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.@en

Aquifer Thermal Energy Storage (ATES) is an increasingly popular technology which can be used to seasonally store thermal energy in the subsurface. This can significantly reduce the consumption of energy for space heating and cooling in larger buildings, when combined with a heat pump. However, the adoption of ATES by building owners puts pressure on available subsurface space in dense urban areas. The spatial planning of ATES requires a minimum clearance between neighboring systems to avoid thermal interactions; in the Netherlands, some cities already have a scarcity of space for new systems, limiting the energy savings which could be delivered. This situation is in part caused by the overallocation of subsurface space under current methods for ATES planning and operation. In this context, previous work using an idealized case study has shown that cooperative ATES operation could support more efficient spatial planning, by dynamically managing thermal interactions between neighboring systems in response to operating conditions. This work extends this approach for a realistic case study of ATES operation in the city of Utrecht, in the Netherlands. This case is simulated using a coupled agentbased/ geohydrological environment, under 5 different scenarios for spatial planning (representing different layout guidelines for ATES systems). In addition, a distributed stochastic model predictive control (DSMPC) approach is used to impose coupling constraints on the stored thermal volumes of neighboring ATES systems. Compared to a case without coordination, the dynamic management of thermal interactions with DSMPC can significantly improve the energy savings obtained per unit of subsurface volume allocated for ATES, without penalizing the thermal performance of individual systems. Furthermore, the DSMPC controller provides comparable computational performance, with runtimes scaling linearly with the number of simulated systems. @en

Aquifer Thermal Energy Storage (ATES) systems contribute to reducing fossil energy consumption by providing sustainable space heating and cooling for buildings by seasonal storage of heat. ATES is important for the energy transition in many urban areas in North America, Europe and Asia. Despite the modest current ATES adoption level of about 0.2% of all buildings in the Netherlands, ATES subsurface space use has already grown to congestion levels in many Dutch urban areas. This problem is to a large extent caused by the current planning and permitting approach, which uses too spacious safety margins between wells and a 2D rather than 3D perspective. The current methods for permitting and planning of ATES do not lead to optimal use of available subsurface space, and, therefore, prevent realization of the expected contribution of the reduction of greenhouse gas (GHG) emissions by ATES. Optimal use of subsurface space in dense urban settings can be achieved with a coordinated approach towards the planning and operation of ATES systems, so-called ATES planning. This research identifies and elaborates crucial practical steps to achieve optimal use of subsurface space that are currently missing in the planning method. Analysis from existing ATES plans and exploratory modeling, coupling agent-based and groundwater models were used to demonstrate that minimizing GHG emissions requires progressively stricter regulation with intensifying demand for ATES. The simulations also quantified both the thresholds beyond which such stricter rules are needed as well as the effectiveness of different planning strategies, which can now effectively be used for ATES planning in practice. The results provide scientific insight in how technical choices in ATES well design, location and operation affect optimal use of subsurface space, and what trade-offs exist between the energy efficiency of individual systems and the combined reduction of the GHG emissions from a plan area. The presented ATES planning method following from the obtained insights now fosters practical planning and design rules suitable to ensure optimal and sustainable use of subsurface space – that is, maximizing GHG emission reductions by accommodating as many ATES systems as possible in the available aquifer, while maintaining a high efficiency for the individual ATES systems.

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Methods for testing and analyzing agent-based models have drawn increasing attention in the literature, in the context of efforts to establish standard frameworks for the development and documentation of models. This process can benefit from the use of established software environments for data analysis and visualization. For instance, the popular NetLogo agent-based modelling software can be interfaced with Mathematica and R, letting modellers use the advanced analysis capabilities available in these programming languages. To extend these capabilities to an additional user base, this paper presents the pyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language. Given Python’s increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts. PyNetLogo’s features are demonstrated by controlling one of NetLogo’s example models from an interactive Python environment, then performing a global sensitivity analysis with parallel processing.

