CN114049007A - Method for calculating bearing capacity of electric automobile by urban power grid - Google Patents

Method for calculating bearing capacity of electric automobile by urban power grid Download PDF

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CN114049007A
CN114049007A CN202111345531.0A CN202111345531A CN114049007A CN 114049007 A CN114049007 A CN 114049007A CN 202111345531 A CN202111345531 A CN 202111345531A CN 114049007 A CN114049007 A CN 114049007A
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CN114049007B (en
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高山
姚婉蕾
吴传申
刘宇
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Southeast University
State Grid Jiangsu Electric Power Co Ltd
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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State Grid Jiangsu Electric Power Co Ltd
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention relates to the field of urban power grids, in particular to a method for calculating the bearing capacity of an electric vehicle by an urban power grid, which comprises the steps of firstly setting the initial scale of the electric vehicle, solving a charging load curve of each functional area by utilizing a trip chain theory and a Monte Carlo method, distributing the charging load to each node of the power grid and overlapping the charging load with the original load, calculating the network load flow distribution after the electric vehicle is accessed, evaluating whether the voltage deviation of the node and the load rate of a transformer exceed a normal range, and if both indexes are not out of limit, increasing the scale of the electric vehicle until any index is out of limit, thus obtaining a bearing capacity calculation result; the method can consider the influence of the space-time randomness of the charging behavior of the electric automobile on the charging load prediction when calculating the bearing capacity of the urban power grid on the electric automobile, accurately calculate the charging load, consider the safety and stability of the power grid operation on the basis of the charging load, provide the evaluation index of the bearing capacity and a quantitative calculation method, and has practical significance.

Description

Method for calculating bearing capacity of electric automobile by urban power grid
Technical Field
The invention relates to the field of urban power grids, in particular to a method for calculating the bearing capacity of an electric automobile by an urban power grid.
Background
With the continuous improvement of the permeability of the electric vehicles and the charging of more and more electric vehicles connected to the urban power grid, the influence of the disordered and considerable electric load on the safe and stable operation of the power grid is more and more, and therefore, the bearing capacity of the urban power grid on the electric vehicles needs to be researched.
The method has various solutions for evaluating the bearing capacity of the urban power grid on the electric automobile, and an evaluation index system can be established in a scoring mode to measure the bearing capacity by using decision methods such as a fuzzy theory, an analytic hierarchy process, an entropy weight method and the like; and different evaluation indexes can be selected to carry out quantitative calculation of the bearing capacity from the aspects of economy, reliability, safety and stability and the like. The bearing capacity of the urban power grid to the electric automobile is calculated quantitatively, and the method has more significance for planning the power grid and exerting the advantages of distributed energy of the electric automobile.
Disclosure of Invention
In order to solve the above mentioned drawbacks in the background art, the present invention provides a method for calculating the load-bearing capacity of an electric vehicle by an urban power grid.
The purpose of the invention can be realized by the following technical scheme:
a method for calculating the carrying capacity of an electric automobile by an urban power grid comprises the following steps:
s1, acquiring urban power grid topology and basic load data of each node, and performing functional area division on the power grid nodes;
acquiring time characteristic quantity, space characteristic quantity and auxiliary variable for describing the travel activity rule of the electric automobile on time and space;
acquiring battery parameters of the electric vehicle;
obtaining the maximum node voltage deviation allowed by the power grid and the maximum load rate of the transformer;
s2, setting the initial scale and the circulation step length of the electric automobile;
s3, solving a charging load curve of each functional area of the urban power grid under the current electric automobile scale by utilizing a trip chain theory and a Monte Carlo method;
s4, distributing the charging load of the electric automobile to each node of the power grid, distributing according to the proportion that the basic load of each node occupies the original total load of the area, overlapping the charging load with the original load, and updating the total load of each node;
s5, calculating the network load flow distribution after the electric automobile is connected, and calculating the node voltage deviation and the transformer load rate;
s6, judging whether the voltage deviation of each node and the load rate of the transformer exceed the normal range, if not, increasing one step length of the number of the electric automobiles, and executing S3, otherwise, executing S7;
and S7, subtracting the circulation step length by the current number of the electric vehicles to obtain the maximum bearing capacity of the urban power grid to the electric vehicles.
