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Towards Sustainable Heat Supply with Decentralized Multi-Energy Systems by Integration of Subsurface Seasonal Heat Storage

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In the energy transition, multi-energy systems are crucial to reduce the temporal, spatial and functional mismatch between sustainable energy supply and demand. Technologies as power-to-heat (PtH) allow flexible and effective utilisation of available surplus green electricity when integrated with seasonal heat storage options. However, insights and methods for integration of PtH and seasonal heat storage in multi-energy systems are lacking. Therefore, in this study, we developed methods for improved integration and control of a high temperature aquifer thermal energy storage (HT-ATES) system within a decentralized multi-energy system. To this end, we expanded and integrated a multi-energy system model with a numerical hydro-thermal model to dynamically simulate the functioning of several HT-ATES system designs for a case study of a neighbourhood of 2000 houses. Results show that the integration of HT-ATES with PtH allows 100% provision of the yearly heat demand, with a maximum 25% smaller heat pump than without HT-ATES. Success of the system is partly caused by the developed mode of operation whereby the heat pump lowers the threshold temperature of the HT-ATES, as this increases HT-ATES performance and decreases the overall costs of heat production. Overall, this study shows that the integration of HT-ATES in a multi-energy system is suitable to match annual heat demand and supply, and to increase local sustainable energy use.
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energies
Article
Towards Sustainable Heat Supply with Decentralized
Multi-Energy Systems by Integration of Subsurface Seasonal
Heat Storage
Els van der Roest 1, 2, * , Stijn Beernink 1,2 , Niels Hartog 1,3 , Jan Peter van der Hoek 2,4
and Martin Bloemendal 1, 2, *


Citation: van der Roest, E.; Beernink,
S.; Hartog, N.; van der Hoek, J.P.;
Bloemendal, M. Towards Sustainable
Heat Supply with Decentralized
Multi-Energy Systems by Integration
of Subsurface Seasonal Heat Storage.
Energies 2021,14, 7958. https://
doi.org/10.3390/en14237958
Academic Editor: Phillip Ligrani
Received: 27 October 2021
Accepted: 19 November 2021
Published: 29 November 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1KWR Water Research Institute, Groningenhaven 7, 3430 BB Nieuwegein, The Netherlands;
stijn.beernink@kwrwater.nl (S.B.); niels.hartog@kwrwater.nl (N.H.)
2Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1,
2638 CN Delft, The Netherlands; j.p.vanderhoek@tudelft.nl
3Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
4Waternet, Korte Oudekerkerdijk 7, 1096 AC Amsterdam, The Netherlands
*Correspondence: els.van.der.roest@kwrwater.nl (E.v.d.R.); martin.bloemendal@kwrwater.nl (M.B.)
Abstract:
In the energy transition, multi-energy systems are crucial to reduce the temporal, spatial
and functional mismatch between sustainable energy supply and demand. Technologies as power-
to-heat (PtH) allow flexible and effective utilisation of available surplus green electricity when
integrated with seasonal heat storage options. However, insights and methods for integration of PtH
and seasonal heat storage in multi-energy systems are lacking. Therefore, in this study, we developed
methods for improved integration and control of a high temperature aquifer thermal energy storage
(HT-ATES) system within a decentralized multi-energy system. To this end, we expanded and
integrated a multi-energy system model with a numerical hydro-thermal model to dynamically
simulate the functioning of several HT-ATES system designs for a case study of a neighbourhood
of 2000 houses. Results show that the integration of HT-ATES with PtH allows 100% provision of
the yearly heat demand, with a maximum 25% smaller heat pump than without HT-ATES. Success
of the system is partly caused by the developed mode of operation whereby the heat pump lowers
the threshold temperature of the HT-ATES, as this increases HT-ATES performance and decreases
the overall costs of heat production. Overall, this study shows that the integration of HT-ATES in
a multi-energy system is suitable to match annual heat demand and supply, and to increase local
sustainable energy use.
Keywords:
high temperature aquifer thermal energy storage (HT-ATES); heat storage; sustainable
heating; multi-energy system
1. Introduction
To limit global warming, governments aim to reducing greenhouse gas (GHG) emis-
sions caused by the use of fossil fuels [
1
4
] and transition to renewable energy sources. As
a result of the transition to renewable sources, the energy system will, in part, become more
decentralized with energy production by e.g., photovoltaic systems (PV) and, on a small
scale, wind, brought closer to consumers [
2
,
3
]. These types of renewable energies are inter-
mittent and thus storage facilities are required to overcome the temporal mismatch between
availability of and demand for energy [
5
7
]. These mismatches can (partly) be overcome
by the introduction of multi-energy systems (MES), as they provide possibilities for system
integration [
8
11
]. In such integrated systems, production of sustainable electricity and
heat, as well as storage and conversion of these commodities, are integrated with the goal
to efficiently maximize the utilisation of available sustainable energy and to balance supply
and demand. A MES consists of sustainable energy sources and conversion and storage
Energies 2021,14, 7958. https://doi.org/10.3390/en14237958 https://www.mdpi.com/journal/energies
Energies 2021,14, 7958 2 of 31
facilities. Different forms of conversion can be applied in a MES, such as power-to-heat
and power-to-gas (i.e., hydrogen). Consequently, traditionally separate operating sectors
have become connected, such as the heat, transport and electricity sectors [1217].
In order to overcome seasonal mismatches between supply and demand, storage of gas
from power-to-gas technologies is often applied. Additionally, great potential is attributed
to underground thermal energy storage techniques, like aquifer thermal energy storage
(ATES) systems, as these techniques offer efficient and large storage capacities at relatively
low costs [
18
]. Yet, the authors notice that seasonal heat storage is often overlooked in the
design and evaluation of sustainable multi-energy systems
[12,14,19,20]
, despite the large
potential benefits for overall system efficiency and GHG emission reduction
[13,2123]
.
Probably, there is a lack of attention for including seasonal storage in MES because
methods for the evaluation of integration based on cost-effectiveness and robustness
are lacking
[12,14,19,20]
. In previous work [
13
,
24
], we evaluated the potential benefit of
a high temperature aquifer thermal energy storage (HT-ATES) system that is used for
seasonal heat storage in a multi-energy system, and compared four MES designs [
25
].
Here it was shown that a scenario with HT-ATES led to the most balanced energy de-
mand profile compared to an all-electric and hydrogen scenario, the highest amount of
local electricity use, and lower costs for households (250–300
/year) compared to an
all-electric scenario [
25
]. To further explore the novel application of HT-ATES in a MES, a
more detailed description and evaluation of different innovative designs for the combined
HT-ATES and heat pump system is necessary. This was not included in earlier publications.
Therefore, in this research, we elaborate on the connection between the HT-ATES and the
heat pump of the MES, to provide methods for integration and foster the use of HT-ATES
within multi-energy systems.
1.1. Goal and Approach
The goal of this study is to develop novel methods for integration and control of a
HT-ATES system in an innovative power-to-heat energy system. Moreover, we assess
how these methods affect the costs and provision of sustainable heat year-round. The
methods are tested for a case-study neighbourhood to assess how conditions and the
different components in the integrated HT-ATES system affect the efficiency and ability to
sustainably meet the heat demand.
More specifically, three aspects are studied; firstly, the effect of the heat pump design
(size, condenser temperature, modes of operation between heat pump and HT-ATES) on
the heat delivery by the HT-ATES system is assessed. Secondly, the performance of the HT-
ATES system is assessed to evaluate the effectiveness of this storage component. Thirdly,
an analysis is done on the levelized costs of the heating system (in /GJ) as a whole.
To do this, a detailed model of a multi-energy system is expanded and integrated with
a numerical hydro-thermal model to simulate the functioning of a HT-ATES system. The
integration of the multi-energy system and the HT-ATES system is applied in a case study
of a neighbourhood in Nieuwegein (The Netherlands) that has been the focus of earlier
work on a multi-energy and water system [
13
,
24
27
]. In this decentralized multi-energy
system, renewable electricity (PV) is converted to high temperature heat and stored in the
HT-ATES system during summer. In winter, the HT-ATES system is used to fulfil the heat
demand of the neighbourhood.
1.2. Structure
In this paper, we first explain the HT-ATES model (2.1) and the relevant parts of the
multi-energy system model (2.2) in the methods, followed by a description of the field case
and how the multi-energy system and HT-ATES are integrated (2.3). Then, we describe the
scenarios for the case where we compare different heat pump designs (2.4) and we discuss
the assessment framework (2.5). In the results, we compare the results of the different
scenarios in terms of fulfilment of heat demand by the HT-ATES system (3.1), the HT-ATES
performance (3.2), and the levelized costs of heat production (LCOE, 3.3). In Section 4, we
Energies 2021,14, 7958 3 of 31
discuss the results and combine the different aspects from the results to more overarching
insights. Finally, in Section 5, we draw conclusions on the integration of HT-ATES in a
multi-energy system.
2. Materials and Methods
2.1. HT-ATES Model
Aquifer thermal energy storage (ATES) systems utilize groundwater to store heat (or
cold) in water-bearing layers, so-called aquifers, in the subsurface. A conventional ATES
system consists of (multiple) cold and warm wells. During heat demand, warm water
is extracted from the warm well, heat is released to the system and the cooled water is
pumped into the cold well. When cooling is needed, groundwater is extracted from the
cold well, heated and stored through the warm well [28].
For this study, a high temperature (HT-)ATES system is studied, which means that
storage temperatures are above 25
C [
29
], Figure 1. In contrast to LT-ATES systems, the
temperatures of the warm and the cold well are both above the ambient groundwater
temperature. Therefore, we use the terms hot and warm instead of warm and cold for the
wells in this study.
Energies 2021, 14, x FOR PEER REVIEW 3 of 33
we discuss the assessment framework (2.5). In the results, we compare the results of the
different scenarios in terms of fulfilment of heat demand by the HT-ATES system (3.1),
the HT-ATES performance (3.2), and the levelized costs of heat production (LCOE, 3.3).
In Section 4, we discuss the results and combine the different aspects from the results to
more overarching insights. Finally, in Section 5, we draw conclusions on the integration
of HT-ATES in a multi-energy system.
2. Materials and Methods
2.1. HT-ATES Model
Aquifer thermal energy storage (ATES) systems utilize groundwater to store heat (or
cold) in water-bearing layers, so-called aquifers, in the subsurface. A conventional ATES
system consists of (multiple) cold and warm wells. During heat demand, warm water is
extracted from the warm well, heat is released to the system and the cooled water is
pumped into the cold well. When cooling is needed, groundwater is extracted from the
cold well, heated and stored through the warm well [28].
For this study, a high temperature (HT-)ATES system is studied, which means that
storage temperatures are above 25 °C [29], Figure 1. In contrast to LT-ATES systems, the
temperatures of the warm and the cold well are both above the ambient groundwater
temperature. Therefore, we use the terms hot and warm instead of warm and cold for the
wells in this study.
Figure 1. High temperature aquifer thermal energy storage (HT-ATES) system working principle. (Left) In summer, avail-
able heat from solar + heat pump (PtH) is stored in the HT-ATES system. (Right) In CHT-ATES hot wells.
Compared to LT-ATES, the higher storage temperatures require consideration of
temperature-dependent density and viscosity to simulate the heat transport [30,31].
Therefore, the simulations for heat injection, storage and extraction are performed using
the coupled groundwater flow model MODFLOW and the multi-species transport code
MT3DMs in connection with SEAWAT [3234]. In this study, we use Flopy, a software
package to operate SEAWAT from within a Python environment [35]. The multi-energy
system model is also programmed in Python code to facilitate coupling with the ATES
model.
2.1.1. Modelling Approach
Depending on the needed maximal capacity of a specific HT-ATES system (e.g.,
MWth), the productivity of the aquifer and the well screen length (dependent on the aqui-
fer thickness), multiple hot and warm wells could be needed. In practice, these wells
should be placed in such a configuration that interaction between wells has a positive
effect. Beernink et al. (2020) showed that positive interaction between the hot and warm
Figure 1.
High temperature aquifer thermal energy storage (HT-ATES) system working principle. (
Left
) In summer,
available heat from solar + heat pump (PtH) is stored in the HT-ATES system. (Right) In CHT-ATES hot wells.
Compared to LT-ATES, the higher storage temperatures require consideration of
temperature-dependent density and viscosity to simulate the heat transport [
30
,
31
]. There-
fore, the simulations for heat injection, storage and extraction are performed using the cou-
pled groundwater flow model MODFLOW and the multi-species transport code MT3DMs
in connection with SEAWAT [
32
34
]. In this study, we use Flopy, a software package to
operate SEAWAT from within a Python environment [
35
]. The multi-energy system model
is also programmed in Python code to facilitate coupling with the ATES model.
