# The Impact of Future Offshore Wind Farms on Wind Power Generation in Great Britain

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## Abstract

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## 1. Introduction

## 2. Estimating the Long Term Power Output of a Defined Wind Farm Distribution

#### 2.1. The GB-Aggregated Capacity Factor Time Series

**Figure 1.**Transformation function used to convert the hub-height wind speed to power output (the “adjusted” curve from [12]).

**Figure 2.**A comparison of MERRA derived data and measured data from a cluster of offshore wind farms: (

**a**) the hourly capacity factor aggregated across the three farms; (

**b**) the cumulative distribution of the capacity factor.

#### 2.2. Wind Farm Distributions

- All Round 3 zones are developed to full capacity (details provided in Table 1).
- All onshore wind farms under construction or with planning permission are fully commissioned.
- All existing farms remain generating at their current capacity. It therefore does not consider the decline in wind turbine performance with age, which is on average 1.6 ± 0.2 of their output per year [18].

Farm | Size (MW) | Median distance from coastline (km) |
---|---|---|

Moray Firth | 1116 | 28 |

Firth of Forth | 3465 | 54 |

Dogger Bank | 7200 | 160 |

Hornsea 1 (Heron & Njord) | 1200 | 112 |

East Anglia | 7200 | 56 |

Rampion | 665 | 50 |

Navitus Bay Wind Park | 970 | 21 |

Celtic Array | 4185 | 30 |

_{j}is the wind farm capacity, d

_{ij}is the distance between wind farms, N is the number of wind farms and C

_{T}is the total installed capacity. Despite the clustering of turbines in offshore wind zones, overall the capacity is more spatially dispersed in the future distribution. The mean distance between each MW unit of capacity, D, increases from 420 km for the current distribution to 460 km for the future distribution. This is a result of the large distances between the Round 3 wind zones and the coastline (given in Table 1). If the future distribution did not include any of the Round 3 offshore projects, there would be a small reduction in the spatial dispersion of the capacity (D = 410 km). In contrast, if the future capacity did not include any new onshore projects (only offshore), the capacity would become more spatially dispersed than the current distribution D = 450 km, but not as dispersed as the assumed future distribution which includes a combination of new onshore and offshore farms.

## 3. Results and Discussion

#### 3.1. GB-Aggregated Capacity Factor: Long Term Statistics

**Figure 4.**(

**a**) The annual mean capacity factor and (

**b**) the annual mean energy production derived using different wind distribution scenarios; current (blue), future (red), current + onshore (black) and current + offshore (green).

**Figure 5.**The frequency density distribution of the hourly GB-aggregated capacity factor derived from the full (1980–2013) time series for (

**a**) the “current” wind farm distribution (blue) along with the “current + onshore” distribution (black, circles); and (

**b**) the “future” wind farm distribution (red) along with the “current + offshore” distribution (green, crosses). The plot shows the mean value for each capacity factor bin (bin size 1%) across the 34 years and the shaded areas represent ±1 standard deviation from the mean (solid) and the dashed lines indicate the minimum and maximum annual frequencies.

^{−1}, compared to 7.8 ms

^{−1}for all onshore farms. As a result, the frequency distribution of hourly capacity factor values for offshore wind farms is relatively flat, while the onshore frequency distribution is clearly skewed towards low capacity factor values (Figure 6b). The 34-year mean capacity factor for the onshore farms is 31.6%, compared to 42% for the offshore farms. The latter result is broadly in agreement with that for the Danish offshore wind fleet [21]. However, it is significantly larger than the observed values from current offshore wind farms in GB. For the period 2004–2007, the Round 1 wind farms achieved a mean capacity factor of 29.5% [22]. For the same period, the mean capacity factor of the Round 1 offshore wind farms derived from the MERRA data is 33%. The difference in these values is likely to be attributable to the relatively low availability of the offshore turbines in the Round 1 wind farms. For onshore farms, the annual turbine availability is typically above 97%, however for the UK Round 1 offshore wind farms this figure is reduced to just 80% [22]. This difference is largely due to increased difficulty in accessing offshore turbines for repair or maintenance. The power curve used in this study is predominantly based on onshore wind turbines, and therefore does not take into account this reduced availability. Consequently, the capacity factors estimated in this study for the future wind farm distribution assume that the availability of offshore turbines in the larger Round 3 projects will approach the levels of availability currently found at onshore turbines.

