CN101958580A - Division calculation method for real time online trend flow of large power grid - Google Patents

Division calculation method for real time online trend flow of large power grid Download PDF

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CN101958580A
CN101958580A CN2010105084804A CN201010508480A CN101958580A CN 101958580 A CN101958580 A CN 101958580A CN 2010105084804 A CN2010105084804 A CN 2010105084804A CN 201010508480 A CN201010508480 A CN 201010508480A CN 101958580 A CN101958580 A CN 101958580A
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subregion
power grid
equivalent
load
trend
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CN101958580B (en
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李昌
刘恩鸽
林丽华
宣弘
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Jiaxing Saiyun Amperex Technology Limited
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SHANGHAI SUNRISE POWER AUTOMATION CO Ltd
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Abstract

The invention relates to a division calculation method for real time online trend flow of a large power grid, which refers to the technical field of steady-state analysis of a power grid and solves the technical problem of improving flow calculation speed and precision. The method comprises the following steps of: dividing the large power grid into a plurality of sub-blocks which are interconnected, then performing word equivalence for each sub-block to calculate the boundary node equivalent load values of the equivalent networks of sub-blocks; then predicting the boundary node equivalent load value of the equivalent network of each sub-block according to the load type of the sub-block; and then combing the equivalent networks of sub-blocks with predicted values as nodes which are infused to the combined block so as to form the equivalent network of the large power grid; finally, calculating the real time flow of the large power grid. The method has the advantages of high calculation speed and high calculation precision.

