Wind Power in Power Systems

Wind Power in Power Systems 371
at a higher resolution (i.e. with smaller grid cells), and the physics and data incorporated
into the models are specifically configured for high-resolution simulations for wind
forecasting applications. The current eWind TM configuration uses a single numerical
model [the Mesoscale Atmospheric Simulation System (MASS) model] to generate the
forecasts.
The statistical models are a set of empirical relationships between the output of the
physics-based atmospheric models and specific parameters that are to be forecasted for a
particular location. In this application, the specific parameters are the wind speed and
direction and air density at the location of the wind turbines that provide power to each
of the five substations. The role of the statistical model is to adjust the output of the physicsbased
model in order to account for subgrid scale and other processes that cannot be
resolved or otherwise adequately simulated on the grids that are used by the physical model.
The third component of the system is a wind turbine output model. This model is a
relationship between the atmospheric variables and the wind turbine output. The wind
turbine output can be either a fixed relationship that applies to a particular wind turbine
configuration or it can be an empirical statistical relationship derived from recent (e.g.
30-day) atmospheric data and wind turbine output data. The final piece is the forecast
delivery system. The user has the option of receiving the forecast information via email,
an FTP transmission, a faxed page or on a web page display.
For the SCE day-ahead power generation, the system is configured to deliver two
forecasts per day: an afternoon forecast at 5 p.m. PT, and a morning forecast at 5:30
a.m. PT. Each cycle consists of the following three parts (Bailey, Brower and Zack,
2000; Brower Bailey and Zack, 2001):
. execution of the physical model;
. reconstruction of the statistical model equations based on the previous 30 days of
physical model forecasts and measured data;
. evaluation of these statistical equations for each forecast hour in the current cycle.
17.3.6 SIPREO ´ LICO
SIPREO´ LICO is a statistics-based prediction tool developed by the University Carlos
III, Madrid, Spain, and the transmission system operator Red Ele´ ctrica de Espan˜a
(REE). It is presently a prototype that provides hourly predictions with a prediction
horizon of up to 36 hours. They are generated by using meteorological forecasts, as well
as online power measurements, as input data to time series analysis algorithms. REE
already uses these predictions for its online system operation. First, SIPREO´ LICO
produces predictions for single wind farms. Once there are predictions for each wind
farm, they are aggregated in zones. Finally, the production forecast for the whole of
Spain is generated. For a given wind farm, SIPREO´ LICO uses four types of input: the
characteristics of the wind farm; historical records of incoming wind and output power,
to arrive at a real power curve; online measurements of power output; and meteorological
predictions provided by HIRLAM. The algorithms that SIPREO´ LICO uses to
generate the predictions depend on what type of input is available. Input data may be
basic, additional or complete. Basic data are those that are available for every wind
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