Optimization approaches for the design and operation of open-loop shallow geothermal systems
Chair of Renewable and Sustainable Energy Systems, Technical University of Munich, Garching, Germany
Fabian Böttcher
Chair of Hydrogeology, Technical University of Munich, Munich, Germany
Department for Climate and Environmental Protection (RKU), City of Munich, Munich, Germany
Kai Zosseder
Chair of Hydrogeology, Technical University of Munich, Munich, Germany
Thomas Hamacher
Chair of Renewable and Sustainable Energy Systems, Technical University of Munich, Garching, Germany
Related authors
No articles found.
Haegyeong Lee, Manuel Gossler, Kai Zosseder, Philipp Blum, Peter Bayer, and Gabriel C. Rau
EGUsphere, https://doi.org/10.5194/egusphere-2024-1949, https://doi.org/10.5194/egusphere-2024-1949, 2024
Short summary
Short summary
A systematic laboratory experiment elucidates two-phase heat transport due to water flow in saturated porous media to understand thermal propagation in aquifers. Results reveal delayed thermal arrival in the solid phase, depending on grain size and flow velocity. Analytical modeling using standard local thermal equilibrium (LTE) and advanced local thermal non-equilibrium (LTNE) theory fails to describe temperature breakthrough curves, highlighting the need for more advanced numerical approaches.
Felix Schölderle, Daniela Pfrang, and Kai Zosseder
Adv. Geosci., 58, 101–108, https://doi.org/10.5194/adgeo-58-101-2023, https://doi.org/10.5194/adgeo-58-101-2023, 2023
Short summary
Short summary
In 2019, a fiber optic cable was installed in the middle of a deep geothermal production well to the reservoir in Munich, Germany. This cable hangs freely below the pump and allows continuous measurements of the temperature at every meter of the cable. The well was put into operation for the first time in the summer of 2021. We used the fiber optic cable to monitor the temperature profile during production in the reservoir and to quantitatively interpret the flow zones using an inverse model.
Martin Peter Lipus, Felix Schölderle, Thomas Reinsch, Christopher Wollin, Charlotte Krawczyk, Daniela Pfrang, and Kai Zosseder
Solid Earth, 13, 161–176, https://doi.org/10.5194/se-13-161-2022, https://doi.org/10.5194/se-13-161-2022, 2022
Short summary
Short summary
A fiber-optic cable was installed along a freely suspended rod in a deep geothermal well in Munich, Germany. A cold-water injection test was monitored with fiber-optic distributed acoustic and temperature sensing. During injection, we observe vibrational events in the lower part of the well. On the basis of a mechanical model, we conclude that the vibrational events are caused by thermal contraction of the rod. The results illustrate potential artifacts when analyzing downhole acoustic data.
Cited articles
Audet, C. and Hare, W.: Derivative-free and blackbox optimization, Springer, ISBN 978-3-319-68913-5, https://doi.org/10.1007/978-3-319-68913-5, 2017. a, b, c
Birge, J. R. and Louveaux, F.: Introduction to stochastic programming, Springer Science & Business Media, ISBN 978-1-4614-0237-4, https://doi.org/10.1007/978-1-4614-0237-4, 2011. a
Blum, P., Menberg, K., Koch, F., Benz, S. A., Tissen, C., Hemmerle, H., and Bayer, P.: Is thermal use of groundwater a pollution?, J. Contam. Hydrol., 239, 103791, https://doi.org/10.1016/j.jconhyd.2021.103791, 2021. a, b
