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Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

Journal Article · · Renewable Energy
 [1];  [1];  [2];  [1];  [2];  [1];  [2];  [3];  [1]
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Univ. of Idaho, Moscow, ID (United States)
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High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.

Research Organization:
Idaho National Laboratory (INL), Idaho Falls, ID (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Nuclear Energy (NE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
Grant/Contract Number:
AC07-05ID14517; AC02-05CH11231
OSTI ID:
1924437
Alternate ID(s):
OSTI ID: 1882766
OSTI ID: 1963048
Report Number(s):
INL/JOU-22-65551-Rev000
Journal Information:
Renewable Energy, Journal Name: Renewable Energy Vol. 197; ISSN 0960-1481
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (1)

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files dataset January 2022

Figures / Tables (14)

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15 GEOTHERMAL ENERGY
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machine learning
multi-objective optimization
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reservoir thermal energy storage