CN114254878A - Distribution network elasticity evaluation method and device considering extreme disaster occurrence frequency - Google Patents

Distribution network elasticity evaluation method and device considering extreme disaster occurrence frequency Download PDF

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CN114254878A
CN114254878A CN202111452560.7A CN202111452560A CN114254878A CN 114254878 A CN114254878 A CN 114254878A CN 202111452560 A CN202111452560 A CN 202111452560A CN 114254878 A CN114254878 A CN 114254878A
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typhoon
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CN114254878B (en
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王蕾
孙飞飞
杜诗嘉
沈志恒
郭创新
顾晨临
邬樵风
钱佳佳
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Zhejiang University ZJU
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a method and a device for evaluating the elasticity of a power distribution network by considering the occurrence frequency of extreme disasters, wherein the method comprises the following steps: establishing a typhoon characteristic fault rate model and an icing characteristic fault rate model; selecting a typhoon event set and an icing event set of an area where a power distribution network is located; respectively evaluating the elasticity level of a first power distribution network under each typhoon event and the elasticity level of a second power distribution network under each icing event according to the typhoon characteristic fault rate model and the icing characteristic fault rate model; setting typhoon weight and icing weight according to the occurrence frequency; the elasticity level of the power distribution network in normal operation is respectively different from the elasticity level of the first power distribution network and the elasticity level of the second power distribution network, so that the performance loss of the first power distribution network and the performance loss of the second power distribution network are obtained; and respectively weighting and accumulating the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averaging the accumulated sum, and evaluating the elasticity level of the power distribution network considering the occurrence frequency of the extreme disasters.

Description

Distribution network elasticity evaluation method and device considering extreme disaster occurrence frequency
Technical Field
The application relates to the field of distribution network elasticity assessment, in particular to a distribution network elasticity assessment method and device considering the occurrence frequency of extreme disasters.
Background
In order to reasonably quantify the capability of the elastic power distribution network in resisting disaster accidents, the elasticity of the power distribution network is evaluated by starting from 3 stages of the absorption, adaptation and recovery of the power distribution network to disturbance events and taking the power supply capability of the power distribution network as an index. The elasticity index of the power distribution network is generally represented by selecting some characteristic values in a dynamic response curve of the power distribution network before and after a disaster.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
at present, the research on the elasticity evaluation indexes of the power distribution network under single disaster is mainly used at home and abroad, the quantitative evaluation of the elasticity indexes is realized by calculating the characteristic value of the elasticity function curve of the power distribution network, but the value of the elasticity evaluation indexes in the whole operation scheduling of the actual power distribution network is still questioned. At present, the influence degree of some frequent disasters on power distribution networks in different regions cannot be reflected by quantitative calculation of elastic indexes of single disasters, for example, extreme weather such as typhoon, ice coating and the like is more concerned by power grids in east coastal regions, and the regional characteristics of the power distribution network elastic evaluation are reflected by considering the occurrence frequency of different region disaster types during elastic evaluation.
Disclosure of Invention
The embodiment of the application aims to provide a power distribution network elasticity evaluation method and device considering the occurrence frequency of extreme disasters so as to solve the technical problem that the influence degree of different disaster occurrence frequencies on power distribution networks in different regions cannot be reflected in the related technology.
According to a first aspect of embodiments of the present application, there is provided a power distribution network elasticity evaluation method considering an occurrence frequency of an extreme disaster, including:
establishing a typhoon characteristic fault rate model and an icing characteristic fault rate model;
selecting a typhoon event set and an icing event set of the area where the power distribution network is located;
according to the typhoon characteristic fault rate model, evaluating the elasticity level of the first power distribution network under each typhoon event in the typhoon event set;
evaluating the elasticity level of a second power distribution network under each icing event in the icing event set according to the icing characteristic fault rate model;
setting typhoon weight and icing weight according to the occurrence frequency of typhoon events and icing events in the area of the power distribution network;
the elasticity level of the power distribution network in normal operation is different from the elasticity level of the first power distribution network, so that the performance loss of the first power distribution network is obtained;
the elasticity level of the power distribution network in normal operation is different from the elasticity level of the second power distribution network, so that the performance loss of the second power distribution network is obtained;
and respectively weighting and accumulating the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averaging the accumulated sum, and evaluating the elasticity level of the power distribution network considering the occurrence frequency of the extreme disasters.
