Thesis Revised- 19Aug2008

8/3/2019 Thesis Revised- 19Aug2008

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1.

Introduction

The US currently consumes about 107 exajoules (102 Quadrillion BTUs) of

primary energy annually, which translates into about a quarter of the worlds energy use

(EIA, 2007a). With rising concern about climate change and diminishing fossil fuel

reserves, there has been significant activity in the past 10 years to promote energy

conservation and the use of alternative fuels. While renewable technologies such as wind

and biofuels are growing at substantial rates (45% and 28% annually respectively), they

only comprise of 7% of the countrys total primary energy consumption (EIA, 2007a).

While renewable energy technologies offer many tangible benefits, such as providing

carbon-free sources of electricity, reducing dependency on imported fuel, and utilizing

fuel that is essentially free, they also have higher capital costs and can be more difficult

to integrate into the complex electrical energy supply grid. Various policy mechanisms

have been put in place to encourage renewable energy development. These include

consumer-choice incentives, state mandated green pricing policies such as net metering,

production tax credits, and renewable portfolio standards (Bird et al., 2005). Moreover,

greenhouse gas (GHG) reduction goals are likely to move from the voluntary carbon

trading market into mandated goalsbecoming more prominent in the United States

energy policy.

While renewable energy generation is expanding at a rapid pace, there are still

uncertainties about how future growth will proceed. Wind power is expanding at a

substantial rate in the Upper Midwest and is projected to comprise of the majority of


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electricity supplied by renewable sources in the near future (Menz and Vachon, 2006).

In order to better understand the potential for wind power to reach renewable policy

mechanisms and greenhouse gas reduction goals, it is important to understand this wind

resource.

1.1 Challenges of Wind Power

Competitive prices and government incentives for wind power have caused wind

technology to become the favored renewable energy technology (Heiman and Solomon,

2004). A study by Chen et al. (2007) analyzing renewable portfolio standard (RPS)

policies throughout the country showed that wind will make up the majority of the US

renewable mix targeted by RPS policies (Figure 1).

Figure 1: Mix of Incremental Renewable Generation in US (GWh,%)


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Long term energy planning is more difficult with intermittently available

renewable power such as wind than for fossil or nuclear fueled power plants. The

electrical energy grid relies on a readily dispatchable mix of power from coal, natural gas

and nuclear fueled power plants. When the demand for electric power increases, electric

grid operators respond by increasing the supply using reserve fossil fuel generating

capacity. The choice of generation depends on the reserve capacity available, the

marginal cost of production (based primarily on fuel type) and the ability to respond fast

enough to match instantaneous increases in demand. Dispatchable generation can be

relied upon in known quantities at any time. Coal and nuclear are the primary base load

fuel sources to meet electric demands that do not quickly change during the day. This is

due to their lower marginal cost of electricity production and their relatively slower

response rate than generation fueled by natural gas. The capacity factor of a base load

generating facility can reach over 72% for coal-burning plants and 89.6% for nuclear

power plants (EIA, 2007b). Capacity factors for wind power typically range from only

20% to 45%. Wind power is rarely thought of as a base load power source due to

intermittency of wind speeds, lack of steady wind flow and challenges in forecasting

energy production. The value of the energy generated depends upon its dispatchability.

In comparison to other non-dispatchable and renewable resources, wind power is cited as

the least certain and the most variable (Figure 1; Piwko et al., 2005). Current

transmission tariffs include imbalance charges which reduce the value of energy if the

day-ahead scheduled energy is actually different than the real-time demand. This


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strategy promotes good scheduling practices and provides some security that there will be

sufficient generation to meet demand at the time it is needed. (Wan et al. 2007)

Figure 2: Variability and Predictability of Non-dispatchable Generating Resources (Piwko et al. 2005)

Intermittency is a major concern when integrating wind power into the electric

grid on a large scale. Policies have been put in place to reduce penalties against wind

power producers for being unable to meet the forecasted output. The Federal Energy

Regulatory Commission (FERC) passed Order 890 which states that wind power cannot

be charged for imbalance charges, when the scheduled energy production does not meet

delivered production, in the same way that conventional fuel sources are charged (FERC,

2007). As exemplified in the February 2008 near-brownout in Texas due to a lapse in

wind power, intermittency must be examined when developing large amounts of wind

power (Dalton, 2008). With an increased understanding of the characteristics of wind on

a large geographic scale, some of these challenges can be minimized. Geographic


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distribution of wind power can relieve some challenges of intermittency (Archer and

Jacobson, 2007). When wind is not blowing in one area, with a connected transmission

grid, other wind power resources may be able to provide that power.

Accurately forecasting wind can be a challenge to increasing wind power

development. Wind power relies on forces of nature that can be predicted to some degree,

but prediction error increases when forecasted over a longer time horizon (Figure 3).

Forecasting studies have focused on understanding high ramp periods: times when

electric demand changes rapidly and changes in wind can have a strong effect on power

Figure 3 : Wind Forecasting Accuracy for a Single Wind Farm (Piwko et al. 2005)


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production (Zack, 2007). While wind power cannot be predicted with complete certainty,

technological advances that decrease forecast error have reduced costs associated with

variability of wind power (Piwko et al., 2005).

Transmission capacity also presents a challenge for an electrical fuel mix that

relies heavily on renewable sources. As wind power is developed over a wider

geographical area, the need for transmission capacity required to move this power to end

users increases. While transmission issues will not be analyzed quantitatively in this

thesis, it is important to understand the need for increased investment in transmission in

order to support a geographically disperse wind generation network. There is a

substantial amount of wind power in the Upper Midwest and Great Plains, but these areas

lack the transmission infrastructure needed to get wind power access to the market. The

Department of Energy released a report in 2006 examining congestion in the transmission

grid. They concluded that while currently there is little congestion in the Dakotas and

Minnesota, significant congestion would result if large amounts of new generation

resources were to be developed without simultaneous development of associated

transmission capacity (DOE, 2006).

1.2 Background on the Minnesota Wind Integration Study

In May 2005 the Minnesota State Legislature mandated that the MN Public

Utilities Commission (MN PUC) perform a wind integration study (MWIS) to evaluate

impacts on reliability and costs of increasing wind capacity within the state. The goal of

the MWIS was to analyze the effect of integrating up to 20% of the states electric

demand with wind power on the requirements for reserve generation capacity. (The


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Minnesota RPS is 25% of electrical power generated by renewable source by the year

2025). This study also identified strategies for utilities to manage the inherent

intermittency of wind in their generation mix. The study examined the effect of wind

integration on the reserve capacity requirements to estimate this external cost of wind

development. The MN PUC used an independent consultant to conduct the MWIS and

all MN utilities were involved. The MN PUC ordered that the utilities must incorporate

the findings of the study into their resource plans and renewable energy objectives reports

(EnerNex, 2006).

The MWIS, which began in September of 2003, included analyses of the impacts

and costs of increasing wind capacity to 15%, 20% and 25% of MN retail electric sales

by 2020, and compared the incremental costs and benefits to each other (EnerNex, 2006).

