Renewable energy power forecasting technology

IEC TR 63043:2020(E), which is a technical report, describes common practices and state of the art for renewable energy power forecasting technology, including general data demands, renewable energy power forecasting methods and forecasting error evaluation. For the purposes of this document, renewable energy refers to variable renewable energy, which mainly comprises wind power and photovoltaic (PV) power – these are the focus of the document. Other variable renewable energies, like concentrating solar power, wave power and tidal power, etc., are not presented in this document, since their capacity is small, while hydro power forecasting is a significantly different field, and so not covered here.
The objects of renewable energy power forecasting can be wind turbines, or a wind farm, or a region with lots of wind farms (respectively PV systems, PV power stations and regions with high PV penetration). This document focuses on providing technical guidance concerning forecasting technologies of multiple spatial and temporal scales, probabilistic forecasting, and ramp event forecasting for wind power and PV power.
This document outlines the basic aspects of renewable energy power forecasting technology. This is the first IEC document related to renewable energy power forecasting. The contents of this document will find an application in the following potential areas:
• support the development and future research for renewable energy power forecasting technology, by showing current state of the art;
• evaluation of the forecasting performance during the design and operation of renewable energy power forecasting system;
• provide information for benchmarking renewable forecasting technologies, including methods used, data required and evaluation techniques

General Information

Status
Published
Publication Date
26-Nov-2020
Current Stage
PPUB - Publication issued
Completion Date
27-Nov-2020
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IEC TR 63043
Edition 1.0 2020-11
TECHNICAL
REPORT
colour
inside
Renewable energy power forecasting technology
IEC TR 63043:2020-11(en)
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---------------------- Page: 2 ----------------------
IEC TR 63043
Edition 1.0 2020-11
TECHNICAL
REPORT
colour
inside
Renewable energy power forecasting technology
INTERNATIONAL
ELECTROTECHNICAL
COMMISSION
ICS 29.020 ISBN 978-2-8322-9079-8

Warning! Make sure that you obtained this publication from an authorized distributor.

® Registered trademark of the International Electrotechnical Commission
---------------------- Page: 3 ----------------------
– 2 – IEC TR 63043:2020  IEC 2020
CONTENTS

FOREWORD ........................................................................................................................... 7

INTRODUCTION ..................................................................................................................... 9

1 Scope ............................................................................................................................ 10

2 Normative references .................................................................................................... 10

3 Terms, definitions and abbreviated terms ...................................................................... 10

3.1 Terms and definitions ............................................................................................ 11

3.2 Abbreviated terms ................................................................................................. 13

4 General introduction to renewable energy power forecasting ......................................... 15

4.1 History of RPF ...................................................................................................... 15

4.1.1 General ......................................................................................................... 15

4.1.2 Development of wind power forecasting ......................................................... 16

4.1.3 Development of PV power forecasting ........................................................... 17

4.2 Use of RPF ........................................................................................................... 17

4.2.1 General ......................................................................................................... 17

4.2.2 RPF for system operations ............................................................................ 18

4.2.3 RPF for power trading .................................................................................... 18

4.2.4 RPF for operations and maintenance ............................................................. 19

4.3 Methods for forecasting renewable power ............................................................. 19

4.3.1 General ......................................................................................................... 19

4.3.2 Classification of forecasting methods ............................................................. 19

4.3.3 Classification based on time scale ................................................................. 21

4.3.4 Classification based on spatial range ............................................................. 22

4.3.5 Classification based on the forecasting model ............................................... 22

4.3.6 Classification based on the forecasting form .................................................. 24

4.4 Summary .............................................................................................................. 25

5 NWP technology ............................................................................................................ 25

5.1 General ................................................................................................................. 25

5.2 Concept and characteristics of NWP ..................................................................... 25

5.3 Influence on RPF accuracy ................................................................................... 27

5.3.1 Sensitivity analysis ........................................................................................ 27

5.3.2 Error source analysis ..................................................................................... 28

5.4 Technology progress for improving NWP .............................................................. 29

5.4.1 General ......................................................................................................... 29

5.4.2 Global model ................................................................................................. 29

5.4.3 Regional model .............................................................................................. 31

5.5 Key techniques for improving the forecast accuracy of regional models ................ 31

5.5.1 Improve the accuracy of the initial conditions ................................................. 31

5.5.2 Ensemble prediction systems......................................................................... 32

5.5.3 Establish regional customized forecasting model ........................................... 38

5.5.4 NWP post-processing .................................................................................... 39

5.6 Summary .............................................................................................................. 39

