(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
A01-16965AIAA-2001-0060
WIND ENERGY RESOURCE ASSESSMENT OF WESTERN AND CENTRAL MASSACHUSETTS
Jason R. Potts,1 Stephen W. Pierson,2 Paul P. Mathisen,3 Jeff R. Hammel,1 and Vlad C. Babau1
'Worcester Polytechnic Institute (WPI), 100 Institute Road, Worcester, MA 016092 Assistant Professor, Department of Physics, WPI, 100 Institute Road, Worcester, MA 01609; email: [email protected]
•"Associate Professor, Department of Civil and Environmental Engineering, WPI, 100 Institute Road,Worcester, MA 01609; email: [email protected]
ABSTRACT
A wind energy resource assessment of western and central Massachusetts has been performed usingWindMap, a GIS-assisted software package. Data from five remote locations in westernMassachusetts, from five airports, and upper-air data from Albany, NY were entered into WindMapto produce estimated wind speed maps at 50 meters. We find that the northwestern corner of the statehas the most potential, as previously expected. The region around Worcester also has large winds,while the Connecticut River Valley near Springfield has Class 2 and 3 wind strengths. In contrast, theridge tops of much of western Massachusetts have weaker wind speeds than predicted in the WindEnergy Resource Atlas.
I. INTRODUCTION
In the last several years, wind energy resourceassessment methods have taken advantage ofGeographic Information System (GIS) packages. Sincethese GIS approaches provide powerful capabilities forspatial analysis, they have tremendous potential forassessing wind potential. In 1993, a group from theUnion of Concerned Scientists (UCS) completed thefirst major study in the United States using GISmethods when they explored the energy potential ofvarious Midwestern states.1 Since then, the applicationsof GIS assisted wind resource assessment methods havegrown considerably. With that growth has comeincreased complexity of the models, as seen, forexample, in the studies of the northern Great Plains,2New Mexico,3 and Colorado.4 Wind resourceassessments, such as the study of Minnesota,5 are alsostarting to use GIS-packages that incorporate numericalmodels, like the commercially available WASP6 andWindMap.7 The progression of these studiesdemonstrates the increasing use of GIS as well as anincreasing enthusiasm for wind energy development inthe United States.
Currently, this interest is sweeping into the northeasternUnited States. For example, with the deregulation ofthe electric utilities in Massachusetts comes arenewables portfolio standard (RPS) that will requirenew sources of renewable energy in the mix. Moreover,the mountainous regions of the western portion of
Massachusetts (which can be seen in Figure 1) are nowbeing uncovered as potential areas for wind energydevelopment. Because wind energy from the westernpart of Massachusetts could play a key role in fulfillingthe RPS, it is important to understand the wind energyresources of that region.
However, information on wind energy in westernMassachusetts is limited. Well-known efforts includethe assessment done in the late 1970s and early 1980sthat is part of the Wind Energy Resource Atlas.8 In thatwork the ridge tops of western Massachusetts wereestimated to be Class 4: Kaminsky et al of theUniversity of Massachusetts (UMass)9 examined windenergy potential for selected sites and also developed anestimate for the maximum wind energy production inMassachusetts. More recently, Kirchhoff of UMassreported10 wind speed and direction measurements atvarious levels at five locations in westernMassachusetts. In 1999, Gogos and Hunter11 completeda resource assessment of Massachusetts following thePowering the Midwest1 GIS-assisted model.
To date, however, no wind energy resource assessmenthas been done for western Massachusetts using theGIS-packages that incorporate numerical models. Inthis paper, we report our findings of such an assessmentfor western Massachusetts using WindMap.12 Ourobjectives are to demonstrate the effective use ofWindMap and to highlight some considerations that
1 For the'wind class categorization at 50 m based on a1/7 power law, Class 1: 0-5.6; Class 2: 5.6-6.4; Class 3:6.4-7.0; Class 4: 7.0-7.5; Class 5: 7.5-8.0; Class 6: 8.0-8.8; and Class 7: 8.8-11.9 (in meters/second).
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
arise when WindMap is used to develop wind resourceassessments.
This paper is organized as follows. Section II explainsWindMap and the inputs used. In Section III, wedescribe the surface and upper-air wind data used hereand how it was entered into WindMap in the form ofwindroses. Our wind energy resource map is presentedin Section IV and their accuracy is discussed. Wesummarize our work in Section V and mention the needfor further wind speed measurements not only to learnmore about the wind speeds but also the wind shear.
