Deep Reinforcement Learning‐Based Load‐Side Resonance Forcing via Hyper‐Scale Data Center Load Modulation

ABSTRACT Hyper‐scale data centers, with rapid load modulation capabilities, introduce a novel cyber‐physical attack vector for power systems. This paper investigates coordinated resonance attacks wherein adversaries exploit data center load flexibility to excite inter‐area oscillation modes. We formulate the attack as a Markov Decision Process and develop a Deep Reinforcement Learning framework using Proximal Policy Optimization that enables model‐free adversaries to discover mode‐matched forcing patterns through local frequency observations alone. We extend the analysis to multi‐agent scenarios using Centralised‐Training‐Decentralised‐Execution, demonstrating that geographically distributed data centers can learn anti‐phase coordination without real‐time communication. Simulations on a reduced‐order dynamic equivalent (tuned to the IEEE 39‐bus 0.64 Hz mode) show that: (1) a single agent learns mode‐targeted forcing using 200 MW of controllable load; and (2) two coordinated agents achieve comparable amplification with only 100 MW each. Sensitivity analysis reveals attack severity scales linearly with load magnitude and inversely with system inertia, and that the learned forcing pattern degrades gracefully under mode frequency mismatch (> 50% effectiveness at 6% mismatch, still ∼3 × random at 25%). High‐fidelity validation using ANDES on IEEE 39‐bus and WECC 179‐bus benchmarks confirms that narrowband forcing produces consistently larger frequency deviations than random modulation—specifically, 1.20 × and 2.20 × the random baseline on the respective benchmarks—while remaining bounded (tens of mHz) under standard control loops. The active‐power focus of the attack is justified by the predominantly real‐power nature of data center loads and the electromechanical coupling governing inter‐area modes; secondary reactive power effects are analysed and shown to be an order of magnitude smaller. These findings highlight an emerging security concern as data center penetration increases and grid inertia declines.