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

Grant Buster, Paul Siratovich, Nicole Taverna, Michael Rossol, Jon Weers, Andrea Blair, Jay Huggins, Christine Siega, Warren Mannington, Alex Urgel, Jonathan Cen, Jaime Quinao, Robbie Watt and John Akerley
Additional contact information
Grant Buster: National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA
Paul Siratovich: Upflow Limited, Taupo 3330, New Zealand
Nicole Taverna: National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA
Michael Rossol: National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA
Jon Weers: National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA
Andrea Blair: Upflow Limited, Taupo 3330, New Zealand
Jay Huggins: National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA
Christine Siega: Contact Energy Limited, Wairakei 3352, New Zealand
Warren Mannington: Contact Energy Limited, Wairakei 3352, New Zealand
Alex Urgel: Contact Energy Limited, Wairakei 3352, New Zealand
Jonathan Cen: Contact Energy Limited, Wairakei 3352, New Zealand
Jaime Quinao: Ngati Tuwharetoa Geothermal Assets Limited, Kawerau 3169, New Zealand
Robbie Watt: Ngati Tuwharetoa Geothermal Assets Limited, Kawerau 3169, New Zealand
John Akerley: Ormat Technologies Inc., Reno, NV 89519, USA

Energies, 2021, vol. 14, issue 20, 1-20

Abstract: Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements 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.

Keywords: geothermal power plant; systems modeling; machine learning; neural networks; system optimization; digital twins (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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