Virtual Inertia Control in Autonomous Microgrids via a Cascaded Controller for Battery Energy Storage Optimized by Firefly Algorithm and a Comparison Study with GA, PSO, ABC, and GWO

Farhad Amiri, Mohsen Eskandari () and Mohammad Hassan Moradi
Additional contact information
Farhad Amiri: Electrical Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan 6516738695, Iran
Mohsen Eskandari: The School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Mohammad Hassan Moradi: Electrical Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan 6516738695, Iran

Energies, 2023, vol. 16, issue 18, 1-22

Abstract: Modern (micro) grids host inverter-based generation units for utilizing renewable and sustainable energy resources. Due to the lack of physical inertia and, thus, the low inertia level of inverter-interfaced energy resources, the frequency dynamic is adversely affected, which critically impacts the stability of autonomous microgrids. The idea of virtual inertia control (VIC), assisted by battery energy storage systems (BESSs), has been presented to improve the frequency dynamic in islanded microgrids. This study presents the PD-FOPID cascaded controller for the BESS, a unique method for enhancing the performance of VIC in islanded microgrids. Using the firefly algorithm (FA), the settings of this controller are optimally tuned. This approach is robust to disruptions due to uncertainties in islanded microgrids. In several scenarios, the performance of the suggested approach is compared with those of other control techniques, such as VIC based on an MPC controller, VIC based on a robust H-infinite controller, adaptive VIC, and VIC based on an optimized PI controller. The simulation results in MATLAB show that the suggested methodology in the area of VIC is better than previous methods.

Keywords: autonomous microgrids; battery energy storage systems (BESSs); cascaded controller; inverter-interfaced energy resources; optimization algorithm; renewable energy resources; virtual inertia (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/18/6611/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/18/6611/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:18:p:6611-:d:1239494

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().