By analyzing the varying levels of integration in wind capacity on the system, the MWIS

hoped to provide guidance in the best practices to expand wind into the electric supply

mix. The MWIS used real time comparisons of both energy demand and wind power

availability to examine how wind power availability interacts with demand to provide a

more accurate estimate of which fuel sources would be displaced by wind power, which

will always have a lower marginal cost of production than power plants consuming fossil

or nuclear fuels. The study also investigated how wind integration would influence

reserve capacity requirements. The MWIS simulated wind generation historical data

using the Mesoscale Model 5 (MM5) for 152 locations grouped into 14 development

zones across Minnesota and the eastern Dakotas in areas that already have substantial

wind development and areas with development potential as determined by wind map


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developed by the MN Dept of Commerce wind map. The MWIS does not provide detail

on how the wind speeds at hub heights of 80m and 100m were calculated or extrapolated.

MM5 was developed by researchers at Penn State and NCAR (MM5, 2008) and

uses 4 km x 4 km spatial resolution. Wind speed and direction, temperature, pressure and

air density were extracted from the MM5 model and applied in a model developed by

WindLogics to form a time series of wind power density model for the 152 sites chosen

for the MWIS for 5 minute and 1 hour intervals and hub heights of 80 and 100 meters.

EnerNex then used these time series to model the wind power potential of these 152

proxy towers and the effects of several levels of wind power penetration on the electric

grid. The report did not state how the wind speeds at 80 and 100 meters were calculated.

The MWIS validated its model with one meteorological tower in Breckenridge, MN. The

model compared favorably to this one validation point; however, it is difficult to ascertain

accuracy using only one point of validation.

The wind development zones used in the MWIS are shown in Figure 4 and Table

1 presents the installed capacity for each area according to the percentage of wind

integration into the system.


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The MWIS concluded that the addition of wind generation up to 20% of MNs

electric sales would be reliably accommodated by the electric power system if sufficient

transmission investments are made to support it (EnerNex, 2006). The MWIS study

assumes that transmission capacity will be upgraded in the near future to support

increased wind generation and substantiates the need for this future growth and the

reduced risk for transmission investment (Appendix A).

Figure 4 shows the zones where wind development was modeled to occur. Table

1 presents the installed capacity for each area according to the percentage of wind

integration into the system.

Figure 4: Wind generation regions and network injections buses- MWIS (EnerNex, 2006)


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Table 1: Meteorological Tower Assignments by region and scenario (EnerNex, 2006)

Area Bus Voltage Towers MW-

15%

MW-

20%

MW-

25%

1 Forbes 500 kV 79, 80, 81, 82, 83, 84, 85 200 280 2892 Winger 230 kV 63, 64, 133, 134, 138, 142, 143 200 280 280

3 Leland 345 kV 113, 114, 115, 116, 117, 123, 124, 125 280 320 320

4 Maple River 345 kV 2, 3, 91, 92, 126, 140, 141 240 280 280

5 Ellendale 230 kV 111, 112, 118, 119, 120, 121, 122 261 261 280

6 Alexandria 345 kV 10, 13, 65, 68, 88, 89, 127, 146, 147,

148

280 400 400

7 Watertown 345 kV 98, 99, 100, 101, 102, 103, 104, 105,

106, 109, 110

0 200 440

8 Granite Falls 345 kV 75, 78, 128, 129, 131, 135, 136, 137,

145, 150

360 400 400

9 Lyon County 345 kV 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,

32, 33, 34, 35, 50, 51, 52, 53, 54, 55, 67,

68, 69, 70, 71, 72, 73, 74, 93, 94, 95, 96,97

607 767 1364

10 Adams 345 kV 38, 39, 40, 41, 42, 43, 44, 45, 139, 151 364 404 404

11 Willmarth 345 kV 8, 9, 11, 12, 86, 87, 130, 132, 144, 149,

152

283 443 443

12 Lakefield

Junction

345 kV 1, 4, 5, 6, 7, 14, 15, 16, 17, 18, 19, 20,

36, 37, 90

432 552 600

13 Nobles County 345 kV 46, 47, 48, 49, 56, 57, 58, 59, 60, 61, 62,

76, 77

127 207 520

14 Ft Thompson 230 kV 107, 108 40 80 80

Another interesting conclusion of the MWIS was the implications of geographic

dispersion of wind generation resources. Previous studies have found that fewer ancillary,

or back-up systems, are needed with a more geographically dispersed wind regime

because wind correlation decreases as distance between turbines increase as shown in

Figure 5 (Ernst et al, 1999). The MN Wind Study found that an increase in geographic

separation or diversity of wind plants allows for higher and more consistent capacity

factors. Figure 6 shows the percentage of time in which there is a less than 5% capacity

factor is reduced significantly with wind turbines that are geographically dispersed

(EnerNex, 2006).


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Figure 6: Annual hourly capacity factor

Figure 5: Correlation of wind generation power changes to distance between plants/turbines (NREL, 1999)


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1.3 Meteorological Data for Wind Analysis

Numerous studies have analyzed wind resources using annual average wind

speeds which are typically presented in the form of wind resource maps (Schwartz, 2001;

Manwell, 2002; Archer and Jacobson, 2005; Ramachandra and Shruthi, 2005; Acker et

al., 2007; EERC, 2008). Some detailed wind resource maps are proprietary. Others do

not provide sufficient and/or temporal resolution to analyze the complexities of

integrating wind generated electric power with existing generation and transmission

resources to meet geographically dispersed demand. Many open-source wind maps, such

as those found on the National Renewable Energy Laboratory (NREL) website, offer

annual or monthly wind speed averages. Meteorological data can be used to study

weather patterns on a finer temporal scale (typically hourly data) but is often based on

ground level measurements which may not provide accurate estimates of the wind speed

at typical hub heights of modern wind-electric conversion devices.

Wind resources can be quantified over any time period but common temporal

resolutions include annual averages, monthly averages, 3 hour averages, 1 hour averages

and 5 minute averages. It is important to use data that have an appropriate temporal

resolution to understand seasonal and diurnal variations in the availability of wind power.

Hourly meteorological data are available for weather stations across the United States but

typically measured at 10 meter heights, and are often recorded at airports that are

protected from high winds. In order to extrapolate for wind turbine hub heights, the 1/7

Power Law is often used (Manwell, 2002). The 1/7 refers to a generalized wind shear

exponent that measures surface roughness surrounding the wind turbine. This


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generalized number, while accepted for purposes of extrapolation, also can be a source of

error in evaluating wind resources from 10 meter wind measurements (Archer and

Jacobson, 2003). These data have limited value in accurate prediction of the diurnal

values of wind power produced from wind generation devices with hub heights of 80

meters or more. These data also have limited geographical coverage. Accurate hourly

wind speed data for 80 meter surface heights (the typical hub height of a modern wind

turbine) at locations well suited for the development of wind power resources generally

relies on the use of tall monitoring towers. It is therefore difficult and expensive to

obtain, and is often proprietary information.

The North American Regional Reanalysis (NARR) offers a database of

meteorological modeled data that has not been previously tested to evaluate it ability to

accurately estimate wind resource in the Upper Midwest. The NARR data was produced

by the National Center for Environmental Prediction (NCEP) and the National Center for

Atmospheric Research (NCAR) and spans periods from 1979 to the present day

(Mesinger et al., 2004). The datas temporal resolution is 3-hour intervals and the spatial

resolution is 32 km2. This temporal resolution is sufficient to understand diurnal and

seasonal wind patterns and the spatial resolution is sufficient for prediction wind potential

on a regional basis.

The NARR data is derived from the Eta model and assimilation systems to

analyze meteorological data (Mesinger, 2004). Completed in 2004, it expands upon and

improves the resolution and accuracy of the NCEP/NCAR Global Reanalysis first


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developed in 1997 (Kalnay et al. 2006). It consists of modeled atmospheric data over the

vertical span of the earths atmosphere above North America, and was developed to

address questions related to the variability of weather and climate with a focus on

precipitation patterns.