2.1.1. Modelling Approach
Depending on the needed maximal capacity of a specific HT-ATES system (e.g., MW
th
),
the productivity of the aquifer and the well screen length (dependent on the aquifer
thickness), multiple hot and warm wells could be needed. In practice, these wells should
be placed in such a configuration that interaction between wells has a positive effect.
Beernink et al. (2020) showed that positive interaction between the hot and warm well(s)
of a HT-ATES system causes a relatively small positive effect (<7%) on overall system
performance compared to a situation without interaction [
36
]. Therefore, to simplify the
modelling exercise, we assume that the total thermal volume is stored and recovered from
one hot and one warm well which do not interact. Our simulations, compared to a 3D
Energies 2021,14, 7958 4 of 31
model, thus represents a slight underestimation of actual overall performance. Because
of these simplifications, an axisymmetric hydrogeological model could be used, as was
done previously [
30
,
37
]. For both the hot and the warm well, a separate axisymmetric
model is initialized. From monitoring data, Lopik et al. (2016) [
30
] established a calibrated
axisymmetric model of a high temperature (80
C) ATES system. In this study, we therefore
use a similar axisymmetric model set-up and parameter values. Several modelling aspects
of the model are described below:
-
Spatial discretization. The spatial discretization in the horizontal direction is 0.5 m
close to the well location. Because flow velocity decreases with the radial distance
from the well, the cell size may increase outwards. This was done logarithmically to a
maximum of 50 m at the model domain boundary. To prevent boundary conditions
from influencing simulation results, several test runs were carried out. The test runs
showed that outcomes did not change significantly (<1%) with an increasing grid
size of 1500 m around the well. This was therefore set as the outer model boundary
distance. To allow for sufficient detail/insight in the vertical (free convection) flow
component, the vertical layers are also discretized at high resolution; 0.5 m thick-
ness. Further refining of the modelling grid did not result in different results. The
discretization was therefore assessed to be sufficient.
-
Temporal discretization. Internal time steps of the SEAWAT model are limited because of
the courant number that is implemented in the advection package. We use a value of
0.8, meaning that the maximal distance that advection will be allowed in one internal
transport time step is 0.8 cell length; the length of the time step is automatically
adjusted accordingly. Model input and output is changed and transferred with the
multi-energy system model with a daily time step. This means that the flow in/out of
the wells is changed once a day and evenly divided for this timespan.
-
Boundary conditions. The boundary conditions are set as constant head and constant
temperature at the outer boundary of the modelling domain. The top and bottom of
the model are set as constant temperature and no-flow boundary. The constant tem-
perature is equal to the starting temperature of the aquifer groundwater,
Tamb = 12 C
.
Therefore, water enters the model via the well or via the outer model boundary (at
1500 m distance). Heat can leave the model domain via the top, bottom, and outer
model boundaries.
-
Subsurface characterization and hydrogeology. The characterization of the subsurface
parameters follows the subsurface model presented in [
24
], which is a 30 m thick
homogeneous aquifer. The model consists of an aquifer confined by 25 m thick
aquitards at the top and bottom. Aquifer horizontal hydraulic conductivity is 35 m/d,
aquitard horizontal hydraulic conductivity is 0.05 m/d, and hydraulic conductivity
anisotropy was used as Kh/Kv= 5 for both the aquifer and the aquitard.
-
Well implementation. The simulated wells have a fully penetrating well screen (
Lwell = 30 m
).
The well screen consists of multiple grid cells based on the vertical discretization.
The flow for each well screen cell is calculated proportionally to the transmissivity
distribution along the layers belonging to the cells. For the modelled homogeneous
aquifer, this results in equal flow distribution over the well screen length. The ex-
traction temperature of each well cell is calculated by SEAWAT as the average well
temperature at the well screen during extraction or injection.
-
Parameter values. The parameter values assigned to MODFLOW follow Lopik et al.
(2016) [
30
] and are corrected for axisymmetric flow according to Langevin (2008) [
37
],
and given in Table 1.
-
Solvers. The groundwater flow is solved using the Preconditioned Conjugate Gradient
2 package. The standard finite-difference method with upstream or central-in-space
weighting is applied in the advection package.
Energies 2021,14, 7958 5 of 31
Table 1. MODFLOW, MT3DMs and SEAWAT simulation parameters.
Parameter Value Used for Package Class [35]
Water heat capacity 4.18 kJ/kg/C RCT
Solid heat capacity * 710 kJ/kg C RCT
Water reference density 1000 kg/m3RCT
Solid density * 2640 kg/m3RCT
Water thermal conductivity 0.58 W/m/C RCT
Solid thermal conductivity 3 W/m/C RCT
Thermal distribution coefficient #1.7 ×104m3/kg RCT
Thermal retardation +2.21 RCT
Porosity 0.3 BTN
Specific storage aquifer 6×104/m LPF
Longitudinal dispersion 0.5 m DSP
Transversal dispersion 0.05 m DSP
Vertical dispersion 0.005 m DSP
Effective molecular diffusion heat #0.157 m2/day DSP
Effective molecular diffusion salt 8.6 ×106m2/day DSP
* quartz,
#
Calculated following Langevin et al. (2008) [
34
],
+
Calculated following Hecht-Mendez et al. (2010) [
38
].
RCT = MT3DMs Chemical Reaction package, BTN = MT3DMs Basic Transport package, LPF = MODFLOW Layer
Property Flow Package, DSP = MT3DMS Dispersion Package.
2.1.2. Implementation of Variable Viscosity and Density
The viscosity and density of groundwater decrease with increasing temperature. This
has an effect on the behaviour of the groundwater at elevated temperatures compared to
the ambient groundwater. Implementation of this relationship with temperature, therefore,
is important for this modelling exercise. In reality, both viscosity and density have a
non-linear relationship with temperature. In this study, we use the viscosity and density
dependency of water as implemented by Langevin et al. (2008) [
34
]. Here, the non-
linear viscosity–temperature relationship of VOSS (1984) [
39
] was used to determine the
groundwater viscosity accurately. For density, however, a linear relationship was used to
calculate the density at different temperatures. This was done because SEAWAT does not
simply allow for a non-linear density function. Therefore, we used an approximation based
on the injection temperature to include the possible influence of buoyancy flow according
to the following linear relationship, similar to previous research [40]:
ρ(T)=ρ0+δρ
δT(TTamb)(1)
where Tis the water temperature in the aquifer
C and
ρ
is the density in kg/m
3
. Here, the
density change gradient per temperature difference (
δρ
/
δ
T) is changed according to the
maximal injection temperature, resulting in a value of
0.22 for T
inj
= 50
C and a value of
0.29 for Tinj = 65 C.
2.2. Multi-Energy System Model
To analyse different layouts for future multi-energy systems (in neighbourhoods) a
simulation model was developed that matches supply and demand on an hourly basis and
integrates different renewable energy sources, conversion and storage mechanisms [
13
,
25
].
In Figure 2, an overview of the model components is given, with a highlight on the aspects
that are the focus of this study. Most of the modelling methodology has been explained
in detail earlier, including both energy and water aspects [
13
,
24
,
25
]; here we focus on the
coupling between the multi-energy system model and HT-ATES integration. The Python
simulations of different model setups are done with an hourly time step for 10 years. The
most important aspects of the model related to heat production and storage are described
in the following paragraphs.
Energies 2021,14, 7958 6 of 31
Energies 2021, 14, x FOR PEER REVIEW 6 of 33
explained in detail earlier, including both energy and water aspects [13,24,25]; here we
focus on the coupling between the multi-energy system model and HT-ATES integration.
The Python simulations of different model setups are done with an hourly time step for
10 years. The most important aspects of the model related to heat production and storage
are described in the following paragraphs.
Figure 2. Overview of the components in the multi-energy system model, with a highlight on the model parts that are the
focus of this paper.
2.2.1. COP Heat Pump
The coefficient of performance (COP) curve of the heat pump (HP) is based on sup-
plier information (GEA: cooling agent R717, extra heat recovery by oil cooling, 1.8 MWel
installation at 65 °C and 50 °C condenser temperature) for heat pump condenser temper-
atures of both 65 °C and 50 °C. From literature [41], we know that the COP of a heat pump
(COPHP) is mainly based on the temperature lift and less on the exact condenser tempera-
ture (heat sink). Therefore, we can use one equation for different condenser output tem-
peratures (THP,cond) (heat sinks), as long as the temperature lift between condenser and
evaporator temperature (THP,evap) is taken into account:
                
(2)
2.2.2. Heat Demand Pattern
The model input for heat demand is a fixed yearly heat demand that varies per type
of household. Based on the degree-day method [42], the total heat demand is distributed
over the runtime with the adaptation of using degree-hours, according to:
 
  
(3)
with hDH_t being the number of degree-hours in a certain hour and Tair,t being the out-
side air temperature, based on KNMI data from De Bilt [43]. The base temperature Tbase is
set to be 14 °C to calculate the degree-hours for a certain moment in time. In the degree
days method, it is important to choose the right base temperature, which is often set
around 18 °C [44]. We have chosen a value of 14 °C because the houses we consider in this
Figure 2. Overview of the components in the multi-energy system model, with a highlight on the model parts that are the
focus of this paper.
2.2.1. COP Heat Pump
The coefficient of performance (COP) curve of the heat pump (HP) is based on supplier
information (GEA: cooling agent R717, extra heat recovery by oil cooling, 1.8 MW
el
installa-
tion at 65
C and 50
C condenser temperature) for heat pump condenser temperatures of
both 65
C and 50
C. From literature [
41
], we know that the COP of a heat pump (COP
HP
)
is mainly based on the temperature lift and less on the exact condenser temperature (heat
sink). Therefore, we can use one equation for different condenser output temperatures
(T
HP,cond
) (heat sinks), as long as the temperature lift between condenser and evaporator
temperature (THP,evap) is taken into account:
COPHP =0.00007·(TH P,cond THP,eva p )3+0.0097·(THP,co nd THP,ev ap )20.5311 THP,cond THP,evap+14.68 (2)
2.2.2. Heat Demand Pattern
The model input for heat demand is a fixed yearly heat demand that varies per type
of household. Based on the degree-day method [
42
], the total heat demand is distributed
over the runtime with the adaptation of using degree-hours, according to:
hDH,t=Tbase Tair,t
24 i f Tair,t<Tbase (3)
with h
DH_t
being the number of degree-hours in a certain hour and T
air,t
being the outside
air temperature, based on KNMI data from De Bilt [
43
]. The base temperature T
base
is set to
be 14
C to calculate the degree-hours for a certain moment in time. In the degree days
method, it is important to choose the right base temperature, which is often set around
18 C
[
44
]. We have chosen a value of 14
C because the houses we consider in this case are
well isolated and probably have a low-temperature heating system (such as floor heating)
and large building inertia, and therefore space heating is not necessary above 14
C outside
air temperature. The heat degree hours are also weighted based on the season. This means
Energies 2021,14, 7958 7 of 31
h
DH_t
is multiplied by 1.1 in November–February, by 1.0 in March and October, and by 0.8
in the remainder of the year [44].
The next step is to calculate the heat demand of the neighbourhood for every hour of
the run period, based on the specific fraction of the total amount of heat degree hours:
Qspacehe at,t=hDH_t
HD H ·Espaceheat,total (4)
Here, Q
spaceheat,t
is the heat demand of the neighbourhood in a specific hour (t) in MJ,
HDH is the sum of all heat degree hours over the total run period (in this case 10 years),
and Espaceheat,total is the total heat demand for the run period in MJ.
Using this method implies that the distribution of the heat demand varies with the
run period chosen. On the one hand, this is an advantage as yearly variations in outside
air temperature are taken into account. On the other hand, this implies that the space
heat demand of a particular hour can vary slightly for a run period of i.e., 2010–2012 vs.
2017–2019. It is therefore important to only compare scenarios with the same run period; in
our case data from 2010–2019 are used for all scenarios.
2.2.3. Heat Supply Pattern
To ensure enough heat is stored to cover the heat demand in winter, a certain amount
of heat needs to be stored in spring and summer. This should be the heat demand plus
the expected loss during storage. The model applies a pattern with weekly values that
represent a percentage of the total yearly heat demand that should be stored in the months
from March to October, as elaborated upon in an earlier publication [
13
]. To compensate
for the heat loss, a factor is applied to the total heat demand. The model will adapt this
factor every year based on the difference in volume that is stored either in the hot or warm
well. If the difference in volume is more than 15%, the factor is either increased by 0.1 (if
the volume stored in the hot well is too small) or decreased by 0.15 (if the volume in the
warm well is too small). The starting value of the loss factor is 1.8 for this study, meaning
that the heat storage target for the HT-ATES is 1.8 times the yearly heat demand of the
neighbourhood at the start of the run time. This value of 1.8 is higher than the actual
expected heat loss over time, because in the first years of system operation a shell with
elevated temperature around the stored volume is developed. After this period, the loss
factor will start to decrease and will finally stabilize around the yearly heat loss value of
the wells (which is more around 1.1–1.4).
