Pseudo-Monte Carlo stochastic power flow calculation method based on digital interleaving technology
A stochastic power flow calculation and quasi-Monte Carlo technology, which is applied to AC network circuits, AC networks with different sources of the same frequency, circuit devices, etc., can solve the problem of affecting the pseudo-Monte Carlo simulation effect and cannot guarantee low sequence deviation and other problems to achieve the effect of improving simulation speed and accuracy, solving aggregation phenomenon, and improving simulation efficiency
Inactive Publication Date: 2019-12-24
郓城金河热电有限责任公司 +1
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Problems solved by technology
Pseudo-random sampling represented by Latin hypercube sampling improves sampling efficiency, but cannot guarantee low bias of the sequence
However, the quasi-Monte Carlo simulation method using a low-deviation sequence can guarantee the uniformity of random numbers, but when the sequence dimension is high, the adjacent two-dimensional random numbers will aggregate and have certain regularity. Will affect the pseudo-Monte Carlo simulation effect
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Embodiment 1
[0048] Taking IEEE118 nodes as an example, nodes 20, 35, 38, 51, 54, 67, 81, 93, 97, and 110 are respectively connected to wind farms with a rated power of 10 MW. The fans are all run at a constant power factor of 1. Assume that there is a correlation between the wind power of each node, and the correlation coefficient is 0.5. All loads obey the normal distribution, the mean is the steady-state value of the nodal load, the standard deviation is 5% of the mean, and the power factor is 0.9. Then calculate as follows:
[0049] Step 101: For the load, the active power is described by a normal distribution, the expected value is the original data value, the standard deviation is a certain percentage of the expectation, and the reactive power follows the change of the active power according to a given power factor;
[0050] Step 102: The Weilbull probability distribution model is used to describe the wind power of each wind farm.
[0051]
[0052] In the formula, p i is the w...
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The present invention discloses a Quasi-Monte Carlo stochastic load flow calculation method based on the digital interleaving technology. The method mainly comprises: the digital interleaving technology is applied to the load flow calculation of an electric power system so as to effectively improve the efficiency and the precision of the Quasi-Monte Carlo load flow calculation. The method comprises: establishing probability distribution models of output and load of a plurality of wind power plants, and employing the equal probability transformation and the Nataf transformation to obtain an equivalent correlation coefficient matrix in the standard normal distribution; employing the Sobol sequence based on the digital interleaving technology to generate S-dimensional random numbers, wherein the digital interleaving technology performs line transformation of the matrix generated by the original Sobol sequence to effectively optimize the distribution of high-dimensional random numbers; and finally, employing the Cholesky decomposition technology to obtain the random input variation samples having the correlation to perform stochastic load flow calculation and obtain node voltage amplitude and branch active and reactive power injection. The algorithm can perform Quasi-Monte Carlo load flow calculation in the condition that a plurality of wind power plants access the system, is high in efficiency and high in computational accuracy.
Description
technical field [0001] The invention relates to a pseudo-Monte Carlo stochastic power flow calculation method, in particular to a pseudo-Monte Carlo stochastic power flow calculation method based on digital interleaving technology, which belongs to the technical field of power supply. Background technique [0002] Wind power generation has attracted widespread attention due to its advantages of wide distribution, convenient use, and no pollution. With the grid-connected operation of large-scale wind farms, the operating characteristics of the power system have changed from weak randomness to strong randomness. And for multiple wind farms with relatively close geographical distance, each wind farm is basically in the same wind speed zone at the same time, and the wind power will have a certain spatial correlation. This spatial correlation will exacerbate random fluctuations in the grid. Therefore, it is of great significance to study the stochastic power flow calculation of...
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IPC IPC(8): H02J3/06
CPCH02J3/06H02J2203/20
Inventor 王风雷朱晓荣金绘民
Owner 郓城金河热电有限责任公司