Description
This code is built-in Python to interface with the DIgSILENT power system dynamic model. The main objective of the code is to accelerate the time domain simulation process in DIgSILENT PowerFactory. This piece of code can help change the parameter of the model inside DIgSILENT and quickly generate a large number of CSV files. It can also be used to apply various disturbances to the model and export thousands of data about the model response during transient simulations.
The code would be of interest to researchers in the area of power system dynamic analysis who develop power system model scenarios to analyze challenges facing the grid while approaching the Net Zero targets.
Note that this code first appeared in Appendix A4 of the connected PhD thesis (pp. 277-281).
Note the code is embargoed until 31 July 2027.
The code would be of interest to researchers in the area of power system dynamic analysis who develop power system model scenarios to analyze challenges facing the grid while approaching the Net Zero targets.
Note that this code first appeared in Appendix A4 of the connected PhD thesis (pp. 277-281).
Note the code is embargoed until 31 July 2027.
| Date made available | Aug 2022 |
|---|---|
| Publisher | Queen's University Belfast |
| Date of data production | Apr 2020 |
Student theses
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Power system dynamics with increasing distributed generation penetrations
Al Kez, D. (Author), Foley, A. (Supervisor) & Laverty, D. (Supervisor), Jul 2022Student thesis: Doctoral Thesis › Doctor of Philosophy
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Datasets
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Python: DIgSILENT computational intensive approach to identify optimal FFR-based battery energy storage locations
Al Kez, D. (Creator), Queen's University Belfast, Aug 2022
DOI: 10.17034/8182fa9c-cf30-4509-8e2e-7c701280d17d
Dataset