Description

The zoning method for calculating of large-scale power grid real-time online trend trend
Technical field
The present invention relates to large-scale power grid steady-state analysis technology, particularly relate to a kind of technology of zoning method for calculating of large-scale power grid real-time online trend trend.
Background technology
The appearance of intelligent grid reaches the appearance of extreme high voltage electrical network and smart machine, has broken the independent operating of traditional electrical network, and each isolated electrical network can be interconnected, and forms large-scale power grid.For guaranteeing large-scale power grid safety, stable operation, when operation of power networks, need monitor in real time the state information of the various electric equipments in defeated, the power distribution network, following running status to electrical network gives warning in advance, so large-scale power grid all wants high to the computational speed of trend calculating and the requirement of computational accuracy.
Conventional electric network swim computational methods are when calculating electric network swim, the time of calculating cost becomes quadratic relationship with the interconnected network scale, when for example adopting conventional tidal current computing method to calculate electric network swim by 10 interconnected large-scale power grids that form of island network, can increase about 100 times its computing time than the computing time of single island network; Therefore, conventional tidal current computing method has been difficult to satisfy the real-time monitoring demand of current intelligent grid, and the following running status of electrical network also is difficult to give warning in advance.
Summary of the invention
At the defective that exists in the above-mentioned prior art, technical problem to be solved by this invention provides a kind of zoning method for calculating of the large-scale power grid real-time online trend trend that computational speed is fast, computational accuracy is high of trend calculating.
In order to solve the problems of the technologies described above, the zoning method for calculating of a kind of large-scale power grid real-time online trend trend provided by the present invention is characterized in that concrete steps are as follows:
1) adopts the maximum impedance Furthest Neighbor that large-scale power grid is carried out subregion, obtain a plurality of subregions of interconnection;
2) generate the Jacobian matrix of all subregion according to the maximum impedance Furthest Neighbor;
3) each subregion is carried out the word equivalence, and keep boundary node constant, obtain the equivalent network and the equivalent load value of boundary node of all subregion;
4) according to the equivalent load value of the boundary node of subregion load type prediction all subregion equivalent network;
5) equivalent network of all subregion is merged into one and merged the zone, the duty values such as boundary node of all subregion equivalent network of being predicted in the step 4 inject this merging zone as node;
6) adopt Newton method to calculate the trend trend that merges the zone, with the real-time trend of result of calculation as large-scale power grid, concrete computing formula is:
Figure 382516DEST_PATH_IMAGE001
Wherein,
Figure 610367DEST_PATH_IMAGE002
Be the load of the border behind the equivalence, Be the large power supply differential behind the equivalence,
Figure 990849DEST_PATH_IMAGE004
Be the node voltage phase angle behind the equivalence,
Figure 286833DEST_PATH_IMAGE005
,
Figure 634768DEST_PATH_IMAGE006
Be the amplitude differential behind the equivalence,
Figure 482639DEST_PATH_IMAGE007
For meritorious to the voltage phase angle differential,
Figure 407869DEST_PATH_IMAGE008
For meritorious to voltage magnitude differential and voltage magnitude product,
Figure 504614DEST_PATH_IMAGE009
For idle to the voltage phase angle differential,
Figure 831690DEST_PATH_IMAGE010
For idle to voltage magnitude differential and voltage magnitude product.
Further, the method for the equivalent load value of boundary node of prediction all subregion equivalent network is as follows in the step 4:
A) the single subregion of load type is adopted of the same type day predicted method;
B) be the subregion of comprehensive load to load type, adopt many factors correlation predictive method;
C) to random load proportion greater than 10% subregion, adopt intelligent neural network prediction method;
D) subregion of and random load large percentage numerous to load type adopts multistage interpolative prediction method.
The zoning method for calculating of large-scale power grid real-time online trend trend provided by the invention, the a plurality of subregions that large-scale power grid are divided into interconnection, again each subregion is carried out the word equivalence, calculate the equivalent network and the equivalent load value of boundary node of all subregion, dope the equivalent load value of boundary node of all subregion equivalent network then according to the subregion load type, and then the equivalent network of all subregion merged, each predicted value is injected as node and is merged the zone, form the equivalent network of large-scale power grid, calculate the real-time trend of large-scale power grid at last, have computational speed and reach the high characteristics of computational accuracy soon.
Description of drawings
Fig. 1 is the calculation flow chart of zoning method for calculating of the large-scale power grid real-time online trend trend of the embodiment of the invention.
Embodiment
Below in conjunction with description of drawings embodiments of the invention are described in further detail, but present embodiment is not limited to the present invention, every employing analog structure of the present invention and similar variation thereof all should be listed protection scope of the present invention in.