2.2.4. Coupling of the HT-ATES Model and the Multi-Energy System-Model
The input of the SEAWAT model (see Section 2.1) is adjusted with a daily time step,
while the multi-energy system model calculates the energy flows on an hourly basis. To
bridge the hourly and the daily models, the hourly data of the multi-energy system is
stored and combined into daily input data for the SEAWAT model. This can be inter-
preted as having an above-ground buffer tank that controls the daily fluctuations of the
energy system. From the hourly multi-energy system model, both the weighted average
temperature and the corresponding net flow from/to the HT-ATES are used as input for
the SEAWAT model. The SEAWAT model subsequently returns the temperature of the
extracted groundwater of both the warm and the hot well after simulation. These values
are then used for the next 24 h of the overall energy system model calculations.
2.3. Application of the Multi-Energy System Model in a Case Study
2.3.1. Case Description; Nieuwegein, The Netherlands
In order to apply our multi-energy system with HT-ATES integration, we present
a field case located in the eastern part of Nieuwegein (The Netherlands). We assume
2000 houses
will be connected to a low-temperature heat grid and the houses will be
suitable for low-temperature floor heating. The energy for domestic hot water production
is provided with a booster heat pump. Assumptions for heat demand are given in
Table 2
Energies 2021,14, 7958 8 of 31
and are based on the Dutch energy label B, which is not the most efficient building type but
is on a European level in the range of a Nearly Zero Energy Building (40–70 kWh/m
2
) [
45
].
Energy demand related to domestic hot water consumption is set at 3.3 GJ/person/year [
46
].
In this publication, we use the case study only to determine the heat demand of the
neighbourhood and the role of heat storage in the fulfilment of heat demand. For exact
temporal energy flows (local use, import and export) and comparison of the MES with a
HT-ATES with other possible system designs, the reader is referred to an earlier publication
focussing on the same neighbourhood case [25].
Table 2. Neighbourhood energy demand parameters.
Terraced House Apartment Total
Number of houses 1000 1000 2000
People per household 2.4 2 -
Energy demand domestic hot water 7.9 GJ/y 6.6 GJ/y 14.5 TJ/y
Space heat demand 24.4 GJ/y 17.6 GJ/y 42 TJ/y
The main heat source of the neighbourhood is heat from surface water (aquathermal
energy) provided by the Lekkanaal, a 100 m wide channel located east of the case study
area. The surface water heat is extracted in summer (April–September) and the temperature
is raised by a large scale heat pump. Part of the heat is used to provide heat directly, and
the remainder of the heat is stored in a HT-ATES system to be utilized during winter. A
HT-ATES system is chosen as the most suitable heat storage option, because of the large
storage size required for 2000 houses, combined with a relatively low amount of space
available on the surface level. The large scale heat pump will use excess electricity from
local sources such as a solar farm and home solar PV systems on houses, supplemented
with electricity from the grid to ensure enough heat is stored to fulfil the total heat demand
of the neighbourhood in winter. This system layout could assist in peak reduction on the
electricity grid by PV systems in summer and prevent high peaks in electricity demand in
winter if houses would be heated by i.e., an (air-sourced) heat pump. In this way, it is thus
possible to decouple supply and demand, which gives more flexibility in a future energy
system based on renewable energy.
Hourly weather data on outside temperature from the period 2010–2019 are used in
this analysis, from the weather station De Bilt (closest to the project location) [47].
2.3.2. Modes of Operation for the Heat Pump and HT-ATES
The heating system has different modes of operation defined in the model, depending
on the situation. In the explanation, we have chosen a heat pump condenser temperature
of 50
C and heat delivery via a district heating network (DHN) with a minimum of 40
C.
Schemes for a heat pump condenser temperature of 65
C are given in the Supplementary
Information. There are five possible modes of operation:
1.
Heat production and storage in the HT-ATES (no heat demand) In this mode of
operation, heat is produced with surface water (14–26
C) as input during the summer
months (see Figure 3A). Heat is injected into the hot aquifer at 48.5
C (assumed
1.5 C
loss over the heat exchanger).
2.
Simultaneous heat production and delivery If there is a heat demand at a time when
the heat pump is producing heat (during summer), the heat demand is fulfilled
directly from the heat pump (50
C) via the hot distributor (see Figure 3B). After
fulfilling the heat demand, the remainder of the heat is injected into the hot aquifer at
48.5 C.
3.
Heat delivery from the HT-ATES above the inlet temperature of the district heating
network (hot well > 43
C) In the period from October–April (approximately), the heat
pump is not able to produce heat with surface water as the surface water temperature
is too low (<14
C). Heat demand from the multi-energy system is now fulfilled by
extracting heat from the hot aquifer of the HT-ATES system (see Figure 3C). Water
Energies 2021,14, 7958 9 of 31
from the hot well (43–48.5
C) is extracted and exchanged with the return flow from
the DHN (25
C) and subsequently injected into the warm well (at 26.5
C). The heat
supply flow of the DHN is heated to 41.5–47 C.
4.
HT-ATES is shut off. (Hot well under threshold temperature < 43
C) In this mode
of operation, the temperature of the hot aquifer has dropped below the threshold
temperature (43
C) to guarantee heat delivery at 40
C. The HT-ATES system is now
shut off and cannot be used to fulfil the heat demand of the multi-energy system. In
this case, heat must be delivered from another (external) source, such as a peak boiler
or a sustainable heat source. This is not included in this analysis.
For this study, we added an extra mode of operation to prolong the heat delivery from
the HT-ATES system and to assess its effect on the efficiency of the heat production system
and the heat production costs.
5.
HT-ATES feeds heat pump mode; heat delivery from the HT-ATES with alternative
threshold temperature (hot well temperature between 30
C and 43
C) To prolong
heat delivery from the HT-ATES system, we designed an extra mode of operation
for the heat pump and HT-ATES. In this mode of operation, the heat delivery from
the HT-ATES continues when the temperature of the hot well is below 43
C but
above 30
C. This is possible because of additional heat exchange with the HT-ATES
in combination with a heat lift by the heat pump (see Figure 3D). First, the return flow
of the DHN is exchanged with water from the hot well (30–43
C) and thereby the
return flow is heated to 28.5–41.5
C. This already heated return flow of the DHN is
then fed to the condenser side of the heat pump and raised to 50
C. The flow from
the hot aquifer is thus first exchanged with the return flow of the DHN, which results
in a temperature of 26.5
C. Then, the same flow is led to a second heat exchanger,
to extract more heat by a (closed) loop that is connected to the evaporator side of
the heat pump (inlet temperature of 25
C). The return flow from the evaporator
side of the heat pump is then finally exchanged with the flow that is injected in the
warm well (at 16–22.5
C). In this way, the heat from the hot well is exchanged in
two stages before being injected into the warm well, making the heat exchange more
efficient. Moreover, the injection temperature in the warm well is decreased, creating
a larger
Tbetween the wells which could be beneficial for the HT-ATES efficiency.
The electricity demand of the heat pump in this mode is retrieved directly from local
RES (renewable energy systems) when possible, or from the grid when no electricity
from local RES is available.
2.4. Scenarios
The goal of this study is to identify methods for integration and control of a HT-ATES
within a multi-energy system, as well as assess how these methods affect the performance
of the energy system. In Section 2.3, we have introduced an additional mode of operation
for the heat pump/HT-ATES system. We hypothesise that this mode of operation prolongs
heat delivery from the hot well and could increase the overall performance of the heat
pump/HT-ATES system. Therefore, we have selected ten scenarios (Table 3) to examine
this hypothesis, as well as obtain general insights on how the HT-ATES system performs
within the multi-energy system.
The heat demand and heat delivery temperature are kept constant for all scenarios.
The amount of heat that should be provided to the neighbourhood houses is 55.2 TJ per
year at a temperature of at least 40
C. We have chosen to vary the condenser temperature,
the threshold temperature (HT-ATES feeds heat pump mode) and the size of the heat pump.
Table 3gives an overview of the ten scenarios that are investigated in this study. The
description code is built up as: condenser temperature|HT-ATES threshold temperature|heat
pump size.
Energies 2021,14, 7958 10 of 31
Energies 2021, 14, x FOR PEER REVIEW 10 of 33
Figure 3.
Overview of the hydraulic schemes for the different modes of operation. (
A
) Mode of
operation 1: Heat production and storage in the HT-ATES. (
B
) Mode of operation 2: Simultaneous
heat production and delivery. (
C
) Mode of operation 3: Heat delivery from the HT-ATES above
the inlet temperature of the DHN. (
D
) Mode of operation 5: HT-ATES feeds heat pump mode for
charging the HT-ATES with a heat pump condenser temperature of 50
C. In the mode of operation
4, the HT-ATES is shut-off, therefore no hydraulic scheme is shown. The shaded lines are inactive
hydraulic connections, needed for other modes of operation. An indication of the temperature (range)
of the different flows is given.
Energies 2021,14, 7958 11 of 31
Table 3. Overview of scenarios.
Heat Pump Condenser
Temperature
HT-ATES Threshold
Temperature Heat Pump Size
65|43|2 65 C 43 C 2 MWel
50|43|2 50 C 43 C 2 MWel
65|30|2 65 C 30 C 2 MWel
50|30|2 50 C 30 C 2 MWel
65|43|1.5 65 C 43 C 1.5 MWel
50|43|1.5 50 C 43 C 1.5 MWel
65|30|1.5 65 C 30 C 1.5 MWel
50|30|1.5 50 C 30 C 1.5 MWel
65|30|1 65 C 30 C 1 MWel
50|30|1 50 C 30 C 1 MWel
We compare two condenser temperatures because we hope to learn more about their
actual effects on heat delivery, recovery efficiency and costs. We know that a higher con-
denser temperature of 65
C leads to a lower COP of the heat pump compared to 50
C,
decreasing the amount of energy that can be stored with a given heat pump size. Addition-
ally, the heat pump will be more expensive at a higher condenser temperature and more
heat loss can occur at a higher storage temperature. On the other hand, storage at
65 C
leads to a smaller storage volume for the HT-ATES system, which saves costs and space.
By comparing scenarios with both condenser temperatures, we obtain more insight into
the combined result of these contradictory effects on the chosen performance parameters.
To ensure heat delivery at 40
C, the threshold value for the hot well is set at 43
C.
In Section 2.3, we introduced an HT-ATES feeds heat pump mode that allows a lower
threshold temperature of the hot well of 30 C, as the heat pump is then used for an extra
temperature lift. Scenarios 65|30|2, 65|30|1.5, 50|30|2 and the 50|30|1.5 will give insight
into the effects of including this HT-ATES feeds heat pump mode, and can be compared
to their respective twins where the HT-ATES feeds heat pump mode is not used and the
threshold temperature is set at 43 C.
Lastly, we investigate the effect of different heat pump sizes. A smaller heat pump
will save costs, but it should be large enough for the HT-ATES system to provide sufficient
heat in winter. We hypothesise that the scenarios with an extra heat pump mode lead to a
more efficient HT-ATES system, which could imply that the amount of heat stored in the
HT-ATES could be reduced. For a good comparison, we included scenarios with a 2 MW
el
,
1.5 MW
el
and 1 MW
el
heat pump. We expect the 1 MW
el
heat pump to be too small to
fulfil the heat demand of the neighbourhood in the multi-energy system (55.2 TJ/y). With
an average COP of 4 and approximately five months of heat production, a maximum of
51.8 TJ/y
could be stored with a 1 MW
el
heat pump, which is less than the heat demand of
the neighbourhood (55.2 TJ/y). However, to investigate the effect of a relatively small heat
pump on the HT-ATES system, we included two scenarios with a 1MW
el
heat pump, both
with a lower (30 C) threshold temperature.
2.5. Assessment Framework
2.5.1. Fulfilment of Heat Demand
The main goal of the combined heat pump and HT-ATES system is to fulfil the heat
demand of the neighbourhood in the multi-energy system in a reliable and affordable way.
To fulfil the heat demand at a certain moment, there are multiple options. In our system,
we first check if the heat demand can be fulfilled by direct heat production, which mainly
happens during summer. Next, it is checked if the heat demand can be fulfilled by the
HT-ATES system, which mostly happens in the winter season. If the HT-ATES feeds heat
pump mode (see Section 2.3.2) is switched on, it is possible to increase the temperature
of the HT-ATES with the heat pump, and the heat demand is fulfilled by the HT-ATES
system with some additional electricity use. Lastly, if the temperature of the hot aquifer is
below the set threshold temperature (43
C or 30
C in our case), the HT-ATES system will
Energies 2021,14, 7958 12 of 31
not be used for heat production. In this case, an external source of heat is necessary. This
external source is not defined in this paper, but could, for example, be a peak gas boiler, or
a renewable option.
