📝 SummarXiv

Abstract

The rapid expansion of data center infrastructure is reshaping power system dynamics by significantly increasing electricity demand while also offering potential for fast and controllable flexibility. To ensure reliable operation under such conditions, the frequency secured unit commitment problem must be solved with enhanced modeling of demand side frequency response. In this work, we propose a data-driven linearization framework based on decision tree based constraint learning to embed nonlinear nadir frequency constraints into mixed-integer linear programming. This approach enables tractable optimization of generation schedules and fast frequency response from data centers. Through case studies on both a benchmark system and a 2030 future scenario with higher DC penetration, we demonstrate that increasing the proportion of flexible DC load consistently improves system cost efficiency and supports renewable integration. However, this benefit exhibits diminishing marginal returns, motivating the introduction of the Marginal Flexibility Value metric to quantify the economic value of additional flexibility. The results highlight that as DCs become a larger share of system load, their active participation in frequency response will be increasingly indispensable for maintaining both economic and secure system operations.