6 Statistical methods ........................................................................................................ 39

6.1 General ................................................................................................................. 39

6.2 Methods ................................................................................................................ 40

6.3 Applications .......................................................................................................... 42

6.3.1 General ......................................................................................................... 42

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IEC TR 63043:2020  IEC 2020 – 3 –

6.3.2 Time series models ........................................................................................ 42

6.3.3 Model output statistics (MOS) ........................................................................ 47

6.3.4 Ensemble composite models (ECM) ............................................................... 51

6.3.5 Power output models ..................................................................................... 53

7 Wind power forecasting (WPF) technology..................................................................... 54

7.1 General ................................................................................................................. 54

7.2 Short-term WPF .................................................................................................... 54

7.2.1 Relationship between wind power output and meteorological elements .......... 54

7.2.2 Framework of short-term WPF ....................................................................... 57

7.2.3 Short-term WPF methods .............................................................................. 58

7.3 Ultra-short-term WPF ............................................................................................ 62

7.4 Probabilistic WPF ................................................................................................. 65

7.4.1 General ......................................................................................................... 65

7.4.2 Basic concepts and model framework definition ............................................. 65

7.4.3 Uncertainty modeling approaches .................................................................. 66

7.4.4 Probabilistic WPF model ................................................................................ 67

7.5 Wind power ramp event forecasting ...................................................................... 71

7.5.1 General ......................................................................................................... 71

7.5.2 Quantitative description of wind power ramp events ....................................... 71

7.5.3 Forecasting methods of wind power ramp events ........................................... 74

7.6 WPF for wind farm clusters ................................................................................... 75

7.6.1 General ......................................................................................................... 75

7.6.2 Basic concepts of WPF for wind farm clusters................................................ 75

7.6.3 Overall framework of the WPF for wind farm clusters ..................................... 76

7.6.4 Physical hierarchy of WPF for wind farm clusters ........................................... 78

7.6.5 WPF methods of wind farm clusters ............................................................... 79

7.7 Other WPF techniques .......................................................................................... 82

7.7.1 Medium-term and long-term WPF .................................................................. 82

7.7.2 WPF for offshore wind farms .......................................................................... 82

7.8 Summary .............................................................................................................. 83

8 PV power forecasting technology ................................................................................... 83

8.1 General ................................................................................................................. 83

8.2 Short-term PVPF ................................................................................................... 83

8.2.1 General ......................................................................................................... 83

8.2.2 Meteorological influence factors of PV power generation ............................... 83

8.2.3 Basic concepts for short-term PVPF .............................................................. 86

8.2.4 Short-term PVPF model ................................................................................. 87

8.2.5 Trends in PVPF development and key technical issues .................................. 89

8.3 Ultra-short-term PVPF........................................................................................... 89

8.3.1 General ......................................................................................................... 89

8.3.2 Basic concepts for ultra-short-term PVPF ...................................................... 90

8.3.3 Ultra-short-term PVPF models ....................................................................... 90

8.3.4 Trends in development and key technical issues ........................................... 92

8.4 Minute-time-scale PVPF........................................................................................ 92

8.4.1 Basic concepts for minute-time-scale solar power forecasting ........................ 93

8.4.2 Technique routine of minute-time-scale solar power forecasting .................... 93

8.4.3 Trends in development and key technical issues ........................................... 94

8.5 Probabilistic PVPF ................................................................................................ 95

8.5.1 Basic concepts of PV power probabilistic forecasting ..................................... 95

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– 4 – IEC TR 63043:2020  IEC 2020

8.5.2 Probabilistic PVPF model .............................................................................. 96

8.5.3 Trends in development and key technical issues ........................................... 98

8.6 Distributed PVPF .................................................................................................. 98

8.6.1 General ......................................................................................................... 98

8.6.2 Basic concepts for distributed PVPF .............................................................. 99

8.6.3 Distributed PVPF methods ............................................................................. 99

8.6.4 Trends in development and key technical issues ......................................... 102

8.7 Summary ............................................................................................................ 102

9 Renewable energy power forecasting (RPF) evaluation ............................................... 103

9.1 General ............................................................................................................... 103

9.2 Deterministic forecasts of continuous variables ................................................... 104

9.2.1 General ....................................................................................................... 104

9.2.2 Metrics ........................................................................................................ 104

9.2.3 Mean bias error ........................................................................................... 104

9.2.4 Mean absolute error..................................................................................... 105

9.2.5 Root mean square error ............................................................................... 105

9.2.6 Skill score .................................................................................................... 106

