Calculation method of electric vehicle carrying capacity in flexible interconnected distribution network considering traffic flow

Abstract

The present invention relates to a method for calculating the carrying capacity of electric vehicles in a flexible interconnected distribution network considering traffic flow, comprising the following steps: establishing an electric vehicle space-time control model containing multiple electric vehicle access modes under the influence of traffic flow; using the electric vehicle space-time control model as the electric vehicle space-time control constraint containing multiple electric vehicle access modes under the influence of traffic flow, considering the control means including intelligent soft switch power regulation, and establishing a calculation model for the carrying capacity of electric vehicles in a flexible interconnected distribution network considering traffic flow; converting the calculation model for the carrying capacity of electric vehicles in a flexible interconnected distribution network into a mixed integer second-order cone programming model; using a nested tightening relaxation algorithm that integrates a sequential boundary tightening algorithm and a gradually tightening linear cutting algorithm to solve the mixed integer second-order cone programming model, and obtaining the maximum number of electric vehicles that can be carried by the flexible interconnected distribution network under the influence of traffic flow. Compared with the prior art, the present invention has the advantages of high accuracy and high computational efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, in particular to a flexible interconnected distribution network containing large-scale electric vehicles, and in particular to a method for calculating the electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow. Background Art

[0002] With the popularity and rapid development of electric vehicles (EVs), their impact on distribution networks has become increasingly significant. The large-scale and disorderly access of electric vehicles has also led to severe challenges to the electric vehicle carrying capacity of distribution networks. Under the constraints of safe operation, the maximum number of electric vehicles allowed to be connected to the distribution network is the distribution network's carrying capacity for electric vehicles. The accurate calculation of this value is crucial to ensure the safe operation of the distribution network and promote the large-scale access of electric vehicles.

[0003] At present, there have been many studies on the calculation method of the electric vehicle carrying capacity of the distribution network. For example, patent application CN117833337A discloses a method and medium for calculating the carrying capacity of the distribution network considering the spatial scheduling of electric vehicles. However, there are still the following limitations to be solved: 1) Existing studies have taken into account the controllable characteristics of electric vehicles in the time or space dimension when calculating the carrying capacity of electric vehicles in the distribution network, but all assume that the control of electric vehicles is not affected by traffic flow. Controlling electric vehicles without considering traffic conditions may cause traffic congestion and affect the willingness of electric vehicle users to participate in the control. In practice, electric vehicles may not be able to reach the designated charging station within a given time, which restricts the accuracy of the calculation results of the carrying capacity of electric vehicles in the distribution network. The time-space control of electric vehicles that integrates the information of the power grid and the traffic network can effectively enhance the adaptability of the distribution network to the access of electric vehicles, but the coupling of energy flow and traffic flow makes the problem present the characteristics of higher dimensions and more non-convex constraints, which increases the difficulty of calculating the carrying capacity of electric vehicles in the distribution network. 2) Existing studies on the calculation method of the carrying capacity of electric vehicles in the distribution network are all applied to the distribution network with radial topology structure, which limits the scale of electric vehicles that the distribution network can carry to a certain extent. Flexible interconnected distribution networks containing flexible interconnected devices such as intelligent soft switches have wide-area power flexible regulation capabilities and can provide reactive power compensation, which helps support large-scale access to electric vehicles. Existing technologies have not taken flexible interconnected distribution networks into consideration.

[0004] Therefore, the calculation method of electric vehicle carrying capacity in flexible interconnected distribution network under the influence of traffic flow deserves further study. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method for calculating the electric vehicle carrying capacity of a flexible interconnected distribution network taking into account traffic flow, which can efficiently and accurately calculate the maximum number of electric vehicles that a flexible interconnected distribution network can carry under the influence of traffic flow.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for calculating electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow comprises the following steps:

[0008] Establish an electric vehicle space-time control model with multiple electric vehicle access modes under the influence of traffic flow;

[0009] Taking the electric vehicle spatiotemporal control model as the electric vehicle spatiotemporal control constraint with multiple electric vehicle access modes under the influence of traffic flow, considering the control means including intelligent soft switch power regulation, a calculation model of electric vehicle carrying capacity of flexible interconnected distribution network considering traffic flow is established;

[0010] Based on the quadratic convex envelope relaxation method, the big-M method and the second-order cone relaxation method, the electric vehicle carrying capacity calculation model of the flexible interconnected distribution network is transformed into a mixed integer second-order cone programming model;

[0011] The nested tightening relaxation algorithm that integrates the sequential boundary tightening algorithm and the gradually tightening linear cut algorithm is used to solve the mixed integer second-order cone programming model to obtain the maximum number of electric vehicles that the flexible interconnected distribution network can carry under the influence of traffic flow.

[0012] Furthermore, the establishment of the electric vehicle space-time control model containing multiple electric vehicle access modes under the influence of traffic flow is specifically as follows:

[0013] Based on a semi-dynamic traffic flow model covering fuel vehicles and electric vehicles with various electric vehicle access modes, the electric vehicle charging position regulation and the electric vehicle charging and discharging regulation are considered to construct the electric vehicle time-space control model.

[0014] Furthermore, when the electric vehicle charging position is regulated, the electric vehicle charging and discharging prices of each charging station in different time periods are adjusted, and user satisfaction is taken into consideration to guide the electric vehicle to change the charging position. Factors considered when obtaining the user satisfaction include the distance between charging stations, the electric vehicle charging and discharging prices, the charging waiting time and the charging station service quality.

[0015] Furthermore, when performing the charging and discharging regulation of the electric vehicle, it is ensured that the state of charge of the electric vehicle is within an allowable range when it arrives at the charging station, and different electric vehicle power calculation methods are designed for different electric vehicle access modes.

[0016] Furthermore, the electric vehicle carrying capacity calculation model of the flexible interconnected distribution network aims to maximize the total number of electric vehicles in various access modes that the flexible interconnected distribution network can carry, and the decision variable is the number of electric vehicles that the flexible interconnected distribution network can accommodate for charging from each starting point to each charging station. The constraints considered include the time and space control constraints of the electric vehicles and the upper limit constraints on operating costs, substation node power constraints, on-load tap-changing transformer regulation constraints, node voltage constraints, line current constraints, flexible interconnected distribution network flow constraints, distributed power generation operation constraints, static VAR compensator operation constraints, energy storage operation constraints and intelligent soft switch operation constraints.

