GIS-Based Renewable Energy Site Selection Model for Offshore Wind Farms

Global energy demand is shifting towards renewables, leading to growth in offshore wind energy. The construction of offshore wind farms is considered more complicated than onshore due to the wide range of technical, environmental, and social considerations. This paper summarizes a geo-spatial model for offshore wind energy site selection that incorporates environmental impact and wind energy potential assessments in a cost-effective framework. The primary function of the model is to evaluate and rank offshore wind farm sites using a spatially integrated multi-criteria framework that addresses energy sustainability and optimization. The model is structured in four layers: the first is the data layer, which is the repository for wind, spatial, and environmental data. The second layer is the GIS-based processing layer, which applies Multi-Criteria Decision-Making Analysis (MCDA) to derive high-value potential zones. The third layer is the WindPro simulator, which calculates annual energy production, layouts of turbine placements, and wake interactions. The last layer publishes data in the form of spatially weighted site suitability maps and ranked site folders. The North Sea offshore inspection case studies provide the boundary conditions for the site selection model that uses wind resource, ecological, and environmental data. The model effectively determined areas of high development potential with wind resources and low ecological risk of development. Data received thus far show that there is a strong match between the model outputs and the already functioning wind farms in the area. This affirms the usefulness of the GIS-enabled decision-support tools for developers, policy makers, and regulators alike. The model decreases the site selection uncertainty, improves clarity in site selection and regulatory decision making, and aids the marine spatial planning objectives by integrating environmental sustainability and energy efficiency. All in all, this model promotes a data-centric and environmentally compliant, cost-effective approach to site selection for offshore wind farms. It fosters the advancement of offshore wind development.