Best Practice Guidelines for Mesoscale Wind Mapping Projects for the World Bank

Transcription

1 Best Practice Guidelines for Mesoscale Wind Mapping Projects for the World Bank October 2010 ESMAP The World Bank s Energy Sector Management Assistance Program

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3 Table of Contents Introduction Mesoscale Wind Modeling Mesoscale Wind Modeling: Potential Use Mesoscale Wind Modeling: Basic Principles Quality Issues in Mesoscale Wind Modeling Climatology Description Spatial Resolution Data Sources, Sampling Techniques Model Specification Verification Using Ground-Based Wind Measurements Adapting Modeling Results to Ground-Based Measurements Limitations of Mesoscale Wind Mapping Coarseness of Scale Model Imperfections Mesoscale Model Output Usability Issues Choice of Heights Above Ground Level Coloring Schemes for Maps Avoid Color Smoothing Between Cells Use Stepwise Color Coding or Overlaid Contour Maps Traditional Maps of Mean Wind Speeds and Power Density Alternative»Wind Atlas«Maps of Mean Wind Speeds and Power Density Weibull Distribution Maps Special Calculation of Weibull Parameters Wind Direction (Wind Rose) Map Elevation Map (Digital Elevation Model) Surface Roughness Length Map Terrain Complexity (Surface Inclination) Map Estimated Annual Wind Energy Production Maps Using Mesoscale Maps in Practice: Why all Maps are Important Printed Maps vs. Computer Searchable Maps, Formats, Layers Computer Display Software for Mesoscale Maps Mesoscale Integration with Microscale Wind Models Data Bank of Simulation Results User Training Dissemination Plan Copyright Issues Appendix 1 Draft Terms of Reference for Mesoscale Wind Mapping

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5 Introduction The purpose of this paper is to provide a draft set of best practice guidelines for the procurement of mesoscale wind maps in the World Bank Group (WBG). The primary audience is task team leaders (TTLs), who are relatively unfamiliar with the details of wind energy technology and wind resource assessment. The paper explains the basics of the method, its use and its limitations, and provides a set of guidelines to assess the quality and in particular the usability of products supplied by suppliers of mesoscale modeling work. Recommendations for use in terms of reference for consultants (ToRs) are included in bold italics in the text. 1 The work is the result of a number of internal reviews, interviews and cross-support work within the World Bank Group on mesoscale wind modeling from 2006 to 2009 supported by ESMAP, the Energy Sector Management Assistance Program operated by the World Bank. In addition, the report incorporates comments from discussions with a number of academic researchers, modelers, bankers, task teams in the World Bank Group and practical users of mesoscale maps over a number of years. 2 1 Readers who are not familiar with wind energy technology may wish to consult a text on the basics of wind energy such as to which references are included in the footnotes. 2 The author wishes to give special thanks to the peer reviewers, Lars Landberg from Garrad Hassan Limited, Colin Murray from Mainstream Renewable Power, Rolf Gebhard form KfW, Jake Badger from Risoe National Laboratory/DTU. Also thanks to Jens Carsten Hansen and Niels Gylling Mortensen from Risoe National Laboratory/DTU, Susan Bogach from the World Bank, Julio Patiño and Dana Younger from the International Finance Corporation for valuable cooperation on previous mesoscale work. 5

6 1 Mesoscale Wind Modeling 1.1 Mesoscale Wind Modeling: Potential Use Most developing countries have very poor wind data. The sole source available is often meteorology stations and wind measurements at airports, but the precision of classical meteorology measurements are inadequate for deploying wind energy. 3 Existing wind speed measurements of poor quality may therefore be a poor guide to where to look for the best wind resources. Actually one may be better off not relying on any ground-based wind measurement in the area at all, and instead examine how the local landscape characteristics combined with global meteorology data would suggest where to look for windy sites. This is where modern mesoscale wind mapping comes into the picture, since it is based on data from earth observation satellites, historical reanalysis data, and global meteorology models. Mesoscale wind modeling is increasingly used to obtain a preliminary, crude mapping of likely locations for commercially exploitable wind resources in a country or a region without using ground based measurement data for anything but verification purposes. This mapping can subsequently be used for finding suitable areas for further exploration for wind resources doing wind measurements 4 using local anemometer masts to provide data for microscale wind resource models. Mesoscale wind models generate huge simulated datasets (often many terabytes) of hourly wind speeds and wind directions for each grid point and at multiple heights above ground level in a 3-dimensional geographical grid covering a whole country or a region. If of adequate quality, such simulated datasets offer a myriad of possibilities of data extraction and can provide useful analyses with a view to both planning for wind farms as well as e.g. doing estimates of economically exploitable wind resources in a country, when properly combined with other geographically referenced data (GIS-data) for e.g. transmission grids and roads. The most typical examples of useful modeling results are discussed in chapter 4 of this paper. This report explains key requirements in ToRs for mesoscale wind modeling, but by providing a mostly generic set of ToRs it cannot cover all possible uses of such modeling work. It is recommended that project task teams in the WBG use independent (nonsupplier) expertise with knowledge of practical issues related to mesoscale mapping 3 If an anemometer in one location has a higher mean wind speed reading than another in another location at the same height above ground level, it may simply reflect lack of calibration as well as the varying degrees of sheltering (wind shade) of the surrounding landscape or local wind obstacles - or surface roughness differences or roughness changes. 4 Wind speeds measurement is explained in more detail by the author on the web pages and 6

