Resource Documents: U.K. (103 items)
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Responses of dispersing GPS-tagged Golden Eagles (Aquila chrysaetos) to multiple wind farms across Scotland
Abstract: Wind farms may have two broad potential adverse effects on birds via antagonistic processes: displacement from the vicinity of turbines (avoidance), or death through collision with rotating turbine blades. Large raptors are often shown or presumed to be vulnerable to collision and are demographically sensitive to additional mortality, as exemplified by several studies of the Golden Eagle Aquila chrysaetos. Previous findings from Scottish Eagles, however, have suggested avoidance as the primary response. Our study used data from 59 GPS-tagged Golden Eagles with 28 284 records during natal dispersal before and after turbine operation &ly; 1 km of 569 turbines at 80 wind farms across Scotland. We tested three hypotheses using measurements of tag records’ distance from the hub of turbine locations: (1) avoidance should be evident; (2) older birds should show less avoidance (i.e. habituate to turbines); and (3) rotor diameter should have no influence (smaller diameters are correlated with a turbine’s age, in examining possible habituation). Four generalized linear mixed models (GLMMs) were constructed with intrinsic habitat preference of a turbine location using Golden Eagle Topography (GET) model, turbine operation status (before/after), bird age and rotor diameter as fixed factors. The best GLMM was subsequently verified by k-fold cross-validation and involved only GET habitat preference and presence of an operational turbine. Eagles were eight times less likely to be within a rotor diameter’s distance of a hub location after turbine operation, and modelled displacement distance was 70 m. Our first hypothesis expecting avoidance was supported. Eagles were closer to turbine locations in preferred habitat but at greater distances after turbine operation. Results on bird age (no influence to 5+ years) rejected hypothesis 2, implying no habituation. Support for hypothesis 3 (no influence of rotor diameter) also tentatively inferred no habituation, but data indicated birds went slightly closer to longer rotor blades although not to the turbine tower. We proffer that understanding why avoidance or collision in large raptors may occur can be conceptually envisaged via variation in fear of humans as the ‘super predator’ with turbines as cues to this life-threatening agent.
Alan H. Fielding, Natural Research Ltd, Brathens, Aberdeenshire
David Anderson, Forestry and Land Scotland, Aberfoyle
Stuart Benn, RSPB Scotland, Inverness
Roy Dennis, Roy Dennis Wildlife Foundation, Forres
Matthew Geary, Department of Biological Sciences, University of Chester
Ewan Weston, Natural Research Ltd, Brathens, Aberdeenshire
D. Philip Whitfield, Natural Research Ltd, Brathens, Aberdeenshire
Ibis: International Journal of Avian Science
Published on line ahead of print 20 July 2021. doi: 10.1111/ibi.12996
Author: Scottish Forestry
[A Scottish citizen made a freedom-of-information request, to which Scottish Forestry replied as follows.]
Thank you for your request dated 26 November and received on the 5 December and the clarification dated 19 December 2019 under the Environmental Information (Scotland) Regulations 2004 (EIRs).
You asked for:
a) the number of trees felled for all onshore wind farm development in Scotland to date.
b) the area of felled trees, in hectares, for all onshore wind farm development in Scotland to date.
I enclose some of the information you requested.
Specifically data covering renewable developments on Scotland’s national forests and lands, which is managed on behalf of Scottish Ministers by Forestry and Land Scotland. The area of felled trees in hectares, from 2000 (the date when the first scheme was developed, is 6,994 hectares [70 km², 17,283 acres]. Based on the average number of trees per hectare, of 2000, this gives an estimated total of 13.9M.
While our aim is to provide information whenever possible, in this instance the Scottish Government does not have some of the information you have requested. Namely data on renewable developments on privately owned woodlands.
Download original document: “Scottish Forestry information request 19-02646”
Author: Renewable Energy Foundation
2019 was the tenth year in which British wind farms have received constraint payments to reduce their output because of electricity grid congestion. There has been a total of £649 million paid out over the decade for discarding 8.7 TWh of electricity. To put this in context, this quantity of energy would be sufficient to provide 90% of all Scottish households with electricity for a year.
