A comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow

Abstract

The present invention discloses a comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow. Based on a dynamic traffic assignment model, the flexible charging and discharging capabilities of electric vehicles are maximized through price signals, the comprehensive spatiotemporal flexible operation domain of electric vehicles is characterized, and a dynamic traffic assignment model considering vehicle-to-vehicle (V2G) vehicle-grid interaction is established. In the model, the price guidance of V2G vehicle-grid interaction and charging and discharging coupons for electric vehicles is considered; a second-order cone DistFlow optimal power flow model is established in the distribution network, and a reasonable power purchase and power generation strategy for the distribution network is formulated; the distribution network and the transportation network are coupled through charging stations and V2G stations, and a distribution-transportation coupling system cost model considering vehicle-grid interaction is constructed. The flexible operation domain of electric vehicle bidirectional charging stations is depicted through convex hull fitting, resulting in a comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow, which can characterize the flexible adjustment capability of electric vehicle charging and discharging under different subsidy amounts.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric-traffic network collaborative optimization, and in particular relates to a comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow. Background Art

[0002] With the continued increase in the proportion of renewable energy generation and the accelerated construction of new power systems, the volatility and uncertainty of both the supply and demand sides of the power system have significantly increased. Traditional "source-grid" flexibility regulation resources, primarily thermal power units and pumped hydroelectric storage, are increasingly unable to meet the demand for a high proportion of renewable energy due to their response speed, regulation costs, and spatial properties. Against this backdrop, the development and coordinated regulation of massive load-side flexibility resources have become a key direction for power system transformation. These massive flexibility resources generally refer to distributed power sources, distributed energy storage, and adjustable loads widely distributed on the user side. Through macro-dispatching of the power system, they can enhance the dynamic balance and stability of the power system across time and space. As a new type of load-side flexible resource, electric vehicles (EVs) have attracted widespread attention for their vehicle-to-grid (V2G) capabilities as mobile energy storage. They are transforming from a "unidirectional rigid load" to a "bidirectional flexible resource." By releasing price signals on the transportation network, they can, to a certain extent, guide the distribution of traffic flows and dispatch EVs to specific bidirectional charging stations for charging and discharging at required times. This not only mitigates the impact of peak EV charging loads on the power grid, but also enables the flexible charging and discharging of EVs within the distribution system to shift loads and reduce peak loads. This allows for load transfer and absorption on a spatiotemporal scale, improving the flexibility and reliability of the distribution-transportation coupling system.

[0003] Current research on load-side flexibility resources primarily focuses on traditional load-side photovoltaic (PV) and energy and thermal storage devices. For example, studies have examined the flexible operating characteristics of PV, energy storage, and controllable loads in AC / DC hybrid distribution networks, helping dispatchers more intuitively understand the flexible operating space of these networks. Other research focuses on the thermal storage flexibility of distribution systems and district heating systems, using projection steps to identify the dynamic boundaries of electric heaters and characterizing the flexibility of district heating systems using polyhedron sets. However, as a new type of load-side flexibility resource, less attention has been paid to the charging and discharging flexibility of electric vehicles. Current research on EV flexibility primarily focuses on exploring EV users' plug-in time habits, quantifying their flexibility in terms of capacity, charging time, and power, and developing real-time assessment models for EV dispatchable capacity. However, this research neglects the complementary benefits of EV V2G capabilities to the grid and lacks modeling of vehicle flow within transportation networks. To fully explore the charging and discharging flexibility of EVs, it is crucial to examine their flow within transportation networks. Most studies on traffic flow modeling use static traffic distribution models and semi-dynamic traffic distribution models, which have low accuracy and are difficult to simulate in real time. Dynamic traffic distribution models can simulate traffic flow at a finer time granularity and are more suitable for real-time scheduling of electric vehicles. Summary of the Invention

[0004] The purpose of the invention is to address the technical problems that the present invention aims to solve by providing a comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flows, addressing the shortcomings of existing technologies. The present invention takes into account the synergy of the power-transportation coupling system. First, a dynamic traffic assignment model is established that considers V2G vehicle-grid interaction. The model considers the price guidance of electric vehicle V2G vehicle-grid interaction and charging and discharging coupons. Second, a second-order cone DistFlow optimal power flow model is established in the distribution network to formulate a reasonable distribution network power purchase and power generation strategy. Finally, the present invention achieves dual-network coupling of the distribution network and the transportation network through charging stations and V2G stations, constructing a distribution-transportation coupling system cost model that considers vehicle-grid interaction. By continuously iteratively rotating the tangent plane to the feasible region, the boundary points of the feasible region are found, and the flexible operating domain of the electric vehicle bidirectional charging station is depicted using convex hull fitting. This accurately depicts the flexible adjustment capability of electric vehicle charging and discharging under different subsidy amounts, making the flexibility of the distribution-transportation coupling system more intuitive and concrete, and facilitating optimal scheduling based on the needs of the coupling system.

