Phase-Coordinated Model Predictive Control for AI Training Power Smoothing

Periodic power fluctuations from artificial intelligence (AI) training workloads can excite oscillations in power systems. This paper proposes a model predictive control (MPC) framework that exploits short start-time delays to mitigate power fluctuations at the computing source. By coordinating the relative phases of independent training jobs, the proposed method reduces the superposition of oscillatory power components without interrupting running jobs or requiring additional power-smoothing hardware. To obtain a tractable optimization formulation, cosine and sine projections are employed to quantify the dominant oscillatory power components, and their magnitudes are used to construct a convex quadratic power-smoothing objective. The receding-horizon framework updates launch decisions as new workload information becomes available while maintaining scheduling feasibility under limited computation time. Numerical results demonstrate reductions of 74.39\% in mean-square power fluctuations within the 0.2--2-Hz band and 46.6\% in power standard deviation, with an average launch delay of 2.188~s. All launch deadlines are satisfied, and the maximum end-to-end computation time is 0.390~s, below the 0.50-s MPC update period.