Global offshore wind turbine dataset

Wind turbine database for intelligent operation and maintenance strategies

With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5 MW wind turbines is presented. The database contains 312 analogous variables recorded at 5-minute intervals, from 78 different sensors. The reported values for each sensor are minimum, maximum, mean, and standard deviation. The database also contains the alarm events, indicating the system and subsystem and a small description. Finally, a set of functions to download specific subsets of the whole database is freely available in Matlab, R, and Python. To demonstrate the usefulness of this database, an illustrative example is given. In this example, different gearbox variables are selected to estimate a target variable to detect whether or not the estimate differs from the actual value provided for the sensor. By using this normality modelling approach, it is possible to detect rotor malfunction when the estimate differs from the actual measured value.

Behavioral responses to offshore windfarms during migration of a declining shorebird species revealed by GPS-telemetry

EU member countries and the UK are currently installing numerous offshore windfarms (OWFs) in the Baltic and North Seas to achieve decarbonization of their energy systems. OWFs may have adverse effects on birds; however, estimates of collision risks and barrier effects for migratory species are notably lacking, but are essential to inform marine spatial planning. We therefore compiled an international dataset consisting of 259 migration tracks for 143 Global Positioning System-tagged Eurasian curlews (Numenius arquata arquata) from seven European countries recorded over 6 years, to assess individual response behaviors when approaching OWFs in the North and Baltic Seas at two different spatial scales (i.e. up to 3.5 km and up to 30 km distance). Generalized additive mixed models revealed a significant small-scale increase in flight altitudes, which was strongest at 0-500 m from the OWF and which was more pronounced during autumn than during spring, due to higher proportions of time spent migrating at rotor level. Furthermore, four different small-scale integrated step selection models consistently detected horizontal avoidance responses in about 70% of approaching curlews, which was strongest at approximately 450 m from the OWFs. No distinct, large-scale avoidance effects were observed on the horizontal plane, although they could possibly have been confounded by changes in flight altitudes close to land. Overall, 28.8% of the flight tracks crossed OWFs at least once during migration. Flight altitudes within the OWFs overlapped with the rotor level to a high degree in autumn (50%) but to a significantly lesser extent in spring (18.5%). Approximately 15.8% and 5.8% of the entire curlew population were estimated to be at increased risk during autumn and spring migration, respectively. Our data clearly show strong small-scale avoidance responses, which are likely to reduce collision risk, but simultaneously highlight the substantial barrier effect of OWFs for migrating species. Although alterations in flight paths of curlews due to OWFs seem to be moderate with respect to the overall migration route, there is an urgent need to quantify the respective energetic costs, given the massive ongoing construction of OWFs in both sea areas.

Offshore Wind Energy and Marine Biodiversity in the North Sea: Life Cycle Impact Assessment for Benthic Communities

Large-scale offshore wind energy developments represent a major player in the energy transition but are likely to have (negative or positive) impacts on marine biodiversity. Wind turbine foundations and sour protection often replace soft sediment with hard substrates, creating artificial reefs for sessile dwellers. Offshore wind farm (OWF) furthermore leads to a decrease in (and even a cessation of) bottom trawling, as this activity is prohibited in many OWFs. The long-term cumulative impacts of these changes on marine biodiversity remain largely unknown. This study integrates such impacts into characterization factors for life cycle assessment based on the North Sea and illustrates its application. Our results suggest that there are no net adverse impacts during OWF operation on benthic communities inhabiting the original sand bottom within OWFs. Artificial reefs could lead to a doubling of species richness and a two-order-of-magnitude increase of species abundance. Seabed occupation will also incur in minor biodiversity losses in the soft sediment. Our results were not conclusive concerning the trawling avoidance benefits. The developed characterization factors quantifying biodiversity-related impacts from OWF operation provide a stepping stone toward a better representation of biodiversity in life cycle assessment.

Understanding uncertainties in contemporary and future extreme wave events for broad-scale impact and adaptation planning

Understanding uncertainties in extreme wind-wave events is essential for offshore/coastal risk and adaptation estimates. Despite this, uncertainties in contemporary extreme wave events have not been assessed, and projections are still limited. Here, we quantify, at global scale, the uncertainties in contemporary extreme wave estimates across an ensemble of widely used global wave reanalyses/hindcasts supported by observations. We find that contemporary uncertainties in 50-year return period wave heights ([Formula: see text]) reach (on average) ~2.5 m in regions adjacent to coastlines and are primarily driven by atmospheric forcing. Furthermore, we show that uncertainties in contemporary [Formula: see text] estimates dominate projected 21st-century changes in [Formula: see text] across ~80% of global ocean and coastlines. When translated into broad-scale coastal risk analysis, these uncertainties are comparable to those from storm surges and projected sea level rise. Thus, uncertainties in contemporary extreme wave events need to be combined with those of projections to fully assess potential impacts.

Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition

Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided. Over two years (2019-2020), power generation and weather-related data were collected at 15-minute intervals. The dataset was used in the Renewable Energy Generation Forecasting Competition hosted by the Chinese State Grid in 2021. The process of data collection, data processing, and potential applications are described. The use of this dataset is promising for the development of data-driven forecasting models for renewable energy generation and the optimization of electricity demand response (DR) programs for the power grid.