[0017] Furthermore, the iterative framework of the nested tightening relaxation algorithm is a double-layer iterative framework based on a sequential boundary tightening algorithm and a gradually tightening linear cutting algorithm. The process of solving the mixed integer second-order cone programming model includes:

[0018] Step 1: Set the second-order cone relaxation gap threshold ε SOCR,avg , and set the number of iterations of the gradually tightening linear cutting algorithm n = 1;

[0019] Step 2: Set the secondary convex envelope relaxation gap threshold ε QC,avg , and set the number of iterations of the sequential boundary tightening algorithm u=1;

[0020] Step 3: Solve the mixed integer second-order cone programming model;

[0021] Step 4: Calculate the average relaxation gap χ introduced by the quadratic convex envelope relaxation QC,avg , if χ QC,avg Below the threshold ε QC,avg , go to step 5, otherwise update the upper and lower limits of each variable, u=u+1, and go to step 3;

[0022] Step 5: Calculate the average relaxation gap χ introduced by the second-order cone relaxation SOCR,avg , if χ SOCR,avg Below the threshold ε SOCR,avg , go to step 6, otherwise add a linear tangent plane constraint, n = n + 1, and go to step 2;

[0023] Step 6: Get the objective function value and the solution of each variable.

[0024] Furthermore, the linear tangent plane constraint is:

[0025]

[0026] Where: l ij,t is the square of the current of line ij in time period t; P ij,n-1,t , Q ij,n-1,t With v i,n-1,tare respectively the active power / reactive power flowing through line ij and the square of voltage amplitude of node i in the n-1th iteration of the asymptotically tight linear cutting algorithm, n≥2; Ω L and Ω T They are line sets and operating time period sets respectively.

[0027] Furthermore, the average relaxation gap χ introduced by the secondary convex envelope relaxation is QC,avg The calculation method is:

[0028]

[0029] Where: a,t , is the gap introduced when performing relaxation based on the quadratic convex envelope relaxation method; <·> is the operator symbol for the number of set elements; χ QC,total The total relaxation gap introduced by the secondary convex envelope relaxation; Ω L and Ω T are line sets and operation time period sets respectively; g , and are the OD pairs (r, s) of fuel vehicles, electric vehicles with non-adjustable charging power, electric vehicles with adjustable charging power, and electric vehicles with adjustable charging and discharging power, where r represents the starting point, s represents the end point, and K rs is the corresponding path set.

[0030] Furthermore, the average relaxation gap χ introduced by the second-order cone relaxation is SOCR,avg The calculation method is:

[0031]

[0032] Where: ij,t is the relaxation gap introduced by the power constraint at the head end of line ij during relaxation period t based on the second-order cone relaxation method; Ω L and Ω T are line sets and operating time sets respectively; <·> is the operator for the number of set elements.

[0033] The present invention also provides a flexible interconnected distribution network electric vehicle carrying capacity calculation system taking into account traffic flow, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the steps of the method described above can be implemented.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The present invention establishes a calculation model for the electric vehicle carrying capacity of a flexible interconnected distribution network under the influence of traffic flow. In the model, the time-space control constraints of electric vehicles including multiple electric vehicle access modes under the influence of traffic flow are considered, the influence of traffic flow is considered in detail, and the flexible and adjustable capabilities of intelligent soft switches are taken into account, so as to realize the accurate calculation of the electric vehicle carrying capacity of a flexible interconnected distribution network.

[0036] 2. The present invention takes into account multiple electric vehicle access modes including non-adjustable charging power, adjustable charging power and adjustable charging and discharging power, which conforms to the actual electric vehicle access situation and can carefully consider the impact of traffic flow, thereby improving the calculation accuracy of the electric vehicle carrying capacity of the flexible interconnected distribution network.

[0037] 3. The present invention takes into account the non-convex nonlinearity of the model, transforms the constructed calculation model of the electric vehicle carrying capacity of the flexible interconnected distribution network, and designs a nested shrinkage relaxation algorithm that integrates the sequential boundary tightening algorithm and the gradually tightening linear cutting algorithm to solve the convexly relaxed model, which can effectively reduce the calculation time while ensuring the solution accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a flow chart of calculation of electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow corresponding to an embodiment of the present invention;

[0039] Figure 2 A flowchart of a nested tightening and relaxing algorithm according to an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of an improved 24-node flexible interconnected distribution network according to an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of a 29-node transportation network according to an embodiment of the present invention;

[0042] Figure 5 A graph showing the photovoltaic power of the flexible interconnected distribution network and the load power of each functional area according to an embodiment of the present invention;

[0043] Figure 6 A vehicle travel demand curve diagram of a transportation network according to an embodiment of the present invention;

[0044] Figure 7 A result diagram of electric vehicle user satisfaction and charging position adjustment amount according to an embodiment of the present invention;

[0045] Figure 8 This is a graph showing the charging and discharging power results of electric vehicles in different access modes of various charging stations according to an embodiment of the present invention;

[0046] Fig. 9 This is a graph showing the output power results of SOP 4-15 of an embodiment of the present invention;

[0047] Fig.10 This is a result diagram of the iterative process of the nested tightening and relaxing algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0050] like Figure 1 As shown, in one embodiment of the present invention, a method for calculating the electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow is provided. The method is applied to a flexible interconnected distribution network containing large-scale electric vehicles, and comprises the following steps:

[0051] Step S101: establishing an electric vehicle space-time control model containing multiple electric vehicle access modes under the influence of traffic flow.

[0052] In this embodiment, the establishment of the electric vehicle space-time control model with multiple electric vehicle access modes under the influence of traffic flow specifically includes:

[0053] 1) Establish a semi-dynamic traffic flow model for electric vehicles covering fuel vehicles and various electric vehicle access modes.

