Multi-objective optimization of medium-enthalpy geothermal Organic Rankine Cycle plants

Geothermal energy is a renewable energy source that can contribute to a decarbonized European energy mix. Geothermal Organic Rankine Cycle (ORC) units can produce power from medium enthalpy hydrothermal resources that are commonly available in Europe. However, their techno-economic and environmental performance is greatly dependent on the site-specific geological conditions. This study proposes a two-step framework to optimize the design and investigate the Levelized Cost Of Electricity (LCOE), Global Warming Impact (GWI), and fifteen other environmental indicators of geothermal ORC units for various geological conditions. First, the LCOE and GWI of the system are calculated via integrated geo-technical, ORC process, techno-economic and life cycle analysis calculations. Second, artificial neural networks (ANN) are used to model for the system and genetic algorithms are used to optimize its design for multiple techno-economic and environmental objectives. It is shown that the techno-economic and environmental performance of the geothermal ORC are driven by the same factors. A higher geofluid temperature results into higher power production and lower LCOE and environmental impacts. Similarly, the LCOE and environmental impacts reduce for increasing reservoir permeability and thickness because the pumping capacity to extract the geofluid reduces. This study shows that geothermal ORCs can be a promising alternative for power production in Europe, though their techno-economic and environmental performance are strongly dependent on the local geological conditions. These conditions can also influence the optimal ORC design. This study also demonstrates the benefits of using ANNs for the optimization of geothermal ORC units.