A successful design and operation of geothermal energy systems require a multidisciplinary approach combining engineering, geoscience and economics. The complex interactions between individual geothermal system components can be captured using techno-economic models. An example of such model is the GEOPHIRES software developed at the Cornell Energy Institute, which allows users to determine the optimal configurations of geothermal systems and quantify their technical and economic performance. The main objective of this work was to improve the competitiveness of geothermal energy by developing improved energy conversion and distribution technologies and by providing well cost models used for the GEOPHIRES software. The first part of this work focused on the development of organic Rankine cycle (ORC) power plants used in geothermal applications. This goal was addressed in multiple ways. First, the efficiency of ORCs was correlated with the molecular structure of working fluids. The developed methodology can be used to evaluate performance of ORCs using less common working fluids, for which no accurate equations of state (EOS) exist. This dissertation also supported the development of more accurate EOS models for next-generation working fluids by providing measurements of isobaric heat capacity (Cp) of pure fluids and mixtures. To expand the thermodynamic data library for these fluids, a flow calorimeter for measuring Cp in liquid, vapor, and supercritical phases was developed. Lastly, this work evaluated the ways to effectively incorporate geothermal utilization systems into the existing energy infrastructure. A feasibility study of a hybrid geothermal-biomass-natural gas energy system for Cornell University campus was done to analyze the opportunities for improving the integration of low-temperature geothermal systems.
In addition to the work on geothermal utilization systems, this dissertation quantified the costs and uncertainties associated with drilling and completion of geothermal wells. The well cost correlations were developed using a predictive well cost model and the records of recently drilled geothermal wells. The presented analysis can reduce the financial risk involved in geothermal systems by quantifying the well cost uncertainty and its impact on the project economics.