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Aquifer Thermal Energy Storage (ATES) is a building technology which can be used to seasonally store thermal energy in natural subsurface formations. In combination with a heat pump, ATES can reduce energy use for heating and cooling by more than half in larger buildings, while supporting the electrification of building energy systems. This has made the technology increasingly popular in Northern Europe. Furthermore, the climactic and subsurface conditions required for ATES can be found across Europe, Asia and North America. The potential contribution of large-scale ATES use for urban energy efficiency has not yet been evaluated in the literature. This makes it difficult for analysts and policymakers to assess its potential on an equal basis with other energy-efficient technologies. Given that ATES performance is highly site-specific and sensitive to climate conditions, this requires a spatially-explicit analysis which accounts for local properties, as well as plausible future changes in operating conditions. This work therefore synthesizes existing data sources for building, climate and aquifer properties, to estimate the long-term energy savings which could be achieved from ATES use in 588 urban areas worldwide at the 2050 horizon. Local energy demand for heating and cooling was first estimated using data from an ensemble of 8 Climate Model Intercomparison Project phase 5 (CMIP5) models, under the standard RCP8.5 and RCP2.6 future trajectories for greenhouse gas concentrations. These results were combined with forecasts for the energy intensity of building heating and cooling as a function of region and building type, and with forecasts for building floor space. Finally, energy savings from ATES were estimated across a set of plausible scenarios for ATES use, where ATES performance and adoption were assumed to be dependent on climate and subsurface properties. These scenarios were derived from current ATES adoption patterns and targets for the Netherlands. Regionally-aggregated results indicate that the potential energy savings from ATES use would be largest in North America, with an upper bound of 490 PJ/year (or 7% of energy use in the commercial building sector in large metropolitan areas). Due to favorable climate conditions for ATES, the largest potential contribution of ATES to energy savings is found in Western and Eastern Europe (8-12% of commercial building energy use). Furthermore, although China is currently not a significant market for ATES, the analysis indicates that ATES could reduce energy use in large metropolitan areas by 420 PJ/year in a high-adoption scenario. However, with the exception of Eastern Europe and Russia, increased climate change tends to reduce the energy-saving potential of ATES by shifting energy demand towards cooling. @en

Methods for decision making under deep uncertainty (DMDU) have attracted increasing interest in the context of problems such as climate adaptation, and more generally for the management of environmental systems. In contrast to “predict-then-act” management, DMDU methods aim to identify policies which are robust to uncertain future conditions. In addition, the management of complex systems typically involves meeting multiple performance objectives. DMDU methods which have been used for this purpose include many-objective robust decision making (MORDM) (Kasprzyk, Nataraj, Reed, & Lempert, 2013), and many-objective robust optimization (RO) (Watkins & McKinney, 1997; Kwakkel et al., 2015). MORDM is used to generate multiple policy alternatives and assesses their robustness a posteriori across multiple states of the world, while many-objective RO directly optimises the robustness of alternatives. @en

Complex environmental models typically require global sensitivity analysis (GSA) to account for non-linearities and parametric interactions. However, variance-based GSA is highly computationally expensive. While different screening methods can estimate GSA results, these techniques typically impose restrictions on sampling methods and input types. As an alternative, this work evaluates two decision tree-based methods to approximate GSA results: random forests, and Extra-Trees. These techniques are applicable with common sampling methods, and continuous or categorical inputs. The tree-based methods are compared to reference Sobol GSA and Morris screening techniques, for three cases: an Ishigami-Homma function, a H1N1 pandemic model, and the CDICE integrated assessment model. The Extra-Trees algorithm performs favorably compared to Morris elementary effects, accurately approximating the relative importance of Sobol total effect indices. Furthermore, Extra-Trees can estimate variable interaction importances using a pairwise permutation measure. As such, this approach could offer a user-friendly option for screening in models with inputs of mixed types.

