IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions

390 Pages·2022·7.291 MB·English

by  Corinna Möhrlen, John W. Zack, Gregor Giebel

IEA WIND
RECOMMENDED
PRACTICE FOR THE
IMPLEMENTATION OF
RENEWABLE ENERGY
FORECASTING
SOLUTIONS
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IEA WIND
RECOMMENDED
PRACTICE FOR THE
IMPLEMENTATION OF
RENEWABLE ENERGY
FORECASTING
✩
SOLUTIONS
CORINNAMÖHRLEN
WEPROG
Assens,Denmark
JOHNW.ZACK
MESOInc.
Troy,NY,UnitedStates
GREGORGIEBEL
TechnicalUniversityofDenmark
DepartmentofWindandEnergySystems
Roskilde,Denmark
✩CoordinatedbyTask36WindEnergyForecastingandTask51ForecastingfortheWeather-driven
EnergySystem
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This IEA Wind Recommended Practice guideline is dedicated to the
renewable energy industry, especially those who are seeking ways to obtain
the most value from wind and solar forecasting information for their
applications. These include the pioneers who have courageously found
innovative ways to extract value from forecasting information as well as those
that are still in doubt about the value of forecasting, or those who are still
trying to understand how to best and most efficiently integrate wind and
solar forecasting into their business processes.
In short, it is dedicated to all those that have to use forecasting of wind
and solar energy and can see the benefit of standardizing business processes
for the integration of wind and solar energy connected to the electric grid and,
to a large extent, participating in power exchange markets.
This guideline is also dedicated to all those that took part in the process
of assembling and refining the recommended practices presented in this
document. Many valuable contributions have been made by those who have
brought up relevant issues in the discussion forums that we organized at
various workshops and conferences and thereby helped us to formulate
common and internationally acceptable procedures.
In addition, the authors want to acknowledge those that guided us to
publicly available information, provided relevant pictures and graphs and also
made us aware of proprietary information that otherwise is only available to
those in contractual relationships. By anonymizing the latter type of
information, we are now able to provide a compendium with comprehensive
and practical information of how to optimally integrate wind and solar
generation forecasting in a wide range of business processes.
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Contents
Listoffigures xv
Listoftables xxi
Biography xxv
Preface xxvii
AbouttheIEAWindTCPandTask36and51 xxix
PART1 Forecastsolutionselectionprocess
1. Forecastsolutionselectionprocess 3
1.1. Beforeyoustartreading 3
1.2. Backgroundandintroduction 3
1.3. Objectives 4
1.4. Definitions 5
2. Initialconsiderations 7
2.1. Tacklingthetaskofengagingaforecasterforthefirsttime 7
2.2. Purposeandrequirementsofaforecastingsolution 10
2.3. Addinguncertaintyforecaststoforecastingsolutions 11
2.4. Informationtableforspecifictopictargets 12
3. Decisionsupporttool 15
3.1. Initialforecastsystemplanning 17
3.2. ITinfrastructureconsiderations 18
3.2.1. ITrequirementsforsingleversusmultipleforecastvendors 18
3.2.2. ITrequirementsfordeterministicversusprobabilisticforecasts 19
3.3. Establishmentofarequirementlist 19
3.3.1. Requirementlist 21
3.4. Short-termsolution 22
3.5. Long-termsolution 23
3.6. GoingforwardwithanestablishedITsystem 23
3.7. ComplexityleveloftheexistingITsolution 24
3.8. Selectionofanewvendorversusbenchmarkingexistingvendor 25
3.9. RFPevaluationcriteriaforaforecastsolution 25
3.9.1. Forecastsolutiontype 26
3.9.2. Vendorcapabilities 30
3.9.3. Evaluationofservices 33
vii
viii Contents
3.10. Forecastmethodologyselectionforuseofprobabilisticforecasts 38
3.10.1. Definitionsofuncertainty 39
3.10.2. Uncertaintyforecastingmethods 40
3.10.3. Trainingtoolsforensembleforecasting 43
3.10.4. Applicationsofuncertaintyforecastsintheenergyindustry 44
3.10.5. Visualizationofforecastuncertainty 49
4. Datacommunication 53
4.1. Terminology 55
4.2. Datadescription 56
4.2.1. LEVEL1–datadescription 57
4.3. Dataformatandexchange 69
4.3.1. LEVEL1dataformatandexchange 69
4.3.2. LEVEL2–dataformatandexchange 70
4.4. Sampleformattedtemplatefilesandschemas 73
5. Concludingremarks 75
PART2 Designingandexecutingforecastingbenchmarks
andtrials
6. Designingandexecutingbenchmarksandtrials 79
6.1. Beforeyoustartreading 79
6.2. Backgroundandintroduction 79
6.3. Definitions 80
6.3.1. Renewableenergyforecastbenchmark 80
6.3.2. Renewableenergyforecasttrial 80
6.4. Objectives 80
7. Initialconsiderations 83
7.1. Decidingwhethertoconductatrialorbenchmark 83
7.2. Benefitsoftrialsandbenchmarks 83
7.3. Limitationswithtrialsandbenchmarks 84
7.4. Timelinesandforecastperiodsinatrialorbenchmark 86
7.5. 1-Page“cheatsheet”checklist 87
8. Conductingabenchmarkortrial 89
8.1. Phase1:preparation 89
8.1.1. Keyconsiderationsinthepreparationphase 89
8.1.2. Metadatagatheringinthepreparationphase 92
Contents ix
8.1.3. Historicaldatagatheringinthepreparationphase 92
8.1.4. IT/dataconsiderationsinthepreparationphase 92
8.1.5. Communicationinthepreparationphase 94
8.1.6. Testruninthepreparationphase 94
8.2. Phase2:Duringbenchmark/trial 95
8.2.1. Communicationduringtheb/t 95
8.2.2. Forecastvalidationandreportingduringtheb/t 96
8.3. Phase3:Posttrialorbenchmark 96
8.3.1. Communicationattheendoftheb/t 96
8.3.2. Forecastvalidation&reportingaftertheb/t 96
9. Considerationsforprobabilisticbenchmarksandtrials 97
9.1. Preparationphasechallengesforprobabilisticb/t 97
9.2. Evaluationchallengesforprobabilisticb/t 98
10.Bestpracticerecommendationsforbenchmarks/trials 101
10.1. Bestpracticeforb/t 101
10.2. Pitfallstoavoid 102
PART3 Forecastsolutionevaluation
11.Forecastsolutionevaluation 107
11.1. Beforeyoustartreading 107
11.2. Backgroundandintroduction 107
12.Overviewofevaluationuncertainty 111
12.1. Representativeness 111
12.1.1. Sizeandcompositionoftheevaluationsample 112
12.1.2. Dataquality 114
12.1.3. Forecastsubmissioncontrol 114
12.1.4. Processinformationdissemination 115
12.2. Significance 115
12.2.1. Quantificationofuncertainty 116
12.3. Relevance 117
13.Measurementdataprocessingandcontrol 119
13.1. Uncertaintyofinstrumentationsignalsandmeasurements 119
13.2. Measurementdatareportingandcollection 120
13.2.1. Non-weatherrelatedproductionreductions 121
13.2.2. Aggregationofmeasurementdataintimeandspace 121