Bonnans, J.-F., Gilbert, J. C., Lemaréchal, C., and Sagastizábal, C. A.: Numerical optimization: theoretical and practical aspects, Springer Science & Business Media, ISBN 978-3-540-35447-5, https://doi.org/10.1007/978-3-540-35447-5, 2006. a
Böttcher, F. and Zosseder, K.: Thermal influences on groundwater in urban environments – A multivariate statistical analysis of the subsurface heat island effect in Munich, Sci. Total Environ., 810, 152193, https://doi.org/10.1016/j.scitotenv.2021.152193, 2021. a
Böttcher, F., Casasso, A., Götzl, G., and Zosseder, K.: TAP – Thermal aquifer Potential: A quantitative method to assess the spatial potential for the thermal use of groundwater, Renew. Energy, 142, 85–95, https://doi.org/10.1016/j.renene.2019.04.086, 2019. a
Bozorg-Haddad, O., Solgi, M., and Loáiciga, H. A.: Meta-heuristic and evolutionary algorithms for engineering optimization, John Wiley & Sons, ISBN 978-1-119-38705-3, 2017. a
Conn, A. R., Gould, N. I., and Toint, P. L.: Trust region methods, SIAM, ISBN 978-0-89871-460-9, 2000. a
Davis, K., Leiteritz, R., Pflüger, D., and Schulte, M.: Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps, arXiv [preprint], arXiv:2302.08199, https://doi.org/10.48550/arXiv.2302.08199, 2023. a
Diersch, H.-J. G.: FEFLOW, Springer, Berlin, Heidelberg, ISBN 978-3-642-38738-8, https://doi.org/10.1007/978-3-642-38739-5, 2014. a
Epting, J. and Huggenberger, P.: Unraveling the heat island effect observed in urban groundwater bodies – Definition of a potential natural state, J. Hydrol., 501, 193–204, https://doi.org/10.1016/j.jhydrol.2013.08.002, 2013. a
Epting, J., Böttcher, F., Mueller, M. H., García-Gil, A., Zosseder, K., and Huggenberger, P.: City-scale solutions for the energy use of shallow urban subsurface resources – Bridging the gap between theoretical and technical potentials, Renew. Energy, 147, 751–763, https://doi.org/10.1016/j.renene.2019.09.021, 2020. a
Florides, G. and Kalogirou, S.: Ground heat exchangers – A review of systems,models and applications, Renew. Energy, 32, 2461–2478, https://doi.org/10.1016/j.renene.2006.12.014, 2007. a
Gao, Q., Zhou, X.-Z., Jiang, Y., Chen, X.-L., and Yan, Y.-Y.: Numerical simulation of the thermal interaction between pumping and injecting well groups, Appl. Therm. Eng., 51, 10–19, https://doi.org/10.1016/j.applthermaleng.2012.09.017, 2013. a
García-Gil, A., Mejías Moreno, M., Garrido Schneider, E., Marazuela, M. Á., Abesser, C., Mateo Lázaro, J., and Sánchez Navarro, J. Á.: Nested Shallow Geothermal Systems, Sustainability, 12, 5152, https://doi.org/10.3390/su12125152, 2020. a
García Gil, A., Garrido Schneider, E. A., Mejías Moreno, M., and Santamarta Cerezal, J. C.: Shallow Geothermal Energy, Springer, https://doi.org/10.1007/978-3-030-92258-0, 2022. a, b
Gelhar, L. W., Welty, C., and Rehfeldt, K. R.: A Critical Review of Data on Field-Scale Dispersion in Aquifers, Water Resour. Res., 28, 1955–1974, https://doi.org/10.1029/92WR00607, 1992. a
Guimerà, J., Ortuño, F., Ruiz, E., Delos, A., and Pérez-Paricio, A.: Influence of ground-source heat pumps on groundwater, in: Conference Proceedings: European Geothermal Congress, 30 May–1 June 2007, Unterhaching, Germany, https://www.geothermal-energy.org/pdf/IGAstandard/EGC/2007/250.pdf (last access: 18 December 2023), 2007. a
Hähnlein, S., Molina-Giraldo, N., Blum, P., Bayer, P., and Grathwohl, P.: Ausbreitung von Kältefahnen im Grundwasser bei Erdwärmesonden, Grundwasser, 15, 123–133, https://doi.org/10.1007/s00767-009-0125-x, 2010. a
Halilovic, S. and Böttcher, F.: Optimization of GWHP well layouts using analytic models, Zenodo [code], https://doi.org/10.5281/zenodo.7230875, 2022. a
