Table Of ContentGreen Energy and Technology
For furthervolumes:
http://www.springer.com/series/8059
Marius Paulescu Eugenia Paulescu
•
Paul Gravila Viorel Badescu
•
Weather Modeling
and Forecasting
of PV Systems
Operation
123
Marius Paulescu PaulGravila
Department of Physics Department of Physics
West Universityof Timisora West Universityof Timisora
Timisora Timisora
Romania Romania
EugeniaPaulescu Viorel Badescu
Department of Physics CandidaOancea Institute
West Universityof Timisora Polytechnic University ofBucharest
Timisora Bucharest
Romania Romania
and
Romanian Academy
Bucharest
Romania
ISSN 1865-3529 ISSN 1865-3537 (electronic)
ISBN 978-1-4471-4648-3 ISBN 978-1-4471-4649-0 (eBook)
DOI 10.1007/978-1-4471-4649-0
SpringerLondonHeidelbergNewYorkDordrecht
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(cid:2)Springer-VerlagLondon2013
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To those who know that prediction with no
place for doubt is a superstition
Foreword
The world population is constantly increasing and the world electricity con-
sumption will presumably double by 2050 with potential dramatic effects on our
climate. It is expected that worldwide primary energy demand will increase by
45 %, and demand for electricity will grow by 80 % between 2006 and 2030.1
Consequently, without decisive action, energy-related greenhouse gas (GHG)
emissionswillmorethandoubleby2050,andincreasedoildemandwillintensify
concerns over the security of supply. There are different paths toward stabilizing
GHG concentrations, but a key issue in all of them is the replacement of fossil
fuels by renewable energy sources.
TheEU’sdependenceonimportsoffossilfuels(naturalgas,coalandcrudeoil)
fromnon-EUcountries,asashareoftotalprimaryenergyconsumption,rosefrom
50.8 % in 2000 to 54.2 % in 2005.2 In addition, baseline scenarios show a rising
dependence on imports for most fossil fuels, although this is particularly relevant
forgas,withimports(asapercentageofprimaryenergyconsumption)risingfrom
around59%in2005toupto84%by2030.Inordertocorrectthissituation,and
consideringthatmanycountrieshavedecidedtolessentheirdependenceonnuclear
energy,theEuropeanUnionhasadoptedthegoalofhaving20 %ofitselectricity
supply from renewable energy sources by 2020, along with a commitment to
achieveatleasta20 %reductionofgreenhousegasesby2020,comparedto1990
(European Directives 2009/28/EC and2009/29/EC).
Wind and solar power are presently considered as the sources of renewable
energywiththebestchancetocompetewithfossil-fuelenergyproductioninthenear
future.However,forallthepresentandfuturewindturbinesandsolarpowerplants
tobeworthwhile,theremustbesufficientwindandsolarenergypotentialavailable.
Whathappenswhentheseconditionsarenotmet?Windandsolarenergyforecasting
1 IEA(2009)Worldenergyoutlook.InternationalEnergyAgency,OECDPublicationService,
OECD,Paris.
2 EEA(2008)EnergyandEnvironmentReport2008.EuropeanEnvironmentalAgencyReport
EEAReportNo6/2008,Chap.2.http://www.eea.europa.eu/.
vii
viii Foreword
techniquesaswellaselectricalgridmanagementdevelopmentsaimtoanswersuch
questionsandhavethegoalofhelpingdevelopersofrenewableenergypowerplants
to decide where to install and how to operate them as well as to help the grid
operatorsmanagethisperdefinitionintermittentproductioninputmoreefficiently.
Indeed, for the operational management of electrical grids, integrating different
power sources and dealing with the highly spatially distributed locations of the
powerplantstogetherwiththeintermittent,weather-dependentproductionbecomes
a very important aspect and determines if the energy production will remain bal-
anced with the demand. In other words, the increased penetration of renewable
energiesimpliesthattheelectricalgridswillhavetoadapttothisnewsituation:the
intermittent,difficult-to-predictcharacterofthiskindofenergyproductionwillbea
growing challenge for the Transmission System Operators (TSOs) which have to
copewiththedangerousrisksofgridinstabilities.Newforecastingsystemsaswell
as enhanced grid management techniques are therefore needed to increase the
predictabilityandintegrationofrenewableenergiesforwidespreadpenetration.
