Abstract
Due to the large dispersion and uncertainty of wind power operation, it is very difficult to predict the wind power. Even though there are many prediction methods considering the uncertainty of wind power, there is still not a method to accurately fit the corresponding relationship between wind speed and wind power, and thus the wind power prediction is not accurate. According to this paper, the probability density curve of wind power under different wind speeds is firstly studied, and the combined cloud model is established with the peak value as the boundary to represent the uncertain corresponding relationship between wind speed and power. Secondly, in order to avoid the error increase of the combined cloud model caused by the sample peak disturbance, L2 norm theory is introduced to update the peak point to enhance the robustness of the model. Finally, The parameters of L2 norm cloud model are calculated based on Bayesian theory. The simulation results show that the combined cloud fitting method can obtain high fitting accuracy for the irregular single peak or multi peak wind speed power probability distribution with uncertainty.
Highlights
With the depletion of energy resources and the increasingly serious environmental problems, renewable energy generation, especially wind power technology, has become the development trend of the world's power industry[1].with the large-scale development of wind power generation, the volatility and randomness of wind power pose great challenges to the safe operation and dispatch control of the power grid.it is critical to provide accurate wind farm power prediction for power systems, in order to safe power production[2,3].At present, the research on wind power prediction focuses on establishing a nonlinear corresponding relationship between wind speed and wind power
In 1995, Professor Li Deyi proposed a cognitive model of qualitative and quantitative conversion based on probability and statistics – cloud model[16], which opened up a new way to study the uncertainty of wind speed-power probability distribution of wind farms
The following conclusions were obtained through simulation verification: 1) The combined cloud model is suitable for wind speed-power fitting with characteristics such as asymmetric distribution, multi-peak distribution, and irregular distribution, and can fully reflect the probability and uncertainty of wind speed-power operation in wind farms; 2) Based on the L2 norm method, the fitting accuracy under peak disturbances can be improved
Summary
With the depletion of energy resources and the increasingly serious environmental problems, renewable energy generation, especially wind power technology, has become the development trend of the world's power industry[1].with the large-scale development of wind power generation, the volatility and randomness of wind power pose great challenges to the safe operation and dispatch control of the power grid.it is critical to provide accurate wind farm power prediction for power systems, in order to safe power production[2,3]. In 1995, Professor Li Deyi proposed a cognitive model of qualitative and quantitative conversion based on probability and statistics – cloud model[16], which opened up a new way to study the uncertainty of wind speed-power probability distribution of wind farms. The representative meanings of the three parameters of the cloud model are as follows: Ex is the point that can best represent the qualitative concept, which is statistically the mean value of the data sample; En is the probabilistic measure of the qualitative concept, reflecting the discreteness of the cloud drop It is expressed as the width of the cloud in FIGURE 1; He is the uncertainty measure of entrop En, which characterizes the uncertain stochastic feature of the qualitative concept. It isexpressed as the thickness of the cloud layer [20]
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