Because of a rapid growth in wind farms, particularly in Scotland, the total paid has tended to increase year on year in spite of grid reinforcements and new grid lines such as the £1 billion Western Link from Hunterston to Deeside, which was built specifically to export wind power from Scotland to English and Welsh consumers. Figure 1. displays this trend, showing payments rising from £174,000 in 2010 to a new record cost of more than £139 million. The quantity of electricity discarded in 2019 was also a new record at 1.9 TWh.
Scottish onshore wind farms are far and away the largest beneficiaries of constraint payments, receiving 94% of the total in 2019, and approximately the same proportion averaged over the last ten years (see Figure 2). Scottish onshore wind received nearly £130 million in 2019, and more than £607 million over the decade. The remaining 6% of payments has largely gone to English offshore wind farms, with smaller fractions for Welsh onshore and Scottish and Welsh offshore wind farms. No English onshore wind farms have received constraint payments via the Balancing Mechanism.
The number of Scottish windfarms receiving constraint payments has increased from three in 2010 to sixty-eight in 2019. The largest increase in wind farm numbers occurred in 2017, when eighteen new windfarms received constraint payments for the first time.
Of the sixty-six onshore wind farms in Scotland receiving constraint payments over the decade, two large windfarms – Whitelee and Clyde – received nearly a third of the decade’s total, taking £108 million and £80 million respectively. The animated bar chart (Figure 4) shows how the costs of constraints to windfarms have accumulated over the decade from a slow start in 2010 when payments were made on only three days, increasing to eighty-two days in 2011, with a peak in 2017 when constraint payments were made on 244 days of the year. Wind farm constraint payments were made on 229 days of 2019.
It is perhaps unsurprising that Whitelee, being the largest UK onshore wind farm and one of the earliest entrants into the constraint market, has received the largest constraint payment total. However, recent years have seen newer and smaller wind farms overtaking Whitelee, suggesting that the sites currently being chosen for wind farm development are in locations with poorer grid connection. Whether this is a deliberate choice, designed to maximise average earnings per MWh generated, is open to debate.
The animated bar chart below shows how constraint costs grew in 2019 and reveal that Kilgallioch, which was built in 2017 and is less than half the size of Whitelee, has received more in constraint payments in 2019. Similarly, Stronelairg, built in 2018 and also less than half the size of Whitelee, has risen immediately to fourth in the annual league table of constraint payments.
In 2019, six wind farms were responsible for 50% of the constraint payment receipts, namely Clyde, Kilgallioch, Whitelee, Stronelairg, Fallago Rig, and Dunmaglass. It is particularly notable that of these six highly constrained wind farms:
a) Stronelairg received planning permission in spite of being behind a grid bottleneck and was subject to a Judicial Review due to its impact on wild land. Moreover, there are currently two further neighbouring applications in process for Glenshero owned by the GFG Alliance, and Cloiche, which is proposed by SSE, the operator of Stronelairg.
b) Whitelee, which opened in 2007 with a capacity of 322 MW, and was one of the first wind farms constrained off in the Balancing Mechanism, has been extended very significantly, with a further 217 MW entering service in 2012.
c) Clyde was completed in 2009, but permission to extend the site with an additional 74 (172.8 MW) turbines was granted in July 2014 and completed in 2017.
d) Fallago Rig is currently seeking an extension to add a further 12 turbines.
e) Kilgallioch is also seeking an extension.
Wind farm owners charge more per unit to reduce output than they earn through generating. For wind farms subsidised under the Renewables Obligation (RO) the income foregone when instructed to reduce output is the value of the Renewable Obligation Certificates (ROC). Typically, wind farms ask to be paid much more than the lost income, and in the early days of wind farm constraint payments, the premiums charged for not generating were very high indeed. For example, in 2011, Crystal Rig 2 charged £991 per MWh to reduce output compared to the value of the ROC at that time of £42 per MWh. Kilbraur, Millennium, Farr, An Suidhe were charging between £200 to £320 per MWh constrained-off in 2011.
This was regarded as an abuse of market power, and the Government introduced the Transmission Constraint Licence Condition (TCLC) in 2012, which sought to prevent excessive bid prices in the event of a constraint. While there can be no doubt that the TCLC resulted in a reduction in prices, they are still well in excess of the subsidy foregone in 2019 as Figures 6 and 7 demonstrate.
