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研究生:蔡 紹 偉
研究生(外文):SORNWIT UDOMPANIT
論文名稱:不同國家的再生能源預測方法之回顧
論文名稱(外文):Renewable Generation Forecasting Methods in Different Countries: A Review
指導教授:吳 元 康
指導教授(外文):WU YUAN KANG
口試委員:蔡少宏李清吟
口試委員(外文):CAI SHAO HONGLI QING YU
口試日期:2019-10-18
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程學系碩士在職專班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:108
語文別:英文
論文頁數:95
中文關鍵詞:不同國家的再生能源預測方法之回顧
外文關鍵詞:Review Renewable Generation Forecasting Methods in Different Countries, Wind and solar power forecasting in different countries.
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Nowadays, the world is shifting to renewable energy sources because of fossil fuels emissions which have a harmful impact on the environment. Despite of the large number of renewable energy sources, the use of wind energy and solar energy is increasing all over the world. The integration of wind and solar sources in generation of energy are quite a difficult task since various issues are created during the energy transaction process from generation to transmission. Wind and Solar sources depend upon various factors such as size of the turbine, topography, terrain and many other factors as well.
In recent years, many organizations, standards and pacts have been developed on an international level which leads to the degradation of global environment, lack of traditional energy and development of energy production from a source which is inexhaustible. This strategic development is the priority of all the countries. Obviously, wind and solar power which lie among renewable energy sources which are famous because of its energy output, clean power production, non-polluting etc. These are why wind and solar power production have been employed in energy markets with the reduction of costs. Also, the cost ratio between production and, implementation and maintenance has greatly affected by the maturity of the technology. Though, the stochastic behavior of wind and solar resources are responsible for the integration in the grid and its variable behavior throughout its lifetime which creates complications in the operation as well as management of the asset.
The above-mentioned behavior for energy production by wind farms or solar farms depend upon the annual wind speed, sunshine irradiance, conditions of weather, the capacity of wind and solar power, connection with the electrical grid, maintenance of wind schedule and ability of the grid to accept wind power when it is available in the grid.
The power that cannot be dispatched from a wind farm or solar farm to the electric grid may have an adverse effect on the quality as well as the balance of power as required by the system due to which the profit of wind or solar farm owners will be compromised. However, these issues are taken into consideration by many researchers and energy markets which are being presented as a new idea of predicting wind and solar power and energy production by the mentioned resources. In this way, the potential for energy production can be properly monitored and the optimization of wind or solar power resources are carried out. In this thesis, timescales have been used for the classification of the methods for forecasting of wind and solar power and the idea with pros and cons of several methods. Also, this thesis surveys a forecasting methods and techniques for improvement of efficiency and ramp events. Many countries including USA, China, Germany, Nordic countries, UK, Denmark, Ireland and other countries are forecasting wind to avoid any disturbance in economic dispatch and fulfillment of unit commitment. This thesis provides the information related to the improvement of methods and efficiency in different types of forecasting techniques and the recommendations are given to advance a method with further research and development.

Table of Content
PAGE
Acknowledgement i
Abstract ii
Table of Content iv
List of Figures vii
List of Tables ix
Chapter 1 Introduction 1
1.1 Background 1
1.2 Organization of Thesis 2
1.3 Contributions of this Thesis 3
Chapter 2 Literature Review 4
2.1 Theory 4
2.1.1 Wind Forecasting Methods 4
2.1.2 Concept of Measurement Accuracy 4
2.1.3 Accuracy of Wind forecasting 10
2.1.4 Wind power Forecasting by Academic Research and Industrial Application 15
2.1.5 Methods of Deterministic Wind Power Forecasting 16
2.1.6 Handling Uncertainties in Power System 17
Chapter 3 Methods and experience of wind power forecasts 18
3.1 Wind Forecasting 18
3.1.1 Physical Methods 18
3.1.2 Statistical Methods 18
3.1.3 Hybrid Methods 19
3.1.4 Artificial Intelligence Methods 20
3.1.5 Persistence method 21
3.1.6 Spatial Correlation Models 21
3.2 Research Work on the methods 22
3.2.1 Probabilistic Forecasting 22
3.2.2 Parametric and non-parametric Probabilistic Forecasting 24
3.2.3 Ramp Forecasting 27
3.2.4 Ramp forecasting experience 29
3.3 Summary research work on the methods 32
Chapter 4 Review of Wind and Solar Power in Selected Countries 33
4.1 Forecasting of Wind Energy by Countries 33
4.1.1 Forecasting of Wind Energy by Germany 33
4.1.2 Forecasting of Wind Energy by United Kingdom 33
4.1.3 Forecasting of Wind Energy by Denmark 34
4.1.4 Forecasting of Wind Energy by Nordic Countries 35
4.1.5 Forecasting of Wind Energy by Ireland 36
4.1.6 Forecasting of Wind Energy by USA 37
4.1.7 Forecasting of Wind Energy by Spain 42
4.1.8 Forecasting of Wind Energy by Australia 43
4.1.9 Comparison Table of forecasting of Wind Energy for different Countries 44
4.1.10 Summary of forecasting of Wind Energy 48
4.2 Forecasting of Solar Energy by Countries 48
4.2.1 Forecasting of Solar Energy by Germany 48
4.2.2 Forecasting of Solar Energy by USA 50
4.2.3 Forecasting of Solar Energy by UK 54
4.2.4 Forecasting of Solar Energy by Denmark 55
4.2.5 Forecasting of Solar Energy by China 57
4.2.6 Forecasting of Solar Energy by France 59
4.2.7 Forecasting of Solar Energy by Spain 62
4.2.8 Forecasting of Solar Energy by Australia 64
4.2.9 Comparison Table of Forecasting of Solar Energy for different Countries 66
4.2.10 Summary of forecasting of Solar Energy 69
Chapter 5 Conclusion and Discussion 70
5.1 Conclusion 70
5.2 Future Work 70
References 71
APPENDIXES 77
Appendix I: 2010 Year end Wind Power Capacity 77
Appendix II: Probabilistic Forecast 78
Appendix III: The Power Curve Model 80
Appendix IV : Wind Forecast Extra Information 82



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