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Python bindings and tools based on the DSS C-API project, the alternative OpenDSS implementation from

Project description

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DSS-Python: Extended bindings for an alternative implementation of EPRI's OpenDSS

Python bindings and misc tools for using our alternative OpenDSS (EPRI Distribution System Simulator) engine, materialized in the DSS C-API library. Based on DSS C-API, CFFI and NumPy, aiming for enhanced performance and full compatibility with the official COM object API on Windows, Linux and macOS. Support includes Intel-based (x86 and x64) processors, as well as ARM processors for Linux (including Raspberry Pi devices) and macOS (including Apple M1 and later).

Please see FAQ for a couple of notes. Check also the other projects from

  • DSS C-API library: the base library that exposes a modified version (alternative implementation) of EPRI's OpenDSS through a more traditional C interface, built with the open-source Free Pascal compiler instead of Delphi. As of 2022, this base library includes several extensive changes, while retaining very good compatibility.
  • dss.hpp: header-only library for C++, hosted within DSS C-API (include/ directory). Allows using DSS C-API more comfortably from C++, abstract memory management and low-level details such as API conventions of the DSS C-API library.
  • if you don't need COM compatibility, or just would like to check its extra functionalities. You can mix DSS-Python and — for example, if you have old code using the official COM objects, you could quickly switch to DSS-Python with very few code changes, and then use opendssdirect.utils to generate some DataFrames.
  • OpenDSSDirect.jl is a Julia module, created by Tom Short (@tshort), migrated with the help of Dheepak Krishnamurthy (@kdheepak) to DSS C-API instead of the DDLL in Feb 2019.
  • DSS Sharp is available for .NET/C#, packaged on NuGet, also mimics the COM classes (drop-in replacement for OpenDSSengine.DLL). The current version is now multi-platform too! Soon it will be possible to use it via COM.
  • DSS MATLAB presents multi-platform integration (Windows, Linux, MacOS) with DSS C-API and is also very compatible with the API of the official OpenDSS COM classes.

While we plan to add a lot more functionality into DSS-Python, the main goal of creating a COM-compatible API has been reached in 2018. If you find an unexpected missing feature, please report it! Currently missing features that will be implemented eventually are interactive features and diakoptics (planned for a future version).

This module mimics the COM structure (as exposed via win32com or comtypes) — see The DSS instance for some docs — effectively enabling multi-platform compatibility at Python level. Compared to other options, it provides easier migration from code that uses the official OpenDSS through COM. See also DSS-Extensions — OpenDSS: Overview of Python APIs. Most of the COM documentation can be used as-is, but instead of returning tuples or lists, this module returns/accepts NumPy arrays for numeric data exchange, which is usually preferred by the users. By toggle DSS.AdvancedTypes, complex numbers and matrices (shaped arrays) are also used to provide a more modern experience.

The module depends mostly on CFFI, NumPy, typing_extensions and, optionally, SciPy.Sparse for reading the sparse system admittance matrix. Pandas and matplotlib are optional dependencies to enable plotting and other features.

Release history

Check the Releases page and the changelog.

Missing features and limitations

Most limitations are inherited from dss_capi, i.e., these are not implemented:

  • DSSEvents from DLL/ImplEvents.pas: seems too dependent on COM.
  • DSSProgress from DLL/ImplDSSProgress.pas: would need a reimplementation depending on the target UI (GUI, text, headless, etc.).
  • OpenDSS-GIS features are not implemented since they're not open-source.

In general, the DLL from dss_capi provides more features than both the official Direct DLL and the COM object.

Extra features

Besides most of the COM methods, some of the unique DDLL methods are also exposed in adapted forms, namely the methods from DYMatrix.pas, especially GetCompressedYMatrix (check the source files for more information).

Since no GUI components are used in the FreePascal DLL, we map nearly all OpenDSS errors to Python exceptions, which seems a more natural way of working in Python. You can still manually trigger an error check by calling the function CheckForError() from the main module or manually checking the DSS.Error interface.


On all major platforms, you can install directly from pip:

    pip install dss_python

Or, if you're using the Anaconda distribution, you can try:

    conda install -c dss-extensions dss_python

Binary wheels are provided for all major platforms (Windows, Linux and MacOS) and many combinations of Python versions (3.5 to 3.10). If you have issues with a specific version, please open an issue about it. Conda packages support at least Python 3.7 to 3.10 (varying according to the release).

After a successful installation, you can then import the dss module from your Python interpreter.


Since v0.14.0, dss_python itself is a pure-Python package, i.e., the usual install methods should work fine for itself. However, you may still need to build the backend yourself in some situations.

The backend is now in dss_python_backend. The

Get the repositories

    git clone
    git clone

Assuming you successfully built or downloaded the DSS C-API DLLs (check its repository for instructions), keep the folder organization as follows:


Open a command prompt in the dss_python_backend subfolder and run the build process:

python build
python install
cd ..
cd dss_python
python -m pip install .

If you are familiar with conda-build, there is a complete recipe to build DSS C-API, KLUSolve(X) and DSS-Python in the conda subfolder.

Example usage

If you were using win32com in code like:

import win32com.client 
dss_engine = win32com.client.gencache.EnsureDispatch("OpenDSSEngine.DSS")

or comtypes (incidentally, comtypes is usually faster than win32com, so we recommend it if you need the official OpenDSS COM module):

import comtypes.client
dss_engine = comtypes.client.CreateObject("OpenDSSEngine.DSS")

you can replace that fragment with:

from dss import DSS as dss_engine

If you need the mixed-cased handling (that is, you were not using early bindings with win32com), add a call to dss.set_case_insensitive_attributes().

Assuming you have a DSS script named master.dss, you should be able to run it as shown below:

from dss import DSS as dss_engine

dss_engine.Text.Command = "compile 'c:/dss_files/master.dss'"
voltages = dss_engine.ActiveCircuit.AllBusVolts

for i in range(len(voltages) // 2):
    print('node %d: %f + j%f' % (i, voltages[2*i], voltages[2*i + 1]))


Since the DLL is built using the Free Pascal compiler, which is not officially supported by EPRI, the results are validated running sample networks provided in the official OpenDSS distribution. The only modifications are done directly by the script, removing interactive features and some other minor issues. Most of the sample files from the official OpenDSS repository are used for validation.

The validation scripts is tests/ and requires the same folder structure as the building process. You need win32com to run it on Windows.

As of version 0.11, the full validation suite can be run on the three supported platforms. This is possible by saving the official COM DLL output and loading it on macOS and Linux. We hope to fully automate this validation in the future.

Roadmap: docs and interactive features

Besides bug fixes, the main functionality of this library is mostly done. Notable desirable features that may be implemented are:

Expect news about these items by version 1.0.

While the base library (DSS C-API) will go through some API changes before v1.0, those do not affect usage from the Python side.


If you have any question, feel free to open a ticket on GitHub (here or at, or contact directly me through email (pmeira at Please allow me a few days to respond.

Credits / Acknowledgment

DSS-Python is based on EPRI's OpenDSS via the dss_capi project, so check its licensing information too.

This project is licensed under the (new) BSD, available in the LICENSE file. It's the same license OpenDSS uses (OPENDSS_LICENSE). OpenDSS itself uses KLUSolve and SuiteSparse, licensed under the GNU LGPL 2.1.

I thank my colleagues at the University of Campinas, Brazil, for providing feedback and helping me test this package during its inception in 2016-2017, as well as the many users and collaborators that have been using this or other DSS-Extensions since the public releases in 2018.

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