Data-Driven Model Predictive Control for Fast-Frequency Support

Low-inertia microgrids experience significant frequency deviations compared to bulk-power systems. In such microgrids, energy storage systems (ESSs) can be a viable option to provide fast-frequency support to keep frequency variations within allowable bounds. A model predictive control (MPC)-based strategy is one of the efficient control strategies to enable fast-frequency support through ESSs. MPC provides the capability to explicitly incorporate physical constraints of the microgrid and the ESS into the control formulation while allowing signifi-cant operational flexibility. MPC allows near-optimal control by optimizing the system over a rolling horizon based on a predictive model of the system. However, the effectiveness of MPC relies on the accuracy of this predictive model. This paper proposes a data-driven system identification (SI) based approach to obtain an accurate yet computationally tractable predictive model for frequency support in microgrids. The proposed data-driven MPC is compared with the conventional MPC that utilizes a simplified transfer-function-based predictive model of the system. Results show that the data-driven MPC offers a better quality of service in terms of lower frequency deviations and rate-of-change of frequency (ROCOF).