IEEE Power & Energy Magazine - November/December 2019 - 57

Decision-making processes that leverage forecast uncertainty
information have almost always been demonstrated to produce
consistently better decisions than those that don't.
have described some of the forefront and experimental
applications of probabilistic forecasts, including to assess
grid security, fine-tune unit commitment, capture extreme
conditions, prepare for extreme events more effectively, plan
for reserves more efficiently, better maintain system stability, and design flexible ramping products.
One of the biggest hurdles for implementing probabilistic forecasting in industry is the lack of case studies, as
well as reference materials in the form of documentation,
standards, and guidelines. IEA Wind Task 36, Wind Energy
Forecasting, is an international collaboration that has, as one
of its tasks, to facilitate optimal use of forecasting solutions
with a special focus on uncertainty/probabilistic forecasting
for the power industry. The task concentrates on the use of
forecast uncertainties for operation and management of the
power system with substantial variable generation, for operation, trading, and balancing in power markets. The goal is
to develop best-practice guidelines for the implementation,
measurement, and quantification of the value of probabilistic forecasts based on experience from experts around the
globe. Additional information can be found in the "For Further Reading" section.
Many users say that wind and solar forecasts would have
more value in their decision-making processes if they were
more accurate. We must recognize, however, that the atmosphere contains inherent variability that cannot be precisely
predicted. A more realistic approach embraces that uncertainty,
quantifies it, and uses that probabilistic information to optimize system operation. Probabilistic forecasts show promise
for growth as a tool to manage the variability of wind and solar
generation in electric systems. Decision-making processes
that leverage forecast uncertainty information have almost
always been demonstrated to produce consistently better decisions than those that don't use this information. Therefore, a
near-term opportunity remains to glean more value from wind
and solar forecasts by more effectively using currently available uncertainty information without having to wait for future
improvements in weather forecasting technology.

For Further Reading
R. J. Bessa et al., "Towards improved understanding of the
applicability of uncertainty forecasts in the electric power industry," Energies, vol. 10, no. 9, p. 1402, 2017. doi: 10.3390/
en10091402.
Z. Ben Bouallègue, T. Heppelmann, S. E. Theis, and P. Pinson,
"Generation of scenarios from calibrated ensemble forecasts

november/december 2019

with a dual-ensemble copula-coupling approach," Monthly
Weather Rev., vol. 144, pp. 4737-4750, 2016. doi: 10.1175/
MWR-D-15-0403.1.
J. Dobschinski et al., "Uncertainty forecasting in a nutshell,"
IEEE Power Energy Mag., vol. 15, no. 6, pp. 40-49, 2017. doi:
10.1109/MPE.2017.2729100.
G. Giebel et al., "IEA wind task 36 forecasting," IEA Wind
Task 36, 2017. [Online]. Available: http://www.ieawindforecasting
.dk/-/media/Sites/IEA_task_36/Posters/EGU2017-Poster
-IEAWindTask36.ashx
S. L. Joslyn, L. Nadav-Greenberg, M. U. Taing, and R. M.
Nichols, "The effects of wording on the understanding and
use of uncertainty information in a threshold forecasting decision," Appl. Cognit. Psychol., vol. 23, no. 1, pp. 55-72, Jan.
2009. doi: 10.1002/acp.1449.
S. J. Joslyn and J. E. LeClerc, "Uncertainty forecasts improve weather-related decisions and attenuate the effects of
forecast error," J. Exp. Psychol. Appl., vol. 18, no. 1, pp. 126-
140, 2012. doi: 10.1037/a0025185.
S. Wende-von Berg et al, "SysDL2.0: System services
from area distribution networks: Methods and applications" (in German), in Proc. 14th Symp. Energy Innovation,
Feb. 2016, Graz, Austria. [Online]. Available: https://www
.tugraz.at/fileadmin/user_upload/Events/Eninnov2016/files/
lf/Session_C4/LF_Wende-von-Berg.pdf

Biographies
Sue Ellen Haupt is with the National Center for Atmospheric Research, Boulder, Colorado.
Mayte Garcia Casado is with Red Eléctrica de Espana,
Madrid, Spain.
Michael Davidson is with Australian Energy Market
Operator, Adelaide, Australia.
Jan Dobschinski is with Fraunhofer IEE, Kassel,
Germany.
Pengwei Du is with ERCOT, Austin, Texas.
Matthias Lange is with energy & meteo systems, Oldenburg, Germany.
Timothy Miller is with Southwest Power Pool, Little
Rock, Arkansas.
Corinna Möhrlen is with WEPROG, Assens, Denmark.
Amber Motley is with CAISO, Folsom, California.
Rui Pestana is with R&D Nester, Sacavém, Portugal.
John Zack is with UL-AWS Truepower, Albany,
New York.
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http://www.ieawindforecasting.dk/-/media/Sites/IEA_task_36/Posters/EGU2017-Poster-IEAWindTask36.ashx http://www.ieawindforecasting.dk/-/media/Sites/IEA_task_36/Posters/EGU2017-Poster-IEAWindTask36.ashx http://www.ieawindforecasting.dk/-/media/Sites/IEA_task_36/Posters/EGU2017-Poster-IEAWindTask36.ashx http://www.tugraz.at/fileadmin/user_upload/Events/Eninnov2016/files/lf/Session_C4/LF_Wende-von-Berg.pdf http://www.tugraz.at/fileadmin/user_upload/Events/Eninnov2016/files/lf/Session_C4/LF_Wende-von-Berg.pdf

IEEE Power & Energy Magazine - November/December 2019

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