Construction method of flexible domain of power distribution system under vehicle-pile-road-network coupling

Abstract

The invention discloses a method for constructing a flexible domain of a power distribution system under vehicle-pile-road-network coupling, which belongs to the technical field of construction of the flexible domain of the power distribution system, and comprises the following steps: aiming at road network coupling, dividing electric automobiles into electric private cars and electric taxies according to travel rules of the electric automobiles; respectively constructing an electric vehicle driving power consumption model and a V2G response model of an electric vehicle access power grid; respectively analyzing the interaction between the electric taxi and the active power distribution network and the interaction between the electric private car and the active power distribution network according to the charging and discharging behaviors of the electric taxi and the electric private car; constructing an active power distribution network operation model; and an active power distribution network operation flexible domain under vehicle-pile-road-network coupling is constructed. According to the method, the influence of the space-time scheduling flexibility and the charging and discharging flexibility of the electric vehicle as a flexible resource on the flexible domain construction of the power distribution system is considered, the system flexibility and the schedulable range visualization of the controllable resource are realized, and a reasonable support is provided for intuitively analyzing the operation state of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution system flexible domain construction, and particularly relates to a method for constructing a power distribution system flexible domain under a vehicle-pile-road-network coupling. BACKGROUND

[0002] The power supply and power consumption structure of the new power system has changed significantly, the proportion of new energy power generation is increasing, and the system flexibility demand is greatly improved. At the same time, the distributed regulation resources in the active distribution network are rapidly developing, providing a new way for system flexible regulation.

[0003] Under the background of road network coupling, electric vehicles, as flexible resources, are different from other regulation resources, and have traffic attributes. Their time and space variable charging and discharging behaviors will change the power grid operation and put forward higher requirements for the flexibility of the power grid. However, if the time and space scheduling flexibility of electric vehicles is regulated, it can provide rich flexible resources for the coordinated economic operation of the power-traffic coupling network. Existing research can analyze the influence of electric vehicles as flexible resources on system flexibility, but system flexibility is closely related to time. The interweaving and coupling of the time and space characteristics of electric vehicles and many time sequence flexible resources such as micro gas turbines connected to the power grid in continuous time periods increase the complexity of flexible analysis at different time sections, and it is difficult to directly present the changes of continuous time domain flexibility. In view of this problem, by analogy with the widely used safety domain model in the system, the time sequence changes of system flexibility at different time scales can be visualized by considering the "flexible domain", the dynamic and intuitive display of the operation range of the system under safety constraints is realized, and the real-time changes of the system operation state under different scenarios and scheduling measures are directly compared, so as to assist the operation personnel to formulate scheduling strategies. The existing technology can realize the construction of the flexible domain of the power distribution system, but does not comprehensively consider the influence of the "vehicle-pile-road-network" coupling network on the construction of the flexible domain. SUMMARY

[0004] The purpose of the present application is to provide a method for constructing a flexible domain of a power distribution system under a vehicle-pile-road-network coupling, which can comprehensively consider the influence of the time and space scheduling flexibility and charging and discharging flexibility of electric vehicles as flexible resources under the background of road network coupling on the construction of the flexible domain of the power distribution system, and realize the visualization of the scheduling range of system flexibility and controllable resources, thereby providing reasonable support for directly analyzing the system operation state.

[0005] To achieve the above-mentioned purpose, the present application provides a method for constructing a flexible domain of a power distribution system under a vehicle-pile-road-network coupling, comprising the following steps:

[0006] S1, for road network coupling, electric vehicles are divided into electric taxis and electric private cars according to the travel law of electric vehicles, and an electric vehicle driving power consumption model and a V2G response model of electric vehicles connected to the power grid are constructed respectively.

[0007] S2, according to the charging and discharging behavior of electric taxis and electric private cars, respectively analyzing the interaction of electric taxis and active power distribution network and the interaction of electric private cars and active power distribution network;

[0008] S3, according to the construction of the active power distribution network operation model, the output of the uncontrollable equipment is regarded as a fixed value, the output of the controllable equipment is regarded as a state variable to optimize the system operation state, a multi-dimensional state space is constructed to represent the scheduling of controllable resources in the active power distribution network, and in the multi-dimensional state space, the coordinate values of each point represent the scheduling of each controllable resource, reflecting the operation state of the whole system;

[0009] S4, constructing the active power distribution network operation flexible domain under the coupling of car-pile-road-network, specifically including:

[0010] S401, according to different electric vehicle models, the travel characteristics of electric taxis and electric private cars are simulated by Monte Carlo method, the time and space distribution of charging and discharging load of different types of electric vehicles is predicted, and then the active power distribution network operation model considering electric vehicle participation under road network coupling is constructed;

[0011] S402, the operation characteristics of electric vehicle time and space coupling resources are different, and the active power distribution network operation flexible domain changes dynamically with the operation state of various resources, based on the idea of domain, the active power distribution network operation flexible domain under road network coupling is constructed, for easy solving, the second-order cone relaxation method is used to linearize the active power distribution system operation flexible domain under road network coupling;

[0012] S403, the controllable resource scheduling strategy is obtained by centralized solving method, and a large number of load samples are repeatedly sampled by Monte Carlo method, the optimization results under each sample, i.e. feasible points, are obtained, and then the feasible point boundaries under all samples are connected to obtain the active power distribution network operation flexible domain under road network coupling.

[0013] Preferably, in S1, the electric vehicle driving power consumption model is:

[0014]

[0015] In the formula, v sd-h,t is the driving speed of the electric vehicle on the hth straight road segment at time t; v sd-0 is the zero flow speed of the straight road segment (s, d); C sd-h is the traffic capacity of the hth straight road segment in the road (s, d), which is related to the road grade; q sd-h,t is the flow of the hth road segment in the road (s, d) at time t; q sd-h,t and C sd-his the ratio of road saturation at time t; β is an empirical coefficient; a, b, n are adaptive coefficients for different road grades; for the main road, a, b, n take values of 1.726, 3.15 and 3 respectively; for the secondary road, a, b, n take values of 2.076, 2.870 and 3 respectively;

[0016] Assuming that the power consumption of the electric vehicle increases linearly with the driving mileage, the electric vehicle selects the shortest path in the simulation process, and uniformly drives in the same section, then the driving time t of the electric vehicle is sd and the remaining grid-connected electric quantity E z,s and the state of charge S z,s , as shown in formula (3):

[0017]

[0018] In the formula, l sd-h is the length of the hth direct link section; v sd-h is the driving speed of the electric vehicle on the hth direct link section; E0 is the initial electric quantity of the electric vehicle; N m is the number of all direct link sections between the start point s and the end point d; ΔE is the electric quantity consumed per kilometer; E z is the battery capacity of the electric vehicle; the energy efficiency coefficient λ is the electric quantity loss caused by starting and braking in the actual driving process, and λ takes a value in the range of 0.9-1.