Further, the urban power grid topology in S1 includes the number of power sources in the system, the number of nodes, a network topology graph, a voltage class, a power reference value, and branch impedance data, the basic load data includes an active load and a reactive load of each node before the electric vehicle accesses the network, and the functional areas are considered as a residential area, a working area, and a business area;
the time characteristic quantity comprises the running time of each section of travel of the electric automobile on a working day and a holiday and the probability distribution parameter of the stop time at a destination, the space characteristic quantity comprises the running distance of each section of travel of the working day and the holiday and the probability distribution parameter of destination selection, the auxiliary variables comprise the first-time trip location of the working day and the holiday, the first-time trip time probability distribution parameter, the length of a trip chain and the probability distribution parameter of the lowest psychological threshold value of the charge state of a user;
the battery parameters of the electric automobile comprise battery capacity, cruising mileage, normal charging power, quick charging power and allowable minimum state of charge (SOC)min。
Further, in the charging load calculation process of S3, taking one minute as a time interval, dividing one day into 1440 time intervals, and respectively obtaining the charging load of each time interval in each area, wherein the specific steps of obtaining the charging load curve are as follows:
s3.1, if the travel scene is a working day, setting the travel time of each section of travel, the stop time at the destination, the travel distance, the first-day travel place and the probability distribution parameters of the first-day travel time as the type of the working day, and if the travel scene is a holiday, setting the probability distribution parameters of the variables as the type of the holiday;
s3.2, assigning the current electric automobile scale to a variable x, initializing the charging load of each region and the total charging load array to be 0, and initializing the Monte Carlo sampling frequency k to be 0;
s3.3, respectively extracting the length y of the trip chain, the first trip time and area of the day and the lowest psychological threshold value of the charge state of the user, and initializing a trip chain circulation variable i to be 2;
s3.4, extracting a first travel destination and the travel time, and calculating first travel arrival time; extracting a first travel distance, and calculating consumed electric quantity and residual electric quantity when the vehicle arrives; extracting the estimated residence time of the first journey, and calculating the estimated leaving time;
s3.5, taking the starting point of the current journey as the end point of the previous journey, and extracting the destination of the current journey according to the end time of the previous journey; extracting the running time length and calculating the travel arrival time; extracting the running distance, and further calculating the electric quantity consumed by the journey; extracting the estimated residence time, and calculating the estimated leaving time of the journey; determining a charging strategy, and updating a charging load array of each area, the estimated residence time of the previous journey, the estimated leaving time and the state of charge when the current journey departs;
s3.6, if i is i +1, determining whether i reaches y and whether the estimated departure time of the previous trip is greater than 1440min, if yes, going to S3.7, otherwise, going to S3.5;
s3.7, calculating the charging load of the last stroke: if the trip chain is open-loop, extracting the destination according to the starting time of the last trip; if the travel chain is closed-loop travel chain, directly setting the destination of the last trip as a residential area, and setting the charging target after the last trip as SOC (state of charge) 1, wherein the SOC is the state of charge of the battery of the electric automobile, and when a charging load array is updated, if the predicted charging end time is greater than 1440min, adding the load data of the exceeding part to the initial part of the corresponding array;
s3.8, if k is k +1, judging whether k reaches a preset upper limit x of sampling times, if so, obtaining the data of loads and total loads of each region, proportionally distributing the electric vehicles to each region according to the charging loads of each region, and dividing the total loads of each region by the number of the electric vehicles in the corresponding region to obtain the average charging power of a single trolley in each region, otherwise, executing S3.3;
wherein, for each section of journey except the last section of journey in the trip chain, the charging strategy needs to be decided independently, and the specific process is as follows:
predicting power consumption and SOC based on a tripminCalculating the minimum electric quantity required by the user for completing the journey, comparing the minimum electric quantity with the lowest psychological threshold of the state of charge of the user, taking the larger value of the minimum electric quantity and the minimum psychological threshold as expected electric quantity, and charging at the destination of the previous journey if the residual electric quantity is less than the expected electric quantity after the previous journey is completed;
if the expected electric quantity can be charged in the expected residence time of the previous stroke by adopting the conventional charging mode, immediately starting charging by adopting the conventional charging mode; if the demand cannot be met, considering whether the destination can be rapidly charged, if the demand cannot be met, prolonging the retention time, and performing conventional charging until the expected electric quantity is charged; if so, considering whether the quick charge can be charged to the expected electric quantity within the expected residence time, if so, carrying out the quick charge, otherwise, prolonging the residence time, and carrying out the quick charge until the expected electric quantity is reached.
