Due to population growth and industrial development in the Kingdom of Saudi Arabia (KSA), energy demand has increased. On the other hand, the consumption of fossil fuels has led to adverse environmental effects. This has led to government investment in clean energy. In many sectors of this country, there is good potential for using wind energy. Production of hydrogen from wind farms is a method for reducing energy swings, and it may also be utilized as a fuel in Saudi Arabia's businesses. As a result, the purpose of this research is to discover the best location for hydrogen generation from wind energy utilizing Multi-Criteria Decision-Making (MCDM) techniques. The criteria were weighted using the Step-wise Weight Assessment Ratio Analysis (SWARA) approach. The most important criteria were determined to be "average wind speed," "number of refineries in the region," and "wind power density" with values of 0.2780, 0.2570, and 0.1570, sequentially. Then, the Weighted Aggregated Sum Product Assessment (WASPAS) approach was used to rank the locations. Finally, three other techniques, namely the Evaluation based on Distance from Average Solution (EDAS), the COmplex PRoportional ASsessment (COPRAS), and the Weighted Sum Model (WSM) were used to validate the results. Saudi Arabia's Eastern Province was recognized as the best position for hydrogen expansion in the country. The Eastern Province, with a rated capacity of 900 kW, was anticipated to produce 1863 MWh of energy and 30.16 tons of hydrogen every year.
Highlights• Wind potential in Saudi Arabia was evaluated for hydrogen production.• Location planning was analyzed to rank locations.List of Symbols and Abbreviations: COPRAS, the complex proportional assessment; EDAS, the evaluation based on distance from average solution; MCDM, multi-criteria decision-making; SWARA, the stepwise weight assessment ratio analysis; WASPAS, the weighted aggregated sum product assessment; WPM, the weighted product model; WSM, the weighted sum model; c, scale parameter; C F , the capacity factor; E WT , the annual wind power generation; ec el , the power needed for electrolysis; f v ð Þ, the distribution function of Weibull probability; h 1 , height of measuring wind speed; h 2 , height of wind turbine tower; i, the alternatives for decision making problem; j, the criteria for decision making problem; k, shape parameter; M hydrogen , the amount of hydrogen production; q j , the local weight of criterion j; S j , the relative value of criterion j; w j , the final weight of criterion j; x ij , the performance of alternative i in terms of criterion j; x ij , the normalized performance of alternative i in terms of criterion j; v, average wind speed; v 1 , wind speed at the height of measurement (h 1 ); v 2 , wind speed at the height of measurement (h 2 ); v i , the cut-in speed; v r , the nominal speed; v 0 , the cut-out speed; WPS i , the generalized criterion of weighted aggregation of additive and multiplicative methods for alternative i; η conv , the rectifier performance; Γ, gamma funct...