[0005] Technical solution: In order to solve the above technical problems, the present invention proposes a comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow, which includes the following steps:

[0006] Step 1: Obtain the network parameters and operating coefficients of the distribution network. The network parameters include the distribution network topology and line impedance. The operating coefficients include the upper-level power grid power supply coefficient, the distributed power supply coefficient, and the photovoltaic inverter coefficient.

[0007] Step 2: Obtain scenario data such as load demand at each node of the power grid, distributed photovoltaic output, and time-of-use electricity price. Use the power purchase amount of the upper power grid and the power generation amount of distributed generation as control variables, and the operation constraints of the distribution network as constraints to establish a second-order cone DistFlow optimal power flow model for the distribution network.

[0008] Step 3: Obtaining transportation network parameters, including transportation network topology, capacity of each road section, and location and capacity of bidirectional charging stations;

[0009] Step 4: Obtain scenario data on transportation network travel demand, electric vehicle penetration rate, and V2G user penetration rate. Using charging and discharging coupons as control variables and the basic operating constraints of the transportation network as constraints, a dynamic traffic allocation model that considers V2G vehicle-grid interaction is established.

[0010] Step 5: Based on the second-order cone DistFlow optimal power flow model of the distribution network in step 2 and the dynamic traffic assignment model considering V2G vehicle-grid interaction in step 4, the distribution network and transportation network are coupled through charging stations and V2G stations, and a distribution-transportation coupling system cost model considering vehicle-grid interaction is constructed;

[0011] Step 6. Set the subsidy amount for the bidirectional charging station coupons of the distribution-transportation coupling system. By rotating the tangent plane to be tangent to the feasible domain, the convex hull fitting is used to depict the flexible operation domain of the electric vehicle bidirectional charging station, and the flexible adjustment capability of electric vehicles under different subsidy amounts under dynamic traffic flow is obtained.

[0012] Furthermore, the second-order cone DistFlow optimal power flow model of the distribution network in step 2 is as follows:

[0013] (1) Grid operation constraints

[0014] (A-1)

[0015] (A-2)

[0016] (A-3)

[0017] (A-4)

[0018] (A-5)

[0019] (A-6)

[0020] (A-7)

[0021] (A-8)

[0022] Where N is the set of distribution network nodes; i, j, m are distribution network nodes; 、 and 、 are the active injection power and reactive injection power of nodes i and j respectively; and are the resistance and reactance of line ij respectively; 、 and 、 are the active power and reactive power of lines ij and jm respectively; is the square of the current in line ij; and is the square of the voltage at nodes i and j; and are the active and reactive power generated by the upper power grid at node i respectively; and are the active and reactive powers generated by the distributed generation at node i respectively; and are the active and reactive power generated by distributed photovoltaic at node i respectively; is the V2G power at node i; is the active load of the charging station at node i; and are the conventional active and reactive loads at node i, They are State aggregated traffic flow on road section a at time instant; and is the average charging power and V2G power; It is a parameter used to describe the one-to-one correspondence between charging stations or V2G stations on traffic network segment a and distribution network nodes;

[0023] (2) PV inverter model and upper and lower limit constraints

[0024] (A-9)

[0025] (A-10)

[0026] (A-11)

[0027] (A-12)

[0028] Where, is the distributed photovoltaic capacity at node i; is the reactive capacity of the photovoltaic inverter at node i, represents the absolute value of the voltage amplitude at node i at time t; and are the lower and upper limits of the voltage at node i respectively; represents the absolute value of the current amplitude of circuit ij at time t; is the upper limit of the current of line ij; 、 and 、 are the lower and upper limits of active and reactive power transmitted by the upper power grid at node i, respectively; 、 and 、 are the lower and upper limits of the active and reactive power generated by the distributed generation at node i, respectively.

[0029] Furthermore, the dynamic traffic assignment model considering V2G vehicle-grid interaction in step 4 is as follows:

[0030] (1) Section state equation and flow propagation constraints:

[0031] (A-13)

[0032] (A-14)

[0033] (A-15)

[0034] (A-16)

[0035] (A-17)

[0036] (A-18)

[0037] (A-19)

[0038] (A-20)

[0039] (A-21)

[0040] (A-22)

[0041] (A-23)

[0042] (A-24)

