Wind measurements and data analysis. EG2340 Wind Power Systems

Wind measurements and data analysis EG2340 Wind Power Systems

Agenda Why do we need wind measurements? Why are accurate wind measurements so important? Importance of long-term wind measurements Wind measurements Data analysis Wind farms, wake effect and siting Software and Example

Why do we need wind measurements?

Cost distribution over the project s lifetime Source: EWEA, The Economics of Wind Energy, 2009

Compared to conventional energy sources Cost distribution over the project s lifetime Larger costs upfront Source: EWEA, The Economics of Wind Energy, 2009

Compared to conventional energy sources Cost distribution over the project s lifetime Larger costs upfront Smaller O&M costs (zero fuel costs) Source: EWEA, The Economics of Wind Energy, 2009

Compared to conventional energy sources Cost distribution over the project s lifetime Larger costs upfront Smaller O&M costs (zero fuel costs) Source: EWEA, The Economics of Wind Energy, 2009 Wind Power Planners: minimize the cost of energy

Cost distribution over the project s lifetime Feasibility studies very important! Source: EWEA, The Economics of Wind Energy, 2009 Wind Power Planners: minimize the cost of energy

Feasibility studies: assess the economical feasibility Wind measurements crucial for making accurate feasibility studies Source: EWEA, The Economics of Wind Energy, 2009

Why are accurate wind measurements so important?

Power in the wind Power in the wind Wind speed U Assumed constant E kin 1 = mu 2 2 2 dekin 1 d( mu ) 1 dm P= = = dt 2 dt 2 dt U 2

Power in the wind Power in the wind Wind speed U Assumed constant A Udt Swept area Mass of air going through the turbine per dt : dm m = ρ Volume = ρ A U dt = ρau dt

Power in the wind Power in the wind Wind speed U Assumed constant 1 2 Ekin = mu 2 dekin d mu dm P = = = U = ρ AU dt 2 dt 2 dt 2 2 1 ( ) 1 2 1 3

Power in the wind P = 1 ρ AU 2 3 Standard condition: density = 1.225 kg/m 3 A: Swept area = πr 2, R: radius of the blades U: Wind speed Power proportional to the cube of the wind speed: 10% error on the wind speed => 33% error on the power available in the wind

Generated power Betz limit: Max extracted power: 16/27 (=0.59) of the power in wind Pmax 1 16 2 27 ρ AU In general: less than that (additional aerodynamical losses, losses in the drive train) - C p (U): power coefficient = P rotor /P wind <16/27 - η: Drive train efficiency= P gen /P rotor - ηc p (U): overall efficiency = P gen /P wind Cannot be higher than the rated power = 1 Pgen = ηc p ( U ) ρau 2 3 3

Extracted power R=30m

Power curve Generated power 6 Power (MW) 5 4 3 About proportional to the cube of the wind speed 2 1 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Wind Speeds

Power curve The power curve is measured under standard conditions. Ex: air density in standard conditions: 1.225 kg/m 3 Air density varies with - Altitude - Temperature - => assignment 1 Other local factors important: orography, turbulence, Source: Eric Hau, Wind turbines. Fundamentals, Technologies, Application, Economics, Chapter 14, 2013

Importance of long-term measurements

Wind variations

Wind variations The shorter the time horizon, the larger the variations in average, because there is an averaging effect when considering large time horizons. Important to get long-term data.

Wind measurements

Do we need measurements? Existing measurements from - Meteorological data close to the site: must be processed with care: Assumptions when the data was collected? Height? Roughness length, obstacles, contour of the local terrain: must be taken into account when processing the data. => necessity of making calculations which compensate for the local conditions under which the meteorological measurements were made. - Data from existing turbines close to site: excellent guide of the local wind conditions

Where to measure? Which site to choose? Wind atlas and maps: - provide estimates of wind energy resource - Indicate general areas where high wind resource might exist - Allow wind energy developers to choose a general area for more detailed examination Source: Hans Bergström, Wind resource mapping of Sweden using the MIUU method, 2007

Wind atlas and maps Source: European Wind Atlas, http://www.windatlas.dk/europe/index.htm

Instruments Wind speed - Anemometers: they rotate with the wind and, hence, can give a measure of the wind speed at a given height. Problem with ice/dust that can lodge in the bearing. - LIDAR/SODAR: use the Doppler effect to measure wind speeds: Need not be put at a given height. No problem with ice or dust. But more costly and less reliable. Wind direction: wind vane. If possible: 10 minute averages for several years

Data analysis

What to do with the measurements? Height (m) 80 60 40 20 Wind shear 0 0 2 4 6 Wind speed (m/s) Analyze the data to identify measurement errors (usually strange values such as -999). Scale the measurements - Remember what the wind shear looks like (wind profile with height) - What if the measurements are made at 20m and the hub height will be at 70m? Wind speed at 20m Wind speed at 70m

