Cluster wind power field fluctuation modeling method

Abstract

The invention provides a cluster wind power field fluctuation modeling method. The method comprises the following steps: S1, defining a relative first-order center distance mathematic model, a relative third-order center distance mathematic model and a relative fourth-order center distance mathematic model of area wind power field cluster fluctuations; S2, establishing a standard deviation, a bias and a kurtosis classic indicator of a cluster wind power field; S3, establishing a inter-wind-power-field correlation coefficient and field interval index relation model and a standard deviation and wind power field average utilization hour polynomial relation model; S4, according to the output historical data and geographical information of a typical area wind power field cluster, obtaining modeling correlation parameters by use of a least square method; S5, by use of a target wind power field cluster field interval, by means of a specifically established filed interval and correlation coefficient index relation model, solving a center distance factor; S6, by use of a target wind power field average utilization hour quantity, by means of the specifically established standard deviation and average utilization hour quantity polynomial relation model, solving the standard deviation; and S7, calculating the standard deviation, the bias and the kurtosis of an area wind power field cluster fluctuation indicator, and analyzing a cluster wind power field fluctuation characteristic.

Description

technical field [0001] The invention relates to the field of new energy power generation in electric power systems, in particular to a volatility modeling method for clustered wind farms. Background technique [0002] Due to the influence of natural characteristics, geographical environment, wind farm's own conditions and other factors, wind power output has random fluctuations. And this is the fundamental reason why wind power has an impact on grid access, especially when the installed capacity of grid-connected wind power grows to a certain scale, the uncontrollable fluctuation of wind power will have a negative impact on the balance of active and reactive power in the grid. These impacts include the local voltage level of the wind power access point, the arrangement of the rotating reserve capacity of the whole network, and the frequency regulation of the system. For a long time, due to the lack of sufficient wind power fluctuation data, it is difficult to quantitatively...

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Problems solved by technology

For a long time, due to the lack of sufficient wind power fluctuation data, it is difficult to quantitatively evaluate the above-mentioned impacts, which is very unfavorable for maximizing the impact of wind power fluctuations on the grid and further improving the grid's ability to accept wind power.

Compared with the fluctuation of the output power of a single wind farm, the dispatching and planning departments of the power grid are more concerned about the impact of the fluctuation characteristics of several wind farm groups after forming a cluster wind farm on the stable operation of the power grid. Operation and planning are of great significance. In view of the limitations of previous wind power fluctuation characteristic indicators and the lack of quantitative analysis methods for wind power cluster volatility, there are still deficiencies in the research on the volatility of wind farm clusters. Most of the research is limited to the qualitative research of wind farms. Moreover, the analysis angle of the calculation method of large-scale wind power output data is only analyzed from the two aspects of central tendency and degree of dispersion, and lacks an in-depth study on the distribution characteristics of wind power output from the perspective of distribution shape. A cluster wind farm fluctuation proposed by the present invention The characteristic modeling method provides a fast and effective calculation method for classic indicator calculation

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