In this study the resource base for EGS (enhanced geothermal systems) in Europe was quantified and economically constrained, applying a discounted cash-flow model to different techno-economic scenarios for future EGS in 2020, 2030, and 2050. Temperature is a critical parameter that controls the amount of thermal energy available in the subsurface. Therefore, the first step in assessing the European resource base for EGS is the construction of a subsurface temperature model of onshore Europe. Subsurface temperatures were computed to a depth of 10 km below ground level for a regular 3-D hexahedral grid with a horizontal resolution of 10 km and a vertical resolution of 250 m. Vertical conductive heat transport was considered as the main heat transfer mechanism. Surface temperature and basal heat flow were used as boundary conditions for the top and bottom of the model, respectively. If publicly available, the most recent and comprehensive regional temperature models, based on data from wells, were incorporated.
With the modeled subsurface temperatures and future technical and economic
scenarios, the technical potential and minimum levelized cost of energy (LCOE)
were calculated for each grid cell of the temperature model. Calculations for a
typical EGS scenario yield costs of EUR 215 MWh
Enhanced or engineered geothermal systems (EGS) have increased the
number of locations that could be suitable for geothermal power production. In
the past, geothermal power production was limited to shallow high-enthalpy reservoirs
(
Breakthroughs in binary power plant technology (e.g., organic Rankine cycle and
Kalina plants) have enabled the use of medium enthalpy heat sources by using a
binary working fluid to power the turbines
Consequently, these developments should allow for more flexibility and a
significant increase in the number of suitable locations for geothermal power
production. In practice, development of EGS is not straightforward and so far in
Europe most of the EGS power plants currently operational are limited to areas
around the failed rift system of the Rhine Graben and the Molasse Basin of the
northern Alpine foreland
For EGS and other geothermal systems, flow rate
This European resource assessment for EGS was conducted as part of the
GeoElec European project to favor the development of geothermal electricity production
in Europe
The first large-scale resource assessment for EGS was conducted for the United
States
The most important input for the resource assessment in this study is a 3-D subsurface temperature model of Europe. The basic methodology of this temperature model is given in Sect. 2. The most recent and comprehensive regional temperature models available are incorporated, and combined with lithosphere-scale models to construct the model geometry and distribute thermal properties.
For the resource assessment of Europe we propose an approach similar
to
The results of this approach do not only delineate prospective areas for future EGS in Europe, but also show an economically constrained depth-dependent distribution of the technical potential. Finally, implications of the results and potential improvements are discussed.
This model mainly relies on temperature and heat flow values measured at Earth's surface and on a simple distribution of thermal properties in the upper crust. The modeling routine is designed in a way that it can easily be extended with additional information such as local temperature models.
The model assumptions of the temperature model in this study are similar to the
protocol proposed by
Model geometry and boundary conditions.
In this model a two-layer setup
is used to assign values for
The model works on a voxet (a regular 3-D grid representation), which for
the European assessment was chosen at a resolution of 10 by 10 km in northing
and easting and by 0.25 km in depth. Depending on the location, each vertical
column of stacked grid cells can represent two layers: one layer that represents
sedimentary cover and the other layer that represents the crustal basement
(Fig. 1, Table 1).
Both layers have two thermal properties: thermal conductivity (
Values for
The sediment thickness or the depth of
For the sediments, an average value for
To obtain values for
In nature, radiogenic heat production can show variations of up to several orders
of magnitude even in samples that have been taken within a 1 km distance
from each other
The model works generally well in stable cratonic areas but, in more
tectonically active regions, heat flow measurements can be severely affected by
transient effects
For the top of the model, constant values for the surface temperature
(
As a Neumann boundary condition for the base of the model at 10 km below ground
level, constant heat flow values are imposed. The heat flow at 10 km (
At the vertical edges of the model, values of zero heat flow are imposed, which can be considered as a special case of a Neumann boundary condition. Finally, this model calculates temperature values in the 3-D grid, given the 3-D thermal conductivity and radiogenic heat production structure.
Input depth slices of subsurface temperature models (b.g.l. – below ground level)
Depth
slices of the modeled temperature voxet. Depths are below ground level.