Scaling The wind shear depends on the roughness length, z 0, of the terrain Examples - Lawn, water: z 0 = 0.01 m - Bushland: z 0 = 0.1 m - Towns, forests: z 0 = 1m z 0 is the height above ground at which the wind speed is zero, due to friction with the terrain

Scaling logarithmic profile Has its origins in boundary layer flow in fluid mechanics and atmospheric research U ln( z/ z0) = U ln( z / z ) ref ref 0 z ref : altitude at which we know the wind speed U ref : wind speed at z ref z: altitude at which we want to calculate the wind speed U: wind speed at z (to be calculated)

Logarithmic profile Height (m) 80 60 40 20 Wind shear z0 = 1 m z0 = 0.1m 0 0 2 4 6 Wind speed (m/s)

Scaling Power law The power law is used by many wind energy researchers. U U ref z = z ref α: power law coefficient. Two ways to calculate it - From the reference values - From the roughness length α 0.37 0.088ln( U ref ) α = 1 0.088ln /10 ( zref ) α = 0.24 + 0.096 log z + 0.016(log z ) 10 0 10 0 2

Power law Height (m) 80 60 40 20 Wind shear z0 = 1m z0 = 0.1m 0 2 3 4 5 6 7 Wind speed (m/s)

Comparison Height (m) 80 60 40 20 Wind shear Power law Logarithmic law 0 0 2 4 6 Wind speed (m/s)

After scaling Draw the wind distribution: - Gather the wind speed measurements in classes ( 0-1 m/s,, 24-25 m/s, ) - Draw an histogram showing the frequency of occurrence of each class versus the wind speeds Distribution = probability of occurrence of each wind speed f( U)?

Wind speed distributions From time series to frequency distributions: Source: Robert Gasch and Jochen Twele, Wind Power Plants. Fundamentals, Design, Construction and Operation. Chapter 4, 2012.

Wind speed distributions Different sites have different characteristics. You would not choose the same turbines for different sites.

Wind speed distributions Something is wrong!

Wind speed distributions Larger wind speeds in Visby Distribution shifted to the right (compared to Bromma/Malmö) => Need a turbine that withstands these wind speeds

Wind speed distributions Bromma/Malmö: Smaller wind speeds Distribution shifted to the left => Need a turbine that can optimally extract power from the wind at these low wind speeds.

Limited information about the site Probability 0.25 0.2 0.15 0.1 Mean wind speed Suppose that you only have access to the mean wind speed at the site, and possibly to the standard deviation. 0.05 0-5 0 5 10 15 Wind speed (m/s) How can you predict the wind speed distribution from this limited information?

Rayleigh distribution π U π U f( U) = exp 2 2 U mean 4 U mean 2

Weibull distribution k 1 k U U f( U) = exp c c c k k>0: shape parameter c>0: scale parameter k=2 => Rayleigh distribution

Weibull distribution

Weibull distribution Calculating k and A from the mean wind speed and the standard deviation k σ U = U mean 1.086 c U mean = Γ (1 + 1 / k) The Gamma function is included in Matlab and Excel.

Comparison measurements vs. Rayleigh and Weibull

Power curve vs. Wind distribution p Extracted power Data (Bromma airport) Weibull Rayleigh Distribution = f(u) Power Curve = P(U) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Energy yield How to assess how much energy a wind turbine will produce each year? From the wind speed distribution, f(u), and the power curve P(U), we can calculate the expected power production. U = cut out Pmean = E P prod = P( U ) f ( U ) du wind class U = 0 PU ( ) hu ( ) h(u): probability of occurence of wind class U

Energy yield E E prod = Pme an time availability =µ T P( U ) h( U ) Units: - μ: availability < 1 - h(u): probability < 1 - P(U): W / kw / MW (power) wind class - T: usually hours (8760 hours per year for example) - E[E prod ] = expected energy yield under T: Wh, kwh, MWh (energy) Example: expected yearly energy yield, assume availability of 98% E Eprod = 0.98 8760 PU ( ) hu ( ) wind class

Availability Accounts for stops due to maintenance, failure, Onshore: Usually around 98-99%. 98% = 7.3 days per year when the turbine is unavailable. But can have a high impact on the energy yield if these are windy days. Offshore: 93-95%. Larger than conventional sources: - Nuclear/coal: 70 90 % - Gas turbines: 80-99% But wind turbines do not produce at rated power. See capacity factor.

Energy yield Source: Robert Gasch and Jochen Twele, Wind Power Plants. Fundamentals, Design, Construction and Operation. Chapter 4, 2012.