[0054] Specifically, according to the time scale, traffic flow models can be divided into static (>90min), semi-dynamic (15-90min) and dynamic (<15min) traffic flow models. The static traffic flow model assumes that traffic flows are independent of each other in different time periods. It is suitable for situations where traffic conditions remain unchanged or change slowly over time, and is often used in traffic network planning scenarios; the dynamic traffic flow model takes into account the changes in traffic flows over short time scales, but the solution speed is slow. It is suitable for situations where traffic conditions change in real time and is often used in real-time navigation scenarios; and the semi-dynamic traffic flow model, as a compromise between the first two models, can take into account the coupling of traffic flows between different time periods, taking into account the accuracy and solution efficiency of the model, and is suitable for situations where traffic conditions change significantly over time but the time interval between changes is relatively long, and can be used in vehicle travel guidance scenarios. In addition, the time scale for calculating the carrying capacity of electric vehicles in distribution networks considering the time or space adjustable characteristics of electric vehicles is usually 1h. Therefore, a semi-dynamic traffic flow model suitable for a time scale of 15-90min is used to describe the operating status of the traffic network. The electric vehicle access mode includes three access modes: non-adjustable charging power, adjustable charging power, and adjustable charging and discharging power.

[0055] The semi-dynamic traffic flow model established in this embodiment, which covers fuel vehicles and three types of electric vehicles, is shown in equations (1) to (9). Equation (1) is a commonly used function of the Federal Highway Administration to describe the travel time of a road section; equations (2) and (3) respectively give the traffic residual flow and revised travel demand The calculation method of ; Formula (4) is the traffic flow conservation constraint; Formula (5) shows the road section flow x a,t is the flow rate of each path passing through this section The sum of; Formula (6) reflects the non-negativity of the path flow; Formula (7) defines the path travel time Formula (8) limits the maximum congestion level of the traffic network; Formula (9) is used to ensure that vehicles can reach the maximum tolerable time. Arrive at destination within.

[0056]

[0057] Where: Ω R and Ω T are the road section and operation time period sets respectively; g , and are the OD pairs (r, s) of fuel vehicles, electric vehicles with non-adjustable charging power, electric vehicles with adjustable charging power, and electric vehicles with adjustable charging and discharging power, where r represents the starting point, s represents the end point, and K rs is the corresponding path set, g,e 1 ,e 2 ,e 3They correspond to fuel vehicles, electric vehicles with non-adjustable charging power, electric vehicles with adjustable charging power, and electric vehicles with adjustable charging and discharging power; t a,t is the travel time of section a in time period t; and c a are the free travel time and capacity of section a respectively; Δt is the time interval; is the travel demand of OD for (r, s) in period t; Indicates the subordinate relationship between segment a and path k; TTI max is the upper limit of the travel time ratio, which is the ratio of the actual travel time to the travel time under free flow conditions.

[0058] 2) Based on the semi-dynamic traffic flow model, considering the electric vehicle charging location control and electric vehicle charging and discharging control, an electric vehicle spatiotemporal control model is constructed, which includes an electric vehicle charging location control model with multiple electric vehicle access modes considering traffic flow and an electric vehicle charging and discharging control model.

[0059] Semi-dynamic traffic flow model and electric vehicle control model are and The traffic flow distribution and the charging and discharging behavior of electric vehicles are mutually influenced and restricted. a,t The temporal and spatial changes of reflect the congestion of the traffic network, which will affect the charging price of electric vehicles at each charging station at different times. ch,s,t With discharge price c dis,s,t , which in turn affects the travel demand of electric vehicles to different charging stations Further affects the charging and discharging power of electric vehicles at each charging station On the other hand, the temporal and spatial distribution of electric vehicle charging and discharging power will affect the travel demand of electric vehicles, and thus affect the flow of electric vehicles and fuel vehicles on each path. Distribution, change the traffic flow distribution of the transportation network.

[0060] 21) When regulating the charging location of electric vehicles, by adjusting the charging and discharging prices of electric vehicles at each charging station at different times and considering user satisfaction, electric vehicles are guided to change their charging locations, improving the traffic flow distribution of the transportation network and the load distribution of the flexible interconnected distribution network, thereby improving the electric vehicle carrying capacity of the flexible interconnected distribution network. The specific model of electric vehicle charging location regulation considering traffic flow and multiple electric vehicle access modes is:

[0061] By adjusting the charging and discharging prices of electric vehicles at various charging stations at different times, electric vehicles are guided to change their charging locations. ch,s,t and discharge price c dis,s,t The constraints are as follows:

[0062]

[0063] Where: c EV,ch,max and c EV,ch,min are the upper and lower limits of electric vehicle charging prices; c EV,dis,max and c EV,dis,min are the upper and lower limits of the electric vehicle discharge price; Ω TE It is the collection of charging station nodes in the transportation network.

[0064] The regulation of the charging location of electric vehicles needs to consider user satisfaction. Only when the satisfaction λ of the charging location s′ changes to s rs′,rs,t Exceeding the minimum min When λ is reached, the electric vehicle will respond to the charging position regulation, as shown in formula (11). rs′,rs,t It is related to factors such as the distance to the charging station, the price of charging and discharging electric vehicles, the charging waiting time and the service quality of the charging station.

[0065]

[0066] Where: is the travel demand of electric vehicles transferred from OD pair (r, s′) to (r, s) in period t.

[0067] Based on the M / M / S model, the waiting time for electric vehicle charging is T wait,m,s,t The calculation method is shown in equations (12) to (16). Equation (12) can be fitted by piecewise linearization method. The average service rate of charging stations for electric vehicles with non-adjustable charging power is It can be obtained by formula (15), and the average service rate of electric vehicles with adjustable charging power and adjustable charging and discharging power can be obtained from the historical statistical data of the charging station.

[0068]

[0069] Where: is the service intensity; when m=e 1 hour, is the number of common charging piles in charging station s, when m=e 2 or e 3 hour, is the number of intelligent and orderly charging piles; φ m,s,t is the arrival rate; P s,EVC,max is the charging pile power in charging station s; E EV,rated is the rated capacity of a single electric vehicle; η EVC,ch Charging efficiency for electric vehicles; ΔSoC EV,avg The average state of charge value after charging the electric vehicle; T wait,m,max The maximum allowed charging waiting time.

[0070] Charging station service quality Q m,s The time required to charge an electric vehicle per unit distance:

[0071]

[0072] Where: T m,s is the charging time required for an electric car of type m to travel a distance of L at a charging station s. When m = e 1 When T e1,s The calculation method is shown in formula (18), m = e 2 or e 3 When T m,s Can be obtained from the historical statistics of the charging station.

