dtocean-economics

The DTOcean Economics Module provides functions to assess and compare the economic performance of arrays designed by DTOcean. It generates metrics such as the levelised cost of energy (LCOE). The module can accept multiple operational expenditure and energy production records to generate statistical analysis.

Part of the DTOcean suite of tools.

Installation

pip install dtocean-economics

Usage

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 (in Euro):

>>> 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           1.0

Now build two independent OPEX records (in Euro):

>>> 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  100000.0

And the related energy production records (in Wh):

>>> energy_dict = {'project_year': [0, 1, 2, 3, 4, 5],
...                'energy 0': [0, 1e6, 2e6, 0, 10e6, 20e6],
...                'energy 1': [0, 1e6, 32e6, 0, 0, 20e6]}
>>> 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         0.0
1             1   1000000.0   1000000.0
2             2   2000000.0  32000000.0
3             3         0.0         0.0
4             4  10000000.0         0.0
5             5  20000000.0  20000000.0

Process the inputs to calculate the discounted values:

>>> from dtocean_economics import add_costs_to_bom, get_discounted_values
>>> discount_rate = 1 / 5
>>> add_costs_to_bom(bom_df, discount_rate)
>>> bom_df
  phase  project_year  quantity  unitary_cost     costs  discounted_costs
0   One             0         1           0.0       0.0          0.000000
1   One             1         1      100000.0  100000.0      83333.333333
2   One             2         1      100000.0  100000.0      69444.444444
3   Two             0         1           1.0       1.0          1.000000
4   Two             1        10           1.0      10.0          8.333333
5   Two             2        20           1.0      20.0         13.888889

>>> discounted_opex = get_discounted_values(opex_df, discount_rate)
>>> discounted_opex
0    152779.240612
1    123522.151492
dtype: float64

>>> discounted_energy = get_discounted_values(energy_df, discount_rate)
>>> discounted_energy
0    1.508230e+07
1    3.109311e+07
dtype: float64

Now calculate the mean of the LCOE (in Euro/kWh):

>>> discounted_capex = bom_df['discounted_costs'].sum()
>>> discounted_costs = discounted_opex + discounted_capex
>>> lcoe = discounted_costs / discounted_energy * 1000
>>> lcoe
0    20.260845
1     8.886958
dtype: float64

>>> float(lcoe.mean())
14.573901960338254

Development

Development of dtocean-economics uses the Poetry dependency manager. Poetry must be installed and available on the command line.

To install:

poetry install

Tests

A test suite is provided with the source code that uses pytest.

Install the testing dependencies:

poetry install --with test

Additional tests are available for the plugins to dtocean-core. Enable these tests by installing the test-extras group:

poetry install --with test --with test-extras

Run the tests:

poetry run pytest

Code quality can also be audited using the ruff and pyright tools. Install the dependencies:

poetry install --with audit

Run the audit:

poetry run ruff
poetry run pyright src

The above tests can be run across all compatible Python versions using tox and tox-uv. To install:

poetry install --with test --with test-extras --with audit --with tox

To run the tests:

poetry run tox

Contributing

Please see the dtocean GitHub repository for contributing guidelines.

Credits

This package was initially created as part of the EU DTOcean project by:

• Mathew Topper at TECNALIA
• Marta Silva at WavEC

It is now maintained by Mathew Topper at Data Only Greater.

License

GPL-3.0