In the first few years of operation, it is likely that the HT-ATES system has not yet
stored enough heat to ensure continuous heat delivery throughout the year. However, the
goal would be to reach continuous operation within a few years, so no external heat source
is necessary and the heat pump-HT-ATES system can function independently.
Because the heat pump can work on 100% renewable electricity with surface water as
a source, there are no direct GHG emissions of the system if it operates independently from
an external source. Energy use during production and recycling of system components is
not taken into account here. The GHG emissions of the external heat source are not taken
into account in this study as we do not define a particular heat source.
2.5.2. HT-ATES Performance
The performance of the HT-ATES system is assessed with two main parameters, the
well recovery efficiency of stored energy and the volume balance between the wells.
Regarding the recovery efficiency, we use three different indicators that give insight
into the efficiency of the heat system as part of the multi-energy system. The performance
of the HT-ATES system is calculated in terms of hot well recovery efficiency, warm well
recovery efficiency and system recovery efficiency. For each indicator, a separate
Tis
used, see below. The average recovery efficiency (
η
) over the total run period (10 years) is
calculated and analysed in the results section.
η=10y
0T·Vout ·cw
10y
0T·Vin ·cw
(5)
Tsystem =ˆ
Thot ˆ
Twarm (6)
Thot well =ˆ
Thot Tamb (7)
Twarm well =ˆ
Twarm Tamb (8)
T
hot
and T
warm
are the temperatures of the hot and warm well (in
C) per day, v
in
and
v
out
are the flows in or out of the hot well (m
3
/h), and c
w
is the heat capacity of water
(4180 kJ/K/m
3
). T
ambient
is the ambient background temperature of the aquifer (12
C).
Depending on the
Tapplied in Formula (5), either the system recovery efficiency (6), hot
well recovery efficiency (7), or warm well recovery efficiency (8) is calculated.
Secondly, the volume balance ratio (r
VB
) between the hot and warm wells is calculated
as described by Beernink et al. (2019) [48]:
rVB =Vheat,storage Vheat,production
Vheat,storage +Vheat,production
(9)
where V
heat,storage
is the yearly total storage volume (m
3
) and V
heat,production
is the yearly total
production volume (m
3
). Here, a positive ratio means that more volume is used to store
heat in the hot well. Therefore, this also means that less volume is stored in the warm wells.
Oppositely, a negative ratio means that more volume is stored in the warm wells.
2.5.3. MES Performance
For improved integration of HT-ATES within a multi-energy system, it is relevant to
know the efficiency of the MES as a whole, under varying modes of HT-ATES integration.
This indicator gives insight into the final efficiency by which the heat is delivered from the
Energies 2021,14, 7958 13 of 31
MES to the houses. Thus, this parameter is the amount of total delivered energy (heat) to
the neighbourhood divided by the used total energy (electricity) for heat production:
ηMES =Endel, 10y avg
COP10y avg ·Eldem, 10y av g
(10)
With
ηMES
as the MES efficiency, En
del,10 y avg
is the average amount of delivered heat
to the neighbourhood (in TJ/year over the first ten years), which can thus be lower than
the actual heat demand. COP
10 y avg
is the average COP of the heat pump over the run time
(10 years), and El
dem,10 y avg
is the average electricity demand of the total heat system over
the run time (10 years).
It includes the efficiency of both directly produced as well as stored heat and heat
loss in the district heating network. In case a certain system design is not able to provide
the total heat demand, this is corrected for by only taking the delivered heat into account
and not the total heat demand of the neighbourhood. The part of the heat that must be
delivered from an external source is thus not taken into account by this parameter. The
MES efficiency purely gives the efficiency of heat delivery of the MES including a heat
pump and HT-ATES system, without other sources.
2.5.4. Economic Analysis/Levelized Cost of Energy
We determine the levelized costs of heat production (LCOE) in
/GJ, including
the costs for heat storage, and excluding the heat delivery (district heating network),
according to:
LCOEheat =α·CAPEXi+OPEXi+Ecosti
Qheat,delivered
(11)
in which:
α=r
1(1+r)Li(12)
where αis the capital recovery factor (no unit), representing a fraction of the total CAPEX
costs depending on the yearly depreciation. ris the discount rate (as a fraction of 1) and L
i
is the lifetime of a system component i. The CAPEX
i
are the total capital expenditures for a
particular system component i, such as the heat pump, heat exchanger or HT-ATES wells.
OPEX
i
are the operational expenditures and maintenance in
/year for a system component
i, expressed as a percentage of the CAPEX. E
cost,i
are the average (in this publication, for
10 years) electricity costs for the heat system (in
/year). These costs are thus only for
the modelled heat system, and not for any external source that might be necessary to
fulfil the total heat demand. Q
heat,delivered
is the part of the heat demand delivered by the
multi-energy system with HT-ATES in GJ/year. We thus only take into account the heat
that can actually be delivered by the multi-energy system with HT-ATES and not by an
external source. This ensures a fair LCOE comparison between different scenarios.
Cost data is given in Table 4. A discount rate (r) of 6% (0.06) is applied, and electricity
costs are set at 60
/MWh
e
based on CBS data for non-households with an electricity
demand of >70,000 MWhein 2019 [49], and is similar to Wesselink et al. 2018 [22].
Table 4. Investment and operational costs for HT-ATES components.
System Component CAPEX (in ) OPEX (%) Lifetime
Aquifers and equipment a(75,860 ×ln(kWth/6.69) 115,000) ×1.25 4% 30
Heat exchanger b1500 ×kWth ×1.1 2% 20
Heat pump—65 C condenser temp. c600 /kWth 1% 20
Heat pump—50 C condenser temp. c400 /kWth 1% 20
a
Formula was originally for ATES systems [
50
], and is adapted for HT-ATES by multiplying with 1.25, because at higher temperatures
more expensive materials have to be used.
b
Costs data based on supplier data for ATES systems, multiplied by 1.1 for higher temperatures
(HT-ATES).
c
Costs estimation based on different sources [
41
,
51
] in combination with supplier data. Heat pump size in MW
th
is calculated
by multiplying the electrical power (MWel) with the average COP of the heat pump in that scenario.
Energies 2021,14, 7958 14 of 31
Lastly, the electricity use of the heat pump is calculated, averaged over the run period
(here 10 years).
3. Results
In the previous section, we described the models, the coupling of the models and
the scenarios that are used as input for the coupled model infrastructure. In this section,
we describe the results of the scenario model runs assessed on the heat fulfilment by the
(HT-ATES) system to the neighbourhood, the performance of the HT-ATES, the differences
in costs (LCOE) between scenarios, and finally we combine these results in an overall
analysis of the performance.
3.1. Fulfillment of Heat Demand with HT-ATES
Simulation results show that in all cases, 12–14% of the heat demand in summer
is delivered to the neighbourhood directly by the heat pump for all scenarios (
Figure 4
)
during the ten-year simulation (A) and the last five years of the simulation (B). This
percentage is the same for each scenario because the net demand is the same and in all
cases, the heat pump capacity is much larger than the heat demand in summer, when the
heat pump produces heat that otherwise would be stored in the HT-ATES.
The simulation results show that at the heat delivered from the HT-ATES, a distinction
is made between scenarios that are able to reach heat delivery by the HT-ATES system
within five years, and scenarios that take longer to reach 100% delivery of demand by the
heat pump and HT-ATES system. In scenarios that take longer than five years to reach 100%
delivery, the HT-ATES system is shut off for >20% of the time, which negatively influences
the amount of heat delivered from the HT-ATES system (40–65%). The other scenarios that
reach a stable heat supply within five years have 70–80% of the heat demand delivered
from the HT-ATES system. In Figure 4B, the origin of the heat delivered during the last
five years of operation of the heat pump-HT-ATES system are shown. Five scenarios can
operate independently of an external source after an initial start-up period.
For this case study, we show that the addition of a HT-ATES to a multi-energy system
with power-to-heat results in >90% fulfilment of the heat demand during the first 10 years
of operation, while after 3–5 years, the combined system can supply the full heat demand
of the neighbourhood in the multi-energy system sustainably.
The HT-ATES feeds heat pump mode of operation allows a lower threshold temper-
ature. As a result, the HT-ATES system can be operated longer during winter. Hence,
with a 2 MW
el
heat pump, the amount of heat provided from the HT-ATES is increased by
6–7% when using the HT-ATES feeds heat pump mode (see Figure 4A). When specifically
comparing the scenarios at 50
C condenser temperature (see 50|43|2 and 50|30|2 in
Figure 4A), the HT-ATES feeds heat pump mode decreases the time needed to reach 100%
heat delivery from 5 to 3 years. This positive effect of the HT-ATES feeds heat pump
mode is even more pronounced with a 1.5 MW
el
heat pump and leads to 9–11% more heat
delivery from the HT-ATES system.
For the 65
C and the 1MW
el
and 1.5 MW
el
heat pump scenarios, it is not possible
to reach 100% heat delivery in 10 years. Without the HT-ATES feeds heat pump mode,
an external heat source is still necessary after 10 years of operation (see 65|43|1.5 and
65|30|1.5 in Figure 4A).
At 50
C, the HT-ATES feeds heat pump mode is able to reduce the time it takes to
reach stable operation to five years, compared to nine years for a scenario without the
HT-ATES feeds heat pump mode (see 50|43|1.5 and 50|30|1.5 in Figure 4A). Thus, the
HT-ATES feeds heat pump mode makes it possible to create a reliable HT-ATES system
with a smaller heat pump (1.5 MWel vs. 2 MWel ) at 50 C condenser temperature.
The amount of electricity necessary for this mode comes from local RES when possible,
or from the grid otherwise. In Figure 5, the electricity demand for heat production is
shown over time in relation to the heat demand. This graph makes it clear that the
electricity demand for the extra mode of operation is small in relation to the total electricity
Energies 2021,14, 7958 15 of 31
demand of the HT-ATES system. In all scenarios, it is never more than 2–6% of the total
electricity demand of the HT-ATES system (6% for the 65|30|1.5 scenario in Figure 5).
This small amount of electricity used in winter decreases the utilization of an external heat
source for fulfilment of heat demand by 6–12%, depending on the scenario (12% for the
65|30|
1.5 scenario
). Furthermore, Figure 5clearly shows the temporal decoupling of heat
production and demand made possible by the integration of HT-ATES in the MES.
Figure 4.
Stacked bar plots of the distribution of heat delivery from direct production of heat and
from the HT-ATES system. In grey, the amount of heat that cannot be fulfilled by the heat pump and
HT-ATES system is shown. The left plot (
A
) shows the distribution for the first ten years of operation.
The numbers on top of the bars indicate the number of years when full heat delivery from the heat
pump-HT-ATES system is reached, with no external source requirement. The right plot (
B
) shows the
distribution of heat delivery for the last five years of operation, with five scenarios fully able to fulfil
the heat demand.
3.2. HT-ATES Performance
3.2.1. Well Temperature Development
Results show that the fulfilment of the heating demand by the HT-ATES system varied
significantly between the 10 scenarios (Figure 4), to a large extent as a result of the varying
performance of the HT-ATES system. The HT-ATES system cannot supply the entire
demand if the threshold temperature is reached during heat extraction from the hot well.
Energies 2021,14, 7958 16 of 31
Figure 6shows the well temperature over time of the relatively large 2 MW
el
heat pump
size scenarios. The hot well temperature drops below the threshold temperature during
recovery in the first years. After year 4, the hot well temperature always stays above the
threshold temperature, both for the T
threshold
= 43
C and the HT-ATES feeds heat pump
mode (T
threshold
= 30
C). The effect of the HT-ATES feeds heat pump mode is visible as a
sharp drop in warm well temperature when the hot well temperature drops below the first
threshold of T = 43
C. For the 65
C storage temperature, the HT-ATES feeds heat pump
mode is no longer activated after the system has sufficiently warmed up after the first years,
as shown in Figure 6A. For the 50
C storage scenarios (Figure 6B), the HT-ATES feeds heat
pump mode is active for most of the modelled years, because the hot well temperature
often drops below the threshold of 43
C at the end of extraction season. This is also visible
in the warm well temperature of this scenario in Figure 6B; the warm well temperature
drops once the HT-ATES feeds heat pump mode becomes active, which results in a very
irregular warm well temperature pattern.
Energies 2021, 14, x FOR PEER REVIEW 16 of 33
Figure 4. Stacked bar plots of the distribution of heat delivery from direct production of heat and
from the HT-ATES system. In grey, the amount of heat that cannot be fulfilled by the heat pump
and HT-ATES system is shown. The left plot (A) shows the distribution for the first ten years of
operation. The numbers on top of the bars indicate the number of years when full heat delivery from
the heat pump-HT-ATES system is reached, with no external source requirement. The right plot (B)
shows the distribution of heat delivery for the last five years of operation, with five scenarios fully
able to fulfil the heat demand.