Halilovic, S., Böttcher, F., Kramer, S. C., Piggott, M. D., Zosseder, K., and Hamacher, T.: Well layout optimization for groundwater heat pump systems using the adjoint approach, Energ. Convers. and Manag., 268, 116033, https://doi.org/10.1016/j.enconman.2022.116033, 2022a. a, b, c, d
Halilovic, S., Odersky, L., Böttcher, F., Davis, K., Schulte, M., Zosseder, K., and Hamacher, T.: Optimization of an Energy System Model Coupled with a Numerical Hydrothermal Groundwater Simulation, in: Mapping the Energy Future – Voyage in Uncharted Territory, 43rd IAEE International Conference, 31 July–3 August 2022, International Association for Energy Economics, http://www.iaee.org/proceedings/article/17725 (last access: 18 December 2023), 2022b. a
Halilovic, S., Odersky, L., and Hamacher, T.: Integration of groundwater heat pumps into energy system optimization models, Energy, 238, 121607, https://doi.org/10.1016/j.energy.2021.121607, 2022c. a
Halilovic, S., Böttcher, F., Zosseder, K., and Hamacher, T.: Optimizing the spatial arrangement of groundwater heat pumps and their well locations, Renew. Energy, 217, 119148, https://doi.org/10.1016/j.renene.2023.119148, 2023. a
Hammond, G., Lichtner, P., Lu, C., and Mills, R. T.: PFLOTRAN: Reactive flow & transport code for use on laptops to leadership-class supercomputers, Groundwater React. Trans. Models, 5, 141–159, 2012. a
Hannah, L. A.: Stochastic optimization, Int. Encycloped. Social Behav. Sci., 2, 473–481, 2015. a
Hinze, M., Pinnau, R., Ulbrich, M., and Ulbrich, S.: Optimization with PDE constraints, in: vol. 23, Springer Science & Business Media, ISBN 978-1-4020-8839-1, https://doi.org/10.1007/978-1-4020-8839-1, 2008. a, b
Kim, J. and Nam, Y.: A Numerical Study on System Performance of Groundwater Heat Pumps, Energies, 9, 4, https://doi.org/10.3390/en9010004, 2016. a
Le Digabel, S.: Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm, ACM Trans. Math. Softw., 37, 1–15, https://doi.org/10.1145/1916461.1916468, 2011. a
Leiteritz, R., Davis, K., Schulte, M., and Pflüger, D.: A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps, arXiv [preprint], arXiv:2203.14961, https://doi.org/10.48550/arXiv.2203.14961, 2022. a
Li, C. and Grossmann, I. E.: A review of stochastic programming methods for optimization of process systems under uncertainty, Front. Chem. Eng., 2, 34, https://doi.org/10.3389/fceng.2020.622241, 2021. a
Logg, A., Mardal, K., and Wells, G. N.: Automated Solution of Differential Equations by the Finite Element Method, Springer, https://doi.org/10.1007/978-3-642-23099-8, 2012. a
Lo Russo, S. and Civita, M. V.: Open-loop groundwater heat pumps development for large buildings: A case study, Geothermics, 38, 335–345, https://doi.org/10.1016/j.geothermics.2008.12.009, 2009. a
Marler, R. T. and Arora, J. S.: The weighted sum method for multi-objective optimization: new insights, Struct. Multidiscip. Optimiz., 41, 853–862, https://doi.org/10.1007/s00158-009-0460-7, 2010. a
Menberg, K., Bayer, P., Zosseder, K., Rumohr, S., and Blum, P.: Subsurface urban heat islands in German cities, Sci. Total Environ., 442, 123–133, https://doi.org/10.1016/j.scitotenv.2012.10.043, 2013. a
Naumann, U.: The Art of Differentiating Computer Programs, Society for Industrial and Applied Mathematics, https://doi.org/10.1137/1.9781611972078, 2011. a
Nocedal, J. and Wright, S. J.: Numerical optimization, Springer, ISBN 978-0-387-22742-9, https://doi.org/10.1007/b98874, 1999. a
