A collaborative optimization configuration method and system based on dynamic evolution characteristics of a power distribution network
By constructing a tensile strength time series function based on material aging, environmental erosion, and mechanical stress factors, and combining it with the total load and failure rate function of freezing rain disaster, a multi-index comprehensive cloud model is established. This solves the problem of poor robustness in distribution network planning and enables refined planning of distribution networks under new energy vehicles and extreme disasters.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA AGRI UNIV
- Filing Date
- 2026-02-04
- Publication Date
- 2026-05-22
AI Technical Summary
Existing power distribution network planning schemes are not robust enough to cope with the rapid development of new energy vehicles and changes in load demand under extreme disasters, making it difficult to achieve refined planning.
A time-series function of tensile strength based on material aging, environmental erosion, and mechanical stress factors is established. Combined with the total load and failure rate function of freezing rain disaster, a multi-index integrated cloud model is constructed to conduct dynamic evaluation and stage division of distribution network planning.
It has enabled the scientific and refined planning of the distribution network, improved the resilience of the distribution network under normal and extreme disasters, and adapted to changes in load demand.
Smart Images
Figure CN122073385A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of distribution network planning, and in particular relates to a collaborative optimization configuration method and system based on the dynamic evolution characteristics of distribution networks. Background Technology
[0002] In recent years, due to the rapid development of new energy vehicles and the frequent occurrence of low-probability, high-loss extreme events, distribution networks are facing multiple challenges, including increased load demand under normal conditions and improved resilience under extreme disasters such as freezing rain. Existing "one-time planning" schemes based on forecasts from the planning year have poor robustness and are ill-suited to the dynamic evolution of distribution networks brought about by these challenges, failing to meet the requirements for refined distribution network planning. Therefore, comprehensively considering both the load demand from new energy vehicles under normal conditions and the changes in distribution network resilience under extreme disasters such as freezing rain is of great significance for the scientific allocation of distribution network planning. Summary of the Invention
[0003] To address the aforementioned technical problems, this invention proposes a collaborative optimization configuration method and system based on the dynamic evolution characteristics of distribution networks.
[0004] The first aspect of this invention proposes a collaborative optimization configuration method based on the dynamic evolution characteristics of a distribution network, the method comprising: Based on material aging factors, environmental corrosion factors, and mechanical stress factors, a time series function of tensile strength of power distribution network lines is established. Based on the impact of freezing rain disaster on the load of the distribution network lines, a time series function of the total load per unit length of the distribution network lines is established; Based on the tensile strength time series function and the total load time series function, a line failure rate function per unit length of the distribution network lines under the influence of freezing rain disaster is established. Establish indicators related to the load demand of the distribution network and indicators related to the line failure rate growth rate function to form a dynamic evaluation index system for assessing distribution network planning; The indicators in the dynamic evaluation indicator system are processed to establish a multi-indicator comprehensive cloud model; By combining the multi-index integrated cloud model to calculate the similarity of indicators in the dynamic evaluation index system for adjacent years, the different stages of the distribution network planning are divided.
[0005] A second aspect of the present invention proposes a collaborative optimization configuration system based on the dynamic evolution characteristics of a distribution network. The system includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to implement the method described in the first aspect of the present invention.
[0006] In summary, the present invention proposes a collaborative optimization configuration method and system based on the dynamic evolution characteristics of distribution networks. By comprehensively considering the load demand of distribution networks under the periodic planning of distribution networks, and taking into account the uncertainties and dynamic evolution characteristics of factors such as the development of new energy sources and the degree of disaster impact, it provides a scientific method and system for distribution network planning, thereby achieving the scientific and refined nature of distribution network planning and design. Attached Figure Description
[0007] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0008] Figure 1 This is a flowchart illustrating a collaborative optimization configuration method based on the dynamic evolution characteristics of a distribution network, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a collaborative optimization configuration system based on the dynamic evolution characteristics of a power distribution network, according to an embodiment of the present invention. Detailed Implementation
[0009] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0010] Appendix Figure 1 The first aspect of this invention proposes a collaborative optimization configuration method based on the dynamic evolution characteristics of a distribution network, comprising the following steps: Based on material aging factors, environmental corrosion factors, and mechanical stress factors, a time series function of tensile strength of power distribution network lines is established. Based on the impact of freezing rain disaster on the load of the distribution network lines, a time series function of the total load per unit length of the distribution network lines is established; Based on the tensile strength time series function and the total load time series function, a line failure rate function per unit length of the distribution network lines under the influence of freezing rain disaster is established. Establish indicators related to the load demand of the distribution network and indicators related to the line failure rate growth rate function to form a dynamic evaluation index system for assessing distribution network planning; The indicators in the dynamic evaluation indicator system are processed to establish a multi-indicator comprehensive cloud model; In one embodiment, the multi-index integrated cloud model includes the expectation of the indicators in the dynamic evaluation index system, the entropy of the indicators in the dynamic evaluation index system, and the hyperentropy of the indicators in the dynamic evaluation index system. By combining the multi-index integrated cloud model to calculate the similarity of indicators in the dynamic evaluation index system for adjacent years, the different stages of the distribution network planning are divided.