Figure 5. Electricity demand (left axis) for heat production is shown together with the heat demand
(right axis) for the 65|30|1.5 scenario, which is the scenario with the highest utilization of the HT-
ATES feeds heat pump mode. The electricity demand is divided into heat production for the HT-
Figure 5.
Electricity demand (left axis) for heat production is shown together with the heat demand
(right axis) for the 65|30|1.5 scenario, which is the scenario with the highest utilization of the
HT-ATES feeds heat pump mode. The electricity demand is divided into heat production for the
HT-ATES (Elec—HT-ATES), the direct production of heat for the MES (Elec—direct heat) and the
electricity for the HT-ATES feeds HP mode (Elec—HP mode).
For the smaller heat pump scenarios (1.5 and 1 MW
el
), the temperature in the hot
and warm wells is presented in Figure 7. Here we observe the same behaviour, but the
temperature drops in both the hot and the warm wells are stronger. Hence, for these
smaller heat pump capacity scenarios, less heat is stored compared to a 2 MW
el
heat
pump, resulting in a more depleted heat storage at the end of the extraction season. As
a result, the threshold temperature of 43
C is reached for both the 65
C scenarios and
the
50 C
scenarios in almost all years. However, at the end of the simulated period, we
see that higher temperatures are maintained and the threshold temperature is reached less
often. This indicates that the losses of previous years have created a shell with elevated
temperature around the stored volume over time. As a consequence, the heat loss decreases
and the system moves towards an equilibrium situation.
Energies 2021,14, 7958 17 of 31
Energies 2021, 14, x FOR PEER REVIEW 18 of 33
Figure 6. Overview of the temperature variations of the hot well and warm well for a 65 °C condenser temperature (A)
and 50 °C condenser temperature (B). Scenarios with a 2 MWel heat pump are shown, both with (65|30|2 or 50|30|2) and
without (65|43|2 and 50|43|2) the HT-ATES feeds heat pump mode. The green lines show the threshold temperatures at
43 °C and 30 °C (with the HT-ATES feeds heat pump mode). With the HT-ATES feeds heat pump mode (threshold tem-
perature of 30 °C ), a sharp drop in the temperature of the warm well is visible when the 43 °C threshold temperature in
the hot well is reached. The HT-ATES feeds heat pump mode is utilized mostly during the first four years of operation at
the 65 °C condenser temperature and during most of the run period at the 50 °C condenser temperature. The legend in (B)
is valid for (A) as well.
Figure 6.
Overview of the temperature variations of the hot well and warm well for a 65
C condenser temperature (
A
) and
50
C condenser temperature (
B
). Scenarios with a 2 MW
el
heat pump are shown, both with (65|30|2 or 50|30|2) and
without (65|43|2 and 50|43|2) the HT-ATES feeds heat pump mode. The green lines show the threshold temperatures
at 43
C and 30
C (with the HT-ATES feeds heat pump mode). With the HT-ATES feeds heat pump mode (threshold
temperature of 30
C), a sharp drop in the temperature of the warm well is visible when the 43
C threshold temperature in
the hot well is reached. The HT-ATES feeds heat pump mode is utilized mostly during the first four years of operation at
the 65
C condenser temperature and during most of the run period at the 50
C condenser temperature. The legend in (
B
)
is valid for (A) as well.
3.2.2. HT-ATES Performance Results
The hot well, warm well and system recovery efficiency are presented in Table 5.
In the previous section, we showed that the heat that is delivered by the multi-energy
system is influenced strongly by the size of the heat pump. With a large heat pump size,
more energy is stored in the subsurface in summer, which results in more energy that is
recovered in winter. Table 5shows that, while this might feel counter-intuitive, the system
recovery efficiency is lowest (50–55%) for the scenario where most heat is provided to the
multi-energy system (largest heat pump scenarios) and vice versa. Because the amount of
stored energy varies between the scenarios, the delivery of heat from the HT-ATES system
cannot be directly coupled to the recovery efficiency of the HT-ATES system.
Energies 2021,14, 7958 18 of 31
Energies 2021, 14, x FOR PEER REVIEW 19 of 33
Figure 7. Overview of the temperature variations of the hot well and warm well for a 65 °C condenser temperature (A)
and 50 °C condenser temperature (B). Scenarios with a 1.5 MWel heat pump are shown, both with (65|30|1.5 or 50|30|1.5)
and without (65|42|1.5 and 50|43|1.5) the HT-ATES feeds heat pump mode. Furthermore, scenarios with a 1 MWel heat
pump are included (65|30|1 and 50|30|1). The green lines show the threshold temperatures at 43 °C and 30 °C (with the
HT-ATES feeds heat pump mode). The legend in (B) is valid for (A) as well.
3.2.2. HT-ATES Performance Results
The hot well, warm well and system recovery efficiency are presented in Table 5.
In the previous section, we showed that the heat that is delivered by the multi-energy
system is influenced strongly by the size of the heat pump. With a large heat pump size,
more energy is stored in the subsurface in summer, which results in more energy that is
recovered in winter. Table 5 shows that, while this might feel counter-intuitive, the system
recovery efficiency is lowest (5055%) for the scenario where most heat is provided to the
multi-energy system (largest heat pump scenarios) and vice versa. Because the amount of
stored energy varies between the scenarios, the delivery of heat from the HT-ATES system
cannot be directly coupled to the recovery efficiency of the HT-ATES system.
In the large heat pump scenarios, more volume is stored in summer than required
(and thus recovered) in winter, resulting in low recovery efficiencies. Oppositely, for the
small heat pump scenarios, more volume is extracted in winter compared to what was
stored in summer. The balance between these two volume flows, the volume balance ratio
(rVB) varies from 0.15 for small heat pump scenarios to 0.21 for the large heat pump sce-
narios (Table 5).
Figure 7.
Overview of the temperature variations of the hot well and warm well for a 65
C condenser temperature (
A
) and
50
C condenser temperature (
B
). Scenarios with a 1.5 MW
el
heat pump are shown, both with (65|30|1.5 or 50|30|1.5)
and without (65|42|1.5 and 50|43|1.5) the HT-ATES feeds heat pump mode. Furthermore, scenarios with a 1 MW
el
heat
pump are included (65|30|1 and 50|30|1). The green lines show the threshold temperatures at 43
C and 30
C (with the
HT-ATES feeds heat pump mode). The legend in (B) is valid for (A) as well.
In the large heat pump scenarios, more volume is stored in summer than required
(and thus recovered) in winter, resulting in low recovery efficiencies. Oppositely, for the
small heat pump scenarios, more volume is extracted in winter compared to what was
stored in summer. The balance between these two volume flows, the volume balance ratio
(r
VB
) varies from
0.15 for small heat pump scenarios to 0.21 for the large heat pump
scenarios (Table 5).
In Figure 8, the recovery efficiency and r
VB
during the last five years of simulation are
shown. The r
VB
has an opposite effect on the hot and the warm well efficiency. For the hot
well, a negative r
VB
means that relatively large amounts of water is extracted, compared to
what was initially injected, resulting in high recovery efficiency. The opposite is true for
positive rVB.
Energies 2021,14, 7958 19 of 31
Table 5.
Ten-year average HT-ATES performance results. The amount of stored energy is mainly determined by the size of
the heat pump and the temperature levels. This subsequently results in different amounts of stored volumes and recovery
efficiencies. The scenario name abbreviation consists of: condenser temperature|HT-ATES threshold temperature|heat pump size.
Scenario
Vhot
Injection
10y
Average
(103m3/y)
Vhot
Extraction
10y
Average
(103m3/y)
Volume
Balance
Ratio (rVB)
Hot Well
Recovery
Efficiency
10y
Average (-)
Warm Well
Recovery
Efficiency
10y
Average (-)
HT-ATES
Heat
Delivered
10y Total
(Endel,10 y avg
in TJ)
HT-ATES
Heat
Stored 10y
Total (TJ)
HT-ATES
System
Recovery
Efficiency
10y
Average (-)
MES
Efficiency
10y
Average
(ηMES )
65|43|2 446 339 0.14 0.64 0.88 413 778 0.53 0.53
65|30|2 450 397 0.06 0.69 0.85 445 780 0.57 0.56
65|43|1.5 330 282 0.08 0.70 0.82 322 569 0.57 0.57
65|30|1.5 315 389 0.10 0.83 0.59 377 568 0.66 0.63
65|30|1 193 259 0.15 0.86 0.51 239 354 0.68 0.64
50|43|2 774 509 0.21 0.63 0.92 436 900 0.48 0.48
50|30|2 736 570 0.13 0.71 0.89 480 838 0.57 0.55
50|43|1.5 692 450 0.21 0.62 0.90 381 810 0.47 0.48
50|30|1.5 680 540 0.11 0.71 0.87 448 785 0.57 0.55
50|30|1 432 441 0.01 0.83 0.69 351 522 0.67 0.61
Energies 2021, 14, x FOR PEER REVIEW 20 of 33
In Figure 8, the recovery efficiency and rVB during the last five years of simulation are
shown. The rVB has an opposite effect on the hot and the warm well efficiency. For the hot
well, a negative rVB means that relatively large amounts of water is extracted, compared
to what was initially injected, resulting in high recovery efficiency. The opposite is true
for positive rVB.
Figure 8. HT-ATES System, hot well and warm well recovery efficiency (last five years) (y-axis) vs.
the volume balance ratio (rVB) during the last five years (x-axis) for the ten modelled scenarios. The
average recovery efficiency during the last five years of simulation is shown to take out any effects
of the first start-up years of HT-ATES operation.
The system recovery efficiency is a result of the recovery efficiency of the hot and the
warm well. However, the hot well has a stronger influence, compared to the warm well,
because relatively more heat is stored in this side of the system. Consequently, the highest
system recovery efficiency is observed for the highest hot well recovery efficiencies at
negative rVB. However, for the lowest rVB ratios, the system recovery efficiency stops in-
creasing because the warm well recovery decrease has a stronger effect compared to the
hot well recovery efficiency increase. It thus seems that maximal system recovery is ob-
tained at slightly negative rVB.
The recovery efficiency is not only influenced by the volume balance ratio; the stor-
age temperature affects the recovery efficiency as well. For higher storage temperature,
more losses due to buoyancy flow occur, resulting in lower recovery efficiency. This is
clearly visible for the scenario operating in volume balance (last five years) in Figure 8.
Here, the warm well recovery efficiency is higher compared to the hot well recovery effi-
ciency. Moreover, we observe two linear relationships for the hot well efficiencies, corre-
sponding to the two varied storage temperatures, which is due to higher energy losses
during storage for the 65 °C storage scenarios. Due to the limited difference in storage
temperatures (65 °C vs. 50 °C) and the strong effect of volume balance, the recovery effi-
ciency is dominated by the volume balance ratio for the simulated scenarios.
Figure 8.
HT-ATES System, hot well and warm well recovery efficiency (last five years) (y-axis) vs.
the volume balance ratio (r
VB
) during the last five years (x-axis) for the ten modelled scenarios. The
average recovery efficiency during the last five years of simulation is shown to take out any effects of
the first start-up years of HT-ATES operation.
The system recovery efficiency is a result of the recovery efficiency of the hot and
the warm well. However, the hot well has a stronger influence, compared to the warm
well, because relatively more heat is stored in this side of the system. Consequently, the
highest system recovery efficiency is observed for the highest hot well recovery efficiencies
at negative r
VB
. However, for the lowest r
VB
ratios, the system recovery efficiency stops
increasing because the warm well recovery decrease has a stronger effect compared to
Energies 2021,14, 7958 20 of 31
the hot well recovery efficiency increase. It thus seems that maximal system recovery is
obtained at slightly negative rVB.
The recovery efficiency is not only influenced by the volume balance ratio; the storage
temperature affects the recovery efficiency as well. For higher storage temperature, more
losses due to buoyancy flow occur, resulting in lower recovery efficiency. This is clearly
visible for the scenario operating in volume balance (last five years) in Figure 8. Here,
the warm well recovery efficiency is higher compared to the hot well recovery efficiency.
Moreover, we observe two linear relationships for the hot well efficiencies, correspond-
ing to the two varied storage temperatures, which is due to higher energy losses during
storage for the 65
C storage scenarios. Due to the limited difference in storage tempera-
tures (
65 C vs. 50 C
) and the strong effect of volume balance, the recovery efficiency is
dominated by the volume balance ratio for the simulated scenarios.
