Small wind power for rural locations - part 1

Wind energy harvesting basics, resource assessment and

application for off grid systems.Hanan Einav-Levy M.Sc.

Thursday, November 10, 2011

A bit about meHanan Einav-Levy M.Sc

• Aeronautical engineer• Wind turbine technology advocate• Experience in installing and building small

wind turbines in Israel and abroad for rural electrification• Consultant to several wind energy NGO’s • Conducting PhD research in wind turbine

resource assessment


Aim of lecture


Aim of lecture• Wind turbine systems are complicated systems



• We have 4 hours...




• You will gain a basic and comprehensive understanding





• Many valuable references will be mentioned for your future use





• Many valuable references will be mentioned for your future use

• You will receive a starting point for developing wind in rural communities in your countries


Outline


Outline• Part 1 (2 hours)



• Global wind resource (10)




• Modern wind turbine history (10)





• Wind energy theory (10)






• Technology - HAWT, VAWT, Lift, Drag, BIG, small (15)







• Environmental considerations (5)








• Wind speed variability (15)









• Estimating the resource (15)










• Off grid wind system components (5)











• Economic considerations(10)












• part II (2 hours)













• Example project (50)














• Small wind turbine product comparison (10)















• Case studies















• Case studies

• Practical action - Peru (10)















• Case studies


• AWP - Zimbabwe (10)















• Case studies



• WindAid - Peru (10)















• Case studies



• WindAid - Peru (10)

• CometME - Israel/PAU (10)


American Economic Review 101 (August 2011): 1649–1675http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.5.1649

1649

An important and enduring issue in environmental economics has been to develop both appropriate accounting systems and reliable estimates of environmental dam-ages (Wassily Leontief 1970; Yusuf J. Ahmad, Salah El Serafay, and Ernst Lutz 1989; Nordhaus and Edward Charles Kokkelenberg 1999; Kimio Uno and Peter Bartelmus 1998).

Some of this literature has focused on valuing natural resources such as water resources, forests, and minerals (Henry M. Peskin 1989; World Bank 1997; Robert D. Cairns 2000; Haripriya Gundimeda et al. 2007; Michael Vardon et al. 2007). Other studies have focused on including pollution. For example, the earliest papers that focused on pollution relied on material !ows analysis to calculate the tons of emissions per unit of production by industry (Robert U. Ayres and Allen V. Kneese 1969). This has been formalized in the Netherlands (Steven J. Keuning 1993) and in Sweden (Viveka Palm and Maja Larsson 2007). The materials-!ow approach is use-ful for tracking physical !ows, but it is inappropriate for national economic accounts because it does not contain values and because the damages associated with differ-ent source locations and toxicity are not included.

This paper contributes to this literature in two ways. First, we present a frame-work to integrate external damages into national economic accounts. The gross

Environmental Accounting for Pollution in the United States Economy †

By N"#$%&'( Z. M)&&*+, R%,*+- M*./*&(%$., './ W"&&"'0 N%+/$')(*

This study presents a framework to include environmental externali-ties into a system of national accounts. The paper estimates the air pollution damages for each industry in the United States. An inte-grated-assessment model quanti!es the marginal damages of air pol-lution emissions for the US which are multiplied times the quantity of emissions by industry to compute gross damages. Solid waste com-bustion, sewage treatment, stone quarrying, marinas, and oil and coal-!red power plants have air pollution damages larger than their value added. The largest industrial contributor to external costs is coal-!red electric generation, whose damages range from 0.8 to 5.6 times value added. (JEL E01, L94, Q53, Q56)

* Muller: Department of Economics, Environmental Studies Program, Middlebury College, 303 College Street, Middlebury, VT 05753 (e-mail: [email protected]); Mendelsohn: School of Forestry and Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511 (e-mail: [email protected]); Nordhaus: Department of Economics, Yale University, 28 Hillhouse, New Haven, CT, 06511 (e-mail: [email protected]) The authors wish to thank the Glaser Progress Foundation for their generous support of this research. Muller acknowledges the support of the USEPA: EPA-OPEI-NCEE-08-02. We also would like to thank seminar participants at Yale University, Harvard University, USEPA, NBER, and the anonymous referees for their helpful comments.

† To view additional materials, visit the article page at http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.5.1649.

Before we start - a bit of extra motivation


http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.5.1649








































































































































































































































































































































































































































































































































































































































































































































































































































American Economic Review 101 (August 2011): 1649–1675http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.5.1649

1649

An important and enduring issue in environmental economics has been to develop both appropriate accounting systems and reliable estimates of environmental dam-ages (Wassily Leontief 1970; Yusuf J. Ahmad, Salah El Serafay, and Ernst Lutz 1989; Nordhaus and Edward Charles Kokkelenberg 1999; Kimio Uno and Peter Bartelmus 1998).

