IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v90y2015ip2p1349-1359.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421500835X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.06.079?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. 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).
    5. 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.
    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.
    7. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qi, Cuiting & Zhou, Renjie & Zhan, Hongbin, 2023. "Analysis of heat transfer in an aquifer thermal energy storage system: On the role of two-dimensional thermal conduction," Renewable Energy, Elsevier, vol. 217(C).
    2. Xiao, Xiao & Jiang, Zhenjiao & Owen, Daniel & Schrank, Christoph, 2016. "Numerical simulation of a high-temperature aquifer thermal energy storage system coupled with heating and cooling of a thermal plant in a cold region, China," Energy, Elsevier, vol. 112(C), pages 443-456.
    3. Liu, Xueling & Wang, Yuanming & Li, Shuai & Jiang, Xin & Fu, Weijuan, 2020. "The influence of reinjection and hydrogeological parameters on thermal energy storage in brine aquifer," Applied Energy, Elsevier, vol. 278(C).
    4. Fleuchaus, Paul & Schüppler, Simon & Godschalk, Bas & Bakema, Guido & Blum, Philipp, 2020. "Performance analysis of Aquifer Thermal Energy Storage (ATES)," Renewable Energy, Elsevier, vol. 146(C), pages 1536-1548.
    5. 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.
    6. Brown, C.S. & Kolo, I. & Lyden, A. & Franken, L. & Kerr, N. & Marshall-Cross, D. & Watson, S. & Falcone, G. & Friedrich, D. & Diamond, J., 2024. "Assessing the technical potential for underground thermal energy storage in the UK," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    7. Kastner, O. & Norden, B. & Klapperer, S. & Park, S. & Urpi, L. & Cacace, M. & Blöcher, G., 2017. "Thermal solar energy storage in Jurassic aquifers in Northeastern Germany: A simulation study," Renewable Energy, Elsevier, vol. 104(C), pages 290-306.
    8. Jin, Wencheng & Atkinson, Trevor A. & Doughty, Christine & Neupane, Ghanashyam & Spycher, Nicolas & McLing, Travis L. & Dobson, Patrick F. & Smith, Robert & Podgorney, Robert, 2022. "Machine-learning-assisted high-temperature reservoir thermal energy storage optimization," Renewable Energy, Elsevier, vol. 197(C), pages 384-397.
    9. Xie, Kun & Nian, Yong-Le & Cheng, Wen-Long, 2018. "Analysis and optimization of underground thermal energy storage using depleted oil wells," Energy, Elsevier, vol. 163(C), pages 1006-1016.
    10. 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.
    11. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    12. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    13. Emmanouil Androulakis & Christos Koukouvinos & Kalliopi Mylona & Filia Vonta, 2010. "A real survival analysis application via variable selection methods for Cox's proportional hazards model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1399-1406.
    14. Singh, Rakhi & Stufken, John, 2024. "Factor selection in screening experiments by aggregation over random models," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    15. Koki Momoki & Takuma Yoshida, 2024. "Hypothesis testing for varying coefficient models in tail index regression," Statistical Papers, Springer, vol. 65(6), pages 3821-3852, August.
    16. Lili Pan & Ziyan Luo & Naihua Xiu, 2017. "Restricted Robinson Constraint Qualification and Optimality for Cardinality-Constrained Cone Programming," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 104-118, October.
    17. Gerhard Tutz & Moritz Berger, 2018. "Tree-structured modelling of categorical predictors in generalized additive regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 737-758, September.
    18. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    19. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    20. Hang Yu & Yuanjia Wang & Donglin Zeng, 2023. "A general framework of nonparametric feature selection in high‐dimensional data," Biometrics, The International Biometric Society, vol. 79(2), pages 951-963, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:90:y:2015:i:p2:p:1349-1359. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.