Machine-learning-assisted high-temperature reservoir thermal energy storage optimization
نویسندگان
چکیده
High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving goal of net-zero carbon economy, given its capability balance intermittent nature renewable generation. In this study, a machine-learning-assisted computational framework is presented identify HT-RTES site with optimal performance metrics by combining physics-based simulation stochastic hydrogeologic formation and operation parameters, artificial neural network regression data, genetic algorithm-enabled multi-objective optimization. A doublet well configuration layered (aquitard-aquifer-aquitard) generic simulated for cases continuous seasonal-cycle scenarios. Neural network-based surrogate models are developed two scenarios applied generate Pareto fronts four sites. The solutions indicate operation-scenario (i.e., fluid cycle) reservoir-site dependent, have competing effects cycle. can be suitable sites HT-RTES, proposed sheds light on design resilient systems.
منابع مشابه
High temperature thermal energy
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ژورنال
عنوان ژورنال: Renewable Energy
سال: 2022
ISSN: ['0960-1481', '1879-0682']
DOI: https://doi.org/10.1016/j.renene.2022.07.118