Area distribution transformer available capacity evaluation method and system considering electric vehicle access state, and storage medium

Abstract

The invention provides a transformer area distribution transformer available capacity evaluation method and system considering an electric vehicle access state, and a storage medium. The method comprises the following steps: obtaining transformer area transformer distribution transformer capacity and transformer area residential electricity consumption data in a preset time period; based on the inventory of the electric vehicles in the court, constructing a trip chain model for the charging behaviors of the electric vehicles by adopting a trip chain method, and calculating a daily charging load value of the court according to a travel schedule generated by the trip chain model; calculating a total load value of residential electricity consumption of the transformer area to obtain a transformer area basic load value, and calculating a total load value of the power distribution transformer area based on the daily charging load value and the transformer area basic load value; and on the basis of the transformer distribution transformer capacity of the transformer area and the total load value of the distribution transformer area, the available capacity evaluation of the distribution transformer area considering the access state of the electric vehicle is completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system management and electric vehicle technology, and in particular relates to a method, system and storage medium for evaluating the available capacity of a distribution transformer in an area taking into account the access status of electric vehicles. Background Art

[0002] In recent years, the scale of electric vehicles in my country has developed rapidly, among which the proportion of electric vehicles owned by passenger cars exceeds 50%. Due to the charging characteristics of passenger cars and the peak period of household electricity consumption at night, there is a high degree of overlap, which leads to a widening of the peak-to-valley difference in the power grid load. Especially in the low-voltage distribution network area, which has the characteristics of small basic load and high proportion of new load of electric vehicle charging, it is greatly impacted by the disorderly charging of electric vehicles. In order to effectively respond to the safety challenges of distribution network areas brought by the charging and discharging of electric vehicles, my country has carried out research and application of technologies such as time-of-use electricity price mechanism and orderly charging and discharging regulation, and achieved good results. However, existing research only considers the adjustable capacity of electric vehicle load, lacks the evaluation of the available capacity of distribution transformers in the area considering the access status of electric vehicles, and ignores the carrying capacity of the power grid during the vehicle-grid interaction, making it difficult to guarantee the effect of coordinated vehicle-grid interaction between multiple distribution network areas or multiple charging stations. Summary of the invention

[0003] To achieve the above object, the present invention provides a method for evaluating the available capacity of distribution transformers in a substation considering the access status of electric vehicles, the method comprising:

[0004] Step S1, obtaining the distribution capacity of transformers in the substation area and the electricity consumption data of residents in the substation area within a preset time period;

[0005] Step S2: Based on the number of electric vehicles in the substation, a travel chain model is constructed for the charging behavior of electric vehicles using a travel chain method, and the daily charging load value of the substation is calculated according to the itinerary generated by the travel chain model;

[0006] Step S3, calculating the total load value of the residential electricity consumption in the substation area based on the residential electricity consumption data in the substation area, obtaining the basic load value of the substation area, and calculating the total load value of the distribution substation area based on the daily charging load value and the basic load value of the substation area;

[0007] Step S4: Based on the distribution transformer capacity of the substation and the total load value of the distribution substation, the evaluation of the available capacity of the substation distribution transformer considering the access status of electric vehicles is completed.

[0008] Optionally, in step S2, the process of constructing a travel chain model for the electric vehicle charging behavior using the travel chain method specifically includes:

[0009] The log-normal distribution method is used to construct the departure-destination probability matrix, and the probability density function is:

[0010]

[0011] Among them, x is the first trip time of the electric vehicle on that day, β and α are the probability density function parameters, β α is the exponential term of the gamma distribution, x α-1 is the time variable exponential term, e -βx is the natural exponential term of the time variable;

[0012] A Markov chain method is used to construct a spatial transfer matrix, and a probability distribution of mileage is calculated based on the spatial transfer matrix;

[0013] A travel chain model is constructed based on the departure point-destination probability matrix and the spatial transfer matrix.

