CN117060473A - Intelligent power distribution network energy storage optimal configuration method - Google Patents
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Abstract
The invention discloses an intelligent power distribution network energy storage optimizing configuration method, which relates to the technical field of energy storage optimizing, and comprises power distribution network load information acquisition, power distribution network load information analysis, energy placement information monitoring, energy transmission information acquisition, energy transmission information analysis and result display.
Description
Technical Field
The invention relates to the technical field of energy storage optimization, in particular to an intelligent power distribution network energy storage optimization configuration method.
Background
The intelligent power distribution network energy storage optimization is widely popularized and applied, the utilization rate of energy sources can be improved better, the power distribution network energy storage is planned reasonably, the energy sources are balanced and transferred better, the purpose of fully utilizing low-cost electric energy to charge is achieved, the energy storage is released in a peak period to meet load demands, the utilization efficiency of the energy sources is improved, the energy source cost is reduced, the bearing capacity of a power grid is improved better, and the running stability is guaranteed.
The prior art is also shallow in understanding, the analysis of the power grid energy storage battery is mainly or one-to-one, the analysis is single at present, the analysis is not carried out from multiple aspects of the power distribution network, the analysis is not carried out according to the load, the energy storage battery capacity and the environment of the power grid, therefore, the implementation mode of optimizing the energy storage of the power distribution network cannot be accurately obtained, the optimization of the energy storage of the power distribution network cannot be well realized, stable service cannot be provided, and the use of a power supply is not facilitated, because the operation load of the power distribution network is not known, the operation load of the power distribution network can be increased to a certain extent, the demand trend of the power distribution network cannot be accurately known, and the power distribution network cannot obtain better energy storage distribution.
Disclosure of Invention
Aiming at the technical defects, the invention aims to provide an intelligent power distribution network energy storage optimal configuration method.
In order to solve the technical problems, the invention adopts the following technical scheme: the invention provides an intelligent power distribution network energy storage optimizing configuration method, which comprises the following steps: step one, acquiring load information of a power distribution network: dividing the target power distribution network into areas to obtain all subareas, and further obtaining load information corresponding to all subareas at the current time point, wherein the load information comprises electricity consumption and electricity consumption unit consumption;
step two, analyzing load information of the power distribution network: according to the load information corresponding to each subarea at the current time point, analyzing to obtain a load evaluation coefficient of each subarea, and further judging the load state of each subarea according to the load evaluation coefficient of each subarea so as to obtain each target subarea;
step three, energy placement information monitoring: according to the load evaluation coefficients of the subareas, further analyzing and obtaining the energy storage battery capacity of each target subarea;
step four, energy conveying information acquisition: acquiring conveying information and environment information of each target subarea at the current time point, wherein the conveying information comprises wire size, conveying distance and residual electric quantity of an energy storage battery, and the environment information comprises temperature and humidity;
step five, energy conveying information analysis: analyzing and obtaining the conveying evaluation coefficient of each target subarea according to the conveying information of each target subarea at the current time point and the load evaluation coefficient of each subarea, further calculating and obtaining the environmental impact factor of each target subarea according to the environmental information of each target subarea, analyzing and obtaining the priority conveying coefficient of each target subarea energy storage battery according to the conveying evaluation coefficient and the environmental impact factor, and judging the conveying state of each target subarea energy storage battery;
step six, displaying the result: and displaying the conveying state of the energy storage batteries of each target subarea.
Preferably, the analysis obtains the load evaluation coefficient of each subarea, and the specific analysis process is as follows:
by calculation formulaAnalyzing to obtain the load evaluation coefficient theta of each subarea i I represents the number of each sub-region, i=1.2. Once again, n is, v (v) 1 、ν 2 Weight factors respectively expressed as preset total electricity consumption and electricity consumption unit consumption, w i 、j i The power consumption and the power consumption unit consumption corresponding to the ith sub-area are respectively represented, and w 'and j' are respectively represented as preset power consumption and power consumption unit consumption.