[0011] In one embodiment, establishing the tensile strength time series function of the distribution network lines based on material aging factors, environmental erosion factors, and mechanical stress factors includes: establishing the material tensile strength time series function, the environmental tensile strength time series function, and the mechanical stress tensile strength time series function of the distribution network lines based on the material aging factors, the environmental erosion factors, and the mechanical stress factors, respectively. The time series function of the tensile strength of the material is: ; in, For the first The remaining tensile strength of the distribution network lines based on material aging factors in the year. The tensile strength of the distribution network lines at the initial commissioning stage. The material attenuation coefficient, Let be the ordinal number of the year in one cycle of the power distribution network planning. The aging index, For the first The aging index of the year; The environmental tensile strength time series function includes a corrosion tensile strength time series function and a temperature cycling tensile strength time series function; the corrosion tensile strength time series function is: ; in, The corrosion coefficient is... For environmental erosion intensity, This refers to the cross-sectional area of the conductors in the power distribution network. The corrosion time index, For the first Corrosion time index per year, For the first The residual tensile strength of the distribution network lines based on corrosion factors in the year; The time series function of the temperature cycling tensile strength is: ; in, This is the thermal fatigue coefficient; This represents the daily average temperature variation. For the front The sum of annual temperature cycle coefficients For the first The residual tensile strength of the distribution network lines based on temperature cycling factors in the year; The mechanical stress tensile strength time series function includes a static load tensile strength time series function and a dynamic stress tensile strength time series function; wherein, the static load tensile strength time series function is: , ; in, The mass per unit length of the lines in the aforementioned distribution network. It is the acceleration due to gravity. The span of the lines in the aforementioned distribution network. The sag of the lines in the aforementioned distribution network under their own weight. The sag of the power distribution network lines under icing conditions. The density of ice, The diameter of the lines in the power distribution network is given. For the first The icing thickness of the distribution network lines in that year. For the first The mass per unit length of the distribution network lines after annual icing. The distribution network lines are in front. The equivalent icing duration per year For the first The remaining tensile strength of the distribution network lines based on static load factors in the year. The weight of the distribution network lines themselves. For the first The static load caused by icing on the distribution network lines mentioned in the previous year. By making the first The number of times the distribution network lines were iced in the year and the number of times they were iced in the year. The value is obtained by multiplying the icing duration of the distribution network lines in a year and then averaging the results.
[0012] The dynamic stress tensile strength time series function is: , ; in, For the first The frequency of line galloping in the aforementioned power distribution network caused by annual winds. For the first The duration of wind that causes the lines of the aforementioned distribution network to gallop annually. No. The average stress amplitude of the power distribution network lines caused by annual wind. For the first The cumulative damage to the distribution network lines after annual stress. air density, The drag coefficient, For the first Average annual wind speed, All of these are material coefficients for the lines of the aforementioned distribution network. For the first The icing thickness of the distribution network lines in that year. For the first The remaining tensile strength of the distribution network lines based on dynamic stress factors in the year.
[0013] In one embodiment, establishing the tensile strength time-series function of the distribution network lines based on material aging factors, environmental corrosion factors, and mechanical stress factors further includes: integrating the material tensile strength time-series function, the environmental tensile strength time-series function, and the mechanical stress tensile strength time-series function to obtain the tensile strength time-series function of the distribution network lines through the following method: ; in, For the first The remaining tensile strength of the distribution network lines in the year.