Further, when the charging load and the original load are superimposed in S4, the mutual charging load synchronization rate is used in consideration that the peak of the charging load of the electric vehicle is not completely synchronized with the grid base load
Figure BDA0003353945010000051
Performing a calculation wherein PmFor peak load of the grid including charging load, PlmFor the original load peak, P, of the gridemIs a charging load peak.
Further, in S5, using MATPOWER when performing the power flow calculation, the node voltage is shifted
Figure BDA0003353945010000052
Wherein, U is the voltage of each functional area node in the load flow calculation result, and U isNRated voltage of system, transformer load factor
Figure BDA0003353945010000053
Wherein, P is the active power of each functional area node in the load flow calculation result, SNCos θ is the power factor for the rated capacity of the transformer.
The invention has the beneficial effects that:
firstly, setting an initial scale of the electric automobile, solving a charging load curve of each functional area by utilizing a trip chain theory and a Monte Carlo method, distributing the charging load to each node of a power grid and overlapping the charging load with the original load, calculating network load flow distribution after the electric automobile is accessed, evaluating whether node voltage deviation and transformer load rate exceed a normal range, and if both indexes are not out of limit, increasing the scale of the electric automobile until any index is out of limit, thus obtaining a bearing capacity calculation result;
the method can consider the influence of the space-time randomness of the charging behavior of the electric automobile on the charging load prediction when calculating the bearing capacity of the urban power grid on the electric automobile, accurately calculate the charging load, consider the safety and stability of the power grid operation on the basis of the charging load, provide the evaluation index of the bearing capacity and a quantitative calculation method, and has practical significance.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without creative efforts;
FIG. 1 is a schematic flow chart diagram of a method for calculating a load-bearing capacity according to the present invention;
FIG. 2 is a schematic flow chart of the method for determining the charging load of the electric vehicle in each functional area of the urban power grid.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A method for calculating the carrying capacity of an electric vehicle by an urban power grid is disclosed, as shown in FIG. 1, and the calculation method comprises the following steps:
s1, acquiring urban power grid topology and basic load data of each node, and performing functional area division on the power grid nodes;
acquiring time characteristic quantity, space characteristic quantity and auxiliary variable for describing the travel activity rule of the electric automobile on time and space;
acquiring battery parameters of the electric vehicle;
obtaining the maximum node voltage deviation allowed by the power grid and the maximum load rate of the transformer;
the urban power grid topology in the S1 comprises the number of power sources in the system, the number of nodes, a network topology graph, voltage levels, power reference values and branch impedance data, basic load data comprises active loads and reactive loads of the nodes before the electric automobile is connected into the network, and functional areas are considered to be a residential area, a working area and a business area;
the time characteristic quantity comprises the running time of each section of travel of the electric automobile on a working day and a holiday and the probability distribution parameter of the stop time at a destination, the space characteristic quantity comprises the running distance of each section of travel of the working day and the holiday and the probability distribution parameter selected by the destination, the auxiliary variables comprise the first-time trip location of the working day and the holiday, the first-time trip time of the day, the length of a trip chain and the probability distribution parameter of the lowest psychological threshold value of the charge state of a user;
the battery parameters of the electric automobile comprise battery capacity, cruising mileage, normal charging power, rapid charging power and allowable minimum state of charge (SOC)min。
S2, setting the initial scale and the circulation step length of the electric automobile;
s3, solving a charging load curve of each functional area of the urban power grid under the current electric automobile scale by utilizing a trip chain theory and a Monte Carlo method;
as shown in fig. 2, in the charging load calculation process of S3, one day is divided into 1440 periods at one-minute intervals, and the charging load for each period in each area is obtained. The specific steps of obtaining the charging load curve are as follows:
s3.1, if the travel scene is a working day, setting the travel time of each section of travel, the stop time at the destination, the travel distance, the first-day travel place and the probability distribution parameters of the first-day travel time as the type of the working day, and if the travel scene is a holiday, setting the probability distribution parameters of the variables as the type of the holiday;
