Mosaik is a flexible Smart-Grid co-simulation framework.
Project description
Mosaik
Mosaik is a simulation compositor for Smart Grid simulations.
It lets you re-use existing simulators and couple them to simulate large-scale Smart Grid scenarios. Mosaik offers powerful mechanisms to specify and compose these scenarios.
Example
A simple demo scenario with mosaik:
>>> import mosaik >>> >>> sim_config = { ... 'ExampleSim': {'python': 'example_sim.mosaik:ExampleSim'}, ... } >>> >>> def create_scenario(world): ... exsim1 = world.start('ExampleSim') ... exsim2 = world.start('ExampleSim') ... ... a = [exsim1.A(init_val=0) for i in range(3)] ... b = exsim2.B.create(2, init_val=0) ... ... for i, j in zip(a, b): ... world.connect(i, j, ('val_out', 'val_in')) >>> >>> world = mosaik.World(sim_config) >>> create_scenario(world) >>> world.run(until=2) Progress: 25.00% Progress: 50.00% Progress: 75.00% Progress: 100.00%
Installation
Mosaik requires Python >= 3.3. Use pip to install it, preferably into a virtualenv:
$ pip install mosaik
Documentation, Source code and issues
The documentation is available at https://mosaik.readthedocs.org.
Please report bugs and ideas for improvment to our issue tracker.
Changelog
2.0 – 2014-09-22
Mosaik 2 is a complete rewrite of mosaik 1 in order to improve its maintainability and flexibility.
Removed features:
The mosl DSL (including Eclipse xtext and Java) are now gone. Mosaik now only uses Python.
Mosaik now longer has executables but is now used as a library.
The platform manager is gone.
The database is now a separate package, see mosaik-hdf5.
The old web UI is gone.
Mosaik now consists of four core components with the following feature sets:
mosaik Sim API
The API has bean cleaned up and simplified.
Simulators and control strategies share the same API.
There are only four calls from mosaik to a simulator: init, create, step and get_data.
Simulators / processes can make asynchronous requests to mosaik during a step: get_progress, get_related_entities, get_data, set_data.
ZeroMQ with JSON is replaced by plain network sockets with JSON.
Scenarios:
Pure Python is now used to describe scenarios. This offers you more flexibility to create complex scenarios.
Scenario creation simplified: Start a simulator to get a model factory. Use the factory to create model instances (entities). Connect entities. Run simulation.
Connection rules are are no based on a primitive connect function that only connects two entities with each other. On top of that, any connection strategy can be implemented.
Simulation Manager:
Simulators written in Python 3 can be executed in process.
Simulators can be started as external processes.
Mosaik can connect to an already running instance of a simulator. This can be used as a replacement for the now gone platform manager.
Simulation execution:
The simulation is now event-based. No schedule and no synchronization points need to be computed.
Simulators can have different and varying step sizes.
Mosaik ecosystem:
A high-level implementation of the mosaik 2 API currently exists for Python and Java.
mosaik-web is a simple visualization for mosaik simulations. See https://bitbucket.org/mosaik/mosaik-web.
mosaik-pypower is an adapter for the PYPOWER load flow analysis library. See https://bitbucket.org/mosaik/mosaik-pypower and https://github.com/rwl/PYPOWER.
mosaik-csv and mosaik-householdsim are simple demo simulators that you can use to “simulate” CSV data sets and load-profile based households. See https://bitbucket.org/mosaik/mosaik-csv and https://bitbucket.org/mosaik/mosaik-householdsim.
There is a repository containing a simple demo scenario for mosaik. See https://bitbucket.org/mosaik/mosaik-demo.
You can find information about older versions on the history page
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