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 , but a 3-D finite difference method is used to solve the boundary value problem and to generate a steady-state solution for the temperature. The methodology of the protocol was based on earlier work of and has been used to assess the geothermal potential of the USA . When data are scarcely available it is a fast way to generate an adequate temperature model for a large area such as Europe or the USA. It makes optimal use of data that are relatively easy to acquire and the variability of the model parameters can be easily adjusted whenever more data have become available. For this method, considering the European scale of the application, local convection is neglected and it is assumed that heat is transported via thermal conduction.

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 (k) and radiogenic heat production (A).

Values for k are assigned according to the vertical position relative to the boundary between the sedimentary cover and the crustal basement. This boundary represents the depth of the sediment–basement interface (S) that divides the two layers.

The sediment thickness or the depth of S is created by using the sediment thickness map from the high-resolution (0.25∘ by 0.25∘) EuCRUST-07 model from . This model is a compilation of existing sediment thickness maps that, where possible, have been improved by using seismic profiles. Because the EuCRUST-07 model does not fully cover the area of interest (eastern Turkey and eastern Ukraine are missing) the CRUST 2.0 model from (with the sediment maps from ) is used. This model is largely based on the sediment thickness from the Tectonic Map of the World, created by .

For the sediments, an average value for k of 2.0 W m-1 K-1 was used, based on basin modeling predictions for lithologies which have not been subject to metamorphism e.g.,. For the European crystalline basement, dominated by plutonic and metamorphic rocks, a value of 2.6 W m-1 K-1 was adopted e.g.,.

To obtain values for A in each grid cell the partition model of was applied. Using the surface heat flow Q0 and depth ZM of the Moho, A was calculated for every grid cell by

Az=0.4Q00.5zM, which forces Az (W m-3) to be constant with depth, but to vary laterally according to Q0 (W m-2) and zM (m). It was assumed that the upper crust forms half of the thickness of the total crust, which is approximately half of the depth of the Moho. The Moho depth in Europe varies from 15 to 63 km and was also derived from the EuCRUST-07 model from and is complemented by the CRUST 2.0 model from to cover eastern Turkey and parts of Ukraine.

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 . A constant heat production with depth may not be realistic, but the advantage of the adopted model is that it reduces A to a simple function of Q0, which is capable of capturing the most important cause for regional heat flow variations, as reflected by correlation of regional variations of the surface heat flow Q0 and the average radiogenic heat production observed in upper parts of the crust .

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 .

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 ∘C in volcanically active regions such as Iceland, parts of Italy, Greece and Turkey. Especially in Iceland and around volcanic regions in Italy, temperatures can reach more than 300 ∘C at a depth of 5 km and more than 500 ∘C at a depth of 10 km. What really stands out, apart from the regions with elevated temperatures, is the profound division between relatively high temperatures in the southwestern part of Europe and low temperatures in the northeastern part. These colder zones are mostly constrained to the East European Craton and to the Fennoscandian or Baltic Shield.

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 . The Precambrian zone has large lithosphere thicknesses and the Moho lies deeper, while in the Phanerozoic part of Europe the lithosphere is thinner and the Moho lies more shallow .

At 5 km depth (Fig. 2e) the model has a mean temperature of 111 ∘C and a total range varying between 40 and 310 ∘C and a standard deviation σ of 44. At 10 km depth (Fig. 2g) the mean temperature is 201 ∘C, a total range between 80 and 590 ∘C and σ=74.

The lowest temperatures at 10 km depth are around 80 ∘C, which is in line with geothermal gradients of 5–10 ∘C km-1 that are observed in old cratonic crust . In the model, large anomalies between the observed and modeled temperature could be an indication for the presence of thermal convection . These temperature anomalies can be used as a proxy for high permeability as was shown for the Netherlands by .

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.

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 and a discounted cash-flow model from and . The model is digitally available as an Excel spreadsheet. As depicted schematically in Fig. 3, the temperature model voxet is used to generate voxets for the heat in place H (J), the theoretical potential Ptheory (MWe), the technical potential Ptechnical (MWe) and finally the LCOE (EUR MWh-1). The LCOE values are used to restrict Ptechnical to obtain the economic potential Peconomic (MWe). It is important to keep in mind that this methodology is based on a number of assumptions and that these potentials provide only an indication of the global European prospective resource base. In these following subsections the main concepts and assumptions used in this methodology are described.

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, It, that needs to be invested in a geothermal energy project in the year t. It includes the investment costs such as the exploration costs, the drilling costs for the wells, the construction costs of the power plant including grid connection, the costs of reservoir stimulation and the costs of financing and commissioning the whole system. OPEX is the sum of all the operational and maintenance expenses, Mt, that are made in the year t. Mt includes costs of personnel and equipment, costs of maintenance and, if required, costs of re-stimulation of the reservoir or the drilling of replacement wells. Taxt is the amount of tax that is paid in the year t, Et is the amount of electricity produced in the year t, and r is the discount rate or inflation. Et=Q×ρwater×Cpwater(Tz-Tr)×ηth×10-3×tload Following Eq. (6), Et is calculated with flow rate Q (L s-1), water density ρwater (kg m-3) and the heat capacity of water Cpwater (J kg-1 K-1). ηth is used to convert from thermal power to electrical power and tload is the time (hours per year) at which the plant is fully operational. Et will only be calculated for grid cells for which the temperature is sufficiently high (Tz>Tr).

