Electric vehicle traffic-electric quantity flow collaborative optimization method considering multi-complementary energy and V2G

Abstract

The invention belongs to the technical field of traffic-electric power crossing, and discloses an electric vehicle traffic-electric quantity flow collaborative optimization method considering multi-complementary energy and V2G, and the method specifically comprises the following steps: obtaining traffic network data and power distribution network data, and constructing a traffic-electric power coupling network; constructing a double-layer optimization model based on the traffic-power coupling network, wherein the double-layer optimization model comprises an upper-layer optimization model and a lower-layer optimization model; the upper-layer optimization model is constructed based on a dynamic traffic distribution method, and the upper-layer optimization model comprising a road model, a dynamic energy complementing station model and an electric vehicle circulation flow model is formed through a mapping relation between electric vehicle traffic flow and electric quantity flow and is used for optimizing vehicle path selection and energy complementing decision; the lower-layer optimization model comprises a power distribution network alternating current optimal power flow model containing an electric vehicle load and is used for optimizing the operation state of the power distribution network; the problem that in the prior art, due to the fact that a traffic-power system is single in energy supplementing mode, vehicle network interaction and cooperation are insufficient is solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of traffic-power intersection, and particularly relates to a traffic-power flow coordination optimization method for electric vehicles considering multiple energy compensation and V2G. BACKGROUND

[0002] With the acceleration of traffic electrification, the rapid popularization of electric vehicles is reshaping the coupling relationship between power and traffic systems. As a new type of load with both travel tool and mobile energy storage attributes, the dynamic energy compensation behavior of electric vehicles forms a complex two-way interaction mechanism with power grid operation: electricity price fluctuations, traffic conditions and energy compensation facility layout directly affect user travel decisions, while large-scale electric vehicle charging and discharging loads significantly change the operation characteristics of distribution networks.

[0003] Existing research still has obvious deficiencies in power-traffic coordination optimization. The grid side method mostly regards electric vehicles as passive loads, ignoring the spatiotemporal dynamic characteristics of their traffic behavior. Traffic side research generally uses static assumptions, which are difficult to reflect the influence of real-time traffic conditions on the power grid. More critically, most existing models only consider a single charging mode, lacking systematic modeling of battery swap stations and failing to fully integrate vehicle-to-grid (V2G) technology, resulting in an inability to accurately depict the dynamic interaction process in actual operation. This model fragmentation and functional deficiency seriously restricts the effectiveness of coordinated optimization. Therefore, the existing technology has the problem of insufficient vehicle-to-grid interaction coordination due to single energy compensation mode in traffic-power systems. SUMMARY

[0004] In view of the deficiencies of the prior art, the purpose of the present application is to provide a traffic-power flow coordination optimization method for electric vehicles considering multiple energy compensation and V2G, which solves the problem of insufficient vehicle-to-grid interaction coordination due to single energy compensation mode in traffic-power systems in the prior art.

[0005] The purpose of the present application can be achieved by the following technical solutions:

[0006] The traffic-power flow coordination optimization method for electric vehicles considering multiple energy compensation and V2G specifically comprises the following steps:

[0007] Obtain traffic network data and distribution network data, and construct a traffic-power coupled network;

[0008] Based on the traffic-power coupled network, a two-level optimization model is constructed, including an upper optimization model and a lower optimization model;

[0009] The upper optimization model is constructed based on a dynamic traffic assignment method. Through the mapping relationship between electric vehicle traffic flow and power flow, an upper optimization model including a road model, a dynamic energy compensation station model and an electric vehicle circulation flow model is formed, which is used to optimize vehicle route selection and energy compensation decisions.

[0010] The lower layer optimization model comprises an alternating current optimal power flow model of a power distribution network including electric vehicle load, used for optimizing the operating state of the power distribution network and obtaining a node marginal price as a complementary energy price;

[0011] Based on a second-order cone relaxation and a polyhedral approximation method, Karush-Kuhn-Tucker conditions in the lower layer optimization model are obtained;

[0012] The Karush-Kuhn-Tucker conditions are substituted into the upper layer optimization model to obtain a single-layer mathematical optimization model;

[0013] The single-layer mathematical optimization model is solved to obtain the space-time distribution of electric vehicle flow and charging load, as well as power generation scheduling of the power distribution system, node marginal price of the power distribution system, and power flow distribution.

[0014] Traffic network data and power distribution network data are obtained, and a traffic-power coupled network is constructed, specifically including the following steps:

[0015] Traffic network data is obtained, including traffic network topology, time for electric vehicles to freely travel when roads are unobstructed, maximum outflow capacity of roads, capacity of electric vehicle complementary energy stations, and travel demand of electric vehicles;

[0016] The traffic network topology includes traffic nodes and road connection information;

[0017] The traffic nodes include ordinary nodes and complementary energy station nodes including electric vehicle complementary energy stations;

[0018] The electric vehicle complementary energy stations include charging stations and battery swap stations, and the charging stations are used to support bidirectional transfer of electric energy between electric vehicles and the power grid;

[0019] The travel demand of electric vehicles includes origin-destination points, the number of electric vehicles, and the initial electric quantity of electric vehicles;

[0020] Power distribution network data is obtained, including power distribution network topology, line capacity, fixed load, distributed generator capacity, and generation cost coefficient and renewable energy output;

[0021] The power distribution network topology includes power distribution network nodes and line connection relationships;

[0022] The complementary energy station nodes in the traffic network are associated with power grid nodes in the power distribution network;

[0023] The coupling relationship between the traffic network and the power distribution network is defined, and the specific coupling relationship is as follows:

[0024] The price of the electric vehicle complementary energy station is obtained according to the node marginal price of the power distribution network;

[0025] The load of the power distribution network node is determined by the energy behavior of the electric vehicles corresponding to the energy supply station node;

[0026] The energy behavior includes the charging and discharging behavior of the electric vehicles at the charging station and the battery replacement behavior of the electric vehicles at the battery replacement station;

[0027] Based on the traffic network data and the power distribution network data, a traffic-power coupling network is established in combination with the coupling relationship.

