TY - GEN
T1 - Implementation of Model Predictive Control for Frequency Support in a Real-time Digital Simulator
AU - Rai, Astha
AU - Bhujel, Niranjan
AU - Hansen, Timothy M.
AU - Tonkoski, Reinaldo
AU - Tamrakar, Ujjwol
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Microgrids experience larger frequency deviations compared to bulk power systems for the same disturbance. Energy storage systems (ESSs) can potentially provide fast frequency support in such microgrids to limit frequency deviation within acceptable limits. One of the effective control approaches to achieve fast-frequency support in ESSs is a model predictive control (MPC)-based approach. Traditionally, MPC is known to use higher computational costs compared to other conventional controllers. In this paper, an MPC-based fast-frequency support mechanism is developed for an ESS and implemented on a real-time digital simulator to provide fast-frequency support in a microgrid model based in Cordova, Alaska. Results show that the computation time of MPC for frequency support is shorter than the simulation time step, justifying real-time applicability. The techniques presented in this paper can be generalized to develop novel MPC-based control approaches for ESSs and analyze their performance through real-time digital simulation techniques before deployment.
AB - Microgrids experience larger frequency deviations compared to bulk power systems for the same disturbance. Energy storage systems (ESSs) can potentially provide fast frequency support in such microgrids to limit frequency deviation within acceptable limits. One of the effective control approaches to achieve fast-frequency support in ESSs is a model predictive control (MPC)-based approach. Traditionally, MPC is known to use higher computational costs compared to other conventional controllers. In this paper, an MPC-based fast-frequency support mechanism is developed for an ESS and implemented on a real-time digital simulator to provide fast-frequency support in a microgrid model based in Cordova, Alaska. Results show that the computation time of MPC for frequency support is shorter than the simulation time step, justifying real-time applicability. The techniques presented in this paper can be generalized to develop novel MPC-based control approaches for ESSs and analyze their performance through real-time digital simulation techniques before deployment.
KW - Energy storage system
KW - fast frequency support
KW - model predictive control
KW - real-time digital simulation
UR - http://www.scopus.com/inward/record.url?scp=85146495584&partnerID=8YFLogxK
U2 - 10.1109/EESAT55007.2022.9998027
DO - 10.1109/EESAT55007.2022.9998027
M3 - Conference contribution
AN - SCOPUS:85146495584
T3 - 2022 IEEE Electrical Energy Storage Application and Technologies Conference, EESAT 2022
BT - 2022 IEEE Electrical Energy Storage Application and Technologies Conference, EESAT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Electrical Energy Storage Application and Technologies Conference, EESAT 2022
Y2 - 8 November 2022 through 9 November 2022
ER -