[0043] In the formula, a is the road section, k is the path, Represents a set of road segments, is the starting node, For the destination node, a pair of starting node r and destination node s is called an OD pair; Represents the rs path set, the path is divided into common paths , charging path and V2G paths ; 、 、 They are the inflow, outflow and state traffic flow on section a at time t under the kth path with r as the starting node and s as the destination node; 、 、 They are the inflow, outflow and state aggregated traffic flow on road section a at time t; is the set of road segments a starting from the starting node r; The penetration rate of vehicles that need to be charged; To select the vehicle penetration rate of V2G; is the travel demand between OD and rs at time t; is the set of road segments a leading to the destination node s; is the arrival traffic flow of the origin-destination node pair rs at time t; is the cumulative arrival traffic flow of the origin-destination node pair rs at time t; for The cumulative arrival traffic flow of the start-destination node pair rs at time instant; is the cumulative arrival traffic flow of the kth path selected in the start-destination node pair rs at time t; They are The state of traffic flow on road section a under the k-th path with r as the starting node and s as the destination node at time; represents the set of road segments a starting from node j, represents the set of road segments a leading to node j; is the free travel time of section a; assuming that section a is the path A section of the road, represents the set of all road segments after road segment a in path k, Indicates that segment b belongs to the segment set after segment a in path k ; for The state traffic flow on the road segment b in the start-destination node pair rs at time instant; for The state traffic flow on the road segment b in the start-destination node pair rs at time instant; for The cumulative arrival traffic flow of the kth path selected in the start-destination node pair rs at time instant;

[0044] (2) Travel time and travel cost constraints:

[0045] (A-25)

[0046] (A-26)

[0047] (A-27)

[0048] (A-28)

[0049] (A-29)

[0050] (A-30)

[0051] (A-31)

[0052] (A-32)

[0053] (A-33)

[0054] Where: For ordinary road sections, For charging section collection, is a collection of virtual road segments, It is a collection of V2G sections; Indicates that road section a has travel time in the case of vehicle inflow; is the free travel time of section a; is the traffic capacity of section a; and is the average charging time and V2G time; and The maximum queuing time for the charging capacity and V2G capacity of the bidirectional charging station; and Configure capacity for charging stations and V2G stations; They are State aggregated traffic flow on road section a at time; virtual road section For the virtual road section set up for the connectivity of the traffic network, the travel time is 0; is the travel cost of section a at time t; is the unit time cost; is the unit charging electricity price of the charging station on road section a at time t; is the unit coupon price of the charging station on road section a at time t; and is the average charging / V2G power; is the unit discharge reward of the V2G station on section a at time t; is the one-time coupon subsidy for the V2G station on road section a at time t; is the travel cost of the kth path starting at time t with r as the starting node and s as the destination node; assuming that section a is the path A section of the road, represents the set of all road segments before road segment a in path k, Indicates that segment b belongs to the set of segments before segment a in path k ; is the free travel time of section b; is a 0-1 parameter that determines the correspondence between road segments and paths. If path k passes through road segment a, then , if not, then ;

[0055] (3) Dynamic user equilibrium conditions, capacity constraints, and initial value conditions

[0056] (A-34)

[0057] (A-35)

[0058] (A-36)

[0059] (A-37)

[0060] (A-38)

[0061] (A-39)

[0062] (A-40)

[0063] Where, express and ; 、 、 are the minimum travel costs of the ordinary path, charging path, and V2G path starting at time t with r as the starting node and s as the destination node, respectively. The state traffic flow on section a at time 0 and time 1 respectively, with r as the starting node and s as the destination node under the k-th path; It is the inflow traffic flow on road section a under the kth path with r as the starting node and s as the destination node at time 0.

[0064] Furthermore, in step 5, the cost model of the distribution-transportation coupling system considering vehicle-grid interaction is:

[0065] (A-41)

[0066] (A-42)

[0067] (A-43)

[0068] (A-44)

[0069] (A-45)

[0070] (A-46)

[0071] (A-47)

[0072] (A-48)

[0073] Where, is the total cost of the distribution-transportation system; Cost of purchasing electricity from the upper power grid; Cost of generating electricity for distributed generation; is the total revenue of the charging station; is the total V2G expenditure; Time-of-use electricity price; is the time span; is the benchmark charging electricity price; the total cost of subsidies for bidirectional charging station coupons for the distribution-transportation coupling system; to cap the total cost of the distribution-transportation system; The upper limit of the subsidy amount for bidirectional charging station coupons in the distribution-transportation coupling system.

[0074] Furthermore, in step 6, by continuously rotating the tangent plane to be tangent to the feasible region, the convex hull fitting is used to depict the flexible operation domain of the electric vehicle bidirectional charging station, characterizing the flexible adjustment capability of electric vehicle charging and discharging under a given subsidy amount:

[0075] (A-49)

[0076] (A-50)

[0077] (A-51)

[0078] (A-52)

[0079] (A-53)

[0080] (A-54)

[0081] (A-55)

[0082] (A-56)

[0083] Where, is the polar angle of the normal vector of the tangent plane at each iteration, that is, the angle between the normal vector of the tangent plane and the z-axis; is the azimuth of the normal vector of the tangent plane at each iteration, that is, the angle between the projection of the tangent plane normal vector on the xy plane and the x-axis; is the general equation of a plane The constant term of ; Representation plane The constant term at the extreme position tangent to the feasible region; the moment of observing the flexible feasible region ,Select three bidirectional charging stations in the flexible operation domain, mark the first bidirectional charging station as x, the second bidirectional charging station as y, and the third bidirectional charging station as z. 、 、 They are the net charge and discharge loads of the first, second, and third bidirectional charging stations, 、 、 They are the first, second and third bidirectional charging stations respectively Charging load at the moment, 、 、 They are the first, second and third bidirectional charging stations respectively V2G power at the moment; is the polar angle of the iterative tangent plane normal vector The initial angle, is the rotation step of the iterative tangent plane normal vector in the polar angle direction, Polar angle Number of rotations, Polar angle Maximum number of rotations; is the azimuth of the iterative tangent plane normal vector The initial angle, is the rotation step of the iterative tangent plane normal vector in the azimuth direction, is the azimuth Number of rotations, is the azimuth Maximum number of spins.