H. WIND RESOURCE ASSESSMENT METHOD
Our wind resource assessment method makes use of thecommercial software WindMap along with previouslymeasured wind velocity data for the western andCentral Massachusetts region. WindMap, which isavailable from Brower and Company, is a mass-conserving model based on the code NumericalObjective Analysis of Boundary Layer (NOABL).Since thorough descriptions of these modelingapproaches are available,13 we only provide a briefoverview here.
Following the approach of NOABL, WindMapprovides an initial guess of the wind field for the entiredomain, and improves upon this guess by applying theminimal adjustment required for the wind field tosatisfy conservation of mass and also match availableanemometer data. The modeling approach also makesuse of available data to estimate vertical boundary layerprofiles, and it provides simple parameter adjustmentsto characterize stable, unstable and neutral atmosphericstates. It is noted that WindMap does not account forcomplex atmospheric conditions such as thermalmountain winds, low-level jets, and flow separation.Nevertheless, its accuracy for many wind assessmentapplications has been shown to be similar5'14 to theJackson-Hunt theory based software, WASP (developedby Riso National Lab in Denmark), and to the GIS-based methods of the National Renewable EnergyLaboratory (NREL). Therefore, WindMap's power andaccuracy are considered to be appropriate for the windassessments completed for this paper.
Application of WindMap requires consideration ofmodel domain and geometry, atmospheric conditionsand boundary layer characteristics, model calculationparameters, and wind data. The nature of the modelingapproach presents the investigator (i.e. the model user)with a tremendous amount of flexibility and uncertaintywhen defining each of the relevant parameters. For thepurposes of this paper, we will provide a brief overview
of the final parameters that yielded an optimal matchbetween our wind map and the measured wind speedsfor the 13 sites in our region. We refer the reader tohttp://www.browerco.com or Ref. [14] for or an in-depth discussion and clarification of the inputparameters and characteristics of WindMap.
For this application, WindMap was used to characterizea model domain that included a 85-mile by 60-mileregion encompassing western and CentralMassachusetts as shown in Figure 1. The horizontalresolution of the finite element mesh was set at 912.9 msuch that the model included 150 elements in the xdirection and 105 elements in the y direction. Thedomain's upper boundary was set at an elevation of3023 meters (which matched one of the elevationswhere upper air wind characteristics were measured),while the lower boundary was defined by surfaceelevations indicated by the digital elevation map(DEM) shown in Figure 1. The DEM was obtainedfrom the United States Geological Service (USGS) website15 and converted to the projection SPC83mal, with aresolution of 912.9 meters to accommodate thehorizontal resolution of the finite element mesh. Thevertical discretization included 15 elements with ageometric z-axis spacing such that the minimumvertical mesh size was no greater than 30 m near theground surface.
Atmospheric conditions included a standardtemperature profile with the air density adjusted forelevation. WindMap includes provisions forincorporating a logarithmic near-surface boundary layerand a transition layer to link this boundary layer to theupper air winds. For this case, the near-surfaceboundary layer height was set at 200m, and a 500 mtransition layer height was used. Bottom roughnesswas defined over the domain with the same resolution(912.9 m) as the DEM using a roughness map obtainedfrom Brower and Company and based on the OlsonClassification scheme. Finally, a stability ratio linkedto the vertical profile was defined with a stability lengthof 270 m, which is indicative of slightly stableconditions.
WindMap's calculations include model initializationand subsequent iteration to determine a final wind fieldthat satisfies conservation of mass. For this case,initialization to define an initial wind field was basedon surface data, with adjustments for elevation and datapoints weighted as 1/r2 with a characteristic length of6500 m. All surface data were used for initiation.Model iteration maintained a maximum residual of 10"5
and a 0% tolerance (or effectively an exact match) tothe available surface data.
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
Figure 1: The digital elevation map of the region studied with elevation given in meters. Also shown are thetown lines of Massachusetts and the locations for which wind data was used. The elevation varies greatly fromthe mountains of Western Massachusetts to the low and flat Connecticut River valley in the center. A colorversion of this map is available at http://www.wpi.edu/~pierson/westMass.html.
Since WindMap uses actual wind speed and directionaldata to initialize the wind field, and the data also serveas constraints or boundary conditions for the wind field,the selection and preparation of data is extremelyimportant for effective application of WindMap.Consequently, the next section is devoted to the sourcesand preparation of both the surface data and the upperair data that are incorporated into WindMap.
III. DATA
As noted in the previous section, the use of accuratewind data is an important component of WindMap. Inthis section we will describe the general sources andcharacteristics of the data that we use and our approachfor incorporating these data into WindMap.