[0014] In one embodiment, the total load time-series function per unit length of the distribution network lines is: , ; in, It is the first y The static load per unit length of the distribution network lines in the year. It is the first y The dynamic load per unit length of the distribution network lines in the year. It is the first y The total load borne per unit length of the distribution network lines in the year.
[0015] In one embodiment, the line failure rate function per unit length of the distribution network lines is: , ; in, The design load of the lines in the aforementioned power distribution network. The predetermined correction factor, For the first The ultimate load of the distribution network lines in the year mentioned, For the first The failure rate per unit length of the distribution network lines in the year.
[0016] In one embodiment, the indicators related to the load demand of the distribution network include: The net load growth rate index of the distribution network in the y-th year Its expression is: ; in, For the first Year t The predicted load demand value of the distribution network at time t. For the first Year The predicted photovoltaic output of the aforementioned distribution network at that time. For the first Year The predicted load demand value of the distribution network at time t. For the first Year The predicted photovoltaic output of the power distribution network at the specified time.
[0017] The load / distributed photovoltaic time-series correlation index of the distribution network Its expression is ; in, For the first Year and the The difference between the load demand of the distribution network and the distributed photovoltaic power generation in the year is in the length of N The ratio of the range to the standard deviation over a subinterval. This is a preset constant.
[0018] The first distribution network Annual net load peak-to-valley difference index Its expression is: ; And, the distribution network of the first y The correlation index of source and load time series in a given year Its expression is: ; in, For the first y The average load forecast of the distribution network in that year, For the first y The average value of the photovoltaic output of the distribution network load forecast for the year.
[0019] In one embodiment, the first y The annual line failure rate growth rate indicator for: ; in, For the first The failure rate per unit length of the distribution network lines in the year.
[0020] In one embodiment, processing the indicators in the dynamic evaluation indicator system to establish a multi-indicator comprehensive cloud model includes: The indicators in the dynamic evaluation index system are processed using range normalization. If the multi-indicator comprehensive cloud model is established using positive indicators, then the positive indicators are expressed as follows: ; If the multi-indicator integrated cloud model is established using inverse indicators, then the inverse indicator is expressed as follows: ; in, For the first The normalized value of the z-th indicator in the year. and These are the maximum and minimum values of the z-th indicator in one cycle of the power distribution network plan, respectively, where z∈{1,2,3,4,5}; The weights of the indicators in the dynamic evaluation index system, after being normalized by the range method, are determined using the entropy weight method, and are expressed as follows: , ; in, Let z be the weight of the z-th indicator. Let z be the entropy value of the z-th index. The time length of one cycle in the power distribution network planning. No. y The weight of the z-th indicator in a year, z∈{1,2,3,4,5}, where Z takes the value of 5; A multi-indicator integrated cloud model is constructed, which is expressed as follows: ; in, , ,and The indicators in the dynamic evaluation index system are respectively in the 1st... yThe expected value for the year, and the indicators in the dynamic evaluation index system in the [year] [section] y The entropy of the year, and the indicators in the dynamic evaluation index system in the first year y The annual hyperentropy, Z, takes a value of 5.
[0021] In one embodiment, the calculation of the similarity between adjacent years based on indicators in the dynamic evaluation index system is performed in the following manner: , ; In the formula: For the first y Year and the y Similarity of indicators over -1 year For the first y Year and the y The relative entropy of the indicator in -1 year For the first y Year and the y The mean difference of the indicator over -1 year These are the weighting coefficients. As a regulating factor, and They represent the same meaning; The division of the different stages of the distribution network planning includes: when the first y Year and the y Similarity of indicators over -1 year If the value is less than a predetermined threshold, then year y-1 is marked as a stage boundary point in a cycle of the power distribution network plan.
[0022] Appendix Figure 2 The second aspect of this invention provides a collaborative optimization configuration system based on the dynamic evolution characteristics of a distribution network. The system includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to implement the following method steps: Based on material aging factors, environmental corrosion factors, and mechanical stress factors, a time series function of tensile strength of power distribution network lines is established. Based on the impact of freezing rain disaster on the load of the distribution network lines, a time series function of the total load per unit length of the distribution network lines is established; Based on the tensile strength time series function and the total load time series function, a line failure rate function per unit length of the distribution network lines under the influence of freezing rain disaster is established. Establish indicators related to the load demand of the distribution network and indicators related to the line failure rate growth rate function to form a dynamic evaluation index system for assessing distribution network planning; The indicators in the dynamic evaluation indicator system are processed to establish a multi-indicator comprehensive cloud model; In one embodiment, the multi-index integrated cloud model includes the expectation of the indicators in the dynamic evaluation index system, the entropy of the indicators in the dynamic evaluation index system, and the hyperentropy of the indicators in the dynamic evaluation index system. By combining the multi-index integrated cloud model to calculate the similarity of indicators in the dynamic evaluation index system for adjacent years, the different stages of the distribution network planning are divided.
