A method for calculating the carrying capacity of electric vehicles in distribution networks

Abstract

The present invention discloses a method for calculating the carrying capacity of electric vehicles in a distribution network, and belongs to the technical field of calculation of the carrying capacity of distribution networks. The method for calculating the carrying capacity of electric vehicles in a distribution network includes: constructing a travel chain model of electric vehicles, describing the transfer and stay of electric vehicles between different locations through a state transfer matrix; using a Monte Carlo simulation method to continuously simulate and obtain the charging demand of electric vehicles in various areas at various times of the day; constructing an optimization model for the carrying capacity of electric vehicles in a distribution network with the largest access capacity; using Wasserstein distance to construct a fuzzy set of photovoltaic output, establishing a distributed robust optimization model, and transforming the optimization model for solution. The method for calculating the carrying capacity of electric vehicles in a distribution network described in the present invention can solve the problem of poor accuracy in the calculation of the carrying capacity of electric vehicles in a distribution network by existing calculation methods, and has the advantage of ensuring the safe and stable operation of the distribution network after access to the charging load.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network carrying capacity calculation, and in particular to a method for calculating the carrying capacity of electric vehicles in a distribution network. Background Art

[0002] Currently, a large number of new energy electric vehicles are connected to distribution networks. However, the charging load of electric vehicles is characterized by randomness, volatility, intermittence, and mobility. This large-scale, disorderly connection may overlap with the peak hours of conventional loads, resulting in a "peak upon peak" situation, affecting the safe and stable operation of the distribution network. Therefore, under the premise of meeting node voltage, line capacity, and other safe operation constraints, evaluating the maximum EV connection capacity of the distribution network—that is, the distribution network's EV carrying capacity—is crucial for the safe and stable operation of the distribution network.

[0003] Methods for calculating the electric vehicle carrying capacity of distribution networks primarily include simulation-based methods and optimization model-based methods. The simulation-based calculation method typically involves building a distribution network model using power flow simulation software and gradually increasing the penetration rate of electric vehicles in the distribution network until the relevant constraint indicators exceed the limit. This method is relatively simple in principle, but its universality is limited, and the use of electric vehicle penetration as an indicator of carrying capacity is relatively crude. Optimization model-based methods currently rarely consider the volatility of photovoltaic power output connected to the distribution network when calculating electric vehicle carrying capacity. This is the impact of the uncertainty of photovoltaic output on the electric vehicle carrying capacity of the distribution network, resulting in poor accuracy in the calculated distribution network load carrying capacity. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for calculating the carrying capacity of electric vehicles in a distribution network, which solves the problem of poor accuracy of the existing calculation methods for the carrying capacity of electric vehicles in a distribution network and has the advantage of ensuring the safe and stable operation of the distribution network after connecting to the charging load.

[0005] To achieve the above object, the present invention provides a method for calculating the carrying capacity of electric vehicles in a distribution network, comprising the following steps:

[0006] S1. Analyze the statistics of electric vehicle travel to obtain the travel patterns of electric vehicle users, build an electric vehicle travel chain model, and describe the transfer and stay of electric vehicles between different locations through the state transition matrix;

[0007] S2. Monte Carlo simulation is used to continuously simulate the charging demand of electric vehicles in various areas at various times of the day;

[0008] S3. Construct an optimization model for electric vehicle carrying capacity of the distribution network with the largest access capacity;

[0009] S4. Obtain a sample set of historical data on photovoltaic output, use Wasserstein distance to construct a fuzzy set of photovoltaic output, establish a distributed robust optimization model, and transform the optimization model containing photovoltaic output uncertainty into a deterministic electric vehicle carrying capacity model for solution.

[0010] Preferably, in S1, the travel chain describes the entire process of the user starting from the starting point based on the travel purpose, passing through several places in chronological order, and finally arriving at the destination to complete the travel;

[0011] The state transition matrix expression is:

[0012] ;

[0013] in, express T Time from area M Go to area N The probability of M for H 、 W 、 P or O , N for H 、 W 、 P or O , H Indicates a family residential area. W Indicates the work and business area. P Indicates a public entertainment area. O Indicates other areas.

