IEEE Power & Energy Magazine - November/December 2017 - 41

I

IT IS IN THE NATURE OF CHAOTIC ATMOspheric processes that weather forecasts will
never be perfectly accurate. This natural fact
poses challenges not only for private life, public
safety, and traffic but also for electrical power
systems with high shares of weather-dependent
wind and solar power production.
To facilitate a secure and economic grid and
market integration of renewable energy sources
(RES), grid operators and electricity traders must
know how much power RES within their systems will produce over the next hours and days.
This is why RES forecast models have grown over
the past decade to become indispensable tools for
many stakeholders in the energy economy. Driven
by increased grid stability requirements and market forces, forecast systems have become tailored
to the end user's application and already perform
reliably over long periods. Apart from a residually
moderate forecast error, there are single extremeerror events that greatly affect grid operators.
Nevertheless, there are also forecast systems
that provide additional information about the
expected forecast uncertainty and estimations of
both moderate and extreme errors in addition to
the "best" single forecast. Such uncertainty forecasts warn the grid operator to prepare to take special actions to ensure grid stability.

The State of the Art
in Forecast Generation
ALL IMAGES LICENSED BY INGRAM PUBLISHING

november/december 2017

Today, some forecast systems have been developed specifically to predict the power production of single wind and solar units, differently
sized portfolios, local transformer stations and
subgrids, distribution and transmission grids,
and entire countries. Nearly all forecast systems
have one thing in common: they rely on numerical weather predictions (NWPs) to calculate the
expected RES power production. The way to
transform weather predictions into power forecasts depends crucially on the end user's application and the available plant configuration and
measurement data. If historical measurements are
available, forecast model developers often use
statistical and machine-learning techniques to
automatically find a relation between historical
weather forecasts and simultaneously observed
power measurements. If no historical measurement data are available, e.g., for new installations
of RES units, the transformation of weather to
power is often accomplished by physically based
models that consider the unit's parameters to map
the internal physical processes.
ieee power & energy magazine

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Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - November/December 2017

IEEE Power & Energy Magazine - November/December 2017 - Cover1
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IEEE Power & Energy Magazine - November/December 2017 - Cover3
IEEE Power & Energy Magazine - November/December 2017 - Cover4
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