7 when drafting ToRs and assessing bids for mesoscale modeling work. This may in particular allow for a better definition of the deliverables, including useful by-products or analyses, which are usually much easier and cheaper to obtain, if they are included in the original ToRs. 1.2 Mesoscale Wind Modeling: Basic Principles Mesoscale wind modeling methods consist of building a model of the surface of a country or region plus adjacent areas at a fairly crude level of 2-10 km cells. For each cell data describing both terrain elevation 5 and local surface roughness 6 is used for the model. This model is then subjected to simulated typical wind patterns throughout the depth of the troposphere such as they have historically occurred over a long period of years. The wind speeds at lower heights above ground level ( m) usable for wind turbines are calculated for a typical year using a so-called atmospheric boundary layer model, i.e. a simplified mathematical description of airflows near the surface of the earth. The flowchart in figure 1 summarizes this process. Figure 1. Principles of Mesoscale Wind Modeling using the KAMM model 7 In order to build the surface model, digital terrain data (i.e. elevation data) are obtained from satellite (remote sensing e.g. SRTM) data, which are converted into a description of the geometry of each surface cell. Likewise land-use data, which enables the modeler to distinguish between land cover such as water, fields, forests, and cityscapes can be ob- 5 Elevation is the height above sea level, not to be confused with height above ground level. 6 Roughness is an indication of how much dispersed objects in the landscape such as weeds, bushes, trees, rocks and buildings will brake the wind near ground level. With low roughness (water surfaces, smooth desert surfaces) wind speeds vary relatively little between say 20 and 100 m height above ground level (low wind shear), whereas with high roughness (forests, cities, rugged terrain) wind speeds vary considerably with height (high wind shear). Generally modelers assume that the wind speed varies logarithmically with the height above ground level. Roughness is normally given as a roughness length or a roughness class. These concepts are explained in more detail on the web pages and 7 Adapted from Helmut Frank, Risoe National Laboratory (2006), 7

8 tained from other satellite remote sensing data. Land-use data is subsequently converted into a surface roughness for each of the surface cells. Typical wind patterns to be used in the model simulations are usually obtained by sampling hourly data from so-called reanalysis meteorology data, usually from the NCEP/NCAR a global database system maintained by an agency of the U.S. National Oceanic and Atmospheric Administration (NOAA). Performing atmospheric boundary layer modeling calculations is a very computationally intensive task, which typically requires some type of supercomputer (or a distributed network of individual processors) to be doable within a reasonable time span (often a number of days or weeks). The modeling software used by most of the suppliers doing mesoscale modeling is in the public domain, established by a number major international research institutions dealing with atmospheric physics. 8 The data to be used is available at relatively moderate cost. This, plus the fact that many suppliers use modeling software available in the public domain explains why the method has become quite popular in recent years. 8 Examples of such models are the Mesoscale Compressible Community (MC2) model, WRF (NCAR), and the Karlsruhe Atmospheric Mesoscale Model (KAMM). 3TIER uses the WRF model while AWS Truewind uses the proprietary MASS model. 8

9 2 Quality Issues in Mesoscale Wind Modeling 2.1 Climatology Description Many global and local meteorological phenomena drive wind climates around the world, e.g. sea and land breezes, mountain winds etc. Atmospheric stability conditions vary between different types of climates, landscapes and seascapes. Some physical phenomena are well represented by mesoscale climate models; others may be inadequately represented. The supplier of mesoscale wind maps should in its report give a general climatology description of the area being analyzed indicating which aspects of the local climate are expected to be well represented by the model, and which may be more uncertain. This description should also alert users to the types of areas, where the prediction from the model may be most uncertain. 2.2 Spatial Resolution Mesoscale climate models are built with horizontal cells of a given size, typically with some 15, 10, 7.5, 5 or 2 km resolution. 9 To some extent, the higher the resolution, the better the representation of the topographical landscape, provided the sampling and modeling techniques are of sufficiently high quality. Some suppliers may use a type of nesting algorithm, where they use different techniques at low resolution (say, 5 km) and a different technique at higher resolution. As mentioned previously, high resolution models are invariably much more computationally intensive in the modeling phase, hence they tend to be more expensive to generate. Some wind mesoscale model suppliers claim that their models may have down to, say 200 m resolution, but there is need to caution the customer about this claim. Computational work in a mesoscale model would normally be practically infeasible at such a high spatial resolution (and can strictly speaking no longer be called mesoscale); hence very high resolutions are usually obtained by some sort of post-processing algorithm. Almost without fail this means that some sort of simple smoothing or interpolation algorithm has been used mechanically. Such smoothing techniques must be discouraged, since they give a false sense of the ability to interpolate data from the map. This is explained in more detail in section 4. The supplier must specify the horizontal spatial resolution of the mesoscale model used for the simulations. If the procedure is multi-staged or involves some sort of postprocessing this should be explained in detail. 9 Since the globe is a sphere the cells are not necessarily quite squares, but based on longitude and latitude borders. KAMM uses a rectangular grid in UTM coordinates. Other layouts, e.g. hexagonal are possible, but uncommon. 9

10 Depending on the size of the surface area, types of landscapes and climates of the region being modeled, it may economically be more efficient to do a fairly coarse-scaled modeling of the entire area and subsequently do a higher resolution modeling of the most interesting areas. This is in a sense what was done in Canadian Wind Energy Atlas 10, where a national project did a complete mesoscale modeling of Canada at a resolution of 5 km, and subsequently a number of provinces contracted higher resolution mapping of the areas in proximity to the transmission grid. 11 Developers often use a similar approach, ordering detailed mesoscale maps for particularly interesting areas. 2.3 Data Sources, Sampling Techniques The selection of data for mesoscale models and its conversion to subsequent modeling use is far from straightforward. Different suppliers use different techniques 12, and only a few offer a reasonably detailed description of their method. This lack of transparency, an unfortunately quite prevalent absence of academic peer reviews, plus lack of consensus among modelers about which methods are the most reliable leave the buyers in an uncertain position. Buyers thus have little choice but to go by references from clients and the vendor's track record in general, in order to assess the apparent quality of their work. The supplier should specify previously published or unpublished mesoscale wind modeling projects and identify the clients for these models. If possible, the supplier should enclose examples of previous work in printed or electronic form and give access to web/computer interfaces to previously published maps. The suppliers should specify in detail all the data sources on which the modeling is based and the sampling technique used for each type of data. For climatology data the time horizon used for sampling should also be specified. 2.4 Model Specification Many wind mesoscale model suppliers use standard climatology models, which are in the public domain, and can be downloaded from the web. These models may yield excellent results, but require advanced skills to work properly. Certain suppliers uses a secret»proprietary algorithm«which is not explained at some level of depth. This is clearly an unsatisfactory situation for the client, hence in general terms transparency about methodology is preferable. The supplier should explain in some detail the mesoscale climatology model used for the calculations, explain whether it is in the public domain, peer reviewed or not. In particular, nonstandard or non-mainstream models should be explained in detail, and 10 The Canadian Wind Energy Atlas is in many ways a good prototype for the required functionality for a mesoscale wind model presentation, generously providing multiple layers of useful data for an extremely large area. In practical use the interface and its content is still second to none of the maps available online, despite a very unpretentious interface. See 11 See for an unfortunately not quite updated overview. 12 Note that different vendors use difference approaches to Reanalysis data. Some use ground measurements as well as reanalysis while others use only upper atmosphere measurements in their model. This is difficult to check and even more difficult to understand unless you have an in-depth understanding of mesoscale modeling. 10

