Wind potential assessment of Quebec Province

Renewable Energy 28 (2003) 1881–1897www.elsevier.com/locate/renene

Wind potential assessment of Quebec Province

Adrian Ilincaa,∗, Ed McCarthyb, Jean-Louis Chaumela,Jean-Louis Re´tiveaua

a Universite du Quebec a Rimouski, Department of Engineering, Rimouski, Canadab Wind Economics and Technology Inc., Martinez, California, USA

Received 5 December 2002; accepted 21 January 2003

Abstract

The paper presents the development of a comprehensive wind atlas of the Province of Que-bec. This study differs from previous studies by 1) use of a standard classification index tocategorize the wind resource, 2) extensive review of surface and upper air data available forthe Province to define the wind resource, and 3) integration of available wind data with thetopography of the Province.

The wind resource in the Province of Quebec is classified using the scheme proposed byBattelle—Pacific Northwest Laboratory (PNL). The Battelle—PNL classification is a numeri-cal one which includes rankings from Wind Power Class 1 (lowest) to Wind Power Class 7(highest). Associated with each numerical classification is a range of wind power and associa-ted mean wind speed at 10 m and 50 m above ground level. For this study, a classificationfor 30 m above ground level was interpolated and used.

A significant amount of wind data was gathered for the Province. These data were obtainedfrom Atmospheric Environment Service (AES), Canada, from wind project developers, andfrom climatological summaries of surface and upper air data. A total of 35 primary data siteswere selected in the Province. Although a number of wind data sites in the Province wereidentified and used in the analysis, large areas of the Province lacked any specific wind infor-mation.

The Province was divided into grid blocks having dimensions of 1/4° latitude by 1/3° longi-tude. Each grid block is assigned a numerical Wind Power Class value ranging from 1 to 7.This value is based on the integration of the available wind data and the topography withinthe square. The majority of the Province was classified as 1 or 2. Coastal locations and topo-graphic features in the interior of the Province typically have Wind Power Class 3 or higher. 2003 Elsevier Science Ltd. All rights reserved.

∗ Corresponding author.E-mail address: [email protected] (A. Ilinca).

0960-1481/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved.doi:10.1016/S0960-1481(03)00072-7

1882 A. Ilinca et al. / Renewable Energy 28 (2003) 1881–1897

Keywords: Wind potential; Wind power density; Wind atlas; Meteorological data; Quebec Province

1. Introduction

The analysis of the wind energy resource in Canada [1] shows that Quebec Prov-ince conceals important wind resources. However, due to the abundance and lowprice of hydraulic energy, the exploitation of wind power has been significantlydelayed. Following public consultations, Quebec Ministry of Natural Resources pro-posed an energy policy [2] that gives an important place to wind power. One of themost important results of this policy is the ‘Le Nordais’ project, a 100 MW windpark located in the Gaspe Peninsula region, the most important North Americanproject in recent years. This project is followed by planned 1000 MW installed powerover the next ten years.

The Quebec Government recommendations were based on the province wind atlascarried out by Wind Economics and Technology (WECTEC) Inc. and Universite duQuebec a Rimouski [3]. In this paper we present the most important methodologicalaspects and conclusions of this wind resource assessment project. While there hasbeen much speculation about the wind energy potential and several studies weredone to identify promising areas in the Province, no standard approach using windatlas methods has previously been performed for Quebec Province. The goal of thisstudy is to apply standard approaches and quantify the wind power potential at anelevation 30 m above ground level and identify regions with the best wind resource.

The wind resource in the Province of Quebec is classified based on the windpower scheme proposed by Battelle—Pacific Northwest Labs (PNL) [4]. The PNLclassification is a numerical classification from 1 (lowest) to 7 (highest). Associatedwith each numerical class is a range of wind power and associated mean wind speedat 10 and 50 m above ground level. For this study, a classification for 30 m aboveground level was interpolated and used.

A grid was superimposed on the Province and each grid block was categorizedusing this numerical wind power classification scheme. Each grid block has dimen-sions of 1/4° latitude by 1/3° longitude, the same as the one used in the Battelle—PNL study of the wind resource in the United States [4].

A significant amount of wind data was gathered from Atmospheric EnvironmentService (AES) Canada [5–7], wind project developers [8,9] and climatological sum-maries of surface and upper air data [10]. A total of 35 primary data sites wereselected in the Province. Although other wind data sites in the Province were ident-ified and used in the analysis, large areas lacked any specific wind information.

Annual wind power density in watts per square meter (W/m2) was also estimatedfor each grid block. To simplify the process associated with handling multiple yearsdata records of hourly wind speed data, the Rayleigh distribution was applied tocreate monthly wind speed frequency distributions for the 35 long term sites. Thesedistributions were then used to calculate monthly wind power density. The annualwind power density was determined by averaging the monthly values for the appro-

1883A. Ilinca et al. / Renewable Energy 28 (2003) 1881–1897

priate time period. These values were then applied to categorize the annual windpower density for each grid block of the entire Province.