Numerous studies have been published using NARR data to understand

precipitation patterns in North America (Luo et al, 2006; Karnauska, Barradas, et al.,

2008; Becker and Berbery, 2008; Szeto, Tran, et al. 2008). Precipitation is closely tied to

other weather patterns such as wind. There are no studies that investigate wind power

production using the NARR data. While the spatial resolution is somewhat coarse (32

km2 grid points), this resolution is of the same order of magnitude of large scale wind

farm development. Figure 7 shows an example map of using the NARR database to

show annual average wind speeds over a geographic area.


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The NARR data is modeled over a vertical grid of 29 constant pressure layers, or

isobars, rather than surface heights. The lowest pressure level is 1000 millibars, which

corresponds to the 29th

pressure level (Z29). The pressure level intervals are 50 millibars.

Figure 7: 2003 Average Wind Speed Map of the Midwest-- NARR data from the 29th pressure level


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The NARR data uses geopotential height instead of measured height to

approximate the altitude of varying pressure levels in the atmosphere. Geopotential

height (GPH) approximates the height of each pressure level above sea level (AMS,

2008). The GPH of a region can vary across points on the map and temporally at each 3-

hour reported value, because the GPH is dependent on the pressure gradient. For

example, an atmospheric pressure of 1000 millibars (or one standard atmosphere) would

correspond to a surface measurement at sea level when the barometric pressure is at one

standard atmosphere. Variations in barometric pressure occur above and below one

Figure 8: Average GPH by Month, 2003


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standard atmosphere as weather patterns change so that when a low pressure weather

event is in progress the GPH of a specific location may actually be predicted to occur

below ground level. Conversely, the same location could have a GPH would be above

ground level during a high pressure event. A main focus of this study was to ascertain

which pressure layer would most accurately represent wind potential using the NARR

data. Figure 8 shows an example of the output of data for showing GPH heights for 2003.

This figure demonstrates the variability and range of GPHs dependent on month; for

January, the GPHs are considerably lower than July GPHs, suggesting that the GPHs

vary seasonally.

NARR data are available in netCDF (network Common Data Form) and GRIB

(GRIdded Binary) format from NCEP. Both of these file formats are often used by

meteorologists and atmospheric scientists. For this study, the files were extracted in the

netCDF format. NetCDF is a set of interfaces for array-oriented data access and a freely

distributed collection of data access libraries for C, Fortran, C++, Java and other

languages. NetCDF was designed to promote exchange of and access to scientific data.

Other programs that can be used to analyze netCDF data include EPIC and the Generic

Mapping Tool (GMT) (Unidata, 2008). The NARR data used in this study were

analyzed using an open-source program called Ferret. Ferret is an interactive computer

visualization program used mainly by oceanographers and meteorologist to analyze large

and complex gridded data sets (PMEL, 2008). The gridded output can also be used in

Geographic Information Systems to analyze with other geodatabases.


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1.4 Objectives of This StudyThis study was undertaken to compare wind energy availability predictions from

different meteorological models. Predictions of wind farm capacity factors using the

North American Regional Reanalysis (NARR) were compared to corresponding locations

from the MWIS. This study was geographically bounded within the states of Wisconsin,

Minnesota and North and South Dakota. An additional goal of this study was to

investigate the correlation between geographically dispersed wind developments in the

Upper Midwest.


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2. Methodology2.1 Calculating Capacity Factors from MWIS

Estimates of wind speeds and power production for 6 of the wind development areas in

the MWIS for the years 2003, 2004 and 2005 were compared to estimates made using

NARR meteorological data. The 6 locations were chosen to reflect wind resource

diversity (high and low annual wind speeds) as well as geographic diversity across the

states of Minnesota and North Dakota region used in the MWIS. The longitude and

latitude of each location, grid value used to extract the data from the NARR database,

general location in each state and wind class are given in the first part of Table 2.

Table 2: Study locations

Minnesota Wind Study versus NARR data Comparison Locations

State / Location

(MWIS

development area) Latitude Longitude

NARR

X Grid

value

NARR

Y Grid

Value

Location

in the

state

Wind

Class

at 80mMinnesota

Hibbing (1) 47.427N -92.937W 206 137 NE 3

Rochester (10) 44.021N -92.469W 210 126 SE 4

Slayton (9) 43.987N -95.755W 203 125 SW 5

Thief river falls (2) 48.119N -96.18W 199 138 NW 4

North Dakota

Berthold(3) 48.315N -101.735W 187 138 NW 5

Kulm (5) 46.301N -98.954W 194 132 SE 5

Geographical time correlation Study Points (in addition to those above)

North Dakota

Devils Lake 48.112N -98.864W 194 138 NE 3

Dickinson 46.879N -102.789W 185 133 WSW 4

South Dakota

Bison 45.520N -102.46W 185 128 NE 5


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Hot Springs 43.431N -103.473W 183 121 SW 4

Huron 44.363N -98.213W 196 125 E Central 3

Stockholm 45.099N -96.801W 199 127 NE 5

Winner 43.376N -99.858W 193 121 S Central 6

Wisconsin

Bristol 42.558N -88.049W 222 123 SE 3

Luxemburg 44.548N -87.707W 221 134 NE 4

Mason 46.465N -91.111W 212 135 N 2

Menominee 45.019N -88.699W 219 132 NW 4

Platteville 42.734N -90.478W 216 123 SW 4

Hourly energy production data from the MWIS were provided by a co-author of

the MWIS (Shuerger, 2008). The MWIS used 152 proxy towers with a total installed

capacity of about 40 MW for each proxy wind farm to create a model of geographically

dispersed wind generation. The data were grouped together into 14 wind development

areas as shown in Figure 4. Details of the 14 wind development areas with their installed

capacities for each wind development area are given in Table 2. The raw data used for

this study included estimated MWh output from each MWIS zone for each hour time

period over the 3 years of the study.

The MWIS studies three wind energy penetration levels: 15%, 20% and 30% of

the total annual electrical energy use in the state of Minnesota. Data from the 20% wind

energy penetration level reflecting the middle level used in the MWIS were used in this

study to calculate capacity factors for each of the 6 regions studied. The 20% penetration

level is the level that the MWIS concluded was a viable option for the Minnesota power

system and corresponds to target levels for the Midwest Governors Association


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electricity goals for 20% renewable energy by 2020 in the Midwestern states (MGA,

2007).

2.2 Calculating Capacity Factors of NARR Data

Data was extracted from NARR using the Ferret program for 4 months (January,

April, July and October) from each of the years 2003, 2004 and 2005 for the 6 MWIS

locations selected for this study. The following four variables were extracted from the

NARR dataset: the u-vector wind velocity, the v-vector wind velocity, GPH and surface

elevation. The wind vectors describe the instantaneous wind direction and speed; the u-

vector represents the east/west wind direction and the v-vector represents the north/south

direction (AMS, 2008).

The NARR database includes 29 pressure levels or isobars modeled throughout

the atmosphere. The earths surface layer is approximated by the 29th

pressure level

pressure level (Z29), which corresponds to an atmospheric pressure of 1000 millibars

(mb) or one standard atmosphere. This isobar was thought to best represent the boundary

layer of the atmosphere in which wind turbines operate. These isobars have differing

geopotential height (GPH) and absolute height above the ground surface which both vary

with atmospheric conditions.