11 it should be explained how they differ from mainstream models. A useful model for the level of detail required in the methodology description may be found in the Canadian wind Energy Atlas, A note of caution is worthwhile at this point: Some bidders in tenders for mesoscale wind modeling work occasionally offer models based on ground-based anemometer mast data, e.g. data from meteorology stations and from airports, which is subsequently generalized to larger areas - sometimes using different types of interpolation methods. This is not always apparent at first sight for the untrained eye, hence the need for suppliers to specify their data sources mentioned above in the previous section. Even if such methods at first sight may look like a classical wind atlas method, which can be done with reasonably high quality, such as is the case in the now 20 year old European Wind Atlas, 13 it should be underlined that this type of modeling does not qualify as mesoscale wind mapping. 14 There are some frightening examples of nonsense wind mapping analysis in the public domain, e.g. where the authors have literally taken hundreds of time series of anemometer readings, recalibrated them to the same height above ground level (assuming a uniform surface roughness of the landscape) and then interpolating between anemometers. 15 This type of mapping may be worse than useless. 2.5 Verification Using Ground-Based Wind Measurements Mesoscale wind model simulations often differ systematically from actual precision ground-based wind measurement. As previously explained there are good reasons for such differences, in particular the fact that mesoscale climatology models are only simplified, coarse representations of real world weather phenomena. Verification of modeling results with existing ground-based precision wind measurements from meteorology masts is an essential component of any mesoscale-modeling contract. Otherwise the user has little way of knowing the order of magnitude of the uncertainties of the modeling. In many developing countries, however, the amount of high 13 European Wind Atlas. Eds. Ib Troen and Erik Lundtang Petersen. Publ. for the Commission of the European Communities. Directorate-General for Science, Research and development. Roskilde For this type of analysis to be usable, the location and data for each and every anemometer mast has to be analyzed thoroughly, which requires on-site inspection, to verify e.g. that the anemometer has not been moved, and in particular the roughness rose has to be estimated for each anemometer location. In addition, historical anemometer data done or weather forecasting purposes are generally extremely inaccurate compared to what is required in the wind industry. In other words, only if long-term measurements were done with calibrated scientific instruments and data is properly recalibrated using roughness assessments and microscale wind modeling software such as WAsP, are the analyses credible. Such a methodology can in fact be used to construct a wind atlas map (discussed later, below), but the central problem in this type of analysis, is how to generalize results to areas where high-precision anemometer readings are not available. 15 An example may be found here: The author suspects other maps of this type can be found, but has not had time to review some other questionable wind atlases. 11

12 quality wind data is very scarce or nonexistent; hence second-best solutions (such as using recent meteorology station data) may have to be used. The task team leader for a mesoscale project will need to prepare for this work by checking with national or regional meteorological institutes, wind energy research organizations, universities etc. for the availability of high quality wind data, or will need to specify how the supplier will get access to the relevant sources. It is essential that the supplier's offer include a program for verification of the mesoscale wind modeling based on high-quality measurements already done in the region being modeled. The proposal should include procurement and analysis of such data for [specify number of, e.g. 20] typical, different scattered locations in the area, preferably locations representative of zones with different (but relevant) wind climates and areas differing modeling precision. [Specify how data will be made available and/or acquired by the supplier]. The validation report should be an integral part of the final report. As a minimum, the validation report should contain the following: 1. Geographical names of all the meteorology stations used for the purpose of this validation with footnotes indicating the source institution; 2. Exact geographic coordinates for the mast location; 3. Exact geographic coordinates for the corresponding grid center points 4. The sample period used and recovery rate of data (please comment on seasonal bias in sample, if any); 5. The heights above ground level at which measurements were taken; 6. Surface roughness estimate from actual wind mast (roughness rose), if available. At least effective roughness for each sector is required, since the wind profile is determined by upwind roughness and roughness change; 7. Model-based surface roughness for cell; 8. Measurement-based mean annual wind speed;* 9. Model-based mean annual wind speed;* 10. Percentage error (with sign) for mean annual wind speed;* 11. Measurement-based Weibull parameters, plus the corresponding wind rose;* 12. Model-based Weibull parameters, plus the corresponding wind rose;* 13. Measurement-based diurnal and seasonal wind pattern. 14. Model-based diurnal and seasonal wind pattern. 15. A detailed description of the verification methodology. * = Provide measurements from each height for wind measurements. A table should be included with the supplier's interpretation of the data for the entire sample, to include: Mean wind speed bias in %; Mean absolute error in %; RMS (root mean squared) error in %. 12