The majority of the Province was classified as 1 or 2. Coastal locations and topo-graphic features in the interior of the Province typically have Wind Power Class 3or higher. The areas with the highest wind power class are found in the GaspePeninsula, on the Northeast coast of St. Lawrence near Blanc Sablon and on Magda-lene’s Island.

2. Methodology

The methodology is based on general techniques in wind resource assessmentclassifications. The model for these general techniques is presented in EnvironmentCanada—Atmospheric Research Report [1] and the US Department of Energy Report[3]. The Environment Canada report presents the standardization and processing ofhourly meteorological data into useful wind energy information for 18 specific AESsites in the province. The DOE Report uses a standardized classification scheme toidentify the wind power throughout the United States. Surface data [5–9], upper-airdata [10], and topography are integrated with meteorological insight to fit theexpected wind resource into seven wind power categories.

Previous studies of the classification of the wind resource in the Province werefirst reviewed, identifying the most relevant results. This includes the one conductedin 1981 by COGESULT [8] and a monitoring program conducted in the Lower St.Lawrence River Valley by Hydro-Quebec [9].

The Province was divided into six geographic regions: Gulf of St. Lawrence,Northern Quebec, Gaspe Peninsula, North Shore, Quebec City and Interior & South(Fig. 1). This division into six areas is based on the differences in the wind resourcecharacteristics of the Province based on the topography and geography. For example,the annual seasonal wind resource characteristics for coastal locations in the Gulfof St. Lawrence may be different than the characteristics of a site in the interior ofthe Province.

Monthly wind speed data was then obtained from Environment Canada [5–7],from special studies undertaken by Hydro-Quebec and from wind energy developers.These monthly data are adjusted to an elevation of 30 m above ground level usingthe wind speed power law. Experience and professional insight are used to integratethe results of the analysis with existing meteorological data and topography withinthe Province to arrive at the classifications in areas with no current meteorologicaldata.

The determination of seasonal wind power density posed a special challenge. Thewind power density is a function of the distribution of wind speeds and the effectof air density. To avoid the time and expense associated with processing multipleyear data records of hourly wind speed data, the Rayleigh distribution was appliedto create monthly wind speed frequency distributions for the 35 long term sites inthe AES data archive [5–7]. These distributions were then used to calculate monthlywind power density and the associate PNL power class.


Fig. 1. Geographic regions of Quebec Province with location of sources of meteorological data.

2.1. Wind speed frequency distribution

The Weibull function is a two parameter function used to estimate wind speedfrequency distribution. It is defined as:

f(V) �kc�V

c�k-1

exp��Vc�k� (1)

where f(V) is the Weibull probability density function with the probability of havinga wind speed of V(m/s) is f(V), c, expressed in m/s, is the Weibull scale factor, whichcould be related to the average wind speed through the shape factor, k, whichdescribes the distribution of the wind speeds.

The relationship between the Weibull scale factor c, Weibull shape factor k andaverage wind speed is given by the following formula:

c �V

�(1 � 1/k)(2)

where � is the Gamma Function and V is the average wind speed.


The Weibull function is commonly used because it has been found to give a goodfit to the observed wind speed data at the surface [14–16] and in the upper air [17].

A special form of the Weibull probability density function, known as the Rayleighdistribution, has been found to typically represent the wind speed frequency distri-butions at most sites. In the Rayleigh distribution, the shape factor k is assumed tohave a value of 2. The Rayleigh distribution is expressed as:

f(V) � pV

2V2exp� � pV2

2V2� (3)

where f(V) is the Rayleigh probability density function where the probability ofhaving a wind speed of V(m/s) is f(V), V is the wind speed in m/s, V is the longterm average wind speed in m/s. Corotis [18] and Cliff [19] found that Rayleighdistribution provided a very good fit between the theoretical and actual windspeed distributions.

In order to simplify the calculation of wind power density, wind speed frequencydistributions were determined for each of the 35 primary sites using the Rayleighdistribution. In making this choice, the results of the study tend to be on the conserva-tive side as the shape factors presented in Wind Energy Resource Maps of Canadaindicate values varying from 1.5 to 1.9 for the 18 stations in the Province of Quebecsubject to the analysis.

2.2. Data corrections and adjustments

The meteorological data used in this report are primarily multiple year hourlyobservations collected for other purposes than wind energy. There are a number ofdata quality issues associated with these long term meteorological data sets: com-pleteness, consistency in observation techniques, consistency in meteorologicalinstrumentation over time, unobstructed measurements, consistency in measurementheight. The summarized Atmospheric Environment Service (AES) data used prim-arily in the study is obtained from the AES publications [5,6]. It is assumed that thesummarized meteorological data is accurate in regard to completeness and consist-ency even though, over a long period of time, there are changes in observation tech-niques and instrumentation. Therefore, there are no changes made to the summarizeddata presented in the AES publications. The published anemometer measurementheight is assumed to be the proper height over the entire period of record. Theonly adjustment is a height adjustment from the published measurement level to astandardized height of 30 m, by using the wind speed power law:

V2 � V1�z2

z1�a (4)

where V2, in m/s, is the calculated wind speed at height z2, V1 is the observed windspeed at height z1 and a is the wind speed power law exponent (typically 0.14).