[0028] The road model is constructed by considering traffic congestion, and the driving time under the condition of traffic congestion is determined according to the first-in first-out principle, which specifically includes:

[0029] When the length of the congestion queue does not exceed the maximum outflow capacity of the road, all electric vehicles in the queue can flow out of the road at the next time step, and the driving time of the electric vehicles on the road is the free driving time plus the time length corresponding to one time step;

[0030] When the length of the congestion queue exceeds the maximum outflow capacity of the road, only the number of electric vehicles equal to the maximum outflow capacity can flow out of the road at the next time step, and the remaining electric vehicles will flow out in turn according to the first-in first-out order at subsequent time steps;

[0031] The specific expression of the road model is as follows:

[0032]

[0033] In the formula, i represents the path number; t represents the time number; I a represents the set of paths containing road a; I j represents the subset of paths related to the origin-destination point j; represents the electric vehicle traffic flow entering the first road of path i at time t; represents the number of electric vehicles corresponding to the travel demand of the origin-destination point j at time t; represents the traffic flow in the congestion queue of road a of path i at time t; represents the traffic flow in the congestion queue of road a of path i at time t-1; represents the traffic flow entering the congestion queue of road a of path i at time t; v a,j,t represents the traffic flow leaving the congestion queue of road a of path i at time t; represents the total traffic flow on road a of path i at time t; represents the total traffic flow on road a of path i at time t-1; u a,i,t represents the traffic flow entering road a of path i at time t; represents the maximum outflow capacity of road a; denotes the initial energy level of the electric vehicle corresponding to the trip demand of the origin-destination pair j at time t; denotes the energy level of the traffic flow allocated to the first road segment of path i at time t; denotes the traffic flow corresponding to the traffic flow u a,i,t denotes the electric power flow of the energy level; denotes the traffic flow corresponding to the traffic flow v a,i,t denotes the electric power flow of the energy level; denotes the traffic flow corresponding to the traffic flow denotes the electric power flow of the energy level; denotes the energy consumption of the vehicle flow on road a; denotes the traffic flow corresponding to the congestion queue denotes the electric power flow of the energy level; denotes the traffic flow corresponding to the congestion queue denotes the electric power flow of the energy level; E and E denote the upper and lower limits of the electric vehicle battery capacity, respectively.

[0034] The dynamic energy supplement station model includes a dynamic charging station model and a dynamic battery swap station model;

[0035] In the dynamic charging station model, a waiting queue and a service queue are constructed to represent the dynamic characteristics of vehicles in the charging station, and the specific expressions are as follows:

[0036]

[0037]

[0038] In the formula, a + denotes the upstream road connected to the node; a - denotes the downstream road connected to the node; denotes the traffic flow from the upstream road into the node on path i at time t; denotes the traffic flow from the node to the downstream road on path i at time t; f denotes the charging station node number; I f denotes the subset of paths containing the charging station f; denotes the traffic flow entering the charging station waiting queue on path i at time t; denotes the traffic flow passing through the charging station node f but not entering the charging station on path i at time t; denotes the traffic flow leaving the charging station f from the service queue on path i at time t; denotes the number of electric vehicles in the charging station f waiting queue on path i at time t; denotes the number of electric vehicles in the charging station f waiting queue on path i at time t-1; denotes the traffic flow entering the charging station f service queue from the waiting queue on path i at time t; denotes the number of electric vehicles in the charging station f service queue on path i at time t; represents the number of electric vehicles in the service queue of charging station f on path i at time t-1; represents the corresponding traffic flow represents the electric flow of energy level represents the corresponding traffic flow represents the electric flow of energy level represents the corresponding traffic flow represents the electric flow of energy level represents the corresponding traffic flow represents the electric flow of energy level represents the corresponding traffic flow represents the electric flow of energy level represents the corresponding waiting queue represents the electric flow of energy level represents the corresponding traffic flow represents the electric flow of energy level represents the corresponding service queue represents the electric flow of energy level

[0039] The energy of the electric vehicle supplemented at the battery swap station is determined by the energy when it enters the battery swap station and the energy of the swapped battery, and the specific expression is as follows:

[0040]

[0041] represents the traffic flow leaving the battery swap station after completing the battery swap operation represents the electric flow of energy level represents the traffic flow entering the service queue from the waiting queue of battery swap station b on path i at time t-1, represents the corresponding traffic flow represents the electric flow of energy level

[0042] The waiting queue and the service queue are constructed in the dynamic battery swap station model to represent the dynamic characteristics of vehicles in the battery swap station, and the specific expression is as follows:

[0043]

[0044] In the formula, b represents the node number of the battery swap station; I b represents the subset of paths containing the battery swap station b; represents the traffic flow entering the waiting queue of the battery swap station on path i at time t; represents the traffic flow passing through the battery swap station node b but not entering the battery swap station on path i at time t; represents the traffic flow leaving the battery swap station b from the service queue on path i at time t; represents the number of electric vehicles in the waiting queue of the battery swap station b on path i at time t; denotes the traffic flow on path i at time t entering the service queue from the waiting queue of battery swap station b; denotes the number of electric vehicles in the service queue of battery swap station b on path i at time t; denotes the traffic flow corresponding to the electric power flow of the energy level; denotes the traffic flow corresponding to the electric power flow of the energy level; denotes the traffic flow corresponding to the electric power flow of the energy level; denotes the energy level corresponding to the waiting queue ; denotes the traffic flow corresponding to the electric power flow of the energy level; denotes the traffic flow corresponding to the electric power flow of the energy level; denotes the energy level corresponding to the service queue ;

[0045] In the charging station, a circulating flow model of electric vehicles containing multiple different charging and discharging power levels is constructed, and the specific expression is as follows:

[0046]

[0047] In the formula, l represents the charging and discharging power level number, L f denotes the set of charging and discharging power levels of charging station f; I f denotes the set of paths containing charging station f; denotes the traffic flow on path i at time t entering the charging and discharging power level l from the waiting queue, denotes the traffic flow on path i at time t entering the service queue of charging station f; denotes the traffic flow in the service queue on path i at time t continuing the charging and discharging operation and selecting the power level l; denotes the traffic flow on path i at time t ending the charging and discharging operation and flowing out of charging station f;v l,j,t denotes the traffic flow on path i at time t leaving the charging and discharging power level l; denotes the number of electric vehicles on path i at time t selecting the charging and discharging power level l; denotes the total number of actual charging piles in charging station f; denotes the energy level corresponding to the service queue ; p l denotes the charging and discharging power corresponding to charging and discharging power level number l; η denotes the charging and discharging efficiency; Δt denotes the time length between adjacent two time points;

[0048] The electric vehicle circulation flow model further comprises constraint conditions for ensuring that the electric vehicles in the service queue perform corresponding charging and discharging operations at each time step, and the constraint conditions are expressed as follows:

[0049]

[0050] In the formula, represents the total sum of the traffic flow entering the power level l from 0 to t, represents the total sum of the traffic flow leaving the power level l from 0 to t+1.

