Modelling Tool

Cross-network planning instrument open_eGo

Model open_eGo
With the freely available planning tool open_eGo, the grid infrastructure can be optimised economically for future loads at all voltage levels.
Credit:

www.ego-n.org

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The eGo model was co-developed at the Institute of Networked Energy Systems and consists of diverse software modules for creating and calculating power grid models for Germany that spans grid levels.In the modules Dataprocessing and DiNGO, data relevant to the power system, such as electricity consumption and generation, the grid structures or cost assumptions are compiled in high spatial and temporal resolution. 

In connection with meteorological and georeferenced information as well as assumptions regarding future system scenarios, these data are made available to interested persons on the OpenEnergy platform in the oedb database. The optional, separately executable calculation modules eGo (spanning grid levels), eTraGo (high and extra-high voltage level) and eDisGo (low and medium voltage level) then make use of these data with the software PyPSA for load flow simulations that optimise overall costs and analysis of various research questions.

The eGo model uses an approach of perfect foresight and optimises the cost-minimised utilisation of power stations based on a merit order over the entire period under observation (generally one year). The transformation of the power system to one with a large number of renewable energy sources is expected to require grid expansion measures as well as additional storage capabilities, which are endogenously optimised in the model.

The eGo model is being actively developed by six institutions according to open source principles, which has already led to many additional uses and research initiatives. Accordingly, the model is undergoing dynamic development and can be flexibly adapted for various research questions. In the research project eGoⁿ, the previously depicted power grid model is being expanded to include demands and flexibility factors from the areas of gas, electric mobility and heat.