Simply is an open-source tool to simulate electricity markets using different matching algorithms.
Simply is an electricity market simulation package consisting of modules and scripts for
- scenario generation,
- market simulation and
- results visualisation and analysis.
Simply is an agent-based market simulation tool with market actors sending bids and asks to a market, that can be periodically cleared using different Matching Algorithms. The algorithms take grid fees into account, which can be based on locally differentiated clusters defined by the agent's location in the network. The matching algorithms can also be used individually as a module or via a wrapper using a json formatted order definition.
The latest documentation can be found here.
The simply package is still in development and has not been officially released on PyPi. This means it is not yet listed and indexed in the PyPi repository for general distribution.
In order to use simply, the simply GitHub repository is cloned for local usage and development. After cloning the repository, a virtual environment is created (e.g. using virtualenv or conda):
git clone git@github.com:BESTenergytrade/simply.git # create virtual environment virtualenv venv --python=python3.8 source venv/bin/activate # install cbc-solver to be able to use optimization with pyomo sudo apt-get update sudo apt-get install -y coinor-cbc
Then there are two options to use simply:
- Use the cloned repository directly and install the necessary dependencies using:
pip install -r requirements.txt
- Or install the package in editable mode using:
pip install -e .
The tool uses Python (>= 3.8) standard libraries as well as specific, but well known libraries such as matplotlib, pandas and networkx.
The core of simply is the market :ref:`matching_algorithms`. To be able to use the market matching algorithms, a :ref:`scenarios` is required as an input. The scenario can either be built from own data or randomly generated and simulated by simply as described below.
In all cases for using simply, a :ref:`config` (config.cfg) is required to specify the correct parameters of the scenario and simulation.
In order to build a scenario in a format that simply understands please prepare the data as described by :ref:`build_scenario` (in :ref:`scenarios`), to define which data is associated to which actor of the community and how they are connected by the network.
Running build_scenario
After the network and the community definitions have been set up, build_scenario.py can be executed. This is done by:
python build_scenario.py path/to/your/project/dir
with the option of specifying a path for your scenario inputs if you want to store them outside of your project directory:
python build_scenario.py path/to/your/project/dir --data_dir path/to/your/scenario/inputs
The scenario is then created and automatically saved to path/to/your/project/dir/scenario. The scenario contains a time series for each actor with power generation, power consumption, and market demand or supply (including bid price).
An example of a scenario can be found in projects/example_projects/example_project.
Generating a randomized scenario
There is also the option of generating a random scenario right before matching using match_market.py (as explained in the section below). In this case, the parameters nb_actors, nb_nodes and weight_factor should be specified in config.cfg, otherwise the default parameters are used. The only input required before running the main simply function is the config.cfg file with attribute load_scenario = False:
|-- projects
|-- your_project_name
|-- config.cfg
An example of config file and a generated random scenario can be found in projects/example_projects/random_scenario. For more details please also see :ref:`scenarios`.
A simulation is executed by running the match_market.py script:
python match_market.py path/to/your/project/dir
If you choose to generate a random scenario, the scenario folder will be created in path/to/your/project/dir scenario - here is where time series for each actor with power generation, power consumption, and market demand or supply (including bid price) can be found.
For both instances, once you run match_market.py the results will be stored in path/to/your/project/dir/market_results. Here you can see the results in csv-format for the matches and orders of actors in the network as well as individual results per actor.
MIT License
Copyright (c) 2021 BEST project developers
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.