[0019] Preferably, in S1, the V2G response model of the electric vehicle connected to the power grid is:

[0020]

[0021] In the formula, [t z,s ,t z,d ] is the time period when the electric vehicle is connected to the power grid; [S min ,S max ] is the state of charge range in which the electric vehicle can control the output power; S z,t is the state of charge of the zth electric vehicle at time t; S z,d is the demand for the battery state of charge before the zth electric vehicle travels; S z,s is the initial state of charge value of the zth electric vehicle when connected to the power grid; t is the time; t z,s is the initial time of the time period when the zth electric vehicle is connected to the power grid; t z,e is the time when the zth electric vehicle discharges to the minimum allowed state of charge; t z,g is the start time of the zth electric vehicle forced to charge to ensure the travel demand; t z,d is the end time of the time period when the zth electric vehicle is connected to the power grid; t z,c is the time when the zth electric vehicle charges to the maximum state of charge; P z,ch and Pz,disc Rated power for charging and discharging electric vehicles, respectively; η ch and η disc These refer to the charging and discharging efficiency of electric vehicles, respectively.

[0022] Preferably, in S2, the interaction between the electric taxi and the active power distribution network specifically includes:

[0023] Based on the actual traffic demand of electric taxis, when the battery power is less than the threshold, as shown in Equation (5), it is immediately fast charged at the rated power and does not participate in the V2G process. It is connected to the grid and is not affected by peak and valley time-of-use electricity prices, and is regarded as an uncontrollable load.

[0024] S z,t ≤S ε (5);

[0025] In the formula, S z,t S represents the state of charge of the electric vehicle at time t, representing the remaining charge. ε To set a threshold for when electric vehicles need to be charged immediately, S ε Take 0.2;

[0026] The charging demand generated by the charging station connected to node j of the power grid at time t. The cost of actively controlling electric taxis via the power distribution network is shown in equations (6)-(7):

[0027]

[0028] In the formula, The charging power of electric taxis when interacting with the active power grid; The number of electric taxis that are charged at the charging station at node j of the power grid at time t when the electric taxis interact with the active distribution network. The charging price for the z-th electric taxi; Δt represents the charging cost of the electric taxi; Δt represents the charging time of the electric taxi.

[0029] Preferably, in S2, the interaction between the electric private vehicle and the active power distribution network specifically includes:

[0030] When electric private cars do not respond to peak-valley time-of-use pricing, their operating mode is similar to that of electric taxis. At time t, the charging demand of electric private cars at charging stations connected to the grid at node j is as follows. As shown in equation (8):

[0031]

[0032] In the formula, This represents the number of electric private cars that are charging at the charging station connected to node j of the power grid at time t under this interaction condition. is the charging power of the electric private car at time t;

[0033] When the electric private car participates in the active power distribution network regulation and control, the electric private car interacts with the active power distribution network during the V2G process. Taking the electric private car z as an example, the planning of the charging process needs to meet the state of charge constraint, the charging and discharging constraint, and the charging and discharging power constraint, as shown in formula (9):

[0034]

[0035] In the formula, is the state of charge of the electric private car when it is connected to the j node of the power grid; is the real-time state of charge of the electric private car connected to the j node of the power grid; is the upper limit of the state of charge of the electric private car; is the actual power exchanged between the zth electric private car at the j node of the power distribution network and the active power distribution network; is the discharging power of the electric private car at time t; is the power exchanged between the electric private car in the idle state and the active power distribution network; P z,0 is the rated power exchanged between the electric private car and the active power distribution network; is the time when the electric private car is connected to the j node of the power grid; is the time when the electric private car leaves the j node of the power grid; and respectively represent the 0-1 variables of the charging, discharging, and idle state of the electric private car at the j node at time t, and only one state exists at the same time; when the electric private car is charging; when the electric private car is discharging; and when the electric private car is in the idle state;

[0036] The operation cost of the active power distribution network in which the electric private car participates is shown in formula (10):

[0037]

[0038] In the formula, is the charging and discharging cost of the electric private car; is the charging price of the zth electric private car; is the real-time compensation price of the zth electric private car.

[0039] Preferably, S3 specifically includes:

[0040] The constructed active power distribution network operation model is as follows:

[0041] Objective function:

[0042] Considering the response characteristics of various distributed resources in the active distribution network, the model takes the minimization of the active distribution network operation cost at the current time as the objective function, as shown in equation (11):

[0043]

[0044] In the formula, is the purchase cost of the active distribution network from the upper-level power grid at time t; is the active power purchased by the active distribution network from the main grid; is the operation cost of the micro gas turbine device connected to node j of the distribution network; is the output power of the micro gas turbine device connected to node j of the distribution network at time t; and are the operation costs of photovoltaic and wind power connected to node j of the distribution network, respectively; and are the output powers of wind power and photovoltaic connected to node j of the distribution network, respectively. TN and MT are the node sets of the upper-level power grid and the micro gas turbine device connected to the distribution network, respectively. PV and Wind are the node sets of photovoltaic and wind power connected to the distribution network, respectively. A is the number of electric taxis; V2G is the number of electric private cars;

[0045] Constraints:

[0046] Active distribution network node power balance equation:

[0047]

[0048] In the formula, P ij,t and Q ij,t are the active and reactive powers of line ij in the active distribution network; I ij,t is the square of the current of line ij; r ij and x ij are the resistance and reactance of line ij, respectively; κ1 and κ2 are the head and tail node sets of the line with node j as the tail and head in the active distribution network, respectively. represents the reactive power injected by the upper-level power grid into node j at time t; P w,j,t and Q w,j,t are the active and reactive loads of node j of the system at time t; is the actual interactive power of the electric taxi at the charging station connected to node j and the active distribution network at time t; is the actual interactive power of the zth electric private car at the charging station connected to node j and the active distribution network at time t.jk,t Pjk(t) is the active power of line jk in the active distribution network at time t; jk,t Qjk(t) is the reactive power of line jk in the active distribution network at time t;

[0049] Voltage-related constraints of the active distribution network:

[0050] The voltage drop equation and the upper and lower limits of the node voltage of the active distribution network are shown in equation (13):

[0051]

[0052] In the formula: U j,t is the square of the voltage amplitude of node j; U j,max and U j,min are the upper and lower limits of the square of the voltage amplitude of node j, respectively;

[0053] Line flow constraints of the active distribution network:

[0054] The second-order cone relaxation method is used to linearize the line flow constraints shown in equation (14). The linearized distribution line flow constraints are shown in equation (15):

[0055]

[0056] In the formula: V j,t is the voltage amplitude of node j; i ij,t is the current amplitude of line ij;

[0057] ||2P ij,t 2Q ij,t I ij,t -U j,t ||2≤I ij,t +U j,t (15);

[0058] The output constraints of each resource in the system and the power interaction constraints of the public coupling node are shown in equations (16)-(19). The related constraints of the electric vehicle participating in regulation in the system are shown in equation (9);

[0059] Micro gas turbines are the main flexible resources in the active distribution network. The minimum technical output constraint and the start-stop constraint do not need to be considered. The output does not exceed the maximum technical output and is limited by the ramp rate constraint;

[0060]

[0061] In the formula, is the maximum technical output of the micro gas turbine; is the maximum ramp rate of the micro gas turbine; is the output of the micro gas turbine at node j at time t-1;

[0062] The wind power and photovoltaic power output range is shown in formula (18):

[0063]

[0064] In the formula, and are maximum values of wind power and photovoltaic power output respectively;

[0065] The active / reactive power of the common coupling node is equal to the difference between all power generation and load, electric vehicle charging and discharging load and power loss inside the distribution network;

[0066]

[0067] Preferably, S401 specifically comprises: after reaching the destination, the electric private car accesses the destination charging facility according to E z,s and t z,s The electric private car participates in V2G response according to the peak and valley electricity price and compensation mechanism of the power grid to plan the electric vehicle charging and discharging time with the lowest operation cost of the active distribution network at the current time; during the load peak period, the electric private car will participate in V2G discharging to relieve the power grid peak pressure on the basis of meeting the travel demand; according to the coupling relationship between the traffic network and the distribution network, the electric vehicle charging and discharging load is calculated into the distribution network node.

