OpenDSSDirect

OpenDSS is an open-source distribution system simulator. This Julia package implements a native, "direct" library interface to OpenDSS. Since 2019, this project uses DSS C-API, an alternative API and OpenDSS engine implementation maintained by the community. See the DSS C-API repository for move information in general, as well as alternatives for other programming languages.

The interface can be faster than the more traditional COM interface, and allows for multi-platform support. Currently we support Windows, Linux and macOS, on ARM and x86 processors.

See also the DSS-Extensions site for more.

Installation

For now, this package is registered, so you can use the following to install:

Open the package manager REPL (using ])

(v1.1) pkg> add OpenDSSDirect

To install the latest development version, use the following from within Julia:

(v1.1) pkg> dev OpenDSSDirect

This package includes our alternative OpenDSS as a library. You do not have to install OpenDSS separately. In particular, it includes the OpenDSSDirect dynamically linked library using dss_capi that implements the direct-access API.

Users new to OpenDSS are still encouraged to use EPRI's official documentation and examples in general.

Note that this should work on 32- and 64-bit Windows systems and 64-bit Linux and Mac systems. The Windows, macOS and Linux libraries are taken from the dss_capi releases page, downloaded automatically.

Features

Julia is a fast, flexible numerical language with Matlab-like syntax. Like Python or Matlab, scripting OpenDSS can be used for Monte-Carlo batch runs, parameter sweeps, post processing and plotting of results, and more.

Julia has several key features for advanced operations with OpenDSS:

Parallel operations – Julia supports several ways to process data in parallel. This includes parallel operations on multiple CPU cores and parallel operations on processes in a cluster. See examples/8760_pmap.jl for an example of an annual simulation split among local CPU cores. The parallel support also includes the context API from DSS-Extensions, which allows using multiple DSS engines in the same process. See test/ctx_threads.jl for a minimal example of how to use this feature. Note that this is not possible in the official OpenDSS engine. For completeness, the Parallel API from the PM version of OpenDSS is also available, but using Julia-native operations is recommended.
Optimization – Julia has strong support for optimization.
Custom solutions and custom models – Because Julia compiles to codes that run as quickly as C programs, it is possible to implement custom solutions, and these custom solutions can contain custom models implemented in Julia. The basic idea is to run a custom solution as described in OpenDSS Solution Interface.pdf. Custom control or power control elements can be implemented between each model iteration. See examples/low-level-solver.jl for an example with a custom solver, including a case with an alternate sparse matrix solver.

Example

The dss function is the main function for passing commands to OpenDSS. After that, a number of modules are available to access different data structures in OpenDSS. Here is an example summing the kW and kvar from loads using the Loads module:

using OpenDSSDirect
filename = joinpath(dirname(dirname(pathof(OpenDSSDirect))), "examples", "8500-Node", "Master.dss")
dss("""
    clear
    compile "$filename"
    solve
""")

function main()

    loadnumber = Loads.First()
    kWsum = 0.0
    kvarsum = 0.0
    
    while loadnumber > 0
        kWsum += Loads.kW()
        kvarsum += Loads.kvar()
        loadnumber = Loads.Next()
    end

    kWsum, kvarsum
end

main()

OpenDSSDirect

Installation

Features

Example

Contents