Furthermore,whatistodayneededis aEuropeanapproach which would allow
increasingelectricitytransfersbetweenthecountries.Meteorologicalconditionsin
Europearesuchthatthewindislikelytobloworthesunislikelytoshineatsome
place in Europe: in order to increase the penetration of renewable energies, it is
mandatory to consider the electricity exchanges on a more extensive scale.
Inthenearfuture,thenumberofrelativelysmalldecentralizedproductionunits
will grow dramatically, which will be efficiently managed only by introducing
‘‘intelligent’’ technologies such as smart grids. Another aspect to guarantee
electricalgridstabilityliesinthedevelopmentofflexiblestoragecapacitieswhich
will allow storage of excess energy and delivery of missing energy when neces-
sary. Energy storage is therefore getting a strategic role and will have to be
associated with the smart grids in order to adapt in real time and efficiently the
energy production to the fluctuating demand. It will help to combine centralized
and decentralized (intermittent) production systems. For this purpose, short-term
weather—and production—forecasts will play a major role when considering the
whole of Europe.
Accurate power forecasting, efficient and intelligent grid management, and
increased flexible storage capacity are mandatory for the efficientdevelopment of
the future energy policies inEurope andelsewhere,nottomention the benefits in
terms of climate change.
June 2012 Dr. Alain Heimo
Chair COST Action ES1002 Weather
Intelligence for Renewable Energies WIRE
Preface
In the last years, the weight of solar electricity in the energy mix experienced an
impressive augment and this trend is expected to continue. It means that a higher
number of solar power systems, photovoltaic or solar-thermal, with inherent vari-
ableweatherdependentenergyproduction,arefedintothegrid.Asaresult,fore-
castingtheoutputpowerofsolar systems,for thenext minutes uptoseveral days
ahead,areofhighimportanceforproperoperationthegrid.Accuratepredictionof
solarirradianceisofutmostimportance,asthisisameasureofavailablefuelofthe
solarpowergeneratoratagivenfuturemomentoftime.
Apart from wind resources where the forecasting of wind speed is in a rather
maturestage,forecastingofsolarenergyisjustinanearlystage.Inthelastyears,
a few projects dedicated to this matter, like European COST Action ES 1002—
Weather Intelligence for Renewable Energies,3 were deployed around the world.
Many research groups started to put great efforts into enhancing the performance
of the actual models or to devise more performing better.
The forecasting of the output power of a solar system involves modeling tools
which generically should exhibit two functions: first, to predict the solar resource
andsecond,tomodelitsconversionintoelectricity.Alargevarietyofmodelsand
approachescan beconsideredforimplementingthe firstfunction.Fornowcasting
solarirradiance,statisticalextrapolationofmeasurementsseemstobeanadequate
approach, while for tens of hours ahead numerical weather prediction models
represent the best solution. For fulfilling the second purpose, the model is chosen
inrespecttotheapplication:solar-thermalorphotovoltaics.Allthesedemonstrate
thatthesyntagmaforecastingtheoutputpowerofsolarsystemscoversaverylarge
areaofresearchfromatmosphericphysicsandmeteorologytophysicsofsolarcell
and advanced electronics.
This book is focused on two subjects: (i) modeling and nowcasting of solar
irradiance at the ground and (ii) modeling the output power of PV converters in
specific operation conditions. Models developed by the authors along with other
3 COSTActionES1002WeatherIntelligenceforRenewableEnergies,http://www.wire1002.ch.
ix
x Preface
models reported in the literature, accompanied with computational and handwork
illustrations, are discussed in the book.
The eleven chapters are structured along logical lines of progressive thought.
