Updated U.S. geothermal supply curve

This paper documents the approach used to update the U.S. geothermal supply curve. The geothermal supply curve analysis undertaken in this study estimates the supply of electricity generation potential from geothermal resources in the United States and the levelized cost of electricity (LCOE), capital costs, and operating and maintenance costs associated with developing these geothermal resources. The supply curve data are used as input to annual reporting by the U.s. Department of Energy (DOE) under the Government Performance and Results Act of 1993, the DOE portfolio development support processes, and market penetration models in support of other DOE analyses. Supply curves were developed for four categories of geothermal resources: identified hydrothermal (6.4 GW(e)), undiscovered hydrothermal (30.0 GW(e)), near-hydrothermal field enhanced geothermal systems (EGS) (7.0 GW(e)) and deep EGS (15,900 GW(e)). Two cases were considered: a base case, which assumes modest improvements in EGS reservoir performance from current benchmarks, and a target case, which assumes significant advances in reservoir performance from the Geothermal Technologies Program (GTP or the Program) funding of EGS research, development, and demonstration projects. Project development costs for the geothermal resources in the assessment were estimated using the Geothermal Electricity Technology Evaluation Model (GETEM). Inputs for GETEM were based on probability distributions of geothermal technology costs and performance levels from experts submitted as part of the GTP's 2009 technical risk assessment. Supply curves were generated for each of the four geothermal resource categories for both the base and target cases. Capital costs by project phase for the different technologies were also calculated. For both cases, hydrothermal resources dominate the lower cost range of the combined geothermal supply curve. The supply curves indicate that the reservoir performance improvements assumed in the target case could significantly lower EGS costs and greatly increase EGS deployment over the base case. The paper discusses the results of the supply curve analysis and improvements that can be made to future supply curve representations.