Further, the process of establishing the typhoon characteristic fault rate model comprises the following steps:
simulating the wind speed and wind direction of each point in the influence range of the typhoon wind ring;
calculating the fault rate of the conducting wire or the tower of the power distribution network under the wind load according to the wind speed and the wind direction;
and establishing a typhoon characteristic fault rate model according to the fault rate of the lead or the tower.
Further, the process of establishing the icing characteristic fault rate model comprises the following steps:
calculating the distribution of the maximum icing thickness of the power distribution network along with the line position of the power distribution network;
and establishing an icing characteristic fault rate model according to the distribution and the preset bearable ice thickness of the power distribution network.
Further, the formula for evaluating the elasticity level of the power distribution network considering the frequency of occurrence of the extreme disaster is as follows:
Figure BDA0003386738840000031
wherein, count1Set phi for typhoon events1Total number of events of (a); count2Set phi for icing events2Total number of events of (a); t is t1And t5Respectively setting the starting time and the ending time of the distribution network affected by the disaster; w is a1Weighting factors for the importance degree of the single typhoon disaster; w is a2Weighting factors for the importance degree of single icing disasters;
Figure BDA0003386738840000032
set phi for typhoon events1In each typhoon event s1A first distribution network performance loss;
Figure BDA0003386738840000033
set phi for icing events2Each of which is an icing event s2The second distribution network performance loss.
Further, the method further comprises:
and averaging the accumulation of the performance loss of the first distribution network and the performance loss of the second distribution network, and evaluating the average elasticity level of the distribution network in the event set time.
Further, the formula for evaluating the average elasticity level of the distribution network in the event set time is as follows:
Figure BDA0003386738840000034
wherein, count1Set phi for typhoon events1Total number of events of (a); count2Set phi for icing events2Total number of events of (a); t is t1And t5Respectively setting the starting time and the ending time of the power distribution network affected by the extreme disasters;
Figure BDA0003386738840000035
set phi for typhoon events1In each typhoon event s1A first distribution network performance loss;
Figure BDA0003386738840000036
set phi for icing events2Each of which is an icing event s2The second distribution network performance loss.
According to a second aspect of the embodiments of the present application, there is provided an elasticity evaluation apparatus for a power distribution network considering an occurrence frequency of an extreme disaster, including:
the modeling module is used for establishing a typhoon characteristic fault rate model and an icing characteristic fault rate model;
the selection module is used for selecting a typhoon event set and an icing event set of the area where the power distribution network is located;
the first evaluation module is used for evaluating the elasticity level of the first power distribution network under each typhoon event in the typhoon event set according to the typhoon characteristic fault rate model;
a second evaluation module, configured to evaluate a second distribution network elasticity level under each icing event in the set of icing events according to the icing characteristic fault rate model;
the setting module is used for setting typhoon weight and icing weight according to the occurrence frequency and the influence degree of typhoon events and icing events in the area where the power distribution network is located;
the first difference making module is used for making difference between the elasticity level of the power distribution network in normal operation and the elasticity level of the first power distribution network to obtain the performance loss of the first power distribution network;
the second difference making module is used for making difference between the elasticity level of the power distribution network in normal operation and the elasticity level of the second power distribution network to obtain the performance loss of the second power distribution network;
and the third evaluation module is used for respectively weighting and accumulating the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averaging the accumulated sum, and evaluating the elasticity level of the power distribution network considering the frequency of the extreme disasters.
Further, the formula for evaluating the elasticity level of the power distribution network considering the frequency of occurrence of the extreme disaster is as follows:
Figure BDA0003386738840000041
wherein, count1Set phi for typhoon events1Total number of events of (a); count2Set phi for icing events2Total number of events of (a); t is t1And t5Respectively setting the starting time and the ending time of the distribution network affected by the disaster; w is a1Weighting factors for the importance degree of the single typhoon disaster; w is a2Weighting factors for the importance degree of single icing disasters;
Figure BDA0003386738840000042
set phi for typhoon events1In each typhoon event s1A first distribution network performance loss;
Figure BDA0003386738840000043
set phi for icing events2Each of which is an icing event s2The second distribution network performance loss.
