A scenario-based multi-attribute decision making approach for optimal design of a hybrid off-grid system

For off-grid electrification, hybrid renewable energy power systems have evolved as a more reliable alternative. Solar tracking systems boost PV power generation but come at a high cost, necessitating a thorough investigation of one's goals before deciding on the type of PV module and power generation approach. A hybrid energy configuration's optimum design and capacity scaling also necessitates the evaluation of numerous factors, making the choice process extremely complicated. The goal of this research is to conduct a feasibility analysis and design an optimum power system considering a two-step approach, capable of electrifying a rural community in Sierra Leone. In the first step, Hybrid Optimization Model of Multiple Energy Resources (HOMER) software is used to assess feasibility and optimally design the various scenarios, while Multi-attribute Decision Making [(MADM)-Combinative Distance-Based Assessment Algorithm (CODAS)] is used to rank and select the optimum scenario based on technical, economic, and environmental factors. With a net present cost (NPC) of ($471,993), cost of energy (COE)($0.376/kWh), and CO2 of (1.32 kg/yr), SC#2 is the most cost-effective and sustainable scenario. It has a 107 kW PV system, a 30 kW biogas generator, a 38.4 kW converter, and 129 batteries. Results proved that a tracking system is not required for the specified location, and the Sharp ND-250QCS PV module is preferred to the CanadianSolar Dymond PV in attaining the research goal. The decision-making process is validated by comparing the results of three different MADM approaches; CODAS, The Weighted Aggregated Sum Product Assessment (WASPAS), and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Sensitivity analyses revealed that the optimal scenario is superior and robust, and that its success or triumph is independent of the approach employed in assigning weights to the considered variables.