Influence of the heat network rollout time on the risk and profitability of a deep geothermal plant

The development of geothermal energy is below the European National Renewable Energy Action Plans' anticipated trajectory. For deep geothermal energy projects in particular, multiple sources of uncertainty in combination with high upfront investment costs result in a major investment risk, hampering the mobilization of required capital (Compernolle et al., 2019). The uncertainty sources include market uncertainty, uncertainty regarding new technologies and uncertainty inherent to working with subsurface data. The objectives of the DESIGNATE project for deep geothermal systems in Belgium, including applications in abandoned mines are two folds. First, to create tools for integrated forecasts under uncertainty and second to set-up a methodological framework for territorial Life Cycle Analysis (LCA) considering surface and subsurface impacts. To do so, analytical reservoir models will be developed to assess the effect of uncertainties about geological data and concepts on the performance and impact of the geothermal applications. These will be coupled with a techno-economic analysis in combination with a territorial, environmental life cycle analysis. To evaluate the impact of different policy measures, the techno-economic analysis consists of a Monte Carlo simulation model that integrates both market and geological uncertainties and a project developers' option to wait or abandon the geothermal project development at different steps in the development of the project (Welkenhuysen et al., this conference). As a preliminary step, the influence of the rollout time of a heat network on the risk and on the profitability is investigated. At the start often only a part of the district heating network is in place at the time of commissioning and the geothermal plant operates at much lower capacity. Part of the capacity is foreseen for district heating networks linked to residential districts expected to be built or renovated in the near future. In this research, the change in income of a project considering a stepwise rollout of a district heating network compared to a full load from the start, in combination with a reduced maximum capacity of the geothermal plant compared to the expected output is calculated. This is done with a simplified spreadsheet techno-economic model, limiting variability to the rollout scenarios. For the calculation, data provided by the project developer HITA of the Turnhout NW geothermal project is used. In the next section the four cases used to evaluate the risk and profitability linked to the changes in the rollout time of a heat network are described. In the first case, the base case, the production plant is assumed to work at full capacity once the construction of the geothermal plant is achieved. Full capacity means that the production plant will be working at 100% during the heating season. Additional production for cooling or for heat storage in summertime are not taken into account. The second case considers that the maximum production capacity is 20% lower than in the first case due to lower-than-expected reservoir temperature or flow rate. In the third case, the full capacity is equal to the one of the base case but will be reached in three steps, simulating a growing demand by adding new district heating networks. The demand is expressed as a percentage of the expected maximum production capacity of the geothermal plant. At the start of production, the geothermal plant runs at 50%. After 5 years this is increased to 75% and after 10 years full capacity is reached. The fourth and last case is similar to the third case, with a stepwise increase of the demand, but the maximum production capacity is, as in second case, 20% lower. Because the demand is lower than the total capacity in the first 10 years, the production plant will however be able to supply the required energy. Only after 10 years when the demand rises to the expected maximum production capacity, only 80% of the required energy can be delivered without additional investments. As such, the income of the project will be the same the first 10 years compared to the third case. In a best-case scenario, demand and rollout of a district heating network will be fast and the production plant will run at full capacity during the heating season from the start (case 1). This is however unlikely and assuming this to be the base case will result in many projects not reaching predetermined targets, as the income of the project will be lower during the first years of production. In this respect, the third case or a similar scenario is a better option to use as a base case. This will put more stringent conditions on the expected output parameters of the production well to ensure an economic viable project, and hence provide a more realistic outlook. When using case 3 as the base case this also has the complementary benefit of reducing the risk related to the maximum production capacity. If the real maximum production capacity is lower than expected, the reduction of income will be lower than the decrease in the maximum production capacity. In other words, a reduction of 20% of the maximum production capacity will not lead to a reduction of 20% of the income, but will be between 0 and 20%, depending on the interest rate and on the time frame to reach full capacity. Acknowledgments This research is carried out under the DESIGNATE project, which receives funding from the BELSPO BRAIN-be 2.0 research programme under contract nr B2/191/P1/DESIGNATE. HITA kindly provided input for the development for this case study. References Compernolle, T., Welkenhuysen, K., Petitclerc, E., Maes, D. & Piessens, K., 2019. The impact of policy measures on geothermal energy investments. Energy Economics, 84, 104524. https://doi.org/10.1016/j.eneco.2019.104524 Welkenhuysen, K., Compernolle, T., Kaufmann, O., Laenen, B., Meyvis, B., Piessens, K., Gousis, S., Dupont, N., Harcouet-Menou, V. & Pogacnik, J., this conference. Decision support under uncertainty for geothermal applications: case selection and concept development.

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