According to a third aspect of embodiments of the present application, there is provided an electronic apparatus, including:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method as described in the first aspect.
According to a fourth aspect of embodiments herein, there is provided a computer-readable storage medium having stored thereon computer instructions, characterized in that the instructions, when executed by a processor, implement the steps of the method according to the first aspect.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
according to the embodiments, a typhoon characteristic fault rate model is established, and the elasticity level of the first power distribution network under each typhoon event in a typhoon event set is evaluated according to the typhoon fault rate model; obtaining the elasticity level of the second power distribution network under each icing event in the same way; respectively obtaining the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the elastic level difference between the elastic level and the elastic level of the first power distribution network and the elastic level of the second power distribution network when the power distribution network normally operates, respectively weighting and accumulating the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averaging the accumulated sum, and evaluating the elastic level of the power distribution network considering the occurrence frequency of the extreme disasters; the method can sequence the extreme disasters according to the occurrence frequency and the disaster occurrence frequency aiming at different areas of the power distribution network, and endows different weight coefficients to single extreme events according to the disaster occurrence frequency to obtain the overall elasticity evaluation method of the power distribution network with the regional characteristics of the power distribution network.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flow chart illustrating a method for evaluating the elasticity of a power distribution grid considering the frequency of occurrence of an extreme disaster according to an exemplary embodiment.
FIG. 2 is a flowchart illustrating a process of establishing a typhoon signature failure rate model according to an exemplary embodiment.
FIG. 3 is a flowchart illustrating a process of establishing an icing characteristic failure rate model in accordance with an exemplary embodiment.
Fig. 4 is a diagram illustrating an elastic curve of a power distribution grid system in an extreme disaster according to an exemplary embodiment.
Fig. 5 is a block diagram illustrating an apparatus for evaluating elasticity of a power distribution grid considering an occurrence frequency of an extreme disaster according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination", depending on the context.
Fig. 1 is a flowchart illustrating a method for evaluating elasticity of a power distribution network considering an occurrence frequency of an extreme disaster according to an exemplary embodiment, where the method is applied to the power distribution network, as shown in fig. 1, and may include the following steps:
step S11: establishing a typhoon characteristic fault rate model and an icing characteristic fault rate model;
step S12: selecting a typhoon event set and an icing event set of the area where the power distribution network is located;
step S13: according to the typhoon characteristic fault rate model, evaluating the elasticity level of the first power distribution network under each typhoon event in the typhoon event set;
step S14: evaluating the elasticity level of a second power distribution network under each icing event in the icing event set according to the icing characteristic fault rate model;
step S15: setting typhoon weight and icing weight according to the occurrence frequency of typhoon events and icing events in the area of the power distribution network;
step S16: the elasticity level of the power distribution network in normal operation is different from the elasticity level of the first power distribution network, so that the performance loss of the first power distribution network is obtained;
step S17: the elasticity level of the power distribution network in normal operation is different from the elasticity level of the second power distribution network, so that the performance loss of the second power distribution network is obtained;
step S18: and respectively weighting and accumulating the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averaging the accumulated sum, and evaluating the elasticity level of the power distribution network considering the occurrence frequency of the extreme disasters.
According to the embodiments, a typhoon characteristic fault rate model is established, and the elasticity level of the first power distribution network under each typhoon event in a typhoon event set is evaluated according to the typhoon fault rate model; obtaining the elasticity level of the second power distribution network under each icing event in the same way; respectively obtaining the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the elastic level difference between the elastic level and the elastic level of the first power distribution network and the elastic level of the second power distribution network when the power distribution network normally operates, respectively weighting and accumulating the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averaging the accumulated sum, and evaluating the elastic level of the power distribution network considering the occurrence frequency of the extreme disasters; the method can sequence the extreme disasters according to the occurrence frequency and the disaster occurrence frequency aiming at different areas of the power distribution network, and endows different weight coefficients to single extreme events according to the disaster occurrence frequency to obtain the overall elasticity evaluation method of the power distribution network with the regional characteristics of the power distribution network.
