TY - GEN
T1 - Data-Driven Model Predictive Control for Fast-Frequency Support
AU - Rai, Astha
AU - Bhujel, Niranjan
AU - Tamrakar, Ujjwol
AU - Hummels, Donald
AU - Tonkoski, Reinaldo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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).
AB - 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).
KW - Model predictive control
KW - energy storage system
KW - fast-frequency support
KW - system identification
UR - http://www.scopus.com/inward/record.url?scp=85182949485&partnerID=8YFLogxK
U2 - 10.1109/ECCE53617.2023.10362777
DO - 10.1109/ECCE53617.2023.10362777
M3 - Conference contribution
AN - SCOPUS:85182949485
T3 - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
SP - 222
EP - 229
BT - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
Y2 - 29 October 2023 through 2 November 2023
ER -