The only factor to estimate for the this formula is the value of the power lawcoefficient. The value of the coefficient varies from less than 0.10 over the tops of


steep hills to over 0.25 in sheltered locations. The typical value is 0.14, which istermed the 1/7th power law. After analysis, a value of a = 0.17 was selected as thepower law exponent factor for stations in Quebec. This value most closely matchesthe surface roughness conditions (z0 = 0.03) at the 35 primary sites. The choice ofthe wind speed exponent of 0.17 for determination of the 30 meter wind speed fromthe 10 m wind speed results in monthly and annual wind speed values 3% greaterand wind power density values 9.4% greater than using the 1/7th power law.

2.3. Wind power density

The potential wind power density, P /A, available on a unit area oriented normalto the wind of speed V is represented by:

PA

�12rV3 (5)

where P is the power in Watts, r, the average density of the air in kg/m3, A, thearea perpendicular to the wind speed vector in m2 and V, the wind speed in m/s. Thedensity of the air is dependent on temperature, pressure, and the moisture content.

A widely used procedure in wind energy analysis is to assemble the wind speeddata into a histogram which shows the number of hours of observation of wind speedin discrete 1 m/s bins. A wind speed frequency histogram is then obtained by dividingthe number of observations in each bin by the total number of observations in thedata set. This method relies on the use of an average value for the density which isacceptable since the pressure and temperature will not vary as much as the windspeed. Then, the average wind power density is calculated as:

PA

�1

2Nravg �m

j � 1

nju3j �

12ravg �m

j � 1

fju3j (6)

where N is the total number of observations, ravg is the average air density for theperiod, nj, the number of observations in the jth class, fj, the frequency of occurrenceof winds in the jth class and uj the wind speed at the midpoint of the jth class.

Eq. (6) is used to determine the annual and seasonal wind power densities for theAES stations in Quebec. The number of observations in each wind class is basedon a Rayleigh distribution of the monthly wind speeds. The average air density isbased on the monthly average of temperature and pressure recorded at each site.

2.4. PNL wind power classification

As part of the US Department of Energy’s Federal Wind Energy Program, PNLdeveloped a wind power classification scheme. This scheme, presented in the WindEnergy Resource Atlas of the United States [4] is shown in Table 1. Areas areclassified on the basis of wind power, ranging from 1 (lowest) to 7 (highest). Eachclass represents a range of wind power density (W/m2) or a range of equivalent meanwind speeds (m/s) at specified heights above ground level. In this study, the wind


Table 1Classes of wind power density at 10, 30 and 50 m (Battelle PNL)

10 m (33 ft) 30 m (100 ft) 50 m (164 ft)Wind Wind power Average wind Wind power Average wind Wind power Average windpower class density speed (m/s) density speed (m/s) density speed (m/s)

(W/m2) (W/m2) (W/m2)

1 �100 �4.4 �160 �5.1 �200 �5.62 �150 �5.1 �240 �6.0 �300 �6.43 �200 �5.6 �320 �6.5 �400 �7.04 �250 �6.0 �400 �7.0 �500 �7.55 �300 �6.4 �480 �7.5 �600 �8.06 �400 �7.0 �640 �8.2 �800 �8.87 �1000 �9.4 �1600 �11.0 �2000 �11.9

power classification is applied to a grid block. Each grid block, with dimensions of1/4 degree latitude by 1/3 degree longitude, covers a large area. At 45° North, thisgrid block has dimensions of 28 by 27 km, or nearly 750 km2. In Table 1, theextrapolation of wind speed between 10, 30 and 50 m is based on the 1/7thpower law.

Typically, grid blocks designated as Class 4 or greater are considered to be suitablefor most wind turbine applications. Class 3 areas are suitable for wind energy devel-opment using taller wind turbine towers. Class 2 areas are considered marginal forwind power development and Class 1 areas are unsuitable. As noted by Elliott [4],the interpretation of the wind power classifications is conditional on the followingassumptions:

� The wind power estimates apply to areas free of local obstructions to the wind.� The wind power estimates apply to terrain features that are well exposed to the

wind, such as open plains, hilltops, and, in mountainous areas, exposed ridge tops.

Local conditions can cause the wind resource to vary widely within one grid block.The classification scheme is not designed to handle variability on a local scale,merely to identify the potential wind resource for the best sites within the cell bound-aries.

To maintain consistency with the Battelle—PNL scheme, the wind power classi-fications for each region imply the area is free of local obstructions. The presenceof trees on hills and ridges within the Province pose a significant technical andenvironmental issue for wind turbines siting and estimation of potential energy. For-ested hills and ridgelines result in higher wind shear, greater turbulence, and a lowerwind resource value than similarly shaped hills with low grasses or brush. Nierenberg[11] and Meroney [12] studied the effects of the presence and removal of vegetationon the wind profile. Depending upon the height of the trees, removal of the treesresulted in increases in wind speed and reduction in turbulence at all measurementlevels below 40 m.


