*********** Quick Start *********** This tutorial will show an example by using `SMART-DS `_ models with snapshot impact analysis. Note that you could generally substitute "time-series" for "snapshot" for that type of simulation. Source Data =========== Suppose the DISCO repo is downloaded to the ``~/disco`` directory, where the SMART-DS data is located in the directory ``tests/data/smart-ds/substations/``. Transform Model =============== DISCO transforms the SMART-DS models into DISCO models with this command. .. code-block:: bash $ disco transform-model ~/disco/tests/data/smart-ds/substations/ snapshot Transformed data from ~/disco/tests/data/smart-ds/substations/ to snapshot-feeder-models for Snapshot Analysis. By default, it generates a directory named ``snapshot-feeder-models`` with transformed models. Config Jobs =========== Configure jobs for execution through JADE with this command: .. code-block:: bash $ disco config snapshot ./snapshot-feeder-models Created config.json for Snapshot Analysis A job config file named ``config.json`` was created. Parameters that you may want to configure: - By default, the PyDSS-exported circuit element properties are taken from `snapshot-exports.toml `_. Specify a different file with ``-e ``. - PyDSS will not automatically export results to CSV files by default. You can set ``export_data_tables`` to ``true`` in ``config.json``. - DISCO applies a DC-AC ratio of 1.15 to all PVSystems by default. You can customize it with the option ``--dc-ac-ratio``. Set it to ``1.0`` to prevent any changes to your models. - DISCO uses a standard IEEE volt-var curve by default. You can customize the value with the option ``--volt-var-curve``. This must be a controller name registered with PyDSS. Run ``pydss controllers show`` to see the registered controllers. - DISCO does not store per-element data in reports by default. For example, it stores max/min voltages across all buses and not the max/min voltages for each bus. You can set ``store_per_element_data`` to ``true`` in ``config.json``. - Other PyDSS parameters: Refer to the ``pydss_inputs`` section of ``config.json``. `PyDSS documentation `_ Submit Jobs =========== Then batch of jobs in ``config.json`` can be submitted through JADE. Two examples are shown below: one on a local machine and one on an HPC. .. code-block:: bash $ jade submit-jobs --local config.json $ jade submit-jobs -h hpc_config.toml config.json .. note:: Create hpc_config.toml with ``jade config hpc`` and modify it as necessary. Refer to `JADE instructions `_ for additional information on how to customize execution. The submitted jobs run to completion and generate an output directory named ``output``. Result Analysis =============== To get a quick summary of job results using JADE: .. code-block:: bash $ jade show-results Results from directory: output JADE Version: 0.1.1 01/04/2021 08:52:36 +-----------------------------------------+-------------+----------+--------------------+----------------------------+ | Job Name | Return Code | Status | Execution Time (s) | Completion Time | +-----------------------------------------+-------------+----------+--------------------+----------------------------+ | p1uhs10_1247__p1udt14394__random__1__5 | 0 | finished | 23.069955110549927 | 2021-01-04 08:52:35.939785 | | p1uhs10_1247__p1udt14394__random__1__10 | 0 | finished | 23.06603503227234 | 2021-01-04 08:52:35.942345 | | p1uhs10_1247__p1udt14394__random__2__5 | 0 | finished | 23.062479972839355 | 2021-01-04 08:52:35.943899 | | p1uhs10_1247__p1udt14394__random__2__10 | 0 | finished | 23.05748414993286 | 2021-01-04 08:52:35.944780 | +-----------------------------------------+-------------+----------+--------------------+----------------------------+ Num successful: 4 Num failed: 0 Total: 4 Avg execution time (s): 23.06 Min execution time (s): 23.06 Max execution time (s): 23.07 Each job output directory contains PyDSS-exported data and reports. - Reports (ex: thermal_metrics.json, voltage_metrics.json) are stored in ``/job-outputs//pydss_project/project.zip`` in the ``Results`` sub-directory. - Exported data tables, if enabled, are stored in the ``Exports`` sub-directory. - You can access the PyDSS-exported data in a Jupyter notebook data-viewer UI or programmatically as shown in this `documentation `_. This is the complete workflow for conducting snapshot impact analysis on SMART_DS feeders.