[0068] Preferably, S402 specifically comprises: when the flexible domain is a non-empty set, it indicates that there is a suitable controllable resource operation strategy to make the active distribution network have flexible adjustment ability; when the flexible domain is an empty set, the adjustment ability of the active distribution network is insufficient;

[0069] The distribution system operation flexible domain under the road network coupling is shown in formula (20):

[0070]

[0071] In the formula: Ω ROF is the distribution system operation flexible domain under the road network coupling; x is the state variable; y is the control variable; U is the node voltage matrix; I is the branch current matrix; P and Q are respectively the node injected active and reactive power vectors; f(x, y) = 0 is the line power flow constraint; is the charging and discharging power of the zth electric vehicle at the jth access node at the tth moment; is the system safe operation constraint, including the system voltage constraint; is the node injected power constraint, including the distributed resource output constraint and the like; and are respectively the lower critical value set and the upper critical value set under the system safe operation; and respectively, are the lower and upper critical value sets of system injection power; H(y)≤R T is the set of state variables for analyzing the system security operation constraints; F(y) is the set of each distributed energy power for analyzing the system power constraints; H(y) is the set of distributed energy power related to time domain coupling for analyzing the system time domain coupling constraints; R T is the set of time domain coupling constraints;

[0072] Since there is a nonlinear constraint in formula (20), in order to facilitate calculation, a second-order cone relaxation method is used to linearize formula (20), as shown in formula (21):

[0073]

[0074] In the formula, Ω Ξ-ROF is the linearized active distribution network operation flexible domain; A, B, C are coefficient matrices in the equality constraint; K, Γ are coefficient matrices in the linear inequality; J, Z are coefficient matrices in the second-order cone constraint; U 2 is the node voltage quadratic matrix; I 2 is the branch current quadratic matrix; P D , Q D are the node active and reactive load matrices; P DG , Q DG are the node unit active and reactive output matrices; Ay+Bx=C and ||Jx||2≤Z correspond to the linearized active distribution network power flow equation and node power balance equation and the second-order cone constraint, as shown in formula (12) and formula (15); Kx≤Γ corresponds to the active distribution network voltage related constraint, each controllable device linear inequality constraint, as shown in formula (13), formula (16)-(19).

[0075] Therefore, the application adopts the above-mentioned vehicle-pile-road-network coupling lower distribution system flexible domain construction method, and the following beneficial effects are obtained compared with the prior art:

[0076] 1. Considering the space-time influence of road network coupling on the charging and discharging of various electric vehicles, according to the travel characteristics of electric vehicles, based on the V2G operation area, the peak-valley compensation price mechanism is used to guide the orderly charging and discharging behavior of electric private cars, realize the flexible regulation and control of distributed resources, and in some time period, the active distribution network transmits power to the upper-level power grid, and improves the operation flexibility and economy of the active distribution network.

[0077] 2. According to the space-time adjustable capacity, the electric vehicle changes its traffic behavior and charging and discharging condition to respond to the guidance of the electricity price signal, analyzes the real-time response of the electric vehicle to the power grid regulation and control ability through the power interaction between the charging pile and the active distribution network, optimizes the space-time distribution of system load, and provides abundant flexible resources for the active distribution network.

[0078] 3. Intuitively display the system running state under different time and space scales in different dimensions, and visualize the dynamic change characteristics of the system flexibility. After the active power distribution network uses the electricity price signal to guide the electric private cars to change the charging and discharging behaviors in real time, the visualization of the flexible domain in the two-dimensional space and the three-dimensional space is represented as a significant increase in area and volume.

[0079] The technical solutions of the present application will be described in further detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 The flowchart for constructing the flexible domain of the present application;

[0081] Figure 2 The schematic diagram of the 29-node urban traffic network;

[0082] Figure 3 The schematic diagram of the modified IEEE 33 power distribution system;

[0083] Figure 4 The schematic diagram of the temporal and spatial distribution of the electric taxi charging load at the typical node under scenario 1;

[0084] Figure 5 The schematic diagram of the temporal and spatial distribution of the electric private car charging load at the typical node under scenario 1;

[0085] Figure 6 The schematic diagram of the active interaction between the electric private car and the active power distribution network in the charging pile at the typical time under scenario 1;

[0086] Figure 7 The schematic diagram of the active interaction between the electric private car and the active power distribution network in the charging pile at the typical time under scenario 2;

[0087] Figure 8 The schematic diagram of the controllable resource output in the active power distribution network under scenario 1;

[0088] Figure 9 The schematic diagram of the controllable resource output in the active power distribution network under scenario 2;

[0089] Figure 10 The schematic diagram of the flexible domain under scenario 1 at the typical time in the two-dimensional space;

[0090] Figure 11 The schematic diagram of the flexible domain under scenario 2 at the typical time in the two-dimensional space;

[0091] Figure 12 The schematic diagram of the flexible domain under scenario 1 at the typical time in the three-dimensional space;

[0092] Figure 13Fig. 2 is a schematic diagram of a flexible domain of a three-dimensional space scene 2 at a typical time instant;

[0093] Figure 14 Fig. 3 is a schematic diagram of a three-dimensional flexible domain of a three-dimensional space scene 1 at three consecutive time instants;

[0094] Figure 15 Fig. 4 is a schematic diagram of a three-dimensional flexible domain of a three-dimensional space scene 2 at three consecutive time instants. DETAILED DESCRIPTION

[0095] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the present application are further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present application and should not be used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application. Examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout.

[0096] It should be noted that the terms "comprising" and "having" and any variations thereof are intended to cover not exclusively containing, for example, a process, method, system, product or server containing a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0097] Similar reference signs and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0098] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present application is usually placed, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0099] In the description of the present application, it should be further explained that, unless otherwise explicitly specified and limited, the terms "arrange", "install", "connect" should be understood broadly, for example, it can be fixedly connected, or detachably connected, or integrally connected; it can be mechanically connected, or electrically connected; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0100] Embodiments

[0101] As Figures 1-15 shown, the present embodiment provides a method for constructing flexible domain of power distribution system under vehicle-pile-road-network coupling, comprising the following steps:

[0102] S1, for road network coupling, according to the travel law of electric vehicles, electric vehicles are divided into electric taxis and electric private cars, and electric vehicle driving power consumption model and V2G response model of electric vehicle access to power grid are constructed respectively.

[0103] In S1, the electric vehicle driving power consumption model is:

[0104]

[0105] In the formula, v sd-h,t is the driving speed of the electric vehicle on the hth straight road segment at time t; v sd-0 is the zero flow speed of the straight road segment (s, d); C sd-h is the traffic capacity of the hth straight road segment in the road (s, d), which is related to the road grade; q sd-h,t is the flow of the hth road segment in the road (s, d) at time t; q sd-h,t is the ratio of C sd-h at time t; β is an empirical coefficient; a, b, n are self-adaptive coefficients under different road grades; for the main road, a, b, n take values 1.726, 3.15 and 3 respectively; for the secondary road, a, b, n take values 2.076, 2.870 and 3 respectively.