[0014] Preferably, in S1, after the electric vehicle completes the journey, the time it stays at different destination nodes is fitted by a probability density function. The probability density function of the length of time the electric vehicle stays in the residential area is:

[0015] ;

[0016] in, is the shape parameter, is the scale parameter, The parking time of electric vehicles;

[0017] Electric vehicles in W 、 P 、 O The probability density function of the dwell time in the area is:

[0018] ;

[0019] in, is a positional parameter, is the scale parameter, is the shape parameter; when hour, ;when hour, .

[0020] Preferably, in S2, the specific process of continuously simulating the charging demand of electric vehicles in various areas at various time periods throughout the day using the Monte Carlo simulation method is as follows:

[0021] S21. Model the road network and generate a road distance matrix D, inputting the road distance matrix and the road impedance matrix calculated according to the road impedance model;

[0022] S22. Build an electric vehicle charging load model, initialize basic vehicle information, extract the complete travel chain of each electric vehicle, and plan a travel route with the shortest travel time for the vehicle based on the travel demand of the vehicle at different times according to the road impedance matrix;

[0023] S23. Set different numbers of electric vehicles to perform Monte Carlo sampling simulation, repeat the simulation continuously, analyze and obtain the charging demand corresponding to each road network node at each time, and obtain the spatiotemporal distribution of the electric vehicle charging demand.

[0024] Preferably, in S21, the road network topology is abstracted into a weighted directed graph, and the topological mathematical expression model of the road network is:

[0025] ;

[0026] in, Represents the traffic network, Representation diagram The set of all nodes, Indicates a specific node. is the number of nodes; Representation diagram The collection of all road segments, Represents a connection node and nodes road sections; Represents a set of divided time periods, is the number of time periods divided, t Indicates a specific time period; W is the set of road segment weights, Indicates the time period t Inner section The weight of

[0027] Road section weight W represents the road travel cost, t time The impedance model of the road section is:

[0028] ;

[0029] Among them, saturation , is the traffic flow of the road section, C For traffic capacity; t 0 is the zero flow travel time, Both are impedance influencing factors.

[0030] Preferably, in said S22, a charging load model of an electric vehicle is constructed, including battery capacity, battery state of charge, charging load and charging time;

[0031] The battery capacity of different types of electric vehicles obeys a uniform distribution, which can be expressed as:

[0032] ;

[0033] in, Indicates the battery capacity of electric vehicles, are the upper and lower limits of the electric vehicle battery capacity distribution range, respectively, in kW·h;

[0034] Based on the travel chain model, the battery state of charge (SOC) of an electric vehicle at the end of each trip is:

[0035] ;

[0036] in, Indicates the Electric cars in the SOC at the end of the trip, For the SOC at the end of the trip, is the vehicle battery capacity; For electric vehicles Mileage of the trip; The power consumption of electric vehicles per kilometer, in kW / km;

[0037] When an electric vehicle generates a charging demand, the calculation expression of the charging load and charging time is:

[0038] ;

[0039] in, Indicates the Electric vehicles in t The target charging capacity at all times; is a 0-1 variable, indicating the The charging demand status of electric vehicles; Target charging SOC for electric vehicles; For the Electric vehicles from t The duration of charging from the moment it starts; Indicates charging power.

[0040] Preferably, in S3, constructing an optimization model for the electric vehicle carrying capacity of a distribution network with the largest access capacity includes the following steps:

[0041] S31, establishing an objective function;

[0042] The specific expression of the objective function is:

[0043] ;

[0044] Among them, the collection Indicates the functional area to which EVCS belongs, . For functional areas Middle The electric vehicle charging load that can be connected to each node is Functional areas predicted by simulation Charging needs, For functional areas The number of nodes connected to charging stations, is a parameter that controls the steepness of the satisfaction function. is the parameter for the degree of impact of the penalty term on charging satisfaction. The operator max{X,0} indicates that the larger value is taken, that is, if X≥0, the value is X, otherwise it is 0.

[0045] S32. Construct constraint conditions, which include node voltage constraints, branch current constraints, branch transmission power constraints, power flow constraints, and charging demand constraints.

[0046] Preferably, in said S32,

[0047] The expression of the node voltage constraint is:

[0048] ;

[0049] in, For nodes The voltage, and are the upper and lower limits of the node voltage respectively, A collection of nodes.

[0050] The branch current constraint expression is:

[0051] ;

[0052] in, For branch The current, is the upper limit of the branch current, A collection of branches.

