Method for capacity credit assessment of photovoltaic power generation system

Abstract

The invention discloses a method for capacity credit assessment of a photovoltaic power generation system. The method comprises: putting forward four factors that affect the capacity credit of a photovoltaic power generation system, i.e., the photovoltaic permeability, the data sampling time interval, the photovoltaic-load output fluctuation and the time sequence correlation between photovoltaic output and load fluctuation; calculating the reliability of the power system by using sequential Monte Carlo simulation and solving the capacity credit by the Secant Method; establishing a three-layer error back propagation neural network between the four factors and the capacity credit; training the neural network by using input and output data sets which are obtained under different irradiation modes; and obtaining the capacity credit of the photovoltaic power generation system under the given condition by using the generalization ability of the neural network which is already trained. The method for capacity credit assessment of a photovoltaic power generation system can be used for planning and design of photovoltaic composite generation and transmission systems, requires no sequential Monte Carlo simulation individually for each specific photovoltaic power generation system, and solves the problem of poor universality of the prior art.

Description

technical field [0001] The invention relates to a method for evaluating the confidence capacity of a photovoltaic power generation system, which belongs to the field of photovoltaic power generation. Background technique [0002] With the reduction of the cost of photovoltaic power generation, the advancement of technology and its friendliness to the environment, large-scale ground photovoltaic power generation systems have developed rapidly, the installed capacity has increased day by day, and the penetration rate of photovoltaic power generation in the power system has gradually increased. Existing studies have shown that photovoltaic power generation has not only energy value, but also capacity value, and its capacity value is reflected in the effective and credible capacity of the photovoltaic power generation system, which is the credible capacity of the photovoltaic power generation system. In order to avoid redundant construction and waste of resources, the evaluation...

AI Technical Summary

Problems solved by technology

[0004] 1) A new sequential Monte Carlo simulation is required for a specific photovoltaic power generation system. For the same photovoltaic power generation system, when the external environment such as radiation intensity and load change mode is different, it is necessary to recalculate. The adaptability and robustness of the algorithm are not strong;

[0005] 2) The algorithm consumes a lot of time. When the scale of the conventional power generation system is large and the number of photovoltaic power generation systems is large, the calculation cost will increase significantly, and even in some extreme cases, the algorithm will not converge.

[0007] 1) Although the approximate analytical method evaluation has a small amount of calculation, its accuracy is low. The calculation accuracy of different analytical methods is different, and the error of the exact solution obtained by the Monte Carlo simulation method is different. It is used in different scenarios. What kind of approximate analytical law needs to be determined after specific calculations, which is not conducive to engineering applications in practice;

[0008] 2) For the same approximate analytical method, as the photovoltaic penetration rate, annual irradiance intensity, and load vary, the error of the evaluation result will also change, resulting in uncertainty in the use of this method

This method can calculate the confidence capacity of the photovoltaic power generation system, but for each specific system, the system must be rebuilt to calculate the confidence capacity of the photovoltaic power generation system. The generality is not strong, and the generality and robustness of the model and method are poor.

At the same time, the factors that affect the confidence capacity and the degree of influence on the confidence capacity have not been analyzed in depth

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