Description

Limiting climate change requires the transition to an energy network with a higher proportion of renewable generation. Renewable energy sources such as wind and solar are highly dependent on weather conditions. Combined with energy demand which partly relies on temperature, this signifies that balancing energy demand and supply will be increasingly weather dependent. Therefore, multiple studies have investigated the dependence of periods of high energy demand and low renewable generation on weather, and more specifically on weather regimes.  

Weather regimes are large-scale atmospheric patterns representing most of the low-frequency variability in the mid-latitudes, which impact energy related variables such as temperature, wind and incoming solar radiation. The winter of 1962-1963 is known as the coldest winter in Europe of the 20th century. It was characterized by a very high frequency of blocking regimes such as the negative North Atlantic Oscillation. Following this observation, within this tool the relationship between weather regimes and winter energy conditions is investigated. This is done by setting the proportion of weather regimes during a winter season. Using 40+ years of modelled energy data for European countries and weather regimes, it is possible to create multiple iterations of winters. This allows us to determine the relative influence of weather regimes on energy but also to identify potential worst-case scenarios. 

Potential users: 

•         Transmission system operators
•         Energy providers
•         Energy traders
•         Governmental agencies

Guide

The user needs to set the frequency of weather regimes for the winter season. The user can select how many scenarios are created (1000 being an upper limit to keep computation time moderate). Each scenario will be a random selection of days within the 40-year period covering the winter period and that fit the requirement for the frequency of weather regimes. The user has the possibility to add climatological criteria to make sure that for instance the December days selected are from the December period.

The output is a list of dates for each scenario. Each date is then associated with energy values that can be explored.

Availability

Currently the tool is not available to the public, as it has only been tested once during the XAIDA summer school.

Basic knowledge of python is necessary to use the tool. More advanced knowledge would be necessary to further determine weather conditions related to extremes.

Use Cases

The tool was tested using the winter of 1962-63 as a reference frequency for the weather regimes.