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Power distribution network risk assessment method based on improved quasi-Monte Carlo method

A quasi-Monte Carlo and risk assessment technology, applied in the field of distribution network risk assessment based on the improved quasi-Monte Carlo method, can solve the problem of inability to assess the risk of voltage over-limit and power flow over-limit risk in distribution networks, and the inability to apply probabilistic power flow. Calculation and other problems to achieve the effect of improving efficiency and fast error convergence speed

Pending Publication Date: 2020-02-14
NANJING UNIV OF SCI & TECH
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Problems solved by technology

However, the low-deviation sequence in the QMC method is a fixed sequence. When the number of samples is constant, the final result is a fixed value, so it cannot be applied to the calculation of probability power flow, so that the risk of voltage violation and power flow of the distribution network cannot be analyzed. risk assessment

Method used

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  • Power distribution network risk assessment method based on improved quasi-Monte Carlo method
  • Power distribution network risk assessment method based on improved quasi-Monte Carlo method
  • Power distribution network risk assessment method based on improved quasi-Monte Carlo method

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Embodiment

[0073] In order to verify the effectiveness of the solution of the present invention, the following simulation experiments are carried out.

[0074] combine figure 2 The shown IEEE34 node distribution network implements the distribution network operation risk assessment method based on scenario analysis. The distribution network contains 34 nodes and 33 branches. Among them, node 1 is a power node, and distributed power is at node 34. Access, the simulation platform is Matlab2017b.

[0075] 1) Construct Halton low deviation sequence

[0076] Construct a multi-dimensional Halton low-bias sequence, where the second-dimensional sequence is as image 3 , and combine it with a pseudo-random number sequence (such as Figure 4 )compared to. It can be seen that the pseudo-random number sequence has obvious cluster phenomenon, while the Halton sequence is more evenly distributed in the sample space.

[0077] 2) Use the improved Halton sequence to obtain wind speed, light and load...

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Abstract

The invention provides a power distribution network risk assessment method based on an improved quasi-Monte Carlo method. The power distribution network risk assessment method comprises: constructinga Halton low-deviation sequence and randomizing the Halton low-deviation sequence; obtaining distributed power supply output and load samples by using the improved Halton sequence; performing deterministic power flow calculation under each group of sample points, and counting probability distribution conditions of voltage and power flow; and evaluating the voltage out-of-limit risk and the power flow out-of-limit risk of the power distribution network. According to the method, the low-deviation sequence in the quasi-Monte Carlo method is improved and is applied to the risk assessment of the power distribution network containing the distributed power supply, the voltage off-limit index and the power flow off-limit index are calculated, and the accuracy and the high efficiency of the risk assessment are remarkably improved.

Description

technical field [0001] The power system technology of the invention specifically relates to a distribution network risk assessment method based on an improved quasi-Monte Carlo method. Background technique [0002] With the large-scale access of distributed power generation, the structure of modern distribution network is becoming more and more complex. It is of great significance to quickly and accurately assess the risk level of distribution network to maintain the stable operation of distribution network. The Monte Carlo (MC) method is widely used in the risk assessment of the distribution network due to its unique advantages (simple implementation, the error has nothing to do with the system scale, etc.), but the MC method itself has uncertainty, and its evaluation The accuracy increases with the increase of the number of samples. In the traditional distribution network risk assessment, the randomness of the MC method is usually avoided by setting a very large simulation...

Claims

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Application Information

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IPC IPC(8): G06F17/18G06Q10/06G06Q50/06
CPCG06F17/18G06Q10/0635G06Q50/06Y04S10/50
Inventor 阮思洁张俊芳朱肖镕李娜徐洲杨振宁
Owner NANJING UNIV OF SCI & TECH
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