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Distribution type power distribution grid genetic algorithm optimization method

A technology of distributed power supply and genetic algorithm, which is applied in the field of genetic algorithm optimization of distributed power distribution network, can solve problems such as voltage upper limit and achieve good optimization results

Inactive Publication Date: 2018-04-24
YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, under certain circumstances, during the low load period, the voltage of the entire network exceeds the upper limit. If a distributed power source is connected at this time, the access point will generate a large amount of reactive power, which may eventually cause the voltage to exceed the upper limit.

Method used

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  • Distribution type power distribution grid genetic algorithm optimization method
  • Distribution type power distribution grid genetic algorithm optimization method
  • Distribution type power distribution grid genetic algorithm optimization method

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Experimental program
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Embodiment 1

[0032] see figure 1 , which is a schematic flow chart of a genetic algorithm optimization method for a distributed power distribution network provided by this application.

[0033] A genetic algorithm optimization method for a distribution network containing distributed power sources, comprising the following steps:

[0034] S1 selects the minimum active power loss of the distribution network as the objective function of active power optimization;

[0035] S2 selects the minimum voltage deviation of the distribution network as the objective function of reactive power optimization;

[0036] S3 combines the objective function of active power optimization and the objective function of reactive power optimization to construct a multi-objective optimization function;

[0037] S4 adopts the genetic algorithm to optimize the calculation of the multi-objective optimization function.

[0038] Step 1. Select the minimum active power loss of the distribution network as the objective f...

Embodiment 2

[0043] see figure 2 , is a schematic diagram of a 33-node distribution network including distributed power sources provided in the embodiment of the present application.

[0044] The 33-node distribution network with distributed power sources is connected to a 70kW fuel cell at node 9, which is a PV node. This method is used to obtain optimization results, including the following steps:

[0045] S1 selects the minimum active power loss of the distribution network as the objective function of active power optimization;

[0046] S2 selects the minimum voltage deviation of the distribution network as the objective function of reactive power optimization;

[0047] S3 combines the objective function of active power optimization and the objective function of reactive power optimization to construct a multi-objective optimization function;

[0048] S4 adopts the genetic algorithm to optimize the calculation of the multi-objective optimization function.

[0049]Selecting the minim...

Embodiment 3

[0062] see Figure 4 , is a schematic diagram of a 21-node distribution network without distributed power sources provided in the embodiment of the present application.

[0063] For a 21-node distribution network without distributed power sources, this method is used to obtain the optimization results, including the following steps:

[0064] S1 selects the minimum active power loss of the distribution network as the objective function of active power optimization;

[0065] S2 selects the minimum voltage deviation of the distribution network as the objective function of reactive power optimization;

[0066] S3 combines the objective function of active power optimization and the objective function of reactive power optimization to construct a multi-objective optimization function;

[0067] S4 adopts the genetic algorithm to optimize the calculation of the multi-objective optimization function.

[0068] Selecting the minimum active network loss of the distribution network as t...

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Abstract

The invention belongs to the field of intelligent power grids and in particular relates to a distribution type power distribution grid genetic algorithm optimization method which comprises the following steps: selecting a smallest power distribution grid active power transmission loss as a target function of active power optimization; selecting a smallest power distribution grid power deviation asa target function of reactive power optimization; combining the target function of active power optimization and the target function of reactive power optimization, and establishing multi-target optimization functions; performing optimization calculation on the multi-target optimization functions by using a genetic algorithm. The method is an optimized algorithm based on voltage deviations and active power transmission loss with a distribution power supply grid, optimization of multi-target functions can be directly achieved, and when a system scale is larger, an optimization effect is better.

Description

technical field [0001] The application belongs to the field of smart grids, and in particular relates to a genetic algorithm optimization method for distribution networks containing distributed power sources. Background technique [0002] Distributed power supply refers to a small modular power supply with a power of several thousand watts to 50 megawatts and is compatible with the environment. With the rapid development of my country's power distribution system, the uncertainty and diversity of power loads are likely to cause voltage quality problems. In the actual distribution network, the rapid and unbalanced development and changes of the load in terms of time characteristics and seasonal development and changes in space characteristics often lead to voltage substandard phenomena in local areas. With the access of distributed power supply, the network loss of the system can be reduced to a certain extent, and the local low voltage can also be controlled. [0003] Altho...

Claims

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Application Information

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IPC IPC(8): H02J3/48H02J3/50H02J3/18H02J3/16
CPCH02J3/16H02J3/1821H02J3/48H02J3/50H02J2203/20Y02E40/30
Inventor 李胜男吴水军何廷一彭俊臻孟贤和鹏郭晓宇韩钰
Owner YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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