Once It, Mt and Et are calculated and discounted using r, it is possible to calculate the expected costs per unit power or LCOE . The LCOE is calculated using Eq. (7): LCOE=cumulative discounted yearly net revenuecumulative discounted yearly electricity production=∑t=1TIt+Mt+Taxt(1+r)t∑t=1TEt(1+r)t. Here the total discounted expenditures made during the project's lifetime in EUR are divided by the total discounted energy produced in megawatt hours for an expected lifetime T in years. The total discounted life cycle costs are equal the sum of all the discounted CAPEX, OPEX, and taxes from year t=1 to t=T. The total lifetime energy production is the sum of the discounted energy produced from year t=1 to t=T. The discount is imposed by dividing the sum of the CAPEX and OPEX in year t and the energy production in year t by (1+r)t. LCOE can only be calculated for grid cells where Et>0.

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 , the average CF of all operational geothermal power plants is 74.5 %, while new plants often reach 90 % or more. This is higher than the CF of coal- and gas-fired power plants and much higher than the CF of other renewable energy technologies that are dependent on weather, such as solar and wind power . Conventional geothermal energy systems have proven to be generally reliable and are able to provide base-load electricity. Because EGS systems work on the same principles as conventional geothermal systems, it is assumed that the CF will be in the same range as for conventional systems. For this study we assumed tload=8000 h, corresponding to CF ≈ 91 %.

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 . It is usually smaller than the economic potential, but it can be larger if governments have policies to promote renewable energy, including geothermal energy, such as feed-in tariffs, favorable taxes and favorable risk insurances.

Important for utilizing EGS for periods longer than the initial life time, is the sustainable potential . It describes the fraction of the economic potential that can be used with sustainable production levels, while taking into account the resource degradation over time caused by pressure drawdown or by declining reservoir temperatures . We did not account for this in our study, but the effect of reduced temperatures and flow rates on the LCOE is shown in Fig 5. The effect of stimulation costs on the LCOE in Fig. 5 can also be used to assess the effect of measures countering resource degradation (e.g., additional stimulation or drilling of new/relieve wells).

For this resource assessment, we restricted the technical potential to grid cells where the LCOE was lower than a given threshold c (Eq. 9). IfLCOE<c:Peconomic=Ptechnical For the 2020, 2030, and 2050 scenarios, values for c of 200, 150 and 100 were chosen, respectively. These numbers were adopted to reflect the likely reduction of feed-in tariffs in the future beyond 2020 and renewable energy prices that will eventually become compatible with current fossil fuel-based energy prices .

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 xy column of grid cells. Since fixed flow rates are assumed for the three different scenarios, the subsurface temperature automatically becomes the most important parameter. The LCOE distribution in the maps of 2020 and 2030 (Fig. 6a, b), therefore correspond largely to the areas where elevated temperatures are present at shallower depth. These mainly consist of volcanic areas such as Iceland, Italy and Turkey, but also sedimentary basins such as the Rhine-Graben, Pannonian Basin and the Southern Permian Basin. The LCOE map for the 2050 scenario clearly shows that the cost of drilling is a determining factor for the LCOE. Due to the use of a linear well cost model for the 2050 scenario, LCOE are lower than EUR 100 MWh-1 for almost all of Europe southwest of the the TESZ.

To calculate the total economic potential for each stacked xy column Eq. (10) was used: Peconomic=∑z=0z=7-10km(Peconomic)z. For the country outlooks, the Peconomic for each country is summed and then multiplied by 0.25 to limit the economic potential for land use restrictions. For 2020 and 2030, only the potentials up to a depth of 7 km are considered, while for 2050 the maximum depth is extended to 10 km. The economic potentials for 2020, 2030, and 2050 are 19, 22 , and 522 GWe, respectively.

The effect of the different values of the LCOE threshold c is illustrated in Fig. 7, where the economic potentials are plotted for c values varying between EUR 300 MWh-1 and EUR 50 MWh-1. The economic potentials for the whole area considered in this study can be found in Table 3, along with the most important assumptions for each scenario.

By dividing Peconomic by Ptechnical, we calculated the effective UR. This results in an UR of 0.1 % for 2020, 0.2 % for 2030, and 2.4 % for 2050. The large difference of the Peconomic and UR of 2020 and 2030 compared to 2050, can mostly be ascribed to the use of a linear well cost model combined with the increase in the maximum drilling depth from 7 to 10 km, enabling exploitation of deeper reservoirs with higher temperatures.

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 ∘C and the reservoir is located sufficiently deep e.g.,. Due to the assumed lower drilling costs in 2050, it becomes financially feasible to drill for these deeper reservoirs with higher temperatures. Furthermore, by adopting a threshold value c of EUR 100 MWh-1 for 2050, these higher temperatures and associated larger depths are also implicitly required.