The impact of transparency policies on local flexibility markets in electricaldistribution networks - A case study with artificial neural network forecasts

Published: 7 December 2021| Version 1 | DOI: 10.17632/ckhff2yxzy.1
Contributor:
Erik Heilmann

Description

This file contains the supplementary data of the paper 'The impact of transparency policies on local flexibility markets in electricaldistribution networks - A case study with artificial neural network forecasts' (see FOLLOWS). The sub file 'Synthetiv network data' contains the data basis for the actual investigations, including Python files to re-calculate these. The sub file 'Forecast_models' contains Python files for the methodological approach of the paper as well as the necessary input data. It can be used stand-alone, as the relevant data are copied from the data basis. In addition, all calculation results are provided in the sub sub file 'results'. Further details can be found in the paper.

Files

Steps to reproduce

You need a environment to run Python. A list of all Python packages installed during the develpoing process is appended (note, that probably not all the packages are relevant). Due to high calculation duration, it is not recommended to run the experimental files on a standard desktop PC. Please read the README files. For further information, please contact the author.

Institutions

Universitat Kassel

Categories

Artificial Neural Networks, Electricity Market, Case Study Paper

Licence