Hills or ridgelines are known to cause wind ‘speed-up’ associated with streamlineconvergence. Wind energy experts frequently focus on the potential for wind speedamplification in hilly terrain. Their interest has led to additional field and laboratorydata on the behaviour of neutral and stratified flows over both two dimensional ridgesas well as three-dimensional hills, valley, and gorges. Hills and ridges are know toproduce higher wind speeds at a given height over the crest than farther up-windduring high speed neutrally stratified air-flow. The approximate increase in windspeed should be on the order of h/L, where h is the hill height above the surroundingterrain and L is some characteristic horizontal hill width.

The increase in wind speed at the crest of the hill is termed in the fractional speed-up, or:

�V �V(z) � Vo(z)

Vo(z)(7)

where V(z) is the wind speed at some height z at the crest of the hill and Vo(z) isthe wind speed at height z some distance up-wind. Thus, the presence of hill andridges will lead to substantially increased wind speeds at 30 m above ground level.The wind power classification will therefore be higher in hilly terrain than on adjac-ent flat terrain. The estimation of the fractional speed-up follows the guidelines pro-posed by Taylor et al. [13].

3. Sources of meteorological data

Historical meteorological data for the Province of Quebec have been obtainedfrom Atmospheric Environment Services (AES), Hydro-Quebec (HQ) archives aswell as from private wind power developers, Project Eole and Kenetech. A list ofall the sites, ranked in alphabetical order by station name and source of the data, ispresented in Table 2. This table includes the station name, sensor height (m), annualaverage wind speed (m/s), the source of the data and period of record. A code ofN/A, meaning Not Available, is used for any annual or seasonal average wind speedwhere there is missing or insufficient data. No specific information on the height ofthe wind measurement level was readily available for some of the AES data sites,so a height above ground level of 10.1 m is estimated (E).

Most of the meteorological data for the Province was obtained from the Atmos-pheric Environment Service of Environment Canada. Wind speed, temperature, andpressure data, in summarized form, was provided in the Canadian Climate Normals1961–90 [5], Canadian Climate Normals 1951–80, Vol 5—Wind [6] and from theAES data archives. The data from Canadian Climate Normals 1961–90 [5] and1951–80 [7] were considered the primary data as these data represented the longestperiod of continuous record for any of the sites in the Province. The thirty-five (35)sites considered as the primary data sites are presented in Table 3. Although datawas available from 78 sites in the Province, not all sites could be considered asprimary sites due to completeness of the data records. However, the data from thesesites is considered in the judgment of the available wind resource.


Table 2Meteorological stations in Quebec ranked in alphabetical order (name, annual average wind speed (m/s),sensor height, source of information and period of record)

Station Annual wind Sensor height (m) Data source Period of recordspeed (m/s)

Amos 3.4 E 10.1 AES 1978–89Anticosti, Pointe S-O 7.4 E 10.1 AES 1937–54Arvida 3.5 12.2 AES 1955–72Bagotville A. 4.2 10.1 AES 1961–90Baie Comeau A. 4.4 10.1 AES 1961–90Baie-Comeau CBA 3.6 E 10.1 AES 1931–60Blanc-Sablon 7.0 10.1 AES 1967–80Border A. 5.6 E 10.1 AES 1955–72Cache Lake 3.1 E 10.1 AES 1951–60Cap Tourmente 3.7 E 10.1 AES 1972–84Cap-D’Espoir 4.8 E 10.1 AES 1991–93Cape Hopes Advance 7.7 E 10.1 AES 1955–72Cape Whittle 8.1 E 10.1 AES 1955–72Caplan CDA 5.2 15.2 AES 1955–72Chapais 3.4 12.2 AES 1962–80Chibougamau A. 3.3 10.1 AES 1971–80Deception Bay 5.2 E 10.1 AES 1964–72Duchesnay 2.4 E 10.1 AES 1955–72Foret Montmorency 1.9 E 10.1 AES 1961–90Gaspe A. 3.9 8.5 AES 1965–80Grande Riviere 4.7 18.3 AES 1955–72Grindstone Island 8.6 10.1 AES 1961–90Harrington Harbour 6.1 E 10.1 AES 1955–72Ile Charron 4.4 E 10.1 AES 1991–93Kuujjuarapik A. 4.7 10.1 AES 1961–90La Grande Riviere A. 4.9 10.1 AES 1976–80La Pocatiere CDA 4.4 E 10.1 AES 1955–72Lac Eon Aut. 3.8 10.1 AES 1955–80Megantic A 4.5 E 10.1 AES 1955–72Manicouagan A. 2.7 E 10.1 AES 1961–71Maniwaki 1.9 10.1 AES 1961–90Matagami A. 3.5 10.1 AES 1973–80Mont-Joli A. 5.3 10.1 AES 1961–90Montreal A.(Dorval) 4.2 10.1 AES 1961–90Montreal Ice Controle 4.9 10.1 AES 1967–70Montreal McGill 3.1 10.1 AES 1961–90Montreal Mirabel A. 3.3 10.1 AES 1975–80Natashquan A. 4.6 10.1 AES 1961–90Nitchequon 4.4 10.1 AES 1961–90Normandin CDA 3.9 10.1 AES 1961–90Parent 3.0 10.1 AES 1972–78Quebec A. 4.2 E 10.1 AES 1961–90Riviere-au-Renard 5.8 10.1 AES 1955–72Riviere-du-Loup 3.9 10.1 AES 1965–80