[0053] The branch transmission power constraint is:

[0054] ;

[0055] in, and Branch Active and reactive power flowing through; For branch The maximum apparent power flowing through is converted into a linear form and expressed as:

[0056] ;

[0057] The Distflow model is used for power flow constraints.

[0058] Preferably, in S4, the fuzzy set construction process is specifically as follows:

[0059] The empirical probability distribution is constructed based on the historical data of the uncertainty variable, and its specific expression is:

[0060] ;

[0061] in, is the historical experience distribution, Indicates uncertainty variables Historical data Dirac distribution centered on ;

[0062] The Wasserstein distance is used to measure the distance between two distributions. The expression of Wasserstein distance is:

[0063] ;

[0064] in, is the true distribution, and The support sets of , express and The joint probability distribution of represents the norm;

[0065] Given radius , the data-driven Wasserstein fuzzy set is constructed as:

[0066] ;

[0067] in, express The support set of Construct a radius sphere, used to represent the true distribution Distribution with historical experience The distance between them.

[0068] Preferably, in S4, the construction and transformation process of the distributed robust optimization model is specifically as follows:

[0069] The uncertainty of photovoltaic output is expressed as:

[0070] ;

[0071] in, For nodes The actual photovoltaic output at For nodes The predicted photovoltaic output value at For nodes The photovoltaic output prediction deviation value at ;

[0072] Considering the uncertainty variables Function The expectation under the worst distribution is minimized, and the optimization problem is rewritten as follows:

[0073] ;

[0074] in, is the decision variable, represent The feasible domain of is a random variable The support set of .

[0075] According to the strong duality theorem, the problem can be transformed into the following solvable form:

[0076] ;

[0077] in, The upper and lower bounds are , and is the introduced dual variable.

[0078] The advantages and positive effects of the method for calculating the electric vehicle carrying capacity of the distribution network described in the present invention are as follows: the present invention first obtains the charging demand of electric vehicles based on the simulation of the coupling model of the road network and the distribution network; by obtaining the travel patterns of electric vehicle users, a spatiotemporal distribution model of the charging demand is generated using Monte Carlo simulation; then, an optimization model with the goal of maximizing charging satisfaction is constructed, and a distributed robust optimization model is constructed based on the Wasserstein distance in combination with the uncertainty of distributed photovoltaic output. By solving the dual transformation of the model, the maximum accessible charging load capacity of each node in the distribution network is determined. The present invention can accurately evaluate the load carrying capacity of the node, effectively deal with the uncertainty of photovoltaic output, and improve the safety and reliability of the distribution network operation.

[0079] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 is a flow chart of an embodiment of a calculation method of the present invention;

[0081] Figure 2 A flow chart of generating a spatiotemporal distribution of electric vehicle charging load demand according to a calculation method embodiment of the present invention;

[0082] Figure 3 It is a 13-node Nguyen-Dupuis road network;

[0083] Figure 4 For the improved IEEE-33 node distribution network;

[0084] Figure 5 is the charging load distribution curve of each functional area obtained through Monte Carlo simulation;

[0085] Figure 6 The following are the analysis results of the charging demand and electric vehicle carrying capacity of electric vehicle charging stations (EVCS); (a) the charging demand and electric vehicle carrying capacity of electric vehicle charging stations 1 and 2 in area P; (b) the charging demand and electric vehicle carrying capacity of electric vehicle charging stations 3-5 in area R; (c) the charging demand and electric vehicle carrying capacity of electric vehicle charging stations 6 and 7 in area W; (d) the charging demand and electric vehicle carrying capacity of electric vehicle charging station 8 in area O. DETAILED DESCRIPTION

[0086] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is usually placed when in use. These are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention. In the description of the present invention, it should also be noted that, unless otherwise expressly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the internal parts of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0087] In this application, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. In the event of any inconsistency, the meaning described in this specification or the meaning derived from the contents recorded in this specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0088] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0089] like Figure 1 、 Figure 3 As shown, a method for calculating the electric vehicle carrying capacity of a distribution network includes the following steps:

[0090] S1. Analyze the statistics of electric vehicle travel to obtain the travel patterns of electric vehicle users, build an electric vehicle travel chain model, and describe the transfer and stay of electric vehicles between different locations through the state transition matrix.

