Tuesday, 27 September 2016
17:00 - 18:30 Annual energy production: improved estimates through advanced modelling
Resource assessment  
          

Advanced modelling methods are now standard in wind estimating modelling of annual energy production (AEP). In this session, speakers will present round robin tests of models for spatial variability of wind resource on projects using different modelling approaches. We will look at the consequences of including atmospheric stability in the calculation of AEP offshore and how you can measure wind profiles at heights of 100-200 metres by using LIDARs with emphasis on charactering extreme shear situations such as low-level jets causing extreme loads. Finally, we will hear about the variability of turbulence intensities measured offshore.

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Learning objectives

• Understand the different methods for modelling the spatial variability of wind resource at the project scale and the errors based on the different modelling approaches;
• How to incorporate stability in the AEP modelling in offshore wind farms;
• Understand the effect of using high resolution modelled wind climatologies combined with advanced boundary modelling on the estimation of the tall winds at 100-200 metres;
• How to characterise and define the extreme events such as low-level jets that can cause fatigue loads in a wind farm;
• Understand the variability of the turbulence intensities offshore measured from multiple measurement towers.

This session will be chaired by:

Przemek Marek Prevailing, United Kingdom
Co-authors:
Przemek Marek (1) ^F Thomas Grey (2) Andrew Hay (2)
(1) Prevailing, Glasgow, United Kingdom (2) Prevailing, Bristol, United Kingdom

A study of the variation in offshore turbulence intensity around the British Isles

Introduction

Offshore met mast installation is expensive, so alternatives like remote sensing are often employed. However, the ambient turbulence intensity (TI) derived from remote sensing cannot be directly related to that provided by cup anemometer measurements. Turbulence intensity is an important input to many wake models and a significant driver of turbine loading, so impacts both wind farm energy production and CAPEX. This presentation shows that the application of off-site TI data represents an attractive and practical alternative.

Approach

Eight publicly available offshore met mast datasets have been analysed from locations in the Irish and North seas. This enabled the identification of key drivers impacting the TI values and distributions at each mast. Generalised conclusions explaining how TI varies offshore are then drawn. The conclusions are clear and derived from real-world measurements allowing simple application to offshore wind farm development.

The data was sourced from The Crown Estate (TCE) Marine Data Exchange (MDE) database and the Energy research Centre of the Netherlands (ECN).


Main body of abstract

The measured data was processed, cleaned and adjusted to derive timeseries at a consistent height across all the analysed masts. TI metrics where then derived from the datasets. The variation of these metrics across all the datasets were analysed as functions of the following parameters:
- Wind speed;
- Wind direction;
- Distance to the coast;
- Presence of an upwind mountain range;
- Time of year (seasons);
- Air-to-water temperature difference (one dataset only).

The mean TI values are very consistent across all the considered datasets. The wind speed distributions of TI as well as the TI probability distributions from all the datasets are in very good agreement. Further, a sensitivity study for TI predictions at a hypothetical wind farm showed low variation in results. Therefore, when compared to the inherent uncertainty of available wake models, using off site turbulence measurements as an input to wake modelling carries a relatively low uncertainty penalty.

A relationship between increased TI and upwind mountain ranges was observed while the trend relating TI to the fetch was weak. For locations greater than 10-15 km from the coast the impact of coastal proximity becomes negligible.

Clear seasonal variation in TI was seen, with lower TI observed at most masts during Spring months. It was hypothesized that the slower warming of the sea, as compared to land, creates a stable boundary layer resulting in this observed lower turbulence. One of the considered datasets includes air-to-water temperature difference records. The clear correlation between this metric and the TI distribution supports the stable spring-time boundary layer hypothesis.

In summary, the variation of TI is more strongly connected to local climate, sea temperature, air mass origin and season than the distance from the coast.


Conclusion

For offshore sites located a sufficient distance from the coast (> 10-15 km) mean values and all-directional distributions of TI for a given region are very similar. This geographic consistency of TI offshore enables the application of off-site TI measurements for energy yield prediction. So, energy yield and representative turbulence calculations can be confidently initiated using seasonally representative TI measurements from any offshore mast in the general site region.


Learning objectives
- Use of off-site mast mounted cup anemometer measurements represent a viable method for defining TI for offshore wind farms
- Consideration of multiple offshore met mast datasets enables the key drivers of offshore TI variation to be gleaned
- Some general and practical conclusions that are applicable to financial-grade energy yield analysis offshore are provided