(continued on next page)


Table 2 (continued)

Station Annual wind Sensor height (m) Data source Period of recordspeed (m/s)

Roberval A. 4.2 10.1 AES 1961–90Rouyn A. 3.3 10.1 AES 1955–80Saint Henri-Taillon 4.0 E 10.1 AES 1991–93Saint-Felicien 3.4 E 10.1 AES 1940–53Saywerville Nord 3.6 10.1 AES 1961–90Schefferville A. 4.7 10.1 AES 1961–90Sept-Iles A. 4.4 10.1 AES 1961–90Sherbrooke A. 2.8 10.1 AES 1961–90St-Augustin 4.0 10.1 AES 1961–90St-Hubert A. 4.4 10.1 AES 1961–90Ste Anne-Des Monts 5.9 E 10.1 AES 1991–93Ste-Agathe-des-Monts 3.1 10.1 AES 1961–90Ste-Anne-de-Bellevue 4.0 10.1 AES 1961–90Trois Rivieres 3.8 E 10.1 AES 1991–93Val D’Or A. 3.6 10.1 AES 1961–90Valcartier FES 1.5 E 10.1 AES 1955–72Fonderie Cap-Chat 6.3 10 HQ 1981–83Iles-de-la-Madeleine 8.0 27 HQ 1981–83Pointe-au-Tonnerre 5.2 10 HQ 1981–83Riviere-au-Renard 5.7 10 HQ 1981–83Cap Chat 6.2 27 Kenetech 1994–95Matane 7.2 27 Kenetech 1994–95Eole Project Tower 6.6 60 Eole Project 1994–95Eole Project, tower 1 N/A 27 Eole Project 1994–95Eole Project, tower 2 N/A 27 Eole Project 1994–95Eole Project, tower 3 N/A 27 Eole Project 1994–95

Table 3Selected 35 AES meteorological monitoring sites in the Province of Quebec

Bagotville Kuujjuaq Montreal–McGill Sawyerville NordBaie Comeau Kuujjuarpik Montreal–Mirabel Sept-IlesBlanc Sablon La Grande Riviere Natashquan Schefferville AChapais Lac Eon A Nitchequon SherbrookeChibougamau Maniwaki Normandin CDA St AugustinForet Monmorency Matagami Quebec St HubertGaspe A Mont-Joli Riviere du Loup Ste Agathe des MontsIle de Grindstone Montreal–Dorval Roberval Ste Anne-De BellevueInukjuak Rouyn Val d’Or

Hourly wind speed and wind direction data for four sites were provided by Hydro-Quebec. These sites included Magdalene’s Island, Fondiere, Point Nord Ouest, andPointe Au Tonnerre. Approximately two years of data from 1981–83 is available foreach site for up to four measurement levels from 10 to 60 m. Wind data was provided


by the wind project developers Project Eole and Kenetech. Hourly wind speed andwind direction data was obtained from Project Eole for four sites near Cap Chat.These four sites included the 60 meter level of the Eole Tower and three 30 metertower sites located near Cap Chat. A complete one year period of record was notavailable for Eole Sites 1, 2, and 3. Annual average and seasonal wind speed dataat 27 m was provided by Kenetech for one year at two sites—Cap Chat and Matane.

All of the meteorological data from Hydro-Quebec, AES, and wind power projectdevelopers is surface-based data. That is, the data are gathered from meteorologicaltowers at airports or other sites of interest, and the maximum height of sampling isapproximately 60 m. Other data are available for the Province of Quebec whichallows an understanding of the wind speed and wind direction at other heights abovethe ground level. These data are termed ‘upper air climatic summaries’ . Twice daily,wind profiles are obtained at specific locations around the globe. These profiles,which extend to 30,000 m above the earth’s surface, are principally used in weatherforecasting activities. Climatological summaries of these profiles are available on amonthly and annual basis. The Global Upper Air Climatic Atlas (GUACA) [10] wasobtained from the National Climatic Data Center (NCDC) of the Department ofCommerce of the United States of America. This two-volume CD-ROM set presents12 year (1980–91) 2.5 degree gridded upper air climatic wind speed and wind direc-tion data derived from the European Centre for Medium Range Weather Forecasts(ECMWF) model analysis. Summaries for a total of 15 different vertical levels arepresented in this set. The wind data for each month and for the entire period ofrecord for the 85 kilopascals vertical pressure level (approximately 1500 m) wasreviewed. The pattern and grid point wind data show a wind speed maximum at thislevel over the Province between 45 and 50°N. North of 50°, the grid point scalar windspeed values decrease. These data are used to assist in defining the wind resource atridge tops in the Laurentian Mountains and the Gaspe Peninsula at elevations greaterthan 1000 m.

