The DTOcean Economics Module provides functions to assess and compare the economic performance of arrays designed by DTOcean. It also acts as a support library for the dtocean-maintenance module. It aggregates the "bill of materials" produced by the dtocean-electrical and dtocean-moorings modules and generates metrics such as the levelised cost of energy (LCOE). The module can accept multiple operational expenditure and energy production records.
See dtocean-app or dtocean-core to use this package within the DTOcean ecosystem.
- For python 2.7 only.
Installation and development of dtocean-economics uses the Anaconda Distribution (Python 2.7)
To install:
$ conda install -c dataonlygreater dtocean-economics
Conda can be used to install dependencies into a dedicated environment from the source code root directory:
$ conda create -n _dtocean_econ python=2.7 pip
Activate the environment, then copy the .condrc file to store installation
channels:
$ conda activate _dtocean_econ
$ copy .condarc %CONDA_PREFIX%
Install dtocean-economics and its dependencies using conda and pip:
$ conda install --file requirements-conda-dev.txt
$ pip install -e .
To deactivate the conda environment:
$ conda deactivate
A test suite is provided with the source code that uses pytest.
If not already active, activate the conda environment set up in the Source Code section:
$ conda activate _dtocean_econ
Install packages required for testing to the environment (one time only):
$ conda install -y pytest
Run the tests:
$ py.test tests
To uninstall the conda package:
$ conda remove dtocean-economics
To uninstall the source code and its conda environment:
$ conda remove --name _dtocean_econ --all
An example of calculating the LCOE from a bill of materials, and two different operational expenditure (OPEX) and energy histories.
Create the bill of materials first:
>>> import pandas as pd
>>> bom_dict = {'phase': ["One", "One", "One", "Two", "Two", "Two"],
... 'unitary_cost': [0.0, 100000.0, 100000.0, 1, 1, 1],
... 'project_year': [0, 1, 2, 0, 1, 2],
... 'quantity': [1, 1, 1, 1, 10, 20]}
>>> bom_df = pd.DataFrame(bom_dict, columns=["phase",
... "project_year",
... "quantity",
... "unitary_cost"])
>>> bom_df
phase project_year quantity unitary_cost
0 One 0 1 0.0
1 One 1 1 100000.0
2 One 2 1 100000.0
3 Two 0 1 1.0
4 Two 1 10 1.0
5 Two 2 20 1.0Now build two independent OPEX records:
>>> opex_dict = {'project_year': [0, 1, 2, 3, 4, 5],
>>> 'cost 0': [0.0, 100000.0, 100000.0, 1, 1, 1],
>>> 'cost 1': [0.0, 100000.0, 0, 1, 1, 100000.0]}
>>> opex_df = pd.DataFrame(opex_dict, columns=["project_year",
... "cost 0",
... "cost 1"])
>>> opex_df
project_year cost 0 cost 1
0 0 0.0 0.0
1 1 100000.0 100000.0
2 2 100000.0 0.0
3 3 1.0 1.0
4 4 1.0 1.0
5 5 1.0 100000.0And the related energy production records:
>>> energy_dict = {'project_year': [0, 1, 2, 3, 4, 5],
... 'energy 0': [0, 1, 2, 0, 10, 20],
... 'energy 1': [0, 1, 32, 0, 0, 20]}
>>> energy_df = pd.DataFrame(energy_dict, columns=["project_year",
... "energy 0",
... "energy 1"])
>>> energy_df
project_year energy 0 energy 1
0 0 0 0
1 1 1 1
2 2 2 32
3 3 0 0
4 4 10 0
5 5 20 20Finally, collect the LCOE values, and take the mean:
>>> from dtocean_economics import main
>>> result = main(bom_df, opex_df, energy_df)
>>> lcoe_values = result['LCOE']
>>> lcoe_values
0 12122.242424
1 7547.792453
dtype: float64
>>> lcoe_values.mean()
9835.017438536306Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
See this blog post for information regarding development of the DTOcean ecosystem.
Please make sure to update tests as appropriate.
This package was initially created as part of the EU DTOcean project by:
It is now maintained by Mathew Topper at Data Only Greater.