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

Wencheng Jin, Trevor A. Atkinson, Christine Doughty, Ghanashyam Neupane, Nicolas Spycher, Travis L. McLing, Patrick F. Dobson, Robert Smith and Robert Podgorney

Renewable Energy, 2022, vol. 197, issue C, 384-397

Abstract: 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 identify HT-RTES site with optimal performance metrics 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. 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.

Keywords: Reservoir thermal energy storage; Multi-objective optimization; Machine learning; Pareto front; Neural network (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:197:y:2022:i:c:p:384-397

DOI: 10.1016/j.renene.2022.07.118

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