A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

Grant Buster, Paul Siratovich, Nicole Taverna, Michael Rossol, Jonathan Weers, Andrea Blair, Jay Huggins, Christine Siega, Warren Mannington, Alex Urgel, Jonathan Cen, Jaime Quinao, Robbie Watt, John Akerley

Research output: Contribution to journalArticlepeer-review

10 Scopus Citations

Abstract

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant im-provements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as‐built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data‐driven thermodynamics approach, GOOML is able to accurately model the real‐world performance characteristics of as‐built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state‐of‐the‐art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.

Original languageAmerican English
Article number6852
Number of pages20
JournalEnergies
Volume14
Issue number20
DOIs
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

NREL Publication Number

  • NREL/JA-6A20-80614

Keywords

  • Digital twins
  • Geothermal power plant
  • Machine learning
  • Neural networks
  • System optimization
  • Systems modeling

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