[0051] The ordinary nodes in the upper optimization model should satisfy the balance constraints of inflow and outflow, and the specific balance constraints are as follows:

[0052] The road model, the dynamic energy supplement station model, the electric vehicle circulation flow model and the balance constraints jointly constitute the upper optimization model.

[0053] When the upper optimization model optimizes the vehicle path selection and energy supplement decision, the minimum travel cost of the electric vehicle is taken as the objective function;

[0054] The travel cost of the electric vehicle includes the time cost and the energy supplement cost;

[0055] The time cost includes the driving time cost of the vehicle on the road, the waiting time cost of the vehicle at the energy supplement station, the service time cost of the vehicle at the energy supplement station and the energy supplement cost of the vehicle;

[0056] The energy supplement cost includes the charging and discharging cost of the electric vehicle at the charging station and the battery replacement cost of the electric vehicle at the battery replacement station;

[0057]

[0058] In the formula, Ψ t represents the travel time cost; κ represents the time cost coefficient of the electric vehicle; represents the total time of the electric vehicle driving on the road; represents the total time of the electric vehicle at the charging station; represents the total time of the electric vehicle at the battery replacement station; Ψ e represents the energy supplement cost; represents the energy supplement cost of the electric vehicle at the charging station, when is negative, represents the discharging benefit; represents the energy supplement cost of the electric vehicle at the battery replacement station;

[0059] In the formula, κ

[0060]

[0061] In the formula, denotes the electric vehicle load of the charging station f at time t, denotes the electric vehicle load of the battery swap station b at time t;

[0062] The objective function of the upper-layer optimization model is: Objective function = min (Ψ t +Ψ e ).

[0063] The lower-layer optimization model includes a distribution network alternating current optimal power flow model containing electric vehicle loads, which is composed of the following operation constraint conditions, and the specific expression is as follows:

[0064]

[0065]

[0066] In the formula, m and n are the distribution network node numbers; π(n) denotes the distribution network node set connected to the distribution network node n; M is the distribution network node set; and are the generation cost coefficients; denotes the active power generated by the distributed generator at the distribution network node n at time t; P mn,t denotes the active power on the line (m, n) at time t; denotes the active power generated by the new energy power generation supply unit at the distribution network node n at time t; R mn denotes the resistance value of the line (m, n); I mn,t denotes the current on the line (m, n) at time t; denotes the fixed active load of the distribution network node n at time t; Q mn,t denotes the reactive power on the line (m, n) at time t; denotes the reactive power generated by the distributed generator at the distribution network node n at time t; X mn denotes the reactance value of the line (m, n); denotes the fixed reactive load of the distribution network node n at time t; U n,t denotes the square of the voltage of the distribution network node n at time t; is the upper limit of the transmission current of the line (m, n); P mn and are the upper limits of the transmission active power of the line (m, n); Q mn and are the upper limits of the transmission reactive power of the line (m, n); U n and are the upper and lower limits of the voltage allowed by the distribution network node n; and Pmax(n) and Pmin(n) are the upper and lower limits of active power output of distributed generators at distribution grid node n, respectively. and Qmax(n) and Qmin(n) are the upper and lower limits of reactive power output of distributed generators at distribution grid node n, respectively. Pev(n,t) represents the electric vehicle load at distribution grid node n at time t, and Pev(n,t) = 0 when the distribution grid node is not connected to the energy supplement station. Pmax(n) is the upper limit of active power output of new energy power generation supply unit at distribution grid node n. λ, τ, μ, and are the Lagrange multipliers corresponding to the operating constraints, wherein LMP(n) is the marginal price of distribution grid node n.

[0067] Based on the second-order cone relaxation and the polyhedral approximation method, the lower-level optimization model is converted into the Karush-Kuhn-Tucker condition, which includes the following steps:

[0068] The second-order cone relaxation is used to convex the lower-level optimization model, which is specifically to relax the following expression in the operating constraint condition into the second-order cone constraint:

[0069] The operating constraint condition that needs to be relaxed is as follows:

[0070]

[0071] The expression of the second-order cone constraint is as follows:

[0072]

[0073] The polyhedral approximation method is used to convert the second-order cone constraint into the following two groups of linear constraints:

[0074]

[0075] In the formula, is an auxiliary variable used for polyhedral approximation; k = 1, …, v, wherein v is the parameter of polyhedral approximation;

[0076] λ, τ, μ, and are the Lagrange multipliers corresponding to the linear constraints;

[0077] The Lagrange function L of the lower-level optimization model is constructed, and the specific expression is as follows:

[0078] L = f(x, y) + λ·h(x, y) + τ·g(x, y)

[0079] Then the Karush-Kuhn-Tucker condition of the lower-level optimization model is as follows:

[0080] ​

[0081] h(x,y)=0

[0082] τ·g(x,y)=0

[0083] τ≥0

[0084] wherein, x represents a variable of an upper layer optimization model; y represents a variable of a lower layer optimization model; f(x,y) represents an objective function of the lower layer optimization model; h(x,y) represents an equality constraint in the lower layer optimization model; g(x,y) represents an inequality constraint in the lower layer optimization model; and λ and τ are Lagrange multipliers corresponding to the constraints;

[0085] The nonlinear complementarity relaxed constraint in the Karush-Kuhn-Tucker condition is τ·g(x,y)=0.

[0086] The nonlinear complementarity relaxed constraint in the Karush-Kuhn-Tucker condition is converted into a linear constraint, and the specific expression is as follows:

[0087] 0≤τ≤M·(1-ε)

[0088] 0≤g(x,y)≤M·ε

[0089] wherein, ε represents an auxiliary variable with a value of 0 or 1; and M is a constant.