[0084] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0085] Compared to traditional electric vehicle scheduling optimization schemes for distribution-transportation coupled systems, the present invention utilizes convex hull fitting, based on precise simulation of dynamic traffic flow, to fully characterize the flexibility feasible domain of bidirectional charging stations in the distribution-transportation coupled system. This approach, guided by price signals, maximizes the charging and discharging capabilities of electric vehicles, making the flexibility of the distribution-transportation coupled system more intuitive and concrete, facilitating vehicle scheduling based on the needs of the coupled system. Case study test results demonstrate that the proposed method can intuitively demonstrate the flexibility feasible domain of bidirectional charging stations in the distribution-transportation coupled system for different charging and discharging coupon subsidy amounts, providing guidance for optimizing scheduling schemes for the coupled system. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a flow chart of the method of the present invention;

[0087] Figure 2 It is the topology diagram of the power distribution-transportation coupling system;

[0088] Figure 3 It is a flexible operating area for bidirectional charging stations with a subsidy of 1,000 yuan;

[0089] Figure 4 It is a flexible operating area for bidirectional charging stations with a subsidy of 2,000 yuan;

[0090] Figure 5 It is a flexible operating area for bidirectional charging stations with a subsidy of 4,000 yuan;

[0091] Figure 6 This is a comparison chart of the total cost of the coupling system under three subsidy amounts. DETAILED DESCRIPTION

[0092] The present invention is further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art fall within the scope defined by the claims attached to this application.

[0093] like Figure 1As shown, the present invention provides a comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow, which includes the following steps:

[0094] Step 1: Obtain the network parameters and operating coefficients of the distribution network. The network parameters include the distribution network topology and line impedance. The operating coefficients include the upper-level power grid power supply coefficient, the distributed power supply coefficient, and the photovoltaic inverter coefficient.

[0095] Step 2: Obtain scenario data such as load demand at each node of the power grid, distributed photovoltaic output, and time-of-use electricity price. Use the power purchase amount of the upper power grid and the power generation amount of distributed generation as control variables, and the operation constraints of the distribution network as constraints to establish a second-order cone DistFlow optimal power flow model for the distribution network.

[0096] Step 3: Obtaining transportation network parameters, including transportation network topology, capacity of each road section, and location and capacity of bidirectional charging stations;

[0097] Step 4: Obtain scenario data on transportation network travel demand, electric vehicle penetration rate, and V2G user penetration rate. Using charging and discharging coupons as control variables and the basic operating constraints of the transportation network as constraints, a dynamic traffic allocation model that considers V2G vehicle-grid interaction is established.

[0098] Step 5: Based on the second-order cone DistFlow optimal power flow model of the distribution network in step 2 and the dynamic traffic assignment model considering V2G vehicle-grid interaction in step 4, the distribution network and transportation network are coupled through charging stations and V2G stations, and a distribution-transportation coupling system cost model considering vehicle-grid interaction is constructed;

[0099] Step 6. Set the subsidy amount for the bidirectional charging station coupons of the distribution-transportation coupling system. By rotating the tangent plane to be tangent to the feasible domain, the convex hull fitting is used to depict the flexible operation domain of the electric vehicle bidirectional charging station, and the flexible adjustment capability of electric vehicles under different subsidy amounts under dynamic traffic flow is obtained.

[0100] Furthermore, the second-order cone DistFlow optimal power flow model of the distribution network in step 2 is as follows:

[0101] (1) Grid operation constraints

[0102] (A-1)

[0103] (A-2)

[0104] (A-3)

[0105] (A-4)

[0106] (A-5)

[0107] (A-6)

[0108] (A-7)

[0109] (A-8)

[0110] Where N is the set of distribution network nodes; i, j, m are distribution network nodes; 、 and 、 are the active injection power and reactive injection power of nodes i and j respectively; and are the resistance and reactance of line ij respectively; 、 and 、 are the active power and reactive power of lines ij and jm respectively; is the square of the current in line ij; and is the square of the voltage at nodes i and j; and are the active and reactive power generated by the upper power grid at node i respectively; and are the active and reactive powers generated by the distributed generation at node i respectively; and are the active and reactive power generated by distributed photovoltaic at node i respectively; is the V2G power at node i; is the active load of the charging station at node i; and are the conventional active and reactive loads at node i, They are State aggregated traffic flow on road section a at time instant; and for average charging and V2G power; It is a parameter used to describe the one-to-one correspondence between charging stations or V2G stations on traffic network segment a and distribution network nodes;