Chapter 1 deals with the concept of energy mix, including a more detailed book
outlineinthelastsection.Chapter2introducesterrestrialandsatellite-basedsolar
radiationmeasurementsandsurveysthelargestsolarradiationdatabases.Chapters
3–8relatesolarregimewithweatherparameters,describingandassessingvarious
approachesfornowcastingsolarirradianceandforecastingsolarirradiation.Chapter
3 deals with the state of the sky assessment while Chap. 4 is focused on different
waystocharacterizethesolarradiativeregimeanditsstabilityinagivenperiodof
time.Chapter5surveysthealgorithmsforestimatingsolarradiationattheground
level,targetingtheideaoftheirusageinnowcastingbyinputtingpredictedvaluesof
weather parameters. Chapters 6 and 7 are devoted to statistical extrapolation of
measurements, being focused on ARIMA (Chap. 6) and fuzzy logic (Chap. 7)
forecastingofclearness indexonshort-timehorizon. Chapter 8proposes asimple
way of predicting solar yield by using forecasted values of daily air temperature
extremesintemperature-basedmodelsforsolarirradiation.Inthenexttwochapters,
the issues of modeling the output of photovoltaic systems operating in specific
weather conditions are addressed. Several models which translate the modules
parameters from standard test conditions to real operating outdoor conditions are
reviewedandillustrated inChap.9.InChap.10,acomparative assessment ofthe
resultsreportedintheliteratureregardingtheforecastingofPVsystemsoutputare
performed.ConclusionsandperspectivesaresummarizedinChap.11.
The authors hope this book gathers information that may be useful to both
researchers in the field of solar radiation forecasting and engineers engaged in
powergridcontrol.Also,partsofthebookmaybeusedforteachingundergraduate
and postgraduate students in related courses.
June 2012 The authors
Acknowledgments
TheauthorsthankDr.AlainHeimo(Meteotest,ChairCOSTActionES1002Weather
IntelligenceforRenewableEnergies—WIRE)forsupportandencouragement.
Someresultsreportedinthisbookwereobtainedbytheauthorswhenworking
to a grant of the Romanian National Authority for Scientific Research, CNCS—
UEFISCDI, project number PN-II-ID-PCE-2011-3-0089 and to the European
Cooperation in Science and Technology project COST ES1002.
Some models and testing procedures reported in this book were worked using
data measured on the Solar Platform of the West University of Timisoara,
developed with financial support from the Romanian Ministry of Research and
Education under the frame of the National Research Program PN II, project
PASOR 21039/2007.
The authors affiliated to the West University of Timisoara express special
thanks to Professor Ion I. Cotaescu for his support.
xi
Contents
1 The Future of the Energy Mix Paradigm . . . . . . . . . . . . . . . . . . 1
1.1 Current Status in Photovoltaics. . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Solar Cells Efficiency. . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 PV Market. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 The Energy Mix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Understating PV Systems Variability . . . . . . . . . . . . . . . . . . 9
1.4 Book Outline. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Solar Radiation Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1 Solar Radiation Components at the Ground Level . . . . . . . . . 17
2.2 Ground Measurements of Solar Radiation . . . . . . . . . . . . . . . 20
2.2.1 Solar Radiometers . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.2 Surface Measurements. . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Solar Radiation Derived from Satellite Observation . . . . . . . . 29
2.3.1 Satellite Based Models for Deriving
Solar Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.3.2 Online Available Databases. . . . . . . . . . . . . . . . . . . 34
2.4 Data Assessment Related to PV Power Forecasting . . . . . . . . 35
2.5 Numerical Weather Prediction Models . . . . . . . . . . . . . . . . . 36
2.5.1 NWP Categories. . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5.2 NWP for Renewable Energy Forecasting. . . . . . . . . . 38
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3 State of the Sky Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1 Traditional Indicators for the State of the Sky . . . . . . . . . . . . 44
3.1.1 Cloud Amount. . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.1.2 Relative Sunshine. . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.1.3 Clearness Index . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.2 Sunshine Number. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
xiii