[0090] The present application has the following beneficial effects:

[0091] The present application establishes a collaborative optimization framework composed of an upper layer optimization model and a lower layer optimization model by constructing a traffic-power coupled network containing a traffic network topology and a power distribution network topology; wherein, the upper layer optimization model is based on a dynamic traffic assignment method, integrates a road model adopting a first-in first-out principle, a dynamic energy supplement station model with a waiting queue and a service queue, and an electric vehicle circulation flow model supporting multi-power level selection, and effectively optimizes the path selection and energy supplement decision of electric vehicles; the lower layer optimization model obtains a Karush-Kuhn-Tucker condition by processing nonlinear constraints through a second-order cone relaxation and a polyhedral approximation method, and embeds the obtained Karush-Kuhn-Tucker condition into the upper layer model to form a solvable single-level mathematical optimization model; the collaborative optimization method not only overcomes the defect of insufficient vehicle network interaction and collaboration caused by single energy supplement mode in the prior art, but also realizes the spatio-temporal optimization scheduling of electric vehicle load through a node marginal electricity price mechanism: guiding charging during a low electricity price period to promote renewable energy consumption, and encouraging discharging during a peak period to support power grid operation, thereby simultaneously improving the traffic system operation efficiency, optimizing the power distribution network power flow distribution, and reducing the system operation cost and carbon emissions. BRIEF DESCRIPTION OF DRAWINGS

[0092] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0093] Figure 1 is the whole flow schematic diagram of the optimization method of the present application;

[0094] Figure 2 is the schematic diagram of the traffic network topology and the power distribution network topology structure in the embodiment of the present application;

[0095] Figure 3 is the schematic diagram of the traffic flow space-time distribution and the load of the energy supplement station in the embodiment of the present application;

[0096] Figure 4 is the schematic diagram of the power generation dispatching and the aggregated electricity price of the power distribution system in the embodiment of the present application;

[0097] Figure 5 is the schematic diagram of the active power flow of the power distribution line and the load of the charging station in the embodiment of the present application. DETAILED DESCRIPTION

[0098] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present application.

[0099] As shown in Figures 1 to 5 , the electric vehicle traffic-power flow collaborative optimization method considering multi-energy supplement and V2G includes the following steps:

[0100] Obtain the traffic network data and the power distribution network data, and construct a traffic-power coupling network;

[0101] Based on the traffic-power coupling network, a double-layer optimization model is constructed, including an upper-layer optimization model and a lower-layer optimization model;

[0102] The upper-layer optimization model is constructed based on a dynamic traffic distribution method. Through the mapping relationship between the electric vehicle traffic flow and the power flow, an upper-layer optimization model including a road model, a dynamic energy supplement station model and an electric vehicle circulation flow model is formed, which is used for optimizing the vehicle path selection and the energy supplement decision;

[0103] The lower-layer optimization model includes an alternating current optimal power flow model of the power distribution network including the electric vehicle load, which is used for optimizing the operation state of the power distribution network and obtaining the node marginal price as the energy supplement price;

[0104] Based on the second-order cone relaxation and the polyhedral approximation method, the Karush-Kuhn-Tucker condition in the lower-layer optimization model is obtained;

[0105] The Karush-Kuhn-Tucker condition is substituted into the upper-layer optimization model to obtain a single-layer mathematical optimization model;

[0106] The single-layer mathematical optimization model is solved to obtain the space-time distribution of the electric vehicle flow and the charging load, and the power generation scheduling of the power distribution system, the marginal price of the power distribution network node and the power flow distribution.

[0107] Obtain the traffic network data and the power distribution network data, and construct a traffic-power coupling network, which specifically includes the following steps:

[0108] Obtain the traffic network data, which includes the traffic network topology, the time of the electric vehicle freely driving when the road is unobstructed, the maximum outflow capacity of the road, the capacity of the electric vehicle energy supplement station and the travel demand of the electric vehicle;

[0109] The traffic network topology includes traffic nodes and road connection information;

[0110] The traffic nodes include ordinary nodes (without energy supplement facilities) and energy supplement station nodes containing electric vehicle energy supplement stations;

[0111] The electric vehicle energy supplement station includes a charging station and a battery swap station, and the charging station is used to support the bidirectional transfer of electric energy between the electric vehicle and the power grid;

[0112] The travel demand of the electric vehicle includes the origin and destination, the number of electric vehicles and the initial electric quantity of the electric vehicle;

[0113] Obtain the power distribution network data, which includes the power distribution network topology, the line capacity, the fixed load, the distributed generator capacity and the generation cost coefficient and the renewable energy output;

[0114] The power distribution network topology includes the connection relationship between the power distribution network nodes and the lines;

[0115] Associate the energy supplement station nodes in the traffic network with the power grid nodes in the power distribution network;

[0116] Define the coupling relationship between the traffic network and the power distribution network, and the specific coupling relationship is as follows:

[0117] The electricity price of the electric vehicle energy supplement station is obtained according to the marginal electricity price of the power distribution network node;

[0118] The load of the power distribution network node is determined by the energy behavior of the electric vehicle in the corresponding energy supplement station node;

[0119] The energy behavior includes the charging and discharging behavior of the electric vehicle at the charging station and the battery swapping behavior at the battery swap station;

[0120] Based on the traffic network data and the power distribution network data, a traffic-power coupled network is established in combination with the coupling relationship.

