HomeMy WebLinkAboutAlaska Wind Program Highlights Canada-USCleanEnergyDialogue 10-01-2015-WPhoto by: Cassandra Cerny, GVEA
Alaska Wind Program Highlights
Canada-US Clean Energy Dialogue
Oct. 1, 2015
Failing to fully consider, model and design
secondary loads in hybrid wind systems ensures a
15-20 point gap from expected annual energy
production.
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Secondary Heat Loads –Critical to Project Success
New wind
penetration
classes:
Impacts of curtailment:
*Max wind = village demand –min diesel loading
+ diversion load
Installed Wind
Capacity (kW)
Total Wind Energy
Produced (kWh)
Excess
Electricity
Net Elec
kWh
Net Thermal
kWh
Control Method Fuel Savings
@ $4.5.gal
Potential
Benefit
300 (Hi Pen)888,180 292,307 595,873 292,307 Elec Boiler or ETS units $240,274.89 100.00%
300 (Hi Pen)888,180 292,307 595,873 0 Turbine max setpoint $206,263.73 85.84%
300 (Hi Pen)888,180 292,307 595,873 0 Non value dump load $206,263.73 85.84%
300 (Hi Pen)489,227 0 489,227 0 Curtailment $169,347.81 70.48%
300 (Hi Pen)888,180 262,731 625,449 0 15-min Batt/FW storage $216,501.58 90.11%
200 (Med Pen)592,117 107,310 484,807 107,310 Elec Boiler or ETS units $180,303.78 100.00%
200 (Med Pen)592,117 107,310 484,807 0 Turbine max setpoint $167,817.81 93.08%
200 (Med Pen)592,117 107,310 484,807 0 Non value dump load $167,817.81 93.08%
200 (Med Pen)396,716 0 396,716 0 Curtailment $137,324.77 76.16%
200 (Med Pen)592,117 90,975 501,142 0 15-min Batt/FW storage $173,472.23 96.21%
Simply comparing annual heat demand
with annual excess energy leads to
significant error in system design.
While the health clinic in this village
consumes almost twice as much energy
over the course of a year, the heat load is
much less variable than and doesn’t
coincide with the excess wind. Additional
heat loads must be added to the system
design to avoid significant curtailment of
wind turbines.
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Modeling of Thermal Systems
Community building/load Connected
to HR Loop?
Current annual heating
oil consumption*
Thermal mass - Equiv.
gals. of storage
MMBTU
Equiv
kWh
Equiv
Average
kW
Design
Day Heat
Public Works-HEMF Y 19,216 2,652 743,163 84.84 Suspect boiler setpoint set above level to gain benefit from HR loop. Estimate 20% of total is unmet.
Sewer Plant Y 13,695 1,890 529,639 60.46 Estimate 20% of total load is unmet
School N 116,800 16,118 4,517,240 515.67 1840000
PSO N 6,348 876 245,502 28.03 100000 <BTU/Hr
Health clinic N 14,219 1,962 549,925 62.78 224000 <BTU/Hr
Water plant N 11,426 1,577 441,904 50.45 180000 <BTU/Hr
Fire Station N 16,758 2,313 648,126 73.99 264000 <BTU/Hr
Power plant Y 1,625 224 62,847 7.17 Estimate 20% of total load is unmet
0 0 0.00
0 0 0.00
Totals 200,087 27,612 7,738,346 883.37 331,107 <<Excess kWh from HOMER
^^ Poorly matched excess vs. heat load
Because wind energy is variable, there are times
throughout the year when there is more energy
available (turquoise = excess) from the wind
turbines (purple) than the current net* village
electrical load (gold).
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Detailed modeling of electric load, heat load and wind energy
Thermal loads (gold) for buildings and facilities
in a community can make use of this excess wind
energy (turquoise) to supplement other sources
(power plant heat recovery or oil-fired boiler).
Reasonably well-matched excess and load:
*Max wind = village demand –min diesel loading
+ diversion load
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Modeling building thermal loads is easy
Pull records on annual fuel deliveries for large
community buildings. AKWarm estimates work
too.
Total annual building heat loss = total heating
fuel consumed per year minus boiler inefficiency
AEA can pull ASOS/AWOS data to build an hourly
temperature profile for the community and
calculate hourly delta T and equivalent hourly
heating oil gallons or kilowatt-hours.
Most village buildings do not harness significant
passive solar gain, so the model can remain
simple.
The model doesn’t need to be exact –just good
enough to compare relative loads vs. excess
power.
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Water Systems: More Complex to Model
There are more factors to model, but we can still
answer the question: “Will the heat loads
connected to our system even have a theoretical
chance of taking all the power we can give
them?”
We still need to know the annual fuel
consumption, but should also consider as many of
the following additional factors: coldest/warmest
water temperature at source, storage tank size
and insulation, length diameter and insulation of
circ. loop pipe, washeteria dryers and DHW load.
Systems with storage tanks offer a buffer to take
more heat now for possible benefit later.
Do buildings and water systems already connect
to a waste heat loop? Does proximity to the
power plant allow this?
Limitations to the method and model
The model is far from perfect, but attempts to simulate a typical year for thermal load and excess wind energy.
Think ballpark, not section/row/seat.
Understand that the diurnal thermal profile (warmest during the day and coolest at night) is greatly diminished near
and above the Arctic Circle (Lambert’s Law) both during the winter and the summer. Atmospheric blocking of solar
energy is also magnified at higher latitudes for longer periods of the day (Beer’s Law).
An efficient passive-solar building design will deviate more from the model –but only when the sun angle is above
the atmosphere-blocking level (Beer’s Law) and facing the primary windows. This may impact new schools or
hospitals, but we don’t presently have any passive-solar water treatment or sewer plants built in the Arctic. Passive-
solar buildings must also have unobstructed southern exposure to maximize their benefit.
