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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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    References listed on IDEAS

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    1. Ashouri, Araz & Petrini, Flavio & Bornatico, Raffaele & Benz, Michael J., 2014. "Sensitivity analysis for robust design of building energy systems," Energy, Elsevier, vol. 76(C), pages 264-275.
    2. Jiang, Xingxing & Li, Shuxia & Zhang, Lina, 2012. "Sensitivity analysis of gas production from Class I hydrate reservoir by depressurization," Energy, Elsevier, vol. 39(1), pages 281-285.
    3. Yapparova, Alina & Matthäi, Stephan & Driesner, Thomas, 2014. "Realistic simulation of an aquifer thermal energy storage: Effects of injection temperature, well placement and groundwater flow," Energy, Elsevier, vol. 76(C), pages 1011-1018.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Kim, Jongchan & Lee, Youngmin & Yoon, Woon Sang & Jeon, Jae Soo & Koo, Min-Ho & Keehm, Youngseuk, 2010. "Numerical modeling of aquifer thermal energy storage system," Energy, Elsevier, vol. 35(12), pages 4955-4965.
    6. Tarancon, Miguel Angel & Del Río, Pablo, 2012. "Assessing energy-related CO2 emissions with sensitivity analysis and input-output techniques," Energy, Elsevier, vol. 37(1), pages 161-170.
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    6. 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.

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