Resource Documents: Technology (134 items)
Documents presented here are not the product of nor are they necessarily endorsed by National Wind Watch. These resource documents are provided to assist anyone wishing to research the issue of industrial wind power and the impacts of its development. The information should be evaluated by each reader to come to their own conclusions about the many areas of debate.
Author: Olauson, Jon; Edström, Per; and Rydén, Jesper
[Abstract] We show that Swedish wind turbines constructed before 2007 lose 0.15 capacity factor percentage points per year, corresponding to a lifetime energy loss of 6%. A gradual increase of downtime accounts for around one third of the deterioration and worsened efficiency for the remaining. Although the performance loss in Sweden is considerably smaller than previously reported in the UK, it is statistically significant and calls for a revision of the industry practice for wind energy calculations. The study is based on two partly overlapping datasets, comprising 1,100 monthly and 1,300 hourly time series spanning 5–25 years each.
Jon Olauson, Division of Electricity, Department of Engineering Sciences, Uppsala University, Uppsala, Sweden
Per Edström, Sweco Energuide, Gothenburg, Sweden
Jesper Rydén, Department of Mathematics, Uppsala University, Uppsala, Sweden
Wind Energy 2017; 20(12):2049–2053. DOI: 10.1002/we.2132
Download original document: “Wind turbine performance decline in Sweden”
Author: Paatero, Jukka; and Lund, Peter
[abstract] Irregularities in power output are characteristic of intermittent energy, sources such as wind energy, affecting both the power quality and planning of the energy system. In this work the effects of energy storage to reduce wind power fluctuations are investigated. Integration of the energy storage with wind power is modelled using a filter approach in which a time constant corresponds to the energy storage capacity.The analyses show that already a relatively small energy storage capacity of 3 kWh (storage) per MW wind would reduce the short-term power fluctuations of an individual wind turbine by 10%. Smoothing out the power fluctuation of the wind turbine on a yearly level would necessitate large storage, e.g. a 10% reduction requires 2–3 MWh per MW wind.
Jukka V. Paatero and Peter D. Lund
Helsinki University of Technology, Advanced Energy Systems, Espoo, Finland
Wind Energy 2005; 8:421–441. DOI: 10.1002/we.151
Download original document: “Effect of Energy Storage on Variations in Wind Power”
Author: Barlas, Emre; et al.
The unsteady nature of wind turbine noise is a major reason for annoyance. The variation of far-field sound pressure levels is not only caused by the continuous change in wind turbine noise source levels but also by the unsteady flow field and the ground characteristics between the turbine and receiver. To take these phenomena into account, a consistent numerical technique that models the sound propagation from the source to receiver is developed. Large eddy simulation with an actuator line technique is employed for the flow modelling and the corresponding flow fields are used to simulate sound generation and propagation. The local blade relative velocity, angle of attack, and turbulence characteristics are input to the sound generation model. Time-dependent blade locations and the velocity between the noise source and receiver are considered within a quasi-3D propagation model. Long-range noise propagation of a 5 MW wind turbine is investigated. Sound pressure level time series evaluated at the source time are studied for varying wind speeds, surface roughness, and ground impedances within a 2000 m radius from the turbine.
Emre Barlas, Wen Zhong Shen, and Kaya O. Dag
— Department of Wind Energy, Technical University of Denmark, Kongens Lyngby, Denmark
Wei Jun Zhu – School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou, China
Patrick Moriarty – National Wind Technology Center, National Renewable Energy Laboratory, Boulder, Colorado, USA
The Journal of the Acoustical Society of America 2017 Nov;142(5):3297.
Download original document: “Consistent modelling of wind turbine noise propagation from source to receiver”
Author: Wind Energy Update
- O&M costs for wind power are double or triple the figures originally projected; they are particularly high in the U.S.
- There’s a −21% change in wind farm return on investment. This underperformance of wind assets is most likely attributable to both differences in power production and O&M costs over original estimates.
- $0.027/kWh, or €0.019/kWh, is the average values of O&M costs obtained from report surveys. This compares to early estimates by one of the world’s dominant turbine suppliers of $0.005/kWh.
- A significant amount of R&D is currently going into gearbox reliability. Many gearboxes, designed for a 20-year life, are failing after 6 to 8 years of operation.
- Data suggests that O&M challenges for wind turbines peaked in 2007/2008.
- At 2 cents/kWh, O&M costs are roughly equal to the federal production tax credit offered in the U.S. as a subsidy to make wind energy competitive.
- Engineers are still scratching their heads when it comes to gearboxes. Even though gearboxes are certified to operate for 20 years, none of them on today’s market lasts more than 8 years.
- 66% – the percentage of offshore O+M costs that are caused by unscheduled corrective maintenance
- 2-6 times higher – offshore wind turbines O+M costs compared with on-shore
- 10% – the loss in revenue due to the effect of spattered debris accumulation on the blade’s leading edge
- €100,000 to €300,000 per year – the costs of keeping offshore turbines online vs. an allocation of €45,000 per turbine for onshore wind
As part of our research into failure rates, costs and downtime on US Wind Farms, we have built a model which estimates lifetime costs of scheduled maintenance for a wind farm in the US. The input data used to build this data pack is a 210MW wind farm made up of 105 2MW turbines of 80 metres in height. The tables below show component risk factors, periodic maintenance costs, failure scenarios and supply chain factors [all costs USD]. CMS [complete monitoring system] options play an increasingly important role in both mean time to repair and the time between failures. As a result they have a large impact on costs. These are also taken into account. Finally the data pack provides major component lifetime O&M cost for the wind farm.
Scheduled maintenance cost—
Frequency per year: 2
Cost per action per turbine: $6,000
Reduced cost: $5,100
Lifetime cost per farm: $21,420,000
Component risk factors—
|Components||Replacement cost||Failure rate (%), failures per 100 parts by year 20||Total failures in 20 years (total farm)||Average downtime per failure, days||Average downtime losses per faiure||Total downtime losses for the rest of the||Labor cost per failure||Crane cost per failure|
Supply chain risk factors—
|Spare in stock / No spare||Distance to manufacturing facility (if no spare available)|
|Available / No spare||Lead time, days||Close / Medium / Remote||Time for transportation, days|
CMS factors (per turbine)—
|Capital Sensor Cost (including installation) per turbine||Annual cost (O&M) per turbine||Detectability||Efficiency|
|Monitoring type||Cost||Reduced cost (economies of scale)||Fixed cost||Reduced cost (economies of scale)|
|Lifetime maintenance cost for the farm||Lifetime maintenance cost assuming secondary damage||Lifetime maintenance and CMS operation cost for the farm||Monitoring type|
We have also looked at failure rates across different turbine technology types and designs. The graph below shows major component failure rates for all types of turbines in our dataset during the first ten years of operations. Different failure modes have different repair times, ultimately leading to different costs.