CN112257990A - Power distribution network elasticity evaluation method and device, computer equipment and storage medium - Google Patents

Power distribution network elasticity evaluation method and device, computer equipment and storage medium Download PDF

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CN112257990A
CN112257990A CN202011059367.2A CN202011059367A CN112257990A CN 112257990 A CN112257990 A CN 112257990A CN 202011059367 A CN202011059367 A CN 202011059367A CN 112257990 A CN112257990 A CN 112257990A
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罗欣儿
李艳
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Shenzhen Power Supply Bureau Co Ltd
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Abstract

The application relates to a method and a device for evaluating elasticity of a power distribution network, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring historical operation data of each distribution area of the power distribution network, and calculating an elastic index value of each distribution area according to the historical operation data; calculating to obtain a microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area; calculating a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value, and determining an evaluation index weight coefficient of each station area according to the complex correlation coefficient; and obtaining a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area. And considering the relevance of the elasticity indexes among different transformer areas, analyzing the influence of interaction in the fault recovery process of the different transformer areas of the power distribution network by adopting a complex correlation coefficient, and taking the influence as a determination basis of the weighting coefficient of the evaluation indexes of the transformer areas, further determining the macroscopic elasticity evaluation value of the power distribution network, and realizing accurate comprehensive evaluation of the elasticity of the power distribution network.

Description

Power distribution network elasticity evaluation method and device, computer equipment and storage medium
Technical Field
The application relates to the technical field of power grids, in particular to a method and a device for evaluating elasticity of a power distribution network, computer equipment and a storage medium.
Background
As an important infrastructure related to national security and national economic life lines, an electric power system is required to satisfy not only reliable operation in a normal environment but also maintenance of necessary functions in the event of an extreme disaster. However, in recent years, many accidents occurring around the world have highlighted the weakness that power systems are not well prepared for, and even extremely vulnerable to, extreme disaster events that are difficult to predict.
Distribution network elasticity refers to the ability of the distribution network to resist failure and quickly recover power supply under extreme weather conditions. The important problem of guaranteeing the power safety is to carry out the elasticity evaluation of the power distribution network and analyze the survival capability and the recovery capability of the power distribution network for serious disasters. How to accurately evaluate the elasticity of the power distribution network is a problem to be solved urgently.
Disclosure of Invention
In view of the above, it is desirable to provide a power distribution network elasticity evaluation method, device, computer device, and storage medium that can accurately perform power distribution network elasticity evaluation.
A power distribution network elasticity evaluation method comprises the following steps:
acquiring historical operation data of each distribution area of the power distribution network, and calculating an elastic index value of each distribution area according to the historical operation data;
calculating to obtain a microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area;
calculating a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value, and determining an evaluation index weight coefficient of each station area according to the complex correlation coefficient;
and obtaining a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area.
In one embodiment, the distribution network has distribution areas including important distribution areas and non-important distribution areas, and all the non-important distribution areas are equivalent to an integral equivalent distribution area.
In one embodiment, the elasticity indexes of the important platform area comprise a fault complete restoration time length, a load loss time length and an energy loss percentage; and the elasticity indexes of the non-important platform areas comprise maximum fault complete restoration time length, average load loss time length and energy loss percentage.
In one embodiment, the calculating the elasticity index value of each zone according to the historical operating data includes:
calculating the complete fault repairing duration according to the historical operation data of the important distribution area, specifically:
Ti,RT＝(t2-t0),i∈ηkey
calculating the load loss duration according to the historical operation data of the important distribution area, specifically:
Figure BDA0002711884690000021
Figure BDA0002711884690000022
calculating the energy loss percentage according to the historical operation data of the important distribution area, specifically:
Figure BDA0002711884690000023
wherein, Ti,RTRepresenting the time length t of complete fault repair of the i-number platform area0Representing the time of occurrence of an accident due to extreme weather, t2Representing the moment of complete restoration of the power path from the upper transformer to the area, ηkeyRepresenting a set of all important lands; t isi,OTIndicating the duration of loss of load, Δ tjIndicating the length of observation in the grid fault,
Figure BDA0002711884690000024
represents the time interval atjThe power required for the normal operation of the inner zone,
Figure BDA0002711884690000031
represents the time interval atjThe power that the interior station area can provide for normal operation,
Figure BDA0002711884690000035
and is representative of any of the various aspects of,
Figure BDA0002711884690000036
represents the presence; ri,ELPThe percentage of energy loss is expressed.
