Electricity system expansion is a topic of high interest in a world facing growing populations and associated electricity demands, while at the same time needing to address key vulnerabilities such as flood risks and water availability, and uncertainties regarding new policies, such as decarbonization. Electric power plant siting requires consideration of a wide range of multisectoral and multiscale constraints and opportunities, including energy, water, land, air, and social factors. Understanding the interactions between uncertain system expansion needs at the grid scale and siting issues at the local scale is critical for projecting the future electricity system technology mix and its ability to meet future demands cost-effectively and reliably. The Capacity Expansion Regional Feasibility (CERF) model is an open-source Python package that investigates the feasibility and cost of siting regional fleets of individual power plants (renewables and non-renewable) consistent with an expansion planning scenario. CERF takes technology-specific, multisectoral considerations into account using high-resolution geospatial suitability analyses as well as economic analyses that address interconnection costs and the locational value of new capacity. The model is structured to facilitate uncertainty analysis and the “on-the-ground” realization of capacity expansion plans under a variety of future conditions. This presentation will demonstrate CERF’s siting methodology to show how socioeconomics, human and natural geospatial constraints, interconnection and operational costs, as well as revenue opportunities all jointly influence power plant siting in the U.S.