Power fluctuations in high-installation- density offshore wind fleets

Abstract

Abstract. Detailed simulation of wind generation as driven by weather patterns is required to quantify the impact on the electrical grid of the power fluctuations in offshore wind power fleets. This paper focuses on studying the power fluctuations of high-installation-density offshore fleets since they present a growing challenge to the operation and planning of power systems in Europe. The Belgian offshore fleet is studied because it has the highest density of installation in Europe by 2020, and a new extension is expected to be fully operational by 2028. Different stages of the future installed capacity, turbine technology, and turbine storm shutdown technologies are examined and compared. This paper analyzes the distribution of power fluctuations both overall and during high wind speeds. The simulations presented in this paper use a new Student t-distributed wind speed fluctuation model that captures the missing spectra from the weather reanalysis simulations. An updated plant storm shutdown model captures the plant behavior of modern high-wind-speed turbine operation. Detailed wake modeling is carried out using a calibrated engineering wake model to capture the Belgium offshore fleet and its tight farm-to-farm spacing. Long generation time series based on 37 years of historical weather data in 5 min resolution are simulated to quantify the extreme fleet-level power fluctuations. The model validation with respect to the operational data of the 2018 fleet shows that the methodology presented in this paper can capture the distribution of wind power and its spatiotemporal characteristics. The results show that the standardized generation ramps are expected to be reduced towards the 4.4 GW of installations due to the larger distances between plants. The most extreme power fluctuations occur during high wind speeds, with large ramp-downs occurring in extreme storm events. Extreme ramp-downs are mitigated using modern turbine storm shutdown technologies, while extreme ramp-ups can be mitigated by the system operator. Extreme ramping events also occur at below-rated wind speeds, but mitigation of such ramping events remains a challenge for transmission system operators.

The Need for Dynamic Process Simulation: A Review of Offshore Power‐to‐X Systems

“…These findings form an essential basis for future investigations of the entire integrated process chain, which includes wind turbines, electrolyzers, synthesis plants and processing and treatment technologies. The wind sometimes fluctuates by more than 30 % in the minute range and within one hour, in extreme situations, 75% of the installed power can be lost, but seasonal fluctuations can also occur [63]. For an overestimate calculation of wind fluctuations, we analyzed the freely available data sets of the reanalysis ERA5 with 1 h -1 measured values for the site SEN-1, which was tendered by the Federal Maritime and Hydrographic Agency of Germany (BSH) as a special area in the North Sea for e.g., PtX projects [64,65].…”

The Hydrogen Wind Turbine: Design of a wind turbine optimised for hydrogen production

“…A super-capacitor can mitigate the high-frequency fluctuations from the wind, however, to analyse the most severe case, it has been omitted in this work. Instead, a 1-second resolution WT generator time series is simulated according to the wind conditions described by [13] and used directly as input for the electrolysis system without applying any low-pass super-capacitor filter. [14].…”

Stochastic wind farm flow generation using a reduced order model of LES

“…The ability of PS-ROM to correctly model the cross-correlations and therefore the dynamic interactions across the entire farm is an important result and affects both short-term forecasting and wind farm control. Forecasting and modelling dynamics for large spatiotemporal regions impacts the stability of the electrical grid [24], where particular significant weather changes such as ramp events are challenging to model [25]. Similarly, capturing the correct dynamic wake interaction is crucial for developing robust wind farm control strategies with reduced uncertainties [2,26,27].…”