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Aquifer Thermal Energy Storage (ATES) technology can lead to major reductions in energy demand for heating and cooling in buildings. ATES systems rely on shallow aquifers to seasonally store thermal energy and have become popular in the Netherlands, where a combination of easily accessible aquifers and strict energy regulations makes the technology especially relevant. However, this rapid adoption has made their management in dense urban areas more challenging. For instance, thermal interferences between neighboring systems can degrade storage efficiency. Policies for the permitting and spatial layout of ATES thus tend to be conservative to ensure the performance of individual systems, but this limits the space available for new systems – leading to a trade-off between individual system performance, and the overall energy savings obtained from ATES in a given area. Furthermore, recent studies show that operational uncertainties contribute to poor outcomes under current planning practices; systems in the Netherlands typically use less than half of their permitted water volume. This further reduces energy savings compared to expectations and also leads to an over-allocation of subsurface space. In this context, this work investigates the potential of a more flexible approach for ATES planning and operation, under which neighboring systems coordinate their operation. This is illustrated with a three-building idealized case, using a model predictive control approach for two control schemes: a decoupled formulation, and a centralized scheme that aims to avoid interferences between neighboring systems (assuming perfect information exchange). These control schemes are compared across a range of scenarios for spatial layout, building energy demand, and climate, using a coupled agent-based/geohydrological simulation. The simulation indicates that centralized operation could significantly improve the spatial layout efficiency of ATES systems, by allowing systems to be placed more densely without penalizing their individual performance. This effectively relaxes the trade-off between individual system performance and collective energy savings as observed in the decoupled case. The continued adoption of ATES technology provides a window of opportunity to revisit existing practices for the layout and operation of urban ATES systems, as information exchange – supported by appropriate spatial planning – could offer significant potential towards improved performance under operational uncertainties. @en

Aquifer Thermal Energy Storage (ATES) is an innovative building technology that can be used to store thermal energy in natural subsurface formations [1, 4, 10]. In combination with a heat pump, ATES can reduce the energy demand of larger buildings by more than half, which has made the technology increasingly popular in northern Europe (see Figure 1). Furthermore, the climate and subsurface conditions required for ATES use can be found in areas across Europe, Asia and North America. By the middle of the century, roughly half of the world’s urban population is therefore expected to live in areas technically suitable for ATES [2].@en

The application of seasonal Aquifer Thermal Energy Storage (ATES) contributes to meet goals for energy savings and greenhouse gas (GHG) emission reductions. Heat pumps have a crucial position in ATES systems because they dictate the operation scheme of the ATES wells and therefore play an important role in utilizing the storage potential of the subsurface. In the Netherlands, suitable climatic and geohydrological conditions in combination with progressive building energy efficiency regulation have caused the adoption of ATES to take off, resulting in a situation where demand for ATES exceeds the available subsurface space in many urban areas. The most important aspects in this problem are A) the permanent and often unused claim resulting from static permits for ATES operation, and B) excessive safety zones around wells to prevent interaction between wells. Both aspects result in an artificial reduction of subsurface space for potential new ATES systems. Recent research has shown that ATES systems could be placed much closer to each other, and that a controlled/limited degree of interaction between them can actually benefit the overall energy savings of an entire area. Two different simulation experiments were carried out to evaluate the effect of an adaptive permit capacity policy, as well as revised layout guidelines for ATES wells. Our solution provides a framework in which smaller distances between wells and adaptability of the permit volume plays a key role, to allow for optimal utilization of subsurface space for ATES and maximize GHG emission reduction. This paper shows how the total GHG emission reduction of an area can be increased by intensifying the use of the aquifer by allowing (some) interaction between ATES wells, which opens up unused but claimed subsurface space, and increase the number of heat pumps and ATES systems installed.@en

In the context of increasingly strict requirements for building energy efficiency, Aquifer Thermal Energy Storage (ATES) systems have emerged as an effective means to reduce energy demand for space heating and cooling in larger buildings. In the Netherlands, over 2000 systems are currently active, which has already raised issues with spatial planning in some areas; current planning schemes may lack the flexibility to properly address variations in ATES operation, which are driven by uncertainties across a broad range of time scales – from daily changes in building energy demand, to decadal trends for climate or groundwater conditions. This work is therefore part of a broader research effort on ATES Smart Grids (ATES-SG), which has focused on more adaptive methods for ATES management and control. In particular, improved control schemes which allow for coordination between neighboring ATES systems may offer more robust performance under uncertainty (Rostampour & Keviczky, 2016). The case studies for the ATES-SG project have so far focused on idealized cases, and on a historical simulation of ATES development in the city center of Utrecht. This poster will present an additional case study for the city center of Amsterdam, which poses several geohydrological challenges for ATES: for instance, variable density flow due to salinity gradients in the local aquifer, and varying depths for ATES systems due to the thickness of the aquifer. To study the effect of these conditions, this case uses an existing 15-layer geohydrological model of the Amsterdam region, cropped to an area of 4500m x 2500m around the Amsterdam Zuidas district. This rapidly developing business district is one of the densest areas of ATES use in Amsterdam, with 32 well doublets and 53 monowells currently registered. The geohydrological model is integrated with GIS data to accurately represent ATES spatial planning; simulated well flows are provided by a model predictive control component. This model is then simulated for two cases: a baseline decoupled configuration without coordination, and a case in which a subset of adjacent ATES systems is managed centrally to avoid overlaps between stored thermal volumes. Given that the thickness of the local aquifer offers significant potential for further ATES adoption in the area, such a coordinated approach could help maximize the benefits of future ATES development. @en

