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

2

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

3

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

4

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

5

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

6

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

7

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?

8

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)

9

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

10

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/€

11

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

12

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

Questions ?

Stand V16 Boyd Orr Hall