Modeling and Control of Dynamic Electric Load Demands from Data Centers
Project Description
Given the rapid increase in computational needs, data centers (DC) are expected to consume more than 20% of the electricity generated in the US within the next 5-10 years. Accommodating such a sudden and significant increase in load demand presents challenges to electric power system operators in terms of energy adequacy, transmission capacity, operations, as well as real-time dynamics. This project focuses on the dynamic modeling of DC load demand. Efforts will be directed in three distinct yet intertwined directions: i) Study the sustained oscillatory behavior of DC loads during the training of large language models (LLMs) and their effect when such loads excite a power transmission network from different DC locations; ii) Develop physics- and data-based models of dynamic DC load demand for different types of DCs; and iii) Model the effect of DC tripping on a power network due to voltage and frequency deviations.
Start Date
November 1, 2025
Postdoc Qualifications
A Ph.D. in Electrical Engineering or a related field, expertise in power systems and computation engineering, a strong analytical background, a solid publication record, and the ability to work both independently and collaboratively.
Co-advisors
Vassilis Kekatos, kekatos@purdue.edu, Associate Professor, Elmore Family School of Electrical and Computer Engineering, https://engineering.purdue.edu/~kekatos/.
Xiaonan Lu, Associate Professor, lu998@purdue.edu, Associate Professor, Electrical Engineering Technology, School of Engineering Technology.
Bibliography
- C. Mishra, L. Vanfretti, J. Delaree, T.J. Purcell, K. D. Jones, “Understanding the inception of 14.7 Hz oscillations emerging from a data center”, Sustainable Energy, Grids and Networks, 43, 2025.
- Min-Seung Ko and Hao Zhu, "Wide-Area Power System Oscillations from Large-Scale AI Workloads," IEEE Trans. on Power Systems, Vol. 37, No. 6, pp. 4409-4423, Nov. 2022.
- M. Jalali, V. Kekatos, S. Bhela, H. Zhu, and V. Centeno, "Inferring Power System Dynamics from Synchrophasor Data using Gaussian Processes," IEEE Trans. on Power Systems, Vol. 37, No. 6, pp. 4409-4423, Nov. 2022.
- L. Ding, Y. Ouyang, X. Lu, J. Qin, et al., “Holistic Small-Signal Stability Analysis for Large-Scale Inverter-Intensive Power Systems with Coupled and Full-Order Dynamics from Control Systems and Power Networks,” IEEE Transactions on Industry Applications, vol. 61, no. 1, pp. 1131-1147, 2025.
- J. Zhang, Y. Men, L. Ding, X. Lu and W. Du, "Gray-Box Modeling for Distribution Systems With Inverter-Based Resources: Integrating Physics-Based and Data-Driven Approaches," in IEEE Transactions on Industry Applications, vol. 60, no. 4, pp. 5490-5498, July-Aug. 2024.