Table 3 shows that the monthly average GPH for Z29 are below the surface

elevations for all locations and all seasons. This simply means that the monthly average

barometric pressure is below one atmosphere at the average elevation ofearths surface at

these locations. The GPHs at the 27th

pressure level (Z27), or 950 millibars, were all


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above the grounds surface elevation and comparable to wind turbine hub heights

referenced to sea level, except for Kulm, ND (Table 4). Both pressure levels were

analyzed because while wind data from Z29 may be modeled below ground, it also

represents the pressure level closest to the boundary layer of earth. The wind data at Z27

was analyzed to investigate whether higher level wind patterns were better predictors of

geographically dispersed wind energy conversion systems than boundary layer wind

patterns. Wind data from the 28th

pressure level (or 950 millibars of pressure) was not

analyzed for this study because the GPHs at this pressure level were very similar to the

surface heights. Therefore, this pressure level did not reflect the hub heights of wind

turbines.

Table 3: Average monthly GPH for Z29 and height above surface

Location (surfaceelevation in

meters) January April July October

GPH

AboveSurfac

e GPH

AboveSurfac

e GPH

AboveSurfac

e GPH

AboveSurfac

e

Hibbing MN (379) 181 -198 127 -252 113 -266 123 -256Rochester MN

(379) 181 -198 123 -256 120 -259 132 -247Slayton, MN (487) 186 -301 123 -364 115 -372 128 -359

Thief River Falls,

MN (379) 185 -194 128 -251 109 -270 120 -259Berthold ND (487) 187 -300 128 -359 101 -386 120 -367

Kulm ND (598) 186 -412 126 -472 107 -491 122 -476


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Table 4: Average monthly GPH for Z27 and height above surface level

Location (surface

elevation in meters) January April July October

GPH

Above

Surface GPH

Above

Surface GPH

Above

Surface GPH

Above

Surface

Hibbing MN (379) 570 191 549 170 553 174 542 163Rochester MN (379) 579 200 547 168 564 185 561 182Slayton, MN (487) 587 100 546 59 560 73 554 67Thief River Falls, MN

(379) 575 196 550 171 550 171 540 161Berthold ND (487) 589 102 543 56 544 57 533 45

Kulm ND (598) 585 -13 548 -50 551 -47 550 -48

Four analyses were performed to determine which method was most closely

correlated with the MWIS predictions: Z29 and Z27 pressure level wind speed data both

uncorrected and corrected for hub height. The wind speed at each location was

calculated from the NARR data using equation 1.

Wind speed= (Vv2+Vu

2)^

1/2(1)

where:

Vu= NARR u-wind vector velocity (m/s)

Vv = NARR v-wind vector velocity (m/s)

These wind speeds were then adjusted for 80 meters hub heights using equation 2

for the hub height corrected analyses (Manwell et al 2002; Archer and Jacobson, 2003).

(2)


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where:

V(z)= estimated wind speed (m/s) at hub heightz (m)

VR= NARR wind speed at the reference height zR

= wind shear exponent = 1/7

For the Z29 pressure level data height correction the hub height (z) was taken

relative to sea level as the surface elevation plus 80 meters (assuming a hub height of the

Vestas V80 2MW wind turbine) and the monthly average GPH for each location was

used forzR. For the Z27 pressure level data height correction, the hub height (z) was taken

relative to the surface elevation (z = surface elevation + 80) and zR was taken as the

monthly average GPH minus the surface elevation of each location (Table 4). Kulm, ND

was not calculated in this correlation because the locationszR values are slightly

negative. All 5 other locations were representative of a height similar to that of a wind

turbine hub height.

The 3 hour average power output for each location was calculated using the

NARR wind speed data and a power curve representative of a 2 MW capacity, Vestas

V80 wind turbine (Figure 9 and equation 3, derived from manufacturers data). The 3

hour energy production was obtained by multiplying the average power output by three

hours.

y = 0.068x5

- 3.237x4

+ 54.82x3

- 397.2x2

+ 1345.0x1706 (3)


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Figure 9: Vestas V80 Power curve (2 MW)

Monthly average capacity factors (CFm) were calculated for each location from

the four NARR data sets (Z29 and Z27, both uncorrected and corrected for hub height)

and for the MWIS dataset using Equation (4).

CFm= total monthly energy production (MWh) (4)

rated capacity (mW) x hours/month

The capacity factor was used to compare locations because it is a metric that is easily

scalable to different installed capacities. The rated capacity used for the NARR data was

a single 2 MW wind turbine, while the rated capacity of the MWIS is dependent on the

location of the model zones as given in Table 2. An example MWh monthly production

table for the unadjusted Z29 level is given in Appendix B. A correlation analysis was

performed on the MWIS and NARR CFm datasets for the months of January, April, July

and October in each of the years 2003, 2004 and 2005.

0

500

1000

1500

2000

2500

0 5 10 15 20

KWO

utput

Wind Speed (m/s)


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2.3 Ground-based Weather Data

Ground-based weather data was also compared to the MWIS data. Hourly wind

speed data measured at 10 meters above the surface using the Automated Weather

Observing Systems (AWOS) often used in aviation weather system measurements was

obtained for the city of Worthington, MN (Weather Source, 2008). The wind speeds were

extrapolated to 80 meters using equation 2, takingzR as 10 meters andz as 80 meters.

Hourly energy outputs were calculated using equation 3 and capacity factors calculated

using equation 4 and correlation analysis was performed with MWIS data from Slayton,

MN (the location from the NARR analyses nearest to Worthington at 30 miles north).

2.4 Diurnal Wind Patterns and On-Peak Percentages

Diurnal wind patterns were investigated using the NARR wind speed data from

Z29 uncorrected for hub height. A diurnal power distribution was created for each

season by averaging the same time interval over the three year study period. The

percentage of the produced during on-peak hours was also calculated to determine how

wind power availability coincides with times of day when energy demand is highest. On-

peak production was defined as the hours between 10:00 and 19:00.

2.5 Geographic Wind Correlation

A temporal correlation analysis was done using 3-hour wind speed averages from

the NARR data across 18 locations in the Upper Midwest. This correlation differs from

the previous analysis by answering the question: how are wind speeds at the same point

in time correlated across a wide geographical region? Studies have shown that with


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increased number of wind turbines sited in geographically diverse locations, a smoothing

effect occurs. The 18 locations include the 6 locations in Minnesota and North Dakota

used in the above calculations and also includes locations from South Dakota and

Wisconsin. The additional locations were chosen to represent differing wind classes and

geographical diversity within and among the states. Table 2 provides details on the

additional analysis locations and Figures 10 through 13 indicate the locations of the study

locations.

Correlations were done for the months of January, April, July and October for

each of the 3 study years to represent seasonal and annual variations using unadjusted

Z29 NARR wind speeds. Distances were calculated between locations using both

Berthold, ND (the most northern and western point of the locations) and Hot Springs, SD

(the most southern and Western points) as reference points to analyze the relationship

between wind farm separation distance and correlation in wind speeds.


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Figure 10: South Dakota Locations for Analysis


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Figure 11: North Dakota Locations for Analysis


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Figure 12: Minnesota Locations for Analysis


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Figure 9: Wisconsin Locations for Analysis


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3. Results3.1 NARR comparison to MWIS Results

Figure 14 provides the capacity factor correlation between MWIS and the NARR

Z29 data unadjusted for hub height. Figure 15 shows the same correlation using wind

data adjusted to 80 meter wind measurement. These data represent the estimated capacity

factors for the 6 MWIS development regions and the corresponding estimates made from

the nearest NARR grid point for January, April, July and October for each of the years

2003, 2004 and 2005. Each of the 6 locations is therefore represented by 12 data points

derived from each estimation method. The capacity factors calculated from the NARR

and MWIS data can be found in Appendix C.