13 The supplier should analyze and explain deviations between model results and measurements, and in particular indicate areas or aspects, which require special attention (systematic biases) Adapting Modeling Results to Ground-Based Measurements Some suppliers of mesoscale maps state that they use a combination of reanalysis meteorology data (explained in section 1.2) and ground-based precision wind measurements (from anemometer masts) for their modeling work. This will usually mean that they first do a mesoscale modeling simulation and then afterwards calibrate their results to groundbased wind measurements, presumably after recalibration for height above ground level and effective surface roughness. In other words: Imagine simulated average wind speeds across an x-y coordinate map pictured as the z-coordinate height over the x-y plane. These points would then form some sort of a flexible, semi-rigid mountain surface, which is then scaled up or down to fit with actual observations. There seems to be two schools of thought on the acceptability of this method in the two communities of scientists and commercial developers respectively. 1. The scientific point of view: Such a technique is unacceptable, since it leaves the user completely in the dark as to the level of precision or uncertainty of the wind map. If all available high-quality ground based measurements are used for calibration of the map, then there is no way of verifying the soundness of the underlying method for intermediate points (i.e. non-measured points). 2. The commercial developer point of view: This is a practical way of combining precision data (if available) with modeling results in order to easily extrapolate measurements to unmeasured areas. In any case, mesoscale modeling is only a tool with limited precision, and is only used to get suggestions for sites to visit and possibly to start measurement programs. If the supplier of mesoscale maps uses ground-based wind measurements to recalibrate model-simulated results, there may be no way of validating the quality of the underlying mesoscale method and obtain estimates of the precision or biases in the method. It is desirable that the method used by the mesoscale model supplier allows for subsequent verification. In any case this is taken into account when comparing bids based on different methods. 16 A distribution of the errors is useful to help understand the spread. This allows the user to understand and estimate risk. 13

14 3 Limitations of Mesoscale Wind Mapping It is a fairly common misconception that mesoscale wind mapping can be used directly for siting wind farms. This is however not the case. The precision of wind resource data from mesoscale wind modeling is far too uncertain for this purpose. In addition, mesoscale models do not provide additional climate parameters such as turbulence intensity (important for the turbine design to be used on each site), and data for the micrositing of individual wind turbines (the variation of wind flow within the wind farm due to terrain features and wind obstacles). This latter type of analysis requires wind measurement on the site and analysis in microscale wind models. 3.1 Coarseness of Scale First of all, as the name of the method implies, mesoscale wind mapping is done at a coarse scale. The basic reason for the relatively large cells of 2-10 km used in the mesoscale models is that the number of calculations grows exponentially with the map resolution. Using large cells means that terrain features such as smaller hills and valleys are»averaged out«in the description of the orography and surface roughness of each cell. But particular terrain features may be very significant for whether the actual wind speed in a specific point in the landscape is higher or lower than predicted by a mesoscale map. In fact, even if mesoscale maps were perfect representations of the average wind climate in each cell, the energy content of the wind may easily be half or double of what is predicted as an average for a cell, due to local features such as speed-up effects on hilltops, lower winds in valleys etc., which will have to be modeled in a microscale wind model (e.g. WAsP) in order to be properly accounted for. If the model has high resolution e.g. 200 m these features may be visible in the results. 3.2 Model Imperfections Secondly, the mesoscale wind models are at best simplified descriptions of the real world. In practice models may exhibit quite significant errors and biases, which is why it is as important as the modeling itself to have a well-documented verification scheme in order to be able to assess the probable accuracy of each mesoscale map. Even when this type of numerical flow model is used to predict wind flows at the microscale level using very detailed digital maps and local surface roughness data (i.e. within the area of a single wind farm) errors may easily be 25% or more. 17 It is notoriously difficult to extrapolate wind statistics across and within areas with complex terrain, 18 i.e. typi- 17 This was verified in a comparative study of the work of eight different modeling teams applied to the same basic set of data for the same area. Francesco Durante, Volker Riedel, Jens Peter Molly, DEWI, Round Robin Numerical Flow Simulation in Wind Energy (2008), Paper presented at the 2008 European Wind Energy Conference. The models are listed in 18 The term complex terrain generally refers to areas with steep inclines (15-20% or more) causing flow separation. 14

15 cally mountainous areas. Hence, as mentioned in chapter 4, it is useful to alert users of mesoscale maps to these zones, where there is likely to be particularly large modeling errors or deviations in wind speeds within each cell. In practice, in this type of terrain it is usually necessary to install several anemometer masts even within a moderately sized wind farm site in order to obtain a bankable assessment of the wind climate. 15

16 4 Mesoscale Model Output Usability Issues This chapter is largely concerned with the presentation of output, its dissemination, accessibility, copyright issues and archival as well as its usability for microscale wind resource modeling use. Experience from the World Bank Group and national donor agencies show a wide variety of outputs from mesoscale modeling projects ranging from well-documented and peer reviewed methodology, empirically verified and properly documented modeling, useful maps with an easily accessible data bank of simulation results with proper metadata documentation (i.e. definitions of each data element), to the provision of a single sheet of 8.5 x 11" paper with a map of mean wind speeds as the only partially usable output. 19 In addition, there seems to be a wide variation between mesoscale wind map suppliers in relation to their knowledge and actual experience with commercial wind farm development. This has on occasion proven to give problems in relation to the relevance of the way model output has been analyzed and presented by the suppliers. A general note of caution may be in place here: This chapter lists a large number of outputs, and including them all in a mesoscale modeling contract may not necessarily be optimal. The uncertainties in obtaining the basic maps such as mean W/m 2 are already large, and more detailed sampling such as providing monthly data may yield little additional value. 20 A good GIS database of results is an essential output from any mesoscale modeling, but a very fundamental weakness has been the lack of quality and lack of maintenance of the software for accessing such databases. 4.1 Choice of Heights Above Ground Level Since wind speeds vary with the height above ground level 21 wind maps are always produced for a given height above ground level. The cost of producing maps for several heights is fairly small, since the surface roughness data already included in the modeling data makes it simple to recalculate wind speeds to a different height. 22 The choice of suitable heights depends on the hub height of the wind turbines envisaged to be used in the region. If small off-grid, village-type electrification schemes are envisaged for wind turbines up to, say, 50 kw, 20 m height may be appropriate. It should be noted, however, that at such low heights the increased influence of small (sub-grid scale) 19 This is the case for the mesoscale wind map for Yemen. 20 However, experience has proved that e.g. in the verification phase systematic deviations between the diurnal wind pattern in the model and measurements actually led to revealing a bug in the simulations in one case. 21 This is explained this in more detail on the web pages and 22 The basic method can be tried out in the author's web-based calculator Wind directions may differ slightly with height above ground level due to the Coriolis force, though not all models account for this. 16

