A Survey of Wind Power Ramp Forecasting

Ouyang, Tinghui; Zha, Xiaoming; Qin, Liang

doi:10.4236/epe.2013.54b071

Cited by 48 publications

(34 citation statements)

References 27 publications

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“…These results are also supported by other authors, who associate atmospheric phenomena (e.g., cold fronts and troughs) [4,8] as the critical weather situations for the TSO, namely to maintain the balance between production and consumption in order to ensure the stability of the power system, especially during the so-called "wind power ramp events" [9]. In fact, in [8] the authors suggest an automated cyclone detection algorithm for recognition of critical weather situations for wind power integration.…”

Section: Introductionsupporting

confidence: 79%

“…According to [32], a wind power ramp occurs whenever there is a variation in energy production with considerable amplitude within a relatively short period of time. A power ramp can then be characterized by parameters [9] such as magnitude, lifetime, growth-rate, direction and occurrence time. In order to deploy a methodology capable of detecting wind power ramps, some definitions found in the literature were considered.…”

Section: Ramp Definitionmentioning

confidence: 99%

“…Type-2 ramps are determined based on a definition found in [9] that considers the ratio between the variation in power for two time-steps over a certain period of time and states that this ratio must be greater than a reference value PRR val :…”

Section: Ramp Definitionmentioning

confidence: 99%

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Wind Power Ramps Driven by Windstorms and Cyclones

2017

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Abstract:The increase in the wind power predictability assumes a very important role for secure power system operation at minimum costs, especially in situations with severe changes in wind power production. In order to improve the forecast of such events, also known as "wind power ramp events", the underlying role of some severe meteorological phenomena in triggering wind power ramps must be clearly understood. In this paper, windstorm and cyclone detection algorithms are implemented using historical reanalysis data allowing the identification of key characteristics (e.g., location, intensity and trajectories) of the events with the highest impact on the wind power ramp events in Portugal. The results show a strong association between cyclones/windstorms and wind power ramp events. Moreover, the results highlight that it is possible to use some features of these meteorological phenomena to detect, in an early stage, severe wind power ramps thus creating the possibility to develop operational decision tools in order to support the security of power systems with high amounts of wind power generation.

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Section: Introductionsupporting

confidence: 79%

Section: Ramp Definitionmentioning

confidence: 99%

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Wind Power Ramps Driven by Windstorms and Cyclones

2017

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“…Cui et al [16] derived the probabilistic distribution of ramps using stochastical scenarios of wind power generation, which were generated by an NN model trained using cumulative density function (CDF)-and auto-correlation function (ACF)-based objective functions. A thorough review of ramp forecasting can be found in [6,17].…”

Section: Related Workmentioning

confidence: 99%

Improving the prediction of wind power ramps using texture extraction techniques applied to atmospheric pressure fields

Musilek

Lozowski

2017

Int J Data Sci Anal

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Much recent research in wind power forecasting has been focused on predicting large, sudden changes in wind power output, called wind ramps. State-of-the-art wind ramp prediction methods estimate future wind ramps from forecast power time series. We suggest that, by analyzing the weather associated with wind ramps, their forecasting can be improved. In particular, we propose a new method for wind ramp forecasting based on the analysis of forecast atmospheric pressure fields. Feature vectors relating to the pressure gradient are extracted from the pressure fields using an image texture extraction technique, called Gabor filtering. Numerical experiments show that these Gabor feature vectors are well correlated with power generation. They are used as inputs to a new wind power forecasting model. Compared with a basic state-of-the-art wind power forecasting model that does not use Gabor features as input, the proposed model exhibits better performance for power prediction, for two of the three wind farms chosen for this study. However, the ability of the model to forecast actual wind ramps is worse than that of the basic model, as measured by ramp capture rate and forecast accuracy. We also describe a second method to pre- Earth and Atmospheric Sciences, University of Alberta, Edmonton, Canada dict the magnitude of a sudden power change (wind ramp), using several input variables, in addition to Gabor features. Numerical experiments show that this second approach has better performance than the basic model, with respect to ramp capture rate and forecast accuracy. We suggest that it could be used operationally to supplement a current state-of-the-art ramp prediction model. We present an example of using this approach to provide a warning of a potential ramp.

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“…Significant fluctuations are called ramps [158,159]. The ramps can be classified into up ramp where the power generated significantly increases in a short period and down ramp where the power generated significantly decreases in a short period [160].…”

Section: Chapter 7 Random Vector Functional Link Neural Network For Wmentioning

confidence: 99%

Ensemble time series forecasting with applications in renewable energy

Ren¹

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I would like to take this opportunity to express my deep gratitude to my project supervisor Associate Professor P. N. Suganthan, who has given me invaluable advice and suggestions throughout my work towards the project. I am also very grateful to Dr N. Srikanth for giving many useful suggestions on my project. I would like to thank Economic Development Board of Singapore (EDB) for their academic and financial support on Clean Energy PhD Scholarship Program. I would like to thank Energy Research Institute @ NTU (ERI@N) for Joint Industrial PhD Program (JIPP) management and opportunities for exchange and workshops. I would like to thank the Singapore National Research Foundation (NRF) under its Campus for Research Excellence And Technological Enterprise (CREATE) programme, and Cambridge Advanced Research Centre in Energy Efficiency in Singapore (CARES), C4T project for resource and technical support. Last but not least, I wish to give thanks to a number of my friends and colleagues for their support.

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A Survey of Wind Power Ramp Forecasting

Cited by 48 publications

References 27 publications

Wind Power Ramps Driven by Windstorms and Cyclones

Wind Power Ramps Driven by Windstorms and Cyclones

Improving the prediction of wind power ramps using texture extraction techniques applied to atmospheric pressure fields

Ensemble time series forecasting with applications in renewable energy

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