Preferably, the judging the load state of each subarea comprises the following specific judging process:
comparing the load evaluation coefficient threshold value of each subarea with the preset load evaluation coefficient threshold value of each subarea, if the load evaluation coefficient threshold value of a certain subarea is smaller than the preset load evaluation coefficient threshold value of each subarea, judging that the load state of the subarea is lighter, and if the load evaluation coefficient threshold value of the certain subarea is larger than or equal to the preset load evaluation coefficient threshold value of each subarea, judging that the load state of the subarea is heavier, and taking the subarea as a target subarea, thereby obtaining each target subarea.
Preferably, the analyzing obtains the energy storage battery capacity of each target subarea, and the specific judging process is as follows:
comparing the load evaluation coefficient of each target subarea with the reference energy storage battery capacity corresponding to each load evaluation coefficient stored in the database, if the load evaluation coefficient of a certain target subarea is smaller than the reference energy storage battery capacity corresponding to a certain load evaluation coefficient stored in the database, the reference energy storage battery capacity is not used as the energy storage battery capacity of the target subarea, and if the load evaluation coefficient of a certain target subarea is larger than or equal to the reference energy storage battery capacity corresponding to a certain load evaluation coefficient stored in the database, the reference energy storage battery capacity is used as the energy storage battery capacity of the target subarea, so that the energy storage battery capacity of each target subarea is obtained.
Preferably, the analysis obtains the conveying evaluation coefficient of each target subarea, and the specific analysis process is as follows:
according to the wire size and the conveying distance corresponding to each target subarea, calculating to obtain a first conveying influence coefficient corresponding to each target subarea, and marking the first conveying influence coefficient as beta 1 r R represents the number of each target subregion, r=1.2..m;
calculating to obtain a conveying state coefficient corresponding to each target subarea according to the residual electric quantity and the load evaluation coefficient of the energy storage battery corresponding to each target subarea, and recording the conveying state coefficient as beta 2 r ；
By calculation formulaAnalysis to obtain the conveying evaluation coefficient beta of each target subarea r ，s 1 The compensation factors are respectively expressed as a preset first conveying influence coefficient and a conveying state coefficient.
Preferably, the calculating obtains a first conveying influence coefficient corresponding to each target subarea, and the specific calculating process is as follows:
by calculation formulaCalculating to obtain a first conveying influence coefficient beta 1 corresponding to each target subarea r ，x 1 、x 2 Respectively expressed as weight factors of preset wire size and conveying distance, and m 'and c' respectively expressed as energy storage capacity of preset wire size and conveying distance, m r 、c r The energy storage capacity is respectively expressed as the wire size and the conveying distance corresponding to the r target subarea.
Preferably, the calculating obtains the conveying state coefficient corresponding to each target subarea, and the specific calculating process is as follows:
by calculation formulaCalculating to obtain a conveying state coefficient beta 2 corresponding to each target subarea r ，z 1 、z 2 The weight factors respectively expressed as preset residual electric quantity and load evaluation coefficient, and n 'and theta' respectively expressed as preset residual electric quantity and load evaluation coefficient, n r Representing the residual electric quantity corresponding to the (r) th target subarea, theta i The load evaluation coefficient of the i-th sub-region is represented.
Preferably, the calculating obtains the environmental impact factor of each target subarea, and the specific analysis process is as follows:
by calculation formulaCalculating the environmental influence factor of each target subarea>r denotes the number of each target sub-region, r=1.2..m, alpha 1 、α 2 Respectively expressed as preset temperature and humidityG ', h' are respectively expressed as preset temperature, humidity, g r 、h r Respectively expressed as the temperature and the humidity corresponding to the r target subarea.
Preferably, the analysis obtains the priority transport coefficient of the energy storage battery of each target subarea, and the specific analysis process is as follows:
by calculation formulaAnalyzing and obtaining the priority transmission coefficient χ of each target subarea energy storage battery r R denotes the number of each target subregion, r=1.2..m, epsilon 1 Compensation factors, respectively denoted as preset environmental impact factors, delivery assessment factors, +.>β r The score is expressed as the environmental impact factor of the r-th target subregion, the delivery evaluation coefficient, and e represents the natural constant.