Figure 14: Capacity Factor Correlation between MWIS and NARR Z29 unadjusted data.

R = 0.6122

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60

NARRDataCapacityFactor

MWIS Capacity Factor


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Figure 15: Capacity Factor Correlation between MWIS and NARR Z29 data adjusted for 80 meter hub height.

While the unadjusted NARR Z29 CFm estimates were more highly correlated with

the MWIS estimates than the hub height adjusted Z29 estimates (R2

= 0.61 versus R2

=

0.46) , the average error of hub height adjusted NARR CFm estimates were lower.

Figures 16 and 17 show the correlation between capacity factors estimated from

MWIS data and NARR Z27 data, both adjusted and unadjusted for 80 meters. The

correlation between the MWIS and the NARR Z27 unadjusted data was much lower than

for the Z29 unadjusted data and the correlation was further reduced when the hub height

adjustment was applied to the Z27 data.

R = 0.4556

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60

N

ARRdataCacpacityFactor



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R = 0.2708

-0.05

0.000.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

NARRdataCapacityFactor


R = 0.439

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60

NARRDataCapacityFactor


Figure 11: Capacity factor correlations between MWIS and NARR Z27data adjusted for 80 meter hub height.

Figure 10: Capacity factor correlations between MWIS and NARR Z27 unadjusted data.


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Geographic differences in the correlation between the MWIS and NARR CFm

datasets are presented in Table 5. Further detail of this analysis is presented in Appendix

D.

Table 5: R2 values of the capacity factor correlations between MWIS and NARR data.

LocationZ29

unadjusted

Z29

adjusted for

80 m

Z27

unadjusted

Z27 adjusted

for 80 m

Hibbing, MN 0.791 0.071 0.456 0.488

Rochester, MN 0.895 0.894 0.833 0.852

Slayton, MN 0.883 0.850 0.675 0.420

Thief River

Falls, MN0.426 0.791 0.141 0.188

Berthold, ND 0.373 0.391 0.394 0.501

Kulm, ND 0.677 0.232 0.657 N/A

Table 6: Error Analysis for NARR Capacity Factors

NARR

Cfm,

Z29

NARR

CFm,

Z29 80m

NARR

CFm,

Z27

NARR CFm,

Z27 80m

Average Error 0.15 0.06 0.10 0.12

Max Error 0.27 0.17 0.21 0.30

Min Error 0.04 0.00 0.00 0.00


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3.2 Correlation between MWIS and Ground-based Weather Data

The CFm correlation between the MWIS and ground-based weather are presented

in Figure 18. The R2

for this correlation was lower than for any of the 6 locations using

NARR data.

y = 0.72x + 0.0063

R = 0.2211

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0 0.1 0.2 0.3 0.4 0.5 0.6

MWISDataCapacityFac

tor

Ground-based Data Capacity Factors

Worthington, MN Ground-based Data Capacity Factors vs. Slayton, MN


Figure 12: Correlation between ground-based capacity factor and MWIS for Slayton, MN (Worthington,

MN)


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3.3 Diurnal Wind Patterns and On-Peak Percentages

The diurnal power output patterns produced from NARR data for the six MWIS locations

are presented in Figures 19 to 23. Diurnal patterns for the other 12 study locations care

presented in Appendix E.

Figure 19: Daily Average Power Output by Month for 2MW Turbine- Slayton MN

0

100

200

300

400

500

600

1:00 4:00 7:00 10:00 13:00 16:00 19:00 22:00

PowerOutput(KW)

January

April

July

October


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Figure 13: Daily Average Power Output by Month for 2MW Turbine- Thief River Falls, MN

Figure 14: Daily Average Power Output by Month for 2MW Turbine- Hibbing, MN

0

50

100

150

200

250

300

350

400

450

500

1:00 4:00 7:00 10:00 13:00 16:00 19:00 22:00

PowerOuput(MW)

Time

January

April

July

October

0

50

100

150

200

250

300

350

400

450

500

1:00 4:00 7:00 10:00 13:00 16:00 19:00 22:00

PowerOutput(KW)

Time

January

April

July

October


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Figure 15: Daily Average Power Output by Month for 2MW Turbine- Berthold, ND

Figure 16: Daily Average Power Output by Month for 2MW Turbine- Kulm, ND

0

100

200

300

400

500

600

700

800

900

1:00 4:00 7:00 10:00 13:00 16:00 19:00 22:00

PowerOutput(kW)

Time

January

April

July

October

0

100

200

300

400

500

600

700

800

1:00 4:00 7:00 10:00 13:00 16:00 19:00 22:00

PowerOutput(KW)

Time

January

April

July

October


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Figure 17: Daily Average Power Output by Month for 2MW Turbine- Rochester, MN

The percentage of energy produced during the on-peak hours of 10:00-19:00

estimated using the NARR data for the six MWIS study locations are presented in

Figures 25 to 27. Each figure represents one of the three study years to provide an

estimate of the annual variation in on peak energy production.

Figure 18: Percentage of Power Produced during On-Peak Hours (2003)

0

50

100

150

200

250

300

350

400

450

500

1:00 4:00 7:00 10:00 13:00 16:00 19:00 22:00

PowerOutput(KW)

Time

January

April

July

October

30%

35%

40%

45%

50%

55%

Hibbing Rochester Slayton Thief River Berthold Kulm

Jan-03

Apr-03

Jul-03

Oct-03


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30%

35%

40%

45%

50%

55%


Jan-04

Apr-04

Jul-04

Oct-04

30%

35%

40%

45%

50%

55%


Jan-05

Apr-05

Jul-05

Oct-05


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3.4 Geographical Wind Correlation

The results of the geographical dispersion correlation analysis using Berthold, ND as the

reference location are presented in Figure 28 combining all seasons and with separate

trend lines by season in Figure 30. The results of this same analysis using Hot Springs,

SD as the reference location are presented in Figures 29 and 31. The correlation between

wind speeds was fitted with exponential decay curves in these figures. The correlation

between geographically separated wind speeds decreased as their geographical separation

increased and reached correlation coefficients of +/- 0.1 at separation distances of about

1200 kilometers and there was little difference in this result between the two

geographical reference points.

Figure 28: Geographic Wind Correlation (4 months) - Distance from Berthold, ND

R = 0.4454

-0.1

0.1

0.3

0.5

0.7

0.9

0 200 400 600 800 1000 1200 1400 1600

CorrelationofWindSpeed

Distance from Berthold, ND (km)


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Figure 29: Geographic Wind Correlation by Month- Distance from Berthold, ND

R = 0.7953

R = 0.4444

R = 0.5117

R = 0.4915

-0.10

0.10

0.30

0.50

0.70

0.90

0 200 400 600 800 1000 1200 1400 1600

CorrelationofWindSpeed

Distance from Berthold, ND (km)

January

April

July

October

R = 0.3685

-0.2

-0.1

00.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 200 400 600 800 1000 1200 1400 1600

Correlation

Distance from Hot Springs South Dakota (km

Figure 21: Geographic Wind Correlation (4 months) - Distance from Hot Springs, SD


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Figure 22: Geographic Wind Correlation by Month - Distance from Hot Springs, SD

January

R = 0.805

April

R = 0.039

July

R = 0.677

October

R = 0.514

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 200 400 600 800 1000 1200 1400 1600

Correlation

Distance from Hot Springs, South Dakota (km)

January

April

July

October


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4. Discussion4.1 Use of NARR data to examine wind resources

The NARR database has been used in the past to understand precipitation and

near-surface weather patterns. The results from this study show that NARR data can also

be used to understand wind power regimes in a geographically dispersed network of wind

farms.