s3.2, assigning the current electric automobile scale to a variable x, initializing the charging load of each region and the total charging load array to be 0, and initializing the Monte Carlo sampling frequency k to be 0;
s3.3, respectively extracting the length y of the trip chain, the first trip time and area of the day and the lowest psychological threshold value of the charge state of the user, and initializing a trip chain circulation variable i to be 2;
s3.4, extracting a first travel destination and the travel time, and calculating first travel arrival time; extracting a first travel distance, and calculating consumed electric quantity and residual electric quantity when the vehicle arrives; extracting the estimated residence time of the first journey, and calculating the estimated leaving time;
s3.5, taking the starting point of the current journey as the end point of the previous journey, and extracting the destination of the current journey according to the end time of the previous journey; extracting the running time length and calculating the travel arrival time; extracting the running distance, and further calculating the electric quantity consumed by the journey; and extracting the estimated residence time and calculating the estimated leaving time of the journey. Determining a charging strategy, and updating a charging load array of each area, the estimated residence time of the previous journey, the estimated leaving time and the state of charge when the current journey departs;
s3.6, if i is i +1, determining whether i reaches y and whether the estimated departure time of the previous trip is greater than 1440min, if yes, going to S3.7, otherwise, going to S3.5;
s3.7, calculating the charging load of the last stroke: if the trip chain is open-loop, extracting the destination according to the starting time of the last trip; if the travel chain is closed-loop travel chain, directly setting the destination of the last trip as a residential area, and setting the charging target after the last trip as SOC (state of charge) 1, wherein the SOC is the state of charge of the battery of the electric automobile, and when a charging load array is updated, if the predicted charging end time is greater than 1440min, adding the load data of the exceeding part to the initial part of the corresponding array;
and S3.8, if k is k +1, judging whether k reaches a preset sampling frequency upper limit x, if so, obtaining the data of the loads and the total loads of all the regions, proportionally distributing the electric vehicles to all the regions according to the charging loads of all the regions, and dividing the total loads of all the regions by the number of the electric vehicles in the corresponding regions respectively to obtain the average charging power of the single-trolley in each region, otherwise, executing S3.3.
Wherein, for each section of journey except the last section of journey in the trip chain, the charging strategy needs to be decided independently, and the specific process is as follows:
according toPredicted power consumption and SOC for one tripminAnd calculating the minimum electric quantity required by the user for completing the journey, comparing the minimum electric quantity with the lowest psychological threshold of the state of charge of the user, taking the larger value of the minimum electric quantity and the minimum psychological threshold of the state of charge of the user as the expected electric quantity, and if the residual electric quantity is less than the expected electric quantity after the last journey is completed, charging the destination of the last journey.
If the expected electric quantity can be charged in the expected residence time of the previous stroke by adopting the conventional charging mode, immediately starting charging by adopting the conventional charging mode; if the demand cannot be met, considering whether the destination can be rapidly charged, if the demand cannot be met, prolonging the retention time, and performing conventional charging until the expected electric quantity is charged; if so, considering whether the quick charge can be charged to the expected electric quantity within the expected residence time, if so, carrying out the quick charge, otherwise, prolonging the residence time, and carrying out the quick charge until the expected electric quantity is reached.
S4, distributing the charging load of the electric automobile to each node of the power grid, distributing according to the proportion that the basic load of each node occupies the original total load of the area, overlapping the charging load with the original load, and updating the total load of each node;
when the charging load and the original load are superimposed in S4, the mutual synchronization rate of the charging load is adopted in consideration that the peak of the charging load of the electric vehicle is not completely synchronized with the power grid base load
Figure BDA0003353945010000091
Performing a calculation wherein PmFor peak load of the grid including charging load, PlmFor the original load peak, P, of the gridemIs a charging load peak.