[0023] In one embodiment, establishing the tensile strength time series function of the distribution network lines based on material aging factors, environmental erosion factors, and mechanical stress factors includes: establishing the material tensile strength time series function, the environmental tensile strength time series function, and the mechanical stress tensile strength time series function of the distribution network lines based on the material aging factors, the environmental erosion factors, and the mechanical stress factors, respectively. The time series function of the tensile strength of the material is: ; in, For the first The remaining tensile strength of the distribution network lines based on material aging factors in the year. The tensile strength of the distribution network lines at the initial commissioning stage. The material attenuation coefficient, Let be the ordinal number of the year in one cycle of the power distribution network planning. The aging index, For the first The aging index of the year; The environmental tensile strength time series function includes a corrosion tensile strength time series function and a temperature cycling tensile strength time series function; the corrosion tensile strength time series function is: ; in, The corrosion coefficient is... For environmental erosion intensity, This refers to the cross-sectional area of the conductors in the power distribution network. The corrosion time index, For the first Corrosion time index per year, For the first y The residual tensile strength of the distribution network lines based on corrosion factors in the year; The time series function of the temperature cycling tensile strength is: ; in, This is the thermal fatigue coefficient; This represents the daily average temperature variation. For the front y The sum of annual temperature cycle coefficients For the first The residual tensile strength of the distribution network lines based on temperature cycling factors in the year; The mechanical stress tensile strength time series function includes a static load tensile strength time series function and a dynamic stress tensile strength time series function; wherein, the static load tensile strength time series function is: , ; in, The mass per unit length of the lines in the aforementioned distribution network. It is the acceleration due to gravity. The span of the lines in the aforementioned distribution network. The sag of the lines in the aforementioned distribution network under their own weight. The sag of the power distribution network lines under icing conditions. The density of ice, The diameter of the lines in the power distribution network is given. For the first y The icing thickness of the distribution network lines in that year. For the first y The mass per unit length of the distribution network lines after annual icing. The distribution network lines are in front. y The equivalent icing duration per year For the first y The remaining tensile strength of the distribution network lines based on static load factors in the year. The weight of the distribution network lines themselves. For the first y The static load caused by icing on the distribution network lines mentioned in the previous year. By making the first y The number of times the distribution network lines were iced in the year and the number of times they were iced in the year. y The value is obtained by multiplying the icing duration of the distribution network lines in a year and then averaging the results.
[0024] The dynamic stress tensile strength time series function is: , ; in, Let y be the frequency of line galloping in the aforementioned power distribution network caused by wind in year y. For the first y The duration of wind that causes the lines of the aforementioned distribution network to gallop annually. No. y The average stress amplitude of the power distribution network lines caused by annual wind. For the first y-1 The cumulative damage to the distribution network lines after annual stress. air density, The drag coefficient, For the first y The annual average wind speed, m, and C are both material coefficients of the power distribution network lines. For the first y The icing thickness of the distribution network lines in that year. For the first y The remaining tensile strength of the distribution network lines based on dynamic stress factors in the year.
[0025] In one embodiment, establishing the tensile strength time-series function of the distribution network lines based on material aging factors, environmental corrosion factors, and mechanical stress factors further includes: integrating the material tensile strength time-series function, the environmental tensile strength time-series function, and the mechanical stress tensile strength time-series function to obtain the tensile strength time-series function of the distribution network lines through the following method: ; in, For the first y The remaining tensile strength of the distribution network lines in the year.
[0026] In one embodiment, the total load time-series function per unit length of the distribution network lines is: , ; in, It is the first y The static load per unit length of the distribution network lines in the year. It is the first y The dynamic load per unit length of the distribution network lines in the year. It is the first y The total load borne per unit length of the distribution network lines in the year.
[0027] In one embodiment, the line failure rate function per unit length of the distribution network lines is: , ; in, The design load of the lines in the aforementioned power distribution network. The predetermined correction factor, Let y be the ultimate load of the distribution network lines described in year y. Let be the failure rate per unit length of the distribution network lines in year y.
