EGU24-17213, updated on 14 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Comparing PV and Wind Models to Analyse Dunkelflaute Events in Ireland

Boris Morin1, Damian Flynn2, Conor Sweeney3, and Aina Maimo Far4
Boris Morin et al.
  • 1University College Dublin, School of Statistics and Mathematics, Ireland (
  • 2University College Dublin, School of Electrical and Electronic Engineering (
  • 3University College Dublin, School of Statistics and Mathematics, Ireland (
  • 4University College Dublin, School of Statistics and Mathematics, Ireland (

The 2024 Government of Ireland Climate Action Plan aims to increase the share of renewable energy sources (RES) from 38% to 80% by 2030. In 2022, the installed capacity of wind power will surpass 4.5 GW, and the goal is to reach the same level as solar power by 2025. As the proportion of energy generated from these weather-dependent sources increases, there is a need to more accurately quantify periods when the energy generated from such sources is low for an extended period, in order to plan for appropriate reserve capacity.

The terms "Dunkelflaute" and “Renewable Drought” have been used to refer to extended periods of time when the capacity factor of both wind and solar power falls below a given threshold for a set period of time. In this study, we define a Dunkelflaute event as occurring when the combined capacity factor for wind and solar falls below a fixed threshold for at least 24 hours. The effect of choosing different values for this fixed threshold is also investigated in our study.

This study aims to investigate how the expected frequency and duration of Dunkelflaute events identified in different RES datasets may change depending on the assumptions made by the underlying RES datasets.

The first RES dataset investigated is an hourly estimate of electricity generation based on ERA5 climate variables, made by C3S Energy, which was produced using statistical and physical models. The C3S Energy dataset provides a time series of electricity supply from wind and solar photovoltaic and is trained using European Network of Transmission System Operators for Electricity (ENTSO-E) data.

This dataset has certain limitations. First, it assumes a homogeneous spatial distribution of the installed capacity of wind and solar energy production, to maintain a methodological coherence between the two RES sources. Second, the energy conversion models applied, contain simplifying approximations, such as using a single wind turbine model with a fixed hub height for all locations.

The second RES dataset has been created by the authors, which uses more detailed information about the location of the wind and PV farms. Relevant atmospheric variables are interpolated from ERA5 data to the location of each RES farm. In addition, the characteristics of the wind and PV panels at each farm are taken into account.

Both datasets are compared against the actual wind and PV capacity factor data supplied by the national grid operator of Ireland, EirGrid, for the year 2023, to indicate the performance of each model. The two datasets are then analysed across the full range of the time series, from 1979 to 2023, to determine the frequency and duration of all Dunkelflaute events during this period.

Differences in the identified Dunkelflaute events highlight the importance of considering results in the context of the driving data, which would be important for future policy decisions such as planning reserve capacity requirements, or locating future RES farms.

How to cite: Morin, B., Flynn, D., Sweeney, C., and Maimo Far, A.: Comparing PV and Wind Models to Analyse Dunkelflaute Events in Ireland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17213,, 2024.