Energy yield - Bromma

6 Generated power Capacity factor Power (MW) 5 4 3 2 1 Wind turbines do not always produce at rated power. Capacity factor: CF = Actual Energy Production Hypothetical Energy Production at Rated Power E E = P rated Higher wind speeds => higher capacity factor Can be used to compare different sites. prod T 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 Wind Speeds Usually between 0.15 and 0.3

Capacity factor Bigger is not always better Ex: smaller rated power - => larger capacity factor but smaller energy production Criteria often used to assess how good a project is: - costs of produced energy: $/MWh, /MWh,

Capacity credit Measure the contribution of wind power to system security. Two ways of calculating it - How much conventional capacity can we replace while maintaining the same level of system security? - How much load can we add while maintaining the same level of system security?

Different coefficients, different definitions Be careful about the following notions; they are different (click on the words to go to the slide of interest): - Availability - Power coefficient - Drive train efficiency - Capacity factor - Capacity credit

Agenda Why do we need wind measurements? Why are accurate wind measurements so important? Importance of long-term wind measurements Wind measurements Data analysis Wind farm, wake effect and siting Software and Example

Wind farms, wake effect and siting

Wind direction Wind rose: plot with information sorted by wind direction. Information can be - Frequency of winds ->prevailing wind direction - Frequency*cube of wind speeds ->prevailing direction from which most wind power comes

Wind rose Can be used to design layouts of wind farms: make most of the wind turbines face the prevailing direction (wake effect) Prevailing wind direction

Other issues than good wind conditions Looking for good locations: no obstacles; make use of speed-up effect (ex: hill effect) Space: - Wake effect: Wind turbines reduce wind speeds as they extract energy from the wind => shading effect for the turbines standing behind => Put the turbines as far apart as possible - Grid connection: the longer the distance between the turbines, the higher the cost => compromise between grid connection costs and wake effect. - Rule of thumb: 5 to 9 diameters apart in the prevailing direction, and 3 to 5 diameters apart in the directioni perpendicular to wind direction

Other issues than good wind conditions Soil conditions - Must be able to build foundations. - Accessibility of the construction site (shipment of material with heavy trucks). Electrical grid - Connection point to the transmission grid as close as possible. - Strong grid; otherwise, may need reinforcement. Distance to neighbouring houses

Energy yield of a wind farm Wake effect: Behind a turbine the air flow is affected => other turbines standing behind other ones do not produce as much as the first one. Different wake effects for different terrains (different roughness length). Different onshore and offshore.

Wake effect Source: Robert Gasch and Jochen Twele, Wind Power Plants. Fundamentals, Design, Construction and Operation. Chapter 4, 2012.

Wake effect Many different ways of handling this effect: - Simple coefficient: E E po rd = Nµ wake µ T PU ( ) hu ( ) wind class - Energy per sector, different wake effect per sectors, sum up over the sectors - CFD: Computational fluid dynamics -

Wind power in Sweden 1 October 2013: 3970 MW installed wind power capacity Peak Load = 27 000 MW Others: - Hydropower: 16 000 MW - Nuclear power: 9 360 MW - From fossil fuel: 4 666 MW - Biomass: 3 000 MW Source: https://www.entsoe.eu/publications/statistics/yearly-statistics-andadequacy-retrospect/pages/default.aspx 1004 MW 1409 MW 330 MW SE 2 SE 3 SE 1 Map source: Skellefteå Kraft s web page 1227 MW SE 4

Wind power variations SE 1 Year 2011 SE SE 2 SE 3 SE 4

Agenda Why do we need wind measurements? Why are accurate wind measurements so important? Importance of long-term wind measurements Wind measurements Data analysis Wind farms, wake effect, siting. Software and example

Software and Example

Software RETScreen WAsP WindPro WindFarmer Homer

Example where to start? Where would you install a wind farm in Sweden?

Example where to start? Where would you install a wind farm in Sweden? Let s choose Malmö We now need to analyze the wind conditions there.

Example local wind conditions In Sweden, SMHI publishes wind measurements from its weather stations -> www.smhi.se

Example getting the data Download the data http://opendata-download-metobs.smhi.se/explore/ Import it to analyze it (to Matlab, Excel, ) Identify the wind speeds and directions in the data Remove bad values (negative values, usually -999 indicates bad measurements) Scale the data Plot the distributions and wind rose.

Example distribution

Example - scaling z U( z) = U( zr ) zr α Distribution shifted to the right because wind speed increases with altitude.

Example comparison: wind speed

Example comparison: power 2.5 3 x 104 Power distribution (Watt) Data (Bromma airport) Weibull Rayleigh 2 Power (Watt) 1.5 1 0.5 0 0 5 10 15 20 25 Wind speed (m/s)

Example comparison: yearly energy yield

Getting the electricity price

The electricity price varies!

Example: Yearly revenue