[0028] In one embodiment, the indicators related to the load demand of the distribution network include: The net load growth rate index of the distribution network in the y-th year Its expression is: ; in, For the first y Year t The predicted load demand value of the distribution network at time t. For the first y Year t The predicted photovoltaic output of the aforementioned distribution network at that time. For the first y -1 year t The predicted load demand value of the distribution network at time t. For the first y -1 year t The predicted photovoltaic output of the power distribution network at the specified time.
[0029] The load / distributed photovoltaic time-series correlation index of the distribution network Its expression is ; in, For the first y Year and the y The load demand of the distribution network and the difference between distributed photovoltaic power generation in the year -1 are in the length of N The ratio of the range to the standard deviation over a subinterval. This is a preset constant.
[0030] The first distribution network y Annual net load peak-to-valley difference index Its expression is: ; And, the distribution network of the first y The correlation index of source and load time series in a given year Its expression is: ; in, For the first yThe average load forecast of the distribution network in that year, For the first y The average value of the photovoltaic output of the distribution network load forecast for the year.
[0031] In one embodiment, the first y The annual line failure rate growth rate indicator for: ; in, For the first y -1 year Failure rate per unit length of the distribution network lines.
[0032] In one embodiment, processing the indicators in the dynamic evaluation indicator system to establish a multi-indicator comprehensive cloud model includes: The indicators in the dynamic evaluation index system are processed using range normalization. If the multi-indicator comprehensive cloud model is established using positive indicators, then the positive indicators are expressed as follows: ; If the multi-indicator integrated cloud model is established using inverse indicators, then the inverse indicator is expressed as follows: ; in, Let z be the normalized value of the z-th indicator in year y. and These are the maximum and minimum values of the z-th indicator in one cycle of the power distribution network plan, respectively, where z∈{1,2,3,4,5}; The weights of the indicators in the dynamic evaluation index system, after being normalized by the range method, are determined using the entropy weight method, and are expressed as follows: , ; in, Let z be the weight of the z-th indicator. Let z be the entropy value of the z-th index. The time length of one cycle in the power distribution network planning. No. y The weight of the z-th indicator in a year, z∈{1,2,3,4,5}, where Z takes the value of 5; A multi-indicator integrated cloud model is constructed, which is expressed as follows: ; in, , ,and The indicators in the dynamic evaluation index system are respectively in the 1st... yThe expected value for the year, and the indicators in the dynamic evaluation index system in the [year] [section] y The entropy of the year, and the indicators in the dynamic evaluation index system in the first year y The annual hyperentropy, Z, takes a value of 5.
[0033] In one embodiment, the calculation of the similarity between adjacent years based on indicators in the dynamic evaluation index system is performed in the following manner: , ; In the formula: For the first y Year and the y Similarity of indicators over -1 year For the first y Year and the y The relative entropy of the indicator in -1 year For the first y Year and the y The mean difference of the indicator over -1 year These are the weighting coefficients. As a regulating factor, and They represent the same meaning; The division of the different stages of the distribution network planning includes: when the first y Year and the y Similarity of indicators over -1 year If the value is less than a predetermined threshold, then year y-1 is marked as a stage boundary point in a cycle of the power distribution network plan.
[0034] In summary, the present invention proposes a collaborative optimization configuration method and system based on the dynamic evolution characteristics of distribution networks. By comprehensively considering the load demand of distribution networks under the periodic planning of distribution networks, and taking into account the uncertainties and dynamic evolution characteristics of factors such as the development of new energy sources and the degree of disaster impact, it provides a scientific method and system for distribution network planning, thereby achieving the scientific and refined nature of distribution network planning and design.
[0035] It should be noted that, in the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units or steps is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or steps may be combined or integrated into another system or step, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.
[0036] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0037] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.
[0038] It should be noted that the embodiments provided in this invention are all illustrative, and different embodiments can be arbitrarily and reasonably combined. For the sake of brevity, not all possible combinations of the various technical features in the above embodiments are described; however, as long as such combinations do not contradict each other, they should all be considered to fall within the scope of this specification. Furthermore, it should be understood that the systems and methods disclosed in the embodiments provided in this invention can be implemented in other ways or with modifications. Any substitutions made in hardware or software, or any modifications made without departing from the concept of this invention, are within the protection scope of this invention.
