Electrical Engineering and Systems Science > Systems and Control
[Submitted on 3 Sep 2026]
Title:Grid-Mode-Aware Model Predictive Control of Hybrid Energy Storage Systems for AI Data Center Power Smoothing
View PDF HTML (experimental)Abstract:To facilitate the grid-friendly integration of highly variable AI data center loads, this paper proposes a grid-mode-aware model predictive control (G-MPC) framework for managing a hybrid energy storage system (HESS) to smooth grid-side power demand. The framework optimally coordinates a battery energy storage system (BESS) and a supercapacitor (SC) by solving a multi-step optimization problem in a receding-horizon manner. In particular, band-pass filter dynamics are directly embedded in the G-MPC formulation to extract and suppress grid-side power components associated with vulnerable grid oscillatory modes, thus mitigating load-induced grid oscillations. The resulting G-MPC optimization jointly minimizes violations of grid-side power-envelope, ramp-rate, and modal-power requirements and the degradation and power-ramping costs of the BESS and SC, while satisfying power limits, state-of-charge limits, and other operational constraints. To enable real-time implementation, a fix-and-re-optimize algorithm is developed to solve each G-MPC problem efficiently while preventing simultaneous charging and discharging. Extensive simulations demonstrate the effectiveness, flexibility, and computational efficiency of the proposed framework. The results also highlight the importance of explicitly suppressing power components associated with vulnerable grid modes, rather than merely reducing overall load variations, to effectively mitigate grid oscillations.
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Facts Only
* A grid-mode-aware model predictive control (G-MPC) framework is proposed for managing a hybrid energy storage system (HESS).
* The objective is to smooth grid-side power demand from highly variable AI data center loads.
* The framework coordinates a battery energy storage system (BESS) and a supercapacitor (SC).
* Band-pass filter dynamics are embedded in the G-MPC formulation to extract and suppress grid-side power components associated with vulnerable grid oscillatory modes.
* The optimization minimizes violations of grid-side power-envelope, ramp-rate, and modal-power requirements.
* Costs included are BESS and SC degradation and power-ramping costs.
* Operational constraints include power limits, state-of-charge limits, and other operational constraints.
* A fix-and-re-optimize algorithm is developed for real-time implementation to avoid simultaneous charging and discharging.