17 terrain features and other influences such the shading effects of buildings and trees will increase uncertainties significantly. It is often useful to plan for large, grid connected turbines even if at present logistical difficulties may exclude wind turbines above 50 m hub height (typical for 850 kw turbines). State-of-the-art commercial turbines in the large markets for wind energy at present (2009) generally have hub heights of 80 m, but hub heights of m are in fact used in places like Germany. Hence to prepare for the future, heights of up to 100 m may be considered, even if present electricity tariffs may not make such hub heights economic. Large commercial wind turbines (with a given rotor diameter) come in different standard hub heights, e.g. 55, 60 or 80 m. The wind speed gain of using a taller tower is relatively higher in high surface roughness areas. If a feed-in (fixed) tariff system is used, then the economic benefit of using a taller tower is obviously higher, the higher the tariff. However, the size of turbine and towers that can be used in a given location are often limited by logistic (e.g. road curvature, craning cost) constraints or local planning requirements. The supplier must supply all wind maps and data for several heights above ground level: [...to be agreed for each study e.g., depending on final use] 20, 50, 80, [100] m. 4.2 Coloring Schemes for Maps Standard output from mesoscale modeling suppliers includes color-coded maps representing data for each height a.g.l. such as mean wind speed in m/s, power density in W/m 2, Weibull scale and shape parameters, 23 prevailing wind direction and the basic input data for elevation, surface roughness, terrain complexity etc Avoid Color Smoothing Between Cells As mentioned in section 2.2 some suppliers may optically increase the resolution displayed by their model by smoothing the colors between adjacent cells, creating the (normally false) impression that the map can be used to interpolate between cells. Such interpolation is normally very risky, given that local terrain features may influence wind speeds by more than 100%: Mountain ridges may create much higher wind speeds on the ridge itself. If the terrain is very rugged or with steep slopes or escarpments 24 this may create high turbulence, which may make the use of the site impossible in practice. 25 It is important that the user of mesoscale maps is aware of these problems when looking for areas of interest for additional ground-based wind measurements, hence its is required that maps are not presented with smoothed colors within/between cells. It is important that the supplier does not smooth map colors between the cells for which data has been calculated (e.g. through interpolation or other techniques). Each cell should be of uniform color in order to alert users to zones with abrupt color changes 23 Weibull distributions are explained in more detail in section 4.5 below. 24 See e.g Due to high fatigue loads on the wind turbines. Fatigue load and extreme loads are explained in more detail here: 17

18 between cells, e.g. due to topographical phenomena such as escarpments, mountain ridges etc. The supplier may have the capability of doing higher-resolution wind resource analysis using microscale wind models for wind resource estimation, based on additional highresolution digital maps and possibly ground-based wind measurement data. This possibility may be useful, and is discussed further in section Color schemes, which use continuous color changes to represent numbers such as wind speeds or power density, are difficult to read. It is much easier to identify interesting spots visually when e.g. wind speeds are indicated in bins or groupings of, say m/s, m/s etc., as was done in e.g. the Canadian Wind Energy Atlas. Continuous color schemes can be used, however, if overlaid with a contour map that enables the user e.g. to see where the integer values are located. Usually there are additional possibilities to obtain a more precise readout from the computer interface with the map Use Stepwise Color Coding or Overlaid Contour Maps Color-coding of data values (e.g. wind speeds, power density, surface roughness classes etc.) should either be done stepwise (and not continuously) in order to facilitate reading the maps and locating interesting areas easily or (possibly better) overlaying continuous coloring with contour maps, e.g. at integer values. Data should be coded in appropriate unit ranges, e.g. for wind speeds groupings should be in 1 m/s, i.e m/s, m/s etc. 4.3 Traditional Maps of Mean Wind Speeds and Power Density This is a standard output from all suppliers of mesoscale maps. The maps are sometimes made available both with annual and seasonal (monthly) data. Power density maps are done calculating the power of the wind per square meter rotor area 26 for each hour of the year and averaging on a monthly and annual basis respectively. 27 The reason why this latter concept of power density is generally more useful for wind developers than the mean wind speed is that the power of the wind varies with the third power of the instantaneous wind speed. 28 This means that the exploitable wind resource depends not just on the mean wind speed, but it also depends very heavily on the local wind speed frequency distribution. 29 For a given mean wind speed the annual energy content of the wind may differ by up to around 50%, depending on the exact shape of the statistical distribution of wind speeds. 26 The calculation of power of the wind is explained in simple terms and in more detail on the web page a web-based power calculator shows how the calculations are done in practice 27 The concept of power density is examined in more detail on this web page: 28 See e.g This is explained in more detail 18

19 It may be worthwhile to study the seasonal variation in wind patterns, 30 since the value of having wind energy in the grid depends partly on how well wind resources are correlated with electricity demand. Although monthly maps can be produced, it may be more convenient to group a number of months, so that they coincide with the seasonal electricity demand patterns in the country or region being examined. It should be kept in mind that the uncertainty on monthly or seasonal mean values will be higher than on annual mean values. The supplier will deliver sets of color-coded maps of both mean wind speeds in m/s and power density in W/m2 for each of the heights above ground level. In each case both with an annual mean and a monthly [or other seasonal] mean. 4.4 Alternative»Wind Atlas«Maps of Mean Wind Speeds and Power Density As mentioned above, standard output from mesoscale model maps typically includes mean wind speeds per cell in m/s (or better, power density in W/m 2 rotor area, since this measure will include the effect of the local wind speed distribution). Such a map is shown in figure See e.g. 19

20 Figure 2 Predicted wind climate of Egypt determined by mesoscale modeling The predicted wind climate of Egypt determined by mesoscale modeling. The map colors show the mean power density in [Wm-2] at a height of 50 m over the actual (model) land surface. The horizontal grid point resolution is 7.5 km. 31 Looking for a site for further investigation purely on the basis of this type of map can be misleading, however, since the best sites may typically be found locally on rounded hills, which are typically»averaged out«by the large size of cells used for mesoscale models. Likewise, good sites may also be found in smaller low-surface roughness areas within what is otherwise a high average surface roughness cell on these maps. In other words: By looking at the average wind resource in each cell we may be missing some of the potentially best sites, which may be located outside the cells, which on average appear to be the best judging by the maps. With this perspective in mind it may be better to look for zones, which have unusually high wind speeds in general, and then afterwards look for rounded hills or low surface 31 Source: Wind Atlas for Egypt: Measurements, Micro- and Mesoscale Modelling by Niels G. Mortensen1, Jens Carsten Hansen, Jake Badger, & al., Roskide and Cairo

