mine_thermal_energy_transport

Evaluate the techno-economic feasibility of shallow geothermal energy transport; see XXXXX (pending link for article).

Requirements

Overall

• Python 3.10 (code has not been tested with other Python versions).
• Python package dependencies:
• Numpy
• Pandas
• Scipy
• Matplotlib
• Optional: TSAM for time series aggregation.

Model Execution

• Python package dependencies:
  • COMANDO and its dependencies (Optimisation framework).
  • Psweep 0.10.0 or lower (Parametric analysis and housekeeping).
• GUROBI 10.0 or higher

Setup

• Check for Python version and package dependencies (virtual environment or separated Conda environment is recommended)
• SETUP GUROBI solver installation, further details can be found in the COMANDO readthedocs

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Required Inputs

• Thermal load time series. The original study considered time series for single-family houses generated using RC_BuildingSimulator
• Ambient temperature time series. The original study used TMY data from PV-GIS
• Ground temperature time series (for pipe losses calculation). The original study derived the ground temperature from the ambient temperature, as indicated in the manuscript.
• Horizon of analysis and interest rate (n = 30 years and i = 0.03 by default).
• Cost of components and commodities.

Usage

Base_Code

The code includes a sample case for a decentralised thermal system, considering conditions for the Rheinish Coal Mining Area (Rheinisches Braunkohlerevier), NRW, Germany region and a non-insulated PE transmission pipeline. The code is executed by running the study.py file. All the required inputs and functions are in the _data and _script folders. Results will be stored in a newly generated _results folder, and a summary of the results will be stored in a pandas data frame inside a pickle file (.pk).

Further component examples are found in the COMANDO list of examples.

Considerations

• COMANDO is solver agnostic; minor modifications to the code should be needed to run the code with other solvers, although the authors haven't tested this so far. Part of the code used for post-processing relies on GUROBI outputs, which would need to be adjusted as well. The individual heat pumps/RCAC components are reversible. If time aggregation is used, be careful with simultaneous heating and cooling demand, as this will result in an unfeasible model.
• The study file is set up to run using Python multiprocessing capabilities. If this isn't desired, the code must be changed accordingly.