Critical weather situations for renewable energies – Part A: Cyclone detection for wind power


Evaluation of day-ahead wind power forecasts for Germany.

Large wind power errors are linked to special weather patterns.

Automatic cyclone detection to indicate difficult weather patterns.


A constantly increasing share of weather dependent renewable energies in Germany’s power mix poses new challenges concerning grid management and security of energy supply. An evaluation of the three year period from 2012 to 2014 reveals, that 60% of days with largest errors in the day-ahead wind power forecasts for Germany are linked to cyclones and troughs traversing the North Sea, the Baltic Sea or Germany. A cyclone detection algorithm has been developed to automatically indicate these critical weather situations. The algorithm is based on Numerical Weather Prediction model forecasts of mean sea level pressure. The cyclone detection is used to design an automated weather information tool for end-users such as Transmission System Operators (TSOs). For 2014, it identified a critical weather development in 38% of all days. The root mean square error of day-ahead wind power forecasts increased by 1% of installed capacity during these periods. A real time application of the tool is being implemented in order to support a sustainable and save integration of the increasing wind power production. It will then be provided to, and will be tested by, three German TSOs with the purpose of an operative usage to guarantee the security of supply.


  • Wind energy;
  • Weather dependency;
  • Cyclone detection;
  • Net stability;
  • Transmission system operator

1. Introduction

The share of renewable energies in the German power mix is constantly increasing. In 2015, 35% of the country’s net electricity production was provided by solar-, wind-, hydro-power and biomass, whereof the largest contribution was due to wind power with 15.1% [1]. The total installed net capacity of wind power is 43.7 GW and during favorable weather conditions it supplies more than half of the country’s total energy production [2]. By nature, wind energy is strongly variable and highly weather-dependent. For an accurate detection of these strong fluctuations, transmission system operators (TSOs) need precise wind power forecasts to guarantee system stability. These, in turn, depend also on the quality of the underlying Numerical Weather Prediction (NWP) models. Weather and power forecasts are, however, afflicted with forecast errors. Certain weather situations are particularly hard to forecast and thus are challenging for TSOs.

In the mid-latitudes, day to day weather is fundamentally influenced by extra-tropical cyclones [3]. Within a cyclone, air masses circulate around a center of low air pressure and thus cyclones are also called low pressure systems or lows. On the northern hemisphere, this rotation is counter-clockwise. In the process of cyclone development, well-defined frontal systems are formed, which represent the borders between low-energy (cold) and high-energy (warm) air masses. These fronts are attached to the cyclone and especially to its movement. The presence of intense low pressure systems not only causes rather unstable, wet and windy weather conditions but also amplifies the wind power production. The associated frontal systems can cause fast changes in wind speed as well as in wind direction and may lead to critical, sharp ramps in the wind power production. Fronts with strong wind speeds in Northern Germany are even regarded as critical events concerning the net stability [4]. Large amounts of wind energy are then produced in the North of the country and need to be transported towards the South. Such a scenario, especially in combination with low temperatures and consequently a high energy demand, stresses the power grid and poses a challenge to the TSOs. This paper addresses day-ahead wind power forecast errors, identifies cyclones and fronts as problematic weather situations and presents an automated tool to recognize such challenging weather elements.

As low pressure systems strongly govern our weather conditions, the ability of atmospheric models to predict cyclones is intensively studied by meteorologists and climatologists. A comprehensive overview of previous extra-tropical cyclone predictability studies focusing on short to medium-range forecasts is given by Ref. [5]. Nine global ensemble prediction systems (EPS) and their ability to forecast cyclones for a 6-month period was investigated in Ref. [6]. EPS produce multiple weather forecasts, which represent a sample of possible future atmospheric states. In accordance with previous findings [7] it is shown that global deterministic models forecast the position of a cyclone with a higher accuracy than the cyclone intensity. EPS instead can add valuable information to the latter, as they show a higher skill in forecasting the strength of a cyclone. In most of the 14 reviewed global forecast systems cyclones also tend to propagate too slowly. With respect to seasonal forecasts, Ref. [8] investigated wintertime extra-tropical cyclones using the European Centre for Medium-Range Weather Forecast (ECMWF) model and concluded that higher model resolution leads to better simulation of extra-tropical cyclones. Furthermore, there is strong interest on past and future changes in cyclone intensity, frequency and changes in cyclone tracks. The latter have also a special implication on future wind resource assessments as they introduce substantial uncertainty (see Ref. [9]). Ref. [10] gives a review of mid-latitude cyclone climatologies with focus on the present climate and possible changes in the future. In Europe, special interest lies on cyclone tracks over the Mediterranean (e.g. Refs. [11] and [12]). Their future changes as simulated by regional climate models is addressed, e.g., in Refs. [13] and [14].

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