21 roughness areas within these zones. There is a way do such searches using a different kind of mapping: Some suppliers can in addition supply a wind atlas map, i.e. an alternative mapping similar to what was used for the now classical European Wind Atlas, i.e. a mapping, which assumes that the area of interest is a flat surface with uniform surface roughness. In this type of mapping the modeler so to speak compensates for the effects of cell surface roughness and orography (terrain elevation variations) and delivers a map of promising zones, where one may afterwards add or subtract the influence of local orography and surface roughness in order to approximate local wind speeds. Such a map is shown in figure 3. Figure 3»Wind Atlas«Map of Egypt determined by mesoscale modeling The regional wind climate of Egypt determined by mesoscale modeling. The map colors shows the mean power density in [Wm-2] at a height of 50 m over an idealized flat, uniform land surface of surface roughness class 0 (z 0 = m, corresponding to a smooth water surface) Source: Wind Atlas for Egypt: Measurements, Micro- and Mesoscale Modelling by Niels G. Mortensen1, Jens Carsten Hansen, Jake Badger, & al., Roskide and Cairo

22 It is interesting to compare the two maps in figure 2 (i.e. with the actual model landscape) and figure 3. (with the idealized, smooth and flat landscape) since some new, potentially interesting patches occur in figure 3, where wind speeds appear to be higher than what would be normal for areas with the given surface roughness and orography. For wind professionals this second type of map may be useful in the search for high wind sites. It is considered desirable [but possibly optional] that the supplier in addition provides a set of»wind atlas maps«, i.e. a set of mesoscale maps of mean wind speeds and power density respectively, where wind speeds are recalculated to a uniform, flat land surface e.g. of surface roughness length z 0 = m. This type of map, which eliminates orography and surface roughness effects (as in the European Wind Atlas) allows users to search for areas with exceptional wind speeds, and manually compensate for local terrain features and surface roughness. The supplier should explain in some detail how the normalization of data is done for this type of mapping. 4.5 Weibull Distribution Maps As explained in section 4.3 wind speed distributions are as important for calculating annual energy output as are mean wind speeds on a site. Wind speed distributions on most sites tend to be skewed distributions, usually with frequent low wind speeds and infrequent high wind speeds. Such distributions are as an industry standard represented as socalled Weibull distributions, a statistical distribution defined by two parameters called the scale factor and the shape factor respectively. 33 The supplier should give the directional Weibull distribution parameters in the GIS output database, which is more convenient to process than maps. The supplier may provide separate color-coded maps for each of the two Weibull distribution parameters (shape and scale) for the area analyzed Special Calculation of Weibull Parameters In order to avoid statistical bias in the energy content displayed by the modeling, the technique used for estimating Weibull distribution parameters for wind speed frequency distributions is different in the wind industry than in other statistical uses of the Weibull distribution (e.g. for quality control sampling). The correct estimation of the Weibull parameters is important for their subsequent use in microscale wind modeling programs, as explained in more detail in section In order for mesoscale modeling output to be compatible with mainstream microscale wind models such as WAsP, the supplier should estimate Weibull frequency distribution parameters using the technique found in the European Wind Atlas. (European Wind Atlas. Eds. Ib Troen and Erik Lundtang Petersen. Publ. for the Commission of the European Communities. Directorate-General for Science, Research and development. Roskilde 1989.) Essentially this method estimates the parameters so as to conserve the energy of the wind calculated for the empirical/simulated wind speed sample. 33 This is explained in more detail where the reader may also plot actual Weibull distributions. 22

23 4.6 Wind Direction (Wind Rose) Map Prevailing wind directions may be difficult to represent graphically. Nevertheless, they may be very useful for assessing the influence of local terrain features on a given location. The supplier should provide a separate color-coded map for prevailing wind directions or alternatively use other means to represent local wind directions, such as arrows or representative miniature wind roses on a map. The map should specify the height above ground level the map represents, which should be the most probable hub height for commercial wind projects. In addition this data should be included in the GIS database. 4.7 Elevation Map (Digital Elevation Model) Elevation maps are a»free«by-product of the analysis, since the basic modeling software requires the elevation for each cell to be represented directly. Elevation maps may be useful for estimating air density, which is important for the assessment of annual energy production. The supplier should provide an elevation map for the elevations actually used in each cell of the mesoscale model. 4.8 Surface Roughness Length Map The surface roughness for each cell is a»free«by-product of the analysis, since each cell will have a surface roughness length assigned to it in the model. This information is e.g. used to generate the different wind speeds at different heights above ground level (wind shear). This information will be important to the user in order to evaluate what the wind speed will be in a part of the cell where surface roughness deviates from the rest of the cell. 34 The unit normally used to describe surface roughness, the roughness length in meters, z 0 varies by several orders of magnitude between smooth surfaces and cityscapes, hence it is most practical to display either the logarithm of the surface roughness length or so-called surface roughness classes, which groups these logarithms in a way, which is convenient to describe typical landscape types. 35 The supplier should provide a surface roughness length map for the roughness lengths actually used in the mesoscale model. It is most practical if the map is divided into roughness classes (0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4). 34 This is illustrated on the author's web page from which figure 4 was excerpted. 35 The definitions of roughness classes may be found here: 23