[0073]

[0074] The satisfaction of electric vehicles changing charging locations with non-adjustable and adjustable charging power rs′,rs,t The calculation method of is shown in formula (19), rs′,rs,t Distance to charging station d s′,s , the price of electric vehicle charging and the waiting time for charging are related to the service quality of the charging station.

[0075]

[0076] Where: 11 ,ω 12 ,ω 13 and ω 14 is the weight coefficient, which can be determined by fuzzy comprehensive analysis; c c ' h,t,max , Q m,max is the saturation value of the difference in charging price and charging service quality between charging stations; d m,max It is the maximum control distance of electric vehicles.

[0077] The charge and discharge power of electric vehicles can be adjusted. rs′,rs,t It is also related to the price of discharging electric vehicles between charging stations, which is calculated as follows:

[0078]

[0079] Where: 21 ,ω 22 ,ω 23 ,ω 24 and ω 25 is the weight coefficient; c′ dis,t,max is the saturation value of the electric vehicle discharge price difference between charging stations.

[0080] After considering that the charging location is adjustable, the travel demand of electric vehicle OD for (r, s) is corrected as follows:

[0081]

[0082] Where: is the original travel demand of electric vehicle OD for (r,s) in period t.

[0083] 22) When regulating the charging and discharging of electric vehicles, ensure that the state of charge of the electric vehicle is within the allowable range when it arrives at the charging station, and design different electric vehicle power calculation methods for different electric vehicle access modes. The specific electric vehicle charging and discharging regulation model considering traffic flow and multiple electric vehicle access modes is:

[0084] The state of charge of electric vehicles should be within the permissible range when they arrive at the charging station to avoid rapid battery aging:

[0085]

[0086] Where: and The SoC is the state of charge of the electric vehicle at the starting point and the destination charging station respectively; EV,l and SoC EV,min They are the state of charge that is reduced per unit mileage of electric vehicles and the minimum state of charge allowed; l a is the length of section a.

[0087] The number of electric vehicles that can be charged simultaneously in a charging station should be limited by the number of charging piles and the number of electric vehicles present, as shown in equations (24) to (26).

[0088]

[0089]

[0090] Where: Ω EVC It is a collection of charging station nodes of the flexible interconnected distribution network; and are the number of electric vehicles of type m that are charged / contained simultaneously at charging station i during time period t.

[0091] The charging power of electric vehicles in the first access mode (i.e., the charging power cannot be adjusted) at charging station i during time period t The calculation method is shown in equations (27) to (28). Equation (29) is used to ensure that the charging needs of electric vehicles in the first access mode can be met.

[0092]

[0093] Where: s∈C(i) refers to the coupling between the transportation network node s and the flexible interconnected distribution network node i.

[0094] The electric vehicle power control model of the second access mode (i.e., charging power adjustable access mode) and the third access mode (i.e., charging and discharging power adjustable access mode) is shown in equations (30) to (38). In the second access mode, electric vehicles only participate in charging power control, while electric vehicles in the third access mode can participate in charging and discharging power control. Equation (30) shows the total power of the mth type of electric vehicles contained in charging station i. The total power of the previous period and the total charging power of electric vehicles Discharge power and the total amount of electric vehicles arriving / leaving Determine; Formulas (31) to (32) respectively give the range of the total charging and discharging power of the mth type of electric vehicle; Formula (33) shows that the second access mode electric vehicle cannot discharge to the grid; Formula (34) gives The upper and lower limits of ; Equations (35) to (36) respectively give and ; Formula (37) gives the calculation method of the number of electric vehicles of type m contained in charging station i; Formula (38) shows that the duration of electric vehicles participating in power regulation in the second and third access modes can be dynamically adjusted.

[0095]

[0096]

[0097] Where: η EVC,dis Discharge efficiency of electric vehicles; Characterizes the charging / discharging state of the electric vehicle, 1 indicates that it is in the charging state, and 0 indicates that it is in the discharging state; SoC EV,max and SoC EV,exp are the maximum / desired state of charge of the electric vehicle, respectively; It is the duration of electric vehicles participating in charging (discharging) power regulation; The duration of path k participating in charging (discharging) power regulation in time period t is τ in The number of electric vehicles.

[0098] Step S102: Using the electric vehicle spatiotemporal control model as the electric vehicle spatiotemporal control constraint containing multiple electric vehicle access modes under the influence of traffic flow, considering control means including intelligent soft switch power regulation, and establishing a flexible interconnected distribution network electric vehicle carrying capacity calculation model considering traffic flow.

[0099] Specifically, the electric vehicle carrying capacity calculation model of the flexible interconnected distribution network of this embodiment aims to maximize the total number of electric vehicles with various access modes that the flexible interconnected distribution network can carry, and the decision variable is the number of electric vehicles that the flexible interconnected distribution network can accommodate for charging from each starting point to each charging station. The constraints considered include the time-space control constraints of the electric vehicles and the upper limit constraints on operating costs, substation node power constraints, on-load tap-changing transformer regulation constraints, node voltage constraints, line current constraints, flexible interconnected distribution network flow constraints, distributed power supply operation constraints, static VAR compensator operation constraints, energy storage operation constraints and intelligent soft switch operation constraints.

[0100] In this embodiment, the objective function of the calculation model of electric vehicle carrying capacity of the flexible interconnected distribution network is shown in formula (39), and the decision variable is the number of electric vehicles that the flexible interconnected distribution network can accommodate from each starting point to each charging station for charging.

[0101]

[0102] The constraints are described as follows:

[0103] 1) Temporal and spatial control constraints of electric vehicles with multiple electric vehicle access modes under the influence of traffic flow

[0104] The time-space control constraints of electric vehicles with multiple electric vehicle access modes under the influence of traffic flow are shown in the above equations (1) to (38).

[0105] 2) Operating cost ceiling constraints

[0106] System comprehensive operating cost C OPE Including the electricity purchase cost C of the flexible interconnected distribution network PU , network loss cost C loss and the traffic network travel cost C travel In order to ensure the economical operation of the system, C OPE Do not exceed the upper limit

[0107]

[0108] Where: Ω S ,Ω DG and Ω L They are substation nodes, distributed generation nodes, and line collections; and are the unit electricity prices of electricity purchased from the upper grid and distributed power sources respectively; c travel is the unit travel cost of the road section; and are the active power output of the substation / distributed power source at node i in time period t; lij,t is the square of the current of line ij in time period t; R ij is the resistance value of line ij.