Preferably, the determining the conveying state of the energy storage battery in each target subarea specifically includes the following steps:
comparing the priority transmission coefficient threshold value of each target subarea energy storage battery with the preset priority transmission coefficient threshold value of each target subarea energy storage battery, judging that the transmission of each target subarea energy storage battery is not priority if the priority transmission coefficient threshold value of a certain target subarea energy storage battery is smaller than the preset priority transmission coefficient threshold value of each target subarea energy storage battery, and judging that the transmission of each target subarea energy storage battery is priority if the priority transmission coefficient threshold value of a certain target subarea energy storage battery is larger than or equal to the preset priority transmission coefficient threshold value of each target subarea energy storage battery, so as to obtain the transmission state of each target subarea.
1. Compared with the prior art, the invention has the beneficial effects that: the invention provides an intelligent power distribution network energy storage optimizing configuration method, which is characterized in that the load evaluation coefficient of each subarea is analyzed, so that the load state of each subarea is better judged, the reference energy storage battery capacity of each target subarea is obtained through analysis, so that energy can be better placed, the conveying evaluation coefficient of each target subarea is obtained through analysis, the environmental impact factor of each target subarea is calculated according to the environmental information of each target subarea, the priority conveying coefficient of the energy storage battery of each target subarea is obtained through analysis according to the conveying evaluation coefficient and the environmental impact factor, the conveying state of the energy storage battery of each target subarea is judged, and the power distribution network is better optimized for energy storage through combining the analysis, so that the defects existing in the prior art are overcome, the operation of the power distribution network can be better stabilized, the use of the power distribution network is better ensured, the cost and the waste of resources are reduced, the energy storage sufficiency of the power distribution network is ensured, and the more reasonable energy storage distribution is realized, and the bearing capacity of the power distribution network is improved.
2. According to the invention, in the analysis of the load information of the power distribution network, the load evaluation coefficients of all sub-areas of the load of the power distribution network are deeply analyzed, so that the state of the power distribution network is better known, the electricity consumption condition of a user is better known, and accordingly, the power distribution network can be better conveyed with corresponding energy sources, and the use of the electricity quantity of the user is better ensured.
3. According to the invention, in the energy conveying information analysis, the conveying evaluation coefficient and the priority conveying coefficient of energy conveying are deeply analyzed, so that the conveying state of the energy storage batteries of each target subarea is better obtained, the energy storage of the power distribution network is better balanced, and more power utilization availability is provided for users.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an intelligent power distribution network energy storage optimizing configuration method, which comprises power distribution network load information acquisition, power distribution network load information analysis, energy placement information monitoring, energy delivery information acquisition, energy delivery information analysis and result display.
The power distribution network load information acquisition is connected with the power distribution network load information analysis and the energy placement information monitoring respectively, the power distribution network load information analysis is connected with the energy delivery information acquisition and the energy delivery information analysis respectively, and the power distribution network load information analysis is connected with the energy delivery information analysis and the result display respectively.
Step one, acquiring load information of a power distribution network: dividing the target power distribution network into areas to obtain all subareas, and further obtaining load information corresponding to all subareas at the current time point, wherein the load information comprises electricity consumption and electricity consumption unit consumption;
the power consumption of each sub-area at the current time point is obtained and then added up, so that the power consumption corresponding to each sub-area is obtained, and the power consumption of each device is divided by the service time, so that the power consumption of each hour is obtained and is used as the power consumption unit consumption corresponding to each sub-area.
Step two, analyzing load information of the power distribution network: according to the load information corresponding to each subarea at the current time point, analyzing to obtain a load evaluation coefficient of each subarea, and further judging the load state of each subarea according to the load evaluation coefficient of each subarea so as to obtain each target subarea;
as an alternative embodiment, the analysis obtains the load evaluation coefficients of the subareas, and the specific analysis process is as follows:
by calculation formulaAnalyzing to obtain the load evaluation coefficient theta of each subarea i I represents the number of each sub-region, i=1.2. Once again, n is, v (v) 1 、ν 2 Weight factors respectively expressed as preset total electricity consumption and electricity consumption unit consumption, w i 、j i The power consumption and the power consumption unit consumption corresponding to the ith sub-area are respectively represented, and w 'and j' are respectively represented as preset power consumption and power consumption unit consumption.