[0091] A trip chain describes the entire journey of a user, starting from a starting point, passing through several stopover points in chronological order, and finally arriving at the destination, based on their travel purpose. Based on my country's national conditions and the living habits of urban residents, the travel destination areas are divided into residential areas (H zone), work and business areas (W zone), public entertainment areas (P zone), and other areas (O zone).

[0092] The state transition matrix expression is:

[0093] ;

[0094] in, express T Time from areaM Go to area N The probability of M for H 、 W 、 P or O , N for H 、 W 、 P or O From the statistics of electric vehicle travel, we can analyze the important travel characteristics of electric vehicle users, such as the number of daily trips, first trip time, and travel destination. Based on this, we can construct the travel state transition matrix of electric vehicle users throughout the day.

[0095] After the electric vehicle completes its journey, the time it spends at different destination nodes is fitted by a probability density function. The probability of the electric vehicle staying in the residential area (area H) satisfies the Weibull distribution, and the probability density function is:

[0096] ;

[0097] in, is the shape parameter, is the scale parameter, The parking time of electric vehicles.

[0098] In other functional areas, the probability distribution of the parking time of electric vehicles satisfies the generalized extreme value distribution. W 、 P 、 O The probability density function of the dwell time in the area is:

[0099] ;

[0100] in, is a positional parameter, is the scale parameter, is the shape parameter; when hour, ;when hour, .

[0101] S2. Monte Carlo simulation method is used to continuously simulate the charging demand of electric vehicles in various areas at various times of the day.

[0102] like Figure 2 The specific process of using the Monte Carlo simulation method to continuously simulate the charging demand of electric vehicles in various areas at various times of the day is as follows:

[0103] S21. Model the road network to generate a road distance matrix D, and input the road distance matrix and the road impedance matrix calculated according to the road impedance model.

[0104] Abstract the topological structure of the road network as a weighted directed graph. The topological mathematical expression model of the road network is:

[0105] ;

[0106] Among them, represents the traffic road network, represents the set of all nodes of the graph, represents a specific node, is the number of nodes; represents the set of all road segments of the graph, represents the road segment connecting node and node ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

[0111] Construct an electric vehicle charging load model, including battery capacity, battery state of charge, charging load and charging time.

[0112] The battery capacity of different types of electric vehicles obeys a uniform distribution, which can be expressed as:

[0113] ;

[0114] in, q Indicates the battery capacity of electric vehicles, s 、 h are the upper and lower limits of the electric vehicle battery capacity distribution range, respectively, in kW·h;

[0115] The state of charge (SOC) of an electric vehicle battery can be used to reflect the change in battery charge during driving. Based on the travel chain model, the SOC of an electric vehicle battery at the end of each trip is:

[0116] ;

[0117] in, Indicates the Electric cars in the SOC at the end of the trip, For the SOC at the end of the trip, is the vehicle battery capacity; For electric vehicles Mileage of the trip; The power consumption of electric vehicles per kilometer, in kW / km;

[0118] When an electric vehicle generates a charging demand, the calculation expression of the charging load and charging time is:

[0119] ;

[0120] in, Indicates the Electric vehicles in t The target charging capacity at all times; is a 0-1 variable, indicating the The charging demand status of electric vehicles; Target charging SOC for electric vehicles; For the Electric vehicles from t The duration of charging from the moment it starts; Indicates charging power.

[0121] S23. Set different numbers of electric vehicles to perform Monte Carlo sampling simulation, repeat the simulation continuously, analyze and obtain the charging demand corresponding to each road network node at each time, and obtain the spatiotemporal distribution of the electric vehicle charging demand.

[0122] S3. Construct an optimization model for the electric vehicle carrying capacity of the distribution network with the largest access capacity.

[0123] Building an optimization model for electric vehicle carrying capacity of the distribution network with the largest access capacity includes the following steps:

[0124] S31, establishing an objective function with the goal of maximizing the charging satisfaction of both the charging station and the electric vehicle user;

[0125] The specific expression of the objective function is:

[0126] ;

[0127] Among them, the collection Indicates the functional area to which EVCS belongs, . For functional areas Middle The electric vehicle charging load that can be connected to each node is Functional areas predicted by simulation Charging needs, For functional areas The number of nodes connected to charging stations, is a parameter that controls the steepness of the satisfaction function. is the parameter for the degree of impact of the penalty term on charging satisfaction. The operator max{X,0} indicates that the larger value is taken, that is, if X≥0, the value is X, otherwise it is 0.

