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Complexity reduction during the modeling process

Complexity Management

Complexity management in energy system modeling evaluates the trade-off between complexity and accuracy. Complexity in models needs to be managed in order to use computational resources and development time efficiently wherefore we holistically do it in METIS. More: Complexity Management …

Photovoltaic capacity factors clustered with eight typical days using k-means clustering

Temporal Aggregation

The main task of temporal aggregation consists of the complexity reduction of temporally resolved input data in order to keep energy system models mathematically tractable and to reduce the computing time while preserving the overall optimization result as good as possible. In contrast to spatial aggregation, temporal aggregation focuses on summarizing redundant repetitions along the time axis. More: Temporal Aggregation …

The Illustration of Spatial Aggregation using administrative borders to aggregate data from regional resolution to national resolution

Spatial Aggregation

Spatial Aggregation is the aggregation of all data points within a group of ressources over a specific time period (granularity). What does that mean in view of energy systems analysis? Time series such as wind speeds or solar radiant power as well as attributes of existing components such as demand profiles of households are spatially condensed for example using averaging. More: Spatial Aggregation …

Sketch showing a suboptimal distribution of work under multiple workers

High Performance Computing

The art of high performance computing consists of the optimal distribution of the work, such that all elements of a supercomputer are at every point performing useful computations. For the case of energy system design optimization, this is challenging since at large parts the currently available optimization algorithms are distributeable to only a limited extent, if they are at all. More: High Performance Computing …

Sketch showing the decompsition and distribution of the conditional matrix of an optimization model

Decomposition Methods

The energy system optimization models we study all have in common that they are very large wherefore we come to the boundaries of what we can compute. Nevertheless, the models are also highly repetitive. For instance, replicable structures are found regarding the spatial resolution, e.g. a similar superstructure of technologies is found in every node, or the temporal resolution, e.g. the model for a storage is similar for every day. More: Decomposition Methods …

Included processes in the single-node model

Single-Node Model

In order to derive CO2 emissions reduction strategies by analyzing energy system models, these models must describe the energy system and all its sub system extensively. One way of building an energy system is to assess a given region as a whole and aggregate all of its components and subsystems into one virtual point (node). More: Single-Node Model …

Example visualization of a three-node energy system model

Multi-Node Model

Multi-node models expand the assumptions of single-node models by the spatial dimension. The area of interest is split up into several regions – so-called nodes. Every node is represented by its own supply and demand profiles of specified commodities. More: Multi-Node Model …

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