[0014] Optionally, in step S2, the method for calculating the daily charging load value of the substation according to the itinerary generated by the travel chain model specifically includes:

[0015] Based on the constructed travel chain model, the Monte Carlo simulation method is used to extract travel information and form a preset one-day itinerary for electric vehicles;

[0016] Charging the preset electric vehicle according to the itinerary and the low SOC threshold of the electric vehicle, and calculating the charging load of the charging pile when the preset electric vehicle is charged;

[0017] Summarize the charging loads of all charging piles in the distribution area to obtain the daily charging load value of the distribution area:

[0018]

[0019] Among them, P t is the charging load value in the distribution station area at time t, p it The charging power of the i-th charging pile, is the charging probability of the i-th charging pile.

[0020] Optionally, in step S4, based on the distribution transformer capacity of the distribution transformer in the distribution area and the total load value of the distribution area, the process of completing the evaluation of the available capacity of the distribution transformer in the distribution area considering the access status of the electric vehicle specifically includes:

[0021]

[0022] Among them, ξ t is the load rate of the distribution station area at time t, F t is the total load value in the distribution substation area, Q is the total capacity of the distribution substation area, and θ is the safe operation boundary of the distribution substation area.

[0023] The present invention also discloses a system for evaluating the available capacity of distribution transformers in a substation area considering the access status of electric vehicles, the system comprising:

[0024] A data acquisition module is used to obtain the distribution capacity of transformers in the substation area and the electricity consumption data of residents in the substation area within a preset time period;

[0025] The electric vehicle load calculation module is used to construct a travel chain model for the charging behavior of electric vehicles based on the number of electric vehicles in the substation area by using the travel chain method, and calculate the daily charging load value of the substation area according to the itinerary generated by the travel chain model;

[0026] A residential electricity load calculation module is used to calculate the total load value of residential electricity in the substation area based on the residential electricity consumption data of the substation area, obtain the basic load value of the substation area, and calculate the total load value of the distribution substation area based on the daily charging load value and the basic load value of the substation area;

[0027] The available capacity assessment module is used to complete the evaluation of the available capacity of the distribution transformer in the substation considering the access status of electric vehicles based on the distribution transformer capacity of the substation transformer and the total load value of the distribution substation.

[0028] Optionally, in the electric vehicle load calculation module, the workflow of constructing a travel chain model for the electric vehicle charging behavior using the travel chain method specifically includes:

[0029] The log-normal distribution method is used to construct the departure-destination probability matrix, and the probability density function is:

[0030]

[0031] Among them, x is the first trip time of the electric vehicle on that day, β and α are the probability density function parameters, β α is the exponential term of the gamma distribution, x α-1 is the time variable exponential term, e -βx is the natural exponential term of the time variable;

[0032] A Markov chain method is used to construct a spatial transfer matrix, and a probability distribution of mileage is calculated based on the spatial transfer matrix;

[0033] A travel chain model is constructed based on the departure point-destination probability matrix and the spatial transfer matrix.

[0034] Optionally, the method for calculating the daily charging load value of the substation according to the itinerary generated by the travel chain model specifically includes:

[0035] Based on the constructed travel chain model, the Monte Carlo simulation method is used to extract travel information and form a preset one-day itinerary for electric vehicles;

[0036] Charging the preset electric vehicle according to the itinerary and the low SOC threshold of the electric vehicle, and calculating the charging load of the charging pile when the preset electric vehicle is charged;

[0037] Summarize the charging loads of all charging piles in the distribution area to obtain the daily charging load value of the distribution area:

[0038]

[0039] Among them, P t is the charging load value in the distribution station area at time t, p it The charging power of the i-th charging pile, is the charging probability of the i-th charging pile.

[0040] Optionally, in the available capacity evaluation module, based on the distribution transformer capacity of the distribution transformer in the distribution area and the total load value of the distribution area, the process of completing the evaluation of the available capacity of the distribution transformer in the distribution area considering the access status of electric vehicles specifically includes:

[0041]

[0042] Among them, ξ t is the load rate of the distribution station area at time t, F t is the total load value in the distribution substation area, Q is the total capacity of the distribution substation area, and θ is the safe operation boundary of the distribution substation area.

