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Andes.jl

The Julia API for ANDES.

Introduction

Andes.jl is the Julia API for ANDES, a power system simulation tool for symbolic modeling and numerical simulation.

Andes.jl provides APIs through PyCall and Conda for calling ANDES from Julia.

Installation

Install Andes.jl with

using Pkg
Pkg.add("Andes")

Customize Python Environment

If you have installed andes in an existing Python environment and do not want to reinstall it from conda, you can configure it in Pycall.

You can manually set the Python path with the following commands in Julia:

ENV["PYTHON"] = "... path of the python executable ..."
# ENV["PYTHON"] = "C:\\Python37-x64\\python.exe"        # example for Windows
# ENV["PYTHON"] = "/usr/bin/python3.7"                  # example for *nix
# ENV["PYTHON"] = "/home/name/miniconda3/envs/andes"    # example for conda
Pkg.build("PyCall")

Check out the documentation of PyCall.jl for more details.

Usage

Andes.jl exposes all Python APIs under andes.py. Use the package with

using Andes
[ Info: Precompiling andes [93a26e3f-343a-4ab9-b467-a68c67574964]

All subsequent usages can be made to andes.py in the same way as in Python.

For example, to run power flow for kundur_full.xlsx (assume exists in the current directory), run

julia> system = Andes.py.run("kundur_full.xlsx")
Parsing input file <kundur_full.xlsx>
Input file kundur_full.xlsx parsed in 0.0768 second.
-> Power flow calculation with Newton Raphson method:
Power flow initialized.
0: |F(x)| = 14.9283
1: |F(x)| = 3.60859
2: |F(x)| = 0.170093
3: |F(x)| = 0.00203827
4: |F(x)| = 3.76414e-07
Converged in 5 iterations in 0.0063 second.
Report saved to <kundur_full_out.txt> in 0.0007 second.
-> Single process finished in 0.1666 second.
PyObject <andes.system.System object at 0x1522910b8>

Visit ANDES Documentation for tutorial and API details

Development

Contributions to Andes.jl are welcome. Please see CONTRIBUTING.md for code contribution guidelines.

License

Andes.jl (the ANDES Julia interface only) is released under MIT license.

Andes.jl has been developed as part of the Large Scale Testbed (LTB) project at the Center for Ultra-Wide-Area Resilient Electric Energy Transmission Networks (CURENT), a National Science Foundation Engineering Research Center that is jointly supported by NSF (National Science Foundation) and the DoE (Department of Energy) of the United States.