The 35 principal meteorological monitoring sites are listed in Table 4. Estimatesof annual average wind speed, annual Wind Power Density (WPD) and associatedWind Power Class, based on the PNL classification scheme, are also presented inthe table. The 10 meter monthly average wind speed data for each of these sites wasadjusted to a 30 meter height level using the wind power law with a power lawexponent of 0.17. As can be seen from Table 4, the majority of sites are characterizedas Wind Power Class 1 or Wind Power Class 2. Only five sites have a wind powerclassification of Class 3 or higher. These sites are Blanc Sablon (Class 7), GrindstoneIsland (Class 7), Inukjuak (Class 4), Kuujjuarpik (Class 3), and Mont-Joli (Class 3).

Wind power density (WPD), expressed in Watts per square meter (W/m2), is calcu-lated for each of the primary monitoring sites using the monthly average wind speed,the monthly average temperature, the monthly average pressure and the RayleighDistribution. In the event of missing data, the values for a nearby, climatologicallysimilar site are substituted. Wind power density could not be calculated for ‘LaGrand Riviere’ station as there is no temperature or pressure data available in theclimatic records for this site.

For each grid block, an annual wind power class at 30 m above ground level is


Table 4Province of Quebec—principal monitoring sites 30 m annual average wind speed (m/s) and wind powerdensity (WPD)

Station Wind speed at Wind Location (region) WPD (annual)30 m (m/s) power (W/m2)

class

Bagotville 5.1 1 Quebec City 166Baie Comeau 5.4 2 North Shore 216Blanc Sablon 8.4 7 Golf of St. Lawrence 825Chapais 4.1 1 Interior and South 83Chibougamau 4.0 1 Interior and South 76Foret Monmorency 2.4 1 Quebec City 18Gaspe A 4.9 1 Gaspe Peninsula 148Grindstone Island 10.5 7 Golf of St. Lawrence 1402Inukjuak 6.8 4 Northern Quebec 395Kuujjuaq 5.3 2 Northern Quebec 192Kuujjuarpik 5.9 2 Northern Quebec 269La Grande Riviere A 5.9 2 Northern Quebec N/ALac Eon 4.5 1 North Shore 109Maniwaki 2.5 1 Interior and South 20Matagami 4.5 1 Interior and South 111Mont-Joli 6.5 3 Gaspe Peninsula 356Montreal–Dorval 4.9 1 Interior and South 148Montreal–McGill 3.7 1 Interior and South 64Montreal–Mirabel 3.9 1 Interior and South 84Natashquan 5.5 2 North Shore 216Nitchequon 5.3 2 Northern Quebec 176Normandin CDA 4.7 1 Interior and South 128Quebec 4.9 1 Quebec City 150Riviere du Loup 4.7 1 Gaspe Peninsula 133Roberval 4.9 1 Interior and South 144Rouyn 4.0 1 Interior and South 76Sawyerville Nord 4.3 1 Interior and South 98

assigned on the map presented at Fig. 2. Associated with the wind power class isan WPD range (see Table 1). The wind power density values in the PNL Report arebased on standard conditions (288 K, 101.3 kPa). For each region, air density iscalculated using regional values of temperature and pressure. These regional valuesare compared to the values based on standard conditions and an adjustment factoris determined. This adjustment factor range from 1.02 in Quebec City to 1.05 inNorthern Quebec.

4. Classifications

The discussion of the classifications in the Province is organized along the linesof the six geographical area presented in the previous section.


Fig. 2. Wind power class distribution for Quebec Province.

4.1. Gulf of St. Lawrence

This area includes Anticosti Island, Magdalene’s Island and the Northeast CoastalSections of the Province. Measurements taken at Anticosti SW Point indicate Class5 wind power is possible. The island was judged to have Class 4–5 Wind Power onthe west and south facing coasts and in the interior elevations above 500 m. Class3 wind power was estimated along the north and east coasts of the island. Someisolated locations along this coastline may have a Class 4 resource.

The Magdalene’s Island has an excellent wind resource. Historical wind speeddata from Grindstone Island indicates a Class 7 wind power class at this specificlocation. Overall, the Magdalene’s Island is judged to have a Class 6 wind resource.


One of the best wind resource areas in the northeast appears to be at the easternend of the Province near Blanc Sablon. The data from the AES Site indicates a Class7 wind resource at this specific location. On the basis of this measurement, the areaat Blanc Sablon was classified as Class 6. The coastal areas and the interior fromBlanc Sablon southwestward was classified as Wind Power Class 4 and Wind PowerClass 5.