S5, calculating the network load flow distribution after the electric automobile is connected, and calculating the node voltage deviation and the transformer load rate;
in S5, MATPOWER is used in the process of carrying out load flow calculation, and the voltage of the node is deviated
Figure BDA0003353945010000092
Wherein, U is the voltage of each functional area node in the load flow calculation result, and U isNRated voltage of system, transformer load factor
Figure BDA0003353945010000093
Wherein, P is the active power of each functional area node in the load flow calculation result, SNCos θ is the power factor for the rated capacity of the transformer.
S6, judging whether the voltage deviation of each node and the load rate of the transformer exceed the normal range, if not, increasing one step length of the number of the electric automobiles, and executing S3, otherwise, executing S7;
and S7, subtracting the circulation step length by the current number of the electric vehicles to obtain the maximum bearing capacity of the urban power grid to the electric vehicles.
In summary, the calculation process of the present invention is: firstly, setting an initial scale of the electric automobile, solving a charging load curve of each functional area by utilizing a trip chain theory and a Monte Carlo method, distributing the charging load to each node of a power grid and overlapping the charging load with the original load, calculating network load flow distribution after the electric automobile is connected, evaluating whether node voltage deviation and transformer load rate exceed a normal range, and if both indexes are not out of limit, increasing the scale of the electric automobile until any index is out of limit, thus obtaining a bearing capacity calculation result.
The method can consider the influence of the space-time randomness of the charging behavior of the electric automobile on the charging load prediction when calculating the bearing capacity of the urban power grid on the electric automobile, accurately calculate the charging load, consider the safety and stability of the power grid operation on the basis of the charging load, provide the evaluation index of the bearing capacity and a quantitative calculation method, and has practical significance.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (5)

1. A method for calculating the carrying capacity of an electric automobile by an urban power grid is characterized by comprising the following steps:
s1, acquiring urban power grid topology and basic load data of each node, and performing functional area division on the power grid nodes;
acquiring time characteristic quantity, space characteristic quantity and auxiliary variable for describing the travel activity rule of the electric automobile on time and space;
acquiring battery parameters of the electric vehicle;
obtaining the maximum node voltage deviation allowed by the power grid and the maximum load rate of the transformer;
s2, setting the initial scale and the circulation step length of the electric automobile;
s3, solving a charging load curve of each functional area of the urban power grid under the current electric automobile scale by utilizing a trip chain theory and a Monte Carlo method;
s4, distributing the charging load of the electric automobile to each node of the power grid, distributing according to the proportion that the basic load of each node occupies the original total load of the area, overlapping the charging load with the original load, and updating the total load of each node;
s5, calculating the network load flow distribution after the electric automobile is connected, and calculating the node voltage deviation and the transformer load rate;
s6, judging whether the voltage deviation of each node and the load rate of the transformer exceed the normal range, if not, increasing one step length of the number of the electric automobiles, and executing S3, otherwise, executing S7;
and S7, subtracting the circulation step length by the current number of the electric vehicles to obtain the maximum bearing capacity of the urban power grid to the electric vehicles.
2. The method according to claim 1, wherein the urban power grid topology in S1 includes the number of power sources in the system, the number of nodes, a network topology graph, voltage levels, power reference values, and branch impedance data, the base load data includes active load and reactive load of each node before the electric vehicle accesses the network, and the functional areas are considered as residential areas, working areas, and business areas;
the time characteristic quantity comprises the running time of each section of travel of the electric automobile on a working day and a holiday and the probability distribution parameter of the stop time at a destination, the space characteristic quantity comprises the running distance of each section of travel of the working day and the holiday and the probability distribution parameter of destination selection, the auxiliary variables comprise the first-time trip location of the working day and the holiday, the first-time trip time probability distribution parameter, the length of a trip chain and the probability distribution parameter of the lowest psychological threshold value of the charge state of a user;
the battery parameters of the electric automobile comprise battery capacity, cruising mileage, normal charging power, quick charging power and allowable minimum state of charge (SOC)min。
3. The method for calculating the carrying capacity of the electric vehicle through the urban power grid according to claim 1, wherein in the charging load calculation process in S3, one minute is taken as a time interval, one day is divided into 1440 time periods, the charging load in each time period of each area is respectively obtained, and the specific steps of obtaining the charging load curve are as follows:
