IEEE Power & Energy Magazine - November/December 2015 - 58

One of the most intuitive factors that impacts forecast performance
is the forecast look-ahead time (also known as the forecast time
horizon or lead time).

forecast in concert with colocated winds from an NWP model
to capture the movement of clouds at different heights. In the
NCAR SunShot project, a cloud nowcasting prototype based on
multiple satellite infrared sensors and a simplified NWP model
compares infrared radiance observations with their equivalents
from a numerical model to develop a more accurate cloud profile that can be used with the other forecasting methods.

Probabilistic Forecasting
Ongoing research in this area includes improved probabilistic treatment of atmospheric physics models, as well as
improved representation of uncertainties in cloud formation
and decay. This will result in increased reliability and sharpness. One novel method is the analog ensemble approach,
which, instead of generating multiple model forecasts,
searches the past history for similar forecasts and makes
corrections to the forecast according to the error in prior
forecasts. The prior analogs become an ensemble that quantifies the uncertainty of the forecast.

Integration into System Operations
While improving the underlying forecasts is important, it is
also crucial that they are integrated into system operations so
that operators can obtain the full value from the forecast. Many

utilities now receive forecasts; however, most are still learning how to use them in their decision-making processes. There
are significant efforts to integrate solar power forecasts more
deeply into system operations. For example, Hawaiian Electric
Company has supported the development of a Solar and Wind
Integrated Forecast Tool, which is built on an optimized mix of
various approaches described earlier and has deterministic and
probabilistic elements as well as variability and ramp-rate predictions. Work is underway to integrate these forecast outputs
directly into the energy management systems. The Red El├ęctrica Control Centre of Renewable Energies in Spain, shown in
Figure 9, can identify risks, anticipate the behavior of solar and
wind, and compensate for their variability, without compromising the quality and security of supply.
Research on methods for using probabilistic forecasts to optimize system operations based on stochastic optimization techniques is being carried out in various institutions. For example,
Sandia National Laboratories is developing a toolkit to demonstrate use of stochastic unit commitment, while the Electric
Power Research Institute is investigating the use of probabilistic information to develop more intelligent operating reserve
requirements. Power system operators and independent power
producers could potentially see benefits by incorporating forecast uncertainty directly into their decision making.

figure 9. Red El├ęctrica Control Centre of Renewable Energies in Spain. (Courtesy of Red Electrica Espana.)
58

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