[0106] Assuming that the electric vehicle power consumption increases linearly with the driving mileage, the electric vehicle chooses the shortest path to travel in the simulation process, and travels at a uniform speed in the same road segment, then the electric vehicle driving time t sd and the remaining power E z,s and the state of charge S z,s of grid-connected are as shown in formula (3):

[0107]

[0108] In the formula, l sd-his the length of the hth direct link; v sd-h is the driving speed of the electric vehicle on the hth direct link; E0is the initial electric quantity of the electric vehicle; m is the number of all direct links between the origin and the destination s, d; ΔEis the electric quantity consumed per kilometer; E z is the battery capacity of the electric vehicle; the energy efficiency coefficient λ is the electric quantity loss caused by starting and braking in the actual driving process, and λ ranges from 0.9 to 1.

[0109] In S1, the V2G response model of the electric vehicle accessing the power grid is as follows:

[0110]

[0111] In the formula, [t z,s ,t z,d ] is the time period when the electric vehicle accesses the power grid; [S min ,S max ] is the state of charge range in which the electric vehicle can control the output power; S z,t is the state of charge of the zth electric vehicle at time t; S z,d is the demand for the state of charge of the battery of the zth electric vehicle before traveling; S z,s is the initial state of charge value of the zth electric vehicle when accessing the power grid; t is the time; t z,s is the initial time of the time period when the zth electric vehicle accesses the power grid; t z,e is the time when the zth electric vehicle discharges to the minimum allowable state of charge; t z,g is the start time of the forced charging of the zth electric vehicle to ensure the traveling demand; t z,d is the end time of the time period when the zth electric vehicle accesses the power grid; t z,c is the time when the zth electric vehicle charges to the maximum state of charge; P z,ch and P z,disc are the rated power of the charging and discharging of the electric vehicle respectively; η ch and η disc are the charging and discharging efficiencies of the electric vehicle respectively.

[0112] In S2, the interaction of the electric taxi and the active power distribution network and the interaction of the electric private car and the active power distribution network are analyzed respectively according to the charging and discharging behaviors of the electric taxi and the electric private car.

[0113] In S2, the interaction of the electric taxi and the active power distribution network specifically includes:

[0114] Based on the actual traffic demand of the electric taxi, when the battery electric quantity is less than the threshold value, the electric taxi is immediately fast-charged at the rated power and does not participate in the V2G process, accesses the power grid without being affected by the peak-valley time-of-use electricity price, and is regarded as an uncontrollable load, as shown in formula (5);

[0115] S z,t ≤S ε (5);

[0116] where S z,t is the state of charge of the electric vehicle at time t; S ε is the threshold value set for the electric vehicle to need to be charged immediately, S ε is 0.2.

[0117] The charging demand of the charging station connected to the j node of the power grid at time t and the cost of the active power distribution network to regulate the electric taxi are shown in equations (6) and (7):

[0118]

[0119] where P is the charging power of the electric taxi under the interaction with the active power distribution network; is the number of electric taxis charging at the charging station connected to the j node of the power grid at time t under the interaction with the active power distribution network; is the charging price of the zth electric taxi; is the charging cost of the electric taxi; and Δt is the charging duration of the electric taxi.

[0120] In S2, the interaction of the electric private car with the active power distribution network specifically includes:

[0121] When the electric private car does not respond to the peak-valley time-of-use electricity price, the working mode is similar to that of the electric taxi, and the charging demand of the electric private car at the charging station connected to the j node of the power grid at time t is shown in equation (8):

[0122]

[0123] where N is the number of electric private cars charging at the charging station connected to the j node of the power grid at time t under the interaction; is the charging power of the electric private car at time t.

[0124] When the electric private car participates in the regulation of the active power distribution network, the electric vehicle interacts with the active power distribution network during the V2G process. Taking the zth electric private car as an example, the planning of the charging process needs to satisfy the state of charge constraint, the charging and discharging constraint, and the charging and discharging power constraint, which are shown in equation (9):

[0125]

[0126] where S is the state of charge of the electric private car when connected to the j node of the power grid; ​Real-time state of charge of the electric private car accessing the j node of the power grid Upper limit of state of charge of the electric private car Actual power exchanged between the zth electric private car and the active power distribution network at the j node of the power grid Discharge power of the electric private car at time t Power exchanged between the electric private car in idle state and the active power distribution network z,0 Rated power exchanged between the electric vehicle and the active power distribution network Time when the electric private car accesses the j node of the power grid Time when the electric private car leaves the j node of the power grid And 0-1 variables respectively representing charging, discharging and idle state of the electric private car at the j node at time t, and only one state exists at the same time; when the electric private car is charging; when the electric private car is discharging; and when the electric private car is in idle state.

[0127] The operation cost of the active power distribution network in which the electric private car participates is shown in formula (10):

[0128]

[0129] In the formula, is the charging and discharging cost of the electric private car; is the charging price of the zth electric private car; is the real-time compensation price of the zth electric private car.

[0130] S3, according to the active power distribution network operation model, the output of uncontrollable equipment is regarded as a fixed value, the output of controllable equipment is regarded as a state variable to optimize the system operation state, and a multi-dimensional state space is constructed to represent the dispatching situation of controllable resources in the active power distribution network. In the multi-dimensional state space, the coordinate values of each point represent the dispatching situation of each controllable resource, reflecting the operation state of the entire system.

[0131] S3 specifically includes:

[0132] The constructed active power distribution network operation model is as follows:

[0133] Objective function:

[0134] Considering the response characteristics of various distributed resources in the active power distribution network, the model takes the minimization of the operation cost of the active power distribution network at the current time as the objective function, as shown in formula (11):

[0135]

[0136] where, is the cost of purchasing power from the upper grid at time t; is the active power purchased from the upper grid by the active distribution grid; is the operating cost of the micro gas turbine equipment connected to node j of the distribution grid; is the output power of the micro gas turbine equipment connected to node j of the distribution grid at time t; and are the operating costs of the photovoltaic and wind power connected to node j of the distribution grid, respectively; and are the output powers of the wind power and photovoltaic connected to node j of the distribution grid, respectively. TN and MT are the sets of nodes of the upper grid and micro gas turbine equipment connected to the distribution grid, respectively. PV and Wind are the sets of nodes of the photovoltaic and wind power connected to the distribution grid, respectively. A is the number of electric taxis; V2G is the number of electric private cars.

[0137] Constraints:

[0138] Active distribution grid node power balance equation:

[0139]

[0140] where, ij,t and Q ij,t are the active and reactive power of line ij in the active distribution grid, respectively; ij,t is the square of the current of line ij; ij and x ij are the resistance and reactance of line ij, respectively; κ1 and κ2 are the sets of head and tail nodes of the line with node j as the tail and head in the active distribution grid, respectively. represents the reactive power injected by the upper grid into node j at time t; w,j,t and Q w,j,t are the active and reactive loads of the system at node j of the distribution grid at time t; is the actual interaction power between the electric taxis of the charging station at node j and the active distribution grid at time t; is the actual interaction power between the zth electric private car of the charging station at node j and the active distribution grid at time t; jk,t is the active power of line jk in the active distribution grid at time t; jk,t is the reactive power of line jk in the active distribution grid at time t.