The data used for this investigation include both surfacedata and upper air data. The surface data come fromtwo primary sources [University of MassachusettsRenewable Energy Resource Lab (RERL) and theNational Climatic Data Center (NCDC)], while theupper air data were obtained from the National Oceanicand Atmospheric Association (NOAA) web site.Because site visits by these authors were not feasible,relevant descriptions of the anemometer terrain were
obtained from reports, websites, and privatecommunication where possible.
Most of the data that we use were available in a rawform of time records with 10-minute or 1-hour intervalsbetween successive data points. To use all of the datato develop a consistent wind assessment, we chose thesame approximate time period (1998) to the extentpossible. While WindMap has the capability of utilizingthese data in this raw form, we converted the form ofthe data to wind roses so that preliminary verificationanalyses could be completed. The data and analysisprocedures are clarified in the following paragraphs.
A. Surface and Upper-Air Data
As noted above, the surface data were available fromthe University of Massachusetts RERL and NCDC.First, RERL, in contract to Northeast Utilities ServiceCompany, monitored wind speed and wind direction atfive locations in western Massachusetts from 1995 to1999.10 The five RERL stations are indicated in Figure1 and are described as follows: Century Cable Tower, amountain-top ski area; Petricca Tower, surrounded by aforested canopy; Burnt Hill, a mountain-top blueberry
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
Table 1: The name of the data sites, the elevation, the anemometer height, the dominant wind direction, theaverage speed, the "effective" average measured speed used in WindMap, and the predicted speed for thefive RERL sites, the five NCDC sites, two other sites, and upper-air data. The predicted speed for each set ofwind data is given in two cases: (i) without displacement heights (column 7); and (ii) with displacementheights (column 8). In the latter case, if a displacement height is used, the value is shown in square brackets.The Effective Measured Speed is the average measured speed used by WindMap and differs from the actualvalue shown in column 4 because the frequency windrose of the upper air data was used for all surfacestations in WindMap. The starred data was used for initialization. Directional data for Mt Tom, Mt.Holyoke, and the Mt Wachusett Wind Farm were not available (N/A), and the elevations for these sites areestimates.
farm; Borden Mountain, a forested canopy mountaintop; and Brodie Mountain, a mountain-top ski area.
The data, which are presented in 10 minute intervals,are available from a RERL website.16 Measurementswere provided at two or three heights for each of thesestations. Because of certain gaps in the data for some ofthe stations in late 1998, we have used data from thetime period between June 1997 and May 1998. Anexamination of the data analysis in Ref. [10] showsthat, for the NCDC surface data and the upper-air data(both of which we discuss below), there is nosignificant difference between the data for this timeperiod and the data for the calendar year 1998 that weuse. When processing the data, if two consecutivezeros were found, the zero values were attributed to
icing and the data points were discarded. This is incontrast to the methods of Kirchhoff et al.17 where thedata were attributed to icing if it occurred over a timeperiod of two hours (twelve consecutive zeros).However, the different approaches led to negligibledifferences in average velocity and associateddirections.
For the purposes of this analysis, these data wereanalyzed to estimate average wind speeds anddirections such that wind roses could be developed foreach of the stations. To develop wind roses, the velocityrecords were separated into 12 different directionalcompartments. Then, the average velocity and relativefrequency of occurrence were determined for each ofthese directional compartments.
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
Figure 2 The (a) frequency windrose [%] and (b)wind speed [m/s] windrose from the 40manemometer of Burnt Hill.
Examples of wind roses indicating wind velocity andfrequency for the 40m height at Burnt Hill are shown inFigure 2. Inspection of the two wind roses indicatesthat that site experiences wind from all directionsalthough there is a tendency for predominant windsfrom the west (i.e. 270°). Careful analysis of all windroses provides information on average speed andpredominant wind directions. The average speeds ateach height of each station, the dominant direction andelevation for all stations are shown in Table 1. In mostcases, the predominant direction is 300°. BrodieMountain, located in the northwestern corner of thestate has the largest winds, reaching speeds of 6.5 m/sat 40 meters. Century Cable Tower also has significant
winds while the remaining three are roughly two-thirdsthe value of Brodie.
For each of the five RERL stations, the frequencywindroses were very similar for the different heightswith two exceptions: Petricca Tower and BrodieMountain. For Petricca Tower, Table 1 indicates thatthe dominant direction was from 330° for the 25mheight but from 300° for the 40m and 60m heights.Nevertheless, the frequency windroses remain quitesimilar. For Brodie Mountain, the shift from the lowestheight to the two upper heights is more pronouncedeven though the dominant direction remains the same.In this case, the frequency percentage for each directionof the 25 m anemometer differs by up to 40% from the40m and 60 m anemometers, which could be due tosurface effects associated with the low anemometerheight or a mis-aligned sensor. In general, winddirections are generally consistent for all five stations.