s3.1, if the travel scene is a working day, setting the travel time of each section of travel, the stop time at the destination, the travel distance, the first-day travel place and the probability distribution parameters of the first-day travel time as the type of the working day; if the travel scene is a holiday, setting the probability distribution parameters of the variables as holiday types;
s3.2, assigning the current electric automobile scale to a variable x, initializing the charging load of each region and the total charging load array to be 0, and initializing the Monte Carlo sampling frequency k to be 0;
s3.3, respectively extracting the length y of the trip chain, the first trip time and area of the day and the lowest psychological threshold value of the charge state of the user, and initializing a trip chain circulation variable i to be 2;
s3.4, extracting a first travel destination and the travel time, and calculating first travel arrival time; extracting a first travel distance, and calculating consumed electric quantity and residual electric quantity when the vehicle arrives; extracting the estimated residence time of the first journey, and calculating the estimated leaving time;
s3.5, taking the starting point of the current journey as the end point of the previous journey, and extracting the destination of the current journey according to the end time of the previous journey; extracting the running time length and calculating the travel arrival time; extracting the running distance, and further calculating the electric quantity consumed by the journey; extracting the estimated residence time, and calculating the estimated leaving time of the journey; determining a charging strategy, and updating a charging load array of each area, the estimated residence time of the previous journey, the estimated leaving time and the state of charge when the current journey departs;
s3.6, if i is i +1, determining whether i reaches y and whether the estimated departure time of the previous trip is greater than 1440min, if yes, going to S3.7, otherwise, going to S3.5;
s3.7, calculating the charging load of the last stroke: if the trip chain is open-loop, extracting the destination according to the starting time of the last trip; if the travel chain is closed-loop travel chain, directly setting the destination of the last trip as a residential area, and setting the charging target after the last trip as SOC (state of charge) 1, wherein the SOC is the state of charge of the battery of the electric automobile, and when a charging load array is updated, if the predicted charging end time is greater than 1440min, adding the load data of the exceeding part to the initial part of the corresponding array;
s3.8, if k is k +1, judging whether k reaches a preset upper limit x of sampling times, if so, obtaining the data of loads and total loads of each region, proportionally distributing the electric vehicles to each region according to the charging loads of each region, and dividing the total loads of each region by the number of the electric vehicles in the corresponding region to obtain the average charging power of a single trolley in each region, otherwise, executing S3.3;
for each section of journey except the last section of journey in the trip chain, a charging strategy needs to be decided independently, and the specific process is as follows:
predicting power consumption and SOC based on a tripminCalculating the minimum electric quantity required by the user to finish the travel, comparing the minimum electric quantity with the lowest psychological threshold of the charge state of the user, taking the larger value of the minimum electric quantity and the minimum psychological threshold as the expected electric quantity, and if the last travel is finishedIf the beam residual electric quantity is less than the expected electric quantity, charging is needed at the destination of the previous journey;
if the expected electric quantity can be charged in the expected residence time of the previous stroke by adopting the conventional charging mode, immediately starting charging by adopting the conventional charging mode; if the demand cannot be met, considering whether the destination can be rapidly charged, if the demand cannot be met, prolonging the retention time, and performing conventional charging until the expected electric quantity is charged; if so, considering whether the quick charge can be charged to the expected electric quantity within the expected residence time, if so, carrying out the quick charge, otherwise, prolonging the residence time, and carrying out the quick charge until the expected electric quantity is reached.
4. The method as claimed in claim 1, wherein the mutual charge load synchronization rate is adopted when the charge load is superimposed on the original load in S4, taking into account that the peak of the charge load of the electric vehicle is not completely synchronized with the basic load of the power grid
Figure FDA0003353943000000041
Performing a calculation wherein PmFor peak load of the grid including charging load, PlmFor the original load peak, P, of the gridemIs a charging load peak.
5. The method as claimed in claim 1, wherein in step S5, MATPOWER is used for load calculation, and the node voltage deviation is calculated
Figure FDA0003353943000000042
Wherein, U is the voltage of each functional area node in the load flow calculation result, and U isNRated voltage of system, transformer load factor
Figure FDA0003353943000000043
Wherein, P is the existence of each functional area node in the load flow calculation resultWork power, SNCos θ is the power factor for the rated capacity of the transformer.
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