Energie-Erneuerungsplanung

Topic: Algorithmic solution for valuing consequences in infrastructure maintenance

This project reflects this challenge

Challenge

Maintenance of company assets of the low-voltage power distribution grid should be prioritized based on asset state and asset importance. While the state of the asset, typically the age since the last maintenance, is well-document, there is currently no measure of importance for these assets. One idea to value the importance of the assets is to analyze the amount of energy that is distributed by them. The amount of consumption is, of course, not an optimal measure for how damage is caused by an asset breakdown, or for what consumer would pay for an insurance against power interupts - if they were allowed to pay for it. This challenge, therefore, leave it open how, for the purpose of optimization, the importance will be measured.

Provided Data

The data that was provided consisted of

List and point coordinates of transformer stations
List and point coordinates of distribution enclosures
List and point coordinates of consumers
List and line coordinates of connection lines
Anonymized consumption data for the last four years

Approach

Since no importance measure was given, but consumption can be considered a first good proxy and consumption is only known per consumer but not per connection line, distribution enclosure or transformer station, the first part of the project was dedicated to enriching the data by calculating these figures.

The second part of the project focussed on the optimization of the prioritization of maintenance work based on a flexible importance measure.

Data enrichment

The basic idea is to calculate the amount of energy that flows through transformer stations, distribution enclosures and connection lines by analyzing the topology of the network, starting from the transformer stations. This topology is constructed from the point and line coordinates. The quality of the coordinates is high enough to do this correctly.

After the topology of the network and the distrance from the transformer stations is created, the yearly power consumption is aggregated for each element of this network starting with the ones that are the furthest away from the transformer stations. The maximum depth of the network is 17 hops.

The data is visualized in an open source software QGIS. This calculated data is added to the network elements such that the visualization can be enhanced by this data. The size of the points and the width of the lines now corresponds to the amount of energy that flows through this element.

This is a larger view of the results. (brown - transformer station, red - distribution enclosure, orange - consumer, red - connection line)

This is a more detailled view of the results.

Optimization

References

http://geopandas.org/reference.html

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