In one embodiment, the calculating an elasticity index value for each zone according to the historical operating data further includes:
calculating the maximum fault complete restoration time length according to the historical operation data of the non-important distribution area, specifically:
Tothers,RT＝max{Ti,RT},i∈ηnon-key
calculating the average load loss duration according to the historical operation data of the non-important distribution area, specifically:
Figure BDA0002711884690000032
Figure BDA0002711884690000033
calculating the energy loss percentage according to historical operating data of the non-important distribution area, specifically:
Figure BDA0002711884690000034
wherein eta isnon-keyRepresenting the set of all insignificant lands, Tothers,OTRepresents the maximum fault complete repair duration, max { T, of all non-essential cellsi,RTThe method comprises the steps that the maximum value of repair duration of all non-important platform areas is selected; t isothers,OTRepresenting the average duration of loss of load, R, of all non-essential cellsothers,ELPRepresenting the percentage of energy loss for all insignificant lands.
In one embodiment, the calculating a microscopic elasticity evaluation value of each station region according to the elasticity index value of each station region includes:
giving weight to the elastic index of the transformer area, and obtaining a transformer area microscopic elastic evaluation model according to the elastic index and the corresponding weight;
and respectively obtaining the microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area and the distribution area microscopic elasticity evaluation model.
In one embodiment, after obtaining the macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area, the method further includes:
and obtaining and outputting an elasticity evaluation result of the power distribution network according to the macroscopic elasticity evaluation value of the power distribution network.
An elasticity evaluation device for a power distribution network, comprising:
the data acquisition module is used for acquiring historical operation data of each distribution area of the power distribution network and calculating an elastic index value of each distribution area according to the historical operation data;
the microscopic elasticity evaluation module is used for obtaining the elasticity index value according to each distribution area and calculating to obtain the microscopic elasticity evaluation value of each distribution area;
the weight coefficient calculation module is used for calculating a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value and determining an evaluation index weight coefficient of each station area according to the complex correlation coefficient;
and the macroscopic elasticity evaluation module is used for obtaining a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
According to the method, the device, the computer equipment and the storage medium for evaluating the elasticity of the power distribution network, the elasticity index value of each distribution area is calculated according to the historical operating data of each distribution area of the power distribution network, the relevance of the elasticity index among different distribution areas is considered, the influence of the interaction in the fault recovery process of different distribution areas of the power distribution network is analyzed by adopting a complex correlation coefficient, and the complex correlation coefficient is used as the basis for determining the weight coefficient of the evaluation index of the distribution areas, so that the macroscopic elasticity evaluation value of the power distribution network is determined, and the elasticity of the power distribution network is accurately and comprehensively evaluated.
Drawings
FIG. 1 is a flow chart of a method for evaluating distribution network resiliency in one embodiment;
FIG. 2 is a flow chart of a method for evaluating the elasticity of a distribution network in another embodiment;
fig. 3 is a block diagram of an apparatus for evaluating elasticity of a power distribution network according to an embodiment;
FIG. 4 is a diagram of the internal structure of a computer device in one embodiment;
fig. 5 is a schematic flow chart of a method for evaluating elasticity of a power distribution network according to an embodiment;
FIG. 6 is a schematic diagram illustrating a computing method of node toughness indexes of a power distribution network in an embodiment;
fig. 7 is a schematic diagram of an architecture of a power distribution network macroelasticity evaluation system in an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, a method for evaluating elasticity of a power distribution network is provided, as shown in fig. 1, and includes:
step S110: and acquiring historical operation data of each distribution area of the power distribution network, and calculating the elasticity index value of each distribution area according to the historical operation data.
Historical operation data of each distribution area within a period of time can be obtained from a database of a power distribution network server, and each distribution area is used as a power distribution network node to perform power distribution network elasticity evaluation. The historical operation data may specifically include the time when the accident occurs due to extreme weather in each platform area, the time when the power path is repaired, the power supplied, the energy loss during disturbance, the energy loss in the normal power supply condition, and the like, and the extreme weather may specifically be typhoon weather and the like. Specifically, when elasticity evaluation is performed for a specific area, a power distribution network fault caused by extreme weather is closely related to a specific position of a distribution area, for example, a certain distribution area is often influenced by typhoon weather and has a fault and a power failure. Therefore, the station areas affected by extreme weather can be distinguished from other station areas, and the elasticity index values can be calculated respectively.
