Multi-Attribute Decision-Making Approach For A Cost-Effective And Sustainable Energy System Considering Weight Assignment Analysis

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 CO2 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.