Inside the workshop tackling Texas’ electrical grid challenges
As large data center demands surge, Texas A&M researchers are creating a multidisciplinary platform where industry leaders, engineers, researchers and students can come together to tackle challenges in Texas’ power grid through collaborative workshops.

CALL workshop attendees pose for a group photo.
Artificial intelligence and other emerging technologies are growing at an unprecedented rate, along with the energy needed to power them. With Texas projected to lead the country in data center construction by 2030, the state’s electrical grid faces a massive challenge in meeting the coming demand.
Drs. Xin Chen and Prasad Enjeti, professors in Texas A&M University’s electrical and computer engineering department, are working to address this issue through a series of power technology workshops. As co-directors of the department’s Consortium on AI and Large Flexible Load (CALL), Chen and Enjeti are designing the events specifically for electric power industry leaders, researchers and students. Their goal is to transfer knowledge, identify key challenges, explore innovative solutions and foster cross-field collaboration.
“I felt it was important to create a platform where researchers and professionals could come together to understand what is happening, think critically and come up with creative solutions,” Chen said.
With the Electrical Reliability Council of Texas (ERCOT) projecting that energy demand could double by 2031, Chen sees the workshops as a critical source for developing solutions.
Success of CALL workshops
Since 2023, CALL workshops have served as a vital bridge between power technology research and implementation. The group is actively working to explore how AI, data centers and other large-load facilities can better interact with the existing grid infrastructure.
Participants have praised the interactive and accessible format while students say it helped them better understand their coursework and research. Following sessions, several industry leaders from the data center and manufacturing sectors reached out to deepen their involvement. This level of industry engagement helps to ensure the research remains grounded in real-world application.
“We are making an important impact in the field,” Chen said. “Our workshop fosters collaboration across different sectors, including utilities, power grid operators, data center operators and original equipment manufacturers.”
Fostering industry and student relationships
Beyond industry impact, the workshops give students the chance to participate in solving real-world challenges, receive direct training and connect with industry partners. For both graduate and undergraduate students, this experience can become a major stepping-stone to their future careers.
“We plan to build a talent pipeline,” Chen said. “By involving students in research projects that directly address the challenges our industry partners face, we ensure they are ready to hit the ground running. After graduation, they can transition directly into the industry to continue the work they started here.”
This hands-on involvement allows students at every level to align their academic research with the immediate needs of the Texas power sector, creating a foundation for career-ready innovation.
Looking to the future
As electric power needs for AI data centers increase exponentially, Chen and Enjeti plan to continue expanding the CALL workshops and are reaching out to AI computing and thermal management experts across the college to create a comprehensive, integrated solution. With attendees spanning multiple fields, the workshops unite cross-disciplinary minds and approach problems from a variety of perspectives.
Future workshops will pivot toward critical technical challenges known as low-voltage ride-through and power fluctuation mitigation solutions for enhancing grid-friendly data center integration. Upcoming sessions will serve as both a symposium and a tutorial, sharing Texas A&M’s latest research on data center load dynamic modeling and control.