Ohmer, M., Klester, A., Kissinger, A., Mirbach, S., Class, H., Schneider, M., Lindenlaub, M., Bauer, M., Liesch, T., Menberg, K., and Blum, P.: Berechnung von Temperaturfahnen im Grundwasser mit analytischen und numerischen Modellen, Grundwasser, 27, 113–129, https://doi.org/10.1007/s00767-022-00509-2, 2022. a
Park, D., Lee, E., Kaown, D., Lee, S.-S., and Lee, K.-K.: Determination of optimal well locations and pumping/injection rates for groundwater heat pump system, Geothermics, 92, 102050, https://doi.org/10.1016/j.geothermics.2021.102050, 2021. a, b
Park, D. K., Kaown, D., and Lee, K.-K.: Development of a simulation-optimization model for sustainable operation of groundwater heat pump system, Renew. Energy, 145, 585–595, https://doi.org/10.1016/j.renene.2019.06.039, 2020. a, b, c
Perego, R., Dalla Santa, G., Galgaro, A., and Pera, S.: Intensive thermal exploitation from closed and open shallow geothermal systems at urban scale: unmanaged conflicts and potential synergies, Geothermics, 103, 102417, https://doi.org/10.1016/j.geothermics.2022.102417, 2022. a
Pophillat, W., Attard, G., Bayer, P., Hecht-Méndez, J., and Blum, P.: Analytical solutions for predicting thermal plumes of groundwater heat pump systems, Renew. Energy, 147, 2696–2707, https://doi.org/10.1016/j.renene.2018.07.148, 2020. a, b
Raissi, M., Perdikaris, P., and Karniadakis, G. E.: Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations, arXiv [preprint], arXiv:1711.10561, https://doi.org/10.48550/arXiv.1711.10561, 2017. a
Rathgeber, F., Ham, D. A., Mitchell, L., Lange, M., Luporini, F., Mcrae, A. T. T., Bercea, G.-T., Markall, G. R., and Kelly, P. H. J.: Firedrake, ACM Trans. Math. Softw., 43, 1–27, https://doi.org/10.1145/2998441, 2017. a
Russo, S. L., Taddia, G., and Verda, V.: Development of the thermally affected zone (TAZ) around a groundwater heat pump (GWHP) system: A sensitivity analysis, Geothermics, 43, 66–74, https://doi.org/10.1016/j.geothermics.2012.02.001, 2012. a
Russo, S. L., Taddia, G., Gnavi, L., and Verda, V.: Neural network approach to prediction of temperatures around groundwater heat pump systems, Hydrogeol. J., 22, 205–216, https://doi.org/10.1007/s10040-013-1072-2, 2014. a
Schrijver, A.: Theory of linear and integer programming, John Wiley & Sons, ISBN 978-0-471-98232-6, 1998. a
Stauffer, F., Bayer, P., Blum, P., Giraldo, N. M., and Kinzelbach, W.: Thermal use of shallow groundwater, CRC Press, Boca Raton, Florida, ISBN 9781466560192, 2014. a
Tröltzsch, F.: Optimal control of partial differential equations: theory, methods, and applications, in: Graduate Studies in Mathematics, Band 112, American Mathematical Society, ISBN 978-0821849040, 2010. a
Zhou, Y.-z. and Zhou, Z.-f.: Simulation of Thermal Transport in Aquifer: A GWHP System in Chengdu, China, J. Hydrodynam., 21, 647–657, https://doi.org/10.1016/S1001-6058(08)60196-1, 2009. a
Short summary
This study focuses on the optimization of open-loop shallow geothermal systems to improve their efficiency and sustainability. Different approaches to solve optimization problems in this field are explored, their strengths and limitations are highlighted, and recommendations are given for their use and future developments. The study can be a valuable basis for researchers and practitioners involved in the management and optimization of shallow geothermal systems.
This study focuses on the optimization of open-loop shallow geothermal systems to improve their...