[0111] (2) PV inverter model and upper and lower limit constraints

[0112] (A-9)

[0113] (A-10)

[0114] (A-11)

[0115] (A-12)

[0116] Where, is the distributed photovoltaic capacity at node i; is the reactive capacity of the photovoltaic inverter at node i, represents the absolute value of the voltage amplitude at node i at time t; and are the lower and upper limits of the voltage at node i respectively; represents the absolute value of the current amplitude of circuit ij at time t; is the upper limit of the current of line ij; 、 and 、 are the lower and upper limits of active and reactive power transmitted by the upper power grid at node i, respectively; 、 and 、 are the lower and upper limits of the active and reactive power generated by the distributed generation at node i, respectively.

[0117] Furthermore, the dynamic traffic assignment model considering V2G vehicle-grid interaction in step 4 is as follows:

[0118] (1) Section state equation and flow propagation constraints:

[0119] (A-13)

[0120] (A-14)

[0121] (A-15)

[0122] (A-16)

[0123] (A-17)

[0124] (A-18)

[0125] (A-19)

[0126] (A-20)

[0127] (A-21)

[0128] (A-22)

[0129] (A-23)

[0130] (A-24)

[0131] In the formula, a is the road segment, k is the path, Represents a set of road segments, is the starting node, For the destination node, a pair of starting node r and destination node s is called an OD pair; Represents the rs path set, the path is divided into common paths , charging path and V2G paths ; 、 、 They are the inflow, outflow and state traffic flow on section a at time t under the kth path with r as the starting node and s as the destination node; 、 、 They are the inflow, outflow and state aggregated traffic flow on road section a at time t; is the set of road segments a starting from the starting node r; The penetration rate of vehicles that need to be charged; To select the vehicle penetration rate of V2G; is the travel demand between OD and rs at time t; is the set of road segments a leading to the destination node s; is the arrival traffic flow of the origin-destination node pair rs at time t; is the cumulative arrival traffic flow of the origin-destination node pair rs at time t; for The cumulative arrival traffic flow of the start-destination node pair rs at time instant; is the cumulative arrival traffic flow of the kth path selected from the start-destination node pair rs at time t; They are The state of traffic flow on road section a under the k-th path with r as the starting node and s as the destination node at time; represents the set of road segments a starting from node j, represents the set of road segments a leading to node j; is the free travel time of section a; assuming that section a is the path A section of the road, represents the set of all road segments after road segment a in path k, Indicates that segment b belongs to the segment set after segment a in path k ; for The state traffic flow on the road segment b in the start-destination node pair rs at time instant; for The state traffic flow on the road segment b in the start-destination node pair rs at time instant; for The cumulative arrival traffic flow of the kth path selected in the start-destination node pair rs at time instant;

[0132] (2) Travel time and travel cost constraints:

[0133] (A-25)

[0134] (A-26)

[0135] (A-27)

[0136] (A-28)

[0137] (A-29)

[0138] (A-30)

[0139] (A-31)

[0140] (A-32)

[0141] (A-33)

[0142] Where, For ordinary road sections, For charging section collection, is a collection of virtual road segments, It is a collection of V2G sections; Indicates that road section a has travel time in the case of vehicle inflow; is the free travel time of section a; is the traffic capacity of section a; and is the average charging time and V2G time; and The maximum queuing time for the charging capacity and V2G capacity of the bidirectional charging station; and Configure capacity for charging stations and V2G stations; They are State aggregated traffic flow on road section a at time; virtual road section For the virtual road section set up for the connectivity of the traffic network, the travel time is 0; is the travel cost of section a at time t; is the unit time cost; is the unit charging electricity price of the charging station on road section a at time t; is the unit coupon price of the charging station on road section a at time t; and is the average charging / V2G power; is the unit discharge reward of the V2G station on section a at time t; is the one-time coupon subsidy for the V2G station on road section a at time t; is the travel cost of the kth path starting at time t with r as the starting node and s as the destination node; assuming that section a is the path A section of the road, represents the set of all road segments before road segment a in path k, Indicates that segment b belongs to the set of segments before segment a in path k ; is the free travel time of section b; is a 0-1 parameter that determines the correspondence between road segments and paths. If path k passes through road segment a, then , if not, then ;

[0143] (3) Dynamic user equilibrium conditions, capacity constraints, and initial value conditions

[0144] (A-34)

[0145] (A-35)

[0146] (A-36)

[0147] (A-37)

[0148] (A-38)

[0149] (A-39)

[0150] (A-40)

[0151] Where, express and ; 、 、 are the minimum travel costs of the ordinary path, charging path, and V2G path starting at time t with r as the starting node and s as the destination node, respectively. The state traffic flow on section a at time 0 and time 1 respectively, with r as the starting node and s as the destination node under the k-th path; It is the inflow traffic flow on road section a under the kth path with r as the starting node and s as the destination node at time 0.