The NARR data from the surface layer pressure level (Z29) showed good

correlation to the wind data used in the MWIS (R2 = 0.61) across the 6 locations

compared in this study (Figure 16). Some locations, generally those with higher potential

for wind energy development had much better correlation (R2

up to 0.89, Table 5). The

absolute value of the capacity factors calculated from the NARR the Z29 pressure level

using the raw wind data were about of those predicted by the MWIS data, however.

The Z29 pressure level data was adjusted to approximate typical wind turbine hub

height (80 meters from ground level) in an effort to correct for this discrepancy. While

the overall correlation between capacity factors calculated for the MWIS and adjusted

Z29 NARR was weaker (R2

= 0.45), the absolute values of the capacity factors were

reproduced more accurately (Figure 17).

The correction used to adjust for hub height was a simple estimate of the

difference between the GPH used in the NARR model and the hub height above the

average ground level using the 1/7 power law. A wind shear exponent of 1/7 is the one

most commonly used for typical conditions but has been shown to vary considerably


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with surface roughness, local topography and the degree of mixing in the boundary layer

which is highly influenced by surface heating and cooling conditions (Manwell et al.

2002). Results from Singh et al (2006) support the importance of using a site specific

wind shear exponent for accurate prediction of wind energy potential at differing hub

heights. This study made no special correction of the wind shear exponent for local

topography, surface roughness or mixing in the boundary layer. The use of a wind shear

exponent of 1/7 for all locations and all conditions did improve the correspondence

between the average CFm predicted by the NARR and MRIW data and also likely

contributed to the reduced correlation for the hub-height corrected estimates. It is

interesting to note that the correlation at some locations was much higher than for others,

possibly because the 1/7 was more appropriate for the surface roughness of those

locations. It is likely that predictability of wind power would be substantially improved

by using location specific and climatic specific wind shear exponents.

The correlation between NARR data and MWIS of the Z27 pressure level was

generally poorer than for the Z29 pressure level. The Z27 level was chosen because the

average GPH for this pressure level more closely approximates the elevation of wind

turbine hub heights in the region. The correlation between the raw Z27 data and the

MWIS data was poorer than either the raw data or the hub height corrected data from the

Z29 pressure level (R2

= 0.27, Figure 18) A hub height adjustment, referenced to surface

height, was also applied to the Z27 pressure level data using the 1/7 power law. This

adjustment produced a higher correlation than the unadjusted Z27 data (R2 = 0.45, Figure

19) but the average error did not decrease and maximum error increased compared to the


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unadjusted Z27. This suggests that accurate prediction of near-surface boundary layer

wind conditions is essential in predicting wind power availability.

The results of this analysis suggest that NARR data can be a useful predictor of

wind energy capacity. The correlation between the Z29 pressure level and the more

detailed MWIS study was reasonable for all locations and was quite good for some

locations. Simple attempts to correct the Z29 NARR data for the hub height of wind

turbines improved agreement between the absolute value of the capacity factors predicted

from the MWIS study but reduced the correlation somewhat. The opposite was true for

attempts to adjust Z27 NARR data, where the correlation increased somewhat but

average error also increased. All of the estimates made using the NARR data were more

highly correlated with the MWIS data than was the ground based meteorological data that

was tested in this study. Accuracy of prediction could likely be improved by using site

specific wind shear exponent and by taking into account the degree of mixing in the

boundary layer using seasonally and diurnally adjusted coefficients.

One limitation of the NARR data is the somewhat low special resolution (32km2

gridded cell) which is coarser resolution than many other wind resource maps. This

resolution is not likely sufficient for assessing specific wind farm sites. This resolution is

sufficient, however, to explore areas in which wind farm development is likely to be

distributed over relatively large areas. The advantage of the NARR data is its

availability over a very large geographical area (North America). It is also useful for

examining trends over extended time periods (1979-present). There are difficulties,

however, in combining NARR data sets with others using finer spatial resolution


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typically used in GIS analysis due to data formats that use these different spatial

resolutions.

4.2 Diurnal Patterns

The NARR data was used to analyze diurnal patterns of wind at locations

throughout the Upper Midwest. Diurnal patterns are important for energy planning

purposes as they provide estimates of the coincidence of wind energy with electric

demand, which in turn affects the type of fossil fuels displaced and the economic value of

wind energy. As the MWIS and other studies suggest, understanding typical patterns and

near-term forecasting of the availability of wind power is essential for successful

integration of wind power into electrical energy transmission and distribution grids

(SDEI, 2007). For nearly every location analyzed, the July CFm were lower but with less

diurnal fluctuation than other months. There were no other discernable trends in the

diurnal distribution of wind energy production across locations. Locations such as

Hibbing, MN and Berthold, MN (which are located in class 4 and 5 wind locations) show

a clear pattern of lower energy in the morning with power outputs increasing in the late

afternoon/evening for all seasons. Other locations such as Kulm, ND had relatively

constant energy production potential throughout the day and across seasons. These

diurnal pattern graphs demonstrate the diversity of wind patterns across the Upper

Midwest. The NARR data could be a very useful tool to help energy planners understand

seasonal and diurnal energy production, transmission capacity requirements and changes

in reserve generation capacity needs for geographically dispersed wind farm

development.


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4.3 On-peak Percentages

The analysis of on-peak power production (Figures 26 to 28) can help define the

Effective Load Carrying Capacity (ELCC) which is a metric used to evaluate when the

wind power capacity is available during a certain high-value time period, such as during

on-peak hours (DeMeo et al. 2007). There is considerable variability from year to year,

between locations, and across season on the percentage of energy produced by wind

during the on-peak period assumed in this study. Kulm, ND had the highest variability in

percent of on-peak energy across both year and month. The month of January had the

smallest percentage of on-peak energy production but little variation across year, while

October showed high variability from year to year with the lowest percentage of time on-

peak during 2005 and the highest percentage during 2003. For most locations, July had

the highest percentage of energy produced during on-peak hours. However, the total

daily energy production for July (Figures 2025) is usually lower than other months and

electric demand peaks in the summer on-peak period. The NARR data can be a useful

tool to develop provide appropriate ELCC values for wind energy development, however

interannual variation must be taken into account.

4.4 Geographic Dispersion and Regional Collaboration

When considering further development of wind power across the Upper Midwest,

the correlation of geographically dispersed wind farms plays an important part in the

understanding of how wind energy can best be integrated into existing generating and

transmission infrastructure. This study confirmed previous assessments of the reduced


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Table 7: Organizations involved in Upper Midwest energy policy

Organization Role in the Midwest

Midwest IndependentService Operator

Ensures the reliable operations of the electric transmissionnetwork in the Midwest and Manitoba and identifies

improvements which can be made to wholesale bulk electric

market

Independent Service

Operator/Regional

Transmission Operators

Council (ISO/RTO)

National council of ISOs and RTOs (like MISO) which drive

policy that promotes regional coordination to address demandresponse issues and renewable energy coordination.