In one embodiment, the distribution network has zones including critical zones and non-critical zones, and all the non-critical zones are equivalent to one integral equivalent zone. When the elasticity evaluation of the power distribution network is carried out, the distribution area with the power distribution network fault caused by extreme weather is defined as an important distribution area, other distribution areas are used as non-important distribution areas, and the non-important distribution areas are regarded as an integral equivalent distribution area for integral evaluation. Specifically, the classification can be performed according to the platform area loads, platform area equivalence is performed on the similarity loads respectively, and all the non-important platform areas are equivalent to an integral equivalent platform area. The elastic index of the equivalent transformer area is considered according to the conservative value. During comprehensive evaluation, the influence of different classification load areas can be highlighted, and huge workload of analyzing a large number of areas one by one is avoided.
The type of the elasticity index of the distribution area is not unique and can be various different indexes, and the elasticity index value of the corresponding distribution area is obtained by correspondingly calculating each elasticity index by combining historical operation data of the distribution area. In one embodiment, the elasticity indicators of the important platform areas comprise fault complete restoration time length, load loss time length and energy loss percentage; the elasticity indexes of the unimportant station areas comprise maximum fault complete restoration time length, average load loss time length and energy loss percentage.
Correspondingly, in one embodiment, calculating the elasticity index value of each zone according to the historical operating data comprises: calculating the complete fault repairing duration according to the historical operation data of the important distribution area, specifically:
Ti,RT＝(t2-t0),i∈ηkey
calculating the load loss duration according to the historical operation data of the important distribution area, specifically:
Figure BDA0002711884690000061
Figure BDA0002711884690000062
calculating the energy loss percentage according to the historical operation data of the important distribution area, specifically:
Figure BDA0002711884690000063
wherein, Ti,RTRepresenting the time length t of complete fault repair of the i-number platform area0Representing the time of occurrence of an accident due to extreme weather, t2Representing the moment of complete restoration of the power path from the upper transformer to the area, ηkeyRepresenting a set of all important lands; t isi,OTIndicating the duration of loss of load, Δ tjIndicating the length of observation in the grid fault,
Figure BDA0002711884690000071
represents the time interval atjThe power required for the normal operation of the inner zone,
Figure BDA0002711884690000072
represents the time interval atjThe power that the interior station area can provide for normal operation,
Figure BDA0002711884690000073
and is representative of any of the various aspects of,
Figure BDA0002711884690000074
represents the presence; ri,ELPThe percentage of energy loss is expressed.
The important distribution area elastic index can be calculated by substituting the relevant historical data of the important distribution area into the formula: the specific values of the fault complete restoration time length, the load loss time length and the energy loss percentage.
Further, in an embodiment, the calculating the elasticity index value of each zone according to the historical operating data further includes:
calculating the maximum fault complete restoration time length according to the historical operation data of the non-important distribution area, specifically:
Tothers,RT＝max{Ti,RT},i∈ηnon-key
calculating the average load loss duration according to the historical operation data of the non-important distribution area, specifically:
Figure BDA0002711884690000075
Figure BDA0002711884690000076
calculating the energy loss percentage according to historical operating data of the non-important distribution area, specifically:
Figure BDA0002711884690000077
wherein eta isnon-keyRepresenting the set of all insignificant lands, Tothers,OTRepresents the maximum fault complete repair duration, max { T, of all non-essential cellsi,RTThe method comprises the steps that the maximum value of repair duration of all non-important platform areas is selected; t isothers,OTRepresenting the average duration of loss of load, R, of all non-essential cellsothers,ELPRepresenting the percentage of energy loss for all insignificant lands.
Substituting the related historical data of the unimportant distribution area into the formula to calculate the elasticity index of the unimportant equivalent distribution area: the specific values of the fault complete restoration time length, the load loss time length and the energy loss percentage.
