Posters

Abstract

Abstract As of the European Commission recommendation #3219 dated 18.5.2022, urging Member States to swiftly identify suitable land and sea areas for renewable energy projects, the process of selecting appropriate sites for wind energy entails evaluating diverse factors. These factors include assessing the available technical wind potential, considering exclusion zones, encompassing areas with high environmental sensitivity, military constraints, radar or aviation regulations, and other elements influencing wind-farm placement, and evaluating infrastructure availability, such as grid connectivity and road access. However, a significant challenge is evident: there is currently no unified data set encompassing all European Union Member States. This absence of a comprehensive data set impedes developers in expediting the essential pre-feasibility studies to identify suitable plots for establishing wind power plants. To address this challenge, an AI-driven approach to wind energy site selection can enhance the process, offering higher accuracy, objectivity, and reduced errors. AI algorithms can efficiently analyze legislation, including parsing documents and extracting critical information related to exclusion zones, environmental sensitivity, military constraints, radar or aviation regulations, and plans for infrastructure development—all of which influence wind-farm placement. Moreover, an AI-powered site selection procedure goes beyond technical considerations. Economic indicators such as distance from the grid or substation, wind resource potential, and other relevant economic factors might be integrated into the AI analysis. This holistic approach will ensure a comprehensive understanding of each potential site's suitability, factoring in both environmental and economic goals. The benefits of incorporating AI include: Improving Accuracy AI algorithms analyze vast datasets to identify intricate patterns, ensuring accurate assessments of technical energy potential, exclusion zones, and infrastructure availability. This minimizes the risk of selecting suboptimal locations and enhances the success of renewable energy projects. Eliminating Subjectivity AI operates based on predefined criteria and data analysis, eliminating subjective influences. This ensures a transparent and impartial evaluation of potential wind plots, free from human biases. Reducing Errors Automated data parsing and analysis by AI algorithms minimize the likelihood of inaccuracies associated with manual input, streamlining the decision-making process and enhancing the overall reliability of selected wind energy sites. Assessing Levelized Costs of Energy AI considers economic indicators to evaluate the levelized costs of energy associated with each potential site. This includes factors like infrastructure costs, transmission expenses, and expected energy output, allowing developers to make informed decisions aligning with both environmental and economic goals. By implementing these AI-driven solutions in conjunction with a unified European Renewable Energy Database, Member States can collaboratively overcome challenges associated with the lack of a single data set for wind energy pre-feasibility studies. This approach will ultimately accelerate the development of renewable energy projects across Europe.