Wind data for 1998 were also obtained from theNational Climatic Data Center (NCDC) for five airportsin western and Central Massachusetts: Chicopee, NorthAdams, Orange, Westfield, and Worcester at hubheights of 10 meters. (See Figure 1.) Wind data fromoutside the region of interest were also used to betterthe accuracy of the wind maps at the boundaries. Thestations used were Albany, NY, Schenectady, NY,Bradley International airport (Windsor, CT), andWillimantic, CT. Airports to the north and east of ourregion were not used for reasons such as distance oravailability.
The NCDC data, which come in 1-hour intervals, wereanalyzed using the same procedures used to analyze theRERL data. The wind strengths and predominantdirections for these stations are shown in Table 1. Thefrequency windroses of North Adams, Worcester, andWestfield were similar to that of the RERL stations inthat strong winds (not necessarily the strongest)occurred in the northwesterly direction (± 30°). Incontrast, Chicopee and Orange have strong winds out ofthe south, which we attribute to its proximity to theConnecticut River Valley.
Zeros were treated differently in the NCDC data forfour of the five NCDC stations because, for thosestations (with the exception of Chicopee), the recordedwind speed values (in mph) at low wind speeds waslimited to 0, 3, and 5 m/s. For this reason, thepossibility of anemometer freezing at these stationscould not be discerned from low wind speeds. Inaddition, winds whose directions could not bedetermined, classified as variable by NCDC, were notincluded in the speed or frequency windroses. Still,
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
these effects are expected to be minimal for thesestations.
The locations of the anemometer sites were obtainedfrom the NCDC web pages for the NCDC airport sitesand from R. Kirchhoff of UMass RERL for the RERLsites. Because the resolution of the WindMap maps isrelatively large, it is possible that the correspondingelevations of the anemometer sites in these maps willnot correspond well to the actual elevations. Tominimize these differences, we have shifted thecoordinates of the sites to match elevations withinWindMap (a shift of up to 500 m in one case, buttypically much less, if at all).
Because of the course resolution (912.9 m) of our maps,the characteristics of specific point locations are notaccurately represented. For example, most NCDC sitesare located at airports, which likely have a smallerroughness than their surrounding area. Consequently,the roughness of the NCDC sites were initiallyoverestimated, which likely results in exaggerated windstrengths at heights of 50 m. To counter this problem,we changed all of the roughness values for the NCDCsites from 0.8 to 0.5. Lowering the values any morethan this resulted in unacceptably large differencesbetween the predicted and measured speeds. Thisadjustment provided for more reasonablerepresentations of airport wind profiles.
The upper air data from Albany, NY were used becauseit is the nearest upper wind monitoring location 1 to ourregion. The data, obtained from the NOAA18 websitefor the year 1998, come in hourly measurements atconstant pressure levels. Because the height forconstant pressure level is not a constant, we averagedthe heights and calculated the speed and frequencywindrose for each pressure level. Using the sameprocedures discussed previously, we calculated theaverage speeds and the dominant wind directions fordifferent heights, which correspond to pressure levels of925.0 millibar (767m), 850 millibar (1467m), and 700millibar (3023m). (See Table 1.) The prevailing winddirections and windroses are similar to those of thesurface stations.
B. Windroses in WindMap
Wind speed windroses (similar to those illustrated inFigure 2) for all monitoring stations were incorporatedinto WindMap. However, WindMap allows the user toidentify only one frequency windrose as the referencefrequency windrose. Consequently, the user must takegreat care to choose the frequency windrose thatcharacterizes the majority of the data. Once chosen, the
igure 3: The frequency windrose for the Albanyupper air data at a pressure level of 925.0 millibars,or an average height of 767 m. This frequencywindrose was used as the frequency windrose for allof our data.
wind speed windrose data can be entered for eachstation. We have used the 767m upper air data as thereference frequency windrose, whose dominantdirection is 300 degrees (see Figure 3), because itsimilar to most windroses and represents the winds inthis region that would exist in the absence of terraineffects. For stations that have a differing dominant winddirection, the user can rotate the windrose withinWindMap to compensate for local terrain effects.