[0109] 3) Substation node power constraints

[0110]

[0111] Where: P S,max and P S,min are the upper / lower limits of the active power output of the node i substation in time period t, respectively; is the reactive power output of the node i substation in time period t, Q S,max and Q S,min are their upper / lower limits respectively.

[0112] 4) On-load tap-changing transformer regulation constraints

[0113]

[0114] Where: v i,t is the square of the voltage amplitude at node i during time period t; k i,t is the tap position of the on-load tap-changing transformer, K max and K min are the upper and lower limits respectively; ΔV is the voltage value changed by adjusting the tap of the on-load voltage regulating transformer by one gear; V i,rated is the rated voltage value.

[0115] 5) Node voltage constraints

[0116]

[0117] Where: Ω N is the node set of the flexible interconnected distribution network; V i,max and V i,min are the upper / lower limits of the node i voltage respectively.

[0118] 6) Line current constraints

[0119]

[0120] Where: I ij,max is the upper limit of the line ij current.

[0121] 7) Flow constraints of flexible interconnected distribution networks

[0122]

[0123] Where: κ(i) and ρ(i) are the child nodes and parent nodes of node i respectively; P ij,t and Q ij,tare the active power and reactive power flowing through line ij in time period t respectively; and are the active power and reactive power injected into node i in period t respectively; X ij is the reactance value of line ij; is the reactive power output of the distributed generation at node i in period t; and are the charging / discharging power of the energy storage at node i in time period t respectively; and are the load active power and reactive power of node i in time period t respectively; and are the active power and reactive power output by the intelligent soft switch of node i in time period t respectively; is the reactive power output by the static VAR compensator at node i in period t.

[0124] 8) Distributed power operation constraints

[0125]

[0126] Where: is the output coefficient of distributed power source; and They are the upper / lower limits of the tangent value of the power factor of the distributed power supply.

[0127] 9) Static VAR Compensator Operation Constraints

[0128]

[0129] Where: Ω SVC is a set of static VAR compensator nodes; They are the upper and lower limits of the reactive power output by the static VAR compensator.

[0130] 10) Energy storage operation constraints

[0131]

[0132]

[0133] Where: Ω ESS is a collection of energy storage nodes; Characterize the charge and discharge state of energy storage; and are the installed power and capacity of energy storage at node i respectively; are the charging / discharging power of energy storage at node i in period t; t 0 and T are the initial and end times of the flexible interconnected distribution network control cycle, respectively; is the amount of energy stored at node i during time period t; ηESS,ch and η ESS,dis They are respectively the charging / discharging efficiency of energy storage; SoC ESS,max and SoC ESS,min They are the upper and lower limits of the energy storage state of charge; SoC ESS,ini is the initial state of charge of the energy storage.

[0134] 11) Intelligent soft switch operation constraints

[0135]

[0136] Where: Ω SOP is a collection of intelligent soft switch nodes; is the loss value of the intelligent soft switch at node i in time period t; is the capacity of the intelligent soft switch at node i; The upper limit of reactive power output by the intelligent soft switch at node i in time period t.

[0137] Step S103: In order to facilitate solving by commercial solvers such as Gurobi, based on the quadratic convex envelope relaxation method, the big-M method, and the second-order cone relaxation method, the electric vehicle carrying capacity calculation model of the flexible interconnected distribution network considering traffic flow is converted into a mixed integer second-order cone programming model.

[0138] 1) Secondary convex envelope relaxation method

[0139] Aiming at the problem of the fourth power term in the road section travel time calculation formula (1), an auxiliary variable is introduced based on the quadratic convex envelope relaxation method. and replace and but and Should meet:

[0140]

[0141] Where: x a,t,max and x a,t,min are the upper / lower limits of traffic flow on section a during time period t respectively.

[0142] There are bilinear terms in formula (2) that multiply continuous variables It is linearized by the quadratic convex envelope relaxation method and an auxiliary variable is introduced. replace The following constraints are met:

[0143]

[0144] Where: and are the upper / lower limits of the travel time of path k respectively; and is the upper / lower limit of the traffic flow on path k.

[0145] The bilinear term t in formula (42) a,t x a,t Can be handled similarly.

[0146] 2) Big-M method

[0147] Based on the big-M method, the if-else constraint (11) can be expressed using the following complementary constraints:

[0148]

[0149] Where: γ rs′,rs,t is a 0-1 variable; M and ε are extremely large and extremely small numbers respectively.

[0150] In addition, formula (31) contains a bilinear term that multiplies a 0-1 variable by a continuous variable. Introducing auxiliary variables replace but Should meet:

[0151]

[0152] 3) Second-order cone relaxation method

[0153] Based on the second-order cone relaxation method, equation (54) is converted into a second-order cone form as follows:

[0154]

[0155] After the above transformation, the calculation model of electric vehicle carrying capacity of flexible interconnected distribution network taking into account traffic flow is transformed into a mixed integer convex optimization problem shown in formula (76), which can be solved by calling a commercial solver.

[0156]

[0157] Formula (1) to (10), Formula (12) to (38),

[0158] Formula (40) to (53), Formula (55) to (75) (76)

[0159] Step S104: A nested tightening relaxation algorithm that integrates a sequential boundary tightening (SBT) algorithm and an increasingly tight linear cut (ITLC) algorithm is used to solve the mixed integer second-order cone programming model, so as to efficiently and accurately obtain the maximum number of electric vehicles that the flexible interconnected distribution network can carry under the influence of traffic flow.