Aquifer Thermal Energy Storage (ATES) systems can significantly reduce the energy use and greenhouse gas emissions of buildings in temperate climates. However, the rapid adoption of these systems has evidenced a number of emergent issues with the operation and management of urban ATES systems, which require careful spatial planning to avoid thermal interferences or conflicts with other subsurface functions. These issues have become particularly relevant in the Netherlands, which are currently the leading market for ATES (Bloemendal et al., 2015). In some urban areas of the country, the adoption of ATES technology is thus becoming limited by the available subsurface space. This scarcity is partly caused by current approaches to ATES planning; as such, static permits tend to overestimate pumping rates and yield excessive safety margins, which in turn hamper the energy savings which could be realized by new systems. These aspects are strongly influenced by time-dependent dynamics for the adoption of ATES systems by building owners and operators, and by the variation of ATES well flows under uncertain conditions for building energy demand. In order to take these dynamics into account, previous research (Jaxa-Rozen et al., 2015) introduced a hybrid simulation architecture combining an agent-based model of ATES adoption, a Matlab control design, and a MODFLOW/SEAWAT aquifer model. This architecture was first used to study an idealized case of urban ATES development. This case evidenced a trade-off between the thermal efficiency of individual systems and the collective energy savings realized by ATES systems within a given area, which had already been suggested by other research (e.g. Sommer et al., 2015). These results also indicated that current layout guidelines may be overly conservative, and limit the adoption of new systems. The present study extends this approach to a case study of ATES planning in the city centre of Utrecht, in the Netherlands. This case is particularly relevant due to a combination of dense ATES development and complex subsurface conditions. An agent-based model of ATES adoption was thus parameterized to represent historical development patterns in the area over the 1998-2015 period, as well as plausible future adoption dynamics under a range of socio-technical uncertainties. An existing geohydrological model (Deltares, 2009) was used to represent local subsurface conditions. Preliminary results from this case study indicate that the idealized dynamics obtained in the previous case can also be observed under more realistic conditions; the geographic constraints introduced by building plot layouts and other spatial features tend to further constrain the adoption of new systems, emphasizing the risk of a scarcity of space under current layout guidelines. Furthermore, order effects appear to play a more significant role for system efficiency than in the idealized case. Earlier adopters thus tend to benefit from higher thermal efficiency due to the transient development of thermal bubbles, which could make older systems more robust to thermal interactions. In order to better understand the relationships between these processes and the operation of ATES wells under uncertainty, the case study will be extended by incorporating a Model Predictive Control approach for simulated ATES operation. @en

This paper presents a control-oriented model for combined building climate comfort and aquifer thermal energy storage (ATES) system. In particular, we first provide a description of building operational systems together with control framework variables. We then focus on the derivation of an analytical model for ATES system dynamics. The dynamics of stored thermal energy over time in each well of an ATES system is the most important concept for a building climate control framework. This concept is proportional to the volume and temperature of water in each well of an ATES system at each sampling time. In this paper we develop a novel mathematical model for both dynamical behavior of volume and temperature of water in each well of an ATES system and provide detailed steps for estimating the model parameters. To illustrate the applicability of our proposed model, a comparison based on an extensive simulation study using an aquifer groundwater simulation environment (MODFLOW) is provided. @en