The system recovery efficiency of the HT-ATES only indirectly provides insight into
the amount of heat that is delivered to the multi-energy system, because this depends on
the absolute amount of energy that is actually stored in the HT-ATES system. The largest
heat pump scenarios can store and deliver most heat, but they do this with a relatively
small system recovery efficiency, shown in Figure 9. Consequently, this means that a
relatively high amount of the stored heat is lost to the subsurface. Additionally, we observe
here that for equal heat pump size, the 50
C scenarios can provide more heat, with a lower
system recovery efficiency. This is caused by the fact that more heat is stored for the 50
C
scenario (higher COP of heat pump) and that less heat is lost during storage due to the
lower storage temperature and larger storage volume.
Energies 2021, 14, x FOR PEER REVIEW 22 of 33
Figure 9. The absolute amounts of delivered heat (y-axis) vs. the HT-ATES system efficiency (x-axis)
for the ten modelled scenarios. An optimal system would have both a high amount of delivered heat
(TJ) and a high HT-ATES system efficiency. The HT-ATES feeds heat pump mode provides a more
efficient HT-ATES system for all scenarios.
3.3. LCOE of Heat Production and Storage
The levelized costs of energy (in this case heat) in €/GJ are shown in Table 6. All sce-
narios with a 50 °C condenser temperature have a higher LCOE (2.54.5 €/GJ or 1118%)
than at 65 °C condenser temperature with the same heat pump size. An important reason
is the higher COP for the 50 °C scenarios, which results in more heat injection in the HT-
ATES with the same heat pump size. The difference in COP is proportional to the differ-
ence in Carnot efficiency based on the extra temperature lift when the heat pump conden-
ser temperature is raised to 65 °C instead of 50 °C. This means that it is 30% more efficient
to produce heat from surface water at 50 °C (average COP = 5.5) than at 65 °C (average
COP = 4). Moreover, the heat pump investment costs per kWth are higher for 65 °C (600
€/kWth) than for 50 °C condenser temperature (400 €/kWth). On the other hand, this cost
difference is almost cancelled out as the COP is higher at 50 °C, resulting in a higher ther-
mal heat pump capacity for the same electrical heat pump capacity. Eventually, the in-
vestment costs for the heat pump are slightly lower (around 6%) with 50 °C condenser
temperature. The investments for the HT-ATES depend on the required capacity and with
that the number of doublets (shown in Table 6). There is an increase of one doublet re-
quired at 50 °C vs. 65 °C condenser temperature, as the capacity delivered from the HT-
ATES is quite similar over all scenarios, resulting in limited variations in the HT-ATES
investment costs across the scenarios. The main decrease in costs between 50 °C and 65 °C
condenser temperature is caused by the lower electricity demand (because of higher COP)
at 50 °C vs. 65 °C .
Next to the condenser temperature, the HT-ATES feeds heat pump mode has an im-
pact on the LCOE. The differences in results between 65|43|2 and 65|30|2, as well as the
50|43|2 and 50|30|2 scenarios illustrate this, as the only difference here is the extra mode
Figure 9.
The absolute amounts of delivered heat (y-axis) vs. the HT-ATES system efficiency (x-axis)
for the ten modelled scenarios. An optimal system would have both a high amount of delivered heat
(TJ) and a high HT-ATES system efficiency. The HT-ATES feeds heat pump mode provides a more
efficient HT-ATES system for all scenarios.
Energies 2021,14, 7958 21 of 31
To get more direct insight into the actual efficiency of the delivered heat within the
MES, we have included the system delivery efficiency in the calculations (see Table 5).
The system delivery efficiency of the MES is similar (1–2% lower) to the system recovery
efficiency of the HT-ATES for most scenarios. This outcome indicates that heat loss in the
district heating network and the direct heat production and delivery more or less outweigh
each other in these scenarios. Three scenarios that take longer than eight years to reach
100% delivery and have a negative r
VB
(65|30|1.5, 65|30|1 and 50|30|1) have a slightly
lower MES efficiency (3–6%) than HT-ATES system recovery efficiency. Still, the same
trend is observed that scenarios with a small heat pump have a high MES efficiency, but a
relatively low amount of heat delivered, and vice versa.
The influence of the HT-ATES feeds heat pump mode when recovery temperatures
reach the threshold is observed in Figure 9. For the four scenarios for which both the
normal and the extra mode of operation were simulated, we observe that both the absolute
amount of energy that can be delivered to the multi-energy system and the system recovery
efficiency strongly increase. The HT-ATES feeds heat pump mode thus has a positive
impact on the performance of the HT-ATES, resulting in a higher recovery efficiency of the
system (4–10% depending on the scenario) which increases the amount of heat delivered
(in TJ) to the houses (7–15% depending on the scenario).
In Figure 9, the optimal scenario regarding heat delivery is the 50|30|2 scenario,
because most heat is delivered (difference of 25 TJ), while the same system recovery
efficiency is observed as the 50|30|1.5 and 65|30|2 scenarios.
3.3. LCOE of Heat Production and Storage
The levelized costs of energy (in this case heat) in
/GJ are shown in Table 6. All
scenarios with a 50
C condenser temperature have a higher LCOE (2.5–4.5
/GJ or 11–
18%) than at 65
C condenser temperature with the same heat pump size. An important
reason is the higher COP for the 50
C scenarios, which results in more heat injection in
the HT-ATES with the same heat pump size. The difference in COP is proportional to the
difference in Carnot efficiency based on the extra temperature lift when the heat pump
condenser temperature is raised to 65
C instead of 50
C. This means that it is 30% more
efficient to produce heat from surface water at 50
C (average COP = 5.5) than at 65
C
(
average COP = 4
). Moreover, the heat pump investment costs per kW
th
are higher for
65 C
(600
/kW
th
) than for 50
C condenser temperature (400
/kW
th
). On the other hand,
this cost difference is almost cancelled out as the COP is higher at 50
C, resulting in a
higher thermal heat pump capacity for the same electrical heat pump capacity. Eventually,
the investment costs for the heat pump are slightly lower (around 6%) with 50
C condenser
temperature. The investments for the HT-ATES depend on the required capacity and with
that the number of doublets (shown in Table 6). There is an increase of one doublet required
at 50
C vs. 65
C condenser temperature, as the capacity delivered from the HT-ATES is
quite similar over all scenarios, resulting in limited variations in the HT-ATES investment
costs across the scenarios. The main decrease in costs between 50
C and 65
C condenser
temperature is caused by the lower electricity demand (because of higher COP) at 50
C vs.
65 C.
Next to the condenser temperature, the HT-ATES feeds heat pump mode has an
impact on the LCOE. The differences in results between 65|43|2 and 65|30|2, as well
as the 50|43|2 and 50|30|2 scenarios illustrate this, as the only difference here is the
extra mode of operation. In both cases with the additional mode of operation, there is an
increase in costs for an additional heat exchanger, but a decrease in electricity costs while
the amount of delivered heat increases. For the 65
C condenser temperature, the electricity
costs increase by 2%, while 6% more heat is delivered from the heat pump/HT-ATES
system, which leads to a reduction of 1.0
/GJ for the LCOE; while at 50
C condenser
temperature, the HT-ATES feeds heat pump mode leads to a decrease in electricity costs of
5%, and 7% more heat delivered, resulting in a total LCOE decrease of 1.4 /GJ.
Energies 2021,14, 7958 22 of 31
Table 6.
Energetic and financial performance of the heat production and storage system as part of a multi-energy system.
The % of heat delivered by the HT-ATES/heat pump is the sum of the yellow and orange bars in Figure 4. The scenario
name abbreviation consists of: condenser temperature|HT-ATES threshold temperature|heat pump size.
Scenario
Average COP
Heat Pump
(COP10 y avg)
Max. Capacity
Infiltrated
(MWth)
Max. Capacity
Delivered
(MWth)
Amount of
Doublets
Average
Electricity
Demand—10y
Average
(Eldem,10 y avg
in TJ/y)
% of Heat
Demand
Delivered by
the HT-
ATES/Heat
Pump
Heat
Production
Costs (LCOE
in /GJ)
65|43|2 * 4 8.8 11.7 6 21.8 84% 19.6
65|30|2 * 4 8.8 11.7 6 22.2 90% 18.6
65|43|1.5 4 6.5 10 6 16.5 68% 18.5
65|30|1.5 4 6.5 11 7 17.7 80% 16.6
65|30|1 4 4.3 8.3 6 12.0 56% 16.7
50|43|2 * 5.5 12.6 11.7 7 17.6 85% 17.1
50|30|2 * 5.5 12.6 11.7 7 16.7 92% 15.7
50|43|1.5 5.5 9.4 11.8 8 16.3 78% 15.5
50|30|1.5 * 5.5 9.5 11.6 8 16.3 90% 13.8
50|30|1 5.6 6.2 10.9 7 12.6 75% 12.2
* The * added to the scenario code indicates that the HT-ATES can deliver 100% of the heat to the multi-energy system within the first
five years.
The HT-ATES feeds heat pump mode thus results in extra heat delivered at limited
extra costs, hence the low LCOE for these scenarios. With a smaller heat pump size of
1.5 MWel
, the LCOE decreases further. For the 65
C scenarios, the electricity demand
does significantly decrease as well, although the % of heat demand delivered decreases
with a smaller heat pump. Still, a reduction of 1.1
/GJ is realized compared to a 2 MW
el
heat pump with an LCOE of 18.5
/GJ. The cost reduction is further enhanced by the
HT-ATES feeds heat pump mode with 12% more heat delivery resulting in an LCOE of
16.6
/GJ. For a 50
C condenser temperature, the same effects are visible, although in
this case, the HT-ATES feeds heat pump mode does not lead to an increase in electricity
demand, but raises the heat demand delivered to 90%, which is almost similar to the
50|30|2 scenario, but with a smaller heat pump (1.5 MW
el
vs. 2 MW
el
). In this scenario
(50|30|1.5), the LCOE is 13.8
/GJ, which is 1.7
/GJ lower than without the HT-ATES
feeds heat pump mode.
A 1 MW
el
heat pump with a 65
C condenser temperature (65|30|1) does not lead
to a further decrease in LCOE compared to a similar scenario with a 1.5 MW
el
heat pump
(65|30|1.5). The savings on heat pump investment and electricity costs thus do not com-
pensate for the decrease in heat delivery (56% vs. 80%) by the heat pump/HT-
ATES system
.
Overall, the 50|30|1 scenario with a 1 MW
el
heat pump has the lowest LCOE, with
75% of the heat demand delivered from the heat pump/HT-ATES system. The lower
investment costs for the heat pump do make this scenario the most feasible. Furthermore,
these results show that by installing a HT-ATES system, the heat pump size is not the
determining factor for heat delivery anymore. As the HT-ATES is the main source of heat
delivery during winter, the capacity of the HT-ATES system is more important than the
size of the heat pump. HT-ATES systems thus help to reduce the size of the heat pump.
These results indicate a tipping point in heat pump size in relation to the amount of
heat that can be delivered from the HT-ATES system to the multi-energy system. In the
cases where the heat pump size is becoming a limiting factor, the positive effect of the
extra mode of operation is most pronounced. The actual heat pump size tipping point will
depend on the local conditions (maximum required heating power, temperature level) and
thus vary per project. This analysis showed that for identifying the lowest LCOE scenario,
it is worthwhile to identify this tipping point when a HT-ATES system in a multi-energy
system is realized.
3.4. Overall Performance: An Integral View on Heat Delivery, System Efficiency and Costs
In this study, we considered several scenarios with varying heat pump designs and
storage temperature levels and assessed how these variations impact the fulfilment of the
Energies 2021,14, 7958 23 of 31
neighbourhood heat demand, the performance of the HT-ATES system, and the costs of
heat production including storage (LCOE). The overall performance of the varied scenarios
is assessed here by combining all assessment criteria, as shown in Figure 10. Here, the
arrows indicate the influence of the HT-ATES feeds heat pump mode compared to the
normal operation mode. The most optimal system would have: 1. a large amount of
delivered heat (y-axis); 2. a high HT-ATES system efficiency (x-axis); and 3. low LCOE
(colour). The following observations stand out:
-
At a small heat pump size and a high storage temperature (65|30|1), a relatively
small amount of the total heat demand can be delivered to the multi-energy system
(0.56) and LCOE does not decrease compared to a 1.5 MW
el
heat pump scenario
(both around 16.6
/GJ). However, decreasing the storage temperature to 50
C has a
large positive effect for this small heat pump scenario (50|30|1); the LCOE strongly
decreases (16.7 to 12.2 /GJ) and the delivered amount of heat increases to 75%.
-
Overall, the 50
C storage temperature ensures lower costs and higher fulfilment of
heat demand to the neighbourhood. This is probably caused because the heat pump
is cheaper and more effective, which results in larger amounts of stored heat. This has
a stronger effect than the increase in costs due to the needed number of wells.
