Multi-Attribute Decision-Making Approach for a Cost-Effective and Sustainable Energy System Considering Weight Assignment Analysis

Keifa Vamba Konneh, Hasan Masrur, Mohammad Lutfi Othman, Hiroshi Takahashi, Narayanan Krishna and Tomonobu Senjyu
Additional contact information
Keifa Vamba Konneh: Department of Electrical & Electronics Engineering, Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan
Hasan Masrur: Department of Electrical & Electronics Engineering, Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan
Mohammad Lutfi Othman: Advanced Lightning and Power Energy Research (ALPER), Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Serdang 43400, Malaysia
Hiroshi Takahashi: Fuji Electric Co., Ltd., Tokyo 141-0032, Japan
Narayanan Krishna: Department of Electrical and Electronic Engineering, SASTRA Deemed University, Thanjavur 613401, India
Tomonobu Senjyu: Department of Electrical & Electronics Engineering, Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan

Sustainability, 2021, vol. 13, issue 10, 1-22

Abstract: The need for inexpensive and sustainable electricity has become an exciting adventure due to the recent rise in the local population and the number of visitors visiting the Banana Islands. Banana Islands is a grid-isolated environment with abundant renewable energy, establishing a hybrid renewable energy-based power system may be a viable solution to the high cost of diesel fuel. This paper describes a dual-flow optimization method for electrifying the Banana Islands, a remote island in Sierra Leone. The study weighs the pros and cons of maintaining the current diesel-based power setup versus introducing a hybrid renewable energy system that takes backup component analysis into account. Hybrid Optimization of Multiple Energy Resources (HOMER) software is used in the first optimization to optimally design the various system configurations based on techno-economic and environmental characteristics. A Multi-Attribute Decision-Making (MADM) Model that takes into account in the second optimization, the Combinative Distance-based Assessment System (CODAS) algorithm, and various methods of assigning weights to the attributes is used to rank the best configuration. The results show that the hybrid renewable energy system is a better option for electrifying the Banana Islands than the current stand-alone system. The Analytical Hierarchy Process (AHP) method of weight assignment was found to be superior to the Entropy method. Biogas generator-assisted hybrid configurations outperformed diesel generator-assisted hybrid configurations. With an optimum design of 101 kW PV, 1 wind turbine, 50 kW biogas, 86 batteries, and a 37.8 kW converter, the PV-wind-biogas-battery system is rated as the best configuration. It has a net present cost (NPC) of $487,247, a cost of energy (COE) of $0.211/kWh, and CO 2 emission of 17.5 kg/year. Sensitivity analyses reveal that changes in the rate of inflation and the cost of storage have a significant effect on the overall cost of the configuration.

Keywords: hybrid systems; techno-economic-environmental analysis; off-grid; multi-attributes decision-making; weight assignment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/10/5615/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/10/5615/ (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:13:y:2021:i:10:p:5615-:d:556601

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 ().