Step S120: and calculating to obtain a microscopic elasticity evaluation value of each station area according to the elasticity index value of each station area. After the elasticity index value of each station area is obtained through calculation, calculation is carried out on each station area by combining the elasticity index and the corresponding weight, and the microscopic elasticity evaluation value of each station area can be obtained respectively.
In one embodiment, step S120 includes: giving weight to the elastic index of the transformer area, and obtaining a transformer area microscopic elastic evaluation model according to the elastic index and the corresponding weight; and respectively obtaining the microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area and the distribution area microscopic elasticity evaluation model. Specifically, for three elasticity indexes of the distribution area, the value of the index of the energy loss ratio is selected between [0 and 1], and other two indexes can be normalized by combining with a membership function and then are endowed with corresponding weights, so that the distribution area elasticity evaluation model is obtained through calculation.
Step S130: and calculating a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value, and determining an evaluation index weight coefficient of each station area according to the complex correlation coefficient. After determining the microscopic elasticity evaluation value of each land, the complex correlation coefficient of each land is calculated in combination with the microscopic elasticity evaluation values of all the lands. The smaller the complex correlation coefficient is, the smaller the degree of overlapping with information reflected by the other indexes is. And screening influence factors of the interaction between the layers by adopting a complex correlation coefficient method, and giving quantitative description to the influence factors according to the strength of the correlation.
Step S140: and obtaining a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area. And after the evaluation index weight coefficient of each distribution area is obtained according to the complex correlation coefficient, averaging the weighted sum of the microscopic elasticity evaluation values of all the distribution areas to serve as the macroscopic elasticity evaluation of the power distribution network system.
Further, in one embodiment, as shown in fig. 2, after step S140, the method further includes step S150: and obtaining and outputting an elasticity evaluation result of the power distribution network according to the macroscopic elasticity evaluation value of the power distribution network.
Specifically, the macroscopic elasticity evaluation value of the power distribution network may be directly output as the elasticity evaluation result, or the macroscopic elasticity evaluation value may be analyzed by combining preset data to obtain the elasticity evaluation result and then output. The type of the preset data is not unique, for example, the preset data may be a single preset threshold, and if the macroscopic elasticity assessment value is greater than the preset threshold, the elasticity assessment result is qualified, otherwise, the elasticity assessment result is unqualified. In addition, the preset data may also include a plurality of threshold values, different sections are divided according to the plurality of threshold values, and each section corresponds to a different evaluation level, and the evaluation level may include excellence, good, pass, fail, and the like. And analyzing the section of the macroscopic elasticity evaluation value obtained by calculation to obtain the corresponding evaluation level as an elasticity evaluation result. The mode of outputting the elasticity evaluation result is not unique, and may be output to a database of the server for storage or output to a display screen for display.
According to the elasticity evaluation method of the power distribution network, elasticity index values of all distribution areas are calculated according to historical operation data of all distribution areas of the power distribution network, the relevance of the elasticity indexes among all different distribution areas is considered, the influence of interaction in the fault recovery process of different distribution areas of the power distribution network is analyzed through a complex correlation coefficient, the complex correlation coefficient is used as a determination basis of the weight coefficient of the evaluation indexes of the distribution areas, the macroscopic elasticity evaluation value of the power distribution network is further determined, and accurate comprehensive evaluation of the elasticity of the power distribution network is achieved.
In one embodiment, an apparatus for evaluating elasticity of a power distribution network is provided, as shown in fig. 3, and includes a data acquisition module 110, a micro elasticity evaluation module 120, a weight coefficient calculation module 130, and a macro elasticity evaluation module 140.
The data acquisition module 110 is configured to acquire historical operation data of each distribution area of the power distribution network, and calculate an elastic index value of each distribution area according to the historical operation data; the microscopic elasticity evaluation module 120 is configured to obtain an elasticity index value according to each distribution area, and calculate to obtain a microscopic elasticity evaluation value of each distribution area; the weight coefficient calculation module 130 is configured to calculate a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value, and determine an evaluation index weight coefficient of each station area according to the complex correlation coefficient; the macroscopic elasticity evaluation module 140 is configured to obtain a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area.
