Sustainability Assessment of Potential Areas for Deep Geothermal Energy Systems in Switzer-land

The Energy Strategy 2050 requires that Switzerland replaces nuclear power by sustainable, low-carbon energy technologies. One of those technologies is deep geothermal energy (DGE), which provides a base-load option with a large potential. However, possible power plant locations are constrained by their sustainability performance, which depends on the geological conditions as well as economic, environment and social aspects.

Based on these premises, scientists from the Technology Assessment Group at the PSI developed a tool to assess the sustainability of potential areas for DGE, which combines Multi-Criteria Decision Analysis (MCDA) and Geographical Information System (GIS). MCDA allows considering a wide variety of aspects in a transparent manner, for example environmental and economic conditions. GIS accounts for the spatial variability of the problem.

Average Sustainability map for Deep Geothermal Energy in Switzerland based on a set of sample preference profiles.

For their sustainability assessment, they used a so-called spatial MCDA (sMCDA) approach at the municipality level the Swiss Molasse basin. For this part of Switzerland, more DGE pilot projects are expected due to the more homogeneous – and therefore more suitable – geological conditions compared to the Alpine region. Overall, the scientists evaluated the sustainability performance for 1684 municipalities.

Average Sustainability map for Deep Geothermal Energy in Switzerland based on an equal weight preference profile.

Currently, no DGE plants are operational in Switzerland. Therefore, the scientists defined six hypothetical geothermal plants with two (duplet) or three (triplet) wells and three plant installed capacities for each.

To assess the DGE’s sustainability, they defined 12 indicators covering the three pillars of sustainability (environment, economy and society), which are subsequently estimated for the six hypothetical geothermal plant cases in each municipality. The five environmental indicators are Climate Change, Human Toxicity, Particulate Matter Formation, Water Depletion, and Metal Depletion. When it comes to economy, they evaluated Average Generation Cost, and Company Taxation. The five social indicators are Non-Seismic Accident Risk, Natural Seismic Risk, Induced Seismicity, Population, and Presence of National/Regional Parks.

The scientists used Life-Cycle Assessment (LCA) to calculate the environmental indicators. Climate Change expresses the kg of CO2 released in the atmosphere per kWh produced by the plant during its lifetime. Human Toxicity measures the number of years lost due to ill-health or disability in terms of kWh produced by the plant during its lifetime. Particulate Matter Formation measures the kg of PM10 released to the air per kWh produced by the plant during its lifetime. Water Depletion and Metal Depletion correspond to the kg of the specific resource depletion per kWh produced by the plant during its lifetime. All these indicators vary spatially and are sensitive to the temperature gradient in the subsoil. The Average Generation Cost indicator measures the average costs, including amortized capital, recurring and end-of-life costs, of a DGE project per kWh over the plant lifetime. It is also sensitive to the temperature gradient in the subsoil. Consequently, the scientists calculated different values of these indicators for each municipality, based on the temperature gradient of the subsoil in Switzerland.

In contrast, no spatial effects are present for Non-Seismic Accident Risk, since this indicator depends only on the number of drilled wells. The Company Taxation, which vary at the cantonal level, refers to the taxation in percentage to the industries. The Natural Seismic Risk is expressed on an ordinal scale as low, medium or high, based on the seismic zones according to the Swiss Society of Engineers (SIA) 261 standard. The scientists consider it as a proxy for social acceptance, where high values correspond to lower social acceptance and vice versa. For the Population indicator they used the number of inhabitants. The larger the population in a municipality, the higher is the risk of potentially affected inhabitants due to induced seismicity, for example.

The presence or absence of National/Regional ParksThey estimated the Induced Seismicity Indicator based on the expected total volume of fluid injected for the stimulation. The higher the volume, the higher the risk of induced seismicity.

The scientists estimated the indicator values for the six hypothetical geothermal plants in each municipality and fed them into the sMCDA. The algorithm then assigned a class of sustainability (high, medium-high, medium, medium-low, low) to each municipality considering a decision maker’s acceptance level, i.e. how demanding is a hypothetical decision-maker in accepting the proposed class of sustainability, stakeholder preferences and the uncertainty in the indicators. Since they did not carry out a survey to elicit stakeholder preferences, they defined and used representative, artificial preference profiles.

On average, the results show that the sustainability performance of DGE in Switzerland is high in the Northeast of Switzerland. However, the artificial stakeholder profiles clearly indicate that different profiles can influence the sustainability performance of a municipality. This illustrates the sensitivity of the ranking to subjective preferences by the stakeholders. Therefore, trade-offs must be considered during an informed decision-making process, and to guide the public debate and participatory processes.