Impact Analysis

This section shows how to perform customized analysis with time-series simulations on a set of input models. Note that you could generally substitute “snapshot” for “time-series” for that type of simulation.

Other sections of this documentation describe workflows that rely on DISCO/PyDSS to collect specific metrics from each simulation. For example, the dynamic hosting capacity analysis workflow collects metrics for max instantaneous and moving-average line and transformer loading violations. It does not store values for every line and transformer at every time point because of the amount of storage space that requires. This section shows how to collect all of the data.

Transform Model

As with earlier sections this assumes that you have cloned the disco repo to the ~/disco directory. Transform the source models into DISCO models with this command:

$ disco transform-model ~/disco/tests/data/smart-ds/substations/ time-series
Transformed data from ~/disco/tests/data/smart-ds/substations/ to time-series-feeder-models for TimeSeries Analysis.

Note

For your own models you will likely need to set --start, --end, and --resolution.

Config Jobs

  1. Copy this text into a file called exports.toml. This will instruct PyDSS to store each of these properties for each element at each time point.

[Loads.Powers]
store_values_type = "all"

[PVSystems.Powers]
store_values_type = "all"

[Circuits.TotalPower]
store_values_type = "all"

[Circuits.LineLosses]
store_values_type = "all"

[Circuits.Losses]
store_values_type = "all"

[Lines.Currents]
store_values_type = "all"

[Lines.Losses]
store_values_type = "all"

[Lines.Powers]
store_values_type = "all"

  1. Create the configuration with all reports disabled, custom exports, all data exported, a custom DC-AC ratio, and a specific volt-var curve.

Note

If you’re using a Windows terminal, the \ characters used here for line breaks probably won’t work.

$ disco config time-series time-series-feeder-models \
    --config-file time-series-config.json \
    --volt-var-curve volt_var_ieee_1547_2018_catB \
    --dc-ac-ratio=1.0 \
    --exports-filename exports.toml \
    --export-data-tables \
    --store-all-time-points \
    --store-per-element-data \
    --thermal-metrics=true \
    --voltage-metrics=true \
    --feeder-losses=true

Submit Jobs

Run the jobs with JADE. Two examples are shown: one on a local machine and one on an HPC.

$ jade submit-jobs --local time-series-config.json -o time-series-output
$ jade submit-jobs -h hpc_config.toml time-series-config.json -o time-series-output

Confirm that all jobs passed.

$ jade show-results -o time-series-output

View Output Files

Each job’s outputs will be stored in time-series-output/job-outputs/<job-name>/pydss_project/project.zip. Extract one zip file. You will see exported data for all element properties. For example, this file contains bus voltages for the volt-var scenario: Exports/control_mode/Buses__puVmagAngle.csv. Exports/control_mode/CktElement__ExportLoadingsMetric.csv contains thermal loading values. The same files will exist for the pf1 scenario.

Summary files will be available for thermal and voltage metrics. Refer to Reports/thermal_metrics.json and Reports/voltage_metrics.json.

Make metric table files

Run this command to convert the thermal and voltage metrics into tabular form.

$ disco make-summary-tables time-series-output

Access Results Programmatically

DISCO includes analysis code to help look at thermal loading and voltage violations. Here is some example code:

import logging
import os

from jade.loggers import setup_logging
from disco.pydss.pydss_analysis import PyDssAnalysis, PyDssScenarioAnalysis
from disco.extensions.pydss_simulation.pydss_configuration import PyDssConfiguration

logger = setup_logging("config", "log.txt", console_level=logging.INFO)

output_dir = "time-series-output"
config = PyDssConfiguration.deserialize(os.path.join(output_path, "config.json"))
analysis = PyDssAnalysis(output_path, config)
analysis.show_results()

# Copy name from the output of show_results().
name = analysis.list_results()[1].name

# Look up job-specific parameters.
job = analysis.get_job(name)
print(job)
print(job.model.deployment)
print(job.model.deployment.project_data)

simulation = analysis.get_simulation(name)

# Get access to result dataframes.
results = analysis.read_results(simulation)
scenario = results.scenarios[0]
scenario_analysis = PyDssScenarioAnalysis(simulation, results, scenario.name)

# Get list of voltage magnitudes for each bus.
voltages_per_bus = scenario_analysis.get_pu_bus_voltage_magnitudes()

# Get loading percentages.
line_loading = scenario_analysis.get_line_loading_percentages()
transformer_loading = scenario_analysis.get_transformer_loading_percentages()

# Find out what classes and properties are available.
for element_class in scenario.list_element_classes():
    for prop in scenario.list_element_properties(element_class):
        print(element_class, prop)

for name in scenario.list_element_names("Lines", "Currents"):
    df = scenario.get_dataframe("Lines", "Currents", name)
    print(df.head())

# Browse static element information.
for filename in scenario.list_element_info_files():
    print(filename)
    df = scenario.read_element_info_file(filename)
    print(df.head())

# Use class names to read specific element infomation.
df = scenario.read_element_info_file("Loads")
df = scenario.read_element_info_file("PVSystems")

# Read events from the OpenDSS event log.
event_log = scenario.read_event_log()

# Get the count of each capacitor's state changes from the event log.
capacitor_changes = scenario.read_capacitor_changes()

Use the PyDSS Data Viewer

PyDSS includes a data viewer that makes it easy to plot circuit element values in a Jupyter notebook. Refer to its docs.

Generic Models

This section follows the same workflow except that it uses pre-defined OpenDSS models. Unlike the previous example, DISCO will not make any changes to the model files.

Refer to Generic Power Flow Models for specific details about the input file time_series_generic.json.

$ disco config-generic-models time-series ~/disco/tests/data/time_series_generic.json \
    --config-file time-series-config.json \
    --volt-var-curve volt_var_ieee_1547_2018_catB \
    --exports-filename exports.toml \
    --export-data-tables \
    --store-all-time-points \
    --store-per-element-data \
    --thermal-metrics=true \
    --voltage-metrics=true \
    --feeder-losses=true

$ jade submit-jobs --local time-series-config.json -o time-series-output

$ jade show-results -o time-series-output

$ disco make-summary-tables time-series-output