Subsurface temperature models were collected from several geologic surveys, including France, Germany, Ireland, the UK and the Netherlands (Table 2). Apart from the UK, which only provided a map of 1 km depth, the subsurface temperature models provide constraints of up to a depth of 5 km. All of these models are based on bottom-hole temperature (BHT) or drill-stem test (DST) data, but their methodologies to compute them differ.
The French model from
The Dutch temperature model from
To use as much reliable temperature data as possible, we merged
the regional temperature models and incorporated them in the modeling routine.
To have constraints for the areas where no temperature models were available,
the digitized subsurface temperature maps of 1 and 2 km depths from the
geothermal atlas of
For areas where more than one temperature value was available, we preferred to use values from integrated models over values derived by interpolation. For regional models where a similar methodology was used, we looked at the amount of measurements that were incorporated near the shared boundary. We have chosen the Dutch model for the overlapping areas between the Dutch and German models and the German model for the overlapping areas between France and Germany.
Next, we replaced the calculated temperature values with the values from the merged temperature models without any smoothing. This approach could potentially cause discrepancies along shared borders between countries, as well as inconsistencies between the imported temperatures and the calculated heat flow. However, it enables the inclusion of more reliable data based on temperature measurements.
The outcome of the temperature modeling routine is a 3-D temperature voxet which contains values for every 10 by 10 by 0.25 km cell. Depth slices of the model taken at shallow to intermediate depth levels of 1–10 km are shown in Fig. 2.
The model shows high average geothermal gradients of up to 60
This dichotomy fits with the Trans-European Suture Zone (TESZ), which marks a
clear division between the stable Precambrian Europe and the dynamic Phanerozoic
Europe
At 5 km depth (Fig. 2e) the model has a mean temperature of
111
The lowest temperatures at 10 km depth are around 80
To develop a geothermal system it is necessary to have favorable geological conditions, including a high temperature and appropriate reservoir properties. However, favorable geological conditions alone are not enough to initiate any commercial development. Because the development of a geothermal system involves high upfront costs and high financial risks (mostly related to drilling), it is vital to assess the financial feasibility for different scenarios. For the GeoElec project we applied a methodology that incorporates economic parameters in the estimation of geothermal resources in Europe. The main outputs from this method are the minimum LCOE and the economic power potential. Both are calculated on the basis of the temperature model described earlier. Because it is difficult to constrain the flow rate without information from a well, fixed flow rates have been used for the calculations, building from the generalized assumption that natural permeability can be enhanced – through stimulation – to sustain the assumed flow rates.
Important assumptions on economic parameters and the main results, including LCOE, theoretical potential, economic potential and the effective ultimate recovery.
The techno-economic model uses the 3-D temperature voxet derived from the
temperature modeling routine as input for its calculations. The complexity of
this techno-economic model lies in the large quantity of variables
inherent to economic problems, rather than in the mathematical solution. The
model is based on a combination of the volumetric approach of
Assessment of the potential power output from a
geothermal system. The theoretical capacity is the amount of thermal energy
physically present in the reservoir rocks of a certain area or prospect. The
theoretical potential describes the total amount of power that can be
converted from the theoretical capacity within a certain period of time
using a conversion efficiency. The technical potential is that part of the
theoretical potential that can be exploited with current technology
available calculated using a recovery factor. The economic potential
describes the part of the technical potential that can be commercially
exploited for a range of economic conditions. In this study we used
different cutoff values for the LCOE (
Following the protocol of
Next,
To calculate
The technical potential
Because it is difficult to precisely quantify all the different types of
limitations,
For potential investors it is essential to quantify the economic potential of a geothermal energy system. Any economic potential study should base its calculations on the investment costs, also known as capital expenditures (CAPEX), and the operational costs, known as operational expenditures (OPEX). Both are usually expressed in EUR cents or USD cents per kilowatt or megawatt of installed capacity.
CAPEX is the sum of all the initial capital,
Once
The outcome of this calculation are the levelized costs per unit of energy produced over time in EUR cents per kilowatt hour or EUR per megawatt hour, which represent the costs that an energy provider would need to charge to break even. The LCOE for future EGS were calculated for techno-economic scenarios in 2020, 2030, and 2050 for which the full list of input parameters and default values can be found in Appendix A. Changes to the default values for the specific scenarios can be found in Table 3.