Claims
1. A collaborative optimization configuration method based on the dynamic evolution characteristics of a distribution network, characterized in that, Includes the following steps: Based on material aging factors, environmental corrosion factors, and mechanical stress factors, a time series function of tensile strength of power distribution network lines is established. Based on the impact of freezing rain disaster on the load of the distribution network lines, a time series function of the total load per unit length of the distribution network lines is established; Based on the tensile strength time series function and the total load time series function, a line failure rate function per unit length of the distribution network lines under the influence of freezing rain disaster is established. Establish indicators related to the load demand of the distribution network and indicators related to the line failure rate growth rate function to form a dynamic evaluation index system for assessing distribution network planning; The indicators in the dynamic evaluation indicator system are processed to establish a multi-indicator comprehensive cloud model; By combining the multi-index integrated cloud model to calculate the similarity of indicators in the dynamic evaluation index system for adjacent years, the different stages of the distribution network planning are divided.
2. The collaborative optimization configuration method based on the dynamic evolution characteristics of a distribution network according to claim 1, characterized in that, The method of establishing the tensile strength time series function of the distribution network line based on material aging factors, environmental erosion factors, and mechanical stress factors includes: establishing the material tensile strength time series function, the environmental tensile strength time series function, and the mechanical stress tensile strength time series function of the distribution network line based on the material aging factors, the environmental erosion factors, and the mechanical stress factors, respectively. The time series function of the tensile strength of the material is: ； in, For the first The remaining tensile strength of the distribution network lines based on material aging factors in the year. The tensile strength of the distribution network lines at the initial commissioning stage. The material attenuation coefficient, Let be the ordinal number of the year in one cycle of the power distribution network planning. The aging index, For the first The aging index of the year; The environmental tensile strength time series function includes a corrosion tensile strength time series function and a temperature cycling tensile strength time series function; the corrosion tensile strength time series function is: ； in, The corrosion coefficient is... For environmental erosion intensity, This refers to the cross-sectional area of the conductors in the power distribution network. The corrosion time index, For the first Corrosion time index per year, For the first The residual tensile strength of the distribution network lines based on corrosion factors in the year; The time series function of the temperature cycling tensile strength is: ； in, This is the thermal fatigue coefficient; This represents the daily average temperature variation. For the front The sum of annual temperature cycle coefficients For the first The residual tensile strength of the distribution network lines based on temperature cycling factors in the year; The mechanical stress tensile strength time series function includes a static load tensile strength time series function and a dynamic stress tensile strength time series function; wherein, the static load tensile strength time series function is: ， ； in, The mass per unit length of the lines in the aforementioned distribution network. It is the acceleration due to gravity. The span of the lines in the aforementioned distribution network. The sag of the lines in the aforementioned distribution network under their own weight. The sag of the power distribution network lines under icing conditions. The density of ice, The diameter of the lines in the power distribution network is given. For the first The icing thickness of the distribution network lines in that year. For the first The mass per unit length of the distribution network lines after annual icing. The distribution network lines are in front. The equivalent icing duration per year For the first The remaining tensile strength of the distribution network lines based on static load factors in the year. The weight of the distribution network lines themselves. For the first The static load caused by icing on the lines of the distribution network mentioned in the year; The dynamic stress tensile strength time series function is: ， ； in, For the first The frequency of line galloping in the aforementioned power distribution network caused by annual winds For the first The duration of winds that cause the lines of the aforementioned distribution network to gallop annually. No. The average stress amplitude of the power distribution network lines caused by annual wind. For the first The cumulative damage to the distribution network lines after annual stress. air density, The drag coefficient, For the first Average annual wind speed, All of these are material coefficients for the lines of the aforementioned distribution network. For the first The remaining tensile strength of the distribution network lines based on dynamic stress factors in the year.
3. The collaborative optimization configuration method based on the dynamic evolution characteristics of the distribution network according to claim 2, characterized in that, The method for establishing the tensile strength time-series function of the distribution network lines based on material aging factors, environmental corrosion factors, and mechanical stress factors further includes: integrating the material tensile strength time-series function, the environmental tensile strength time-series function, and the mechanical stress tensile strength time-series function to obtain the tensile strength time-series function of the distribution network lines through the following method: ； in, For the first The remaining tensile strength of the distribution network lines in the year.