[0128] S32. Construct constraint conditions, which include node voltage constraints, branch current constraints, branch transmission power constraints, power flow constraints, and charging demand constraints.

[0129] The expression of the node voltage constraint is:

[0130] ;

[0131] in, For nodes The voltage, and are the upper and lower limits of the node voltage respectively, A collection of nodes.

[0132] The branch current constraint expression is:

[0133] ;

[0134] in, For branch The current, is the upper limit of the branch current, A collection of branches.

[0135] The branch transmission power constraint is:

[0136] ;

[0137] in, and Branch Active and reactive power flowing through; For branch The maximum apparent power flowing through is converted into a linear form and expressed as:

[0138] ;

[0139] The power flow constraint adopts the Distflow model, which is expressed as:

[0140] ;

[0141] in, and Node The active power and reactive power at Represents a node is the set of branches of the root node, Represents a node is the branch set of the child nodes, and For branch ef The resistance and reactance, represents the square of the node voltage amplitude, represents the square of the branch current amplitude, and Node The active load and reactive load at and Node The substation active power and substation reactive power at Representation node The electric vehicle charging load that can be connected, Representation node The photovoltaic output at the site.

[0142] S4. Obtain a sample set of historical data on photovoltaic output, use Wasserstein distance to construct a fuzzy set of photovoltaic output, establish a distributed robust optimization model, and transform the optimization model containing photovoltaic output uncertainty into a deterministic electric vehicle carrying capacity model for solution.

[0143] The specific process of constructing fuzzy sets is as follows:

[0144] The empirical probability distribution is constructed based on the historical data of the uncertainty variable, and its specific expression is:

[0145] ;

[0146] in, is the historical experience distribution, Indicates uncertainty variables Historical data The Dirac distribution centered at .

[0147] The Wasserstein distance is used to measure the distance between two distributions. The expression of Wasserstein distance is:

[0148] ;

[0149] in, is the true distribution, and The support sets of , express and The joint probability distribution of Represents the norm.

[0150] Given radius , the data-driven Wasserstein fuzzy set is constructed as:

[0151] ;

[0152] in, express The support set of Construct a radius sphere, used to represent the true distribution Distribution with historical experience The distance between them.

[0153] The construction and transformation process of the distributed robust optimization model is as follows:

[0154] The uncertainty of photovoltaic output is expressed as:

[0155] ;

[0156] in, For nodes The actual photovoltaic output at For nodes The predicted photovoltaic output value at For nodes The photovoltaic output prediction deviation value at .

[0157] Considering the uncertainty variables Function The expectation under the worst distribution is minimized, and the optimization problem is rewritten as follows:

[0158] ;

[0159] in, is the decision variable, represent The feasible domain of is a random variable The support set of .

[0160] According to the strong duality theorem, the problem can be transformed into the following solvable form:

[0161] ;

[0162] in, The upper and lower bounds are , and is the introduced dual variable.

[0163] In order to facilitate understanding, a simulation test is conducted on the method of the present invention in combination with a specific scenario. Figure 3 13-node Nguyen-Dupuis road network and Figure 4 This study simulated and verified the IEEE-33 node distribution network in the transportation network. The number of electric vehicles (EVs) simulated in this network was set to 1200, including 1000 private cars and 200 taxis. The EV parameters were set as follows: the battery capacity was uniformly distributed within the range [35, 80] kWh; the SOC followed a normal distribution with a mean of 0.8 and a standard deviation of 0.1; and the initial departure time followed a normal distribution with a mean of 7.3 and a standard deviation of 1.3. In the IEEE-33 node system, distributed photovoltaic (PV) units with a rated capacity of 0.3 MW were connected to nodes 8, 12, 17, and 27, while electric vehicle charging stations (EVCS) were connected to nodes 3, 5, 9, 12, 15, 20, 24, and 29.

[0164] Figure 5The charging load distribution curves for each functional area, derived through Monte Carlo simulations, are presented in Figure 2. Summarizing charging demand across regions reveals that the total EV charging load is higher during daytime hours (9:00 AM - 8:00 PM), overlapping with traditional peak electricity demand and potentially placing pressure on the distribution network.

