Abstract
This paper proposes the Compound Inverse Rayleigh distribution as a proper model for the characterization of the probability distribution of extreme values of wind-speed. This topic is gaining interest in the field of renewable generation, from the viewpoint of assessing both wind power production and wind-tower mechanical reliability and safety. The first part of the paper illustrates such model starting from its origin as a generalization of the Inverse Rayleigh model by means of a continuous mixture generated by a Gamma distribution on the scale parameter, which gives rise to its name. Moreover, its validity for interpreting different field data is illustrated resorting to real wind speed data. Then, a novel Bayes approach for the estimation of such extreme wind-speed model is proposed. The method relies upon the assessment of prior information in a practical way, that should be easily available to system engineers. The results of a large set of numerical simulations—using typical values of wind-speed parameters—are reported to illustrate the efficiency and the accuracy of the proposed method. The validity of the approach is also verified in terms of its robustness with respect to significant differences compared to the assumed prior information.
Highlights
In recent years, renewable energy has become one of the major research topics among power system engineers
The electrical power produced by the wind energy is modelled as a statistical distribution of the wind speed (WS) random variable (RV); this determines that the estimation of wind electrical energy production is strictly related to the accuracy of the adopted wind speed distribution [5,6,7,8,9,10,11,12,13,14,15]
All the results reported here refer to real wind speed data, and the many relevant statistical fitting results reported in Tables 3 and 4 and in the Figures show, for the sets of reported data, the superiority of the Compound Inverse Rayleigh (CIR) model with respect to other alternative models that are being used in the literature and in real applications
Summary
Renewable energy has become one of the major research topics among power system engineers. WS forecasting methods, [9] illustrates the comparison of various models in terms of goodness-of-fit, while [10,11,12,13,14] are especially devoted to some more recent developments, and [15] casts WS forecasting in the framework of hybrid wind power generation, giving an account on the related issue of battery life All these references, as well as all the studies on the subject, show that an accurate wind speed distribution modeling is the first step to achieve accurate wind energy production estimation. This “robustness analysis” clearly shows that the same estimation efficiency holds, at least in most of the examined cases, when the true prior model is very different from the one assumed for the random parameter under study
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