[0152] Furthermore, in step 5, the cost model of the distribution-transportation coupling system considering vehicle-grid interaction is:

[0153] (A-41)

[0154] (A-42)

[0155] (A-43)

[0156] (A-44)

[0157] (A-45)

[0158] (A-46)

[0159] (A-47)

[0160] (A-48)

[0161] Where, is the total cost of the distribution-transportation system; Cost of purchasing electricity from the upper power grid; Cost of generating electricity for distributed generation; is the total revenue of the charging station; is the total V2G expenditure; Time-of-use electricity price; is the time span; is the benchmark charging electricity price; the total cost of subsidies for bidirectional charging station coupons for the distribution-transportation coupling system; to cap the total cost of the distribution-transportation system; The upper limit of the subsidy amount for bidirectional charging station coupons in the distribution-transportation coupling system.

[0162] Furthermore, in step 6, by continuously rotating the tangent plane to be tangent to the feasible region, the convex hull fitting is used to depict the flexible operation domain of the electric vehicle bidirectional charging station, characterizing the flexible adjustment capability of electric vehicle charging and discharging under a given subsidy amount:

[0163] (A-49)

[0164] (A-50)

[0165] (A-51)

[0166] (A-52)

[0167] (A-53)

[0168] (A-54)

[0169] (A-55)

[0170] (A-56)

[0171] Where, is the polar angle of the normal vector of the tangent plane at each iteration, that is, the angle between the normal vector of the tangent plane and the z-axis; is the azimuth of the normal vector of the tangent plane at each iteration, that is, the angle between the projection of the tangent plane normal vector on the xy plane and the x-axis; is the general equation of a plane The constant term of ; Representation plane The constant term at the extreme position tangent to the feasible region; the moment of observing the flexible feasible region ,Select three bidirectional charging stations in the flexible operation domain, mark the first bidirectional charging station as x, the second bidirectional charging station as y, and the third bidirectional charging station as z. 、 、 They are the net charge and discharge loads of the first, second, and third bidirectional charging stations, 、 、 They are the first, second and third bidirectional charging stations respectively Charging load at the moment, 、 、 They are the first, second and third bidirectional charging stations respectively V2G power at the moment; is the polar angle of the iterative tangent plane normal vector The initial angle, is the rotation step of the iterative tangent plane normal vector in the polar angle direction, Polar angle Number of rotations, Polar angle Maximum number of rotations; is the azimuth of the iterative tangent plane normal vector The initial angle, is the rotation step of the iterative tangent plane normal vector in the azimuth direction, is the azimuth Number of rotations, is the azimuth Maximum number of spins.

[0172] Case Analysis

[0173] The following example illustrates the superiority of the comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow. The present invention uses a 13-node transportation network and a 33-node distribution network coupling system as an example. The network topology diagram is shown in Figure 2 Three bidirectional charging stations are set up in the coupling system, each equipped with 60 electric vehicle charging piles and 40 electric vehicle V2G piles. The first, second and third bidirectional charging stations are set as flexible operation domain observation objects respectively. 、 、 , and choose the observation time to be Two distributed power sources are installed at the end of the grid to supply energy. To compare the superiority of the proposed method, we considered the spatiotemporal flexibility of electric vehicles in three scenarios: a distribution-transportation coupled system with a bidirectional charging station coupon subsidy of 1,000, 2,000, and 4,000 yuan. This method was implemented using the GAMS optimization platform and the Baron solver for the nonlinear programming problem.

[0174] Based on this example, the upper limit of the subsidy amount for the bidirectional charging station coupons of the distribution-transportation coupling system is set to 1000 yuan, 2000 yuan, and 4000 yuan respectively. By continuously iteratively rotating the plane to be tangent to the feasible region, the convex hull fitting is used to depict the flexible operation domain of the electric vehicle bidirectional charging station. The results are shown in Figure 3-Figure 5 , we can clearly and intuitively see the flexibility of electric vehicle bidirectional charging stations under different subsidy amounts, and compare Figure 3 、 Figure 4 、 Figure 5 It can also be found that different subsidy amounts have different effects on the charging and discharging flexibility of guiding traffic flow through price signals in the current period. The higher the subsidy amount, the larger the flexible operation domain and the stronger the temporal and spatial mobility of the load. For a comparison of the total costs of the distribution-transportation coupling system at the boundaries of the flexible operation domain under the three subsidy amounts, see Figure 6It can be found that the change in the total cost of the coupling system is basically equal to the change in the subsidy amount. That is to say, guiding the charging and discharging of electric vehicles through price signal subsidies has little impact on the costs of other parts of the coupling system. Through subsidy expenditures, the flexible charging and discharging capabilities of electric vehicles can be brought into play, playing the role of load time and space transfer.