[0043] A storage medium stores a computer program, which executes a method for evaluating the available capacity of a distribution transformer in an area when the computer program is running.

[0044] The beneficial effects of the present invention are:

[0045] By considering the charging status of electric vehicles to develop the available capacity of distribution stations, the impact of large-scale electric vehicles connected to the distribution network can be effectively dealt with. Specifically, the present invention can be applied and benefited in two aspects: first, the present invention is used to calculate the available capacity of distribution stations under different access states of electric vehicles, and more accurately predict the problems of heavy overload of distribution stations, so as to support the planning of distribution networks; second, the available capacity of distribution stations calculated by the present invention is combined to further improve the quantification method of vehicle-grid interaction potential, so as to support the optimization of vehicle-grid interaction regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0047] Figure 1 A flowchart of a method for evaluating the available capacity of a distribution transformer in a substation considering the access status of electric vehicles according to an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of the total negative load results of the distribution transformer area under different electric vehicle access quantities in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Embodiment 1

[0052] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the available capacity of a distribution transformer in a substation considering the access status of electric vehicles, including:

[0053] Step S1, obtaining the distribution capacity of transformers in the substation area and the electricity consumption data of residents in the substation area within a preset time period.

[0054] Collect transformer capacity data in the distribution substation area. Consider different seasons, working days and holidays, and obtain the basic load data of residential electricity consumption in the substation area. The basic load of residential electricity consumption refers to the load value of residential electricity consumption that does not include the charging demand of electric vehicles.

[0055] Step S2: Based on the number of electric vehicles in the substation, a travel chain model is constructed for the charging behavior of electric vehicles using a travel chain method, and the daily charging load value of the substation is calculated according to the itinerary generated by the travel chain model.

[0056] First, analyze the level of electric vehicle ownership in the area. If the charging posts and electric vehicle ownership in the area are available, use real data; if the data on charging posts and electric vehicle ownership in the area are not available, predict the level of electric vehicle ownership in the area based on the number of cars per 100 households and the penetration rate of electric vehicles in the area. On this basis, set the number of electric vehicles, and set the battery capacity and power consumption per 100 kilometers of each electric vehicle based on surveys or statistical data;

[0057] Then, the trip chain method is used to model the charging behavior of electric vehicles, including spatial variables and temporal variables. The spatial variables include the destination and mileage of the ith trip; the temporal variables include the start time, end time, driving duration, and parking duration.

[0058] For the time variable, predictions are made for working days and rest days respectively. Here, the gamma distribution is used to fit the time variable in the electric vehicle charging travel chain. The fitted probability distribution can be used to characterize the access charging probability of electric vehicles, and the charging probability threshold can be set to screen electric vehicle users who are expected to charge. The probability density function is shown in formula (1).

[0059]

[0060] Where x is the first trip time of the electric vehicle on that day, β and α are the probability density function parameters, which can be obtained by fitting the historical travel data. Specifically, the maximum likelihood estimation method can be used. α is the exponential term of the gamma distribution, x α-1 is the time variable exponential term, e - βx is the natural exponential term of the time variable.

[0061] The driving time is related to the type of departure point and destination. The travel destinations are divided into five categories: home, work, shopping / business, social / entertainment, and others. The departure point-destination probability matrix between different destinations can be obtained. Here, the log-normal distribution is used for driving prediction, and the probability density function is shown in formula (2).

[0062]

[0063] Where, t x is the driving distance of the electric vehicle, δ x , is the probability density function parameter, which represents the departure point-destination probability function and can be obtained by fitting historical travel data. Specifically, the maximum likelihood estimation method can be used.

[0064] For spatial variables, a spatial transfer matrix is ​​established to characterize the probability of electric vehicle transfer. Here, the Markov chain method is used to establish the spatial transfer matrix, as shown in formula (3).

[0065]

[0066] In the formula, μ x is the spatial transfer matrix of the x-th electric vehicle, m is the number of departure points, n is the number of destinations, x mn is the probability of an electric car going from the mth starting point to the nth destination.

