Equitable generation expansion planning: A data-driven approach to energy justice in hydroelectric power

This study aims to develop a structured framework for segmenting regions and designing energy planning models that promote both cost efficiency and environmental sustainability in hydro-dominated energy systems. Focusing on the United States—with a detailed case analysis of Texas—this research investigates the role of hydroelectric dams in energy landscape, emphasizing challenges encountered across various regions. Utilizing a comprehensive approach that integrates demographic, geographic, and dam property data, the research introduces a two-dimensional population segmentation methodology. By employing population segmentation, the study utilizes supervised learning methods and data analysis to categorize regions within the U.S., with specific attention given to Texas counties. Subsequently, stylized optimization models for generation expansion planning are developed to address the unique needs and requirements of the segmented regions. Results show that minimizing costs led to a 63% reduction in total expenditures but results in higher CO2 emissions. In contrast, an approach that prioritizes emissions reduction achieves a 98% decrease in CO2 emissions, though at a higher cost. Leveraging these models, the study offers significant insights into strategic energy planning, underlining the imperative of transitioning to sustainable energy solutions. Furthermore, this research discusses broader implications for advancing energy justice and suggests a roadmap for extending similar analyses to other regions with significant hydroelectric potential.