4.2. Northern Quebec

Very little meteorological data is available in this region. The stations for whichdata is available include Schefferville, Nitchequon, La Grand Riviere, and Kuujjuara-pik. All four sites have annual average wind speeds around 5.8 m/s. Interior sectionsare judged to have a Class 2 resource in the eastern portion and a Class 3 resourceelsewhere. Based on the measurements at La Grand Riviere and Kuujjuarapik, thecoastal and coastal interior sections along Hudson’s Bay and James Bay are thoughtto have a Class 3 resource. There is an area of potential Class 3 and Class 4 resourceat the crest of a line of hills extending from 52 N 73 W to 52.5 N 70.5 W. TheClass 4 resource will occur along the higher terrain where the elevations exceed1000 m.

The wind resource increases north of 60 degrees. The interior sections and coastalsections along Hudson’s Bay are judged to be Class 3. The far northern section ofthe Province has a wind resource which is judged to be Class 4 and Class 5 in themountains of the far north and Class 6 along the immediate coast. The east facingcoastal sections around Ungava Bay are judged to be Class 3 while the west facingcoastal sections are judged as Class 4–6.

4.3. Gaspe Peninsula

The Gaspe Peninsula appears to have potential wind resources ranging from Class3 to Class 6. This is based on historical wind speed data from the airport at MontJoli, observations at Gaspe and Riviere du Renard, upper air data, and an assessmentof the terrain induced speed up over the mountains and hills of the interior andeastern sections of the peninsula. Southwest of Mont Joli the wind resource alongthe coast and interior sections is Class 3. South-southeast of Mont Joli on the interiorhills the wind resource is estimated as Class 4. The south and east coast of thePeninsula is classified as Wind Power Class 3 on the higher hills and ridges (500–800 m). Class 5 characterizes the coastal sections near Cap Chat and eastward toPoint-de-la-Fregate. The remainder of the coast is Class 4. The ridge crests of theinterior mountains are classified as Class 5 with the exception of the highest terrainnear Mt Jacques Cartier which should have a Class 6 wind resource.

Two of the three monitoring sites for the Lower St. Lawrence River Valley Studywere on the Gaspe Peninsula. The estimate of a Class 4–Class 5 wind resource inthe Cap Chat area is based on the data collected at the Fonderie Site. The 30 meterannual average wind speed is estimated as 7.2 m/s (Class 5). The wind power classi-fication for the higher terrain along the coast is estimated as one wind power class


greater. The second monitoring site was located at Pointe Nord Ouest at the easternend of the Gaspe Peninsula. The 30 meter annual average wind speed is estimatedas 6.9 m/s (Class 4), confirming the wind power estimates for this area.

4.4. North coast

The wind speed resource ranges from Class 2 to Class 3 along the north coast ofthe St. Lawrence River. Data from Baie Comeau (Class 2), Sept-Iles (Class 3), andNatashquan (Class 3) are used to make these estimates. Along the coast from BaieComeau to Sept-Iles, Class 2 wind resources predominate. From Sept-Iles to Natash-quan, a Class 3 wind resource is estimated.

One of the three towers installed as part of the St. Lawrence River Study wasinstalled on the north coast near Pointe au Tonnerre in an area judged as Class 3.The data from the two year period of study at 20 and 40 m indicates the annualaverage wind speed at 30 m above ground level is approximately 6.4 m/s. This agreesquite well with the classification of this area.

The northeast interior, extending from just west of Reservoir Manicouagan to theQuebec–Labrador border is estimated to have a generally Class 3 wind resource.Better wind resources are found on the mountains and ridges east and west of Reser-voir Manicouagan, higher terrain running due north of Sept-Iles, and high terrain( � 500 m) near 64 W. East and west of Reservoir Manicouagan, the ridgetops withelevations greater than 1000 m are characterized as wind power Class 4–5. Elevationsbetween 700 and 1000 m are characterized as Class 3. North of Sept-Iles, along theRiviere Moisie, elevations of 1000 m or greater have a Class 3 rating. The historicalwind speed data at Lac Eon classifies this area as Class 1. The interior sections northof Harve St. Pierre along 64 W at elevations greater than 900 m are Class 3.

4.5. Quebec City

Historical meteorological data from Quebec City, Bagotville, Foret Monmorency,and St. Augustin indicates generally Class 1 and Class 2 wind speeds in the interiorportions of the Province of Quebec north of Quebec City. Better wind resources(Class 3 or higher) are estimated in the Laurentian Mountains north and northeastof Quebec City. This wind power class is associated with elevations in excess of 1000m in the mountains. The mountains in the region from Quebec City—Chicoutimi—Saguenay River—St. Lawrence River and a small mountain range near 51 N 69.5W are though to have Class 4–5 resource at elevations greater than 1000 m. Duethe high terrain along the north coast of the St. Lawrence River, Class 3 wind poweris anticipated in this areas. The resource is thought to be better than the area on thesouth coast of the river.