[0121] The road model is constructed considering traffic congestion, and the driving time under the traffic congestion condition is determined according to the first-in first-out principle, which specifically includes:

[0122] When the congestion queue length does not exceed the maximum outflow capacity of the road, all electric vehicles in the queue can flow out of the road at the next time step, and the driving time of the electric vehicles on the road is the free driving time plus the time length corresponding to one time step;

[0123] When the congestion queue length exceeds the maximum outflow capacity of the road, only the number of electric vehicles equal to the maximum outflow capacity can flow out of the road at the next time step, and the remaining electric vehicles will flow out in turn according to the first-in first-out order at subsequent time steps;

[0124] The specific expression of the road model is as follows:

[0125]

[0126] In the formula, i represents the path number; t represents the time number; I a represents the set of paths containing road a; I j represents the subset of paths related to the origin-destination point j; represents the electric vehicle traffic flow entering the first road of path i at time t; represents the number of electric vehicles corresponding to the travel demand of the origin-destination point j at time t; represents the traffic flow in the congestion queue of road a on path i at time t; represents the traffic flow in the congestion queue of road a on path i at time t-1; represents the traffic flow entering the congestion queue of road a on path i at time t; v a,j,t represents the traffic flow leaving the congestion queue of road a on path i at time t; represents the total traffic flow on road a of path i at time t; represents the total traffic flow on road a of path i at time t-1; u a,i,t represents the traffic flow entering road a on path i at time t; represents the maximum outflow capacity of road a; represents the initial energy level of the electric vehicle corresponding to the travel demand of the origin-destination point j at time t; represents the energy level of the traffic flow allocated to the first road of path i at time t; represents the electric power flow corresponding to the traffic flow u a,i,t energy level; represents the electric power flow corresponding to the traffic flow va,i,t the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; represents the energy consumption of traffic on road a; Indicates the corresponding congested queue the flow of electricity at energy levels; Indicates the corresponding congested queue The flow of electricity at the energy level; E and E represent the upper and lower limits of the electric vehicle battery capacity, respectively.

[0127] The dynamic energy replenishment station model includes the dynamic charging station model and the dynamic battery swap station model;

[0128] In the dynamic charging station model, waiting queues and service queues are constructed to represent the dynamic characteristics of vehicles in the charging station. The specific expressions are as follows:

[0129]

[0130]

[0131] Where a + Indicates the upstream road connected to the node; a - Indicates the downstream road connected by the node; represents the traffic flow from the upstream road into the node on path i at time t; represents the traffic flow from node i to the downstream road at time t; f represents the charging station node number; I f represents the subset of paths that include charging station f; represents the traffic flow entering the waiting queue of the charging station on path i at time t; represents the traffic flow on path i at time t that passes through charging station node f but does not enter the charging station; represents the traffic flow leaving the charging station f from the service queue on path i at time t; represents the number of electric vehicles waiting in the queue at charging station f on path i at time t; represents the number of electric vehicles waiting in the queue at charging station f on path i at time t-1; represents the traffic flow on path i at time t, which enters the service queue from the waiting queue at charging station f; represents the number of electric vehicles in the service queue of charging station f on path i at time t; represents the number of electric vehicles in the service queue of charging station f on path i at time t-1; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; representing the corresponding traffic flow electric power flow at the energy level; representing the corresponding traffic flow electric power flow at the energy level; representing the corresponding traffic flow electric power flow at the energy level; representing the corresponding waiting queue electric power flow at the energy level; representing the corresponding traffic flow electric power flow at the energy level; representing the corresponding service queue electric power flow at the energy level;

[0132] The energy replenished by the electric vehicle at the battery swap station is determined by the energy when the electric vehicle enters the battery swap station and the energy of the battery swapped, and the specific expression is as follows:

[0133]

[0134] representing the traffic flow leaving the battery swap station after the end of the battery swap operation electric power flow at the energy level, representing the traffic flow entering the service queue from the waiting queue of the battery swap station b on path i at time t-1, representing the corresponding traffic flow electric power flow at the energy level;

[0135] The waiting queue and the service queue are constructed in the dynamic battery swap station model to represent the dynamic characteristics of the vehicles in the battery swap station, and the specific expression is as follows:

[0136]

[0137] In the formula, b represents the node number of the battery swap station; I b representing the subset of paths containing the battery swap station b; representing the traffic flow entering the waiting queue of the battery swap station on path i at time t; representing the traffic flow passing through the battery swap station node b but not entering the battery swap station on path i at time t; representing the traffic flow leaving the battery swap station b from the service queue on path i at time t; representing the number of electric vehicles in the waiting queue of the battery swap station b on path i at time t; representing the traffic flow entering the service queue from the waiting queue of the battery swap station b on path i at time t; representing the number of electric vehicles in the service queue of the battery swap station b on path i at time t; representing the corresponding traffic flow electric power flow at the energy level; representing the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding waiting queue energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding service queue energy levels.

[0138] In charging stations, a circulating flow model for electric vehicles with various charging and discharging power levels is constructed. This model allows electric vehicles to flexibly select the charging and discharging power levels and charging and discharging times within the charging station.

[0139] The circular flow model abstracts the charging and discharging behavior of electric vehicles on physical charging piles into charging and discharging currents. Electric vehicles can independently select the charging and discharging power between adjacent time intervals, and form different charging and discharging currents according to the selected power level. Each charging and discharging current corresponds to a unique charging and discharging power level. This charging and discharging current can be composed of electric vehicles that initially select this power level, or it can include vehicles that have changed from other power levels. Electric vehicles that have completed the charging and discharging operation converge into the traffic flow leaving the charging station.

[0140] The specific expression of the electric vehicle circulation flow model is as follows:

[0141]

[0142]

[0143] Where, l represents the charge and discharge power level number, L f represents the charging and discharging power level set of charging station f; I f represents the set of paths containing charging station f; is the traffic flow with charging and discharging power level l entering from the waiting queue on path i at time t, is the traffic flow entering the service queue of charging station f on path i at time t; For the traffic flow in the service queue on path i that continues charging and discharging operations at time t and selects power level l; is the traffic flow from charging station f after the charging and discharging operations on path i at time t; v l,j,t is the traffic flow leaving the charging and discharging power level l on path i at time t; is the number of electric vehicles with charging and discharging power level l selected on path i at time t; is the total number of actual charging piles in the charging station f; represents the energy level of the corresponding service queue p l is the charging and discharging power corresponding to the charging and discharging power level l; η is the charging and discharging efficiency; Δt is the time length between adjacent two time points;

[0144] To ensure that the electric vehicles in the service queue perform corresponding charging and discharging operations at each time point, and avoid the situation that the charging piles are occupied but no energy exchange is performed, the electric vehicle circulation flow model further includes a constraint condition, and the expression of the constraint condition is as follows:

[0145]

[0146] In the formula, represents the total sum of the traffic flow entering the power level l from 0 to t, represents the total sum of the traffic flow leaving the power level l from 0 to t+1.