[0160] In order to improve the accuracy and efficiency of model solution, this embodiment designs a two-layer iterative framework based on the sequential boundary tightening algorithm and the gradually tightening linear cutting algorithm. The inner layer is the iteration of the sequential boundary tightening algorithm, and the outer layer is the iteration of the gradually tightening linear cutting algorithm. The specific process of the nested tightening relaxation algorithm is as follows: Figure 2 As shown, the details are as follows:

[0161] Step 1: Set the second-order cone relaxation gap threshold ε SOCR,avg , and set the number of iterations of the gradually tightening linear cutting algorithm n = 1;

[0162] Step 2: Set the secondary convex envelope relaxation gap threshold ε QC,avg , and set the number of iterations of the sequential boundary tightening algorithm u=1;

[0163] Step 3: solving the convexly relaxed calculation model of electric vehicle carrying capacity of the flexible interconnected distribution network considering traffic flow;

[0164] Step 4: Calculate the average relaxation gap χ introduced by the quadratic convex envelope relaxation QC,avg If χ QC,avg Below the threshold ε QC,avg , go to step 5; otherwise update the variable x a,t , With t a,t The upper / lower limit, u=u+1, go to step 3;

[0165] Step 5: Calculate the average relaxation gap χ introduced by the second-order cone relaxation SOCR,avg If χ SOCR,avg Below the threshold ε SOCR,avg , go to step 6; otherwise, add the linear tangent plane constraint formula (85), n = n + 1, and go to step 2;

[0166] Step 6: Get the objective function value and the solution of each variable.

[0167] Among them, relaxation based on the quadratic convex envelope relaxation method With t a,t x a,t The gap introduced a,t , The calculation methods are shown in equations (77) to (79) respectively.

[0168]

[0169] Where: is an auxiliary variable.

[0170] The sequential bound tightening algorithm iteratively tightens the variable x a,t , With t a,t The upper / lower limit of x a,t For example, in the uth (u≥2) iteration of the sequential boundary tightening algorithm, x a,t Upper / lower limit Updated to:

[0171]

[0172] Where: α is the tightening factor; It is the solution of the traffic flow on the road section at the u-1th iteration of the sequential boundary tightening algorithm.

[0173] When x QC,avg Below the threshold ε QC,avg When , the sequential boundary tightening algorithm stops iterating. QC,avg The calculation method is:

[0174]

[0175] Where: <·> is the operator for the number of set elements; χ QC,total Total relaxation gap introduced for the quadratic convex envelope relaxation.

[0176] The relaxation gap χ introduced by relaxing the line head-end power constraint based on the second-order cone relaxation method ij,t The calculation method is:

[0177]

[0178] The tightening linear cutting algorithm generates a tightened linear cutting plane through iteration to reduce the gap introduced by the second-order cone relaxation. The linear cutting plane generated by the tightening linear cutting algorithm at the nth (n≥2) iteration is:

[0179]

[0180] Where: P ij,n-1,t , Q ij,n-1,t With v i,n-1,t They are respectively the solutions of the active power / reactive power flowing through line ij and the square of the voltage amplitude at node i in the n-1th iteration of the tightening linear cutting algorithm.

[0181] When x SOCR,avg Below the threshold ε SOCR,avg When , the asymptotically tight linear cut algorithm stops iterating. SOCR,avg The calculation method is as follows:

[0182]

[0183] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0184] In another embodiment, a flexible interconnected distribution network electric vehicle carrying capacity calculation system taking into account traffic flow is provided, comprising a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0185] In this embodiment, Figure 3 The improved 24-node flexible interconnected distribution network and Figure 4The 29-node traffic network shown in the figure is used as a test case for analysis. The total power of the conventional load of the flexible interconnected distribution network is 37.48MW, the reference voltage is 20kV, the substation capacity is 10MV·A, and the node voltage range is 0.93pu~1.07pu; the line model is LGJ-95, and the maximum current allowed is 335A; the charging station is connected to the nodes 17, 18, 2, 11, 5, and 10 of the flexible interconnected distribution network, corresponding to the nodes 17, 5, 7, 26, 4, and 20 of the traffic network, and the installed charging pile power is 50, 50, 50, 50, 60, and 60kW / unit respectively; the light The voltmeter is connected to nodes 2, 3, 4, 5, 7, 9, 17, and 18, and the unit electricity price is 0.35 yuan / kW·h; the energy storage is connected to nodes 4, 5, 7, and 12, with a single capacity of 1MW·h, an initial state of charge of 0.5, and a state of charge range of 0.1 to 0.9; the static VAR compensator is connected to nodes 9, 14, and 19, with a single capacity of 0.5MVar; the intelligent soft switch is connected to lines 1-14, 2-3, 4-7, 4-15, 5-6, and 7-11, and the installed capacity of the intelligent soft switch of a single line is 1MV·A. The rated capacity of a single electric vehicle is 50.1kW·h, the charging / discharging efficiency is 95%, the charging demand is generated when the state of charge is lower than 0.2, and the state of charge range is 0.1~1; the charging price range is 0.4~1.5 yuan / kW·h, and the discharge price range is 0.35~1.5 yuan / kW·h; the lower limit of satisfaction for changing the charging location is 0.8; the upper limit of the time for electric vehicles in the second and third access modes to participate in grid interaction is 4h; the ratio of electric vehicles in the three access modes is 3:1:1; the ratio of electric vehicles to fuel vehicles is 1:3. TTI max is 1.4, and the unit travel cost of the road section is 57.6 yuan / h. The second-order cone relaxation gap threshold and the secondary convex envelope relaxation gap threshold are both set to 0.4%. The photovoltaic power of the flexible interconnected distribution network and the load power curve of each functional area are shown in Figure 5 As shown in Figure 2, the vehicle travel demand curve of the transportation network is shown in Figure 2. Figure 6 The model was solved by calling Gurobi 9.5.0 on the Matlab R2020b simulation platform with built-in Yalmip toolkit; the computer processor was R7-5800H, with a main frequency of 3.2 GHz and a memory of 16 GB.