[0141] Voltage-related constraints of active distribution network:

[0142] To ensure the voltage quality of active distribution network and avoid the over-limit of node voltage, the voltage drop equation and the upper and lower limit constraints of node voltage of active distribution network are shown in equation (13):

[0143]

[0144] In the equation, U j,t is the square of voltage amplitude of node j; U j,max and U j,min are the upper and lower limits of the square of voltage amplitude of node j, respectively.

[0145] Line power flow constraints of active distribution network:

[0146] Since the line power flow constraints of active distribution network are nonlinear, in order to facilitate the solution, the original constraints shown in equation (14) are linearized by using the second-order cone relaxation method, and the linearized distribution line power flow constraints are shown in equation (15):

[0147]

[0148] In the equation, V j,t is the voltage amplitude of node j; i ij,t is the current amplitude of line ij.

[0149] ||2P ij,t 2Q ij,t I ij,t -U j,t ||2≤I ij,t +U j,t (15);

[0150] The output constraints of each resource in the system and the power interaction constraints of public coupling nodes are shown in equations (16)-(19); the related constraints of electric vehicles participating in regulation and control in the system are shown in equation (9).

[0151] Micro gas turbine is the main flexible resource in active distribution network, and there is no need to consider the minimum technical output constraint and start-stop constraint. The output does not exceed the maximum technical output, and is limited by the ramp rate constraint.

[0152]

[0153] In the equation, P is the maximum technical output of micro gas turbine; is the maximum ramp rate of micro gas turbine; is the output of micro gas turbine at node j at t-1.

[0154] The output range of wind power and photovoltaic is shown in equation (18):

[0155]

[0156] wherein, and are the maximum wind power and photovoltaic power outputs respectively.

[0157] The active / reactive power of the common coupling node is equal to the difference between all power generation and load, electric vehicle charging and discharging load, and power loss inside the distribution network. In addition, in view of the small magnitude of the power loss in the present application, the power loss is approximately 0 in the actual calculation process.

[0158]

[0159] S4, constructing an active distribution network operation flexible domain under vehicle-pile-road-network coupling, a flow chart is shown as Figure 1 , and specifically includes:

[0160] S401, according to different electric vehicle models, electric taxi and electric private car travel characteristics are simulated by Monte Carlo method respectively, and the space-time distribution of different types of electric vehicle charging and discharging load is predicted, and then an active distribution network operation model considering electric vehicle participation under road network coupling is constructed.

[0161] S401 specifically includes: after arriving at the destination, the electric private car accesses the destination charging facility according to the E z,s and t z,s participates in V2G response according to the power grid peak-valley price and compensation mechanism to plan the electric vehicle charging and discharging time with the lowest operation cost at the current time of the active distribution network; during the peak load period, the electric private car will participate in V2G discharge to relieve the peak pressure of the power grid on the basis of meeting the travel demand; according to the coupling relationship between the traffic network and the distribution network, the electric vehicle charging and discharging load is calculated into the distribution network node.

[0162] S402, the operation characteristics of electric vehicle space-time coupling resources are different, and the active distribution network operation flexible domain dynamically changes with the operation state of various resources, and the flexible domain is constructed under the road network coupling. In order to facilitate solution, the second-order cone relaxation method is used to linearize the distribution system operation flexible domain under road network coupling.

[0163] S402 specifically includes: when the flexible domain is a non-empty set, it means that there is a suitable controllable resource operation strategy to make the active distribution network have flexible adjustment ability; when the flexible domain is an empty set, the adjustment ability of the active distribution network is insufficient.

[0164] The distribution system operation flexible domain under road network coupling is shown as formula (20):

[0165]

[0166] Ω ROF is the operation flexible region of distribution system under the coupling of road network; x is the state variable; y is the control variable; U is the node voltage matrix; I is the branch current matrix; P and Q are the node injected active and reactive power vectors respectively; f(x, y) = 0 is the line power flow constraint; is the charging and discharging power of the zth electric vehicle at the jth access node at time t; is the system safe operation constraint, including system voltage constraint; is the node injected power constraint, including the output constraint of each distributed resource, etc. and are the lower and upper critical value sets respectively under the safe operation of the system; and are the lower and upper critical value sets of the system injected power respectively; H(y) ≤ R T is the system time domain coupling constraint; W(x) is the set of state variables used to analyze the safe operation constraints of the system; F(y) is the set of distributed energy power used to analyze the power constraints of the system; H(y) is the set of distributed energy power related to time domain coupling used to analyze the time domain coupling constraints of the system; R T is the set of time domain coupling constraints.

[0167] Since there is a nonlinear constraint in formula (20), in order to facilitate calculation, the second-order cone relaxation method is used to linearize formula (20), as shown in formula (21):

[0168]

[0169] Ω Ξ-ROF is the linearized active distribution network operation flexible region; A, B and C are the coefficient matrices in the equality constraint; K and Γ are the coefficient matrices in the linear inequality; J and Z are the coefficient matrices in the second-order cone constraint; U 2 is the node voltage quadratic matrix; I 2 is the branch current quadratic matrix; P D and Q D are the node active and reactive load matrices; P DG and Q DG are the node unit active and reactive output matrices; Ay + Bx = C and ||Jx||2≤Z correspond to the linearized active distribution network power flow equation and node power balance equation and the second-order cone constraint, as shown in formula (12) and formula (15); Kx ≤ Γ corresponds to the active distribution network voltage related constraint and the linear inequality constraint of each controllable device, as shown in formula (13), formula (16)-(19).

[0170] S403, obtain the controllable resource scheduling strategy by a centralized solving method, and repeatedly sample a large number of load samples by a Monte Carlo method, to obtain the optimization result under each sample, i.e. a feasible point, and then connect the boundaries of the feasible points under all samples to obtain the operation flexible domain of the active distribution network under the road network coupling.

[0171] Figure 1 The flowchart of the present application for constructing the flexible domain. First, for the traffic behavior of electric vehicles under the coupling of 'car-pile-road-network', the temporal and spatial distribution characteristics of electric vehicle charging load are analyzed based on the travel characteristics of electric vehicles under the coupling of road network; secondly, the real-time response capability of electric vehicles participating in grid regulation under the peak-valley compensation electricity price mechanism is mapped through the power interaction between charging piles and active distribution network; finally, the distribution network operation optimization model is constructed, based on the idea of 'domain', through the area boundary formed by the optimization results of a large number of load samples, the operation flexible domain under multiple time scales and different dimensions is realized.

[0172] The present application adopts the coupling network composed of 29-node road network and IEEE 33-node distribution network in Figure 2 and Figure 3 The coupling network composed of 29-node road network and IEEE 33-node distribution network in the present application verifies the rationality of the method, wherein the coupling relationship of the two networks is shown in Table 1, and the related parameters in the distribution system are shown in Table 2. At the same time, two scenarios are set in the test for comparative analysis, scenario 1 is that all electric vehicles only charge according to the remaining power and do not participate in V2G response; scenario 2 is that electric private cars participate in V2G response according to the peak-valley electricity price and compensation mechanism, wherein the peak-valley electricity price is shown in Table 3, and the generation cost of wind power and photovoltaic power is set to 0. The model calculation program is realized by MATLAB R2022a calculation toolbox, and Yalmip optimization toolbox is used to call IBMILog CPLEX12.0 processor for solving.

