A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

@article{Cui2019ACC,
  title={A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps},
  author={Mingjian Cui and Venkat Krishnan and Bri-Mathias S. Hodge and Jie Zhang},
  journal={IEEE Transactions on Smart Grid},
  year={2019},
  volume={10},
  pages={3870-3882}
}
Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic WPR forecast (cp-WPRF) model based on copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by… 
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41 Citations
Background Citations
9
Methods Citations
5

41 Citations

Citation Type

Citation Type

Multi-distribution ensemble probabilistic wind power forecasting
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TLDR
Case studies on publicly available data indicate that the proposed distributionally robust uncertainty quantification model could provide reliable probability information of ramp events for system operators to benefit the safe operation.
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The proposed approach offers the probability upper bound of a ramp event in the worst-case wind power distribution, and the conservatism can be remarkably reduced when more historical data are at hand.
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The multi-model structure has insignificant advantages for wind speed prediction via GPR; joint point-interval prediction of wind power is realizable and very reasonable; uncertainty enlargement exists for stepwise prediction ofWind power while it is more significant after receding multi-step predictions of wind speed.
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TLDR
The results indicate that the probabilistic small signal stability analysis adopting pair copula model is more accurate and stable than other dependence models under different conditions.
Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting
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Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation
Capacity Planning of Distributed Wind Power Based on a Variable-Structure Copula Involving Energy Storage Systems
TLDR
The study results validate the proposed method and provide effective reference for the planning strategy of DWP, and show that the DWP capacity result is more reasonable after considering the correlation of wind and load by using a variable-structure copula.
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References

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TLDR
Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.
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TLDR
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