[0175] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow, characterized by: The method comprises the following steps: Step 1: Obtain the network parameters and operating coefficients of the distribution network. The network parameters include the distribution network topology and line impedance. The operating coefficients include the upper-level power grid power supply coefficient, the distributed power supply coefficient, and the photovoltaic inverter coefficient. Step 2: Obtain scenario data such as load demand at each node of the power grid, distributed photovoltaic output, and time-of-use electricity price. Use the power purchase amount of the upper power grid and the power generation amount of distributed generation as control variables, and the operation constraints of the distribution network as constraints to establish a second-order cone DistFlow optimal power flow model for the distribution network. Step 3: Obtaining transportation network parameters, including transportation network topology, capacity of each road section, and location and capacity of bidirectional charging stations; Step 4: Obtain scenario data on transportation network travel demand, electric vehicle penetration rate, and V2G user penetration rate. Using charging and discharging coupons as control variables and the basic operating constraints of the transportation network as constraints, a dynamic traffic allocation model that considers V2G vehicle-grid interaction is established. Step 5: Based on the second-order cone DistFlow optimal power flow model of the distribution network in step 2 and the dynamic traffic assignment model considering V2G vehicle-grid interaction in step 4, the distribution network and transportation network are coupled through charging stations and V2G stations, and a distribution-transportation coupling system cost model considering vehicle-grid interaction is constructed; Step 6. Set the subsidy amount for the bidirectional charging station coupons of the distribution-transportation coupling system. By rotating the tangent plane to be tangent to the feasible domain, the convex hull fitting is used to depict the flexible operation domain of the electric vehicle bidirectional charging station, and the flexible adjustment capability of electric vehicles under different subsidy amounts under dynamic traffic flow is obtained.

2. The comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow according to claim 1 is characterized in that: The second-order cone DistFlow optimal power flow model of the distribution network in step 2 is as follows: (1) Grid operation constraints （A-1） （A-2） （A-3） （A-4） （A-5） （A-6） （A-7） （A-8） Where N is the set of distribution network nodes; i, j, m are distribution network nodes; 、 and 、 are the active injection power and reactive injection power of nodes i and j respectively; and are the resistance and reactance of line ij respectively; 、 and 、 are the active power and reactive power of lines ij and jm respectively; is the square of the current in line ij; and is the square of the voltage at nodes i and j; and are the active and reactive power generated by the upper power grid at node i respectively; and are the active and reactive powers generated by the distributed generation at node i respectively; and are the active and reactive power generated by distributed photovoltaic at node i respectively; is the V2G power at node i; is the active load of the charging station at node i; and are the conventional active and reactive loads at node i, They are State aggregated traffic flow on road section a at time instant; and for average charging and V2G power; It is a parameter used to describe the one-to-one correspondence between charging stations or V2G stations on traffic network segment a and distribution network nodes; (2) PV inverter model and upper and lower limit constraints （A-9） （A-10） （A-11） （A-12） Where, is the distributed photovoltaic capacity at node i; is the reactive capacity of the photovoltaic inverter at node i, represents the absolute value of the voltage amplitude at node i at time t; and are the lower and upper limits of the voltage at node i respectively; represents the absolute value of the current amplitude of circuit ij at time t; is the upper limit of the current of line ij; 、 and 、 are the lower and upper limits of active and reactive power transmitted by the upper power grid at node i, respectively; 、 and 、 are the lower and upper limits of the active and reactive power generated by the distributed generation at node i, respectively.

