Python: DIgSILENT computational intensive approach to identify optimal FFR-based battery energy storage locations



This code was 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 code can help change the location and capacity of battery energy storage systems inside the DIgSILENT model. 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 C5 of the connected PhD thesis (pp. 293-299).

Note the code is embargoed until 31 July 2027.
Date made availableAug 2022
PublisherQueen's University Belfast
Date of data productionAug 2021 -

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