[0173] Table 1 Correspondence table of distribution network nodes and traffic network node numbers

[0174] Power distribution network node numbering Transport network node numbering Power distribution network node numbering Road node numbering 1 2 18 24 2 3 19 30 3 4 20 20 4 6 21 21 5 7 22 22 6 8 23 18 7 9 24 19 8 11 25 26 9 12 26 17 10 23 27 25 11 5 28 28 12 13 29 27 13 10 30 - 14 14 31 - 15 15 32 - 16 16 33 - 17 29 - -

[0175] Table 2 Related parameters of IEEE distribution system

[0176]

[0177]

[0178] Table 3 Peak-valley electricity price division

[0179]

[0180] The test system verification result data is as follows:

[0181] (1) Charging and discharging load temporal and spatial distribution

[0182] Assuming that the electric vehicle (EV) charging facilities are reasonably distributed and configured in the urban road network, each node in the test area is provided with a charging station for electric taxis (ET) and electric private cars (EPC) to charge. With a time scale of 15 min, the space-time distribution of charging load at a typical node in the test area under scenario 1 in a day is shown in FIGS. 1A-1D. Figure 4 、 Figure 5

[0183] In the time dimension, Figure 4 、 Figure 5 It is shown that without response, the rapid charging demand of electric taxis after the morning peak passenger carrying and the slow charging demand of electric private cars after arriving at the business district and work area cause the charging load to be at a high level and fluctuate greatly during 10:00-14:00. Similarly, during 18:00-20:00, the electric vehicle charging load is also high, but since the electric private cars may go to other destinations after work, the charging demand is scattered compared with that during 10:00-14:00. In addition, since the slow charging of electric private cars after returning home causes the charging load of electric taxis to be large at the early morning, and the electric taxis need to be charged frequently during the day due to frequent passenger carrying, the charging load demand is high during the day.

[0184] In the spatial dimension, since the start and end points of each trip of electric taxis have randomness when carrying passengers, there is no significant regularity in their charging locations, so the analysis is mainly performed on electric taxis. Electric taxis need to be charged in time to meet the demand of future trips. At night, the charging load demand at nodes 15 and 18 in the residential area is higher than that in the business district and work area; the charging demand of node 19 in the business district coincides with the work period, and only a few electric taxis stay in this area after work and have charging demand; similarly, the charging load curve of node 22 in the business district is at a high level during 10:00-18:00, and as the electric taxi owners gradually return to the residential area, the charging load demand decreases obviously after 18:00; the charging load demand of node 26 in the work area reaches a peak at noon, and as the travel demand decreases in the afternoon, the charging demand fluctuates, and to meet the needs of the next day's work, the charging load demand is slightly higher in the night and early morning than in the business district.

[0185] To further analyze the active interaction between the charging and discharging behavior of electric private cars and the active power distribution network, the active interaction between electric private cars and the active power distribution network in each charging pile under two scenarios is analyzed, in which the charging is positive and the discharging is negative, as shown in FIGS. 2A-2D. Figure 6 、 Figure 7

[0186] ​​Daytime electric private cars travel frequently in business areas and work areas. In scenario 2, at 10:00, which is in the peak period of electricity price, most electric private cars discharge in response to active power distribution network regulation under the influence of compensatory electricity price, and at this time, the discharge behavior of the business area node is more significant. At 13:00, which is in the flat period of electricity price, the compensatory electricity price is also considerable, and the electric private cars are regulated by the active power distribution network, changing from charging only in scenario 1 to charging and discharging coexisting in scenario 2.

[0187] From the evening, in scenario 2, the charging and discharging behavior of electric private cars in the daytime peak and valley period of electricity price is similar, but slightly different. At 20:00, which is in the peak period of electricity price, compared with 10:00, part of the electric private cars return to the residential area, and the obvious discharge interaction response of the residential area is obvious. At 22:00, which is in the flat period of electricity price, compared with 13:00, most electric private cars return to the residential area, and the charging demand of the residential area is high. In addition, at 02:00, which is in the valley period of electricity price, the charging cost is low, but in scenario 1, the electric private cars do not respond to the active power distribution network dispatching, and the electric private cars do not concentrate on charging in the early morning; in scenario 2, the electric private cars respond to the active power distribution network regulation, and at this time, there is almost no travel demand, and most of the electric private cars in the residential area choose to charge. Similarly, compared with 22:00, the charging cost at this time is less than that at 22:00 in the flat period, so the charging demand at 02:00 is high.

[0188] (2) Flexible resource output of power distribution system under "vehicle-pole-road-network" coupling

[0189] The flexible resource output in the two scenarios is shown in Figure 8 , Figure 9 , and in both scenarios, there is a situation of power supply to the upper-level power grid, which is more significant in scenario 2. In scenario 2, considering the influence of compensatory electricity price, electric private cars prefer to discharge in peak periods, and controllable resource output is greater than active power distribution network load demand, resulting in a situation of reverse power supply in load peak.

[0190] At the same time, in scenario 2, considering the guiding role of peak-valley compensatory electricity price on electric private cars, electric private car users prefer to charge in valley period 00:00-08:00, which reduces the load pressure in peak period and optimizes the charging load distribution. The implementation of peak-valley compensatory electricity price mechanism not only relieves the system load level, but also realizes the purpose of transmitting power to the upper-level power grid to a certain extent and improving the system flexibility.

[0191] (3) Description of flexible domain of power distribution system under "vehicle-pole-road-network" coupling

[0192] Figure 10 , Figure 11are the flexible domain result diagrams of typical time in two-dimensional space under two scenarios. By comparison, it can be seen that, due to the participation of electric private cars in active power distribution network regulation under scenario 2, the adjustable range of the system is improved, and the range of the flexible domain under scenario 2 is obviously larger than that under scenario 1 at the same time. Influenced by the discharging behavior of electric private cars, the flexible domain of scenario 2 covers a larger range of negative values, indicating that the active power distribution network supplies power to the upper-level power grid on the basis of meeting its own load demand, and the system flexibility is sufficient and has a surplus. In addition, scenario 2 makes up for the insufficient flexibility at some time sections, and the flexible domain areas at different times have certain differences. The flexible domain at typical times under the two scenarios is as follows: due to the high load at 12:00 and the large output of photovoltaic and electric vehicles, the flexible domain area reaches the maximum under the two scenarios; with the change of time from 12:00 to 13:00, the electric private cars in scenario 2 gradually change from discharging to charging behavior under the guidance of peak-valley compensation price, and the flexible domain area decreases. Compared with 21:00, the load at 19:00 in scenario 1 is higher, and the flexible domain area is slightly larger; in scenario 2, the electric private cars change from charging behavior to discharging behavior under the stimulation of the compensation price, which increases the flexible scheduling range of the system, and the flexible domain area increases. The output of controllable devices in the night system at 04:00 is small, and the flexible domain area is small.