[0147] The ordinary nodes in the upper optimization model should satisfy the balance constraint of inflow and outflow, and the specific balance constraint is as follows:

[0148] The road model, the dynamic energy supplement station model, the electric vehicle circulation flow model and the balance constraint jointly constitute the upper optimization model.

[0149] When the upper optimization model optimizes the vehicle path selection and energy supplement decision, the minimum travel cost of the electric vehicle is taken as the objective function;

[0150] The travel cost of the electric vehicle includes a time cost and an energy supplement cost;

[0151] The time cost includes a driving time cost of the vehicle on the road, a waiting time cost of the vehicle at the energy supplement station, a service time cost of the vehicle at the energy supplement station and a vehicle energy supplement cost;

[0152] The energy supplement cost includes a charging and discharging cost of the electric vehicle at the charging station and a battery replacement cost at the battery replacement station;

[0153]

[0154] In the formula, Ψ t represents the travel time cost; κ represents the time cost coefficient of the electric vehicle; represents the total time of the electric vehicle driving on the road; represents the total time of the electric vehicle at the charging station; represents the total time of the electric vehicle at the battery replacement station; Ψ e represents the energy supplement cost; represents the energy supplement cost of the electric vehicle at the charging station, when is negative, represents the discharge benefit; represents the energy compensation cost of the electric vehicle at the battery swap station;

[0155] wherein,

[0156]

[0157] wherein, represents the electric vehicle load of the charging station f at time t, represents the electric vehicle load of the battery swap station b at time t;

[0158] The objective function of the upper-layer optimization model is: Objective function = min (Ψ t +Ψ e ).

[0159] The lower-layer optimization model includes a distribution network alternating current optimal power flow model containing the electric vehicle load, and the distribution network alternating current optimal power flow model is composed of the following operation constraint conditions, and the specific expression is as follows:

[0160]

[0161] wherein, m and n are the distribution network node numbers; π(n) represents a distribution network node set connected with the distribution network node n; and M is a distribution network node set; and is a generation cost coefficient; represents the active power generated by the distributed generator at the distribution network node n at time t; P mn,t represents the active power on the line (m, n) at time t; represents the active power generated by the new energy power generation supply unit at the distribution network node n at time t; R mn represents the resistance value of the line (m, n); I mn,t represents the current on the line (m, n) at time t; represents the fixed active load of the distribution network node n at time t; Q mn,t represents the reactive power on the line (m, n) at time t; represents the reactive power generated by the distributed generator at the distribution network node n at time t; X mn represents the reactance value of the line (m, n); represents the fixed reactive load of the distribution network node n at time t; U n,t represents the square of the voltage of the distribution network node n at time t; is the upper limit of the transmission current of the line (m, n); P mn and are respectively the upper limits of the transmission active power of the line (m, n); Q mn and are the upper limits of reactive power transmitted by line (m,n); U n and are the upper and lower limits of the voltage allowed at node n in the distribution network, respectively; and are the upper and lower limits of the active power generated by the distributed generator at the distribution network node n; and are the upper and lower limits of the reactive power generated by the distributed generator at the distribution network node n; It represents the electric vehicle load on the distribution network node n at time t. When the distribution network node is not connected to the energy replenishment station, is 0; The upper limit of the active power generated by the renewable energy power supply unit at the distribution network node n; are all Lagrange multipliers corresponding to the operating constraints, where is the marginal electricity price at the distribution network node;

[0162] Preferably, the new energy power generation supply unit includes wind power generation, photovoltaic power generation, etc.;

[0163] Based on the second-order cone relaxation and polyhedron approximation method, the underlying optimization model is transformed into the Karush-Kuhn-Tucker condition, which includes the following steps:

[0164] The lower optimization model is convexified using second-order cone relaxation. Specifically, the following expressions in the running constraints are relaxed into second-order cone constraints.

[0165] The required relaxed operating constraints are as follows:

[0166]

[0167] The expression of the second-order cone constraint is as follows:

[0168]

[0169] The polyhedron approximation method is used to transform the second-order cone constraints to obtain a single-layer mathematical optimization model with equilibrium constraints. The single-layer optimization model is solved to obtain the spatiotemporal distribution of electric vehicle traffic and charging load, as well as the power generation scheduling of the distribution system, the marginal electricity price of the distribution network nodes, and the power flow distribution.

[0170] The polyhedron approximation method is to convert the second-order cone constraint into the following two sets of linear constraints:

[0171]

[0172] Where, auxiliary variable for polyhedral approximation; k = 1, …, v, wherein v is a parameter of polyhedral approximation;

[0173] λ and τ are Lagrange multipliers corresponding to linear constraints;

[0174] The Lagrange function L of the lower optimization model is constructed, and the specific expression is as follows:

[0175] L = f(x, y) + λ · h(x, y) + τ · g(x, y)

[0176] The Karush-Kuhn-Tucker condition of the lower optimization model is as follows:

[0177]

[0178] h(x, y) = 0

[0179] τ · g(x, y) = 0

[0180] τ ≥ 0

[0181] In the formula, x represents the variable of the upper optimization model; y represents the variable of the lower optimization model; f(x, y) represents the objective function of the lower optimization model; h(x, y) represents the equality constraint in the lower optimization model; g(x, y) represents the inequality constraint in the lower optimization model; λ and τ are Lagrange multipliers corresponding to the constraints;

[0182] The nonlinear complementarity relaxation constraint in the Karush-Kuhn-Tucker condition is τ · g(x, y) = 0.

[0183] The nonlinear complementarity relaxation constraint in the Karush-Kuhn-Tucker condition is converted into a linear constraint, and the specific expression is as follows:

[0184] 0 ≤ τ ≤ M · (1-ε)

[0185] 0 ≤ g(x, y) ≤ M · ε

[0186] In the formula, ε represents an auxiliary variable with a value of 0 or 1; M is a constant, and M needs to be large enough relative to other variables, and preferably, the value range of M is 10 to 20.

[0187] The entire Karush-Kuhn-Tucker condition after conversion is substituted into the upper optimization model to obtain a single-layer mathematical optimization model.

