aristopy 0.9.4

Description:

aristopy 0.9.4

Optimizing energy systems with aristopy
The Python package aristopy is a framework for modeling and optimizing the
design and operation of energy systems.
The name of the framework is derived from the great Greek thinker Aristotle.
For Aristotle, planning and the wise use of human goods represented great virtues.
Transferred to today's time and the design of energy systems, this implies using
appropriate tools that support the planning process and contribute to an
optimal use of the available resources (money, fuel, etc.).
Selected highlights


Flexible modeling of energy systems with only a small number of basic
components (Source, Sink, Conversion, Bus, Storage) and a comprehensive API.


Manual scripting of component constraints to enable all types of
mathematical modeling classes (linear [LP], mixed-integer linear
[MILP], mixed-integer non-linear [MINLP], etc.).


Declaration of persistent models to quickly run models iteratively
after applying small changes (e.g., add an integer-cut constraint).


Auto-generated visualization of the optimization results with
flexible plotting routines.


Documentation
The package documentation is hosted on readthedocs.org and can be accessed
here.
Installation
Before you can create your first optimization model with aristopy, you need
to make sure you have Python and aristopy, and at least one suitable
mathematical solver installed on your machine.
The installation of aristopy in your current environment can easily be
executed from the command line via pip:
pip install aristopy

More detailed installation instructions can be found in the
documentation.
Examples
The code of the first simple example from the examples directory, shown
below, illustrates the notation of aristopy.
A detailed description of the code is provided in the
documentation.
import aristopy as ar

# Create basic energy system instance
es = ar.EnergySystem(
number_of_time_steps=3, hours_per_time_step=1,
interest_rate=0.05, economic_lifetime=20)

# Add a gas source, two different conversion units and sinks
gas_source = ar.Source(
ensys=es, name='gas_source', commodity_cost=20, outlet=ar.Flow('Fuel'))

gas_boiler = ar.Conversion(
ensys=es, name='gas_boiler', basic_variable='Heat',
inlet=ar.Flow('Fuel', 'gas_source'), outlet=ar.Flow('Heat', 'heat_sink'),
capacity_max=150, capex_per_capacity=60e3,
user_expressions='Heat == 0.9 * Fuel')

chp_unit = ar.Conversion(
ensys=es, name='chp_unit', basic_variable='Elec',
inlet=ar.Flow('Fuel', 'gas_source'),
outlet=[ar.Flow('Heat', 'heat_sink'), ar.Flow('Elec', 'elec_sink')],
capacity_max=100, capex_per_capacity=600e3,
user_expressions=['Heat == 0.5 * Fuel',
'Elec == 0.4 * Fuel'])

heat_sink = ar.Sink(
ensys=es, name='heat_sink', inlet=ar.Flow('Heat'),
commodity_rate_fix=ar.Series('heat_demand', [100, 200, 150]))

elec_sink = ar.Sink(
ensys=es, name='elec_sink', inlet=ar.Flow('Elec'), commodity_revenues=30)

# Run the optimization
es.optimize(solver='cbc', results_file='results.json')

# Plot some results
plotter = ar.Plotter('results.json')
plotter.plot_operation('heat_sink', 'Heat', lgd_pos='lower center',
bar_lw=0.5, ylabel='Thermal energy [MWh]')
plotter.plot_objective(lgd_pos='lower center')

The method plot_operation returns a mixed bar and line plot that visualizes
the operation of a component based on a selected commodity.

The method plot_objective returns a bar chart that summarizes the cost
contributions of each component to the overall objective function value
(net present value).

Citing and Contributing
You are welcome to test aristopy and use it for your purposes. If you
publish results based on the application of the package, please
cite this GitLab repository or the project documentation on readthedocs.org.
If you have questions, found a bug, or want to contribute to the development
of aristopy, you are invited to open an issue or contact the developers
([email protected]).
License
MIT License
Copyright (c) 2021 Stefan Bruche (TU Berlin)
Acknowledgement
This work was developed during the research project "MINLP-Optimization of
Design and Operation of Complex Energy Systems", funded by the German Federal
Ministry for Economic Affairs and Energy (project reference number 03ET4053A).
The funding is gratefully acknowledged.