In the specific implementation of step S11, a typhoon characteristic fault rate model and an icing characteristic fault rate model are established;
specifically, as shown in fig. 2, the process of establishing the typhoon characteristic failure rate model includes:
step S21: simulating the wind speed and wind direction of each point in the influence range of the typhoon wind ring;
specifically, the wind speed and the wind direction of each point in the range of influence of the typhoon wind ring are simulated, and the formula is as follows:
Figure BDA0003386738840000071
in the formula VwThe wind speed is in the direction of the anticlockwise tangential direction at the simulation range of the wind ring, VmaxThe wind speed at the strongest storm zone, RmaxIs the distance from the center of the typhoon wind ring to the maximum wind speed, and r is the distance from the range action point to the typhoon center.
Step S22: calculating the fault rate of the wire or the tower of the power distribution network under the wind load according to the wind speed and the wind direction;
specifically, the stress on the cross section of the wire is in direct proportion to the sum of the wind load and the gravity load of the wire, and the bending moment on the electric pole is the vector sum of the wind load of each part of the electric pole. The first two specific calculations can be derived from a mechanical wind load analysis reference. Fault rate lambda of a conductor or a pole tower of a power distribution network under wind loadnIs composed of
λn＝P{R-S＜0}
Wherein, P is probability; s is lead stress or pole bending moment caused by wind load; r is the element strength;
wherein the magnitude of the wind load depends on the wind speed and wind direction of the acting point, and the wind load N acting on the leadwThe calculation formula is as follows:
Figure BDA0003386738840000081
in the formula, VwThe wind speed is the size of the action point; d is the outer diameter of the wire at the action point; theta is the angle between the conducting wire and the wind direction. Mu.sZIs the wind pressure height variation coefficient; alpha is the uneven coefficient of the wind pressure of the electric wire; mu.sSCIs the form factor of the wire; lHThe horizontal span of the electric wire. Wind load N acting on towersThe calculation formula is as follows:
Figure BDA0003386738840000082
wherein, VwThe wind speed is the size of the action point; beta is the wind vibration coefficient; mu.sSIs the wind load figure coefficient; a is the projected area of the windward side of the tower structural member;
step S23: and establishing a typhoon characteristic fault rate model according to the fault rate of the lead or the tower.
Specifically, the power transmission and distribution line of the power distribution network is equivalent to a series model of a tower and a wire, and therefore the formula of the typhoon characteristic fault rate model is as follows:
Figure BDA0003386738840000083
in the formula (I), the compound is shown in the specification,
Figure BDA0003386738840000084
the fault rate of the line i under the condition of single typhoon; lambda [ alpha ]fp,k,iFailure rate of the kth pole of line i; lambda [ alpha ]f1,k,iThe fault rate of the k-th conductor of the line i is obtained; m is1Total number of poles of line i, m2The total number of wires of line i.
Specifically, as shown in fig. 3, the process of establishing the ice coating characteristic fault rate model includes:
step S31: calculating the distribution of the maximum icing thickness of the power distribution network along with the line position of the power distribution network;
specifically, the distribution of the maximum icing thickness of the power distribution network with the line location of the power distribution network may be represented by a normalized distribution function, as follows:
Figure BDA0003386738840000091
in the formula, gammaiIs a scale parameter; k is a radical ofiIs a shape parameter; etaiIs a location parameter; x is the position of the line.
Step S32: and establishing an icing characteristic fault rate model according to the distribution and the preset bearable ice thickness of the power distribution network.
Specifically, according to the metal deformation theory, when the bearing capacity of the tower or the conductor reaches the limit, the bearing capacity is reduced by times, and the fault rate of the line is inversely proportional to the bearing capacity. The physical analysis of the power distribution network line can obtain a line fault rate function as follows:
Figure BDA0003386738840000092
wherein y is the thickness of the ice coating; d is the predetermined loadable ice thickness.
In the specific implementation of step S12, selecting a typhoon event set and an icing event set of an area where the power distribution network is located;
specifically, a typhoon event set phi in a preset time period of the area where the power distribution network is located is randomly selected1And set of icing events phi2Wherein the total number of events of the typhoon event set is count1The total number of the icing event set is count2。
In a specific implementation of step S13, evaluating a first distribution network elasticity level under each typhoon event in the set of typhoon events according to the typhoon characteristic fault rate model;
specifically, as shown in fig. 4, a functional curve of the power distribution network for dynamically responding to the disaster is divided on a time scale according to the progress of the disaster, and the overall function curve can be divided into four parts:
1) early warning stage before disaster (t)0—t1). The intelligent performance of the power distribution network is mainly reflected in the stage, the influence range of the disaster is calculated by the power grid according to the weather station forecast information, and a disaster prediction model is constructed and risk assessment is carried out.
