Model Predictive Controlled Application of Power Management Algorithm for Battery Energy Storage System Providing Frequency Ancillary Service

Akpinar, Kubra Nur; Sarma, Nur; Gundogdu, Burcu; Ozgonenel, Okan

Abstract

The active power that must be provided in accordance with frequency ancillary service regulations when battery energy storage systems participate in the frequency ancillary service is modelled in this study using a Model Predicted Controlled (MPC) 2-Level Voltage Source Converter (2L-VSC). The reference active power for the 2 MW battery energy storage system was determined using the rule-based power management method, and the outcome was then used as the Model Predicted Control's input data. The output power for the MPC 2L-VSC, whose simulation study was conducted in the Simulink, successfully remained within the active power-frequency envelope in the frequency ancillary service regulation, and the demanded power was delivered by using the battery state of charge (SOC) value optimally.

Citation

Akpinar, K. N., Sarma, N., Gundogdu, B., & Ozgonenel, O. (2023, November). Model Predictive Controlled Application of Power Management Algorithm for Battery Energy Storage System Providing Frequency Ancillary Service. Presented at 2023 14th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkiye

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 14th International Conference on Electrical and Electronics Engineering (ELECO)
• Start Date: Nov 30, 2023
• End Date: Dec 2, 2023
• Acceptance Date: Oct 31, 2023
• Online Publication Date: Feb 6, 2024
• Publication Date: Nov 30, 2023
• Deposit Date: Dec 13, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1-5
• Book Title: 2023 14th International Conference on Electrical and Electronics Engineering (ELECO)
• DOI: https://doi.org/10.1109/eleco60389.2023.10416032
• Public URL: https://durham-repository.worktribe.com/output/3216742