Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty

Manduleli Alfred Mquqwana () and Senthil Krishnamurthy
Additional contact information
Manduleli Alfred Mquqwana: Department of Electrical, Electronics and Computer Engineering, Centre for Substation, Automation, and Energy Management Systems, Cape Peninsula University of Technology, Bellville P.O. Box 1906, South Africa
Senthil Krishnamurthy: Department of Electrical, Electronics and Computer Engineering, Centre for Substation, Automation, and Energy Management Systems, Cape Peninsula University of Technology, Bellville P.O. Box 1906, South Africa

Energies, 2024, vol. 17, issue 2, 1-21

Abstract: Microgrids can assist in managing power supply and demand, increase grid resilience to adverse weather, increase the deployment of zero-emission energy sources, utilise waste heat, and reduce energy wasted through transmission lines. To ensure that the full benefits of microgrid use are realised, hybrid renewable energy-based microgrids must operate at peak efficiency. To offer an optimal solution for managing microgrids with hybrid renewable energy sources (HRESs) while taking microgrid reserve margins into account, the particle swarm optimisation (PSO) method is suggested. The suggested approach demonstrated good performance in terms of charging and discharging BESS and maintaining the necessary reserve margins to supply critical loads if the grid and renewable energy sources are unavailable. On a clear day, the amount of electricity sold to the grid increased by 58%, while on a partially overcast day, it increased by 153%. Microgrids provide a good return on investment for their operators when they are run at peak efficiency. This is because the BESS is largely charged during off-peak hours or with excess renewable energy, and power is only purchased during less expensive off-peak hours.

Keywords: particle swarm optimisation; hybrid renewable energy resources; microgrids; reserve margins (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/2/422/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/2/422/ (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:17:y:2024:i:2:p:422-:d:1319467

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 ().