Better than average wind resources are found in this area near the Thetford Minesand the Town of St. Philemon immediately south of Quebec City. These betterresources occur at the top of a line of hills with elevations of 800 m or greaterextending in a southwest to northeast line immediately northwest of the Thetford


Mines and an area of higher terrain with elevations greater than 1000 m near theTown of St. Philemon. This second area is rated as Class 3.

4.6. Interior and south

The historical wind speed data indicates the area north and northwest of Montrealis predominantly Class 1 and Class 2. The historical wind speed data from Roberval,Chapais, Chibougamau, Normandin, Ste. Agathe-des-Monts, Maniwaki, Val d’Or,and Matagami confirm this assessment. There is a small area of potentially Class 3resource northwest and north of Ste. Agathe-des-Monts. A series of hills and ridgesrunning perpendicular to the prevailing wind flow with elevations of 800 m to over1000 m should be Class 3. Class 2 wind power predominates in this region in thenorthwest towards James Bay.

Available wind speed data and the lack of any pronounced mountain or ridgelinefeatures indicates the wind resource in the region south of Montreal is generallyClass 1–2. The historical wind speed data from Sherbrooke, Sawyerville Nord, St.Hubert, and the airports south of Montreal confirm this resource. There are isolatedareas in this region with better wind resource: Mt. Megantic (Class 4), terrain inexcess of 1000 m near the Town of Sutton (Class 3), and a small area near the townof Notre Dame-des-Bois (Class 4) where the terrain exceeds 1000 m.

Acknowledgements

The authors acknowledge the financial support from Hydro-Quebec and the Que-bec’s Ministry of Natural Resources for the accomplishment of this work. The firstauthor kindly acknowledge the financial contribution from NSERC.

References

[1] Walmsley JL, Morris RJ, 1992. Wind energy resource maps for Canada. AES Report ARD-92-003-E.[2] Quebec Government Natural Resources Minister, 1996. L’ energie au service du Quebec, une per-

spective de developpement durable.[3] McCarthy E, Ilinca A, Chaumel JL, Dugas C, 1996. Evaluation du potentiel eolien du Quebec.

Technical Report, Universite du Quebec a Rimouski.[4] Elliott DL, Holladay CG, Barchet WR, Foote HP, Sandusky WF, 1987. Wind energy resource atlas

of the United States. DOE/CH10094-4.[5] Canadian climate normals 1961–90, Volume 5, 1993. Quebec, Atmospheric Environment Service,

Environment Canada.[6] Canadian climate normals 1951–80, Volume 5, 1982. Wind, Atmospheric Environment Service,

Environment Canada.[7] Climatic atlas—Canada, Map Series No. 5, 1988. Atmospheric Environment Service, Environ-

ment Canada.[8] COGESULT Inc.—Etude du Marche des Eoliennes de Petite Puissance, 1981. ARD Report 92-003-

E, Montreal, Canada.


[9] Taylor PA, Salmon JR, Berry RL, 1981. A preliminary report on the wind energy resource assess-ment of the three sites in the Lower St. Lawrence River Valley. AES Report AQRB-18-032-L.

[10] Global upper air climatic atlas (GUACA), 1992. Available from the National Climatic Data Center,Asheville, NC.

[11] Nierenberg RD. Detailed measurements on the effects of trees on wind speed, energy, vertical shear,and turbulence. In: Proceedings of the American Wind Energy Conference, 1992, San Francisco,CA. 1992. p. 218–25.

[12] Meroney RN, Neff DE, McCarthy EF. Elevation and vegetation considerations on wind power avail-ability: a wind tunnel study. In: Proceedings of the American Wind Energy Conference, 1992, SanFrancisco, CA. 1992. p. 213–7.

[13] Taylor PA, Walmsley JL, Salmon JR, 1988. Guidelines and models for estimating wind speeds atWECS sites in complex terrain. Boundary Layer Research Division, Atmospheric Environment Ser-vice, pp. 6.

[14] Wentink T, 1976. Study of Alaskan wind power potential and its possible applications. Final Rep.NSF/RANN/SE/AER 74-00239/FR-76/1, Geophysical Institute, U of Alaska.

[15] Justus CG, Mikhail A. Height variations of wind speed and wind distribution statistics. Geophys.Res. Lett 1976;3:261–4.

[16] Justus CG, Hargraves WR, Yalcin A. Nationwide assessment of potential output from wind powergenerators. J. Appl. Meteor 1976;15:673–8.

[17] Barnes CJ, Davenport AG, 1975. Some statistical models for wind climate predictions. Fourth Con-ference on Probability and Statistics in the Atmospheric Sciences, Tallahasse, FL, Amer. Met. Soc.,section 1–7.

[18] Corotis RB, 1977. Stochastic modeling of site wind characteristics. Final Report ERDA EY-76-S-06-2342, pp. 143.

[19] Cliff WC, 1977. The effect of generalized wind characteristics on annual power estimates from windturbine generators. DOE Report PNL-2436.

Documents

Wind potential assessment of Quebec Province