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
Start Date
15-Dec-2020
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
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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

– 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

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

– 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 .
...

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