2) Resisting and absorbing stage (t)1—t2). The phase mainly reflects the robustness of the power distribution network, namely the survival capability of the power distribution network system in an extreme event.
3) Adaptation phase (t)2—t3). The power grid adapts to and responds to disasters at the stage, and the redundancy of the power distribution network is reflected. The actual load loss condition and the current equipment state information of the power distribution network need to be acquired at the stage.
4) Post-disaster recovery phase (t)3—t5): the rapidity and flexibility of the power distribution network are mainly embodied in the stage, namely the system utilizes flexible resources to rapidly recover key functions and reduce power failure loss. Considering the response speed of the flexible resource, the phase can be particularly subdivided into an emergency recovery phase (t)3—t4) And a gradual recovery phase (t)4—t5)2 small stages, as shown by the dashed lines in fig. 4.
Evaluating the elasticity level of the first power distribution network under each typhoon event in the typhoon event set by the curve drawing mode
Figure BDA0003386738840000101
In a specific implementation of step S14, evaluating a second distribution network elasticity level for each icing event in the set of icing events according to the icing characteristic fault rate model;
in particular toAdditionally, the implementation of synchronization step S13 evaluates the first grid elasticity level for each icing event of the set of icing events by plotting
Figure BDA0003386738840000102
In the specific implementation of step S15, setting a typhoon weight and an icing weight according to the occurrence frequency of a typhoon event and an icing event in the area where the power distribution network is located;
specifically, setting the typhoon weight w according to the occurrence frequency of typhoon events and icing events in the area of the power distribution network1And icing weight w2。
In one embodiment, the typhoon weight and the icing weight are set as shown in tables 1 and 2:
TABLE 1
Typhoon frequency (second/year) Typhoon weight w1
>10 1
7-10 0.8
3-6 0.6
1-2 0.3
TABLE 2
Icing frequency (year/time) Icing weight w2
>0.3 1
0.15-0.3 0.8
0.1-0.15 0.6
＜0.1 0.3
In the specific implementation of step S16, making a difference between the elasticity level of the power distribution network in normal operation and the elasticity level of the first power distribution network, so as to obtain a performance loss of the first power distribution network;
in particular, the elasticity level F of the distribution network in normal operation0(t) and the first distribution network elasticity level
Figure BDA0003386738840000111
Making a difference to obtain the performance loss of the first power distribution network
Figure BDA0003386738840000112
In the specific implementation of step S17, making a difference between the elasticity level of the power distribution network in normal operation and the elasticity level of the second power distribution network to obtain a performance loss of the second power distribution network;
in particular, the elasticity level F of the distribution network in normal operation0(t) and the first distribution network elasticity level
Figure BDA0003386738840000113
Making a difference to obtain the performance loss of the first power distribution network
Figure BDA0003386738840000114
In the specific implementation of step S18, the first distribution network performance loss and the second distribution network performance loss are weighted and accumulated according to the typhoon weight and the icing weight, respectively, the accumulated sums are averaged, and the distribution network elasticity level considering the frequency of the extreme disaster is evaluated.
Specifically, the formula for evaluating the elasticity level of the power distribution network considering the frequency of occurrence of the extreme disaster is as follows:
Figure BDA0003386738840000115
wherein, count1Set phi for typhoon events1Total number of events of (a); count2Set phi for icing events2Total number of events of (a); t is t1And t5Respectively setting the starting time and the ending time of the distribution network affected by the disaster; w is a1Weighting factors for the importance degree of the single typhoon disaster; w is a2Weighting factors for the importance degree of single icing disasters;
Figure BDA0003386738840000116
set phi for typhoon events1In each typhoon event s1A first distribution network performance loss;
Figure BDA0003386738840000117
set phi for icing events2Each of which is an icing event s2(ii) a second distribution network performance loss;
Figure BDA0003386738840000118
set phi for typhoon events1In each typhoon event s1A lower first distribution network elasticity level;
Figure BDA0003386738840000119
set phi for icing events2In each icing event s2The elasticity level of the lower second power distribution network; f0And (t) is the elasticity level of the power distribution network in normal operation.
