| Abstract |
Geothermal energy is a key pillar of the global energy transition due to its reliability, low carbon footprint, and ability to provide baseload power. However, its widespread adoption is often constrained by a relatively high Levelized Cost of Energy (LCOE), driven by factors such as high upfront investment and complex subsurface conditions. Optimization of operational parameters offers a practical approach to improving economic performance and making geothermal systems more competitive. In this context, we present TANGO (Techno-economic ANalysis of Geo-energy Operations), a Python-based tool developed for the design, assessment, and optimization of geo-energy systems. TANGO integrates geothermal modeling with techno-economic analysis to evaluate and improve system performance across a range of operating scenarios. By optimizing parameters such as mass flow rate, reinjection temperature, and surface plant specifications, TANGO identifies configurations that minimize LCOE while maintaining technical feasibility. Through representative case studies, we demonstrate how TANGO effectively supports decision-making by highlighting economically optimal strategies tailored to site-specific conditions. The results underscore the tool’s capability to reduce costs and enhance the viability of geothermal projects, thereby reinforcing the critical role of geothermal energy in achieving a sustainable and resilient energy future. |