9.2.7 Correlation coefficient .................................................................................. 106

9.2.8 Maximum prediction error ............................................................................ 107

9.2.9 Pass rate ..................................................................................................... 107

9.2.10 95 % QDR ................................................................................................... 108

9.2.11 Customized metrics ..................................................................................... 109

9.3 Deterministic forecasts of categorical (event) variables ....................................... 109

9.3.1 General ....................................................................................................... 109

9.3.2 Occurrence/non-occurrence metrics ............................................................ 110

9.3.3 Frequency bias ............................................................................................ 110

9.3.4 Probability of detection ................................................................................ 110

9.3.5 False alarm ratio .......................................................................................... 111

9.3.6 Critical success index .................................................................................. 111

9.3.7 Equitable threat score .................................................................................. 111

9.3.8 Heidke skill score ........................................................................................ 111

9.4 Probabilistic forecasts of categorical (event) variables ........................................ 112

9.4.1 General ....................................................................................................... 112

9.4.2 Overall performance .................................................................................... 112

9.4.3 Reliability..................................................................................................... 116

9.4.4 Resolution ................................................................................................... 117

9.5 Probabilistic forecasts of continuous variables .................................................... 118

9.5.1 General ....................................................................................................... 118

9.5.2 Overall performance .................................................................................... 118

9.5.3 Reliability..................................................................................................... 119

9.5.4 Resolution ................................................................................................... 119

9.6 Sources of forecast error .................................................................................... 119

9.7 Comparison of forecast performance................................................................... 120

9.8 Selection of an optimal forecast solution ............................................................. 122

10 Conclusions and recommendations.............................................................................. 123

Bibliography ........................................................................................................................ 126

Figure 1 – Forecasting of PV power at different spatial and temporal scales ......................... 21

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IEC TR 63043:2020  IEC 2020 – 5 –

Figure 2 – Introduced data for PV power forecasting at different spatial and temporal

scales ................................................................................................................................... 21

Figure 3 – Typical process for running a regional model ....................................................... 26

Figure 4 – Power curve of typical wind turbines .................................................................... 27

Figure 5 – Characteristics of three kinds of forecasting errors ............................................... 28

Figure 6 – Evolution of ECMWF's forecasting skills for the 500 hPa potential height

[35], [54] ............................................................................................................................... 30

Figure 7 – Ensemble forecasting sketch [54] ......................................................................... 33

Figure 8 – Illustration of parameterization schemes for sub-grid physical processes [54] ...... 38

Figure 9 – MAE (% of capacity) versus look-ahead time for 0 h to3 h forecasts of the

15 min average wind power production from the TWRA aggregate over the one-year

period from October 2015 to September 2016 for each of 5 source-dependent sets of

predictors employed in the predictor source category experiment [96] .................................. 44

Figure 10 – Percentage MAE reduction over persistence by look-ahead time achieved

by each source-dependent set of predictors for 0 h to 3 h forecasts of the 15 min

average TWRA aggregate (capacity of 2 319 MW) power production over the one-year

period from October 2015 to September 2016 [96] ................................................................ 45

Figure 11 – Percentage MAE reduction by look-ahead time achieved by building
forecasting models with the XGBoost method versus MLR for the “Add existing

external data” (set #4) and “Add targeted sensors” (set #5) predictor sets for 0 h to 3 h

forecasts of the 15 min average TWRA aggregate (capacity of 2 319 MW) power

production over the one year period from October 2015 to September 2016 [96] .................. 46

Figure 12 – Percentage MAE reduction by look-ahead time achieved by using the “rate

of change” (indirect forecasting) versus “the 15 min average power generation” (direct

forecasting) as the target predictand for the XGBoost model for 0 h to 3 h forecasts of

the 15 min average TWRA aggregate (capacity of 2 319 MW) power production over

the one year period from October 2015 to September 2016 [96] ............................................ 47

Figure 13 – Mean absolute error (MAE) in m/s of two 0 h to 18 h NWP-MOS forecasts

of the maximum wind gust in a 15 min period for 33 sites over a 32-case sample of

high wind events as a function of training sample size .......................................................... 48

Figure 14 – Percentage reduction in the mean absolute error of NWP-based 0 h to 15 h

wind power forecasts for the Tehachapi Wind Resource Area (TWRA) over a one-year

period resulting from the application of 26 statistical forecasting methods to the output

from the United States National Weather Service’s High Resolution Rapid Refresh

(HRRR) model [96] ............................................................................................................... 49