4. The collaborative optimization configuration method based on the dynamic evolution characteristics of the distribution network according to claim 3, characterized in that, The time-series function of the total load per unit length of the distribution network lines is: ， ； in, It is the first The static load per unit length of the distribution network lines in the year. It is the first The dynamic load per unit length of the distribution network lines in the year. It is the first The total load borne per unit length of the distribution network lines in the year.
5. The collaborative optimization configuration method based on the dynamic evolution characteristics of the distribution network according to claim 4, characterized in that, The line failure rate function per unit length of the distribution network lines is: ， ； in, The design load of the lines in the aforementioned power distribution network is... The predetermined correction factor. For the first The ultimate load of the distribution network lines in the year mentioned, For the first The failure rate per unit length of the distribution network lines in the year.
6. The collaborative optimization configuration method based on the dynamic evolution characteristics of a distribution network according to claim 5, characterized in that, Indicators related to the load demand of the aforementioned distribution network include: The net load of the distribution network is the first Annual growth rate indicators Its expression is: ； in, For the first Year The predicted load demand value of the distribution network at time t. For the first Year The predicted photovoltaic output of the aforementioned distribution network at that time. For the first Year The predicted load demand value of the distribution network at time t. For the first Year The predicted photovoltaic output of the distribution network at the given time; The load / distributed photovoltaic time-series correlation index of the distribution network Its expression is ； in, For the first Year and the The difference between the load demand of the distribution network and the distributed photovoltaic power generation in the year is in the length of The ratio of the range to the standard deviation over a subinterval. This is a preset constant; The first distribution network Annual net load peak-to-valley difference index Its expression is: ； And, the distribution network of the first The correlation index of source and load time series in a given year Its expression is: ； in, For the first The average load forecast of the distribution network in that year, For the first The average value of the photovoltaic output of the distribution network load forecast for the year.
7. The collaborative optimization configuration method based on the dynamic evolution characteristics of a distribution network according to claim 6, characterized in that, No. The annual line failure rate growth rate indicator for: ； in, For the first The failure rate per unit length of the distribution network lines in the year.
8. The collaborative optimization configuration method based on the dynamic evolution characteristics of a distribution network according to claim 7, characterized in that, The process of processing the indicators in the dynamic evaluation indicator system to establish a multi-indicator comprehensive cloud model includes: The indicators in the dynamic evaluation index system are processed using range normalization. If the multi-indicator comprehensive cloud model is established using positive indicators, then the positive indicators are expressed as follows: ； If the multi-indicator integrated cloud model is established using inverse indicators, then the inverse indicator is expressed as follows: ； in, For the first Year The normalized values of each indicator, and Each of the following is a cycle in the power distribution network planning: The maximum and minimum values of each indicator. ; The weights of the indicators in the dynamic evaluation index system, after being normalized by the range method, are determined using the entropy weight method, and are expressed as follows: ， ； in, For the first The weight of each indicator, For the first The entropy value of each indicator, The time length of one cycle in the power distribution network planning. No. Year The proportion of each indicator , The value is 5; A multi-indicator integrated cloud model is constructed, which is expressed as follows: ； in, , ,and The indicators in the dynamic evaluation index system are respectively in the 1st... The expected value for the year, and the indicators in the dynamic evaluation index system in the [year] [section] The entropy of the year, and the indicators in the dynamic evaluation index system in the first year The superentropy of the year The value is 5.
9. The collaborative optimization configuration method based on the dynamic evolution characteristics of the distribution network according to claim 8, characterized in that: The calculation of the similarity between adjacent years based on the indicators in the dynamic evaluation index system is performed in the following manner: ， ； In the formula: For the first Year and the Similarity of indicators from year to year For the first Year and the The relative entropy of the annual index For the first Year and the The difference in the average value of the indicators over the years These are the weighting coefficients. As a regulating factor, and They represent the same meaning; The division of the different stages of the distribution network planning includes: when the first Year and the Similarity of indicators from year to year If it is less than the predetermined threshold, then the first... The year is marked as a phase boundary point in a cycle of the power distribution network planning.
10. A collaborative optimization configuration system based on the dynamic evolution characteristics of a distribution network, the system comprising: At least one processor; And a memory communicatively connected to at least one processor; wherein the memory stores instructions executable by at least one processor, the instructions being executed by at least one processor to enable the at least one processor to implement the method as described in any one of claims 1-9.