24 4.9 Terrain Complexity (Surface Inclination) Map When preparing input data for the topographical model, the supplier will normally use satellite data for elevations (often on a grid down to 90 m horizontal resolution). 36 This preprocessing can provide a good source of data, which may be used to alert users of the maps to complex terrain. Unfortunately there is no standard definition of complex terrain based on orography data for the purposes of wind climate assessment. Until such a common definition is established, the following proposal may be a useful way of defining complex terrain simply. It is useful if the supplier can offer a map signaling which cells may be affected by complex terrain, e.g. defined as cells in which two adjacent elevation points (among the 8 grid points surrounding each elevation point) in the original data series shows an incline of 20% or more. In any case, it is important that the precise way this mapping is done be defined clearly in the final report. One objective measure of the steepness or ruggedness of the terrain around a site is the so-called ruggedness index or RIX (Bowen and Mortensen, 1996), defined as the percentage fraction of the terrain within a certain distance from a specific site which is steeper than some critical slope, say 0.3 (Wood, 1995). This index was proposed as a coarse measure of the extent of flow separation and thereby the extent to which the terrain violates the requirements of linearized flow models. RIX is worked out for each direction sector. An average for all sectors can also be given Estimated Annual Wind Energy Production Maps This type of map is the simplest way of representing the wind energy potential of the region being analyzed in a way that is comprehensible to non-wind energy specialists. (But would rarely be requested by professionals). It is useful if the supplier can offer a map, which shows annual energy production (or capacity factor) from a typical mainstream pitch controlled wind turbine located in the center of each cell of the simplified model landscape, based on the hourly wind speeds simulated by the mesoscale model, with appropriate corrections for air density based on a standard atmosphere. It is preferable that air density correction be done using the actual power curves for the wind turbine for different air densities rather than simply using a correction proportional to air density. The supplier should specify in the final report in detail which assumptions and methods (turbine power curves, air temperatures and densities etc.) are used in these calculations, and should alert users to probable systematic biases in the calculations This is standard non-us resolution in the SRTM Digital Elevation Database, 37 The reason why it is preferable that air density correction be done using the actual power curves for the wind turbine for different air densities rather than simply using a correction proportional to air density is that energy production from modern pitch-controlled turbines does not vary in proportion to changes in air density, since the turbine will adjust its pitch angle to varying air densities. Production occurring at relatively high wind speeds (when power control sets in) will not change with varying air density. This complex relationship means that a correction proportional to air density will give an estimated production that will typically be biased upwards for high wind sites and biased downward for low wind sites. Low temperature areas will likewise have an upwards bias for estimated production if calculated proportionately. Conversely, high temperature areas will have a downward bias in estimated production. 24

25 An interesting by product of this type of calculation is the ability to calculate an approximation of the wind energy potential for the entire region (or for sub-regions), distributed according to expected annual energy production per turbine and ultimately by cost per kwh of energy. This type of analysis may allow for the construction of a supply curve for wind energy. To be useful in practice, it may be necessary to restrict the analysis to areas near the electrical transmission grid and possibly areas with road access in the proximity. An interesting example of such an analysis was done for the province of Québec, which demonstrated that there is a potential for commercially exploitable wind power installations 38 of 100,000 MW within 25 km of the existing transmission grid. 39 If this type of analysis is required, it should be specified explicitly in the ToRs. 38 Defined as potential of installed wind power for areas with mean annual wind speeds above 7 m/s. The figure for wind speeds above 8 m/s is 3,852 MW. 39 Étude sur l'évaluation du potentiel éolien, de son prix de revient et des retombées économiques pouvant en découler au Québec, Dossier R , Hélimax pour la Régie de l'énergie du Québec, p

26 4.11 Using Mesoscale Maps in Practice: Why all Maps are Important It might seem from the description of the previous map with estimated annual wind energy production, that moving a cursor across the electronic version of such a map is the magic wand, with which one can determine where to look for a good, windy site. Hence, a reader unfamiliar with wind resource assessment may wonder why we would need all the maps mentioned above but cannot simply do with this single map in order to find out where to locate a wind farm. The reason is that the real world is far more complicated that the apparently homogenous cells on the mesoscale maps. Actually the reader may be able to find substantially better (or worse) locations in terms of annual energy output within any cell. These variations can easily be ±50%, i.e. often a far wider range of wind speeds than the variations indicated between two adjacent cells in the mesoscale map. Hence, relying directly of a mesoscale map to find good, windy locations can be very treacherous. These microscale considerations require more detailed topographical maps and/or a good look at elevations and land cover e.g. on Google Earth - or good knowledge of the local area on the map. But actually a desktop study can manually use all of the information contained in all of the mesoscale maps mentioned above. This somewhat idealized example illustrates how all of the maps mentioned previously are used by a wind resource expert: Let us assume the mean wind speed map tells us that the cell has an excellent mean wind speed of 9.0 m/s at 50 m above ground level. If we then check exactly the same cell in the surface roughness length map, we may e.g. find that the roughness length is 0.1 m (also called roughness class 2 in European Wind Atlas methodology). This corresponds to a typical landscape with agricultural land with some houses and 8 m tall sheltering hedgerows with a distance of approx. 500 m. 40 Figure 4 illustrates how a professional reader of a mesoscale map with mean annual wind speeds would extrapolate from the average wind speed information in the mesoscale map to a smaller area within a cell, where surface roughness differs from the rest of the cell. Figure 4. Wind Speeds by Surface roughness Classes and Heights 41 Now, if on Google Earth we find a substantial area of open agricultural area without fences and hedgerows and very scattered buildings within the cell, then that part of the cell will be in roughness class 1 instead, corresponding to a roughness length of 0.03 m. In that column of our table above we find a 40 Roughness lengths and roughness class definitions may be found in the wind energy manual on the web page 41 Table adapted from 26