3. The comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow according to claim 2 is characterized in that: The dynamic traffic assignment model considering V2G vehicle-grid interaction in step 4 is as follows: (1) Section state equation and flow propagation constraints: （A-13） （A-14） （A-15） （A-16） （A-17） （A-18） （A-19） （A-20） （A-21） （A-22） （A-23） （A-24） In the formula, a is the road section, k is the path, Represents a set of road segments, is the starting node, For the destination node, a pair of starting node r and destination node s is called an OD pair; Represents the rs path set, the path is divided into common paths , charging path and V2G paths ; 、 、 They are the inflow, outflow and state traffic flow on section a at time t under the kth path with r as the starting node and s as the destination node; 、 、 They are the inflow, outflow and state aggregated traffic flow on road section a at time t; is the set of road segments a starting from the starting node r; The penetration rate of vehicles that need to be charged; To select the vehicle penetration rate of V2G; is the travel demand between OD and rs at time t; is the set of road segments a leading to the destination node s; is the arrival traffic flow of the origin-destination node pair rs at time t; is the cumulative arrival traffic flow of the origin-destination node pair rs at time t; for The cumulative arrival traffic flow of the start-destination node pair rs at time instant; is the cumulative arrival traffic flow of the kth path selected in the start-destination node pair rs at time t; They are The state of traffic flow on road section a under the k-th path with r as the starting node and s as the destination node at time; represents the set of road segments a starting from node j, represents the set of road segments a leading to node j; is the free travel time of section a; assuming that section a is the path A section of the road, represents the set of all road segments after road segment a in path k, Indicates that segment b belongs to the segment set after segment a in path k ; for The state traffic flow on the road segment b in the start-destination node pair rs at time instant; for The state traffic flow on the road segment b in the start-destination node pair rs at time instant; for The cumulative arrival traffic flow of the kth path selected in the start-destination node pair rs at time instant; (2) Travel time and travel cost constraints: （A-25） （A-26） （A-27） （A-28） （A-29） （A-30） （A-31） （A-32） （A-33） Where, For ordinary road sections, For charging section collection, is a collection of virtual road segments, It is a collection of V2G sections; Indicates that road section a has travel time in the case of vehicle inflow; is the free travel time of section a; is the traffic capacity of section a; and is the average charging time and V2G time; and The maximum queuing time for the charging capacity and V2G capacity of the bidirectional charging station; and Configuration capacity for charging stations / V2G stations; They are State aggregated traffic flow on road section a at time; virtual road section For the virtual road section set up for the connectivity of the traffic network, the travel time is 0; is the travel cost of section a at time t; is the unit time cost; is the unit charging electricity price of the charging station on road section a at time t; is the unit coupon price of the charging station on road section a at time t; and is the average charging power and V2G power; is the unit discharge reward of the V2G station on section a at time t; is the one-time coupon subsidy for the V2G station on road section a at time t; is the travel cost of the kth path starting at time t with r as the starting node and s as the destination node; assuming that section a is the path A section of the road, represents the set of all road segments before road segment a in path k, Indicates that segment b belongs to the set of segments before segment a in path k ; is the free travel time of section b; is a 0-1 parameter that determines the correspondence between road segments and paths. If path k passes through road segment a, then , if not, then ; (3) Dynamic user equilibrium conditions, capacity constraints, and initial value conditions （A-34） （A-35） （A-36） （A-37） （A-38） （A-39） （A-40） Where, express and ; 、 、 are the minimum travel costs of the ordinary path, charging path, and V2G path starting at time t with r as the starting node and s as the destination node, respectively. The state traffic flow on section a at time 0 and time 1 respectively, with r as the starting node and s as the destination node under the k-th path; It is the inflow traffic flow on road section a under the kth path with r as the starting node and s as the destination node at time 0.

4. The comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow according to claim 3 is characterized in that: In step 5, the cost model of the distribution-transportation coupling system considering vehicle-grid interaction is: （A-41） （A-42） （A-43） （A-44） （A-45） （A-46） （A-47） （A-48） Where, is the total cost of the distribution-transportation system; Cost of purchasing electricity from the upper power grid; Cost of generating electricity for distributed generation; is the total revenue of the charging station; is the total V2G expenditure; Time-of-use electricity price; is the time span; is the benchmark charging electricity price; the total cost of subsidies for bidirectional charging station coupons for the distribution-transportation coupling system; to cap the total cost of the distribution-transportation system; The upper limit of the subsidy amount for bidirectional charging station coupons in the distribution-transportation coupling system.

5. The comprehensive spatiotemporal flexibility analysis method for electric vehicles under dynamic traffic flow according to claim 4 is characterized in that: In step 6, by continuously rotating the tangent plane to be tangent to the feasible region, the convex hull fitting is used to depict the flexible operation domain of the electric vehicle bidirectional charging station, characterizing the flexible adjustment capability of electric vehicle charging and discharging under a given subsidy amount: （A-49） （A-50） （A-51） （A-52） （A-53） （A-54） （A-55） （A-56） Where, is the polar angle of the normal vector of the tangent plane at each iteration, that is, the angle between the normal vector of the tangent plane and the z-axis; is the azimuth of the normal vector of the tangent plane at each iteration, that is, the angle between the projection of the tangent plane normal vector on the xy plane and the x-axis; is the plane equation The constant term of ; Representation plane The constant term at the extreme position tangent to the feasible region; the moment of observing the flexible feasible region ,Select three bidirectional charging stations in the flexible operation domain, mark the first bidirectional charging station as x, the second bidirectional charging station as y, and the third bidirectional charging station as z. 、 、 They are the net charge and discharge loads of the first, second, and third bidirectional charging stations, 、 、 They are the first, second and third bidirectional charging stations respectively Charging load at the moment, 、 、 They are the first, second and third bidirectional charging stations respectively V2G power at the moment; is the polar angle of the iterative tangent plane normal vector The initial angle, is the rotation step of the iterative tangent plane normal vector in the polar angle direction, Polar angle Number of rotations, Polar angle Maximum number of rotations; is the azimuth of the iterative tangent plane normal vector The initial angle, is the rotation step of the iterative tangent plane normal vector in the azimuth direction, is the azimuth Number of rotations, is the azimuth Maximum number of spins.

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