In one embodiment, the distribution network has zones including critical zones and non-critical zones, and all the non-critical zones are equivalent to one integral equivalent zone. Further, in one embodiment, the elasticity indexes of the important platform area comprise fault complete restoration time length, load loss time length and energy loss percentage; the elasticity indexes of the unimportant station areas comprise maximum fault complete restoration time length, average load loss time length and energy loss percentage.
In one embodiment, the micro elasticity evaluation module 120 gives a weight to the elasticity index of the distribution room, and obtains a distribution room micro elasticity evaluation model according to the elasticity index and the corresponding weight; and respectively obtaining the microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area and the distribution area microscopic elasticity evaluation model.
In one embodiment, the macroscopic elasticity evaluation module 140 is further configured to obtain an elasticity evaluation result of the power distribution network according to the macroscopic elasticity evaluation value of the power distribution network and output the elasticity evaluation result.
For specific limitations of the power distribution network elasticity evaluation device, reference may be made to the above limitations of the power distribution network elasticity evaluation method, and details are not repeated here. All or part of each module in the power distribution network elasticity evaluation device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
According to the elasticity evaluation device for the power distribution network, elasticity index values of all distribution areas are calculated according to historical operation data of all distribution areas of the power distribution network, the relevance of the elasticity indexes among all different distribution areas is considered, the influence of interaction in the fault recovery process of different distribution areas of the power distribution network is analyzed through a complex correlation coefficient, the complex correlation coefficient is used as a determination basis of the weight coefficient of the evaluation indexes of the distribution areas, the macroscopic elasticity evaluation value of the power distribution network is further determined, and accurate comprehensive evaluation of the elasticity of the power distribution network is achieved.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store historical operating data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a distribution network elasticity evaluation method.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program: acquiring historical operation data of each distribution area of the power distribution network, and calculating an elastic index value of each distribution area according to the historical operation data; calculating to obtain a microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area; calculating a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value, and determining an evaluation index weight coefficient of each station area according to the complex correlation coefficient; and obtaining a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area.
In one embodiment, the processor, when executing the computer program, further performs the steps of: giving weight to the elastic index of the transformer area, and obtaining a transformer area microscopic elastic evaluation model according to the elastic index and the corresponding weight; and respectively obtaining the microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area and the distribution area microscopic elasticity evaluation model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and obtaining and outputting an elasticity evaluation result of the power distribution network according to the macroscopic elasticity evaluation value of the power distribution network.
In one embodiment, there is also provided a computer readable storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of: acquiring historical operation data of each distribution area of the power distribution network, and calculating an elastic index value of each distribution area according to the historical operation data; calculating to obtain a microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area; calculating a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value, and determining an evaluation index weight coefficient of each station area according to the complex correlation coefficient; and obtaining a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area.
In one embodiment, the computer program when executed by the processor further performs the steps of: giving weight to the elastic index of the transformer area, and obtaining a transformer area microscopic elastic evaluation model according to the elastic index and the corresponding weight; and respectively obtaining the microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area and the distribution area microscopic elasticity evaluation model.
In one embodiment, the computer program when executed by the processor further performs the steps of: and obtaining and outputting an elasticity evaluation result of the power distribution network according to the macroscopic elasticity evaluation value of the power distribution network.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
In order to better understand the method, the apparatus, the computer device and the storage medium for evaluating the elasticity of the power distribution network, the following detailed description is made in conjunction with specific embodiments.
The application provides a power distribution network elasticity evaluation method based on complex correlation coefficients, and an evaluation flow chart of the method is shown in fig. 5. When elasticity evaluation is carried out in a specific area, the power distribution network fault caused by extreme weather is closely related to the specific position of a distribution area, for example, a certain distribution area is often influenced by typhoon weather and has a fault and power failure; when the evaluation is carried out, the station areas are defined as important station areas, other station areas are used as non-important station areas, and the non-important station areas with similar load characteristics are equivalent to an integral equivalent station area. And classifying the distribution areas of the power distribution network into important distribution area equivalent distribution areas and non-important distribution area equivalent distribution areas according to the conformity, and numbering the important distribution areas and the non-important distribution areas respectively. Historical operation data of the power distribution network are collected, and station area information such as station area accumulated fault probability, fault points and the like can be determined according to the historical operation data. And taking the important platform area as a key node, and taking the equivalent platform area of the non-important platform area as a non-key node to perform elastic evaluation analysis.