Some of this literature has focused on valuing natural resources such as water resources, forests, and minerals (Henry M. Peskin 1989; World Bank 1997; Robert D. Cairns 2000; Haripriya Gundimeda et al. 2007; Michael Vardon et al. 2007). Other studies have focused on including pollution. For example, the earliest papers that focused on pollution relied on material !ows analysis to calculate the tons of emissions per unit of production by industry (Robert U. Ayres and Allen V. Kneese 1969). This has been formalized in the Netherlands (Steven J. Keuning 1993) and in Sweden (Viveka Palm and Maja Larsson 2007). The materials-!ow approach is use-ful for tracking physical !ows, but it is inappropriate for national economic accounts because it does not contain values and because the damages associated with differ-ent source locations and toxicity are not included.

This paper contributes to this literature in two ways. First, we present a frame-work to integrate external damages into national economic accounts. The gross

Environmental Accounting for Pollution in the United States Economy †

By N"#$%&'( Z. M)&&*+, R%,*+- M*./*&(%$., './ W"&&"'0 N%+/$')(*

This study presents a framework to include environmental externali-ties into a system of national accounts. The paper estimates the air pollution damages for each industry in the United States. An inte-grated-assessment model quanti!es the marginal damages of air pol-lution emissions for the US which are multiplied times the quantity of emissions by industry to compute gross damages. Solid waste com-bustion, sewage treatment, stone quarrying, marinas, and oil and coal-!red power plants have air pollution damages larger than their value added. The largest industrial contributor to external costs is coal-!red electric generation, whose damages range from 0.8 to 5.6 times value added. (JEL E01, L94, Q53, Q56)

* Muller: Department of Economics, Environmental Studies Program, Middlebury College, 303 College Street, Middlebury, VT 05753 (e-mail: [email protected]); Mendelsohn: School of Forestry and Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511 (e-mail: [email protected]); Nordhaus: Department of Economics, Yale University, 28 Hillhouse, New Haven, CT, 06511 (e-mail: [email protected]) The authors wish to thank the Glaser Progress Foundation for their generous support of this research. Muller acknowledges the support of the USEPA: EPA-OPEI-NCEE-08-02. We also would like to thank seminar participants at Yale University, Harvard University, USEPA, NBER, and the anonymous referees for their helpful comments.

† To view additional materials, visit the article page at http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.5.1649.

coal-fired power plants have air pollution damages larger than theirvalue added. The largest industrial contributor to external costs iscoal-fired electric generation, whose damages range from 0.8 to 5.6times value added

Before we start - a bit of extra motivation


















































































































































































































































































































































































































































































































































































Global wind resource


Jacobson et al. 2009

Wind Resource


Wind ResourceWind energy potential at 100 m

Jacobson et al. 2010Thursday, November 10, 2011


Wind ResourceThursday, November 10, 2011



Modern wind turbine history


Modern wind harvesting history1888, USA Cleveland Ohio, 17 m diameter,

12 Kw rated power, 20 year life time, charged lead acid batteries (stand alone system)


Modern wind harvesting history

1980 - Bonus 30 Kw


Modern wind 2 Mw machines and more


Modern wind Source: Garrad Hassan

2 Mw machines and more


Modern wind 2 Mw machines and more


Wind energy theory


How much can we get out of the wind?


Wind energy exploitation

• How much energy can we get out of the wind?

• Wind turbine production profile


Energy vs. wind speed







12mv2 = 1

2·ρAvt·v2 = 1

2ρAtv3



12mv2 = 1

2·ρAvt·v2 = 1

2ρAtv3

12mv2

t= 12ρAv3


Swept areaThursday, November 10, 2011

Swept areaThursday, November 10, 2011

S = Swept AreaThursday, November 10, 2011

P = 12ρSV 3Cp[Watt]

ρ = wind density [Kg /m3]S = swept area [m2 ]V = wind speed [m / s]Cp = power coefficient < 0.593


Energy density

P = 12ρV 3 Watt

m2⎡⎣⎢

⎤⎦⎥

P = 121.225·63 = 132 Watt

m2⎡⎣⎢

⎤⎦⎥


Power curve

1 2 3 4


Power curve

1 2 3 4



Power vs. energy


• The power curve of the turbine is measured in watts vs. m/s

Power vs. energy



• To calculate the energy the turbine will produce in a given time - say 1 hour, we need the average wind speed during this hour

Power vs. energy




• The energy is measured in kWh - kilo-Watt-hour

Power vs. energy





• this is equal to

Power vs. energy






• one thousand watt operating for a hour

Power vs. energy







• a 100 watt operating for 10 hours

Power vs. energy







• a 100 watt operating for 10 hours

• kWh = Watt X hour / 1000

Power vs. energy


TechnologyVAWT - HAWT, Lift - Drag, Big - Small


What a good WT does

• Follows the wind

• Extracts wind energy with high efficiency

• Low cost of energy

• Low maintenance costs

• Long life


HAWTThursday, November 10, 2011


HAWT

7I7T 7hT

6-10. Horizontal-axis configurations. Upwind, downwind, one blade or two-it's all been tried at one time or another.led from j. W. Twidell and A. D. Weir, Renewable Energy Resources.