[0067] The starting point and destination of electric vehicle travel every hour are counted, and the types of starting points and destinations every hour are analyzed to establish a spatial transfer matrix. Based on the predicted driving time, the probability distribution of mileage can be calculated. According to the established travel chain model, the travel information is extracted based on the Monte Carlo simulation method, including characteristic variables such as the first travel time, destination, driving time, driving distance, and stop time, to form a complete itinerary arrangement for electric vehicles in one day.

[0068] In different charging scenarios, the charging frequency can be divided into "one charge per day" and "one charge per multiple days". After returning home from the daily trip, electric vehicle users determine whether to charge on the same day based on their respective "low SOC" thresholds. Based on historical charging data, the "low SOC" threshold of each electric vehicle is extracted. After returning home from the daily trip, electric vehicles with SOC less than the "low SOC" threshold are charged. The charging mode is slow charging, and the charging power is 3.5kW.

[0069] The charging load of all electric vehicles in the distribution area is summarized and the daily charging load value in the distribution area is predicted. The calculation formula is shown in formula (4).

[0070]

[0071] Where P t is the charging load value in the distribution station area at time t, p it The charging power of the i-th charging pile, is the charging probability of the i-th charging pile.

[0072] Step S3: Calculate the total load value of the residential electricity consumption in the area based on the residential electricity consumption data in the area to obtain the basic load value of the area, and calculate the total load value of the distribution station area based on the daily charging load value and the basic load value of the area.

[0073] Analyze the analysis date and classify the date by seasonal characteristics and whether it is a working day. For different types of dates, obtain the basic load of the distribution station area and the predicted electric vehicle charging load data. According to the corresponding time point, add the basic load and charging load to obtain the total load of the distribution station area. The calculation formula is shown in formula (5).

[0074]

[0075] In the formula, F t is the total load value in the distribution station area, P t is the charging load value in the distribution station area at time t, is the basic load value within the distribution station area, and the data is taken from the basic load collection and prediction data of the residential community in step 1.

[0076] Step S4: Based on the distribution transformer capacity of the substation and the total load value of the distribution substation, the evaluation of the available capacity of the substation distribution transformer considering the access status of electric vehicles is completed.

[0077] In this embodiment, the capacity evaluation of the present invention is aimed at the following two situations:

[0078] (I) Based on the available capacity of the distribution area, improve the quantification of the vehicle-grid interaction potential, specifically:

[0079] By subtracting the total load from the safe available capacity of the transformer in the distribution station area, we can get the available capacity of the area at different times, which can then guide the formulation of load management and vehicle-grid interaction strategies within the area.

[0080] (ii) Predict overload problems based on the available capacity of the distribution station area under different access conditions of electric vehicles:

[0081] The distribution transformer load rate can be used to measure the remaining capacity margin of the transformer in the distribution station area and determine whether there is an overload problem. The calculation formula is shown in formula (6).

[0082]

[0083] In the formula, ξ t is the load rate of the distribution station area at time t, F t is the total load value in the distribution area, Q is the total capacity of the distribution area, and θ is the safe operation boundary of the distribution area, which is generally taken as 80%.

[0084] Embodiment 2

[0085] Taking an ordinary residential area in Guangzhou as a representative of the low-voltage area, the influence of charging load on the distribution transformer load rate and distribution transformer load rate is studied. The low-voltage area is powered by a distribution transformer with a capacity of 500kVA and a power factor of 0.9. There are 150 residential users in the area. When the number of electric vehicles is 50, 100, and 150 respectively, the corresponding charging load is calculated and superimposed on the residential load. Finally, the distribution transformer daily load curve is obtained. The total load of the distribution transformer area under different electric vehicle access is as follows: Figure 2 shown.

[0086] The load factor of the distribution transformer under different numbers of electric vehicles is calculated, and the results of the load factor of the distribution transformer area under different numbers of electric vehicles are shown in Table 1.

