Data and code underlying the publication: How battery energy storage impacts grid congestion – An evaluation of the trade-offs between different battery control strategies

DOI:10.4121/3dde2317-817d-4c57-b844-39eca5411c0d.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/3dde2317-817d-4c57-b844-39eca5411c0d

Datacite citation style

van Someren, Christian (2025): Data and code underlying the publication: How battery energy storage impacts grid congestion – An evaluation of the trade-offs between different battery control strategies. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/3dde2317-817d-4c57-b844-39eca5411c0d.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

The goal of this research was to evaluate the effects of different battery control strategies on battery performance and battery grid impacts. This dataset contains the code and data necessary to simulate power flows on a distribution grid in a rural village in the Netherlands, including interactions with household batteries. The model considers current grid topology, current consumer load and generation profiles, and the potential effects of introducing household batteries to the area. All data has 15-minute timesteps; units are in MW unless indicated otherwise.


Includes the following datasets:

  • Electricity grid model parameters, derived from grid topology data (.xlsx file).
  • Average annual load and generation profiles for consumer connections, derived from measurements (.csv files).
  • EPEX electricity market prices (.csv file).
  • Results and data analysis (.xlsm file).


Also included is the .py file with the code used to simulate power flows in the grid and battery energy storage systems.


A readme file provides additional details and contact information.


This work is supported by Hanze University of Applied Sciences and the NO GIMZOS project funded by Topsector Energie - RVO.

History

  • 2025-05-14 first online, published, posted

Publisher

4TU.ResearchData

Format

*.py, *.csv, *.xlsm, *.xlsx, *.txt

Funding

  • NetOptimalisatie voor Grootschalige Inpassing Zon- en windstroom Middels Opslag en Software (grant code MOOI52109) [more info...] Rijksdienst voor Ondernemend Nederland (RVO) - Topsector Energie

Organizations

Hanze University of Applies Sciences - Entrance Centre of Expertise Energy

DATA

Files (8)