Levelized costs and economic impacts of geothermal district heating networks: a decision tree analysis

Geothermal district heating networks are among the key options to decarbonize the heating sector in the State of Geneva in Switzerland. But the development of geothermal district heating requires high capital costs and involves risk of not finding sufficient geothermal resources, which make these systems less competitive. On the other hand, building geothermal district heating creates a wider impact on the economy, domestically and overall. But such impact has rarely been evaluated. Our study aims to analyze the competitiveness of geothermal district heating networks and their wider economic impacts using two competitiveness indicators (levelized costs of geothermal district heating and of district heating system as a whole) and two economic impact indicators (economic impact multipliers and share of domestic economic impacts in Switzerland). We construct a decision tree to generate 9’096 decision paths to develop shallow and medium geothermal district heating in the State of Geneva comprising 10 decision parameters: target of heat demand to be supplied (100 GWh/year and 400 GWh/year), number of districts (1,2,3 and 4 districts), share of geothermal coverage in the district heating system (10%, 40%, 70% and 100%), choice of auxiliary heating source, district heating generation (second, third and fourth), linear heat density (2, 4, 6, and 8 MWH/m•year), geothermal well depths (800 m, 1600 m, 2500 m), geothermal flowrates (20 l/s, 50 l/s, 80 l/s), and 3D seismic exploration program (with or without). We quantify the four indicators for each decision path in a decision tree, including applying probability trees to account for geothermal resource risk through assigning probabilities of success. We then identify the most influential decision parameters using a random forest regression and pinpoint the decision paths that lead to low levelized costs of heat and high economic impact multipliers and share of domestic economic impact. Finally, to analyze the synergies among the four indicators, we identify the common key decision parameters and the decision paths leading to synergies between having low levelized costs and high economic impacts. The results demonstrate significant variation in the values of four indicators of levelized costs and economic impact, depending on the combination of the 10 aforementioned decision parameters. The influence of geothermal coverage is observed in all four indicators, although more strongly in the variation of levelized cost indicators. For the variation of the economic impact indicators, the choice of auxiliary heating source has a stronger influence than geothermal coverage. We identify that synergy could be achieved in scenarios having 40% geothermal heat and 60% heat from centralized waste incineration, deployed together in a district with a linear heat density of 6 or 8 MWh/(m.year), using second district heating generation. Our study shows the importance of integrating a combination of many decision parameters to understand the competitiveness and economic impacts of geothermal district heating. Focusing on geothermal coverage, linear heat density, district heating generation, and choice of auxiliary heating sources makes the biggest difference when setting up economically meaningful strategies.