nareshram256 / opendam Goto Github PK

Read the doc for more information.

Alternatively you can generate the documentation yourself if you have sphinx installed:

cd <to the root of the project>
sphinx-apidoc -o doc/ openDAM/ -f --separate
cd doc; make html; cd ..

The html doc is in _build/html

1. Download the code from Github
2. We highly recommend to use an Anaconda distribution

1. download and install Anaconda for Python 2.7 and your specific OS.
2. Create one environement for this project

conda create --name python_DAM --file conda-{platform}.txt

where "{platform}" must match your OS. Checkout this reference for more information about how to manage Anaconda environments.

3. Activate the environment

For Windows:

activate python_DAM

For OSX and Linux,

source activate python_DAM

The code is currently tuned for CPLEX.

1. First, run create_dam_db_from_csv.py from openDAM/dataio/ to generate an sqlite database, taking as default source scripts the CSV files in folder data/tests. The resulting sqlite database can be browsed using any client for Sqlite.
   • You can modify the call to create_dam_db_from_csv.py if you want to use other input CSV files.
2. Run python openDAM from the master directory with either the --all option, or another option if you want to run a particular day.

The Italian market data can be freely downloaded from the website of the Italian market operator. The raw data required to run the model is stored in 4 different XML files, named:

1. YYYYMMDDMGPOffertePubbliche.xml, located in a zip file named YYYYMMDDMGPOffertePubbliche.zip, contained in another zip file named YYYYMMDDOfferteFree_Pubbliche.zip. The xml file contains the real market order data;
2. YYYYMMDDMGPLimitiTransito.xml, containing the network capacity limits;
3. YYYYMMDDMGPPrezzi.xml , containing the official prices used for comparison;
4. YYYYMMDDMGPQuantita, containing the flows coming from the non-GME zones;

where YYYYMMDD is the date of the requested day in the format: year, month and day. These files can be freely downloaded from the following links, for example for January 27th, 2018:

1. https://www.mercatoelettrico.org/en/Download/DownloadDati.aspx?val=OfferteFree_Pubbliche
2. https://www.mercatoelettrico.org/It/WebServerDataStore/MGP_LimitiTransito/20180127MGPLimitiTransito.xml
3. https://www.mercatoelettrico.org/It/WebServerDataStore/MGP_Prezzi/20180127MGPPrezzi.xml
4. https://www.mercatoelettrico.org/It/WebServerDataStore/MGP_Quantita/20180127MGPQuantita.xml

Then, they can be imported into a suitable SQL database by running the Python script GME_xml_importer.py.

Assuming your data is in the folder data, you can do this by running python openDAM/dataio/GME_xml_importer.py --split -p data/ -d test.sqlite3 --from_date=20180110 --to_date=20180110. The --split option generates a problem per period.

Then you can run from the master directory python openDAM -p openDAM\dataio -d test.sqlite3 -c 2018011019 --pun_strategy=Advanced

See also GME_xml_importer.py --help for further details.

Random block orders can be added to a database by running python openDAM/dataio/generate_block_orders.py. Parameters defining block properties must be modified directly inside this file (they are not accessible through command line arguments).