Metadata-Version: 2.1
Name: energysim
Version: 2.1.9.1
Summary: Python package for cosimulation of multi-energy systems
Home-page: https://github.com/dgusain1/energysim
Author: Digvijay Gusain
Author-email: digvijay.gusain29@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/x-rst
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: tqdm
Requires-Dist: fmpy (==0.2.14)
Requires-Dist: pandapower
Requires-Dist: pypsa
Requires-Dist: networkx
Requires-Dist: matplotlib
Requires-Dist: tables
Requires-Dist: h5py


.. image:: logo.png

Compatible with Python 3.6 and above.
Documentation available `here <https://energysim.readthedocs.io/en/latest/>`_.

What is energysim?
##################

``energysim`` is a python based cosimulation tool designed to simplify multi-energy cosimulations. The tool was initially called ``FMUWorld``, since it focussed exclusively on combining models developed and packaged as Functional Mockup Units (FMUs). However, it has since been majorly updated to become a more generalisable cosimulation tool to include a more variety of energy system simulators.

The idea behind development of ``energysim`` is to simplify cosimulation to focus on the high-level applications, such as energy system planning, evaluation of control strategies, etc., rather than low-level cosimulation tasks such as message exchange, time progression coordination, etc.

Currently, ``energysim`` allows users to combine:

	1. Dynamic models packaged as *Functional Mockup Units*.
	2. Pandapower networks packaged as *pickle files*.
	3. PyPSA models (still under testing) as *Excel files*.
	4. User-defined external simulators interfaced with *.py functions*.
	5. CSV data files


Installation
############
``energysim`` can be installed with ``pip`` using::

	pip install energysim

Dependencies
^^^^^^^^^^^^
``energysim`` requires the following packages to work:

	1. FMPy
	2. Pandapower
	3. PyPSA
	4. NumPy
	5. Pandas
	6. Matplotlib
	7. NetworkX
	8. tqdm
	9. PyTables

Usage
#####

``energysim`` cosimulation is designed for an easy-plug-and-play approach. The main component is the ``world()`` object. This is the "playground" where all simulators, and connections are added and the options for simulation are specified. ``world()`` can be imported by implementing::

	from energysim import world


Initialization
^^^^^^^^^^^^^^
Once ``world`` is imported, it can be initialized with basic simulation parameters using::


	my_world = world(start_time=0, stop_time=1000, logging=True, t_macro=60)

``world`` accepts the following parameters :

	- ``start_time`` : simulation start time (0 by default).
	- ``stop_time`` : simulation end time (1000 by default).
	- ``logging`` : Flag to toggle update on simulation progress (True by default).
	- ``t_macro`` : Time steps at which information between simulators needs to be exchanged. (60 by default).

Adding Simulators
^^^^^^^^^^^^^^^^^
After initializing the world cosimulation object, simulators can be added to the world using the ``add_simulator()`` method::

	my_world.add_simulator(sim_type='fmu', sim_name='FMU1',
	sim_loc=/path/to/sim, inputs=['v1', 'v2'], outputs=['var1','var2'], step_size=1)

where:

	- ``sim_type`` : 'fmu', 'powerflow', 'csv', 'external'
	- ``sim_name`` : Unique simulator name.
	- ``sim_loc`` : A raw string address of simulator location.
	- ``outputs`` : Variables that need to be recorded from the simulator during simulation.
	- ``inputs`` : Input variables to the simulator.
	- ``step_size`` : Internal step size for simulator (1e-3 by default).

Please see documentation on ``add_simulator`` to properly add simulators to ``energysim``.
The values to simulator input are kept constant for the duration between two macro time steps.

Connections between simulators
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Once all the required simulators are added, the connections between them can be specified with a dictionary as follows ::

	connections = {'sim1.output_variable1' : 'sim2.input_variable1',
	   'sim3.output_variable2' : 'sim4.input_variable2',
	   'sim1.output_variable3' : 'sim2.input_variable3',}

This dictionary can be passed onto the world object::

	my_world.add_connections(connections)


Initializing simulator variables
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Initialization is important to start-up simulator in a cosimulation. If the simulators are not internally initialized, or of users want to use different initial conditions for the simulators, it can easily be done in ``energysim``. To provide initial values to the simulators, an ``init`` dictionary can be specified and given to the ``world`` object ::

	initializations = {'sim_name1' : (['sim_variables'], [values]),
	                   'sim_name2' : (['sim_variables'], [values])}
	options = {'init' : initializations}
	my_world.options(options)


Executing simulation
^^^^^^^^^^^^^^^^^^^^
The ``simulate()`` function can be called to simulate the world.
When ``record_all`` is True, ``energysim`` records the value of variables not only at macro time steps, but also at micro time steps specified by the user when adding the simulators. This allows the users to get a better understanding of simulators in between macro time steps. When set to False, variables are only recorded at macro time step. This is useful in case a long term simulation (for ex. a day) is performed, but one of the simulators has a time step in milli-seconds. ``pbar`` can be used to toggle the progress bar for the simulation::

	my_world.simulate(pbar=True, record_all=False)

Extracting Results
^^^^^^^^^^^^^^^^^^
Results can be extracted by calling ``results()`` function on ``my_world`` object. This returns a dictionary object with each simulators' results as pandas dataframe. Additionally, ``to_csv`` flag can be toggled to export results to csv files.

	results = my_world.results(to_csv=True)

More information is provided on the documentation page.

## Citing
Please cite the following paper if you use **energysim**:
Gusain, D, Cvetković, M & Palensky, P 2019, Energy flexibility analysis using FMUWorld. in 2019 IEEE Milan PowerTech., 8810433, IEEE, 2019 IEEE Milan PowerTech, PowerTech 2019, Milan, Italy, 23/06/19. https://doi.org/10.1109/PTC.2019.8810433


