Offshore
Wind Farms & the Role of SeaSonde
Data
Saving Money for Utilities and New Jersey Rate Payers
August
2010
President
Obama wants 20% of United States
power coming from green energy
by 2030. While this sounds like
an aptly ambitious goal, it pales
in comparison to that set by
the state of New Jersey: source
30% of its electricity from green
energy by 2020. Last summer the
state celebrated its 4000th solar
installation, proving it is rising
to the challenge. But to fully
achieve this lofty goal, New
Jersey cannot rely on land-based
equipment and must move to the
water, capturing offshore wind
power.
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Image above shows the future location of
NJ’s 350 MW offshore wind park, set for construction to start
in 2012. Program led by NRG Bluewater Wind. |
The New Jersey Board of Public Utilities (NJ BPU) is funding the  development
of offshore wind farms and along the way aims to save money for the
utilities and NJ rate payers by optimal harnessing of such “green
power”. Once installed, the daily operating cost of running a wind
turbine is relatively uniform, regardless of actual power produced
any given day. Utilities sell power by bidding certain quantities
on spot energy market for prices that are set 24 hours in advance.
If the utility can predict accurately how much wind energy they will
create and have available the following day (to sell) then they can
bid a larger quantity of power produced from the wind (that comes
at no additional cost to the utility), and maximize their profit.
New Jersey rate payers also benefit because a percentage of any such
profits gets refunded to them. However, if the utility estimates
poorly and oversells energy based on expected wind output then they
need to derive that energy from another source (e.g. coal) -- as
a result the utility can lose money and rate payers see no savings
in their utility bills.
NJ BPU has contracted scientists at Rutgers University to improve the atmospheric
forecasts that utilities use in estimating potential green energy production.
Rutgers’ very high resolution atmospheric forecast model, RU-WRF, is running
with a 1 km resolution that is fine enough to resolve the physics of the
critical sea breeze off the New Jersey coast. RU-WRF outputs information
that NJ BPU can share with all utility companies. Rutgers is running an
operational version used to provide information to weather service and
also a research version they can use to experiment and tweak over time.
The SeaSonde data outputs will be a critical tool used to validate the
WRF model. Rutgers manages a SeaSonde network in the New York- New Jersey
area providing 2-D current maps with both 1 km and 6 km resolution settings.
Wind turbines will be positioned near center of 1 km grid coverage areas.
For the modeling and forecasting effort, the biggest variability near shore
during peak power times is the diurnal sea breeze. The sea breeze is a
wind field that moves across the coastal zone towards land, affected significantly
by differences between the warm land surface temperature and the cool sea
surface temperature. You can see its leading onshore edge using microwave
radars, as this front side contains plenty of dust and particulate matter
acting as an ideal scatter wall for the microwave signals. However, that’s
all the microwave radar can see. The HF radar picks up from there by helping
show the extent of the sea breeze and quantifying the spatial and temporal
variability across the breeze field, that has until now been the critical
missing information.
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“Maps showing diurnal variance ellipses (black
crosses) and the major axis (color) of diurnal variability
calculated from the HF radar system for (a) FebruaryMarch
2005 and (b) AprilMay 2005.“
This figure and above description are published in Hunter, E., R.
Chant, L. Bowers, S. Glenn, and J. Kohut (2007), Spatial and temporal
variability of diurnal wind forcing in the coastal ocean, Geophys.
Res. Lett., 34, L03607, doi:10.1029/2006GL028945. |
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How
does the HF radar do this? Not giving away the recipe in this short
article, in summary: Rutgers applies a series of post-processing
techniques to the SeaSonde 2-D surface current maps that filter out
specific influences on the surface currents, such as the tidal constituents,
eventually isolating the wind-induced component of current at each
1 km grid point in the radar field. The intensity of the wind-induced
surface current is very well correlated with what the winds above
are doing spatially.
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Additional Uses For RU-WRF Model & SeaSonde Outputs:
Wind Farm Design and Engineering Typically the technology engineers utilize
what are called “Wind Resource Maps” (WRM) to design equipment, determine
its ideal placement offshore and estimate energy production. The resource
maps are rather crude, in the form of annual average maps. One task of
the Rutgers team is using the model outputs and SeaSonde data in creating
more sophisticated WRMs-- for each month, with data averaged for 3 hour
segment across the day, to better match demand periods.
Verifying Performance
The WRF model and SeaSonde data can also be used to confirm that the wind
turbines are working and delivering the power they’re supposed to over
a range of various wind speeds and durations, and afterwards gauge the
power harnessing effectiveness of that equipment.
Assisting Routine & Emergency
Ocean Operations
In addition to SeaSonde data being used to validate the model outputs,
this same data can also be used to assist with field operations: during
installation, routine O&M and any emergency responses that may be required.
For these activities it’s good to know in realtime what the ocean current
and wave conditions are for the area. |
 Example
of annual Wind Resource Map for New Jersey area. |
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