-
For all scenarios, the HT-ATES system feeds heat pump mode (43
C vs. 30
C shut-
off temperature) has a strong positive effect on all assessment criteria: the LCOE
decreases, the fulfilment of heat demand increases, and the MES efficiency increases.
Hence, this mode of operation has a high potential to be integrated into future HT-
ATES systems. With only a small amount of electricity use in winter (not more than
2–6% of the total electricity use), the amount of heat delivered from the HT-ATES
increases by 6–12%.
-
The most optimal scenario for this neighbourhood, based on all assessment criteria, is
the 50|30|1.5 MW scenario. This scenario can provide 100% of the heat demand year-
round to the neighbourhood within the first five years, while the LCOE is
13.8 /GJ
,
the second cheapest system.
Energies 2021, 14, x FOR PEER REVIEW 25 of 33
Figure 10. The relationships between the three main aspects investigated in this study; the fulfilment of hea t demand by
HT-ATES + heat pump to the MES (10-year average), the 10y MES efficiency and the costs of heat production and storage
(LCOE). The * added to the scenario code indicates that the HT-ATES can deliver 100% of the heat to the multi-ener gy
system within the first five years.
4. Discussion
4.1. The Functioning of a HT-ATES within a Multi-Energy System
We have shown that seasonal heat storage, HT-ATES, is crucial to be able to increase
the amount of locally provided sustainable energy in a multi-energy system. Hence, HT-
ATES is probably a better option for seasonal heat storage instead of the hot water storage
tanks that are usually applied in a multi-energy system [12,14,15,19]. However, HT-ATES
is not applicable everywhere, as it requires the presence of an aquifer.
The use of HT-ATES including the heat pump booster mode enables better utilisation
of the stored heat, and with that, increases HT-ATES performance. We showed that this
mode of operation has a high potential to be integrated into future HT -ATES systems,
because the costs strongly decrease and HT-ATES performance increases, leading to an
increase in local sustainable heat delivery.
The performance of the HT-ATES system is assessed via the recovery efficiency of
heat from the HT-ATES system. The HT-ATES system recovery is determined by the re-
covery efficiency of heat from the hot and the warm well, which are strongly related to
the volume (im)balance between the two wells. When the volume imbalance is negative
(net extraction from the hot well), the recovery efficiency of the hot well increases and
decreases at the warm well. However, the total amount of energy that can be provided to
the MES can be larger than expected based on a simulation with a volume balance. For
example, this could have a significant improvement for the recovery efficiency of HT -
Figure 10.
The relationships between the three main aspects investigated in this study; the fulfilment
of heat demand by HT-ATES + heat pump to the MES (10-year average), the 10y MES efficiency and
the costs of heat production and storage (LCOE). The * added to the scenario code indicates that the
HT-ATES can deliver 100% of the heat to the multi-energy system within the first five years.
Energies 2021,14, 7958 24 of 31
4. Discussion
4.1. The Functioning of a HT-ATES within a Multi-Energy System
We have shown that seasonal heat storage, HT-ATES, is crucial to be able to increase
the amount of locally provided sustainable energy in a multi-energy system. Hence, HT-
ATES is probably a better option for seasonal heat storage instead of the hot water storage
tanks that are usually applied in a multi-energy system [
12
,
14
,
15
,
19
]. However, HT-ATES
is not applicable everywhere, as it requires the presence of an aquifer.
The use of HT-ATES including the heat pump booster mode enables better utilisation
of the stored heat, and with that, increases HT-ATES performance. We showed that this
mode of operation has a high potential to be integrated into future HT-ATES systems,
because the costs strongly decrease and HT-ATES performance increases, leading to an
increase in local sustainable heat delivery.
The performance of the HT-ATES system is assessed via the recovery efficiency of
heat from the HT-ATES system. The HT-ATES system recovery is determined by the
recovery efficiency of heat from the hot and the warm well, which are strongly related to
the volume (im)balance between the two wells. When the volume imbalance is negative
(net extraction from the hot well), the recovery efficiency of the hot well increases and
decreases at the warm well. However, the total amount of energy that can be provided to
the MES can be larger than expected based on a simulation with a volume balance. For
example, this could have a significant improvement for the recovery efficiency of HT-ATES
wells that experience high heat losses during storage. Previous studies [
31
,
52
,
53
] evaluated
the effect of several HT-ATES storage conditions on the recovery efficiency at V
in
= V
out
,
and showed that energy losses can vary significantly. Therefore, it is recommended to
analyse the combined effect of both varying storage conditions and the effect of volume
(im)balance on recovery efficiency to get insight in the optimal combination of both factors
for a given scenario.
In this study, we separately simulated the hot and warm wells of the HT-ATES systems.
In reality, the wells will be placed next to each other, in such a way that positive interaction
from the hot and warm wells is optimal. Previous studies showed that this leads to
increased HT-ATES system performance [36].
4.2. The Impact of HT-ATES in a Multi-Energy System
In a multi-energy system with power-to-heat but without a HT-ATES, the heat pump
size would be determined by the maximum peak in heat demand. By including a HT-ATES
system in the multi-energy system, the heat demand and supply are mostly decoupled and
the (peak) heat demand is delivered from the HT-ATES system during winter. The heat
pump is employed in an optimal way by producing most of the heat in summer, the most
likely time of abundance of (local) solar energy, but is also, to a small extent, employed in
winter for the HT-ATES feeds heat pump mode when necessary.
Figure 11 shows the maximum capacity delivered to the heat users and the condenser
capacity of the applied heat pump. The scenarios which end up in 100% heat delivery
by the HT-ATES in the multi-energy system (*) indicate that the peak heating demand
is
11.8 MWth
. For these scenarios, the ratio of the maximum demand and the applied
condenser capacity shows how much smaller heat pump capacity can be installed to
optimally utilise heat produced by power surplus. The 50|43|2 and 50|30|2 scenarios
have a positive ratio, indicating the heat pump is over-dimensioned. From the 65|43|2,
65|30|2, 50|43|1.5 and the 50|30|1.5 scenario results, it is concluded that the heat pump
can have a 21–25% smaller capacity than the actual peak heating demand. The other
scenarios (not leading to 100% heat delivery) have ratios of 0.6–0.7, indicating that 25%
lower heat pump capacity is the maximum. Besides a smaller capacity, the heat pump
is employed both in summer to store the heat and in winter to provide additional heat
when the HT-ATES heat production is not sufficient. This fosters year-round heat pump
utilisation, although the energy use in winter is a fraction (<6%) of the total energy use
Energies 2021,14, 7958 25 of 31
of the HT-ATES system (see Figure 5). Thus, most energy demand is still during summer,
with a likely oversupply of sustainable local electricity from PV.
Energies 2021, 14, x FOR PEER REVIEW 27 of 33
Figure 11. The maximum heat pump capacity vs. the heat delivery at the peak demand is shown for
all scenarios. Because of the addition of the HT-ATES system, it is possible to deliver the peak de-
mand from the HT-ATES, which explains why the maximum thermal heat pump capacity is lower
than the maximum capacity delivered. The * sign shows which scenarios reach 100% heat delivery
by the HT-ATES to the MES (within the first five years of operation).
Following the basic heat pump dynamics, a simplified calculation for a multi-energy
system with only direct heat production will result in a total average electricity use of 12.8
TJ/year with a 3.1 MWel heat pump (assuming an average heat pump evaporator temper-
ature of 8 °C, and 50 °C condenser temperature), with an electricity demand as shown in
Figure 12. In the first 10 years of operation, a MES with HT-ATES will consume around
1617 TJ/year of electricity and a maximum of 90% of the heat demand will be fulfilled.
Yet, after the first two to four years of operation, the heat demand can be provided com-
pletely from the HT-ATES and the electricity demand of the system decreases. For exam-
ple, in the case of the most optimal 50|30|1.5 scenario, the electricity demand in the 10th
year of operation is 13.6 TJ. This is about 6% higher than a MES with direct heat produc-
tion. The total electricity use thus slightly increases when HT-ATES is included. The total
electricity use by the HT-ATES + HP system is influenced in two ways. Firstly, the source
of heat (canal) is mainly harvested when its temperature is relatively high. This is favour-
able for the heat pump COP and results in relatively less electricity use. Secondly, more
heat needs to be produced than the total yearly heat demand because a part of the pro-
duced heat will be lost during storage. Hence, the second effect is of slightly more influ-
ence on the total electricity demand in this study, for the first 10 years of operation. How-
ever, as pointed out in the previous section, the energy losses of HT-ATES systems, and
thus their electricity use, may vary strongly for different HT-ATES storage conditions
(e.g., storage volume, hydrogeological conditions). Consequently, under more favourable
storage conditions, yearly electricity use of the MES may be lower compared to direct heat
production.
More importantly, the integration of HT-ATES has a strong effect on the distribution
of electricity use over the year. The electricity use is decoupled by the integration of HT-
ATES, and the electricity use peak switches from winter to summer, following the availa-
bility of sustainable electricity by solar energy (Figure 12). Here, we compare the
Figure 11.
The maximum heat pump capacity vs. the heat delivery at the peak demand is shown
for all scenarios. Because of the addition of the HT-ATES system, it is possible to deliver the peak
demand from the HT-ATES, which explains why the maximum thermal heat pump capacity is lower
than the maximum capacity delivered. The * sign shows which scenarios reach 100% heat delivery
by the HT-ATES to the MES (within the first five years of operation).
The addition of HT-ATES in a power-to-heat MES affects the total yearly electricity
use, and, the yearly distribution of electricity use, as energy demand and availability are
decoupled. For a multi-energy system with power-to-heat without HT-ATES, all heat needs
to be produced directly by the heat pump on demand, which can become a problem in
winter because (a) the temperature of the heat source (canal) becomes too low to use, and
(b) sustainable power to run the heat pump is not available. This results in the need for
larger heat exchangers and the need for other sources of heat and associated costs, as well
as a reduction in the use of local sustainable energy.
Following the basic heat pump dynamics, a simplified calculation for a multi-energy
system with only direct heat production will result in a total average electricity use of
12.8 TJ/year with a 3.1 MW
el
heat pump (assuming an average heat pump evaporator
temperature of 8
C, and 50
C condenser temperature), with an electricity demand as
shown in Figure 12. In the first 10 years of operation, a MES with HT-ATES will consume
around 16–17 TJ/year of electricity and a maximum of 90% of the heat demand will be
fulfilled. Yet, after the first two to four years of operation, the heat demand can be provided
completely from the HT-ATES and the electricity demand of the system decreases. For
example, in the case of the most optimal 50|30|1.5 scenario, the electricity demand in
the 10th year of operation is 13.6 TJ. This is about 6% higher than a MES with direct heat
production. The total electricity use thus slightly increases when HT-ATES is included. The
total electricity use by the HT-ATES + HP system is influenced in two ways. Firstly, the
source of heat (canal) is mainly harvested when its temperature is relatively high. This is
favourable for the heat pump COP and results in relatively less electricity use. Secondly,
more heat needs to be produced than the total yearly heat demand because a part of the
produced heat will be lost during storage. Hence, the second effect is of slightly more
Energies 2021,14, 7958 26 of 31
influence on the total electricity demand in this study, for the first 10 years of operation.
However, as pointed out in the previous section, the energy losses of HT-ATES systems,
and thus their electricity use, may vary strongly for different HT-ATES storage conditions
(e.g., storage volume, hydrogeological conditions). Consequently, under more favourable
storage conditions, yearly electricity use of the MES may be lower compared to direct
heat production.
Energies 2021, 14, x FOR PEER REVIEW 28 of 33
electricity use of a MES with and without HT-ATES. The electricity use of scenario
50|30|1.5 is compared to a reference system that uses a HP for direct heat production
only.
Thus, the HT-ATES system enables the use of more sustainable and local electricity
when available (in summer) and reduces the size of the heat pump compared to systems
with direct heat production. The yearly electricity use slightly increases for the modelled
scenarios in this study; however, this may change under more favourable HT-ATES stor-
age conditions.
Figure 12. Comparison of the monthly electricity demand of a reference scenario with direct heat
production by a heat pump with 8 °C evaporator temperature and the 50|30|1.5 scenario with HT-
ATES. The areas under the graph show the total electricity demand over the year, in this case the
year 2019.
4.3. The LCOE Placed in Perspective
The LCOE for the different scenarios of heat pump size, condenser temperature and
threshold temperature varies between 16.619.6 €/GJ for 65 °C heat pump condenser tem-
perature, and between 12.217.1 €/GJ for 50 °C condenser temperature. Choosing a lower
condenser temperature (50 °C instead of 65 °C) leads to lower costs as the COP of the heat
pump is higher and the costs of the heat pump are assumed to be lower. However, exact
cost curves of (large scale) heat pumps are not available, so the cost difference is not
known exactly and needs further investigation. Additionally, at 50 °C condenser temper-
ature, the total stored volume in the HT-ATES is approximately twice as large as at 65 °C,
and therefore requires more hydraulic pumping for the injection and abstraction.
