Analyzing Europe’s Biggest Offshore Wind Farms: A Data Set with 40 Years of Hourly Wind Speeds and Electricity Production

Oliver Grothe,Mira Watermeyer,Fabian Kächele

doi:10.3390/en15051700

Abstract

We provide an open, available, and ready-to-use data set covering 40 years of hourly wind speeds and synthetic hourly production signals for the 29 biggest offshore wind farms in Europe. It enables researchers and industry experts to include realistic offshore time series into their analyses. In particular, we provide data from 1980 to 2019 for wind farms already in operation and those that will be in operation by 2024. We document in detail how the data set was generated from publicly available sources and provide manually collected details on the wind farms, such as the turbine power curves. Correspondingly, the users can easily keep the data set up to date and add further wind farm locations as needed. We give a descriptive analysis of the data and its correlation structure and find a relatively high volatility and intermittency for single locations, with balancing effects across wind farms.

Highlights

The EU has the largest wind energy exploitable maritime space in the world
The main potential for such offshore wind farms in the EU and UK is located in the North Sea, due to relatively steadily blowing winds and shallow sea depths allowing for ground-based installations
We provide detailed information about the currently installed or planned wind turbine types and give analytical expressions for the power curves

Summary

Introduction

The EU has the largest wind energy exploitable maritime space in the world. to reduce net greenhouse gas emissions, the EU plans to expand the current offshore wind capacity of 12 GW to 60 GW by 2030 and 300 GW by 2050 [1,2]. We include wind speeds at hub height based on meteorological reanalysis data and further weather parameters as well as specifications of currently installed and planned wind turbines with parametric forms of their power curves. This aggregation is a simplification of the actual effects of how individual wind turbines combine to form a wind farm It suffices to provide insights into overall variations, intermittencies, their time constants, as well as distributional characteristics of power production at certain locations and dependency patterns between locations. In the first 29 files, we report detailed data for each wind farm, including wind components u and v, the forecast surface roughness (fsr), calculated wind speed, wind direction, scaled wind speed at hub height, and estimated power for each turbine type in the columns. (b) Generated power at wind farm Gwynt y Mor in January 2019

Descriptive Statistics

Dependence Patterns

Findings

Conclusions