***Data underlying the publication: Sizing batteries for power flow management in distribution grids***
Authors: C. van Someren, M. Visser, H. Slootweg
TU Eindhoven, Department of Electrical Engineering
Hanze University of Applies Sciences, EnTranCe - Centre of Expertise Energy
Corresponding author: Christian van Someren
c.e.j.van.someren@pl.hanze.nl; c.e.j.v.someren@tue.nl

***General Introduction***
This dataset contains all of the data and the model used to obtain the results to be published in the article:
Sizing batteries for power flow management in distribution grids (not yet published)

This data is being made public as supplementary material for the article and to allow other researchers to use and/or adapt the model/data as they see fit.

***Purpose of Model/Data***
The model was developed to test the minimum battery capacity requirements needed for power flow management on a distribution grid.
The model allows easy comparison of different battery siting configurations and variable power flow patterns.
The model uses PandaPower to achieve a relatively comprehensive simulation of grid constraints and network losses.
The model can be adapted to different battery applications and/or control techniques.

***Requirements***
The model was designed in Python, and various modules must be downloaded to run the model, particularly PandaPower.

***Description of data in the datasets***
1. The IEEE Test Grid Excel file (.xlsx) contains all relevant grid properties, such as cable impedence, bus coordinates, etc.
   All these data are identical to the IEEE European Low Voltage Test Feeder model available at: https://cmte.ieee.org/pes-testfeeders/resources/
2. The Load Pattern .csv files provide a load pattern for all 55 consumer connections (simultaneity % is indicated after the '_' in the file name).
   The first row contains the consumer connection identifier. Each consumer connection is numbered in the order they are generated in the Python model.
   The following rows contain the consumer load profile in 15-minute increments. Loads are presented in MW.
3. This is identical to the Load Pattern .csv files, but used to model distributed generation patterns for all 55 consumer connections. 
   The file is currently filled with 0's, but can be adapted to test decentral generation effects.
   All generations should be presented in MW.
4. The final results and data analysis (.xlsm file). This contains several macros which were used to generate load patterns and analyze scenario results.
   The data in this file is not used directly by the model, but it is a collection of model outputs and analysis. All data within the file is labelled.