[0087] Table 1

[0088] Number of electric vehicles Distribution transformer load factor (%) 0 80.00 50 89.20 100 98.41 150 107.61

[0089] Embodiment 3

[0090] A system for evaluating the available capacity of distribution transformers in a distribution area considering the access status of electric vehicles, comprising:

[0091] The data acquisition module is used to obtain the distribution capacity of the transformer in the substation area and the electricity consumption data of the residents in the substation area within a preset time period.

[0092] Collect transformer capacity data in the distribution substation area. Consider different seasons, working days and holidays, and obtain the basic load data of residential electricity consumption in the substation area. The basic load of residential electricity consumption refers to the load value of residential electricity consumption that does not include the charging demand of electric vehicles.

[0093] The electric vehicle load calculation module is used to construct a travel chain model for the charging behavior of electric vehicles based on the number of electric vehicles in the substation area using the travel chain method, and calculate the daily charging load value of the substation area according to the itinerary generated by the travel chain model.

[0094] First, analyze the level of electric vehicle ownership in the area. If the charging posts and electric vehicle ownership in the area are available, use real data; if the data on charging posts and electric vehicle ownership in the area are not available, predict the level of electric vehicle ownership in the area based on the number of cars per 100 households and the penetration rate of electric vehicles in the area. On this basis, set the number of electric vehicles, and set the battery capacity and power consumption per 100 kilometers of each electric vehicle based on surveys or statistical data;

[0095] Then, the trip chain method is used to model the charging behavior of electric vehicles, including spatial variables and temporal variables. The spatial variables include the destination and mileage of the ith trip; the temporal variables include the start time, end time, driving duration, and parking duration.

[0096] For the time variable, predictions are made for working days and rest days respectively. Here, the gamma distribution is used to fit the time variable in the electric vehicle charging travel chain. The probability density function is shown in formula (7).

[0097]

[0098] Where x is the first trip time of the electric vehicle on that day, β and α are the probability density function parameters, which can be obtained by fitting the historical travel data. Specifically, the maximum likelihood estimation method can be used. α is the exponential term of the gamma distribution, x α-1 is the time variable exponential term, e -βx is the natural exponential term of the time variable.

[0099] The driving time is related to the types of departure point and destination. The travel destinations are divided into five categories: home, work, shopping / business, social / entertainment, and others. The departure point-destination probability matrix between different destinations can be obtained. Here, the log-normal distribution is used for driving prediction, and the probability density function is shown in formula (8).

[0100]

[0101] Where, t x is the driving distance of the electric vehicle, δ x , is the probability density function parameter, which represents the departure point-destination probability function and can be obtained by fitting historical travel data. Specifically, the maximum likelihood estimation method can be used.

[0102] For spatial variables, a spatial transfer matrix is ​​established to characterize the probability of electric vehicle transfer. Here, the Markov chain method is used to establish the spatial transfer matrix, as shown in formula (9).

[0103]

[0104] In the formula, μ x is the spatial transfer matrix of the x-th electric vehicle, m is the number of departure points, n is the number of destinations, x mn is the probability of an electric car going from the mth starting point to the nth destination.

[0105] The starting point and destination of electric vehicle travel every hour are counted, and the types of starting points and destinations every hour are analyzed to establish a spatial transfer matrix. Based on the predicted driving time, the probability distribution of mileage can be calculated. According to the established travel chain model, the travel information is extracted based on the Monte Carlo simulation method, including characteristic variables such as the first travel time, destination, driving time, driving distance, and stop time, to form a complete itinerary arrangement for electric vehicles in one day.

[0106] In different charging scenarios, the charging frequency can be divided into "one charge per day" and "one charge per multiple days". After returning home from the daily trip, electric vehicle users determine whether to charge on the same day based on their respective "low SOC" thresholds. Based on historical charging data, the "low SOC" threshold of each electric vehicle is extracted. After returning home from the daily trip, electric vehicles with SOC less than the "low SOC" threshold are charged. The charging mode is slow charging, and the charging power is 3.5kW.

[0107] The charging load of all electric vehicles in the distribution area is summarized and the daily charging load value in the distribution area is predicted. The calculation formula is shown in formula (10).