A consequence of the use of only one frequencywindrose is that the average speed for each stationcalculated by WindMap, or the "effective" measuredspeed, will not equal the actual average speed for thesame station. This effect is best examined by comparingtwo columns in Table 1. Column 5 contains the actualaverages while column 6 contains the effectivemeasured speed resulting from using the frequencywindrose for the 767m upper-air data for all of thestations. As one can see, the "effective" averagemeasured speeds underestimates the actual averagespeed by a modest 5-10% in most cases. For Worcester,Orange, and Westfield, the agreement is better than1.5%. One can also see in Table 1 that the agreementbetween the effective and actual measured speeds forBurnt Hill, whose frequency wind rose is shown inFigure 2, is within 6% despite their dominant directionsdiffer by 30°. Because the disagreement is rarely morethan 10%, this approximation (and constraint withinWindMap) is reasonable.
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)1 Sponsoring Organization.
Figure 4: A map of the wind speeds in m/s at a height of 50 m for western and Central Massachusetts alongwith our data sites and the town lines of Massachusetts. Displacement heights were not used for this map. Acolor version of this map is available at http://www.wpi.edu/~pierson/westMass.jpg.
In cases where the difference between the WindMapvalue and the actual value was substantial, the windrosecan be rotated so that the second most dominantdirection corresponds with the dominant direction ofthe reference frequency windrose. This is the case fortwo airport stations, Chicopee and Orange. For thesestations, the frequency wind rose indicates relativelydominant southerly wind components (180 degrees) butwe use little or no rotation to obtain a more accuratevalue for the average speed. If we were to use therotation to the dominant direction of 180 degrees, theresulting average speed in WindMap would be 20%smaller than the actual value.
IV. RESULTS
In this section, we present and discuss the wind energyresource maps that we produced using the datadescribed in Section III and the methods and parametersof Section II. It is important to stress that the maps thatwe present are based on only one year of data andtherefore should not be taken to represent long-termaverages. The maps should therefore be used to identifythe locations with large wind energy potential relativeto other locations in the region. Clearly, these mapsshould serve only as a guide for siting a windfarm and
further wind measurements should be taken beforeactual siting of a windfarm.
While there are a variety of parameters that one canchange in WindMap, we believe that the WindMapparameter settings that we use are optimal. Theparameter settings are optimized by minimizing thedifferences between our maps and the available datawhich includes the NCDC data, the RERL data, anddata from four other stations in or near our region ofinterest: Mt. Tom, Mt. Holyoke, the Mt. Wachusettwind farm and the Searsburg wind farm. Our final mapwas not found to be particularly sensitive to any of theinput parameters.
In Figure 4, we present a map of the wind speedsproduced with WindMap at a height of 50m. Asexpected, the strongest winds are in the northwestcorner of the state on Mt. Greylock and the surroundingpeaks including Brodie Mountain. Parts of thesouthwest corner of the state also have significantwinds. Surprisingly, and in contrast to the Wind Atlas8
maps, much of the region of western and northernMassachusetts are Class 1, less than the wind strengthsof the area from Springfield to Worcester. Therelatively higher elevations and distance from theBerkshires are the probable reason for the larger windsin Central Massachusetts, while the complex terrain and
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)1 Sponsoring Organization.
2 3 4 5 6
Effective Measured Speed (nVs)
Figure 5: The predicted wind speed versus theeffective wind speed for all of the stations with nodisplacement height (squares) and withdisplacement heights (triangles). The excellentagreement is an artifact of the fact that the majorityof the data here was used to initialize the wind field.The data that strays from the line is principally the10m, 25m, and 60m (i.e., non-40m) anemometerheights from the five RERL stations that were notused for initialization. One can see the using adisplacement height improved the accuracy of thenon-40m RERL data.
shadowing effects of the western-most mountains arethe likely reason for the low winds in much of westernand northern Massachusetts.
A comparison of the measured values to those predictedby WindMap is shown in Table 1 and Figure 5. Notethat we have used all five NCDC stations and all 40-meter data from all of the RERL stations to initializethe wind field and therefore the excellent agreement forthose values is an artifact. It follows that it is best tojudge the accuracy of these maps by other methods, towhich we now turn.
To check the high winds predicted for the BrodieMountain and surrounding area, we compare our resultswith a recent wind resource assessment of Vermont19
and the reported wind speed of 8 m/s at 40 m for theSearsburg, VT wind farm.20 We find that ourpredictions for wind class are in good agreement withtheir map. In particular, the NREL study finds Class 6& 7 winds19 just north of North Adams, in the samelocations as our map predicts. The agreement in the restof the southern Vermont area between our map and thatof NREL is also reasonable. In addition our predictionfor the Searsburg site is low by only 10.5%.