In an embodiment, the method may further comprise:
and averaging the accumulation of the performance loss of the first distribution network and the performance loss of the second distribution network, and evaluating the average elasticity level of the distribution network in the event set time.
Specifically, the formula for evaluating the average elasticity level of the distribution network in the event set time is as follows:
Figure BDA0003386738840000121
wherein, count1Set phi for typhoon events1Total number of events of (a); count2Set phi for icing events2Total number of events of (a);
Figure BDA0003386738840000122
a first distribution network performance loss;
Figure BDA0003386738840000123
a second distribution network performance loss;
Figure BDA0003386738840000124
set phi for typhoon events1In each typhoon event s1A lower first distribution network elasticity level;
Figure BDA0003386738840000125
set phi for icing events2Each of which is an icing event s2The elasticity level of the lower second power distribution network; f0And (t) is the elasticity level of the power distribution network in normal operation.
Corresponding to the foregoing embodiment of the method for evaluating elasticity of the power distribution network in consideration of the frequency of occurrence of the extreme disaster, the present application also provides an embodiment of a device for evaluating elasticity of the power distribution network in consideration of the frequency of occurrence of the extreme disaster.
Fig. 5 is a block diagram illustrating an apparatus for evaluating elasticity of a power distribution grid considering an occurrence frequency of an extreme disaster according to an exemplary embodiment. Referring to fig. 5, the apparatus may include:
the modeling module 21 is used for establishing a typhoon characteristic fault rate model and an icing characteristic fault rate model;
the selection module 22 is used for selecting a typhoon event set and an icing event set of the area where the power distribution network is located;
a first evaluation module 23, configured to evaluate, according to the typhoon characteristic fault rate model, a first distribution network elasticity level of each typhoon event in the typhoon event set;
a second evaluation module 24, configured to evaluate a second distribution network elasticity level under each icing event in the set of icing events according to the icing characteristic fault rate model;
the setting module 25 is used for setting typhoon weight and icing weight according to the occurrence frequency and the influence degree of typhoon events and icing events in the area where the power distribution network is located;
the first difference making module 26 is used for making difference between the elasticity level of the power distribution network in normal operation and the elasticity level of the first power distribution network to obtain the performance loss of the first power distribution network;
the second difference making module 27 is used for making difference between the elasticity level of the power distribution network in normal operation and the elasticity level of the second power distribution network to obtain the performance loss of the second power distribution network;
and a third evaluation module 28, which respectively weights and accumulates the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averages the accumulated sum, and evaluates the elasticity level of the power distribution network considering the frequency of the extreme disasters.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
Correspondingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a method for power distribution network resiliency assessment that takes into account the frequency of occurrence of extreme disasters, as described above.
Accordingly, the present application also provides a computer readable storage medium, on which computer instructions are stored, wherein the instructions, when executed by a processor, implement the method for assessing elasticity of a power distribution grid considering the frequency of occurrence of extreme disasters as described above.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings and described above, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. A power distribution network elasticity evaluation method considering the occurrence frequency of extreme disasters is characterized by comprising the following steps:
establishing a typhoon characteristic fault rate model and an icing characteristic fault rate model;
selecting a typhoon event set and an icing event set of the area where the power distribution network is located;
according to the typhoon characteristic fault rate model, evaluating the elasticity level of the first power distribution network under each typhoon event in the typhoon event set;
according to the icing characteristic fault rate model, evaluating the elasticity level of a second power distribution network under each icing event in the icing event set;
setting typhoon weight and icing weight according to the occurrence frequency of typhoon events and icing events in the area of the power distribution network;
the elasticity level of the power distribution network in normal operation is different from the elasticity level of the first power distribution network, so that the performance loss of the first power distribution network is obtained;
the elasticity level of the power distribution network in normal operation is different from the elasticity level of the second power distribution network, so that the performance loss of the second power distribution network is obtained;
and respectively weighting and accumulating the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averaging the accumulated sum, and evaluating the elasticity level of the power distribution network considering the occurrence frequency of the extreme disasters.
2. The method of claim 1, wherein the process of establishing a typhoon signature failure rate model comprises:
simulating the wind speed and wind direction of each point in the influence range of the typhoon wind ring;
calculating the fault rate of the conducting wire or the tower of the power distribution network under the wind load according to the wind speed and the wind direction;
and establishing a typhoon characteristic fault rate model according to the fault rate of the lead or the tower.