7I7T 7hT

6-10. Horizontal-axis configurations. Upwind, downwind, one blade or two-it's all been tried at one time or another.led from j. W. Twidell and A. D. Weir, Renewable Energy Resources.










VAWTThursday, November 10, 2011

VAWT

~///}////////

Figure 6-4. Darrieus configurations. There are several other Darrieus configurations besides the common eggbeaterdesil!n.

~///}////////

Figure 6-4. Darrieus configurations. There are several other Darrieus configurations besides the common eggbeaterdesil!n.








BIG - small


BIG - small


BIG - small


Tilt up tower


Aerodynamic control in high winds







Systems furls its HR3 "'vu~, -running position. This design includes a winch and cable for manually furling the turbine,rip I'ohlriin np Ins Recursos Energeticos in Punta Arenas, Chile.

Systems furls its HR3 "'vu~, -running position. This design includes a winch and cable for manually furling the turbine,rip I'ohlriin np Ins Recursos Energeticos in Punta Arenas, Chile.






Technology summary


Technology summary

-Marlec910F

_A;,.", - RWr.on-Marlec910F

_A;,.", - RWr.on



Technology summary

Figure 1-1. Small wind turbine nomenclature. (1) Spinner or nose cone.(2) Rotor blades. (3) Direct-drive alternator. (4) Mainframe. (5) Yawassembly. (6) Slip rings and brushes. (7) Tail vane. (8) Nacelle cover. (9)Winch for furling the rotor out of the wind. (Bergey Windpower)

Figure 1-1. Small wind turbine nomenclature. (1) Spinner or nose cone.(2) Rotor blades. (3) Direct-drive alternator. (4) Mainframe. (5) Yawassembly. (6) Slip rings and brushes. (7) Tail vane. (8) Nacelle cover. (9)Winch for furling the rotor out of the wind. (Bergey Windpower)


• Rural areas - Small and medium wind turbines

• Main concern - noise

• Non issues -

• Birds

• EM radiation

• Shadow flickr

• View obstruction

Environmental considerations


Noise

Figure 13-22. Calculated and meas-ured noise emissions. This chart

1989 on the relationship between

that diameter could substitute for tipspeed and hence determine soundpower. The top line was derived fromdata on experimental large turbinesdeveloped in the late 1970s and early1980s. The bottom line was derivedfrom data on commercial wind tur-

bines being installed in the late 1980s.Published data for commercial tur-bines in use from the 1990s through2000 has been added. Noise emis-sions from small turbines at the WulfTest Field and other test sites are alsoincluded.

Sound Power level dBA120

-19805 -19905. 1999 . Small

. Micro

L=22 log D + 72110

100

~

..90

L=22 log 0 + 6580

70

6010010

Diameter (meters)

Figure 13-22. Calculated and meas-ured noise emissions. This chart

1989 on the relationship between

that diameter could substitute for tipspeed and hence determine soundpower. The top line was derived fromdata on experimental large turbinesdeveloped in the late 1970s and early1980s. The bottom line was derivedfrom data on commercial wind tur-

bines being installed in the late 1980s.Published data for commercial tur-bines in use from the 1990s through2000 has been added. Noise emis-sions from small turbines at the WulfTest Field and other test sites are alsoincluded.

Sound Power level dBA120

-19805 -19905. 1999 . Small

. Micro

L=22 log D + 72110

100

~

..90

L=22 log 0 + 6580

70

6010010

Diameter (meters)


NoiseThursday, November 10, 2011

NoiseThursday, November 10, 2011

Wind speed Variability


Short term speed fluctuations


Long term speed distribution


Yearly fluctuations


Wind production vs. consumption in Denmark

Source: www.energinet.dk

hours

Mw

Dealing with variability in a grid connected system


Wind production vs. consumption in Denmark

Source: www.energinet.dkStorm fronthours

Mw



T+1 hour T+12 hour

Source: Garrad Hassan



Dealing with variability for off grid systems



Diverts the electricity according to battery status



Stores the excess energy (wind is blowing but nobody is using the electricity)



when the battery is full (and the wind is blowing)




A word about loads

• The “Dump load” is a load used when the battery is full

• A “load” is any electrical appliance connected to the battery

• Such as

• light bulbs

• TV/radio

• computer

• cell phone charger

• Sewing machines ...


