Layout Optimisation Brings Step Change in Wind Farm Yield Dr Andrej Horvat, Intelligent Fluid Solutions Dr Althea de Souza, dezineforce Come and visit.
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Layout Optimisation Brings Step Change in Wind Farm Yield
Dr Andrej Horvat, Intelligent Fluid SolutionsDr Althea de Souza, dezineforce
Come and visit us on Stand V16 Boyd Orr Hall
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Present work is a joint effort of
• David Hartwanger - Intelligent Fluid Solutions• Richard Harding - dezineforce• Althea de Souza1 - dezineforce• Andrej Horvat2 - Intelligent Fluid Solutions
Acknowledgment
2Dr. Andrej Horvat
Principal Engineer
Intelligent Fluid Solutions Ltd.
andrej.horvat@intelligentfluidsolutions.co.uk
www.intelligentfluidsolutions.co.uk
1Dr. Althea de Souza
Senior Design Engineer
dezineforce
althea.desouza@dezineforce.com
www.dezineforce.com
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Presentation Structure
• Motivation and problem definition• Wind turbine modelling methodologies• Wind farm simulation• Maximizing investment yield• Optimisation of the wind farm layout• Conclusions & further work
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Motivation and problem definition
• Significant demand for renewable sources of energy, where wind power is (one of) the largest contributors
• Power output from a wind farm depends on wind availability (intermittency in strength and wind direction), local topology and turbine quantity
• Installation of wind farms is capital intensive
• In such an environment, accurate prediction of wind farm power output is crucial for planning installation capacity and to maximise return on the investment
• Interaction between turbine wakes means turbine layout affects total power output
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Wind turbine modelling methodologies
• Detailed simulations (CFD) of entire wind farms are computationally demanding
• Individual turbines can be modelled using blade element theory to reduce overall computational requirements
• Effects of a rotating turbine on the flow are modelled with momentum sources/sinks
• Correct time-averaged representation of axial and tangential wake velocities
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Wind farm simulation
• Turbines are arranged in staggered (zig-zag) pattern to minimise wake influence
• Covering fixed surface area of 2 x 3 km in streamwise (x) and spanwise (y) direction
• Steady-state simulations were performed for different number of wind turbines in x and y direction
wind direction
21
34
56
87
9ny
12
34
56
7nx
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Maximising Investment Yield
• Reduction of flow velocity in the wake reduces the power output of each subsequent row of turbines
• Which wind farm arrangement provides maximum power generation for a given investment?
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Maximising Investment Yield
• To find maximum power output for given investment costs
– Computational Fluid Dynamics (CFD) calculations of the different wind farm arrangements were performed and total power calculated as a sum of power output from each turbine
– the investment costs were divided into fixed costs (construction, grid connection, development etc.) and variable costs (proportional to the number of installed turbines)
In this case study - 2MW Vestas V80 turbine was used as a representative example of a modern commercial turbine
- single wind velocity of 15 m/s and single rotation speed of 16.7 rpm were considered
- 28 mil EUR of fixed investment costs and 2.5 mil EUR per unit were assumed
- The costs were estimated based on review studies prepared by Dept. of Trade and Industry
(2001) and KEMA Nederland (2007)
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Optimisation of the Wind Farm Layout• A CFD based modelling methodology
was developed to predict wind farm power output for a given investment
• For a set area and staggered layout, the number of turbines in each row (ny) and the number of rows (nx) were varied
• Advanced design search and optimisation techniques were used to search for an optimal wind farm configuration
• This approach cost effectively assessed the range of design options available
• Additional variables can be considered, e.g. wind speed, direction, geographical site etc.
ny
nx
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Optimisation of the Wind Farm LayoutBased on a statistically significant but relatively small number of simulations (~30) the entire design space (~120 designs) is characterised
13 in first row, 5 rows = 63 turbinesPower/Cost metric = 0.55 W/€
9 in first row, 8 rows = 68 turbinesPower/Cost metric = ~0.31 W/€
13 in first row, 8 rows = 100 turbinesPower/Cost metric = 0.39 W/€
13 in first row, 2 rows = 25 turbinesPower/Cost metric = ~0.44 W/€
11 in first row, 6 rows = 63 turbinesPower/Cost metric = ~0.51 W/€
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Conclusions & Further Work
• Blade element model was implemented in a commercial CFD package to simulate operation of wind turbines in a wind farm environment
• Different wind farm layouts were simulated to calculate power output of the wind farm
• The analysis shows that the same number of turbines in different layouts can result in significantly different yield
• With alternate offset rows, wide, shallow wind farms are most profitable
• The use of computational simulation methods and advanced optimisation tools can result in significant performance improvements
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Conclusions & Further Work
Further work
• Different wind angles and speeds for full wind rose
• Alternative staggering
• Assessment of specific geographical topologies
• Allowance for local geographical features
• More complex investment models
...technologies have been developed over more than 10 years by world-renowned professors from the University of Southampton
...provides a unique, end-to-end service integrating computational engineering and optimisation tools, on demand over the web
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