Background: ATES is application is growing Application of seasonal Aquifer Thermal Energy Storage (ATES) contributes to energy saving and Greenhouse Gas (GHG)-reduction goals (CBS, 2015; EU, 2010, 2014). Recently it was shown that ATES is applicable in several parts of the world (Bloemendal et al., 2015). While in most parts of the world adoption is just beginning, in the Netherlands progressive building energy efficiency regulation already caused the adoption of ATES to take off (Heekeren and Bakema, 2015; Sommer et al., 2015). As a result of the large number of ATES systems in the Netherlands, the subsurface plays a crucial role in the energy saving objectives of The Netherlands (Kamp, 2015; SER, 2013). Problem: suboptimal use of the subsurface for energy storage ATES systems accumulate in urban areas, as can be expected with a large growth of ATES systems; at many locations in Dutch cities demand for ATES transcends the available space in the subsurface (Li, 2014; Sommer et al., 2015). Within in the Dutch legal framework and state of technology optimal use of the subsurface is not secured; i.e. minimizing the total GHG emissions in a certain area. (Bloemendal et al., 2014; Li, 2014). The most important aspects in this problem are A) the permanent and often unused claim resulting from static permits and B) excessive safety zones around wells to prevent interaction. Both aspects result in an artificial reduction of subsurface space for potential new ATES systems. Recent research has shown that ground energy storage systems could be placed much closer to each other (Bakr et al., 2013; Sommer et al., 2015), and a controlled/limited degree of interaction between them can actually benefit the overall energy savings of an entire area. Solution: the approach and first results of our research project on ATES Smart Grids The heating and cooling demand of buildings is a dynamic and hard to predict process, due to effects such as weather, climate change, changing function and usage of buildings over time. This naturally also applies to the required storage capacity in the subsurface. Because of these uncertainties the only way to optimally use the subsurface is to shift the organization of the subsurface space use from the planning phase to the operational phase. Our solution therefore provides a framework in which adaptability plays a key role. Optimal use can only be achieved when users have insight in the status and their effect on the resource they are exploiting (Ostrom, 1990). Therefore exchange of information is necessary for individual users to adapt their operation based on the current state of the subsurface and their energy demand via a controller and compensation measures. To arrive at a proof-of-concept based on our approach, we use expertise and models from different fields such as administrative policy and decision making, systems and control, and hydrogeology. A central element is the so-called agent-based model (ABM), which is a technique widely used in administrative policy and decision making in order to simulate the behavior of actors. Each agent has a controller that determines how the geothermal energy system must satisfy the energy demand of the building. Thus properties and characteristics of the building and the system are included in this controller. We apply a so-called Model Predictive Control (MPC) approach, which means that the controller takes into account the expected energy demand in the future, and also how its control strategy influences the performance of its own resources. The computed control action is implemented on a groundwater model of the area including all geothermal energy systems, which then serves as the basis for planning the control actions over the next period. This concept is developed in 3 steps up to the scale of a typical Dutch town. Currently we have a proofof- concept based on a fictitious academic model (1), which we are currently scaling up to the city-center area of Utrecht (2) after which we then incorporate the entire city of Amsterdam (3). The first results of the academic model are promising (Jaxa-Rozen et al., 2015), it shows that stable situations result even for ATES system that are placed a lot closer together than what current regulations would dictate. This has a positive effect on the total @en

Aquifer Thermal Energy Storage (ATES) can yield significant reductions in the energy use and greenhouse gas (GHG) emissions of larger buildings, and the use of these systems has been rapidly growing in Europe – especially in the Netherlands, where over 3000 systems are currently active in urban areas. However, the successful management of this technology poses a range of policy challenges, due to its reliance on subsurface resources and to the possibility of thermal interactions across adjacent systems. In particular, recent research suggests that ATES planning policies should acknowledge a potential trade-off between the total energy or GHG savings which can be obtained by ATES systems within a given area, and the economic returns realized by individual system operators. To better understand this compromise, this paper follows a simplified version of the multi-objective robust decision making framework (Kasprzyk et al., 2013), using an idealized agent-based model of ATES adoption and operation coupled with a geohydrological subsurface model. This simulation approach was used to investigate suitable options for the spatial planning of ATES systems, by exploring the behaviour of the coupled system under a set of sociotechnical and geohydrological uncertainties. A multiobjective evolutionary optimization algorithm was then applied to search for ATES well layout parameters which perform well in relation to the assessment criteria. The optimization identified a set of planning parameters which describe a Pareto-efficient trade-off between the individual and collective performance of ATES systems under uncertainty. @en

This paper proposes a building energy management framework, described by mixed logical dynamical systems due to operating constraints and logic rules, together with an aquifer thermal energy storage (ATES) model. We develop a deterministic model predictive control strategy to meet building thermal energy demand. At each sampling a mixed integer quadratic optimization problem is formulated. We then provide a simulation study using an agent-based model and a geohydrological simulation environment (MODFLOW) to illustrate the performance of the framework.

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