As an optional implementation manner, the determining the load state of each sub-area specifically includes the following steps:
comparing the load evaluation coefficient threshold value of each subarea with the preset load evaluation coefficient threshold value of each subarea, if the load evaluation coefficient threshold value of a certain subarea is smaller than the preset load evaluation coefficient threshold value of each subarea, judging that the load state of the subarea is lighter, and if the load evaluation coefficient threshold value of the certain subarea is larger than or equal to the preset load evaluation coefficient threshold value of each subarea, judging that the load state of the subarea is heavier, and taking the subarea as a target subarea, thereby obtaining each target subarea.
According to the invention, in the analysis of the load information of the power distribution network, the load evaluation coefficients of all sub-areas of the load of the power distribution network are deeply analyzed, so that the state of the power distribution network is better known, the electricity consumption condition of a user is better known, and accordingly, the power distribution network can be better conveyed with corresponding energy sources, and the use of the electricity quantity of the user is better ensured.
Step three, energy placement information monitoring: according to the load evaluation coefficients of the subareas, further analyzing and obtaining the energy storage battery capacity of each target subarea;
as an optional implementation manner, the analyzing obtains the energy storage battery capacity of each target subarea, and the specific judging process is as follows:
comparing the load evaluation coefficient of each target subarea with the reference energy storage battery capacity corresponding to each load evaluation coefficient stored in the database, if the load evaluation coefficient of a certain target subarea is smaller than the reference energy storage battery capacity corresponding to a certain load evaluation coefficient stored in the database, the reference energy storage battery capacity is not used as the energy storage battery capacity of the target subarea, and if the load evaluation coefficient of a certain target subarea is larger than or equal to the reference energy storage battery capacity corresponding to a certain load evaluation coefficient stored in the database, the reference energy storage battery capacity is used as the energy storage battery capacity of the target subarea, so that the energy storage battery capacity of each target subarea is obtained.
Step four, energy conveying information acquisition: acquiring conveying information and environment information of each target subarea at the current time point, wherein the conveying information comprises wire size, conveying distance and residual electric quantity of an energy storage battery, and the environment information comprises temperature and humidity;
the temperature of each target subarea is obtained through a temperature measuring instrument and the humidity of each target subarea is obtained through a humidity measuring instrument by obtaining the wire size and the residual electric quantity of the energy storage battery in a database.
The conveying distance refers to the distance between each target subarea and the place where the energy storage battery is located.
Step five, energy conveying information analysis: analyzing and obtaining the conveying evaluation coefficient of each target subarea according to the conveying information of each target subarea at the current time point and the load evaluation coefficient of each subarea, further calculating and obtaining the environmental impact factor of each target subarea according to the environmental information of each target subarea, analyzing and obtaining the priority conveying coefficient of each target subarea energy storage battery according to the conveying evaluation coefficient and the environmental impact factor, and judging the conveying state of each target subarea energy storage battery;
as an alternative embodiment, the analysis obtains the transport evaluation coefficient of each target subarea, and the specific analysis process is as follows:
according to the wire size and the conveying distance corresponding to each target subarea, calculating to obtainThe first transport influence coefficient corresponding to each target subarea is denoted as beta 1 r R represents the number of each target subregion, r=1.2..m;
calculating to obtain a conveying state coefficient corresponding to each target subarea according to the residual electric quantity and the load evaluation coefficient of the energy storage battery corresponding to each target subarea, and recording the conveying state coefficient as beta 2 r ；
By calculation formulaAnalysis to obtain the conveying evaluation coefficient beta of each target subarea r ，s 1 The compensation factors are respectively expressed as a preset first conveying influence coefficient and a conveying state coefficient.