The costs of the assessed scenarios in this paper are comparable to the costs (20.5
€/GJ) that were determined by Wesselink et al. [22], who considered a HT-ATES system
in combination with geothermal energy. In an overview of different HT-ATES feasibility
studies, the LCOE of heat varied between 14.7–29.3 €/GJ, with larger systems (> 350,000
m3) resulting in a LCOE below 20 €/GJ [54]. The LCOE calculated in this paper thus fits
within the price range known for HT-ATES systems. Heat delivered by a gas boiler costs
on average 1012 €/GJ [49]. For the scenarios considered in this paper, calculated prices
are higher with a minimum difference of 0.22.2 €/GJ (50|30|1). This gap could be bridged
by an increase in the CO2 price or subsidies on the installation or heat price of 100% re-
newable power-to-heat systems with HT-ATES. With a CO2-price of 55.6 €/ton CO2 and
CO2-emission factor of 35.97 kg CO2/GJ of heat [55], the LCOE of a gas boiler would rise
to 1214 €/GJ, which would then fall in the same range as the 50|30|1 and 50|30|1.5 sce-
narios. At a CO2-price of 100 €/tonne CO2, the price range of heat from a natural gas boiler
Figure 12.
Comparison of the monthly electricity demand of a reference scenario with direct heat
production by a heat pump with 8
C evaporator temperature and the 50|30|1.5 scenario with
HT-ATES. The areas under the graph show the total electricity demand over the year, in this case the
year 2019.
More importantly, the integration of HT-ATES has a strong effect on the distribution of
electricity use over the year. The electricity use is decoupled by the integration of HT-ATES,
and the electricity use peak switches from winter to summer, following the availability of
sustainable electricity by solar energy (Figure 12). Here, we compare the electricity use of a
MES with and without HT-ATES. The electricity use of scenario 50|30|1.5 is compared to
a reference system that uses a HP for direct heat production only.
Thus, the HT-ATES system enables the use of more sustainable and local electricity
when available (in summer) and reduces the size of the heat pump compared to systems
with direct heat production. The yearly electricity use slightly increases for the mod-
elled scenarios in this study; however, this may change under more favourable HT-ATES
storage conditions.
4.3. The LCOE Placed in Perspective
The LCOE for the different scenarios of heat pump size, condenser temperature and
threshold temperature varies between 16.6–19.6
/GJ for 65
C heat pump condenser
temperature, and between 12.2–17.1
/GJ for 50
C condenser temperature. Choosing a
lower condenser temperature (50
C instead of 65
C) leads to lower costs as the COP of
the heat pump is higher and the costs of the heat pump are assumed to be lower. However,
exact cost curves of (large scale) heat pumps are not available, so the cost difference is
not known exactly and needs further investigation. Additionally, at 50
C condenser
temperature, the total stored volume in the HT-ATES is approximately twice as large as at
65 C, and therefore requires more hydraulic pumping for the injection and abstraction.
The costs of the assessed scenarios in this paper are comparable to the costs (
20.5 /GJ
)
that were determined by Wesselink et al. [
22
], who considered a HT-ATES system in
combination with geothermal energy. In an overview of different HT-ATES feasibility
studies, the LCOE of heat varied between 14.7–29.3
/GJ, with larger systems (
>350,000 m3
)
Energies 2021,14, 7958 27 of 31
resulting in a LCOE below 20
/GJ [
54
]. The LCOE calculated in this paper thus fits within
the price range known for HT-ATES systems. Heat delivered by a gas boiler costs on
average 10–12
/GJ [
49
]. For the scenarios considered in this paper, calculated prices
are higher with a minimum difference of 0.2–2.2
/GJ (50|30|1). This gap could be
bridged by an increase in the CO
2
price or subsidies on the installation or heat price of
100% renewable power-to-heat systems with HT-ATES. With a CO
2
-price of 55.6
/ton
CO
2
and CO
2
-emission factor of 35.97 kg CO
2
/GJ of heat [
55
], the LCOE of a gas boiler
would rise to 12–14
/GJ, which would then fall in the same range as the 50|30|1 and
50|30|
1.5 scenarios
. At a CO
2
-price of 100
/tonne CO
2
, the price range of heat from a
natural gas boiler would rise to 13.6–15.6, increasing the feasibility of a HT-ATES system
with power-to-heat further.
4.4. Prolonged Lifetime of the Multi-Energy System with HT-ATES
In this study, only the first ten years of operation of the HT-ATES have been simulated.
The lifetime of the multi-energy system and HT-ATES is longer than ten years, and the
results show that most scenarios can reach a 100% heat delivery within the first ten years
of operation (see Figures 6and 7). After this time, the electricity demand is more represen-
tative compared to the first few years of operation as the system is still stabilizing and not
able to provide the total heat demand. Hence, we performed an extra financial analysis
assuming the electricity demand of the 10th year of operation would stay constant for the
next 30 years of operation. This is a conservative estimation because the systems will most
likely continue to improve in performance and system recovery efficiency over the years.
On the other hand, we assumed that all systems would be able to provide the total heat
demand of the neighbourhood after 10 years of operation, although we are not completely
sure about this for i.e., scenario 65|43|1.5, 65|30|1.5, 65|30|1, and 50|30|1. The results
can thus not be used to reliably compare the scenarios with each other, but give an idea
of how the LCOE would develop over time. The results are shown in Table 7and show a
reduction of 1.2–5.6
/GJ, which is a reduction in LCOE of 4–28% and 13% on average. The
LCOE presented in this paper are thus a conservative estimation and will be significantly
lower when considering a lifetime of 30 years, which would be further enhanced when a
reduction in electricity consumption is taken into account as well.
Table 7. LCOE analysis with 30 years of operation of the heat pump/HT-ATES system.
Heat Production Costs
(LCOE) in /GJ—10 Years
Heat Production Costs
(LCOE) in /GJ—30 Years
65|43|2 19.6 17.4
65|30|2 18.6 17.4
65|43|1.5 18.5 14.3
65|30|1.5 16.6 14.3
65|30|1 16.7 11.1
50|43|2 17.1 14.6
50|30|2 15.7 14.2
50|43|1.5 15.5 13.0
50|30|1.5 13.8 12.3
50|30|1 12.2 10.1
5. Conclusions
In this study, it is shown that with the novel integration of a HT-ATES system to a
multi-energy system, it is possible to provide >90% of the heat demand during the first ten
years of operation, and supply the full heat demand after 3–5 years. The system recovery
efficiency of the HT-ATES system was 50–70% depending on the system design chosen. By
including a HT-ATES system in a multi-energy system, the size of the heat pump could be
reduced by maximally 25% as the peak in heat demand can be delivered from the HT-ATES
system, which reduces both the impact on the surface level (smaller installation) and costs.
The integration of HT-ATES allows for the decoupling of energy demand and availability,
Energies 2021,14, 7958 28 of 31
meaning that the yearly distribution of electricity use is shifted completely from mainly
in winter to mainly in summer, to match the availability of sustainable electricity by e.g.,
solar PV.
Choosing a condenser temperature of 50
C led to a 11–18% lower LCOE than a system
with 65
C condenser temperature at the same heat pump size and threshold temperature.
Overall, a heat pump condenser temperature of 50
C with a 1 MW
el
heat pump and a
threshold temperature of 30
C led to the lowest LCOE (12.2
/GJ). Yet, with this heat
pump size, only 75% of the heat demand was fulfilled and 25% of the heat demand had
to be fulfilled by another source (in the first ten years of operation). The scenario with a
50
C condenser temperature, 1.5 MW
el
heat pump size and 30
C threshold temperature
is seen as the most optimal regarding the combination of LCOE (13.8
/GJ), fulfilment of
heat demand (90% in the first 10 years of operation) and MES efficiency (55%).
The extra mode of operation of the heat pump is a booster mode during recovery from
the HT-ATES system and allowed to lower the acceptable temperature threshold value
of the hot well below the actual delivery temperature of the DHN. We have shown that
this innovative “HT-ATES feeds heat pump mode” is able to prolong heat delivery from
the HT-ATES, and therefore the amount of heat delivered increases, as well as the system
recovery efficiency and MES efficiency. The effect of the HT-ATES feeds heat pump mode is
most pronounced in situations where the size of the heat pump is becoming a limiting factor
for a well-functioning system. In these cases, the HT-ATES feeds heat pump mode allows
the installation of a smaller heat pump without compromising on the efficiency of the
HT-ATES, which eventually leads to lower costs of heat within the multi-energy system.
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/
10.3390/en14237958/s1. Figure S1. Mode of operation 1: Heat production and storage in the HT-
ATES. Overview of the hydraulic scheme for charging the HT-ATES with a heat pump condenser
temperature of 65
C. The shaded lines are inactive hydraulic connections, needed for other modes of
operation. An indication of the temperature (range) of the different flows is given. Figure S2. Mode
of operation 2: Simultaneous heat production and delivery. Overview of the hydraulic scheme for
loading and simultaneously delivering (part of) the heat with a heat pump condenser temperature of
65
C. The shaded lines are inactive hydraulic connections, needed for other modes of operation. An
indication of the temperature (range) of the different flows is given. Figure S3. Mode of operation 3:
Heat delivery from the HT-ATES above the inlet temperature of the DHN. Overview of the hydraulic
scheme for heat delivery above the threshold value of 43
C to ensure heat delivery of at least
40 C
. The shaded lines are inactive hydraulic connections, needed for other modes of operation. An
indication of the temperature (range) of the different flows is given.
Author Contributions:
Conceptualization, E.v.d.R. and M.B.; methodology, all authors; software,
E.v.d.R., S.B. and M.B.; validation, E.v.d.R. and S.B.; formal analysis, E.v.d.R. and S.B.; investigation,
E.v.d.R. and S.B.; writing—original draft preparation, E.v.d.R., S.B. and M.B.; writing—review and
editing, all authors; visualization, E.v.d.R. and S.B.; supervision, M.B., N.H. and J.P.v.d.H. All authors
have read and agreed to the published version of the manuscript.
Funding:
This research was co-financed with PPS-funding from the Topconsortia for Knowledge
and Innovation (TKI’s) of the Ministry of Economic Affairs and Climate. The authors would like to
thank the TKI Watertechnology (2016KWR019 or RVO5289) and TKI Urban Energy (TEUE117059) in
The Netherlands for their financial contribution to the research projects that lay at the foundation of
this publication.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are openly available via 4TU:
doi 10.4121/16964773.
Conflicts of Interest: The authors declare no conflict of interest.
Energies 2021,14, 7958 29 of 31
Nomenclature
Symbol Definition Unit
CAPEXiTotal capital expenditure for system component i
COP Coefficient Of Performance -
COP10y avg Average COP of the heat pump over the run time (10 year average) -
COPHP Coefficient of performance of a heat pump -
cwSpecific volumetric heat capacity of water kJ/K/m3
DHN District Heating Network -
Ecost,i Costs of energy (in this case electricity) for the system /year
el electric power -
Eldem,10y avg Average electricity demand of the total heat systemover the run time (10 year average) TJ/y
Endel,10y avg Average amount of delivered heat to the neighbourhood (10 year average) TJ/y
Espaceheat,total Total space heat demand over a run period MJ
hDH_i Number of degree hours at hour t -
HP Heat Pump -
HT-ATES High Temperature Aquifer Thermal Energy Storage -
LCOEheat Levelized cost of heat production /GJ
LiLifetime of a system component i years
MES Multi-Energy System -
OPEXiOperational expenditures and maintenance of systemcomponent i /year
PtH Power-to-Heat -
PV Photovoltaic -
Qheat,delivered Part of the heat demand delivered by MES (HT-ATES + heat pump) GJ/year
Qspaceheat,i Heat demand at time t MJ
rDiscount rate %
RES Renewable Energy Systems -
rVB Volume balance ratio -
TWater temperature C
Tair,i Outside air temperature at time t C
Tambient Background temperature of the aquifer C
Tbase Base temperature for heating C
th thermal power -
Thot Temperature of the hot well of the HT-ATES C
THP,cond Outgoing temperature heat pump condenser C
THP,evap Ingoing temperature heat pump evaporator C
Tinj Injected storage temperature C
Twarm Temperature of the warm well of the HT-ATES C
Vheatproduction Yearly total extraction volume m3
Vheatstorage Yearly total storage volume m3
vin Flow of water into the hot well m3/h
vout Flow of water out of the warm well m3/h
αCapital recovery factor -
HDH Sum of heat degree hours over a run period -
η(recovery) Efficiency %
ηMES MES efficiency %
ρDensity of water kg/m3
ρ0Density of water at ambient groundwater temperature kg/m3
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