Figure 6 shows the five onshore wind farms which received the largest premiums above the subsidy forgone and the five which received the smallest premiumin 2019. It is interesting to note that Andershaw, Blackcraig, Beinneun, Cour and Sanquar, which are receiving a high premium over lost income, are newer wind farms accredited after the ROC banding for onshore wind was reduced such that they receive 0.9 of a ROC per MWh in subsidy. Assuming the 2019 ROC value will be approximately £55, these wind farms would receive £49 per MWh if generating but ask for and receive £96-£98 per MWh not to generate and thus get a premium of £47–£49 above the subsidy when constrained off. The five wind farms with the lowest constraint prices are older wind farms which receive 1 ROC per MWh. In 2019, they were setting constraint prices of £64-£69 per MWh to reduce output, thus getting a premium of £10-£15 per MWh.
The RO-subsidised offshore windfarms which received constraint payments fall into various subsidy bands: 1, 1.5, 1.8 and 2.0 ROCs per MWh. Taking these variations into account, it is again the newer wind farms that are charging higher constraint payments with the most expensive five offshore wind farms making £52 – £74 per MWh more than the site-specific subsidy forgone. The five least expensive received £20 – £38 per MWh over their subsidy.
It is difficult to see any justification for compensation over and above the subsidy lost, and indeed REF has suggested, as many economists would argue, that constraints are a normal and entirely foreseeable commerical risk and should not be compensated at all. Indeed, REF infers from the data presented above that constraint payments are actually encouraging the siting of onshore wind farms in grid constrained areas. This is clearly not in the public interest, and entails a significant cost to electricity consumers, who ultimately fund constraint payments through their bills.
Finally, it must be remembered that the cost of mislocating wind farms in areas with weak grid connection or behind constraints is much greater than the direct payments to wind farms themselves, large though these are. When a wind farm is constrained off the grid on one side of a grid bottleneck, National Grid as the system operator is required to make up the short fall in electricity by paying other generation (usually gas-fired) to increase generation on the other side of the bottleneck. Over the last ten years, the overall cost of constraints has risen by nearly 400%, from £165 million in 2010 to £636 million in 2019, reflecting the expense and difficulty of integrating a large wind fleet, and increasingly a large solar fleet, into the GB electricity grid.
Avian vulnerability to wind farm collision through the year: Insights from lesser black-backed gulls (Larus fuscus) tracked from multiple breeding colonies
Author: Thaxter, Chris; et al.
- Wind energy generation has become an important means to reduce reliance on fossil fuels and mitigate against human‐induced climate change, but could also represent a significant human–wildlife conflict. Airborne taxa such as birds may be particularly sensitive to collision mortality with wind turbines, yet the relative vulnerability of species’ populations across their annual life cycles has not been evaluated.
- Using GPS telemetry, we studied the movements of lesser black‐backed gulls Larus fuscus from three UK breeding colonies through their annual cycle. We modelled the distance travelled by birds at altitudes between the minimum and maximum rotor sweep zone of turbines, combined with the probability of collision, to estimate sensitivity to collision. Sensitivity was then combined with turbine density (exposure) to evaluate spatio‐temporal vulnerability.
- Sensitivity was highest near to colonies during the breeding season, where a greater distance travelled by birds was in concentrated areas where they were exposed to turbines.
- Consequently, vulnerability was high near to colonies but was also high at some migration bottlenecks and wintering sites where, despite a reduced sensitivity, exposure to turbines was greatest.
- Synthesis and applications. Our framework combines bird‐borne telemetry and spatial data on the location of wind turbines to identify potential areas of conflict for migratory populations throughout their annual cycle. This approach can aid the wind farm planning process by: (a) providing sensitivity maps to inform wind farm placement, helping minimize impacts; (b) identifying areas of high vulnerability where mitigation warrants exploration; (c) highlighting potential cumulative impacts of developments over international boundaries and (d) informing the conservation status of species at protected sites. Our methods can identify pressures and linkages for populations using effect‐specific metrics that are transferable and could help resolve other human–wildlife conflicts.
Chris B. Thaxter
Viola H. Ross‐Smith
Nigel A. Clark
Greg J. Conway
Gary D. Clewley
Lee J. Barber
Niall H. K. Burton
British Trust for Ornithology, Norfolk
Computational Geo‐Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, The Netherlands
Elizabeth A. Masden
Environmental Research Institute, North Highland College, University of the Highlands and Islands, Thurso, U.K.
Journal of Applied Ecology 2019; 00: 1–13
First published: 09 September 2019