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Enhancing resilience of DC microgrids with model predictive control based hybrid energy storage system.
- Source :
-
International Journal of Electrical Power & Energy Systems . Jun2021, Vol. 128, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- • A method to enhance the resilience of DC microgrids with hybrid energy storage system is developed. • Model Predictive Control-based scheme is introduced to control the hybrid energy storage system. • For the proposed method, only local communication in the HESS between BESS and SMES is needed. • A power assignment strategy is proposed to achieve a better coordination between the SMES and the battery. Due to the renewable energy resources fluctuations, load changes, failures and unplanned disconnection from the utility grid, DC microgrids (DCMGs) may at various risks of different time scale power mismatch and dc bus voltage instability. The hybrid energy storage system (HESS) composed of power-type energy storage and energy-type energy storage devices is considered as a cost-effective measure to enhance the resilience of DCMGs against those disturbances. This paper proposes a fast model predictive control (MPC) based voltage control and power allocation optimization method for HESS. In this MPC controller, only local information in the HESS is utilized, and the DC bus voltage can be regulated quickly by one-step prediction horizon and simplified switching states, which enhances the resilience of DCMGs against various disturbances. Besides, the power allocation command is optimally achieved by the residual capacity triggered activating-sequence of different types of ESSs based on a dynamic voltage control. Several cases conducted in MATLAB/Simulink demonstrate the feasibility and superiority of fast response ability without causing DC bus voltage oscillation. Furthermore, reducing the charge/discharge cycles and rates of the battery effectively extends the service lifetime of HESS. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01420615
- Volume :
- 128
- Database :
- Academic Search Index
- Journal :
- International Journal of Electrical Power & Energy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 148776472
- Full Text :
- https://doi.org/10.1016/j.ijepes.2020.106738