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Research

Complexity Reduction

Complexity Reduction

In order to derive optimal investment or operation decisions in a complex energy system, a holistic complexity management is developed that evaluates the trade-off between complexity and required model accuracy. For the case of the spatial and temporal complexity dimensions, this is extended by novel data aggregation methods that are able to reduce the required model complexity to design reliable renewable based energy systems.
Complexity Management
Temporal Aggregation
Spatial Aggregation

Parallelization

Parallelization

Today's computational resources are described by many parallel workers. Nevertheless, the currently available solvers for mathematical problems are parallelisable to only a limited extent, wherefore novel decomposition methods are developed and applied in this project.
High Performance Computing
Decomposition Methods

Energy System Model

Energy System Models

Models are required to determine the economic and ecologic optimal path to a carbon neutral energy systems. In METIS three different model types are developed: A nationwide single-node model to derive the optimal trade-off between a changing supply and efficiency measures, a multi-node model for the infrastructure design, and a simplistic model for education purpose.
Single-Node Model
Multi Node Model

Partners

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FZJ - IEK-3, JSC

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RWTH - JERI

FAU logo

FAU - EDOM


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