A rough check of this map around Chicopee, MA andWorcester, MA (See Figure 1) can be made with winddata from time periods different than those used to
5 7
•O 6
> 5
Hit
2 3 4 5 6
Effective Measured Speed (m/s)
Figure 6: The predicted wind speed, in the casewhere each site was not used for initialization,versus the effective wind speed for the five RERLstations (40m) and the five NCDC stations withrepresentative input parameters. The diamonds arethe values used without a displacement height, andthe squares with the displacement height discussedin the text. The line represents ideal agreement.
produce these maps. Two sets of data are available forthe Connecticut River Valley area from the late 1970s.While the exact locations were not available for thispaper, the reported averages were 5.45 m/s at 60 feetand 7 m/s at 150 feet for a DOE site at Mt. Holyoke,21'22
and 6.545 m/s at 40 feet for a UMass site at Mt. Tom,22
as shown in Table 1. A check of our map does revealwind speeds within 13-18% of the DOE values in thevicinity of Mt. Holyoke. Although no values as large asthe UMASS value at Mt. Tom were predicted byWindMap, the winds surrounding that site are known21
to be quite complex. The Mount Wachusett data wastaken in the early 1980s and reveal speeds of 6.8 m/s at24m.23 Again, the agreement with the WindMapprediction is reasonable (within 18%), furthervalidating our wind energy resource map and perhapsindicating that the winds are stronger there thanpredicted in our maps.
To check the accuracy of the WindMap method, weomitted each station from the initialization one at a timeand then checked the accuracy of the predicted windspeed for that station for various input parameters. Wereport on a representative case here. When the rest ofthe RERL stations were omitted from the initialization(see Figure 6), the accuracy varied from 1% for CenturyCable and 8% for Burnt Hill to 40% for Petricca Towerand 50% for Borden Mountain. For the five NCDCsites, the differences varied between 20% and 35%,except for Westfield, which were only 7% from theeffective measured value. The overall mediocreagreement when a station was omitted from the
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
Figure 7: The percentage difference between thewind speed map obtained without a displacementheight (Figure 4) and the map calculated withdisplacement heights in the region where thedifference was non-negligible. The wind speedsobtained with displacement heights are larger by upto 15%. Also shown are Massachusetts county linesand the five RERL and two NCDC data sites.
initialization can be attributed to the complex terrain ofwestern Massachusetts. When examining Figure 6, thereader should bear in mind that the data points for Mt.Wachusett, Mt. Holyoke and Mt. Tom were excludedbut that their agreement was in the 15% range, perhapsdue to the simpler terrain of their respective regions.
By inspecting the predicted wind speeds for the RERLstations at the heights other than the 40 m values usedfor the initialization, the wind shear in our map can bejudged. One can see from Table 1 and Figure 5 that thevalues of the predicted wind speeds at the 10 m, 25 mand 60 m heights for the RERL stations differed fromthe effective measured values by roughly 20-30% andup to double that value, (e.g. Brodie Mountain at 10m).While we attribute most of the disparity for the 10mheight of Brodie Mountain to surface effects, it ispossible that the other large differences for the 25m and60m heights are a result of a non-zero displacementheight, which can be defined as follows. In thickforests, it is common that the wind profile reaches zeroat a distance that is well above the ground. Thisdistance, after accounting for the roughness length, isrepresented by the displacement height and results in asmaller "effective" anemometer height.
For the RERL stations where data is available for twoor more heights, the wind velocity profiles (velocity vversus height z) were analyzed using both the logarithmand power-law formulas: vocln(z/z0) and voc (Z/ZQ)",where ZQ is a roughness length and a is the wind shearexponent. These analyses confirmed the results ofKirchhoff et al.24 that revealed unusually large values(-0.5) of a. By introducing a displacement height h intothe power law formula v<>= [(z-h)/z0]a, we were able tofind more reasonable values (-0.25-0.3) of a.
To test whether non-zero displacement heights couldalso help to achieve better agreement for the non 40-meter anemometer heights in the WindMap analysis,we adjusted the anemometer heights of four of the fiveRERL stations down by their respective displacementheights. The displacement heights resulting from thewind profile analysis were 10-15 m for Petricca Tower,Borden and Century Cable, 5 m for Brodie Mountain,and zero for Burnt Hill. The displacement height forBrodie was limited by the fact that the lowestanemometer was 10m. We found empirically that using15m for the first three stations in our WindMap analysisyielded the best agreement with the data for the non 40-meter anemometer heights. The difference was reducedto less than 10% in most cases. (See Table 1 and Figure5.) Brodie Mountain at 10m still differs significantlyhowever, most likely due to surface effects.