The important platform zone elasticity indexes comprise:
(1) time length T for complete fault repairi,RTIt means the time required from the start of the failure to the complete restoration of the system to normal.
Ti,RT＝(t2-t0),i∈ηkey (1)
In the formula, Ti,RTRepresents the repair duration of the key node I, t0Representing the time of occurrence of an accident due to extreme weather, t2Representing the moment at which the power path from the upper transformer to the node is completely restored, ηkeyRepresenting the set of all key nodes.
(2) Time of loss of load Ti,OTAnd considering that the emergency standby capacity of the distribution power supply or the energy storage equipment in the transformer area can meet the partial load requirement, and the transformer area which cannot meet the power supply requirement has partial load power failure.
Figure BDA0002711884690000131
Figure BDA0002711884690000132
In the formula (2), Δ tjIndicating a certain small observation period in the grid fault,
Figure BDA0002711884690000133
corresponding to time t in FIG. 60To time t2The solid line in between is shown as,
Figure BDA0002711884690000134
corresponding to time t in FIG. 60To time t2The dotted lines in between represent the power required and available for normal operation of the node during that time period, if any
Figure BDA0002711884690000135
The node can completely ensure the normal operation of the node in the period, and the coefficient kjIs 0, whereas the coefficient kjIs 1. This period does not account for power loss.
Figure BDA0002711884690000141
Represents any of,
Figure BDA0002711884690000142
Is representative of presence.
Time of loss of load Ti,OTTime length T for complete restoration of faulti,RTThe influence is simultaneously influenced by a node load curve and the maximum output power of the accessed energy storage device.
(3) Loss of energyLoss percentage Ri,ELPThe percentage of energy loss of the station area during a disturbance compared to the normal power supply situation.
Figure BDA0002711884690000143
As can be seen from FIG. 6 and equation (3), the indicator corresponds to time t of 60To time t2The area between the dotted line and the solid line, the factor affecting the index and the factor affecting Ti,OTThe same factors apply.
The non-important equivalent platform zone elasticity indexes comprise:
(1) maximum fault complete repair duration Tothers,RTIt is the maximum time required from the onset of the non-critical cell failure to the complete recovery of the system.
Tothers,RT＝max{Ti,RT},i∈ηnon-key (4)
The analogy formula (1) assumes that a total of n non-critical nodes of the distribution network are provided, wherein eta is shown in the formula (4)non-keyAnd representing the set of all non-key nodes, and selecting the maximum value of the repair duration of all the non-key nodes.
(2) Average time duration of loss of load Tothers,OTTime duration T for fault recovery of non-essential cellsi,RTIn contrast, the average loss load duration of all the unimportant cells is calculated.
Figure BDA0002711884690000144
Figure BDA0002711884690000145
Analogy to equation (2), the time duration T for fault recovery due to each nodei,RTIn contrast, the index mainly calculates the average load loss duration of all non-critical nodes.
(3) Percentage of energy loss Rothers,ELP
Figure BDA0002711884690000151
Analogy equation (3), equation (6) quantitatively calculates the energy loss percentage of all non-critical nodes.
In the elasticity indexes of the distribution room, the value of the index of the energy loss percentage is between [0 and 1], and other two indexes are normalized by combining with the membership function and then are endowed with corresponding weights, so that the elasticity evaluation model of the distribution room is given. The elasticity evaluation index, the membership function thereof and the selection of the weight are shown in table 1.
Figure BDA0002711884690000152
TABLE 1
In Table 1,. lambda.criticalAnd λothersRespectively representing the upper limit of the load recovery supply time length of the distribution room, gammacriticalAnd gammaothersRespectively representing the upper limit of the load loss duration of the key node and the upper limit of the average load loss duration of other nodes, wi,RT、wi,OTAnd wi,ELPTaking values by combining the classified load characteristics of the distribution areas, for example, if the load of a certain node is mainly industrial load and the yield is most sensitive to the percentage of energy loss, wi,ELPThe arrangement can be large; for the condition that non-key nodes are mainly civil loads and residents mainly pay attention to the restoration duration, the weight wi,RTThe setting can be large.