Midwest Reliability

Organization (MRO)

Ensures the dependability of the bulk electric supply bydeveloping and enforcing regional reliability standards and

providing seasonal and long-term assessments of the bulk

electric system in the MidwestMidwest Renewable

Tracking System (M-

RETS)

Organization that tracks renewable energy credits across

Midwestern states to not only account for all renewable energyprojects but trade credits when applicable

Midwest Governors

Association

Group of 12 Midwestern governors that work together onpublic policy issues that affect the Midwest.

The Midwest Independent Service Operator (MISO) has been directing the

transmission network in the upper Midwest since 2001 (MISO, 2007). Development of

and access to electric transmission services are regulated by the Federal Energy

Regulation Commission (FERC). MISO sets the cost of transmission services and

regulates access to transmission line by power producers and distributors. Sophisticated

modeling and forecasting methods are used to ensure that energy supplies meet the

instantaneous energy demand everywhere on the transmission and distribution grid.

While MISO would most likely not use the NARR data for forecasting because it is

mainly a retrospective analysis, the NARR data can provide useful estimates of

variability across time and space, as well as analysis of the interaction between wind

energy availability and electric demand from a retrospective point of view. The results of


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this study clearly indicate that a coordinated regional development plan can reduce

intermittency of wind power availability and potentially make wind energy more

economical to deploy and will require leadership and coordination from organizations

such as MISO.

Working alongside MISO, the Independent Service Operators (ISO)/Regional

Transmission Operators (RTO) Council (IRC) is a coalition of the seven transmission

operators in the states which collaborate to ensure coordination and effectiveness among

them. In a 2007 study, they explained how the IRC can foster development of demand

response and renewable energy. The report states wholesale markets facilitate this

[renewable energy] development by fostering an environment that is open to investment

by all parties, providing price transparency that informs the developer, operating a 5- to

15-minute dispatch that reduces integration costs of renewable resources such as wind

power, and managing stakeholder planning processes that make it possible to build the

transmission needed to bring renewable energy to market. (IRC, 2007) This promising

statement reveals that regional networks can work in coordination to overcome

constraints of renewable energy. The data provided in this thesis corroborates that using

these strong networks to increase wind generation can also help reduce intermittency of

wind energy supply and improved economic benefits of wind power development

Concurrently, the Midwest Reliability Organization (MRO) works to ensure the

dependability of the bulk electric supply as part of the North American Electric

Reliability Council (NERC) by developing and enforcing regional reliability standards

and providing seasonal and long-term assessments of the bulk electric system in the


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Midwest (MRO, 2008). The MRO is significant to this analysis because large scale

renewable electric generation, especially wind power, has fluctuating power production

with capacity factors considerably lower than conventional fuels. If large of amounts of

wind power are accounted for on the transmission grid network, reliability concerns

increase because of difficulties predicting whether energy supply will meet energy

demand. However increased accuracy in weather forecasting and real-time demand data

are reducing the severity of this problem (Zack, 2007). Numerous studies have shown

that the system can reliably accommodate over 20% wind penetration into the electrical

mix (Piwko, 2005; EnerNex, 2006; DOE, 2008). The NARR database, with

meteorological dating back nearly 30 years, can help understand wind patterns and may

also have potential to increase the accuracy of forecasting.

The Midwest Renewable Tracking System (M-RETS) began in 2007 and tracks

renewable energy credits across Midwestern states to not only account for all renewable

energy projects but trade credits when applicable. M-RETs provide a measurement and

verification system to transparently record renewable generation in Midwest (M-RETS,

2008). Using information gathered by Midwest Independent Service Operator (MISO)

records, the system assists in verifying compliance for RPS policies of the states and

provinces that participate (Illinois, Iowa, Montana, North Dakota, South Dakota,

Wisconsin, and Manitoba).

This analysis suggests that having a widely dispersed but interconnected and

coordinated wind energy production network, using the organizational structure currently


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in place, can improve the feasibility and renewable energy production potential in the

Midwest. With an interconnected network, wind power becomes more economically

feasible because high costs of spinning reserves and on-peak costs can be reduced

through increasing geographic diversity of wind farms.

4.6 Transmission

There have been numerous studies which investigate the need for increased

transmission in general as well as to support renewable energy development (Archer and

Jacobson; 2007; Wiser and Barbose, 2008; Benjamin, 2007; DOE, 2006). The MISO

released a report on transmission expansion in 2007 that states the increasing need for

transmission improvements to accommodate development of wind resources. (MISO,

2007) Such improvements to the transmission lines would not only meet the projected

increases in generation, but also help connect the wind resources available in the western

part of Minnesota and the Dakotas (DOE, 2006). The Midwest Governors Association,

as part of Greenhouse Gas Reduction agreement signed in September 2007, is

encouraging the development of new transmission to accommodate renewable energy

that can offset greenhouse gas emissions (MGA, 2007).

With FERC Order 890, renewable energy generators cannot be issued an

imbalance charge if the energy delivered differs from scheduled energy (FERC, 2007).

This addresses a policy limitation for renewable energy. However, other access issues

for renewable energy to enter the transmission grid can negatively affect future wind

development. If the wind farm is located at large distance from transmission lines, the

costs of building new transmission lines must be rolled into the capital costs of the wind


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farm or must be regulated in the pricing mechanism for electricity. This can increase

overall costs and dissuade further development of prime wind resource locations.

This analysis of NARR data suggests that by developing wind farms across a

broad geographic area, increased transmission would allow the low or negative

geographical correlations to help reduce intermittency of wind power availability. With

an increasingly intertwined network of transmission grid and power producers, wind

power can play a stronger role in renewable power production. Increased transmission

investment by federal or state government can help achieve the geographic dispersion

needed to reduce wind intermittency. Policies that support renewable energy need to be

developed to ensure appropriate access to the transmission grid.

4.7 Renewable Portfolio Standards (RPS)

RPS policies are currently implemented in 25 states with state-specific policy

schemes. Most require a certain percentage of electricity sales to be derived from

renewable sources. North Dakota, South Dakota, Minnesota and Wisconsin have

renewable energy policies which are outlined in Table 8. While currently the RPS

policies are derived on a state by state basis and some states do have in-state production

requirements, none of the states in this study region have in-state production

requirements (DSIRE, 2008). This allows them to fulfill their requirements by

developing renewable energy sources outside of state jurisdictional boundaries.


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Table 8: RPS requirements for Upper Midwest (Wiser and Barbose, 2008)

State First Year

Compliance

Mandatory

RPS?

Current

Ultimate

Target

Current

Plants

Eligible?

Set Asides,

Tiers or

Minimums

In-State

Production

Requirements?

Minnesota 2002 Yes 25% (2025

30% (2020

by Xcel)

Yes Wind for Xcel;

Goal for

Community

Based

Renewables

No

North

Dakota

2015 No 10% (2015) Yes None No

South

Dakota

None No None None No

Wisconsin 2000 Yes 10% (2015) Yes None No

The role of the states working together have been predicted to help increase

renewable energy penetration as well as help foster reductions of greenhouse gas

emissions (Peterson and Rose, 2006). The results from this study confirm that regional

collaboration is beneficial in bringing renewable energy on line in the Midwest in the

most cost-effective and resource-efficient manner. With increased transmission and

regional network for wind resources, wind power may help achieve goals of RPS policies

and encourage the adoption of RPS policies where there are currently none.