[0188] Specifically, the scheme of the application is further described through the following embodiments:

[0189] In this embodiment, a 24-node traffic network and a 21-node power distribution network are used for testing, and the topology structure of the traffic-power coupled network in the embodiment is as shown in Figure 2As shown in the figure; T in the traffic network represents a common node without energy supplement facilities, F is a charging station node, B is a battery swap station node, E4 and E17 in the distribution network are connected with wind turbines, E6 and E10 are connected with distributed generators, and E2, E6, E9, E10, E12, E13, E16 and E20 are connected with charging stations, and E5, E7, E15 and E17 are connected with battery swap stations; wherein there are 8 pairs of origin-destination points of travel demand, node T4 is the origin, and nodes T1, T2, T3, T6, T7, T10, T11 and T12 are the destinations;

[0190] The traffic flow space-time distribution and energy supplement station load obtained by the application are as shown in the figure Figure 3 The lightness and darkness of the road color represent the size of the traffic flow on the road, and it can be seen from the figure that the method of the application can accurately capture the dynamic driving and energy supplement process of electric vehicles in the traffic network; the traffic flow spreads from the origin T4 to the destination, which is manifested as that only the roads around T4 have traffic flow at the beginning of scheduling, and all traffic flows are on the roads near the destination at the end of the scheduling period;

[0191] The power generation scheduling and node aggregated electricity price of the distribution network obtained by the method of the application are as shown in the figure Figure 4 As can be seen from the figure, after considering the electric vehicle load, the electric vehicle supplements energy when the electricity price is low, that is, the wind power is sufficient, which promotes the consumption of wind power and reduces the abandoned wind power; when the electricity price is high, that is, the load of the distribution network is high, the electric vehicle discharges to support the power grid, the output of the distributed generator is reduced, the carbon emission is reduced, and the economic efficiency of the distribution system scheduling is improved;

[0192] Figure 5 The active power transmitted on the line E11-E12 of the distribution network and the charging and discharging of electric vehicles of the charging station related to the line are shown, and it can be seen from the figure that under the power and traffic joint optimization, not only can the electric vehicle user reduce the charging cost and obtain the discharging benefit to reduce the travel cost under the strategy of “valley charging and peak discharging”, but also the distribution system can reduce the over-limit condition of power flow to improve the safety of the distribution network.

[0193] In the description of the present specification, the description of the terms “one embodiment”, “example”, “specific example” and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are contained in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0194] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A method for coordinated optimization of electric vehicle traffic and power flow considering multi-fuel and V2G, characterized in that: The specific steps include: Obtain transportation network data and distribution network data, and build a transportation-power coupling network; A two-layer optimization model is constructed based on the traffic-power coupling network, including an upper-layer optimization model and a lower-layer optimization model; The upper-level optimization model is constructed based on the dynamic traffic assignment method. Through the mapping relationship between electric vehicle traffic flow and power flow, it forms an upper-level optimization model including road model, dynamic charging station model and electric vehicle circulation flow model, which is used to optimize vehicle route selection and charging decision-making. The lower-level optimization model includes an AC optimal power flow model for the distribution network including electric vehicle loads, which is used to optimize the operating status of the distribution network and obtain the node marginal electricity price as the energy replenishment price; Based on the second-order cone relaxation and polyhedron approximation method, the Karush-Kuhn-Tucker conditions in the underlying optimization model are obtained; Substituting the Karush-Kuhn-Tucker condition into the upper-level optimization model, a single-level mathematical optimization model is obtained; By solving the single-layer mathematical optimization model, we can obtain the spatiotemporal distribution of electric vehicle traffic and charging load, as well as the power generation scheduling of the distribution system, the marginal electricity price of the distribution network nodes, and the power flow distribution.

2. The electric vehicle traffic-power flow coordinated optimization method according to claim 1, characterized in that: Obtaining transportation network data and distribution network data and building a transportation-power coupling network involves the following steps: Obtaining transportation network data, including the transportation network topology, the time that electric vehicles can travel freely when the roads are clear, the maximum outflow capacity of the roads, the capacity of electric vehicle charging stations, and the travel demand of electric vehicles; The transportation network topology includes transportation nodes and road connection information; Traffic nodes include ordinary nodes and charging station nodes including electric vehicle charging stations; Electric vehicle charging stations include charging stations and battery swap stations. Charging stations are used to support the bidirectional transmission of electric energy between electric vehicles and the power grid. The travel demand of electric vehicles includes the starting and ending points, the number of electric vehicles, and the initial charge of electric vehicles; Obtain distribution network data, including distribution network topology, line capacity, fixed load, distributed generator capacity and power generation cost coefficient, and renewable energy output; The distribution network topology includes the connection relationship between distribution network nodes and lines; Associating the energy recharging station nodes in the transportation network with the grid nodes in the distribution network; Define the coupling relationship between the transportation network and the distribution network. The specific coupling relationship is as follows: The electricity price of the electric vehicle charging station is obtained based on the marginal electricity price of the distribution network node; The load of the distribution network node is determined by the energy behavior of the electric vehicles at the corresponding charging station node; Energy behavior includes the charging and discharging behavior of electric vehicles at charging stations and the battery swapping behavior at battery swapping stations; Based on the transportation network data and distribution network data, combined with the coupling relationship, a transportation-power coupling network is established.

