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Sensitivity analysis of recovery efficiency in high-temperature aquifer thermal energy storage with single well

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  • Jeon, Jun-Seo
  • Lee, Seung-Rae
  • Pasquinelli, Lisa
  • Fabricius, Ida Lykke

Abstract

High-temperature aquifer thermal energy storage system usually shows higher performance than other borehole thermal energy storage systems. Although there is a limitation in the widespread use of the HT-ATES system because of several technical problems such as clogging, corrosion, etc., it is getting more attention as these issues are gradually alleviated. In this study, a sensitivity analysis of recovery efficiency in two cases of HT-ATES system with a single well is conducted to select key parameters. For a fractional factorial design used to choose input parameters with uniformity, the optimal Latin hypercube sampling with an enhanced stochastic evolutionary algorithm is considered. Then, the recovery efficiency is obtained using a computer model developed by COMSOL Multiphysics. With input and output variables, the surrogate modeling technique, namely the Gaussian-Kriging method with Smoothly Clopped Absolute Deviation Penalty, is utilized. Finally, the sensitivity analysis is performed based on the variation decomposition. According to the result of sensitivity analysis, the most important input variables are selected and confirmed to consider the interaction effects for each case and it is confirmed that key parameters vary with the experiment domain of hydraulic and thermal properties as well as the number of input variables.

Suggested Citation

  • Jeon, Jun-Seo & Lee, Seung-Rae & Pasquinelli, Lisa & Fabricius, Ida Lykke, 2015. "Sensitivity analysis of recovery efficiency in high-temperature aquifer thermal energy storage with single well," Energy, Elsevier, vol. 90(P2), pages 1349-1359.
    • Handle: RePEc:eee:energy:v:90:y:2015:i:p2:p:1349-1359
    DOI: 10.1016/j.energy.2015.06.079
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    1. Shi, Yu & Cui, Qiliang & Song, Xianzhi & Liu, Shaomin & Yang, Zijiang & Peng, Junlan & Wang, Lizhi & Guo, Yanchun, 2023. "Thermal performance of the aquifer thermal energy storage system considering vertical heat losses through aquitards," Renewable Energy, Elsevier, vol. 207(C), pages 447-460.
    2. Els van der Roest & Stijn Beernink & Niels Hartog & Jan Peter van der Hoek & Martin Bloemendal, 2021. "Towards Sustainable Heat Supply with Decentralized Multi-Energy Systems by Integration of Subsurface Seasonal Heat Storage," Energies, MDPI, vol. 14(23), pages 1-31, November.
    3. Lyden, A. & Brown, C.S. & Kolo, I. & Falcone, G. & Friedrich, D., 2022. "Seasonal thermal energy storage in smart energy systems: District-level applications and modelling approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Chen, Kecheng & Sun, Xiang & Soga, Kenichi & Nico, Peter S. & Dobson, Patrick F., 2024. "Machine-learning-assisted long-term G functions for bidirectional aquifer thermal energy storage system operation," Energy, Elsevier, vol. 301(C).
    5. Song, Yang & Wang, Yanhui & Yang, Shaoqiong & Wang, Shuxin & Yang, Ming, 2020. "Sensitivity analysis and parameter optimization of energy consumption for underwater gliders," Energy, Elsevier, vol. 191(C).
    6. Jonathan Banks & Spencer Poulette & Jens Grimmer & Florian Bauer & Eva Schill, 2021. "Geochemical Changes Associated with High-Temperature Heat Storage at Intermediate Depth: Thermodynamic Equilibrium Models for the DeepStor Site in the Upper Rhine Graben, Germany," Energies, MDPI, vol. 14(19), pages 1-23, September.
    7. Fleuchaus, Paul & Godschalk, Bas & Stober, Ingrid & Blum, Philipp, 2018. "Worldwide application of aquifer thermal energy storage – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 861-876.
    8. Li, Shuang & Wang, Gaosheng & Zhou, Mengmeng & Song, Xianzhi & Shi, Yu & Yi, Junlin & Zhao, Jialin & Zhou, Yifan, 2024. "Thermal performance of an aquifer thermal energy storage system: Insights from novel multilateral wells," Energy, Elsevier, vol. 294(C).

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