[0165] Based on the distribution of charging demand, the electric vehicle carrying capacity analysis is carried out. The results are as follows: Figure 6 As shown in Figure 2, it can be seen that the charging load carrying capacity allocated to electric vehicle charging stations in different areas is different and varies throughout the day.

[0166] Table 1 compares the calculation results of different methods during the 12:00 PM period, demonstrating that the method described in this embodiment can more accurately calculate the charging load that the distribution network can carry. Furthermore, to verify the adaptability of the out-of-sample method, 1,000 PV output scenarios generated by Monte Carlo simulation were substituted into the calculated load capacity results to analyze whether the distribution network operates within the constraints. The results demonstrate that the method described in this embodiment can accurately calculate the EV load capacity of the distribution network while ensuring broad applicability.

[0167] Table 1 Calculation results of different methods at 12:00

[0168] ;

[0169] Therefore, the method for calculating the carrying capacity of electric vehicles in the distribution network described in the present invention can solve the problem of poor accuracy in calculating the carrying capacity of electric vehicles in the distribution network using existing calculation methods, and has the advantage of ensuring safe and stable operation of the distribution network after connecting to the charging load.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for calculating the carrying capacity of electric vehicles in a distribution network, characterized in that: The following steps are involved: S1. Analyze the statistics of electric vehicle travel to obtain the travel patterns of electric vehicle users, build an electric vehicle travel chain model, and describe the transfer and stay of electric vehicles between different locations through the state transition matrix; S2. Monte Carlo simulation is used to continuously simulate the charging demand of electric vehicles in various areas at various times of the day; S3. Construct an optimization model for electric vehicle carrying capacity of the distribution network with the largest access capacity; S4. Obtain a sample set of historical PV output data, construct a fuzzy set of PV output using Wasserstein distance, establish a distributed robust optimization model, and transform the optimization model including PV output uncertainty into a deterministic EV carrying capacity model for solution; In S3, constructing an optimization model for electric vehicle carrying capacity of a distribution network with the largest access capacity includes the following steps: S31, establishing an objective function; The specific expression of the objective function is: ； Among them, the collection Indicates the functional area to which EVCS belongs, For functional areas Middle The electric vehicle charging load that can be connected to each node is Functional areas predicted by simulation Charging needs, For functional areas The number of nodes connected to charging stations, is a parameter that controls the steepness of the satisfaction function. is the parameter of the degree of influence of the penalty item on charging satisfaction; the operator max{X,0} indicates that the larger value of the two is taken, that is, if X≥0, the value is X, otherwise it is 0; S32. Construct constraint conditions, which include node voltage constraints, branch current constraints, branch transmission power constraints, power flow constraints, and charging demand constraints.

2. The method for calculating the electric vehicle carrying capacity of a distribution network according to claim 1, characterized in that: In S1, the travel chain describes the entire process of a user starting from a starting point based on the travel purpose, passing through several en route locations in chronological order, and finally arriving at the destination to complete the trip; The state transition matrix expression is: ； in, express Time from area Go to area The probability of for 、 、 or , for 、 、 or , Indicates a family residential area. Indicates the work and business area. Indicates a public entertainment area. Indicates other areas.

3. The method for calculating the electric vehicle carrying capacity of a distribution network according to claim 2, characterized in that: In S1, after the electric vehicle completes its journey, the time it stays at different destination nodes is fitted by a probability density function. The probability density function of the length of time the electric vehicle stays in the residential area is: ； in, is the shape parameter, is the scale parameter, The parking time of electric vehicles; Electric vehicles in 、 、 The probability density function of the dwell time in the area is: ； in, is a positional parameter, is the scale parameter, is the shape parameter; when hour, ;when hour, .

