A Multi-Tiered System Dynamics Approach for Geothermal Systems Analysis and Evaluation

Abstract

and hydroelectric, geothermal energy is unique with regards to our ability to predict the economic viability of a particular site or technology. For instance, measuring wind speed for the purposes of assessing a sites potential as a wind farm is relatively simple when compared to determining the extractable energy from an enhanced geothermal system (EGS) site. While this difficulty does not diminish the promise of geothermal energy production, it does require additional analyses to deal with the higher levels of uncertainty that exist throughout the characterization, development, and deployment process. The cost of not fully understanding how uncertainty in each of these phases impacts the probability of successfully developing a project comes in lower investment rates and longer development times. It is only by either reducing uncertainty or increasing our understanding of the implications of uncertainty that we can hope to more accurately address geothermal performance and its economic viability. To address this issue, we have developed an integrated systems modeling tool that allows a user to perform real-time tradeoff and scenario analyses. This ability allows interested parties (e.g., engineers, project planners, potential investors) to identify the optimal solution space for a given set of site characteristics, and power plant and well configurations. The tool also allows for identifying key areas of uncertainty that if better understood, would provide the largest gain in understanding and predictability and will ultimately be able to identify and assess the set of physical, technological, and economic obstacles that need to be overcome for a geothermal project to become market competitive. The integrated model framework is built using a system dynamics (SD) approach that allows for the simulation and quantification of system and sub-system relationships in order to capture the feedbacks and delays that create the complexity and non-linear behavior that is so difficult to contend with. In this way, the model is able to simulate the efficacy of investing time and/or money into reducing areas of uncertainty while assessing the timing, cost, and market competitiveness of geothermal energy production.