Different Geothermal Power Cycle Configurations Cost Estimation Models

Shamoushaki, Moein; Manfrida, Giampaolo; Talluri, Lorenzo; Niknam, Pouriya H.; Fiaschi, Daniele

doi:10.3390/su132011133

Cited by 2 publications

(2 citation statements)

References 42 publications

(40 reference statements)

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“…The data for the reference case are shown in Table 5. They are site-specific or were adopted from other geothermal projects [2] [19] [20]. The prognosis for the geothermal power in the reference case is 5.5 MW; the total power of the coupled total system is ca.…”

Section: Resultsmentioning

confidence: 99%

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Modeling and economic evaluation of deep geothermal heat supply systems using the example of the Wealden near Hanover, Germany

Schlagermann

Reinicke

2023

Preprint

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Germany desires to become climate-neutral in its heat supply by 2045. From 2024 onward communities are legally required to develop a plan documenting how the objective will be achieved. Geothermal resources can be a major building block to reach the aspirational target if they can be developed at competitive costs. To evaluate the economic potential of geothermal resources is time and money consuming. Questions which need to be addressed in the context of such evaluations are: How can an economic recovery of geothermal heat be achieve, how can subsurface risks associated with an exploration be managed, and how competitive is a deep geothermal energy recovery compared to other options of heat supply? These questions are key to a development of deep geothermal heat, especially if the geothermal conditions are not as prominent as in already realized projects, but less favourable as in the deep clastic sediments of the North German Basin. With this contribution a procedure is presented and used to determine net present values and the associated levelized costs for deep hydrothermal heat recovery systems. It consists of modelling the geothermal cycle, sizing all necessary components, costing them, and calculating net present value and levelized cost. The thermodynamic and economic modeling of the thermal cycle is performed in MATLAB coupled to Refprop. The relationships used in the model to determine the state variables pressure and temperature are presented. Based on their knowledge the components of the thermal cycle are sized. Models are presented to cost activities, components, and operations. Together with market information, the results are used to determine cash-in and cash-out and carry out net present value and levelized cost calculations. The model is set up to enable a coupling of a geothermal with another heat generator, such that the geothermal system can be utilized at baseload. The thermal model is verified by comparing the modelled state variables pressure and temperature at relevant state points of the thermal cycle with actual data of a geothermal project. The cost model is validated with biding results and cost information from actual projects and modified as appropriate. Research results published to date typically address deep high enthalpy systems to generate electrical power and focus on particular aspects only. Few publications address the challenges of lower enthalpy heat systems, allowing determination of net-present values/risk discounted values and levelized costs of heat and systemic optimizations. In applying the model to a setting in the Hannover-Celle area with temperatures of around 70°C, conditions are determined, which lead to positive net present values. The degree of their influence is determined in sensitivity analyses allowing a systemic optimization. The presented thermodynamic and cost models are considered helpful instruments for developing preliminary conceptual estimates, strategies for optimization, and portfolio management.

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Section: Resultsmentioning

confidence: 99%

“…The production process of electrical energy in geothermal power plants is the focus of Sanchez et al [1]. Cost Estimation models for geothermal power cycle configurations are the subject of Shamoushaki et al [2]. Optimal and sustainable geothermal reservoir exploitation is the content of Blank et al [3].…”

Section: Introductionmentioning

confidence: 99%