| Citation:Cheng Yan,Yi Tang,Jianfeng Dai,Chenggen Wang,Shengjun Wu.Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors[J].Protection and Control of Modern Power Systems,2021,V6(3):276-288[Copy] |
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| Abstract: |
| Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup. |
| Key words: Inertial response,
Primary frequency control,
Error distribution,
Mixed skew generalized error distribution,
Uncertainty modeling, |
| DOI：10.1186/s41601-021-00200-3 |
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| Fund: |
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