[0108]

[0109] Where P t is the charging load value in the distribution station area at time t, p it The charging power of the i-th charging pile, is the charging probability of the i-th charging pile.

[0110] The residential electricity load calculation module is used to calculate the total load value of residential electricity in the substation area based on the residential electricity consumption data of the substation area, obtain the basic load value of the substation area, and calculate the total load value of the distribution station area based on the daily charging load value and the basic load value of the substation area.

[0111] The analysis date is analyzed and classified by seasonal characteristics and whether it is a working day. For different types of dates, the basic load of the distribution area and the predicted electric vehicle charging load data are obtained. According to the corresponding time point, the basic load and the charging load are summed to obtain the total load of the distribution area. The calculation formula is shown in formula (11).

[0112]

[0113] In the formula, F t is the total load value in the distribution station area, P t is the charging load value in the distribution station area at time t, It is the basic load value within the distribution station area.

[0114] The available capacity assessment module is used to complete the evaluation of the available capacity of the distribution transformer in the substation considering the access status of electric vehicles based on the distribution transformer capacity of the substation transformer and the total load value of the distribution substation.

[0115] By subtracting the total load from the safe available capacity of the transformer in the distribution station area, we can get the available capacity of the area at different times, which can then guide the formulation of load management and vehicle-grid interaction strategies within the area.

[0116] The distribution transformer load rate can be used to measure the remaining capacity margin of the transformer in the distribution station area and determine whether there is an overload problem. The calculation formula is shown in formula (12).

[0117]

[0118] In the formula, ξ t is the load rate of the distribution station area at time t, F t is the total load value in the distribution area, Q is the total capacity of the distribution area, and θ is the safe operation boundary of the distribution area, which is generally taken as 80%.

[0119] Embodiment 4

[0120] A storage medium stores a computer program, which executes a method for evaluating the available capacity of a distribution transformer in an area when the computer program is running.

[0121] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for evaluating the available capacity of distribution transformers in a substation considering the access status of electric vehicles, characterized in that: include: Obtain the distribution capacity of transformers in the substation area and the electricity consumption data of residents in the substation area within the preset time period; Based on the number of electric vehicles in the substation, a travel chain model is constructed for the charging behavior of electric vehicles using the travel chain method, and the daily charging load value of the substation is calculated according to the itinerary generated by the travel chain model; Calculate the total load value of the residential electricity consumption in the substation area based on the residential electricity consumption data in the substation area to obtain the basic load value of the substation area, and calculate the total load value of the distribution substation area based on the daily charging load value and the basic load value of the substation area; Based on the distribution transformer capacity of the substation and the total load value of the distribution station area, the evaluation of the available capacity of the distribution transformer in the substation considering the access status of electric vehicles is completed.

2. The method for evaluating the available capacity of distribution transformers in a substation considering the access status of electric vehicles according to claim 1 is characterized in that: The process of constructing a travel chain model for electric vehicle charging behavior using the travel chain method specifically includes: The log-normal distribution method is used to construct the departure-destination probability matrix, and the probability density function is: Among them, x is the first trip time of the electric vehicle on that day, β and α are the probability density function parameters, β α is the exponential term of the gamma distribution, x α-1 is the time variable exponential term, e -βx is the natural exponential term of the time variable; A Markov chain method is used to construct a spatial transfer matrix, and a probability distribution of mileage is calculated based on the spatial transfer matrix; A travel chain model is constructed based on the departure point-destination probability matrix and the spatial transfer matrix.

3. The method for evaluating the available capacity of distribution transformers in a substation considering the access status of electric vehicles according to claim 2 is characterized in that: The method for calculating the daily charging load value of the substation according to the itinerary arrangement generated by the travel chain model specifically includes: Based on the constructed travel chain model, the Monte Carlo simulation method is used to extract travel information and form a preset one-day itinerary for electric vehicles; Charging the preset electric vehicle according to the itinerary and the low SOC threshold of the electric vehicle, and calculating the charging load of the charging pile when the preset electric vehicle is charged; Summarize the charging loads of all charging piles in the distribution area to obtain the daily charging load value of the distribution area: Among them, P t is the charging load value in the distribution station area at time t, p it The charging power of the i-th charging pile, is the charging probability of the i-th charging pile.