3. The method of claim 1, wherein the process of modeling the icing characteristic failure rate comprises:
calculating the distribution of the maximum icing thickness of the power distribution network along with the line position of the power distribution network;
and establishing an icing characteristic fault rate model according to the distribution and the preset bearable ice thickness of the power distribution network.
4. The method of claim 1, wherein the formula for evaluating the elasticity level of the power distribution network considering the frequency of occurrence of extreme disasters is as follows:
Figure FDA0003386738830000021
wherein, count1Set phi for typhoon events1Total number of events of (a); count2Set phi for icing events2Total number of events of (a); t is t1And t5Respectively setting the starting time and the ending time of the distribution network affected by the disaster; w is a1Weighting factors for the importance degree of the single typhoon disaster; w is a2Weighting factors for the importance degree of single icing disasters;
Figure FDA0003386738830000022
set phi for typhoon events1In each typhoon event s1A first distribution network performance loss;
Figure FDA0003386738830000023
set phi for icing events2Each of which is an icing event s2The second distribution network performance loss.
5. The method of claim 1, further comprising:
and averaging the accumulation of the performance loss of the first distribution network and the performance loss of the second distribution network, and evaluating the average elasticity level of the distribution network in the event set time.
6. The method of claim 5, wherein the formula for evaluating the average elasticity level of the distribution network over the event set time is as follows:
Figure FDA0003386738830000024
wherein, count1Set phi for typhoon events1Total number of events of (a); count2Set phi for icing events2Total number of events of (a); t is t1And t5Respectively setting the starting time and the ending time of the power distribution network affected by the extreme disasters;
Figure FDA0003386738830000025
set phi for typhoon events1In each typhoon event s1A first distribution network performance loss;
Figure FDA0003386738830000026
set phi for icing events2Each of which is an icing event s2The second distribution network performance loss.
7. An elasticity evaluation device for a power distribution network considering the frequency of occurrence of extreme disasters, comprising:
the modeling module is used for establishing a typhoon characteristic fault rate model and an icing characteristic fault rate model;
the selection module is used for selecting a typhoon event set and an icing event set of the area where the power distribution network is located;
the first evaluation module is used for evaluating the elasticity level of the first power distribution network under each typhoon event in the typhoon event set according to the typhoon characteristic fault rate model;
the second evaluation module is used for evaluating the elasticity level of the second power distribution network under each icing event in the icing event set according to the icing characteristic fault rate model;
the setting module is used for setting typhoon weight and icing weight according to the occurrence frequency and the influence degree of typhoon events and icing events in the area where the power distribution network is located;
the first difference making module is used for making difference between the elasticity level of the power distribution network in normal operation and the elasticity level of the first power distribution network to obtain the performance loss of the first power distribution network;
the second difference making module is used for making difference between the elasticity level of the power distribution network in normal operation and the elasticity level of the second power distribution network to obtain the performance loss of the second power distribution network;
and the third evaluation module is used for respectively weighting and accumulating the performance loss of the first power distribution network and the performance loss of the second power distribution network according to the typhoon weight and the icing weight, averaging the accumulated sum, and evaluating the elasticity level of the power distribution network considering the occurrence frequency of the extreme disasters.
8. The apparatus of claim 7, wherein the formula for evaluating the elasticity level of the power distribution network considering the frequency of occurrence of the extreme disaster is as follows:
Figure FDA0003386738830000031
wherein, count1Set phi for typhoon events1Total number of events of (a); count2Set phi for icing events2Total number of events of (a); t is t1And t5Respectively setting the starting time and the ending time of the distribution network affected by the disaster; w is a1Weighting factors for the importance degree of the single typhoon disaster; w is a2Weighting factors for the importance degree of single icing disasters;
Figure FDA0003386738830000032
set phi for typhoon events1In each typhoon event s1A first distribution network performance loss;
Figure FDA0003386738830000033
for icing event setφ2Each of which is an icing event s2The second distribution network performance loss.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
10. A computer-readable storage medium having stored thereon computer instructions, which, when executed by a processor, carry out the steps of the method according to any one of claims 1-6.
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