In summary, the expression of the station zone elasticity evaluation model is as follows:
xi＝100*(wi,RT*Mi,RT+wi,OT*Mi,OT+wi,ELP*Mi,ELP) (7)
in the formula (7), i ∈ ηkeyYηnon-key，wi,RT、wi,OTAnd wi,ELPAre respectively index weights and satisfy the relation wi,RT+wi,OT+wi,ELP＝1。
The comprehensive elastic evaluation on the power distribution network system level needs to comprehensively consider macro and micro characteristics in the power grid operation process. The distribution network as a whole, the elastic indexes of each distribution area can generate interactive influence with each other, the multiple correlation coefficient concerns the correlation degree between a single index and multiple indexes, and the multiple correlation coefficient has great advantages when considering the common influence of the multiple indexes on the single index. The smaller the complex correlation coefficient is, the smaller the degree of overlapping with information reflected by the other indexes is. The method has the advantages that the influence factors of the interaction between the layers can be screened by adopting a complex correlation coefficient method, quantitative description is given to the influence factors according to the strength of the correlation degree, the elasticity weight of each distribution area is obtained according to the complex correlation coefficient, and therefore the weighted sum of the elasticity of all the distribution areas is averaged to be used as the macroscopic elasticity evaluation of the power distribution network system. The overall power distribution network macroelasticity evaluation model is shown in fig. 7. This model is explained in detail below.
Assuming that the total number of important transformer areas and non-important equivalent transformer areas of a certain power distribution system is p, the corresponding elastic comprehensive evaluation set is { X }1,X2,X3,...,Xi,...Xp}. Taking the microscopic elastic evaluation value obtained by calculating the transformer area under the condition of each fault as a primary score, and under the condition of q faults, each transformer area has a score set X for q timesi＝{x1,x2,x3,......,xqAnd determining the correlation of each score set by introducing a complex correlation coefficient. Wherein, the arbitrary score XiCan be expressed as a linear combination of the remaining scores, i.e.:
Figure BDA0002711884690000161
in the formula (8)
Figure BDA0002711884690000162
Representative elasticity score
Figure BDA0002711884690000163
Fitting value of beta0,β1,β2，......,βk-1Is a constant to be solved. The correlation coefficient between the two is then calculated as follows:
Figure BDA0002711884690000164
in the formula (9), the reaction mixture,
Figure BDA0002711884690000165
represents
Figure BDA0002711884690000166
And
Figure BDA0002711884690000167
cov, denotes the calculated covariance, E denotes the expectation,
Figure BDA0002711884690000168
and
Figure BDA0002711884690000169
each represents XiAnd
Figure BDA00027118846900001610
average value of (a). If it is
Figure BDA00027118846900001611
Multiple correlation coefficient of
Figure BDA00027118846900001612
The larger the index is, the stronger the relevance between the index and other indexes is, the smaller the weight is, and the reciprocal of each correlation coefficient is
Figure BDA00027118846900001613
Weight W used as each index after normalizationiAs shown in formula (10), the overall elasticity score X of the distribution networkDistributionAs shown in equation (11).
Figure BDA0002711884690000171
Figure BDA0002711884690000172
The method for establishing the elasticity evaluation of the power distribution network based on the complex correlation coefficient mainly has the following advantages:
firstly, according to the classification of the platform area loads, platform area equivalence is respectively carried out on the similarity loads, and the elastic index of an equivalent platform area is considered according to conservative values. Therefore, the influence of different classification load areas can be highlighted during comprehensive evaluation, and huge workload of analyzing a plurality of areas one by one is avoided.
Secondly, the macro characteristic of the power grid structure is considered comprehensively, the influence of interaction of fault recovery processes of different distribution areas of the power distribution network is analyzed by adopting a complex correlation coefficient, the relevance of elastic indexes among different distribution areas is fully considered, the relevance is used as a determination basis of a distribution area evaluation index weight coefficient in the comprehensive elasticity evaluation of the power distribution network, and the elasticity of the power distribution network is evaluated comprehensively.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for evaluating elasticity of a power distribution network is characterized by comprising the following steps:
acquiring historical operation data of each distribution area of the power distribution network, and calculating an elastic index value of each distribution area according to the historical operation data;
calculating to obtain a microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area;
calculating a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value, and determining an evaluation index weight coefficient of each station area according to the complex correlation coefficient;
and obtaining a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area.