The LCOE is an economic parameter that is commonly used to describe the costs of energy for conventional and emerging power producing technologies, and provides an easy way to compare the costs between different energy systems. However caution must be taken when comparing the LCOE between sources of power that are dispatchable or nondispatchable. Enhanced geothermal systems that use pumps to produce geothermal fluids can be considered dispatchable since the power output can be adjusted by varying the pumping pressure. Power sources that are nondispatchable cannot simply adjust the power output on demand because they are dependent on energy sources that are strongly variable, such as the wind or the sun.
Besides dispatchability, an important factor for replacing conventional power
plants with an alternative form is the capacity factor CF. CF
is the ratio of the actual energy output and the maximum energy output that a
power plant could produce when always operating at full capacity. The actual
energy output is always lower than the maximum energy output since a power plant
can be out of service or operating at a lower capacity due to equipment
maintenance or failure or, in the case of solar or wind power, due to lack of
resources. According to
To estimate the economic potential we combine the volumetric resource assessment
with the techno-economic model described earlier in Sect. 3.2.
A great portion of the CAPEX is determined by the costs that are related to the
drilling of the wells. Three different well cost models were used to calculate
the investment costs (EUR per well):
The WellCost Lite model has been proposed by
Well costs in
million EUR for 2020, 2030, and 2050.
(Eq. 8). For the 2020 scenario a combination of two well cost
models is used. Above 5200 m the ThermoGIS model from
For 2020, the WellCost Lite model and the ThermoGIS model were combined with
Another advantage of geothermal energy is that the OPEX are relatively low and
do not depend on fuel costs, contrary to the OPEX of conventional power plants,
which can vary strongly due to the erratic development of coal and gas prices.
The problems encountered with the development of geothermal energy systems are
mostly related to the high upfront costs and the related finances. The high
upfront costs are usually caused by the costs involved with the drilling of the
wells. The problems with financing geothermal projects relate to the substantial
uncertainties in the performance of the wells. EGS technology is still in a
research and development stage since only a handful of projects have been
realized
Because most EGS are relatively new and commercial exploitation has just started
it is difficult to assess the LCOE
These costs should enable EGS in the near future to become competitive with
conventional power sources, such as coal and gas, currently priced at USD 65 MWh
To make a comparison we applied our techno-economic model on a hypothetical
EGS project situated near the Rhine-Graben, with a reservoir depth at 5000 m and
a default temperature of 200
Tornado plot showing
the sensitivity of the calculated LCOE to changes in a selection of parameters.
The default settings of the 2030 scenario (bold) were applied to a reservoir at 5 km depth with a temperature of
200
Figure 5 shows the sensitivity of the calculated LCOE to
variations in a selection of parameters. For each of the selected parameters, we
assumed values for what the upside and downside scenarios could be and calculated
the difference compared to the LCOE for the 2030 scenario (EUR 127 MWh
The economic potential describes the part of the technical potential that can
be commercially exploited for a range of economic conditions. The total costs of
the system should ideally fall within the same range as the costs for
operational geothermal energy systems. The developable potential is the part of the economic
potential that can actually be developed taking into account all economic and
noneconomic circumstances
Important for utilizing EGS for periods longer than
the initial life time, is the sustainable potential
For this resource assessment, we restricted the technical potential to grid
cells where the LCOE was lower than a given threshold
Maps depicting the
calculated minimum levelized cost of energy (for each stacked
To visualize the spatial distribution of the LCOE we compiled maps
(Fig. 6) for each future scenario (Table 3),
depicting the minimum value for the LCOE for each stacked
To calculate the total economic potential for each stacked
Economic potential in
GW
The effect of the different values of the LCOE threshold
By dividing
We also assumed that all wells will be self-flowing in 2050, by adopting a COP
of 1000 (Table 3). From theoretical considerations, it can be
argued that well pressures in production and injection wells can be self-flowing, provided the
reservoir temperature is in excess of 220
The economic resource assessment clearly demonstrates the strong sensitivity of the spatial and depth distribution of economic potential to both subsurface and cost parameters. Temperature and flow rate are the most important constraints for the development of an EGS project. These parameters are also the most uncertain since their exact values can only be determined by drilling a well and successfully creating a reservoir. For the LCOE, costs of drilling is the most important parameter, and the models clearly demonstrate the significant impact in the economic potential through a lowered cost curve for the 2020, 2030, and 2050 scenarios.
