Aquifer Thermal Energy Storage (ATES) smart grids: Large-scale seasonal energy storage as a distributed energy management solution

Aquifer Thermal Energy Storage (ATES) is a building technology used to seasonally store thermal energy in the subsurface, which can reduce the energy use of larger buildings by more than half. The spatial layout of ATES systems is a key aspect for the technology, as thermal interactions between neighboring systems can degrade system performance. In light of this issue, current planning policies for ATES aim to avoid thermal interactions; however, under such policies, some urban areas already lack space for the further development of ATES, limiting achievable energy savings. We show how information exchange between ATES systems can support the dynamic management of thermal interactions, so that a significantly denser layout can be applied to increase energy savings in a given area without affecting system performance. To illustrate this approach, we simulate a distributed control framework across a range of scenarios for spatial planning and ATES operation in the city center of Utrecht, in The Netherlands. The results indicate that the dynamic management of thermal interactions can improve specific greenhouse gas savings by up to 40% per unit of allocated subsurface volume, for an equivalent level of ATES economic performance. However, taking advantage of this approach will require revised spatial planning policies to allow a denser development of ATES in urban areas.

[1]  Johan R. Valstar,et al.  The impact of aquifer heterogeneity on the performance of aquifer thermal energy storage , 2013 .

[2]  Riccardo M. G. Ferrari,et al.  Differentially-private distributed fault diagnosis for large-scale nonlinear uncertain systems , 2018 .

[3]  C. Doughty,et al.  A Dimensionless Parameter Approach to the Thermal Behavior of an Aquifer Thermal Energy Storage System , 1982 .

[4]  J. Lofberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).

[5]  M Bakker,et al.  Scripting MODFLOW Model Development Using Python and FloPy , 2016, Ground water.

[6]  Hans Hoes,et al.  An aquifer thermal storage system in a Belgian hospital: Long-term experimental evaluation of energy , 2011 .

[7]  Tariq Samad,et al.  Smart grid technologies and applications for the industrial sector , 2012, Comput. Chem. Eng..

[8]  Philipp Blum,et al.  Sustainability and policy for the thermal use of shallow geothermal energy , 2013 .

[9]  P. Blum,et al.  Worldwide application of aquifer thermal energy storage – A review , 2018, Renewable and Sustainable Energy Reviews.

[10]  P. Blum,et al.  Analytical solutions for predicting thermal plumes of groundwater heat pump systems , 2020 .

[11]  E.H.A. Van Vliet Flexibility in heat demand at the TU Delft campus smart thermal grid with phase change materials , 2013 .

[12]  Martin Bloemendal,et al.  Combining climatic and geo-hydrological preconditions as a method to determine world potential for aquifer thermal energy storage. , 2015, The Science of the total environment.

[13]  Tamás Keviczky,et al.  Distributed Stochastic Model Predictive Control Synthesis for Large-Scale Uncertain Linear Systems , 2018, 2018 Annual American Control Conference (ACC).

[14]  T. Olsthoorn,et al.  Methods for planning of ATES systems , 2018 .

[15]  Martin Bloemendal,et al.  The adoption and diffusion of common-pool resource-dependent technologies: The case of aquifer Thermal Energy Storage systems , 2015, 2015 Portland International Conference on Management of Engineering and Technology (PICMET).

[16]  Ingo Leusbrock,et al.  Optimization and spatial pattern of large-scale aquifer thermal energy storage , 2015 .

[17]  Philip Haves,et al.  Model predictive control for the operation of building cooling systems , 2010, Proceedings of the 2010 American Control Conference.

[18]  G. Hardin,et al.  The Tragedy of the Commons , 1968, Green Planet Blues.

[19]  N. Hartog,et al.  Analysis of the impact of storage conditions on the thermal recovery efficiency of low-temperature ATES systems , 2018 .

[20]  Martin Bloemendal,et al.  How to achieve optimal and sustainable use of the subsurface for Aquifer Thermal Energy Storage , 2014 .

[21]  Philipp Grunewald,et al.  Protecting data privacy is key to a smart energy future , 2018, Nature Energy.

[22]  Admir Ceric,et al.  On using simple time‐of‐travel capture zone delineation methods , 2005, Ground water.

[23]  Philipp Blum,et al.  Greenhouse gas emission savings of ground source heat pump systems in Europe: A review , 2012 .

[24]  E. Ostrom A General Framework for Analyzing Sustainability of Social-Ecological Systems , 2009, Science.