[0186] After optimization calculation, the maximum number of electric vehicles that the flexible interconnected distribution network can carry is 7095. The spatial distribution of traffic flow is relatively balanced, but some sections are congested during the travel peak hours of 7:00-9:00 and 17:00-19:00. The charging and discharging electricity prices of electric vehicles at each charging station vary according to the operation of the flexible interconnected distribution network and the transportation network to guide electric vehicles to change the charging location. Charging stations 10 and 11 are located in the commercial area and office area respectively. At 8:00, the conventional load power in the commercial area is low and that in the office area is high. Therefore, compared with node 11, the remaining accessible load power of node 10 of the flexible interconnected distribution network is higher, and the road sections around charging station 10 are relatively unobstructed, so the charging electricity price of electric vehicles at charging station 10 is lower; because some electric vehicles in the first access mode are transferred to charging station 10 for charging, the nearby power grid has a certain peak shaving demand, so the discharge electricity price of electric vehicles is higher, so as to guide electric vehicles in the third access mode to go to charging station 10 for energy replenishment. The satisfaction and adjustment amount of electric vehicles changing charging locations in the three access modes change dynamically with factors such as the charging and discharging prices of electric vehicles at each charging station and the charging waiting time. The dynamic change curve of electric vehicle user satisfaction and adjustment amount in the first access mode of transferring from OD pair (12,26) to (12,20) is as follows: Figure 7 As shown. It can be seen that only when the user satisfaction is not lower than the threshold, electric vehicles may change the charging location. However, since the distance between charging stations 11 and 10 exceeds the control distance of electric vehicles in the second and third access modes, this type of electric vehicle will not be transferred from the OD pair (12,26) to (12,20). The charging and discharging power of electric vehicles in different access modes at each charging station is shown in Figure 8 As shown in the figure. The charging power of electric vehicles in the first access mode reaches its peak at 7:00-9:00 and 17:00-19:00, which greatly increases the load peak of the flexible interconnected distribution network; while the charging power of electric vehicles in the second access mode is relatively small during the above periods to reduce the superposition of conventional load and charging load peak; electric vehicles in the third access mode return power to the flexible interconnected distribution network at 8:00-9:00 and 17:00-19:00, thereby reducing the risk of voltage exceeding the limit of the distribution network and overload of substations (lines); due to the limited time for participating in grid interaction, the charging demand of electric vehicles in the second and third access modes during peak hours is mainly transferred to 10:00-12:00 and 20:00-22:00.

[0187] In order to verify the necessity of considering the influence of traffic flow when calculating the electric vehicle carrying capacity of the flexible interconnected distribution network, the electric vehicle carrying capacity of the flexible interconnected distribution network without considering the traffic flow was further calculated and compared with the above calculation results. The number of electric vehicles that the flexible interconnected distribution network can carry without considering the traffic flow is 7280, which is 2.61% higher than that considering the traffic flow. When the traffic flow is not considered, more electric vehicles in the first access mode tend to be regulated to charging station 5 and other charging stations from 7:00 to 9:00, but it is easy to cause congestion in nearby roads, further affect the traffic flow distribution of the traffic network, change the vehicle travel time, and make it difficult for users to reach the charging station within the maximum tolerance time. Therefore, the calculation result of the electric vehicle carrying capacity of the flexible interconnected distribution network is too high in this case.

[0188] In order to verify the effectiveness of intelligent soft switch access in improving the electric vehicle carrying capacity of the distribution network, the number of electric vehicles that the distribution network can carry without considering the intelligent soft switch access is calculated, and compared with the calculation results when the intelligent soft switch access is taken into account. Compared with the case without intelligent soft switch access, the electric vehicle carrying capacity of the distribution network is increased by 15.65% after the 6MV·A intelligent soft switch is connected. The output power curve of SOP 4-15 is shown in the figure. Fig. 9 As shown. Substation 23 is directly connected to charging station 10 / 11, and the sum of the net load power of the connected nodes (including nodes 3, 4, 10, 11, and 16) is relatively large. In order to alleviate the power supply pressure of substation 23, SOP 4-15 transmits power from port 15 to 4 in most periods. In periods 1 to 16 and 21 to 24, the change trend of the active power transmitted by SOP 4-15 is basically consistent with the change trend of the net load power of the nodes connected to substation 23; while in periods 17 to 20, due to the high sum of the charging demand of electric vehicles in residential areas and the conventional load power, and the gradual decrease in photovoltaic output power, although the net load power of the nodes connected to substation 23 is high, the active power transmitted by SOP 4-15 to node 4 has decreased or even become negative. SOP 4-15 is close to the node where the charging station is located, and the photovoltaic capacity is limited, so reactive power is provided in most periods; although the reactive compensation demand is high in periods 7-15, the reactive power provided by SOP 4-15 is low because under capacity constraints, SOP prioritizes active power transmission to improve load distribution. The access of intelligent soft switches enhances the wide-area power regulation capability of the distribution network. Most charging stations in the distribution network, especially charging station 10, can meet higher electric vehicle charging needs, and the discharge power demand for electric vehicles is reduced.

[0189] The effectiveness of the nested tightening and relaxation algorithm is verified below.

[0190] If the tightening relaxation strategy is not adopted and the commercial solver is used directly to solve the relaxed model, the average quadratic convex envelope relaxation gap and the average second-order cone relaxation gap are 69.70% and 32.96% respectively. The relaxation gap is relatively high, and the calculated result of the electric vehicle carrying capacity of the flexible interconnected distribution network is 7,200 vehicles, which is far from the optimal solution of the original problem. If only the sequential boundary tightening algorithm or the gradually tightening linear cutting algorithm is used to solve the relaxed model, it can only significantly reduce the relaxation gap caused by the quadratic convex envelope relaxation or the second-order cone relaxation. The average second-order cone relaxation gap or the average quadratic convex envelope relaxation gap is still as high as 36.03% and 65.34% respectively. When the algorithm proposed in the present invention is used to solve the model, the average quadratic convex envelope relaxation gap and the average second-order cone relaxation gap are both lower than 0.4%. The algorithm iteration process is as follows: Fig.10 As shown in the figure, the outer layer linear cutting algorithm is iterated twice, and the inner layer sequential boundary tightening algorithm is iterated 6 times. In the first and second iterations of the linear cutting algorithm, the sequential boundary tightening algorithm is iterated 4 and 2 times respectively to reach the convergence condition.

[0191] In order to further verify the superiority of the nested tightening relaxation algorithm, the following two methods are set up to solve the model and conduct comparative analysis. The results are shown in Table 1.

[0192] Method 1: Use a single-layer tightening relaxation algorithm based on a sequential boundary tightening algorithm and a gradually tightening linear cutting algorithm to solve the relaxed model (that is, update the variable x in each iteration a,t , t a,t The upper / lower limits and linear tangent plane constraints (85));

[0193] Method 2: Use the algorithm proposed in the present invention to solve the relaxed model.

