DEVELOPMENT OF A GIS FRAMEWORK FOR ONSHORE WIND ENERGY FEASIBILITY ASSESSMENT AND SITING EFFORT PRIORITIZATION

Abstract

Already limited by the geophysical requirements inherent to wind energy production, the task of siting wind farms is made all the more daunting for developers by the need to navigate a regulatory environment (undergirded by sociopolitical realities) filled with obstacles to the construction of the tall, rotating structures that comprise a wind farm. Geographic information system analysis is an effective option for managing the vast amounts of data necessary to pinpoint the locations most likely to result in successful development. Past studies have developed methods of combining different limiting factors into a combined score, but efforts within the United States have mostly been theoretical in nature or conducted at a broad scale. A methodology for high resolution wind energy production site prioritization is developed in this thesis, with the state of Ohio being used for a case study. Data ingested into the model were procured from a broad – though mostly governmental – range of reputable sources. The evolving nature of wind energy siting hindrances necessitates a methodology capable of being adapted as new limitations present themselves. The procedures developed in this thesis rely on converting all pertinent data layers into properly aligned rasters. The overall hindrance presented by each layer is quantified on a scale from zero to one, with zero being ascribed to any grid cell rendered incapable of supporting wind energy by the consideration represented by the layer in question. Because the final composite scores are calculated via multiplication of the input layers, a zero in any layer results in a similar preclusion in the composite. The composite scores calculated within this case study confirm that wind farm developers are likely to contend with concerns wherever they attempt to build within Ohio. All of the state’s grid cells have at least one hindrance, largely owing to the mobility of potentially impacted animal species. Nevertheless, the methodology developed in this thesis offers a pathway to identify the areas with the lowest overall hindrance, allowing developers to focus on regions where the concerns necessary to address are kept to a minimum.