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Multivariate regulation and control method for power grid

A power grid and multiple technology, applied in the field of coordinated control of multiple power grids based on multi-model neural network adaptive regulation, can solve problems involving a wide range, uncertainty, and large real-time load errors, so as to achieve fewer human factors and improve The effect of planning accuracy and improving dispatching efficiency

Active Publication Date: 2018-12-07
SHENYANG POLYTECHNIC UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the large number of human factors involved in the current plan, which involves a wide range and many departments, the output of each unit and the real-time load of each node of the grid often have large errors; and due to the uncontrollability and uncertainty of wind power and photovoltaic power generation, etc. Difficulty in power grid regulation

Method used

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  • Multivariate regulation and control method for power grid
  • Multivariate regulation and control method for power grid
  • Multivariate regulation and control method for power grid

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Embodiment Construction

[0040] For the above-mentioned multi-model coordinated control method of power grid based on multi-model neural network adaptive regulation, the method is implemented by taking the actual operation calculation of a certain power grid as an example:

[0041] There are 5 nodes in a local power grid, and the load of the i-th time period every day within 10 days is:

[0042] number of days

1

2

3

4

5

6

7

8

9

10

Load (kVA)

499.5

502.3

511.1

496.2

490.9

503.7

508.9

500.1

492.8

503.3

[0043] The load of each grid node in this period within 10 days is:

[0044]

[0045]

[0046] During this period of 10 days in the power grid, the output of each unit is as follows:

[0047]

[0048] The daily planned output of each generator set in the power grid within 10 days is as follows:

[0049]

[0050]

[0051] In addition, α 0 = α 1 = 1, β 0 = β 1 = 1, estimated value ...

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Abstract

The invention relates to a multivariate regulation and control method for a power grid. According to the invention, the system repeatedly learns historical data in the same time period of the previousday for a period of time and performs multiple iterations so as to continuously adjust a regulation and control coefficient of the system and determine an output value required by a node of each power generation unit of the power grid; then the outputs required by all nodes in the power grid are added to obtain the planned output condition of each power generation unit of the power grid in the time period of the subsequent day; then the steps are repeated in the next time period, finally the output of each power generation unit of the power grid in all time periods of the whole day is finallyadjusted so as to enable the day-ahead plan to be more accurate. Real-time regulation and control can be performed through AGC for generated errors so as to ensure the frequency of the power grid tobe stable.

Description

technical field [0001] The invention belongs to the technical field of smart grids, and in particular relates to a multi-element grid coordination control method based on multi-model neural network self-adaptive regulation. Background technique [0002] In recent years, a new round of energy transformation is advancing globally. The structural adjustment of my country's energy industry is accelerating. The grid-connected capacity of clean energy represented by photovoltaics and wind power is increasing. The utilization of energy is gradually becoming characterized by high efficiency, diversification and intelligence. , requiring accelerated improvement of the power grid to adapt to the optimal allocation of energy across the country. Facing the pressure of global environmental degradation and resource shortage, energy conservation, emission reduction and improvement of energy utilization efficiency have become the requirements that the future power grid must meet. At present...

Claims

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Application Information

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IPC IPC(8): H02J3/00H02J3/46
CPCH02J3/00H02J3/46H02J2203/20
Inventor 滕云王晴瀚
Owner SHENYANG POLYTECHNIC UNIV
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