Figure 15 – Percentage reduction in the mean absolute error (MAE) of wind power

forecasts relative to a baseline of a raw NWP forecast for three NWP models when a

MOS procedure is applied to the NWP output (larger percentages are better) ....................... 51

Figure 16 – Input and output parameters of the three-days-ahead WPF ................................ 54

Figure 17 – Wind power output at different wind speeds under air density of 1,225

kg/m (a typical 2 MW wind turbine) ..................................................................................... 55

Figure 18 – EC distribution of a wind farm at different wind speeds and directions ................ 56

Figure 19 – Wind speed and wind power curves of wind turbines at different air

densities ............................................................................................................................... 57

Figure 20 – Typical framework of short-term WPF ................................................................. 58

Figure 21 – Principle of short-term WPF based on physical approaches ............................... 59

Figure 22 – Flowchart of short-term WPF based on statistical approaches ............................ 60

Figure 23 – Short-term WPF model based on ANN ............................................................... 60

Figure 24 – Input and output parameters of the 4 h ultra-short-term WPF ............................. 62

Figure 25 – Flowchart of ultra-short-term WPF ...................................................................... 63

Figure 26 – Generalized combination methods of ultra-short-term WPF ................................ 64

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– 6 – IEC TR 63043:2020  IEC 2020

Figure 27 – Methods used for probabilistic forecasting .......................................................... 65

Figure 28 – Overview of probabilistic wind power forecasting ................................................ 66

Figure 29 – Wind power probability distribution forecasting results ....................................... 67

Figure 30 – Filtering approach with ensemble NWP as input ................................................. 68

Figure 31 – Dimension reduction approach with ensemble NWP as input .............................. 69

Figure 32 – Direct approach with ensemble NWP as input .................................................... 69

Figure 33 – Two ramp events of a wind farm ......................................................................... 72

Figure 34 – Overall framework of the WPF system for wind farm clusters ............................. 77

Figure 35 – Physical levels of WPF for wind farm clusters .................................................... 78

Figure 36 – Flow chart of the accumulation method .............................................................. 79

Figure 37 – Flow chart of the statistical upscaling method..................................................... 80

Figure 38 – Flow chart of the space resource matching method ............................................ 81

Figure 39 – Volt-ampere characteristic curve of PV modules corresponding to different

irradiance ............................................................................................................................. 84

Figure 40 – Volt-ampere characteristics of PV modules at different temperatures ................. 85

Figure 41 – Short-term forecasting models of PV power generation ...................................... 87

Figure 42 – PV short-term power physical forecasting method technical route ...................... 89

Figure 43 – Basic technology roadmap for pv power ultra-short-term forecasting .................. 91

Figure 44 – Ultra-short-term PVPF based on machine learning model ................................... 91

Figure 45 – Minute-time-scale solar power forecasting technique process ............................ 94

Figure 46 – Example of probabilistic PV model ..................................................................... 96

Figure 47 – Forecasting process of physical PV power probabilistic forecasting model ......... 96

Figure 48 – Forecasting process of statistical probabilistic PVPF model ............................... 97

Figure 49 – Framework of clustering statistical forecasting method for distributed PVPF..... 100

Figure 50 – Framework of grid forecasting method for distributed PVPF ............................. 101

Figure 51 – Comparison between the forecasting results of the clustering statistical

method and the grid forecast method .................................................................................. 102

Figure 52 – Example of a reliability diagram for two probabilistic forecasts (Forecast A

and Forecast B) of a binary event ....................................................................................... 117

Table 1 – Classification of RPF methods ............................................................................... 19

Table 2 – Features of global NWP models ............................................................................ 30

Table 3 – Comparison of different ensemble prediction methodologies and their

attributes [46], [73] ................................................................................................................ 37

Table 4 – Output modes of probabilistic forecasting .............................................................. 67

Table 5 – Advantages and disadvantages of ramp events definitions .................................... 73

Table 6 – Data sources of WPF for wind farm clusters .......................................................... 77

Table 7 – Comparison of WPF methods for wind farm clusters. ............................................. 81

Table 8 – Contingency table for forecasts of the occurrence/non-occurrence of an

event .................................................................................................................................. 110

Table 9 – A summary of recommended metrics for frequently used forecast types .............. 121

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IEC TR 63043:2020  IEC 2020 – 7 –
INTERNATIONAL ELECTROTECHNICAL COMMISSION
____________
RENEWABLE ENERGY POWER FORECASTING TECHNOLOGY
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