Spatial Analysis and Modelling of Wind Farm Site Suitability in Nasarawa State, North-Central Nigeria

Abstract

There has been an increasing global and local interest in developing renewable, clean, and cheap energy towards achieving Goal number 7 of the Sustainable Development Goals (SDG). However, decisions involving suitable and sustainable locations for renewable energy projects remain an important task. This study employed Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) to spatially analyze and model wind farm site suitability in Nasarawa State. The aim is to integrate the environmental, social, and economic aspects of decision-making for identifying sustainable wind farm sites. The study distinguished between two sets of decision criteria: decision constraints and decision factors. The former defined the exclusion zones while the latter were standardized based on fuzzy logic to depict varying degrees of suitability across the State. The MCDA applied the weighted linear combination method, with relative weights generated through pairwise comparisons of the analytic hierarchy process to analyze three policy scenarios: equal weights, environmental/social priority, and economic priority scenario. A combination of resulting composite maps from the constraints and the factors gave the final suitability maps. The resulting suitability index (SI) for the respective policy scenario describes the degrees of suitability: Ideal locations were denoted by one (1) and the not suitable locations by zero (0), with values in-between depicting varying degrees of wind farm site suitability. Based on the SI, priority locations indicating areas with good prospects, in addition to the most suitable parcels of land, were identi-

A Comprehensive Resource Assessment for Wind Power Generation on the Rural Island of Sibuyan, Philippines

“…While mapping wind power resources, each parameter underwent a thorough evaluation by assigning feature weights through reclassification and standardization and transforming levels into a scale ranging from one to five indexes. This scale ranged from poor suitability (1) to high suitability (5) for wind power development. The assigned values were determined considering the significance of each level or category.…”

“…Integrating GIS-based and MCDM techniques has become instrumental in spatial analyses, particularly in risk and suitability assessments for renewable energy projects like solar and wind. Numerous MCDM methods, including AHP [8,9,11,15,28,49,[57][58][59], fuzzy AHP [16,47,60], integrated AHP [36], analytical network process (ANP) [18], decision-making trial and evaluation laboratory (DEMATEL) [18], adaptive neuro-fuzzy inference system (ANFIS) [19], density-based clustering [29], best-worst method (BMW) [1], weighted linear combination (WLC) [1,5,8,47,49], data envelopment analysis (DEA) [61], fuzzy complex proportional assessment (COPRAS) [61], preference ranking organization method for enrichment evaluations (PROMETHEE II) [15], and spatial decision support system (SDSS) [4], have been applied in wind power resource assessments. Among these, AHP stands out as a robust and widely utilized technique due to its simplicity and effectiveness in solving complex decision problems within specific case studies.…”

“…Energy drives countries' economic growth worldwide and is one of the fundamental necessities for assisting scientific and industrial progress [1]. Throughout history, fossil fuels have been the predominant energy source, and their demand has surged since the Industrial Revolution [2][3][4][5]. Due to population growth and increasing urbanization, energy demand will continue to rise, with the Energy Information Administration (EIA) predicting a 50% rise in worldwide power usage from 2018 to 2050.…”

“…Precise resource forecasting is essential to identify optimal site locations for wind farms [2,5,15,18]. Geographic information system (GIS) software programs, such as ArcGIS Pro and QGIS, are widely utilized to analyze attributes with geospatial components.…”