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.

$ 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:

$ 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 <your-file>.

  • 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.

$ jade submit-jobs --local config.json
$ jade submit-jobs -h hpc_config.toml config.json


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:

$ 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 <output-dir>/job-outputs/<job-name>/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.