**Figure 6.**A comparison of (

**a**) the frequency distribution of the hourly capacity weighted GB mean wind speed, the dashed lines represent the capacity weighted mean wind speeds; and (

**b**) the frequency distribution of the hourly GB-aggregated capacity factor, for the future wind farm scenario. Statistics for the onshore (black) and offshore (green) wind farms are plotted separately.

#### 3.2. A 34 Year Climatology of Persistent Low or High Wind Generation

**Figure 7.**The frequency of persistent low generation events for the current (solid lines) and future (dashed lines) wind farm distributions for three capacity factor thresholds: (

**a**) 5%; (

**b**) 10%; (

**c**) 20%. All panels show the mean number of events per year for all 34 years ±1 standard deviation (shaded areas).

**Figure 8.**The frequency of persistent high generation events for the current (solid lines) and future (dashed lines) wind farm distributions for three capacity factor thresholds: (

**a**) 50%; (

**b**) 65%; (

**c**) 80%. All panels show the mean number of events per year for all 34 years ±1 standard deviation (shaded areas).

**Figure 9.**The frequency of events for which: (

**a**) the GB mean power output is persistently below 2 GW; and (

**b**) the GB mean power output is persistently above 8 GW. Both panels show the mean value for the current (blue) and future (red) distributions ±1 standard deviation.

#### 3.3. A 34 Year Climatology of Ramping Events

**Figure 10.**The frequency distribution of ramping events for the current (blue) and future (red) wind farm distributions for three time windows: (

**a**) t

_{win}= 3 h; (

**b**) t

_{win}= 6 h; and (

**c**) t

_{win}= 12 h.

**Figure 11.**The frequency distribution of ramping events for the (

**a**) current and (

**b**) future wind farm distribution. The figures show the mean number of hours per year for which there is a subsequent ramp of at least ΔCF (also expressed as ΔP) within different time windows t

_{win}= 3 (blue), 6 (red) and 12 (black) h. The shaded area represents ±1 standard deviation, and the dashed lines represent the minimum and maximum numbers for any one year.

^{−1}), which can be attributed to turbine cut-out. These events are very rare and only occur on approximately 800 occasions (less than 0.25% of the time), but nonetheless need to managed by the system operator.

_{GB}= 7.5–12.5 ms

^{−1}). Further analysis has shown that for both distributions and all time windows, over 80% of these events occur in the winter months (December to February) and none occur in the summer months (June to August).

**Figure 12.**The frequency of ramping events as a function of the magnitude in the ramp (expressed as a change in GB-aggregated capacity factor) and the GB capacity weighted mean wind speed. The analysis has been completed for three time windows t

_{win}= 3 h, t

_{win}= 6 h and t

_{win}= 12 h for the current (

**a**–

**c**) and future (

**d**–

**f**) wind farm distributions. The individual points show the full dataset, while the contours detail the density of the points in wind speed bins of 0.25 ms

^{−1}and ΔCF of 1% (above 100 occurrences).

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Drew, D.R.; Cannon, D.J.; Brayshaw, D.J.; Barlow, J.F.; Coker, P.J.
The Impact of Future Offshore Wind Farms on Wind Power Generation in Great Britain. *Resources* **2015**, *4*, 155-171.
https://doi.org/10.3390/resources4010155

**AMA Style**

Drew DR, Cannon DJ, Brayshaw DJ, Barlow JF, Coker PJ.
The Impact of Future Offshore Wind Farms on Wind Power Generation in Great Britain. *Resources*. 2015; 4(1):155-171.
https://doi.org/10.3390/resources4010155

**Chicago/Turabian Style**

Drew, Daniel R., Dirk J. Cannon, David J. Brayshaw, Janet F. Barlow, and Phil J. Coker.
2015. "The Impact of Future Offshore Wind Farms on Wind Power Generation in Great Britain" *Resources* 4, no. 1: 155-171.
https://doi.org/10.3390/resources4010155