The wind speed map obtained using displacementheights is very similar to that of Figure 4 and so is notshown here. (A color version is available athttp://www.wpi.edu/-pierson/westMass.html.) Instead,a percentage difference between the two maps is shownin Figure 7. As one can see, the main differencebetween the maps is that the wind speeds for the mapcalculated with displacement heights tend to be nearly15% larger in a region with a radius of about 10 kmaround Century Cable, 11% larger in a small regionaround Petricca Tower, 8% higher around BordenMountain, and 5% higher around Brodie Mountain. Thedifference becomes minimal at a distance of 15-30 kmfrom these sites.
With the use of displacement heights, better agreementbetween the WindMap values and the wind data isobtained for nearly all sites. (See Table 1.) Theexception is Burnt Hill, where the agreement decreasesslightly. When the accuracy check for the WindMapmethod (i.e., omitting each station from theinitialization) is repeated, the overall agreementimproves a small amount. (See Figure 6.) Nonetheless,our results indicate that non-zero displacement heightsimprove the overall accuracy of our maps.
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
Our wind maps are presented for a height of 50 meters,which seems to be a standard in the literature. Becauseonly one of the measurement stations out often has datafor a height greater than 40 m and the majority haveheights less than 40 m, (especially when displacementheights are considered,) the accuracy of our mapswould be better at lower heights. The poorunderstanding of the shear exponents in this regionfurther contributes to the difficulties of extrapolating toheights larger than 40 m. However, a closeexamination of the differences between the 30 m mapand the 50 m map revealed that both showed the samegeneral pattern at each level, the only difference beingthat the wind speed values in each map differed, withthe 30m map having values 12% less (20% in thevalleys) than the 50m map. This reinforces the idea thatthese maps should be used to judge the potential of onesite versus another and not strictly as a prediction forwind speeds.
V. SUMMARY AND DISCUSSION
A wind energy resource assessment was performed forwestern and Central Massachusetts using the GIS-assisted, commercially available WindMap and datafrom around the region. The maps indicate that the mostpromising areas are the ridges in the northwest cornerof the state as previously expected.8 However, strongwinds were also found for the area around Worcester inCentral Massachusetts. Near Springfield, the ridge thatruns roughly parallel to and crosses the ConnecticutRiver near Mt. Tom and Mt. Holyoke seems to have themost potential in that part of the state.
While our map is in qualitative agreement with theWind Energy Resource Atlas's predictions8 forMassachusetts in places, there are some significantdifferences. The primary regions of agreement are forthe northwest corner of the state where Class 4 windsare found, and for the Chicopee and Worcester area,where Class 2 & Class 3 winds, respectively, are found.It should be pointed out that we also find regions ofClass 6 & 7 winds in the northwest corner of the state.The region of significant difference is in themountainous regions of western Massachusetts wherethe Wind Atlas predicts Class 4 along ridge crests. Incontrast, our map, except for the northwest andsouthwest corners, shows mostly Class 1 winds forwestern Massachusetts, even at ridge tops. Furthermore,in northern Massachusetts (west of Borden Mountainand north of Mt. Wachusett), we find Class 1 winds,much less than the Class 3 & 4 winds predicted inReference 8.
For the broad swath of western Massachusetts tonorthern Central Massachusetts where our maps predictlower winds than that of the Wind Energy ResourceAtlas's predictions8, we attribute the low winds toshadowing effects from the mountains to the west andto the complex terrain. That the winds in the regionfrom Springfield to Worcester are larger than the windsof much of western and northern Massachusetts canattributed to the opposite effect: smoother topographyand a larger distance from the western mountains,which subdues their shadowing effects. While it ispossible than the winds around Worcester andSpringfield are somewhat larger than reality due to theroughness effects discussed in Section III, we feel thatthis is minimal, especially since the winds of Mt. Tom,Mt Holyoke and Mt. Wachusett are all underestimatedby upwards of 15%.
To improve and further test the accuracy of the maps,more wind data are needed. In particular, the regionaround Worcester and the region between the fiveRERL stations and the NCDC airports (Orange,Chicopee, and Westfield) are lacking data. A betterunderstanding of the wind shear in the wind velocityprofile, the role of displacement heights in the region,and the daily and seasonal variability of the wind andother atmospheric conditions would also be beneficial.We further recommend a study of the area with othercommercially available wind resource assessmentpackages. Finally, an analysis of the amount ofcontiguous area, zoning laws, distance to major powerlines, environmental suitability, and other social factorsshould be done to assess the feasibility of siting a windfarm in this area.