[0193] Figure 12 、 Figure 13 、 Figure 14 and Figure 15 are the flexible domain result diagrams of typical time in three-dimensional space under two scenarios and the flexible domain result diagrams under three consecutive time periods. As can be seen from the diagrams, at the same time within a day, the operation flexible domain formed between the outputs of different devices is also irregular decoupling, and there is a certain coupling relationship, and different devices have different contributions to the size of the system operation flexible domain. After the electric vehicles participate in the power grid dispatching, the flexible domain volume under scenario 2 increases significantly, and the coverage range of the flexible domain expands to negative values, which indicates that the adjustment capacity provided still has a certain surplus after meeting the load at the current time, improves the system flexibility, and ensures the reliable operation of the system. The three-dimensional space flexible domain result diagrams under three consecutive time periods reflect that the current charging and discharging state of electric private cars will affect the charging and discharging behavior of adjacent time periods, there is a more complex time coupling constraint, and the operation flexible domain presents a more irregular shape, and the coupling relationship is irregular.

[0194] Therefore, the power distribution system flexible domain construction method under the vehicle-pile-road-grid coupling adopted by the application can comprehensively consider the influence of electric vehicles as flexible resources on the construction of the power distribution system flexible domain under the road network coupling background, the spatio-temporal scheduling flexibility and the charging and discharging flexibility, realize the visualization of the system flexibility and the adjustable range of controllable resources, and provide reasonable support for intuitive analysis of the system operation state.

[0195] It should be pointed out finally that the above examples are only used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can still be modified or replaced equivalently, and these modifications or equivalent replacements should not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for constructing a flexible domain of a power distribution system under vehicle-pile-road-network coupling, characterized in that: Comprise the following steps: S1, for the road network coupling, according to the travel law of electric vehicles, electric vehicles are divided into electric taxis and electric private cars, and electric vehicle driving power consumption model and V2G response model of electric vehicle access to power grid are constructed respectively; S2, according to the charging and discharging behavior of electric taxis and electric private cars, the interaction of electric taxis and active distribution network and the interaction of electric private cars and active distribution network are analyzed respectively; S3, according to the construction of active distribution network operation model, the output of uncontrollable equipment is regarded as a fixed value, the output of controllable equipment is regarded as a state variable to optimize the system operation state, a multi-dimensional state space is constructed to represent the scheduling of controllable resources in active distribution network, and the coordinate values of each point in the multi-dimensional state space represent the scheduling of each controllable resource, reflecting the operation state of the whole system; S4, the active distribution network operation flexible domain under the coupling of car-pile-road-network is constructed, specifically including: S401, according to different electric vehicle models, the travel characteristics of electric taxis and electric private cars are simulated by Monte Carlo method, the time and space distribution of charging and discharging load of different types of electric vehicles is predicted, and then the active distribution network operation model considering electric vehicle participation under road network coupling is constructed; S402, the operation characteristics of electric vehicle time and space coupling resources are different, and the active distribution network operation flexible domain changes dynamically with the operation state of various resources, based on the idea of domain, the active distribution network operation flexible domain under road network coupling is constructed, and the second order cone relaxation method is used to linearize the active distribution system operation flexible domain under road network coupling; S403, the controllable resource scheduling strategy is obtained by centralized solving method, a large number of load samples are repeatedly sampled by Monte Carlo method, the optimization results under each sample are obtained, and then the feasible point boundary under all samples is connected to obtain the active distribution network operation flexible domain under road network coupling.

2. The method according to claim 1, wherein the method is characterized by: In S1, the electric vehicle driving power consumption model is: In the formula, v sd-h,t is the driving speed of the electric vehicle on the hth straight road section at time t; v sd-0 is the zero flow speed of the straight road section (s, d); C sd-h is the traffic capacity of the hth straight road section in the road (s, d), which is related to the road grade; q sd-h,t is the flow of the hth road section in the road (s, d) at time t; q sd-h,t is the ratio of C sd-h to the saturation degree of the road at time t; β is an empirical coefficient; a, b, and n are adaptive coefficients under different road grades; for the main road, a, b, and n take values of 1.726, 3.15, and 3, respectively; for the secondary road, a, b, and n take values of 2.076, 2.870, and 3, respectively. Assuming that the power consumption of the electric vehicle increases linearly with the driving distance, the electric vehicle chooses the shortest path to travel in the simulation process, and travels at a constant speed in the same section, then the driving time t of the electric vehicle is sd The remaining grid-connected power E z,s And the state of charge S z,s As shown in equation (3): wherein, l sd-h is the length of the hth straight road segment; v sd-h is the driving speed of the electric vehicle on the hth straight road segment; E0is the initial electric quantity of the electric vehicle; N m is the number of all straight road segments between the start point s and the end point d; ΔEis the electric quantity consumed per kilometer; E z is the battery capacity of the electric vehicle; and the energy efficiency coefficient λ is the electric quantity loss caused by starting and braking in the actual driving process, and λ is in the range of 0.9-1.

3. The method according to claim 2, wherein the method further comprises: determining the flexible domain of the power distribution system based on the vehicle-pile-road network coupling. In S1, the V2G response model of electric vehicle access to power grid is: In the formula, [t z,s ,t z,d ] is the period of time when the electric vehicle accesses the power grid; [S min ,S max ] is the state of charge range in which the electric vehicle can control the output power; S z,t is the state of charge of the zth electric vehicle at time t; S z,d is the demand for the state of charge of the zth electric vehicle before the trip; S z,s is the initial state of charge value of the zth electric vehicle when accessing the power grid; t is the time; t z,s is the initial time of the period of time when the zth electric vehicle accesses the power grid; t z,e is the time when the zth electric vehicle discharges to the minimum allowed state of charge; t z,g is the start time of the zth electric vehicle forced charging to ensure the trip demand; t z,d is the end time of the period of time when the zth electric vehicle accesses the power grid; t z,c is the time when the zth electric vehicle charges to the maximum state of charge; P z,ch and P z,disc are the rated power of the charging and discharging of the electric vehicle respectively; η ch and η disc are the charging and discharging efficiencies of the electric vehicle respectively.

4. The method according to claim 3, wherein the method further comprises: determining the flexible domain of the power distribution system based on the vehicle-pile-road network coupling. In S2, the interaction of electric taxis and active distribution network specifically includes: Based on the actual traffic demand of electric taxis, when the battery capacity is less than the threshold value, the electric taxis are immediately fast-charged at rated power and do not participate in V2G process, the access to power grid is not affected by peak-valley time-of-use price, and is regarded as uncontrollable load, as shown in formula (5); S z,t ≤S ε (5); In the formula, S z,t is the state of charge of the remaining electric quantity of the electric vehicle at time t; S ε is a threshold value set for the electric vehicle to need to be immediately charged, S ε takes 0.2; The charging demand generated by the charging station connected to the grid node j at time t The cost of actively regulating the electric taxi in the distribution network is shown as formula (6)-(7): In the formula, is the charging power of the electric taxi under the interaction between the electric taxi and the active power grid; is the number of electric taxis charging at the charging station connected to node j of the power grid at time t under the interaction between the electric taxi and the active power grid; is the charging price of the zth electric taxi; is the charging cost of the electric taxi; and Δt is the charging duration of the electric taxi.

