The PSS®E has a Python API to perform sample activities. This project provides the tools to automate the complex PSSE®E workflows. They include

• wrappers for sample subsystems APIs
• routines for analysis of:
  • violations
  • contingencies
  • headroom capacity
• JSON writer for headroom and violations statistics

Requirements:

• Python 3.9
• pipenv ≥ 2018.11.15

# Install project dependencies
pipenv install --skip-lock
# Run the simulation
pipenv run python -m pssetools sample_config.json

The config file name, sample_config.json in this example, is the required argument. Its path should be an absolute path or a path relative to this repository root. The config file is described in the next section.

The headroom analysis is performed. The output files for headroom and violations statistics are written in JSON format. They are written to the same directory as a case file if the case file name is an absolute path or to the project root directory if the case file name is a relative path.

All the configuration options are described as a ConfigModel class found in the config.py file.

There are 3 required options:

{
    "case_name": "savnw.sav",
    "upper_load_limit_p_mw": 100.0,
    "upper_gen_limit_p_mw": 80.0
}

The case_name is a file name from the PSS®E EXAMPLE directory or an absolute file path.

There are debugging features that could be enabled using the environment variables.

To start the project with verbose output enabled, run:

$env:PSSE_TOOLS_VERBOSE = ’1’
pipenv run python -m pssetools sample_config.json

pipenv run python -m unittest

To install developer tools, run:

pipenv install --skip-lock --dev

Format code and check typing errors with

pipenv run isort . ; black . ; mypy .

Check unused imports with

pipenv run autoflake --remove-all-unused-imports -r .