Abstract
The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of cluster wind power output do not fully consider timing volatility, or are not suitable for small sample data scenarios. Meanwhile, the existing source-load timing matching evaluation indicator ignores the impact of wind power permeability on the timing matching degree between wind power output and load. Therefore, the authors propose quantitative evaluation methods of cluster wind power output volatility and source-load timing matching in regional power grid. Firstly, the volatility-based smoothing coefficient is defined to quantitatively evaluate the smoothing effect of wind-farm cluster power output. Then, the source-load timing matching coefficient considering wind power permeability is proposed to quantitatively evaluate the timing matching degree of regional wind power output and load, and the corresponding function model of volatility-based smoothing coefficient and source-load timing matching coefficient is established. Finally, the validity and applicability of the proposed methods are verified by MATLAB software based on the actual power output of 10 wind farms and actual grid load in a certain grid dispatching cross-section of northeast China. The results demonstrated that the proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output while maintaining the volatility continuity of wind power output time series and without affect from the data sample size. The source-load timing matching coefficient can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load.
Highlights
It is an inevitable trend for future development to implement renewable energy substitution actions, deepen the reform of power systems and build a new power system dominated by new energy
In order to overcome the deficiencies of the existing source-load timing matching evaluation indicator, a source-load timing matching coefficient considering permeability is proposed, which can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load from the perspective of wind power output and load volatility by introducing the theoretical permeability coefficient of wind power, as shown in Formula (7)
The volatility-based smoothing coefficient gradually increases, and the source-load timing matching coefficient shows a linear increase with the increase of the number of wind farm combinations, indicating that the timing matching between wind power output and grid load becomes worse with the increase of wind power permeability
Summary
It is an inevitable trend for future development to implement renewable energy substitution actions, deepen the reform of power systems and build a new power system dominated by new energy. Scholars have proposed the load tracking coefficient based on volatility consistency to quantitatively evaluate source-load timing matching degree;there are still some problems, which follow:. To solve the aforementioned problems, the quantitative evaluation methods for the smoothing effect of wind farm cluster power output and the timing matching degree between wind power output and load in the regional power grid are proposed in this paper. The proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output, which can maintain the volatility continuity of wind power output time series, but is unaffected by the data sample size. The exponential function model depicts the quantitative relationship between the volatility smoothing effect of wind farm cluster power output and source-load timing matching degree, which makes up for the deficiency of simple qualitative analysis in previous studies.
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