4. The method for evaluating the available capacity of distribution transformers in a substation considering the access status of electric vehicles according to claim 1 is characterized in that: Based on the distribution transformer capacity of the substation and the total load value of the distribution substation, the process of completing the evaluation of the available capacity of the substation distribution transformer considering the access status of electric vehicles specifically includes: Among them, ξ t is the load rate of the distribution station area at time t, F t is the total load value in the distribution substation area, Q is the total capacity of the distribution substation area, and θ is the safe operation boundary of the distribution substation area.

5. A system for evaluating the available capacity of a distribution transformer in a substation considering the access status of electric vehicles, the system being used to implement the method for evaluating the available capacity of a distribution transformer in a substation according to any one of claims 1 to 4, characterized in that: The system comprises: A data acquisition module is used to obtain the distribution capacity of transformers in the substation area and the electricity consumption data of residents in the substation area within a preset time period; The electric vehicle load calculation module is used to construct a travel chain model for the charging behavior of electric vehicles based on the number of electric vehicles in the substation area by using the travel chain method, and calculate the daily charging load value of the substation area according to the itinerary generated by the travel chain model; A residential electricity load calculation module is used to calculate the total load value of residential electricity in the substation area based on the residential electricity consumption data of the substation area, obtain the basic load value of the substation area, and calculate the total load value of the distribution substation area based on the daily charging load value and the basic load value of the substation area; The available capacity assessment module is used to complete the evaluation of the available capacity of the distribution transformer in the substation considering the access status of electric vehicles based on the distribution transformer capacity of the substation transformer and the total load value of the distribution substation.

6. The system for evaluating the available capacity of distribution transformers in substations considering the access status of electric vehicles according to claim 5 is characterized in that: In the electric vehicle load calculation module, the workflow of constructing a travel chain model for electric vehicle charging behavior using the travel chain method specifically includes: The log-normal distribution method is used to construct the departure-destination probability matrix, and the probability density function is: Among them, x is the first trip time of the electric vehicle on that day, β and α are the probability density function parameters, β α is the exponential term of the gamma distribution, x α-1 is the time variable exponential term, e -βx is the natural exponential term of the time variable; A Markov chain method is used to construct a spatial transfer matrix, and a probability distribution of mileage is calculated based on the spatial transfer matrix; A travel chain model is constructed based on the departure point-destination probability matrix and the spatial transfer matrix.

7. The system for evaluating the available capacity of distribution transformers in substations considering the access status of electric vehicles according to claim 6 is characterized in that: The method for calculating the daily charging load value of the substation according to the itinerary arrangement generated by the travel chain model specifically includes: Based on the constructed travel chain model, the Monte Carlo simulation method is used to extract travel information and form a preset one-day itinerary for electric vehicles; Charging the preset electric vehicle according to the itinerary and the low SOC threshold of the electric vehicle, and calculating the charging load of the charging pile when the preset electric vehicle is charged; Summarize the charging loads of all charging piles in the distribution area to obtain the daily charging load value of the distribution area: Among them, P t is the charging load value in the distribution station area at time t, p it The charging power of the i-th charging pile, is the charging probability of the i-th charging pile.

8. The system for evaluating the available capacity of distribution transformers in substations considering the access status of electric vehicles according to claim 5 is characterized in that: In the available capacity evaluation module, based on the distribution transformer capacity of the distribution transformer area and the total load value of the distribution transformer area, the process of completing the evaluation of the available capacity of the distribution transformer area considering the access status of electric vehicles specifically includes: Among them, ξ t is the load rate of the distribution station area at time t, F t is the total load value in the distribution substation area, Q is the total capacity of the distribution substation area, and θ is the safe operation boundary of the distribution substation area.

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when running, executes the method for evaluating available capacity of distribution transformers in a substation area according to any one of claims 1 to 4.

Images