5. The method according to claim 4, wherein the flexible domain of a vehicle-pile-road network coupled power distribution system is constructed by: In S2, the interaction of electric private cars and active distribution network specifically includes: The charging demand of the electric private car at the charging station connected to the j node of the power grid at time t when the electric private car does not respond to the peak-valley time-of-use electricity price As shown in formula (8): In the formula, is the number of electric private cars charging at the charging station of the j node of the power grid at time t in this interaction; is the charging power of the electric private car at time t. When electric private cars participate in active distribution network regulation and control, electric private cars interact with active distribution network during V2G process, for example, electric private car z, the charging process needs to meet the state of charge constraint, charging and discharging constraint and charging and discharging power constraint, as shown in formula (9): wherein, is the state of charge of the electric private car when accessing the grid j node; is the real-time state of charge of the electric private car accessing the grid j node; is the upper limit of the state of charge of the electric private car; is the actual power exchanged between the zth electric private car and the active power grid at the grid j node; is the discharging power of the electric private car at time t; is the power exchanged between the electric private car in idle state and the active power grid; P z,0 is the rated power exchanged between the electric private car and the active power grid; is the time when the electric private car accesses the grid j node; is the time when the electric private car leaves the grid j node; and are 0-1 variables respectively representing the charging, discharging and idle state of the electric private car at the grid j node at time t, and only one state exists at the same time; when the electric private car is charging; when the electric private car is discharging; when the electric private car is in idle state; The active distribution network operation cost participated by electric private cars is shown as formula (10): In the formula, Cost of charging and discharging for electric private cars; Charging price for the zth electric private car; Real-time compensation price for the zth electric private car.

6. The method for constructing a flexible domain of a power distribution system under vehicle-pile-road-network coupling as described in claim 5, characterized in that: S3 specifically includes: The constructed active distribution network operation model is as follows: Objective function: Considering the response characteristics of various distributed resources of active distribution network, the model takes the minimization of active distribution network operation cost at current time as objective function, as shown in formula (11): In the formula, is the cost of purchasing power from the upper grid at time t for the active power grid; is the active power purchased by the active power grid from the main grid; is the operating cost of the micro gas turbine equipment connected to the j node of the power distribution grid; is the output power of the micro gas turbine equipment connected to the j node of the power distribution grid at time t; and are the operating costs of photovoltaic and wind power connected to the j node of the power distribution grid, respectively; and are the output powers of wind power and photovoltaic connected to the j node of the power distribution grid, respectively; TN and MT are the node sets of the upper grid and the micro gas turbine equipment connected in the power distribution grid, respectively; PV and Wind are the node sets of photovoltaic and wind power connected in the power distribution grid, respectively; A is the number of electric taxis; and V2G is the number of electric private cars. Constraint condition: Active distribution network node power balance equation: In the formula, P ij,t and Q ij,t These represent the active and reactive power of line ij in the active distribution network; I ij,t r is the square of the line current ij; ij and x ij κ1 and κ2 are the resistance and reactance of line j, respectively; κ1 and κ2 are the sets of the first and last nodes of the line in the active distribution network with node j as the end and the first end, respectively. P represents the reactive power injected into node j by the upstream power grid at time t; w,j,t and Q w,j,t These represent the active and reactive loads at node j of the distribution network at time t; The actual interaction power between the electric taxi connected to the charging station at node j at time t and the active distribution network; P represents the actual interaction power between the z-th electric private vehicle connected to the charging station at node j at time t and the active distribution network; jk,t Q represents the active power of line jk in the active distribution network at time t; jk,t Let jk be the reactive power of line jk in the active distribution network at time t; Active distribution network voltage related constraint: The voltage drop equation and the upper and lower bound constraints of the node voltage of the active distribution network are shown in equation (13): where: U j,t is the voltage amplitude squared at node j; U j,max and U j,min are the upper and lower limits of the voltage amplitude squared at node j, respectively. The line flow constraint of the active distribution network: The second-order cone relaxation method is used to linearize the flow constraint shown in equation (14), and the linearized distribution line flow constraint is shown in equation (15): where: V j,t is the voltage amplitude for node j; i ij,t is the current amplitude for line ij; ||2P ij,t 2Q ij,t I ij,t -U j,t ||2≤I ij,t +U j,t (15); The output constraints of each resource in the active distribution network and the power interaction constraints of the public coupling node are shown in equations (16)-(19); the related constraints of the electric vehicle participating in the regulation of the active distribution network are shown in equation (9); The micro gas turbine is a flexible resource in the active distribution network, and the output does not exceed the maximum technical output without considering the minimum technical output constraint and the start-stop constraint, and is limited by the ramp rate constraint; wherein is the maximum technical output of the micro gas turbine; is the maximum ramp rate of the micro gas turbine; is the output of the micro gas turbine at the access node j at time t-1; The output range of wind power and photovoltaic power is shown in equation (18): wherein and respectively the maximum wind power and photovoltaic power outputs. The active / reactive power of the public coupling node is equal to the difference between the power generation and load, the electric vehicle charging and discharging load, and the power loss in the distribution network; 7. The method according to claim 6, wherein the method further comprises the steps of: determining the flexible domain of the power distribution system based on the vehicle-pile-road network coupling model. S401 specifically comprises: after the electric private car arrives at the destination, according to E z,s and t z,s Access to the destination charging facilities, according to the grid peak and valley price and compensation mechanism V2G response, to actively power grid current time operation cost lowest planning electric vehicle charging and discharging time; In the peak load period, electric private car will participate in V2G discharge to ease the pressure of the grid peak on the basis of meeting the travel demand; According to the coupling relationship between the traffic network and the power distribution network, the electric vehicle charging and discharging load is calculated to the power distribution network node.

8. The method according to claim 7, characterized in that: S402 specifically includes: when the flexible domain is a non-empty set, it means that there is a suitable controllable resource operation strategy to make the active distribution network have flexible adjustment capability; when the flexible domain is an empty set, the adjustment capability of the active distribution network is insufficient; The operation flexible domain of the distribution system under the road network coupling is shown in equation (20): In the formula: Ω ROF is the operation flexible domain of distribution system under the coupling of road network; x is the state variable; y is the control variable; U is the node voltage matrix; I is the branch current matrix; P and Q are respectively the node injected active and reactive power vectors; f(x, y) = 0 is the line power flow constraint; is the charging and discharging power of the zth electric vehicle at the jth access node at the tth time; is the system safe operation constraint, including the system voltage constraint; is the node injected power constraint, including the output constraint of each distributed resource, etc.; and are respectively the lower critical value set and the upper critical value set under the system safe operation; and are respectively the lower critical value set and the upper critical value set of the system injected power; H(y)≤R T is the system time domain coupling constraint; W(x) is the set of state variables for analyzing the system safe operation constraint; F(y) is the set of each distributed energy power for analyzing the system power constraint; H(y) is the set of distributed energy and time domain coupling related power for analyzing the system time domain coupling constraint; R T is the time domain coupling constraint set; There is a nonlinear constraint in equation (20), and the second-order cone relaxation method is used to linearize equation (20) as shown in equation (21): In the formula, Ω Ξ-ROF is the active power distribution network operation flexible domain of linearization processing; A, B, C are coefficient matrices in the equality constraint; K, Γ are coefficient matrices in the linear inequality; J, Z are coefficient matrices in the second-order cone constraint; U 2 is the node voltage quadratic matrix; I 2 is the branch current quadratic matrix; P D , Q D are the node active and reactive load matrices; P DG , Q DG are the node unit active and reactive output matrices; Ay+Bx=C and ||Jx||2≤Z correspond to the active power distribution network power flow equation and the node power balance equation and the second-order cone constraint after linearization processing, as shown in formula (12) and formula (15); Kx≤Γ corresponds to the active power distribution network voltage related constraint, linear inequality constraint of each controllable device such as formula (13), formula (16)-(19).

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