Very drafty buildings can consider a wind-chill-based Delta T calculation rather than straight temperature.
For structures combining building heat with water heat, an estimate will need to be made on the portion of fuel
attributed to each function. Diurnal profiles for water heating will be driven by both Delta T as well as time-of-day
usage.
The farther away a building is from the reference AWOS station, the less accurate the model. In small villages, this
shouldn’t be a problem. In large metropolitan areas like Anchorage or Fairbanks where inversion layers can affect
parts of the city, but not others, temperatures could be off by as much as 20 degrees F at certain times.
Consider opportunities to implement heat pumps or dispatchable electric loads.
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Typical Permafrost Foundation –Thermopile with Concrete Cap
Bethel Avg Temperature 2013-14 warmest on record
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Warm Permafrost is a Concern
1950 1960 1970 1980 1990 2000 2010-5
-4
-3
-2
-1
0
1
2
Temperature Cy=0.0245x-49.8932
Sensor configuration
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Vibration Monitoring
Snapshot of Unalakleet met tower (blue hues)
and wind turbine anemometers (all other
colors) showing variation but with major wind
speed shifts in sync.
Nov. 5 & 6 very weak and weak icing signals in Nome. Met tower
anemometers (green and olive) follow nacelle 1 and 2 (light and dark blue)
heated anemometers. Heated anemometers still indicate light winds when
cup anemometers indicate no wind, but not in the range where the wind
turbine will produce power.
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Icing Study
Weak icing signal in Nome. Met tower anemometers
(green and olive) follow nacelle 1 and 2 (light and dark
blue) heated anemometers. Wind turbine output (orange
and red) has stopped during the icing period and
resumes when wind speeds increase.
Unalakleet post-icing event showing met tower
anemometers (blue) flat lined during the icing period
and then lagging the nacelle anemometers (green) for
10 hours. Wind turbine production (red) is zero during
the icing period and post-icing for 8-16 hours.
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Icing Impact on Wind Turbine Output
Nome EWT-52 turbine #1 showing overlayed power
curves for non-flagged (blue) periods, met tower icing
events (orange), pre-icing reduced power production
(green), post-icing reduced power production (yellow)
and other possible events where no met tower icing was
observed (grey).
Unalakleet NPS-100 turbine #5 showing overlayed power
curves for non-flagged (blue) periods, met tower icing events
(orange), pre-icing reduced power production (green), post-
icing reduced power production (yellow). Icing (orange) is
masked by other plots/curves, but is essentially flat-lined at
zero power output with wind speeds of 5 m/s or less.
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Icing Impact on Wind Turbine Output
Icing Impact on Wind Turbine Output
When encountered in met tower data sets, icing data should be left “as -is” rather than deleting
and resynthesizing the data values. The original data accurately represents the wind conditions
in the case of hoar frost and represents the expected wind turbine output in the case of hoar
frost and rime ice. Tables below show the expected error in energy production forecasting using
the delete/synthesize method for the study period.
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Cold-weather evaluation to test
equipment accuracy and
survivability.
Light detection and ranging system
weighs 60 kg.
Remote power module weighs 375
kg.
Deployed at Delta Wind Farm –
Latitude 64 deg
Very limited winter performance
data due to warranty
troubleshooting and repair.
Stable performance since reinstall
in May 2015
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SpiDAR Evaluation
After several sunny days of operation
following the field install at Delta Wind
Farm, the unit went offline on an overcast
day. Similar outages were experienced the
following week.
An on-board heater consumes more power
than the nominal 35W to run the LIDAR
system. 1-min average loads of 89W were
observed with instantaneous peaks of 305W.
Power pack is only usable 5 months per year.
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SpiDAR Power Pack Capabilities
SpiDAR Data Accuracy
SpiDAR data were compared to the reference data sets from the met tower at 30 meters (speed)
and 50 meters (speed and direction) and the EWT turbines nacelle met station (speed and
direction).
Trend charts of 5030 rows of data indicated 27 10-minute records that contained outlier data
which were subsequently deleted. Only the most extreme outliers were screened on the initial
pass to assess data quality without preconceived limits. Pre and Post outlier removal is below.
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Performing a Pearson correlation of 10-minute time-
paired data, the results showed high correlation on
temperature and average wind speed data between
the SpiDAR and the met tower or wind turbine
anemometers.
Correlation drops in the wind speed standard deviation
and wind direction measurements.
An analysis of the distributions of wind direction show
better overlap than the Pearson correlation of step-by-
step changes.
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SpiDAR Data Accuracy
RFP issued with $20k to seed development of
datalogger specifically designed to meet the needs
of wind resource assessment in remote Alaska.
Current offerings (12-15 data channels at $1800+ per
unit) targeted at large wind farm resource
assessment market.
Winning design proposal has 3 anemometer
channels and 1 vane, on-board temperature sensor,
1-min logging interval of date & time, min, max,
average and std. dev for anemometer/vane and min,
max, avg for temperature. .CSV format.
Data cable inputs are spring-clip, providing for fast
and reliable connection in harsh weather
installations.
Halus Power Systems is designer, manufacturer and
supplier.
Unit sells for $500-$650 depending on exact
configuration/options.
Datalogger unit at field test site in Palmer, AK
showing controller board with SD card, spring-clip
connectors and water-tight seals around cable
intrusions.
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Wind Datalogger for Alaska
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Optional Buckland photos
Rich Stromberg
907-771-3053 (desk)
rstromberg@aidea.org
AKEnergyAuthority.org
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