4. The method for calculating the electric vehicle carrying capacity of a distribution network according to claim 3, characterized in that: In S2, the specific process of using the Monte Carlo simulation method to continuously simulate and obtain the charging demand of electric vehicles in various areas at various time periods throughout the day is as follows: S21. Model the road network and generate a road distance matrix D, inputting the road distance matrix and the road impedance matrix calculated according to the road impedance model; S22. Build an electric vehicle charging load model, initialize basic vehicle information, extract the complete travel chain of each electric vehicle, and plan a travel route with the shortest travel time for the vehicle based on the travel demand of the vehicle at different times according to the road impedance matrix; S23. Set different numbers of electric vehicles to perform Monte Carlo sampling simulation, repeat the simulation continuously, analyze and obtain the charging demand corresponding to each road network node at each time, and obtain the spatiotemporal distribution of the electric vehicle charging demand.

5. The method for calculating the electric vehicle carrying capacity of a distribution network according to claim 4, characterized in that: In S21, the road network topology is abstracted into a weighted directed graph, and the topological mathematical expression model of the road network is: ； in, Represents the traffic network, Representation diagram The set of all nodes, Indicates a specific node. is the number of nodes; Representation diagram The collection of all road segments, Represents a connection node and nodes road sections; Represents a set of divided time periods, is the number of time periods divided, Indicates a specific time period; is the set of road segment weights, Indicates the time period Inner section The weight of Road section weight represents the road travel cost, time The impedance model of the road section is: ； Among them, saturation , is the traffic flow of the road section, For traffic capacity; is the zero flow travel time, Both are impedance influencing factors.

6. The method for calculating the electric vehicle carrying capacity of a distribution network according to claim 5, characterized in that: In said S22, a charging load model of an electric vehicle is constructed, including battery capacity, battery state of charge, charging load and charging time; The battery capacity of different types of electric vehicles obeys a uniform distribution, which can be expressed as: ； in, Indicates the battery capacity of electric vehicles, are the upper and lower limits of the electric vehicle battery capacity distribution range, respectively, in units of ; Based on the travel chain model, the battery state of charge (SOC) of an electric vehicle at the end of each trip is: ； in, Indicates the Electric cars in the SOC at the end of the trip, For the SOC at the end of the trip, is the vehicle battery capacity; For electric vehicles Mileage of the trip; The power consumption of electric vehicles per kilometer, in kW / km; When an electric vehicle generates a charging demand, the calculation expression of the charging load and charging time is: ； in, Indicates the Electric vehicles in The target charging capacity at all times; is a 0-1 variable, indicating the The charging demand status of electric vehicles; Target charging SOC for electric vehicles; For the Electric vehicles from The duration of charging from the moment it starts; Indicates charging power.

7. The method for calculating the electric vehicle carrying capacity of a distribution network according to claim 6, characterized in that: In the S32, The expression of the node voltage constraint is: ； in, For nodes The voltage, and are the upper and lower limits of the node voltage respectively, is a set of nodes; The branch current constraint expression is: ； in, For branch The current, is the upper limit of the branch current, For branch collection; The branch transmission power constraint is: ； in, and Branch Active and reactive power flowing through; For branch The maximum apparent power flowing through is converted into a linear form and expressed as: ； The Distflow model is used for power flow constraints.

8. The method for calculating the electric vehicle carrying capacity of a distribution network according to claim 7, characterized in that: In S4, the construction process of the fuzzy set is specifically as follows: The empirical probability distribution is constructed based on the historical data of the uncertainty variable, and its specific expression is: ； in, is the historical experience distribution, Indicates uncertainty variables Historical data Dirac distribution centered on ; The Wasserstein distance is used to measure the distance between two distributions. The expression of Wasserstein distance is: ； in, is the true distribution, and The support sets of , express and The joint probability distribution of represents the norm; Given radius , the data-driven Wasserstein fuzzy set is constructed as: ； in, express The support set of Construct a radius sphere, used to represent the true distribution Distribution with historical experience The distance between them.

9. The method for calculating the electric vehicle carrying capacity of a distribution network according to claim 8, characterized in that: In S4, the construction and transformation process of the distributed robust optimization model is specifically as follows: The uncertainty of photovoltaic output is expressed as: ； in, For nodes The actual photovoltaic output at For nodes The predicted photovoltaic output value at For nodes The photovoltaic output prediction deviation value at ; Considering the uncertainty variables Function The expectation under the worst distribution is minimized, and the optimization problem is rewritten as follows: ； Among them, g is the decision variable, represents the feasible region of g, is a random variable The support set of According to the strong duality theorem, the problem is transformed into the following solvable form: ； in, The upper and lower bounds are , and is the introduced dual variable.

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