Estimating the resource


Looking at the long term distribution again


Wind atlas

• Several resources:

• SWERA

• NREL

• RISOE

• Include average yearly wind speed at several heights, and energy density


Wind atlas


• SWERA

• NREL

• RISOE



Wind atlas


• SWERA

• NREL

• RISOE



How to Estimate average yearly/monthly/daily production



Using the wind speed distribution




multiplying by the power curve




multiplying by the power curve

summing up to receive the AEP


E = 12ρV 3·1.91[W /m2 ]

f (u) = uV 2 e

−12

uV

⎛⎝⎜

⎞⎠⎟2

• For k = 2, we get the Rayleigh distribution:• Starting from the Weibull distribution.

• For the Rayleigh distribution the energy density can be calculated in a simpler way:

• where 1.91 comes from the form of the Rayleigh distribution.

A more simplistic way to estimate the AEP


Simple AEP estimates

AEP = 87601000

E·πD2

4Cp[Kwh / year]

E: power density from wind atlas, or measurementCp: power coefficient 0.2-0.25 for small windD: diameter


Simple AEP estimates


Capacity Factor (CF)• Alternative way to describe the wind resource

at a site

• Used wildly in the energy sector - not just in wind energy

•

• The capacity factor is a function of the wind distribution and the power curve

• But can be estimated for a generic power curve

AEP = 8760 × P ×CF[kwh / year]


On land wind capacity factor


Measurement campaign

• Minimal equipment

• 10 meter tilt up tower

• Single Anemometer

• Best practice


• 2 Anemometers

• 1 wind vane

• 1 temperature probe

• Alternatives

• Install small wind turbine immediately






• Best practice


• 2 Anemometers

• 1 wind vane


• Alternatives







• Best practice


• 2 Anemometers

• 1 wind vane


• Alternatives



Wind shear

• Wind speed increases with height

• Putting a small turbine on a tall tower is aways a good economic move

• Insures steady winds - longer life for the blades


Economic considerations


Wind development costs

• Pre-feasibility study

• Big wind - major effort, 200,000$ / Mw

• Off grid small wind - basic measurement campaign, trial and error. 200-1000$ for measurement system.

• Wind turbine system

• Battery bank, Inverter

• BOS (cables, breakers ...)


Example costs - Battery-less wind turbine system (Installed cost)


Diameter [m]

Swept area [m^2]

cost [$]Avg. wind speed

Energy production

Simplistic cost of energy (15 year life time)

2 3.142000 $/m^2 X

3.14 m^2 = 6280$

4 m/s

120 kwh/m^2/year X 3.14 m^2 = 376.8

kwh/year

1.1 $/kwh

2 3.14 $6280 5 m/s

260 kwh/m^2/year X 3.14 m^2 = 816.4

kwh/year

0.51 $/kwh

Example costs - Battery-less wind turbine system (Installed cost)


Balance of system

• Charge controller

• Dump load

• Battery

• System meter

• Inverter


Balance of system

• Charge controller

• Dump load

• Battery

• System meter

• Inverter

Included in previous assessment


Crash course on Batteries



• The heart of an off-grid electric system




• Typically Lead-acid





• 150 year old technology






• Many different models







• A car battery is cheap - and lasts 1-3 years








• A deep-discharge battery is more expansive, but lasts longer









• Typical voltage is 12 volts










• Capacity measured in Ah










• Capacity measured in Ah

• Energy is AhXVolt/1000 in kWh


Example battery costs


Example battery costs

• Israel battery costs (Lead Acid): (source: Comet-ME)

• Gel type - 1000 shekel/90Ah 12V (Israel manufacturer) ~ 250$/kWh, ~50$/kWh/year

• 3000Ah 48V OPZF (2V units) OPK (15 year life) 85,000 euro (German manufacturer) ~820$/kWh, ~55$/kwH/year


Example inverter costs


Dealing with battery costs• Batteries are used frequently in rural areas

• Charged occasionally by transporting to the closest grid connected town for a considerable cost

• If batteries are bought specifically for the wind project they can become a major cost of the system

• If the batteries exist already, they can be charged more cheaply by the wind turbine


Example meter costs• Using a meter to measure the electricity

used is crucial to success of wind-project

• simple meter 100$-150$

• Pay by use meter - costs are the same, but software is expensive - one time licensing fee 10,000Euro

• There is a standard in the world for these type of systems (the encoding method)


Next up -Examples and case studies

part 2


Small wind power for rural locations - part 1

Business

hours global wind resource

modern wind turbine

wind speed variability

small wind turbines

wind energy ngos

lecture wind turbine

technology hawt

environmental considerations