ACKNOWLEDGEMENTS
We gratefully acknowledge the support of theMassachusetts Division of Energy Resources (JohnCosmas and Nils Bolgen in particular) who purchasedWindMap and the airport data from the NationalClimatic Data Center for us. Michael Brower providedus with invaluable advice on this project as well astechnical support on WindMap. We also thank BobKirchhoff of the University of Massachusetts for hishelp is pinpointing the locations of the RERLanemometer stations and Jim Man well of the Universityof Massachusetts for useful conversations and forinformation on the Mt. Tom Data. We are grateful toSteve Clemmer of the Union of Concerned Scientistsfor providing the IDRISI GIS software and suggestingthis topic of study. The support of the IGSD at WPI isalso gratefully acknowledged; without their support, theculmination of this project would have been delayedconsiderably. Finally, we wish to acknowledge WPI
(c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
students Jeremy P. Gogos, Jason H. Hunter, Saman is an extension of a graduation requirement of three ofDasmah, Cuong D. Le and Joseph Tinkham, whosework laid a solid foundation for this project. This workDasmah, Cuong D. Le and Joseph Tinkham, whose the authors (JRP,JRH, and VCB).
1 M. C. Brower, M. W. Tennis, E. W. Denzler, and M. M. Kaplan Powering the Midwest: Renewable Electricity forthe Economy and the Environment (Cambridge: Union of Concerned Scientists, 1993).2 D. Elliot and M. Schwartz, Windpower ^98 Proceedings, American Wind Energy Association, p 333, 1998.3 M. Brower, New Mexico Wind Resources, Final repot to the State of New Mexico Energy, Minerals and NaturalResources Department, September, 1997; available at http://www.browerco.com.4 M. C. Brower, P. Hurley, and R. Simon. Windpower 1996, American Wind Energy Association, 1996; available athttp://www.browerco.com.5 R. Artig, Windpower 1999 Conference Proceedings, American Wind Energy Association, 1999.6 Product of Riso National Lab, Denmark; http://www.wasp.dk/.7 Product of Brower and Company, Andover, MA; http://www.browerco.com.8 Wind Energy Resource Atlas of the United States, (Golden: National Renewable Energy Laboratory, 1987); availableat http://rredc.nrel.gov/wind/pubs/atlas/.9 F. C. Kaminsky, R. H. Kirchhoff, J. Man well, and T. B. James, Windpower "87Proceedings, American Wind EnergyAssociation, p 358,1987.10 R. H. Kirchhoff, Wind Resource Assessment in Western Massachusetts, Final Report to Northeast Utilities ServiceCompany, December, 1999.11 J. P. Gogos and J. H. Hunter, Wind Energy Assessment of Massachusetts Using GIS, WPI Undergraduate Thesis,Advised by S. W. Pierson and D. W. Woods, 1999.12 This work is an extension of J. R. Potts, J. R. Hammel, and V. C. Babau, Wind Energy Resources in WesternMassachusetts: A GIS Assisted Analysis, WPI Undergraduate Thesis, Advised by S. W. Pierson and P. P. Mathisen,2000.13 J. S. Rohatgi and V. Nelson, Wind Characteristics: An analysis for the generation of wind power, (AlternativeEnergy Institute, Canyon, TX, 1994).14 M. Brower, Windpower 1999 Conference Proceedings, American Wind Energy Association, 1999.15 http://edcwww.cr.usgs.gov/glis/hyper/guide/l_dgr_demfig/states.html16ftp://ftp.ecs.umass.edu/pub/rerl/outgoing/raw_wind_data/.17 R. Kirchhoff and F. Simons, European Wind Energy Conference 1999, Nice, France, 1999.18 Radiosonde Database Access, National Oceanic and Atmospheric Administration;http://raob.fsl.noaa.gov/Welcome.cgi.19 Green Mountain Power Corporation, NRG Systems and Vermont Environmental Research Associates,http://www.state.vt.us/psd/ee/wind/ee-wind.htm and National Renewable Energy Lab,http://www.nrel.gov/wind/statemaps.html.20 http://www.eren.doe.gov/wind/green.html.21 D. S. Renne, W. F. Sandusky, and D. L. Hadley, Meteorological Field Measurements at Potential and Actual WindTurbine Sites, 1982, http://rredc.nrel.gov/wind/pubs/candidate/met/.22 J. Manwell, Umass, Amherst, private communication.23 J. Fitch, Princeton Municipal Light Company, private communication.24 R. H. Kirchhoff and D. R. Bruno, European Wind Energy Conference 1998, Dublin, Ireland, 1997.
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