7/30/2019 Lara-Fanego DNI Solar Forecasts WRF Spain for CPV
applications-CPV8
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CPV-8Conf.,Toledo,Spain(2012)
Evaluation Of DNI Forecast Based On The WRFMesoscale Atmospheric
Model For CPV Applications
V. Lara-Fanego1, J. A. Ruiz-Arias
1, A. D. Pozo-Vzquez
1,
C. A. Gueymard2 and J. Tovar-Pescador1
1Department of Physics, University of Jan. Campus Las Lagunillas
A3-066, 23071, Jan (Spain)
2Solar Consulting Services, P.O. Box 392, Colebrook, NH 03576,
USA
[email protected]
Abstract. The integration of large-scale solar electricity
production into the energy supply structures depends es-sentially
on the precise advance knowledge of the available resource.
Numerical weather prediction (NWP) modelsprovide a reliable and
comprehensive tool for short- and medium-range solar radiation
forecasts. The methodologyfollowed here is based on the WRF model.
For CPV systems the primary energy source is the direct normal
irradi-ance (DNI), which is dramatically affected by the presence
of clouds. Therefore, the reliability of DNI forecasts isdirectly
related to the accuracy of cloud information. Two aspects of this
issue are discussed here: (i) the effect of
the models horizontal spatial resolution; and (ii) the effect of
the spatial aggregation of the predicted irradiance.Results show
that there is no improvement in DNI forecast skill at high spatial
resolutions, except under clear-skyconditions. Furthermore, the
spatial averaging of the predicted irradiance noticeably reduces
their initial error.
Keywords: DNI, WRF, NWP, forecast.PACS: 88.05.Lg, 96.60.Ub,
92.60.Vb, 94.20.Cf
INTRODUCTION
Technologies for harnessing the solar resourcehave experienced a
significant development in recentyears. Their future looks even
more promising. TheInternational Energy Agency expects that,
accordingto a reference scenario, the worlds installed solar
power capacity will increase from 14 GW in 2008 to119 GW in
2035, with a 8.3% average annual in-crease [1]. Therefore, the
challenge for the next fewyears is to achieve a high level of
development andintegration, to make this resource competitive
com-
pared to traditional sources of energy, or even tomore
established renewable sources, like wind. A
major effort is being made in this regard [2]. Toachieve this
goal, a key aspect concerns the resourceitself (technology aside).
The safe and optimal inte-gration of large-scale solar electric
power productioninto the energy grid of any country depends on
theknowledge of the solar production capacity, which
in turn is directly related to the available resource.An
important intrinsic characteristic of solar ra-
diation is its very high variability over space andtime, itself
directly dependent on weather character-istics. This intermittency
in the resource makes a so-lar plants operation and management
particularlydifficult. It also makes solar production
troublesomefor grid system operators, since it is hardly con-
trolled and may not be available when it would be ofgreatest
value [1]. This ultimately translates into in-cremental
exploitation and integration costs. There-fore, prior knowledge of
the available resource ofthe near future is essential. Previous
experience with
the wind energy sector has shown that accurate fore-casts play a
key role toward the successful integra-
tion of variable energy sources.
CPV systems use the beam component of solar
radiationor direct normal irradiance (DNI)astheir energy source.
DNI is primarily affected byclouds, aerosols, and water vapor.
Clouds are nor-mally the principal factor affecting the incident
solarradiation at the earths surface, since they are mostoften
completely opaque to DNI. In contrast, aero-
sols are most influential under cloudless conditions.The
uncertainty in the determination of the physical
parameters associated with these atmospheric con-stituents is
the main source of error in DNI predic-tions. This study focuses on
how the latter is affect-ed by cloudiness forecasts.
Numerical weather prediction (NWP) modelshave been proved to be
powerful tools for solar radi-
ation forecasting [3, 4]. One particular tool that iswidely used
by the research community is theWeather Research and Forecasting
(WRF) model[5]. WRF, like other NWP models, has a wide rangeof
physical parameterizations, providing the possi-
bility to achieve high spatial and temporal resolu-tions. It is
commonly assumed that the higher the
resolution, the better the physical description andresults will
be. This should apply, for instance, to therepresentation of
processes that lead to the formationof clouds. In turn, high
resolutions are computation-ally very expensive. Therefore, an
optimal spatialresolution may exist in solar forecasting.
This contribution evaluates the role of the WRF
models horizontal spatial resolution in the reliabil-ity of the
DNI forecasts that it can (indirectly) gen-erate. Additionally, the
intentional use of spatial av-eraging of the gridded WRF-derived
solar field toimprove the models accuracy is evaluated.
Themethodology applied is described first. A descriptionof the
forecast results is presented in a second step.
