A review on the recent history of wind power ramp forecasting

Gallego-Castillo, Cristóbal; Cuerva-Tejero, Alvaro; López-García, Óscar

doi:10.1016/j.rser.2015.07.154

Cited by 146 publications

(127 citation statements)

References 39 publications

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“…The role of atmospheric circulation in triggering wind power ramps is a relatively new research topic, however, there has been a significant increase in the number of published studies and projects on this topic and on the adjacent causes behind the occurrence of wind power ramps [14]. Understanding such events is not an easy task as the weather conditions are rarely the same for different wind parks.…”

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

“…Understanding such events is not an easy task as the weather conditions are rarely the same for different wind parks. In fact, even when two wind parks are located in similar latitudes, local effects may differ due to the terrain characteristics, roughness and topography or even due to phenomena like sea/land breezes [14]. Moreover, also the layout of the wind park, as well as its size, can (or not) justify the occurrence of wind power ramps through meteorological processes involving changes in wind direction [14].…”

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

“…In fact, even when two wind parks are located in similar latitudes, local effects may differ due to the terrain characteristics, roughness and topography or even due to phenomena like sea/land breezes [14]. Moreover, also the layout of the wind park, as well as its size, can (or not) justify the occurrence of wind power ramps through meteorological processes involving changes in wind direction [14]. Additionally, it is well documented that aggregating wind parks with a wide geographical dispersion enables one to take advantage of the lack of spatiotemporal correlation of the wind resource.…”

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

“…Notwithstanding this, several authors [4,14,15] have indicated that the aggregation of wind parks, even when widely dispersed, is not enough to mitigate some events, especially when an entire country (or a control zone) is immersed under certain weather conditions with a coherent structure, which usually extends over several hundreds of kilometers (e.g., cold fronts). Indeed, in [14] the authors identified areas of strong convection, fronts and low-level jets as the major drivers for wind power ramp events.…”

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

“…Indeed, in [14] the authors identified areas of strong convection, fronts and low-level jets as the major drivers for wind power ramp events. Using a classification of the weather situations, in [16] the authors concluded that the greatest impact on the wind power production is expected during: (i) large-scale weather systems, such as cold fronts; and (ii) local/mesoscale circulations, namely sea/land breezes and mountain/valley breezes.…”

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

See 4 more Smart Citations

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: Weather and Wind Power Rampsmentioning

confidence: 99%

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

Section: Weather and Wind Power Rampsmentioning

confidence: 99%

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Wind Power Ramps Driven by Windstorms and Cyclones

2017

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Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting

Dhiman

Deb

2021

Int Trans Electr Energ Syst

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Fluctuation in wind speed over the period of time can impact operation of a utility grid. Sudden large scale variation in turbine power, often termed as ramp event, can threaten the security of power system. In this work, we predict ramp events through a hybrid method based on discrete wavelet transform (DWT) and learning algorithms such as Twin Support vector regression (TSVR), random forest regression (RFR), and Convolutional neural networks (CNN) for onshore, offshore and hilly sites. Wavelet transform-based signal processing helps extract features from wind speed. Results suggest that SVR based prediction models are the best in forecasting among the available models. Besides, CNN predicts ramp events closer to the TSVR model and gives a better prediction performance for larger training datasets. The proposed hybrid version of TSVR, RFR, and CNN models are compared with existing SVM, ANN, and ELM models and indicate significant improvement in predicting ramp events. Compared to SVM, TSVR and RFR are 17.88% and 4.87% efficient in terms of RMSE, respectively. Further, randomness in ramp event signal for all the wind farm sites considered is evaluated using log-energy entropy. Results reveal that compared to wavelet transform, empirical mode decomposition yields lower randomness in predicted ramp event signals. K E Y W O R D Sempirical mode decomposition (EMD), ramp events, support vector regression (SVR), twin support vector regression (TSVR), wavelet transform (WT), wind forecasting | INTRODUCTIONEnvironmental deterioration and greenhouse gas emission is a cause of concern for many developing and developed countries. [1][2][3][4] Among renewable energy sources, by 2020, wind power is likely to cater 12% of global electricity

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Reducing the cost of wind resource assessment: using a regional wind power forecasting method for assessment

Özkan

Karagöz

2021

Int J Energy Res

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Wind energy, with its high potential, has an important place among renewable energy sources. Therefore, the number of investments on wind energy is increasing with new turbine technologies and solutions. For the investors of these technologies, how to determine the location of wind turbines for such investments is a challenging and critical problem. Wind maps of the country are a useful resource to determine high potential locations. However, such maps often present an overview to provide the general picture, and there is a need for a more detailed analysis, specifically on the potential plant area. In this paper, we investigate the use of a recent regional wind power forecast method to determine the wind power potential. By applying the RegionalSHWIP on a potential plant area, investors can obtain the preliminary power prediction, hence the wind potential of the area, without any financial cost recurring for measurements and with negligible manhour for computation. The wind resource assessments of the RegionalSHWIP have been analyzed in comparison to the wind maps of four provinces from different regions of Turkey. In all of these test provinces, it has been observed that the model produces results compatible with the values in the wind potential atlas. In

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A review on the recent history of wind power ramp forecasting

Cited by 146 publications

References 39 publications

Wind Power Ramps Driven by Windstorms and Cyclones

Wind Power Ramps Driven by Windstorms and Cyclones

Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting

Reducing the cost of wind resource assessment: using a regional wind power forecasting method for assessment

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