[0194] Table 1 Comparison of results of different methods

[0195]

[0196] Both methods 1 and 2 shrink the quadratic convex envelope relaxation and the second-order cone relaxation. Compared with method 1, although method 2 has more iterations, the average solution time of a single iteration is less, and the total solution time is reduced by 31.64%, which can effectively improve the solution speed when the relaxation gap meets the accuracy requirements.

[0197] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0198] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0199] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0201] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for calculating the electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow, characterized in that: The following steps are involved: Establish an electric vehicle space-time control model with multiple electric vehicle access modes under the influence of traffic flow; Taking the electric vehicle spatiotemporal control model as the electric vehicle spatiotemporal control constraint with multiple electric vehicle access modes under the influence of traffic flow, considering the control means including intelligent soft switch power regulation, a calculation model of electric vehicle carrying capacity of flexible interconnected distribution network considering traffic flow is established; Based on the quadratic convex envelope relaxation method, the big-M method and the second-order cone relaxation method, the electric vehicle carrying capacity calculation model of the flexible interconnected distribution network is transformed into a mixed integer second-order cone programming model; The mixed integer second-order cone programming model is solved by a nested tightening relaxation algorithm that integrates a sequential boundary tightening algorithm and a gradually tightening linear cutting algorithm to obtain the maximum number of electric vehicles that the flexible interconnected distribution network can carry under the influence of traffic flow; The iterative framework of the nested tightening relaxation algorithm is a double-layer iterative framework based on the sequential boundary tightening algorithm and the gradually tightening linear cutting algorithm. The process of solving the mixed integer second-order cone programming model includes: Step 1: Set the second-order cone relaxation gap threshold ε SOCR,avg , and set the number of iterations of the gradually tightening linear cutting algorithm n = 1; Step 2: Set the secondary convex envelope relaxation gap threshold ε QC,avg , and set the number of iterations of the sequential boundary tightening algorithm u = 1; Step 3: Solve the mixed integer second-order cone programming model; Step 4: Calculate the average relaxation gap χ introduced by the quadratic convex envelope relaxation QC,avg , if χ QC,avg Below the threshold ε QC,avg , go to step 5, otherwise update the upper and lower limits of each variable, u=u+1, and go to step 3; Step 5: Calculate the average relaxation gap χ introduced by the second-order cone relaxation SOCR,avg , if χ SOCR,avg Below the threshold ε SOCR,avg , go to step 6, otherwise add a linear tangent plane constraint, n = n + 1, and go to step 2; Step 6: Get the objective function value and the solution of each variable.

2. The method for calculating electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow according to claim 1 is characterized in that: The establishment of the electric vehicle space-time control model containing multiple electric vehicle access modes under the influence of traffic flow is specifically as follows: Based on a semi-dynamic traffic flow model covering fuel vehicles and electric vehicles with various electric vehicle access modes, the electric vehicle charging position regulation and the electric vehicle charging and discharging regulation are considered to construct the electric vehicle time-space control model.

3. The method for calculating electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow according to claim 2 is characterized in that: When performing the electric vehicle charging position regulation, the electric vehicle charging and discharging prices of each charging station in different time periods are adjusted, and user satisfaction is taken into consideration to guide the electric vehicle to change the charging position. Factors considered when obtaining the user satisfaction include the distance between charging stations, electric vehicle charging and discharging prices, charging waiting time and charging station service quality.

4. The method for calculating electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow according to claim 2 is characterized in that: When performing the charging and discharging regulation of the electric vehicle, it is ensured that the state of charge of the electric vehicle is within the allowable range when it arrives at the charging station, and different electric vehicle power calculation methods are designed for different electric vehicle access modes.

5. The method for calculating electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow according to claim 1 is characterized in that: The electric vehicle carrying capacity calculation model of the flexible interconnected distribution network aims to maximize the total number of electric vehicles in various access modes that the flexible interconnected distribution network can carry. The decision variable is the number of electric vehicles that the flexible interconnected distribution network can accommodate for charging from each starting point to each charging station. The constraints considered include the time-space control constraints of the electric vehicles and the upper limit constraints on operating costs, substation node power constraints, on-load tap-changing transformer regulation constraints, node voltage constraints, line current constraints, flexible interconnected distribution network flow constraints, distributed power supply operation constraints, static VAR compensator operation constraints, energy storage operation constraints and intelligent soft switch operation constraints.

6. The method for calculating electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow according to claim 1 is characterized in that: The linear tangent plane constraint is: Where: l ij,t is the square of the current of line ij in time period t; P ij,n-1,t , Q ij,n-1,t With v i,n-1,t are respectively the active power / reactive power flowing through line ij and the square of voltage amplitude of node i in the n-1th iteration of the asymptotically tight linear cutting algorithm, n≥2; Ω L and Ω T They are line sets and operating time period sets respectively.

7. The method for calculating electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow according to claim 1 is characterized in that: The average relaxation gap χ introduced by the secondary convex envelope relaxation QC,avg The calculation method is: Where: a,t , is the gap introduced when performing relaxation based on the quadratic convex envelope relaxation method; <·> is the operator symbol for the number of set elements; χ QC,total The total relaxation gap introduced by the secondary convex envelope relaxation; Ω R and Ω T are the road segment set and the running time segment set respectively; g , and are the OD pairs (r, s) of fuel vehicles, electric vehicles with non-adjustable charging power, electric vehicles with adjustable charging power, and electric vehicles with adjustable charging and discharging power, where r represents the starting point, s represents the end point, and K rs is the corresponding path set.

8. The method for calculating electric vehicle carrying capacity of a flexible interconnected distribution network considering traffic flow according to claim 1 is characterized in that: The average relaxation gap χ introduced by the second-order cone relaxation SOCR,avg The calculation method is: Where: ij,t is the relaxation gap introduced by the power constraint at the head end of line ij during relaxation period t based on the second-order cone relaxation method; Ω L and Ω T are line sets and operating time sets respectively; <·> is the operator for the number of set elements.

9. A flexible interconnected distribution network electric vehicle carrying capacity calculation system considering traffic flow, characterized in that: The method comprises a memory, a processor and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, the steps of any method as claimed in claim 1 to claim 8 can be implemented.

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