Method for generating and reducing wind power multi-period time sequence scene

A technology of wind power and time series, which is applied in wind power generation, electrical components, circuit devices, etc., can solve the problems of ignoring the correlation of random variables in the scene sequence of high-dimensional scene similarity, and the loss of scene feature information.

A technology of wind power and time series, which is applied in wind power generation, electrical components, circuit devices, etc., can solve the problems of ignoring the correlation of random variables in the scene sequence of high-dimensional scene similarity, and the loss of scene feature information.

CN112271731AActive Publication Date: 2021-01-26HEFEI UNIV OF TECH +1
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  • Method for generating and reducing wind power multi-period time sequence scene
  • Method for generating and reducing wind power multi-period time sequence scene
  • Method for generating and reducing wind power multi-period time sequence scene

Examples

Experimental program
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Embodiment Construction

[0081]In this embodiment, a wind power scene reduction method that comprehensively considers spatial distance and random characteristics, generates a single-period asymptotically optimal scenario based on the Wasserstein probability distance index, and reduces multi-period scenarios based on an improved tabu search algorithm. On the basis of the optimal reduction framework, starting from both the spatial and random features of the scene, the distance index is used to represent the spatial features, and the related loss and probability similarity index are used to represent the retention of random features before and after the high-dimensional scene is subtracted. Specifically, such asfigure 1 As shown, proceed as follows:

[0082]1. A method for generating and subtracting wind power multi-period time series scenarios, characterized by the following steps:

[0083]Step 1: Generation strategy of asymptotically optimal scenario of wind power in a single period:

[0084]The basic idea of ​​scene...

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Abstract

The invention discloses a method for generating and reducing a wind power multi-period time sequence scene. The method comprises the following steps of: 1, establishing a single-period asymptotic optimal scene generation strategy based on a Wasserstein probability distance index and a multi-period scene reduction strategy based on an improved tabu search algorithm; 2, defining a distance index torepresent a spatial feature, and defining a correlation loss and probability similarity index to represent a random feature retention degree before and after reduction of a high-dimensional scene; and3, solving a wind power multi-period time sequence scene by using an improved tabu algorithm. According to the method, the problem of large feature information loss during scene reduction is solved from the two directions of the space and random features of the scene, and the random features of the wind power can be effectively expressed while the wind power simplified scene is accurately and efficiently generated.

Description

Technical field[0001]The invention relates to the field of power system distribution network planning, in particular to a method for generating and reducing wind power multi-period time series scenarios.Background technique[0002]At present, various random variables inevitably exist in the power system. The uncertainty of random variables will greatly affect the economic, stability, and reliability evaluation results of the power system, which also makes stochastic programming have a wide range of application prospects in decision-making, optimization and scheduling problems. As a breakthrough point, scenario analysis provides a good solution for the uncertainty expression of various random variables.[0003]As the proportion of renewable energy generation in the power system gradually increases, generating representative sequence scenarios of renewable energy is an indispensable link in power system planning and decision-making. For example, wind power has strong randomness and volati...

Claims

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Application Information

Patent Timeline
26 Jan 2021
Publication
CN112271731A
IPC
H02J3/00; H02J3/38; G06F17/16; G06F17/18
CPC
H02J3/00; H02J3/381; G06F17/16; G06F17/18; H02J2203/10; H02J2203/20; H02J2300/28; Y04S10/50
Inventors
吴红斌; 何叶