As an optional implementation manner, the calculating obtains the first conveying influence coefficient corresponding to each target subarea, and the specific calculating process is as follows:
by calculation formulaCalculating to obtain a first conveying influence coefficient beta 1 corresponding to each target subarea r ，x 1 、x 2 Respectively expressed as weight factors of preset wire size and conveying distance, and m 'and c' respectively expressed as energy storage capacity of preset wire size and conveying distance, m r 、c r The energy storage capacity is respectively expressed as the wire size and the conveying distance corresponding to the r target subarea.
As an optional implementation manner, the calculating obtains the conveying state coefficient corresponding to each target sub-region, and the specific calculating process is as follows:
by calculation formulaCalculating to obtain a conveying state coefficient beta 2 corresponding to each target subarea r ，z 1 、z 2 The weight factors respectively expressed as preset residual electric quantity and load evaluation coefficient, and n 'and theta' respectively expressed as preset residual electric quantity and load evaluation coefficient, n r Representation ofResidual electric quantity corresponding to the (r) th target subarea, theta i The load evaluation coefficient of the i-th sub-region is represented.
As an alternative embodiment, the calculating obtains the environmental impact factor of each target subarea, and the specific analysis process is as follows:
by calculation formulaCalculating the environmental influence factor of each target subarea>r denotes the number of each target sub-region, r=1.2..m, alpha 1 、α 2 Weight factors respectively expressed as preset temperature and humidity, and g 'and h' respectively expressed as preset temperature and humidity, g r 、h r Respectively expressed as the temperature and the humidity corresponding to the r target subarea.
As an alternative embodiment, the analysis obtains the preferential transport coefficient of the energy storage battery of each target subarea, and the specific analysis process is as follows:
by calculation formulaAnalyzing and obtaining the priority transmission coefficient χ of each target subarea energy storage battery r R denotes the number of each target subregion, r=1.2..m, epsilon 1 Compensation factors, respectively denoted as preset environmental impact factors, delivery assessment factors, +.>β r The score is expressed as the environmental impact factor of the r-th target subregion, the delivery evaluation coefficient, and e represents the natural constant.
As an optional implementation manner, the determining the conveying state of the energy storage battery of each target subarea specifically includes the following steps:
comparing the priority transmission coefficient threshold value of each target subarea energy storage battery with the preset priority transmission coefficient threshold value of each target subarea energy storage battery, judging that the transmission of each target subarea energy storage battery is not priority if the priority transmission coefficient threshold value of a certain target subarea energy storage battery is smaller than the preset priority transmission coefficient threshold value of each target subarea energy storage battery, and judging that the transmission of each target subarea energy storage battery is priority if the priority transmission coefficient threshold value of a certain target subarea energy storage battery is larger than or equal to the preset priority transmission coefficient threshold value of each target subarea energy storage battery, so as to obtain the transmission state of each target subarea.
According to the invention, in the energy conveying information analysis, the conveying evaluation coefficient and the priority conveying coefficient of energy conveying are deeply analyzed, so that the conveying state of the energy storage batteries of each target subarea is better obtained, the energy storage of the power distribution network is better balanced, and more power utilization availability is provided for users.
Step six, displaying the result: and displaying the conveying state of the energy storage batteries of each target subarea.
The invention provides an intelligent power distribution network energy storage optimizing configuration method, which is characterized in that the load evaluation coefficient of each subarea is analyzed, so that the load state of each subarea is better judged, the reference energy storage battery capacity of each target subarea is obtained through analysis, so that energy can be better placed, the conveying evaluation coefficient of each target subarea is obtained through analysis, the environmental impact factor of each target subarea is calculated according to the environmental information of each target subarea, the priority conveying coefficient of the energy storage battery of each target subarea is obtained through analysis according to the conveying evaluation coefficient and the environmental impact factor, the conveying state of the energy storage battery of each target subarea is judged, and the power distribution network is better optimized for energy storage through combining the analysis, so that the defects existing in the prior art are overcome, the operation of the power distribution network can be better stabilized, the use of the power distribution network is better ensured, the cost and the waste of resources are reduced, the energy storage sufficiency of the power distribution network is ensured, and the more reasonable energy storage distribution is realized, and the bearing capacity of the power distribution network is improved.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.