2. The distribution network elasticity evaluation method according to claim 1, wherein the distribution network distribution areas comprise important distribution areas and non-important distribution areas, and all the non-important distribution areas are equivalent to an overall equivalent distribution area.
3. The distribution network elasticity evaluation method according to claim 2, wherein the elasticity indexes of the important distribution areas comprise fault complete restoration time length, load loss time length and energy loss percentage; and the elasticity indexes of the non-important platform areas comprise maximum fault complete restoration time length, average load loss time length and energy loss percentage.
4. The method according to claim 3, wherein the calculating elasticity index values of the zones according to the historical operating data includes:
calculating the complete fault repairing duration according to the historical operation data of the important distribution area, specifically:
Ti,RT＝(t2-t0),i∈ηkey
calculating the load loss duration according to the historical operation data of the important distribution area, specifically:
Figure FDA0002711884680000011
Figure FDA0002711884680000012
calculating the energy loss percentage according to the historical operation data of the important distribution area, specifically:
Figure FDA0002711884680000021
wherein, Ti,RTRepresenting the time length t of complete fault repair of the i-number platform area0Representing the time of occurrence of an accident due to extreme weather, t2Representing the moment of complete restoration of the power path from the upper transformer to the area, ηkeyRepresenting a set of all important lands; t isi,OTIndicating the duration of loss of load, Δ tjIndicating the length of observation in the grid fault,
Figure FDA0002711884680000022
represents the time interval atjThe power required for the normal operation of the inner zone,
Figure FDA0002711884680000023
represents the time interval atjThe power that the interior station area can provide for normal operation,
Figure FDA0002711884680000027
and is representative of any of the various aspects of,
Figure FDA0002711884680000028
represents the presence; ri,ELPThe percentage of energy loss is expressed.
5. The method according to claim 4, wherein the calculating elasticity index values for each zone according to the historical operating data further comprises:
calculating the maximum fault complete restoration time length according to the historical operation data of the non-important distribution area, specifically:
Tothers,RT＝max{Ti,RT},i∈ηnon-key
calculating the average load loss duration according to the historical operation data of the non-important distribution area, specifically:
Figure FDA0002711884680000024
Figure FDA0002711884680000025
calculating the energy loss percentage according to historical operating data of the non-important distribution area, specifically:
Figure FDA0002711884680000026
wherein eta isnon-keyRepresenting the set of all insignificant lands, Tothers,OTRepresents the maximum fault complete repair duration, max { T, of all non-essential cellsi,RTThe method comprises the steps that the maximum value of repair duration of all non-important platform areas is selected; t isothers,OTRepresenting the average duration of loss of load, R, of all non-essential cellsothers,ELPRepresenting the percentage of energy loss for all insignificant lands.
6. The method for evaluating the elasticity of the power distribution network according to claim 1, wherein the step of calculating the microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area comprises:
giving weight to the elastic index of the transformer area, and obtaining a transformer area microscopic elastic evaluation model according to the elastic index and the corresponding weight;
and respectively obtaining the microscopic elasticity evaluation value of each distribution area according to the elasticity index value of each distribution area and the distribution area microscopic elasticity evaluation model.
7. The power distribution network elasticity evaluation method according to any one of claims 1 to 6, wherein after obtaining the macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area, the method further comprises:
and obtaining and outputting an elasticity evaluation result of the power distribution network according to the macroscopic elasticity evaluation value of the power distribution network.
8. An elasticity evaluation device for a power distribution network, comprising:
the data acquisition module is used for acquiring historical operation data of each distribution area of the power distribution network and calculating an elastic index value of each distribution area according to the historical operation data;
the microscopic elasticity evaluation module is used for obtaining the elasticity index value according to each distribution area and calculating to obtain the microscopic elasticity evaluation value of each distribution area;
the weight coefficient calculation module is used for calculating a complex correlation coefficient of each station area according to the microscopic elasticity evaluation value and determining an evaluation index weight coefficient of each station area according to the complex correlation coefficient;
and the macroscopic elasticity evaluation module is used for obtaining a macroscopic elasticity evaluation value of the power distribution network according to the microscopic elasticity evaluation value and the evaluation index weight coefficient of each distribution area.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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