy that would otherwise be wasted. A detailed analysis of curtailment reduction and transmission constraint of ERCOT is conducted through the optimal siting of data center loads. The analysis is conducted using the highly detailed Production Cost software module of PlanOS developed by GE Vernova [5][6], which performs hour-by-hour production cost simulations while accounting for transmission constraints. Two distinct scenarios were tested using PlanOS with different data center load considerations. The key results of the analysis include curtailment analysis, transmission congestion analysis and carbon emission outlook.The study holds broader implications of the power system planning and sustainable infrastructure development. By strategically aligning high-energy-demand facilities such as data centers with regions that frequently experience renewable energy oversupply, the grid can operate more efficiently while advancing decarbonization goals. Furthermore, this approach may reduce the need for expensive grid upgrades and improve economic returns on renewable energy investments. As the digital economy continues to expand, this framework offers a scalable model for integrating flexible, large-scale loads into power system globally, enabling a more balanced and decarbonized energy landscape.