Claims (10)
1. The intelligent power distribution network energy storage optimal configuration method is characterized by comprising the following steps of:
step one, acquiring load information of a power distribution network: dividing the target power distribution network into areas to obtain all subareas, and further obtaining load information corresponding to all subareas at the current time point, wherein the load information comprises electricity consumption and electricity consumption unit consumption;
step two, analyzing load information of the power distribution network: according to the load information corresponding to each subarea at the current time point, analyzing to obtain a load evaluation coefficient of each subarea, and further judging the load state of each subarea according to the load evaluation coefficient of each subarea so as to obtain each target subarea;
step three, energy placement information monitoring: according to the load evaluation coefficients of the subareas, further analyzing and obtaining the energy storage battery capacity of each target subarea;
step four, energy conveying information acquisition: acquiring conveying information and environment information of each target subarea at the current time point, wherein the conveying information comprises wire size, conveying distance and residual electric quantity of an energy storage battery, and the environment information comprises temperature and humidity;
step five, energy conveying information analysis: analyzing and obtaining the conveying evaluation coefficient of each target subarea according to the conveying information of each target subarea at the current time point and the load evaluation coefficient of each subarea, further calculating and obtaining the environmental impact factor of each target subarea according to the environmental information of each target subarea, analyzing and obtaining the priority conveying coefficient of each target subarea energy storage battery according to the conveying evaluation coefficient and the environmental impact factor, and judging the conveying state of each target subarea energy storage battery;
step six, displaying the result: and displaying the conveying state of the energy storage batteries of each target subarea.
2. The energy storage optimization configuration method of the intelligent power distribution network according to claim 1, wherein the analysis obtains load evaluation coefficients of all subareas, and the specific analysis process is as follows:
by calculation formulaAnalyzing to obtain the load evaluation coefficient theta of each subarea i I represents the number of each sub-region, i=1.2. Once again, n is, v (v) 1 、ν 2 Weight factors respectively expressed as preset total electricity consumption and electricity consumption unit consumption, w i 、j i The power consumption and the power consumption unit consumption corresponding to the ith sub-area are respectively represented, and w 'and j' are respectively represented as preset power consumption and power consumption unit consumption.
3. The energy storage optimizing configuration method of intelligent distribution network according to claim 1, wherein the judging of the load state of each subarea comprises the following specific judging process:
comparing the load evaluation coefficient threshold value of each subarea with the preset load evaluation coefficient threshold value of each subarea, if the load evaluation coefficient threshold value of a certain subarea is smaller than the preset load evaluation coefficient threshold value of each subarea, judging that the load state of the subarea is lighter, and if the load evaluation coefficient threshold value of the certain subarea is larger than or equal to the preset load evaluation coefficient threshold value of each subarea, judging that the load state of the subarea is heavier, and taking the subarea as a target subarea, thereby obtaining each target subarea.
4. The energy storage optimizing configuration method of the intelligent power distribution network according to claim 1, wherein the analysis obtains the energy storage battery capacity of each target subarea, and the specific judgment process is as follows:
comparing the load evaluation coefficient of each target subarea with the reference energy storage battery capacity corresponding to each load evaluation coefficient stored in the database, if the load evaluation coefficient of a certain target subarea is smaller than the reference energy storage battery capacity corresponding to a certain load evaluation coefficient stored in the database, the reference energy storage battery capacity is not used as the energy storage battery capacity of the target subarea, and if the load evaluation coefficient of a certain target subarea is larger than or equal to the reference energy storage battery capacity corresponding to a certain load evaluation coefficient stored in the database, the reference energy storage battery capacity is used as the energy storage battery capacity of the target subarea, so that the energy storage battery capacity of each target subarea is obtained.