As shown in Figure 1, the zoning method for calculating of a kind of large-scale power grid real-time online trend trend that the embodiment of the invention provided is characterized in that concrete steps are as follows:
1) adopt the maximum impedance Furthest Neighbor that large-scale power grid is carried out subregion, obtain a plurality of subregions of interconnection, be designated as:
Figure 116041DEST_PATH_IMAGE011
, wherein
Figure 403934DEST_PATH_IMAGE012
Be large-scale power grid, m is a subregion quantity,
Figure 166353DEST_PATH_IMAGE013
Extremely
Figure 223302DEST_PATH_IMAGE014
Be each sub regions,
Figure 678554DEST_PATH_IMAGE015
Extremely
Figure 453744DEST_PATH_IMAGE016
Numbering for all subregion;
2) according to the admittance Jacobian matrix of maximum impedance Furthest Neighbor generation large-scale power grid, be designated as:
Figure 19854DEST_PATH_IMAGE017
Wherein,
Figure 55943DEST_PATH_IMAGE018
Be the admittance Jacobian matrix of large-scale power grid, this matrix is a symmetrical matrix,
Figure 560392DEST_PATH_IMAGE019
Be the number of nodes in the large-scale power grid, each element that is positioned on the matrix leading diagonal is self-admittance (comprising a large amount of 0 elements), and other element is a transadmittance;
Then the Jacobian matrix of subregion is:
Wherein,
Figure 927100DEST_PATH_IMAGE021
Be a sub regions,
Figure 83275DEST_PATH_IMAGE022
Be subregion
Figure 880329DEST_PATH_IMAGE021
Jacobian matrix, Be subregion
Figure 538024DEST_PATH_IMAGE021
In number of nodes, its value is less than the quantity m of subregion;
Obtain:
Figure 424071DEST_PATH_IMAGE024
3) each subregion is carried out the word equivalence, and keep boundary node constant, obtain the equivalent network and the equivalent load value of boundary node of all subregion;
Wherein, the Jacobian matrix of each equivalent network only keeps boundary node and inner individual nodes, and for example a sub regions is , its equivalent network is
Figure 360596DEST_PATH_IMAGE026
, its Jacobian matrix is
Figure 806621DEST_PATH_IMAGE027
, its boundary node is 1,2, its inside does not have power supply, then this subregion Equivalent network Jacobian matrix For:
Figure 660121DEST_PATH_IMAGE028
；
The equivalent load value of the boundary node of subregion is designated as:
Figure 645395DEST_PATH_IMAGE029
；
Wherein,
Figure 830520DEST_PATH_IMAGE030
Be the numbering of subregion,
Figure 901244DEST_PATH_IMAGE031
Be to be numbered
Figure 954651DEST_PATH_IMAGE030
The equivalent load value of boundary node of equivalent network of subregion,
Figure 672727DEST_PATH_IMAGE032
Be the number of nodes in the equivalent network of this subregion,
Figure 153386DEST_PATH_IMAGE033
Extremely
Figure 321194DEST_PATH_IMAGE034
Be each node in the equivalent network of this subregion, Extremely
Figure 872578DEST_PATH_IMAGE036
Numbering for each node;
Wherein, be numbered
Figure 399505DEST_PATH_IMAGE030
The equivalent network of subregion in have at most
Figure 444822DEST_PATH_IMAGE032
(
Figure 715397DEST_PATH_IMAGE032
+ 1) branch road/2, its number of nodes and branch road quantity have much smaller than the number of nodes and the branch road quantity of its corresponding subregion
Figure 264190DEST_PATH_IMAGE032
＜＜
Figure 224668DEST_PATH_IMAGE023
, occur simultaneously The large power supply point of duty values such as individual border and limited several inside;
4) according to the equivalent load value of boundary node of subregion load type prediction all subregion equivalent network, concrete Forecasting Methodology is as follows:
A) the single subregion of load type is adopted of the same type day predicted method;
B) be the subregion of comprehensive load to load type, adopt many factors correlation predictive method;
C) to random load proportion greater than 10% subregion, adopt intelligent neural network prediction method;
D) subregion of and random load large percentage numerous to load type adopts multistage interpolative prediction method, and this predicted method and load type are irrelevant, and its precision is low slightly;
5) equivalent network of all subregion is merged into one and merged the zone, the duty values such as boundary node of all subregion equivalent network of being predicted in the step 4 inject this merging zone as node, should merge the zone and be designated as:
Figure 300389DEST_PATH_IMAGE037
, wherein
Figure 844634DEST_PATH_IMAGE038
For merging the zone,
Figure 103577DEST_PATH_IMAGE039
Extremely
Figure 467693DEST_PATH_IMAGE040
Equivalent network for each sub regions;
Obtain:
Figure 470284DEST_PATH_IMAGE041
；
Wherein,
Figure 848528DEST_PATH_IMAGE042
For merging the number of nodes in the zone, because
Figure 12793DEST_PATH_IMAGE032
＜＜
Figure 129785DEST_PATH_IMAGE023
So,
Figure 670487DEST_PATH_IMAGE042
＜＜
Figure 923745DEST_PATH_IMAGE043
6) adopt Newton method to calculate the trend trend that merges the zone, with the real-time trend of result of calculation as large-scale power grid, concrete computing formula is:
Wherein,
Figure 863200DEST_PATH_IMAGE002
Be the load of the border behind the equivalence,
Figure 207593DEST_PATH_IMAGE003
Be the inner large power supply differential that keeps behind the equivalence,
Figure 705571DEST_PATH_IMAGE004
Be the node voltage phase angle behind the equivalence,
Figure 84075DEST_PATH_IMAGE005
, Be the amplitude differential behind the equivalence,
Figure 58164DEST_PATH_IMAGE007
For meritorious to the voltage phase angle differential, For meritorious to voltage magnitude differential and voltage magnitude product,
Figure 87617DEST_PATH_IMAGE009
For idle to the voltage phase angle differential,
Figure 400918DEST_PATH_IMAGE010
For idle to voltage magnitude differential and voltage magnitude product.
In the embodiment of the invention, the maximum impedance Furthest Neighbor that is adopted, subregion is carried out word equivalence, day predicted method of the same type, many factors correlation predictive method, intelligent neural network prediction method, multistage interpolative prediction method and Newton method be prior art.

