NetworkDynamics.jl -- Composing and simulating complex networks in Julia
Michael Lindner,
Lucas Lincoln,
Fenja Drauschke
et al.
Abstract:NetworkDynamics.jl is an easy-to-use and computationally efficient package for working with heterogeneous dynamical systems on complex networks, written in Julia, a high-level, high-performance, dynamic programming language. By combining state of the are solver algorithms from DifferentialEquations.jl with efficient data structures, NetworkDynamics.jl achieves top performance while supporting advanced features like events, algebraic constraints, time-delays, noise terms and automatic differentiation.
“…Plots.jl is used for visualizations in scientific publications of different fields, such as numerics [28,3,8,10,13,20], mathematics [12], biology [2,5] and geology [9,11] as well as for teaching purposes [7,19].…”
There are plenty of excellent plotting libraries in the world. Each one excels for a different use case: one is good for printed 2D publication figures, the other is good at interactive 3D graphics, another one has excellent L A T E X integration and one is good for creating dashboards on the web.The aim of Plots.jl is to enable the user to use the same syntax for many different plotting libraries, such that it is possible to change the used library without needing to touch the code that creates the content and having to learn yet another application programming interface (API) with different concepts. This is achieved by the separation of plot specification from the backend implementation. Together with a user extendable recipe system that allows users and package authors to create new plotting types from existing ones yields a high reuse potential. Plots.jl is publicly available at https://github. com/JuliaPlots/Plots.jl.
“…Plots.jl is used for visualizations in scientific publications of different fields, such as numerics [28,3,8,10,13,20], mathematics [12], biology [2,5] and geology [9,11] as well as for teaching purposes [7,19].…”
There are plenty of excellent plotting libraries in the world. Each one excels for a different use case: one is good for printed 2D publication figures, the other is good at interactive 3D graphics, another one has excellent L A T E X integration and one is good for creating dashboards on the web.The aim of Plots.jl is to enable the user to use the same syntax for many different plotting libraries, such that it is possible to change the used library without needing to touch the code that creates the content and having to learn yet another application programming interface (API) with different concepts. This is achieved by the separation of plot specification from the backend implementation. Together with a user extendable recipe system that allows users and package authors to create new plotting types from existing ones yields a high reuse potential. Plots.jl is publicly available at https://github. com/JuliaPlots/Plots.jl.
“…As an Open-Source Software PowerDynamics.jl was also already used for creating an online simulation environment for an online course about "inertia requirements for renewable power systems" 10 . The goal of the course is to guide participants through the topic of inertia issues and possible solutions for highly renewable power grids 11 .…”
Section: Commercial Impactmentioning
confidence: 99%
“…The construction of the differential equation system is based on Network-Dynamics.jl [10], a Julia package for simulating large dynamical systems on complex network structures that is also developed at PIK. The numerical integration is based on Julia's DifferentialEquations.jl package [11].…”
PowerDynamics.jl is a Julia package for time-domain modeling of power grids that is specifically designed for the stability analysis of systems with high shares of renewable energies. It makes use of Julia's state-of-the-art differential equation solvers and is highly performant even for systems with a large number of components. Further, it is compatible with Julia's machine learning libraries and allows for the utilization of these methods for dynamical optimization and parameter fitting. The package comes with a number of predefined models for synchronous machines, transmission lines and inverter systems. However, the strict open-source approach and a macro-based user-interface also allows for an easy implementation of custom-built models which makes it especially interesting for the design and testing of new control strategies for distributed generation units. This paper presents how the modeling concept, implemented component models and fault scenarios have been experimentally tested against measurements in the microgrid lab of TECNALIA.
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