A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

Upasana Lakhina, Irraivan Elamvazuthi, Nasreen Badruddin (), Ajay Jangra, Bao-Huy Truong and Joseph M. Guerrero
Additional contact information
Upasana Lakhina: Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Irraivan Elamvazuthi: Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Nasreen Badruddin: Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Ajay Jangra: University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra 136119, India
Bao-Huy Truong: Institute of Engineering and Technology, Thu Dau Mot University, Thu Dau Mot 7500, Vietnam
Joseph M. Guerrero: Centre of Research on Microgrids, Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark

Sustainability, 2023, vol. 15, issue 8, 1-25

Abstract: Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms.

Keywords: cost optimization; energy management; microgrid; multi-verse optimizer; renewable energy sources (RESs) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/8/6358/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/8/6358/ (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:jsusta:v:15:y:2023:i:8:p:6358-:d:1118221

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

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