Claims (2)

1. the zoning method for calculating of a large-scale power grid real-time online trend trend is characterized in that concrete steps are as follows:
1) adopts the maximum impedance Furthest Neighbor that large-scale power grid is carried out subregion, obtain a plurality of subregions of interconnection;
2) generate the Jacobian matrix of all subregion according to the maximum impedance Furthest Neighbor;
3) each subregion is carried out the word equivalence, and keep boundary node constant, obtain the equivalent network and the equivalent load value of boundary node of all subregion;
4) according to the equivalent load value of the boundary node of subregion load type prediction all subregion equivalent network;
5) equivalent network of all subregion is merged into one and merged the zone, the duty values such as boundary node of all subregion equivalent network of being predicted in the step 4 inject this merging zone as node;
6) adopt Newton method to calculate the trend trend that merges the zone, with the real-time trend of result of calculation as large-scale power grid, concrete computing formula is:
Figure 2010105084804100001DEST_PATH_IMAGE001
Wherein,
Figure 970700DEST_PATH_IMAGE002
Be the load of the border behind the equivalence,
Figure 2010105084804100001DEST_PATH_IMAGE003
Be the large power supply differential behind the equivalence,
Figure 514945DEST_PATH_IMAGE004
Be the node voltage phase angle behind the equivalence, ,
Figure 386604DEST_PATH_IMAGE006
Be the amplitude differential behind the equivalence,
Figure 2010105084804100001DEST_PATH_IMAGE007
For meritorious to the voltage phase angle differential, For meritorious to voltage magnitude differential and voltage magnitude product,
Figure 2010105084804100001DEST_PATH_IMAGE009
For idle to the voltage phase angle differential,
Figure 753312DEST_PATH_IMAGE010
For idle to voltage magnitude differential and voltage magnitude product.
2. method according to claim 1 is characterized in that: the method for the equivalent load value of boundary node of prediction all subregion equivalent network is as follows in the step 4:
A) the single subregion of load type is adopted of the same type day predicted method;
B) be the subregion of comprehensive load to load type, adopt many factors correlation predictive method;
C) to random load proportion greater than 10% subregion, adopt intelligent neural network prediction method;
D) subregion of and random load large percentage numerous to load type adopts multistage interpolative prediction method.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102593823A (en) * 2012-02-01 2012-07-18 中国电力科学研究院 Superposition-principle-based on-line power flow calculation method of distribution network
CN103679283A (en) * 2013-11-06 2014-03-26 国家电网公司 New district electric power load prediction method for top design mode
CN107480109A (en) * 2017-10-23 2017-12-15 积成电子股份有限公司 The incomplete Parallel Implementation method of state estimation based on network structure dynamic partition
CN109474077A (en) * 2018-12-29 2019-03-15 国网辽宁省电力有限公司电力科学研究院 Intelligent power distribution switchgear control system and electric load prediction technique
CN111682539A (en) * 2020-06-30 2020-09-18 国网山东省电力公司威海供电公司 Partitioning method and system for power grid with distributed power supply based on simplified comprehensive scene
CN112701678A (en) * 2020-12-18 2021-04-23 国网辽宁省电力有限公司 Power grid evolution trend analysis method

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CN101540505A (en) * 2009-01-09 2009-09-23 南京南瑞继保电气有限公司 Building method of multistage multi-region interconnected network data model

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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102593823A (en) * 2012-02-01 2012-07-18 中国电力科学研究院 Superposition-principle-based on-line power flow calculation method of distribution network
CN102593823B (en) * 2012-02-01 2014-03-12 中国电力科学研究院 Superposition-principle-based on-line power flow calculation method of distribution network
CN103679283A (en) * 2013-11-06 2014-03-26 国家电网公司 New district electric power load prediction method for top design mode
CN107480109A (en) * 2017-10-23 2017-12-15 积成电子股份有限公司 The incomplete Parallel Implementation method of state estimation based on network structure dynamic partition
CN109474077A (en) * 2018-12-29 2019-03-15 国网辽宁省电力有限公司电力科学研究院 Intelligent power distribution switchgear control system and electric load prediction technique
CN109474077B (en) * 2018-12-29 2024-03-15 国网辽宁省电力有限公司电力科学研究院 Intelligent power distribution switch cabinet control system and electric load prediction method
CN111682539A (en) * 2020-06-30 2020-09-18 国网山东省电力公司威海供电公司 Partitioning method and system for power grid with distributed power supply based on simplified comprehensive scene
CN111682539B (en) * 2020-06-30 2024-01-19 国网山东省电力公司威海供电公司 Partitioning method and system for power grid containing distributed power source based on simplified comprehensive scene
CN112701678A (en) * 2020-12-18 2021-04-23 国网辽宁省电力有限公司 Power grid evolution trend analysis method
CN112701678B (en) * 2020-12-18 2023-02-17 国网辽宁省电力有限公司 Power grid evolution trend analysis method

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