5. The energy storage optimization configuration method of the intelligent power distribution network according to claim 1, wherein the analysis is performed to obtain the conveying evaluation coefficients of all target subareas, and the specific analysis process is as follows:
according to the wire size and the conveying distance corresponding to each target subarea, calculating to obtain a first conveying influence coefficient corresponding to each target subarea, and marking the first conveying influence coefficient as beta 1 r R represents the number of each target subregion, r=1.2..m;
calculating to obtain a conveying state coefficient corresponding to each target subarea according to the residual electric quantity and the load evaluation coefficient of the energy storage battery corresponding to each target subarea, and recording the conveying state coefficient as beta 2 r ；
By calculation formulaAnalysis to obtain the conveying evaluation coefficient beta of each target subarea r ，s 1 The compensation factors respectively expressed as a preset first conveying influence coefficient and a conveying state coefficient, and e represents a natural constant.
6. The energy storage optimization configuration method of the intelligent power distribution network according to claim 5, wherein the calculation obtains the first transport influence coefficient corresponding to each target sub-region, and the specific calculation process is as follows:
by calculation formulaCalculating to obtain a first conveying influence coefficient beta 1 corresponding to each target subarea r ，x 1 、x 2 Respectively expressed as weight factors of preset wire size and conveying distance, and m 'and c' respectively expressed as energy storage capacity of preset wire size and conveying distance, m r 、c r The energy storage capacity is respectively expressed as the wire size and the conveying distance corresponding to the r target subarea.
7. The energy storage optimization configuration method of the intelligent power distribution network according to claim 5, wherein the calculation is performed to obtain the conveying state coefficient corresponding to each target sub-region, and the specific calculation process is as follows:
by calculation formulaCalculating to obtain a conveying state coefficient beta 2 corresponding to each target subarea r ，z 1 、z 2 The weight factors respectively expressed as preset residual electric quantity and load evaluation coefficient, and n 'and theta' respectively expressed as preset residual electric quantity and load evaluation coefficient, n r Representing the residual electric quantity corresponding to the (r) th target subarea, theta i The load evaluation coefficient of the i-th sub-region is represented.
8. The energy storage optimization configuration method of the intelligent power distribution network according to claim 1, wherein the environmental impact factors of all target subareas are obtained through calculation, and the specific analysis process is as follows:
by calculation formulaCalculating the environmental influence factor of each target subarea>r denotes the number of each target sub-region, r=1.2..m, alpha 1 、α 2 Weight factors expressed as preset temperature and humidity, g'H' are respectively expressed as preset temperature and humidity g r 、h r Respectively expressed as the temperature and the humidity corresponding to the r target subarea.
9. The energy storage optimization configuration method of the intelligent power distribution network according to claim 1, wherein the analysis obtains the priority transmission coefficient of the energy storage battery of each target subarea, and the specific analysis process is as follows:
by calculation formulaAnalyzing and obtaining the priority transmission coefficient χ of each target subarea energy storage battery r R denotes the number of each target subregion, r=1.2..m, epsilon 1 Compensation factors, respectively denoted as preset environmental impact factors, delivery assessment factors, +.>β r The score is expressed as the environmental impact factor of the r-th target subregion, the delivery evaluation coefficient, and e represents the natural constant.
10. The energy storage optimizing configuration method of the intelligent power distribution network according to claim 1, wherein the specific judging process is as follows:
comparing the priority transmission coefficient threshold value of each target subarea energy storage battery with the preset priority transmission coefficient threshold value of each target subarea energy storage battery, judging that the transmission of each target subarea energy storage battery is not priority if the priority transmission coefficient threshold value of a certain target subarea energy storage battery is smaller than the preset priority transmission coefficient threshold value of each target subarea energy storage battery, and judging that the transmission of each target subarea energy storage battery is priority if the priority transmission coefficient threshold value of a certain target subarea energy storage battery is larger than or equal to the preset priority transmission coefficient threshold value of each target subarea energy storage battery, so as to obtain the transmission state of each target subarea.
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