4.8 Recommendations for Further Study

The NARR data shows good promise as the basis of further studies of the

intricacies of developing geographically dispersed wind generation and the correlation of

this distributed generation with distributed electric demand. Future study areas could

also be easily expanded to include the entire MISO jurisdiction and all of North America.

Further development and validation of estimation techniques using the NARR data would


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also be useful. In particular, methods for adjusting the wind shear exponent for surface

roughness and boundary layer mixing appear to be promising areas of research to

improve the predictive ability of the NARR data. Other tools, such as GEMpak (an

analysis, display, and product generation package for meteorological data), can be used to

model exact heights of wind measurement from NARR data to provide increased

accuracy from the varying GPHs of the isobars (Unidata, 2008). This tool uses complex

modeling techniques but could possibly provide a more accurate prediction of wind

speeds at wind turbine hub heights.


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5. Conclusion

The goal of this study was to compare wind energy availability predictions from

different meteorological models. Using the MWIS as a point of comparison, this study

shows that at the NARR data Z29 pressure level data produced reasonable estimate of

wind power availability over time and space. It is likely that the accuracy of prediction

could be improved through application of more location-specific wind shear exponents as

well as adjustment of wind shear exponents for changes in the degree of vertical mixing

in the boundary layer as influenced by surface heating and cooling. A greater

understanding of the MWIS data sources and calculations is needed. The NARR has

some limitations for assessing specific wind development locations due to its coarse

spatial resolution (32km2). The strength of the dataset include wide geographical

coverage, reasonable temporal resolution (3 hours) and an extensive history (from 1979

to the present) that can be used for statistical analysis to predict the probability of future

events.

A secondary goal of this study was to use the NARR data to investigate the

implications of geographic dispersion of wind resource development. The geographic

wind correlation analysis provided a useful estimate of the relationship between distance

between wind generation locations and the correlation of wind speed and power

production. The analysis performed using the NARR data confirmed the general trend of

decreased correlation with increased separation predicted by other studies and provided a

quantitative estimate of this relationship in the upper Midwest. The results of this


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analysis provide and estimate of the potential benefits of regional wind resource

development on reduction of wind power intermittency


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Appendix A: MWIS Transmission Line Upgrade Assumptions


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Appendix B: Average MWh Monthly Output with 2 MW Installed

Capacity Wind Turbine (Z29 unadjusted for 80 m hub height)

Location 2003 2004 2005

Jan Apr July Oct Jan Apr July Oct Jan Apr July Oct

Hibbing MN 331 291 177 168 253 292 168 168 274 331 264 264

Rochester MN 365 339 194 388 304 324 119 388 264 294 202 278

Slayton, MN 375 365 292 364 327 339 158 438 318 372 334 324

Thief River

Falls, MN 346 372 172 250 240 316 156 412 296 404 244 348

Berthold ND 513 513 312 408 393 429 264 409 468 567 406 426

Kulm ND 535 465 298 407 411 408 238 506 399 553 366 486


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Appendix C: Capacity Factors calculated for MWIS and NARR data

Figure C.1: Capacity factor calculated for the MWIS


2003 2004 2005

Location (MWIS Zone) Jan Apr July Oct Jan Apr July Oct Jan Apr July Oct

Hibbing (1) 0.40 0.34 0.28 0.35 0.29 0.32 0.24 0.42 0.31 0.35 0.34 0.34

Rochester (10) 0.46 0.43 0.30 0.37 0.41 0.38 0.22 0.44 0.36 0.41 0.27 0.40

Slayton (9) 0.41 0.43 0.36 0.40 0.37 0.36 0.25 0.49 0.36 0.42 0.38 0.42

Thief River Falls (2) 0.43 0.42 0.35 0.38 0.33 0.41 0.31 0.54 0.37 0.46 0.37 0.42

Berthold (3) 0.44 0.46 0.39 0.36 0.37 0.38 0.30 0.48 0.37 0.45 0.38 0.46

Kulm (5) 0.48 0.46 0.40 0.40 0.40 0.39 0.33 0.52 0.36 0.46 0.40 0.47

Figure C.2: Capacity factor for NARR Z29 (adjusted to 80m)

Capacity Factor (Z29 adjusted for 80 meter hub height)

2003 2004 2005


Hibbing (1) 0.32 0.32 0.21 0.28 0.33 0.20 0.35 0.35 0.26 0.36 0.30 0.29

Rochester (10) 0.35 0.32 0.20 0.31 0.30 0.30 0.13 0.37 0.25 0.29 0.21 0.27

Slayton (9) 0.36 0.41 0.35 0.38 0.33 0.34 0.18 0.43 0.30 0.37 0.34 0.33


Berthold (3) 0.50 0.45 0.40 0.40 0.39 0.42 0.49 0.49 0.39 0.52 0.46 0.46

Kulm (5) 0.46 0.47 0.31 0.39 0.37 0.41 0.27 0.39 0.43 0.51 0.39 0.40

Figure C.3: Capacity factor for NARR Z29 (raw wind speeds)

Capacity Factor (Z 29 unadjusted wind speeds)

2003 2004 2005


Hibbing (1) 0.22 0.20 0.12 0.20 0.17 0.20 0.11 0.25 0.18 0.23 0.18 0.19

Rochester (10) 0.25 0.24 0.13 0.21 0.20 0.22 0.08 0.26 0.18 0.20 0.14 0.19

Slayton (9) 0.25 0.25 0.20 0.24 0.22 0.24 0.11 0.29 0.21 0.26 0.22 0.22


Berthold (3) 0.34 0.34 0.21 0.27 0.26 0.29 0.18 0.28 0.31 0.38 0.27 0.29

Kulm (5) 0.36 0.32 0.20 0.27 0.28 0.28 0.16 0.34 0.27 0.38 0.25 0.33


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Figure C.4: Capacity factor NARR Z27 (raw data)

Z27 Capacity Factor (adjusted for 80 m hub height)

2003 2004 2005


Hibbing (1) 0.34 0.30 0.14 0.21 0.35 0.32 0.19 0.42 0.38 0.35 0.28 0.36

Rochester (10) 0.37 0.30 0.19 0.29 0.41 0.34 0.14 0.44 0.37 0.35 0.25 0.36

Slayton (9) 0.17 0.14 0.10 0.13 0.21 0.26 0.11 0.31 0.26 0.29 0.23 0.12


Berthold (3) 0.27 0.30 0.29 0.24 0.20 0.26 0.16 0.24 0.23 0.34 0.25 0.25

Kulm (5) 0.28 0.28 0.17 0.23 0.26 0.31 0.17 0.36 0.25 0.41 0.26 0.35

Figure C.5: Capacity factor for NARR Z27 level (raw data)

Z27 - Capacity Factor (unadjusted wind speed)

2003 2004 2005


Hibbing (1) 0.43 0.34 0.16 0.25 0.36 0.29 0.17 0.38 0.39 0.32 0.26 0.33

Rochester (10) 0.46 0.34 0.22 0.34 0.42 0.32 0.14 0.43 0.38 0.32 0.25 0.35Slayton (9) 0.30 0.25 0.20 0.25 0.23 0.24 0.11 0.30 0.28 0.26 0.22 0.22


Berthold (3) 0.34 0.36 0.31 0.27 0.26 0.30 0.18 0.28 0.31 0.39 0.27 0.29

Kulm (5) 0.36 0.32 0.20 0.26 0.28 0.28 0.16 0.33 0.27 0.38 0.24 0.33

8/3/2019 Thes

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Thesis Revised- 19Aug2008