27 mean wind speed of 9.8 m/s. This may as a rule of thumb typically yield approximately at least 11% larger annual energy production. 42 The user would need consult four other mesoscale maps to verify this first guess: 1. We would want to know what the prevailing wind direction is in this cell in order to ensure that zones of higher surface roughness do not obstruct the site upwind, i.e. we will want to crosscheck the most important wind directions with Google Earth or our topographical map. Indeed, we would ideally like to display the wind rose for the cell, since there may be multiple wind directions, which are important for the assessment. Wind roses are difficult to display on a map, so it would be best to have a computer interface to the mesoscale data to extract the correct wind rose. 2. We would want to look at the Weilbull shape factor map mentioned in section 4.5, which indicates whether wind speeds tend to be spread over a wide range of values or are more narrowly clustered around the mean value. If the shape factor in this cell is 3, then we would gain 14% larger energy production than the mean for the cell rather than the 11% mentioned above. 3. We would want to look at the elevation map to do an estimate for the air density. If we are at high elevations, say 1,500 m above sea level air density is typically 14% lower than at sea level, which together with local temperature has an impact on energy production (somewhat less than proportionately with air density). 4. We would check the terrain complexity map for warning signs that the area may be very rugged (steep inclines of 10-20% and above, or even an escarpment). If this is the case, then there may be high turbulence, because the lower level airflows will detach from the terrain. This may mean that the area is unsuitable for wind power, since there may be much tear and wear on the wind turbines, but little useful wind energy, despite the apparently high mean wind speeds indicated by the map. Using the topographical map of the area or Google Earth, we may be able to determine if the site is slightly inclined towards the prevailing wind direction. If this is the case we may be able to get an additional 10% energy production, or even more on a nicely rounded hill or ridge. Conversely, if the site is on a downhill compared to the prevailing wind direction, we may loose just as much annual energy production. We can draw a few conclusions from the above way of analyzing the mesoscale maps: 1. It may be easier to find the most promising locations using the»wind atlas maps«mentioned in section 4.4, which remove the detailed clutter from the map and simply tell us the zones in which the wind speeds are above what is typical for the region. We can then start studying those areas in more detail and do the calculation adjustments for local conditions mentioned above. 2. We need to have very precise geo-referencing of each cell, i.e. we must be able to find the same cell on normal topographical maps and Google Earth. It therefore helps a lot, if we can»switch on«roads, geographical names and transmission lines in order to check the locations with our other map sources It is a somewhat inexact and time consuming exercise to do this analysis manually, it would be useful to be able to move from the mesoscale to the microscale level by downloading the necessary data directly into our microscale model (e.g. WAsP), which can account for surface roughness changes, air density changes,»double humped«weibull distributions, local terrain (orography) features, and park effects (array effects) of wind turbines shading one another. 42 This can be verified using the power calculator on the web page 43 KMZ or KML files (also used on Google earth) can be integrated for this purpose. 27

28 For this we need the wind rose and preferably also the Weibull distributions for each compass direction Printed Maps vs. Computer Searchable Maps, Formats, Layers The maps printed in mesoscale wind modeling reports 45 tend to be of such a low resolution, that they are difficult to use for anything but illustrative purposes. Maps to be used for printing in the final report should be supplied in with a 300 dpi density and lossless compression, suitable for professional printing. In practice professional users will need machine-readable maps, where they are able to pinpoint the location of an individual cell on a computer screen, to zoom in, to switch between data layers, and to relate the wind data to both geographical coordinates (longitude and latitude) and to other types of reference data, e.g. the transmission grid. Often suppliers of mesoscale modeling deliver their own specialized reader application to be used with their digital maps, or they put the maps on their web site with a web interface to access the data. It is generally preferable if standard, free software such as ArcReader or ArcExplorer can be used, since there are unfortunately very few specialized robust computer program interfaces, which have survived more than a few years without major maintenance. In the worst cases the machine-readable maps thus cease to be readable after a few years. Hence it may be more safe and defensive in any case to require the use of a robust standard, non-web solution in addition to whatever nonstandard solution for display is proposed by the supplier. One such possibility is the GEO-PDF format, which is readable on both PC and Mac platforms. 46 In addition, all data maps should be supplied as GIS maps with lossless compression with the following additional layers: 1. Longitude and latitude lines (very important) 2. City names (important) 3. Administrative region names 4. Major road numbers 5. City points or borders 6. Administrative region boundaries 7. Transmission lines (very important) 8. Roads (very important) 9. Protected areas 10. Land/water mask (lakes, rivers) 44 Obviously this type of analysis can be automated, and is in fact done by some suppliers for selected areas. 45 See e.g. or

29 These files may be supplied as GEO-PDF, shape files or a similar standard format, which allows the user to display the maps on his computer screen down to the level of individual cells together with the current cursor longitude and latitude, and to switch each layer on and off. The necessary map layers for transmission lines, roads, cities and administrative regions will be supplied by [e.g. the national authorities of the country in question] either in a standard GIS format [to be negotiated] or in a machine readable graphic format sufficient for the high resolution display required for these files. Please note that the final paragraph is not a requirement for the supplier, but a promise that the national or regional authorities in question will supply the necessary map data in a quality and a format, which is usable by the mesoscale map supplier. The task team leader responsible for the project will need to prepare this coordination between the supplier and the authorities Computer Display Software for Mesoscale Maps The Canadian Wind Energy Atlas provides one of the most useful web interfaces to mesoscale maps. 47 The interface is shown on the next page in figure 6. In addition to the somewhat self-explanatory options in the left side of the display, which allows users to switch map layers on and off and alternate between 15 data layers for mean wind speeds and 15 layers for mean wind energy, the map can be zoomed, verification data can be displayed directly, and clicking on a point of the map brings up the window shown below in figure 5. Selecting the various tabs, the user may display (1) the wind rose for the point, both on average for a year and for each of the four seasons. (2) The histograms for seasonal wind speed distributions. In both case (1) and (2) the user may with a mouse click download the corresponding data for further processing, e.g. in a microscale wind resource assessment program. Finally the last tab allows the user to use a generic wind turbine formula to calculate energy production in the point selected. 47 The example illustration may be displayed here: ds=1&cities=1 29

30 Figure 5. Popup window from the Canadian Wind Atlas. 30

31 Figure 6 Canadian Wind Energy Atlas Display The Help menu on the right side of the screen explains the content of the page and the options available to the user; hence an in-depth explanation of the interface seems superfluous in this context. The importance of this example is that it is a useful yardstick with which to judge map user interfaces from potential suppliers Compared to this example, e.g. the web interface of displays very little useful quantitative data, even in the case of the more detailed Bolivia wind map. The Google Earth integrated wind atlas of Peru offers an elegant interface with much more useful information, somewhere in between the Canadian and Bolivian examples. 31