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Releases: openego/eGo

Release 0.3.4

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@wolfbunke wolfbunke released this 10 Dec 10:23

Update of eDisGo version.

Release 0.3.3

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@wolfbunke wolfbunke released this 07 Dec 12:39

Fixing and Documentation Release

Release 0.3.2

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@wolfbunke wolfbunke released this 27 Oct 16:26

Making eGo quotable with zenodo.

Release 0.3.1

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@wolfbunke wolfbunke released this 27 Oct 11:03
3436d73

This release contains documentation and bug fixes for the new features introduced in 0.3.0.

Find more information on openego.readthedocs.io

Release 0.3.0

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@wolfbunke wolfbunke released this 07 Sep 18:55

Power Flow and Clustering. eGo is now using eTraGo non-linear power flows based on optimization results and its disaggregation of clustered results to an original spatial complexities. With the release of eDisGo speed-up options, a new storage integration methodology and more are now available.

Read more on here

Birthday Release 0.2.0

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@wolfbunke wolfbunke released this 19 Jul 16:26
ec16791

Fundamental structural changes of the eGo tool are included in this release. A new feature is the integration of the MV grid power flow simulations, performed by the tool eDisGo.. Thereby, eGo can be used to perforem power flow simulations and optimizations for EHV, HV (eTraGo) and MV (eDisGo) grids.

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Read more on: openego.readthedocs.io

Result class implementation

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@wolfbunke wolfbunke released this 29 Mar 14:34
03378ba

This is the second release of eGo. The Release introduce the result class and is still under construction and not ready for a normal use.

Added features

  • Update of Interface between eTraGo and eDisGo (specs)
  • New structure of eGo module / resulte class
  • Restructuring of functions
  • Add import function of eTraGo results form oedb

First release of eGo

First release of eGo Pre-release
Pre-release

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@wolfbunke wolfbunke released this 02 Feb 12:30

As this is the first release of eGo. The tool eGo use the Python3 Packages eTraGo (Optimization of flexibility options for transmission grids based on PyPSA) and eDisGo (Optimization of flexibility options and grid expansion for distribution grids based on PyPSA) for an electrical power calculation from extra high voltage to selected low voltage level.