To reduce the uncertainty for the temperature, the temperature model should be
improved. For this work, a simple two-layer conductive model is used where
values for
The underlying cause for variations in radiogenic heat production in the upper
crust are lithological variations (e.g., Hasterok and Chapman, 2011). Inclusion
of lithological interpretations of crustal composition for thermal properties
(
Furthermore, it is widely recognized that locally a conductive approximation for
the temperature distribution may be oversimplified and models need to take into
account the effects of convective fluid flow
Improvements to the quality of the temperature model could be attained by adopting data assimilation to borehole measurements of temperature, consistent with the constitutive equations for heat transfer and fluid flow. The successful implementation of the described improvements for all of Europe can only be achieved when the quality, quantity and accessibility of geological information in Europe improves drastically.
One of the most important assumptions from a geological perspective is that the
model uses a fixed flow rate. Since flow rate is one of the most sensitive
parameters for the technical and economic performance of a geothermal system
Furthermore, no distinction has been made between national differences regarding the economic situation, legislation, regulation and stimulation. These effects could potentially be significant but it is not in the scope of this study to quantify these differences. Nevertheless, for future work the model can easily be adjusted to nation specific scenarios.
Comparing the future economic potential for Europe obtained in this study to
the results of other large-scale resource assessments is problematic because of
differences in methodologies and assumptions; however, the results
in Table 3 appear to be in agreement with other estimations.
For the LCOE calculation, the following input variables are used depending on the specific application. Most of the input parameters use the default values as specified below, whilst the values of some variables, including the base temperature and the relative efficiency, depend on the temperatures derived from the temperature voxet.
Fluid and rock properties:
Power conversion:
Reservoir:
scaling factor for ThermoGIS well cost model = 1.5 well costs (EUR 10 stimulation and other costs (EUR 10 pump investment (EUR 10 number of wells = 2: depends on application subsurface CAPEX (EUR 10 maximum drilling depth (m) = 7000. COP (MW electricity price for driving the pumps (EUR MWh variable OPEX (EUR MWh
Subsurface:
Subsurface parasitic:
Power temperature range used:
outlet temperature power plant ( thermal power for electricity (MW electric power (MW power load time (hours per year) = 8000 power plant investment costs (EUR 10 power distance to grid (m) = 5000 power grid investment (EUR kW power grid connection variable (EUR m power plant CAPEX (EUR 10 power fixed OPEX rate (%) = 1 power fixed OPEX (EUR 10 power variable OPEX (EUR MWh fiscal stimulus on lowering equity before tax (true or false) = false percentage of CAPEX for fiscal stimulus (%) = 0 legal max in allowed tax deduction (EUR 10 NPV (net present value) of benefit to project (EUR 10 inflation or discount rate loan rate (%) = 6 % required return on equity (%) = 15 % equity share in investment (%) = 20 % (100 % minus debt share in
investment) debt share in investment (%) = 80 % (100 % minusequity share in
investment) tax (%) = 25.5 % term loan (years) = 30 depreciation period (years) = 30 .
Power surface facilities:
Fiscal stimulus:
Economics:
The research leading to these results has received funding from the Intelligent Energy Europe Programme under grant agreement no. IEE/10/321 Project GeoElec and from the European Community's Seventh Framework Programme under grant agreement no. 608553 (Project IMAGE). This paper is largely based on the master's thesis of J. Limberger that described the work carried out during an internship at the Dutch Geological Survey (TNO) from April 2012 to March 2013. We thank the editor and two anonymous reviewers for their constructive comments, which have helped us to improve the manuscript. Edited by: G. Beardsmore Reviewed by: two anonymous referees