3. The electric vehicle traffic-power flow coordinated optimization method according to claim 2, characterized in that: The road model is constructed taking traffic congestion into consideration. The travel time under traffic congestion is determined based on the first-in-first-out principle. The first-in-first-out principle specifically includes: When the length of the congested queue does not exceed the maximum outflow capacity of the road, all electric vehicles in the queue can flow out of the road in the next time step. At this time, the driving time of the electric vehicles on the road is the free driving time plus the time length corresponding to one time step. When the length of the congestion queue exceeds the maximum outflow capacity of the road, only electric vehicles with a number equal to the maximum outflow capacity can flow out of the road in the next time step, and the remaining electric vehicles will flow out in the subsequent time steps in a first-in-first-out order. The specific expression of the road model is as follows: Where i is the path number; t is the time number; I a represents the set of paths containing road a; I j represents the subset of paths associated with the origin and destination point j; represents the traffic flow of electric vehicles entering the first section of the road on path i at time t; represents the number of electric vehicles corresponding to the travel demand of the starting and ending points j at time t; represents the traffic flow in the congested queue of road a on path i at time t; represents the traffic flow in the congested queue of road a on path i at time t-1; represents the traffic flow entering the congested queue of road a on path i at time t; v a,j,t represents the traffic flow leaving the congested queue on road a on path i at time t; represents the total traffic flow on road a of path i at time t; represents the total traffic flow on path i road a at time t-1; u a,i,t represents the traffic flow entering road a on path i at time t; represents the maximum outflow capacity of road a; represents the initial energy level of the electric vehicle corresponding to the travel demand of the starting and ending point j at time t; represents the energy level of the traffic flow allocated to the first section of the road on path i at time t; Represents the corresponding traffic flow u a,i,t the flow of electricity at energy levels; Represents the corresponding traffic flow v a,i,t the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; represents the energy consumption of traffic on road a; Indicates the corresponding congested queue the flow of electricity at energy levels; Indicates the corresponding congested queue Energy level flow of electricity; E and They represent the upper and lower limits of electric vehicle battery capacity respectively.

4. The electric vehicle traffic-power flow coordinated optimization method according to claim 3, characterized in that: The dynamic energy replenishment station model includes the dynamic charging station model and the dynamic battery swap station model; In the dynamic charging station model, waiting queues and service queues are constructed to represent the dynamic characteristics of vehicles in the charging station. The specific expressions are as follows: Where a + Indicates the upstream road connected to the node; a - Indicates the downstream road connected by the node; represents the traffic flow from the upstream road into the node on path i at time t; represents the traffic flow from node i to the downstream road at time t; f represents the charging station node number; I f represents the subset of paths that include charging station f; represents the traffic flow entering the waiting queue of the charging station on path i at time t; represents the traffic flow on path i at time t that passes through charging station node f but does not enter the charging station; represents the traffic flow leaving the charging station f from the service queue on path i at time t; represents the number of electric vehicles waiting in the queue at charging station f on path i at time t; represents the number of electric vehicles waiting in the queue at charging station f on path i at time t-1; represents the traffic flow on path i at time t, which enters the service queue from the waiting queue at charging station f; represents the number of electric vehicles in the service queue of charging station f on path i at time t; represents the number of electric vehicles in the service queue of charging station f on path i at time t-1; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding waiting queue the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding service queue the flow of electricity at energy levels; The energy replenished by an electric vehicle at a battery swap station is determined by its own energy when entering the station and the energy of the battery swapped. The specific expression is as follows: Indicates the traffic flow that has completed the battery swap operation and left the battery swap station The flow of electricity at the energy level, represents the traffic flow from the waiting queue at station b to the service queue on path i at time t-1, Indicates the corresponding traffic flow the flow of electricity at energy levels; In the dynamic battery swap station model, waiting queues and service queues are constructed to represent the dynamic characteristics of vehicles in the battery swap station. The specific expressions are as follows: Where, b represents the node number of the battery swap station; I b represents the subset of paths that include the battery swap station b; represents the traffic flow entering the waiting queue of the battery swap station on path i at time t; represents the traffic flow on path i at time t that passes through the battery swap station node b but does not enter the battery swap station; represents the traffic flow on path i leaving the battery swap station b from the service queue at time t; represents the number of electric vehicles waiting in the queue at station b on path i at time t; represents the traffic flow on path i at time t, which enters the service queue from the waiting queue of battery swap station b; represents the number of electric vehicles in the service queue of the battery swap station b on path i at time t; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding waiting queue energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding traffic flow the flow of electricity at energy levels; Indicates the corresponding service queue energy levels.

5. The electric vehicle traffic-power flow coordinated optimization method according to claim 4, characterized in that: In the charging station, a circulating flow model of electric vehicles with various charging and discharging power levels is constructed. The specific expression is as follows: Where, l represents the charge and discharge power level number, L f represents the charging and discharging power level set of charging station f; I f represents the set of paths that include charging station f; is the traffic flow with charging and discharging power level l entering from the waiting queue on path i at time t, is the traffic flow entering the service queue of charging station f on path i at time t; For the traffic flow in the service queue on path i that continues charging and discharging operations at time t and selects power level l; is the traffic flow from charging station f after the charging and discharging operations on path i at time t; v l,j,t is the traffic flow leaving the charging and discharging power level l on path i at time t; is the number of electric vehicles with charging and discharging power level l selected on path i at time t; is the total number of actual charging piles in charging station f; Indicates the corresponding service queue Energy level; p l is the charge and discharge power corresponding to the charge and discharge power level number 1; η is the charge and discharge efficiency; Δt is the time length between two adjacent moments; The electric vehicle circulation flow model also includes constraints to ensure that the electric vehicles in the service queue perform the corresponding charging and discharging operations at each time step. The constraint expressions are as follows: Where, represents the sum of traffic flows with power level l entering from time 0 to time t, It represents the sum of traffic flows with power level l leaving from time 0 to time t+1.

6. The electric vehicle traffic-power flow coordinated optimization method according to claim 5, characterized in that: Ordinary nodes in the upper optimization model should satisfy the inflow and outflow balance constraints. The specific balance constraints are: The road model, dynamic charging station model, electric vehicle circulation flow model and balance constraints together constitute the upper-level optimization model.

7. The electric vehicle traffic-power flow coordinated optimization method according to claim 6, characterized in that: When the upper-level optimization model optimizes vehicle route selection and energy replenishment decisions, it takes minimizing the travel cost of electric vehicles as the objective function; The travel cost of electric vehicles includes time cost and recharging cost; Time cost includes the time cost of the vehicle traveling on the road, the time cost of the vehicle waiting at the charging station, the time cost of the vehicle serving at the charging station and the cost of the vehicle charging; The cost of recharging includes the cost of charging and discharging electric vehicles at charging stations and the cost of replacing batteries at battery swap stations; Where, t represents the travel time cost; κ represents the electric vehicle time cost coefficient; represents the total time that electric vehicles are driven on the road; Indicates the total time that the electric vehicle is at the charging station; Indicates the total time that electric vehicles spend at battery swap stations; e represents the energy replenishment cost; Represents the energy replenishment cost of electric vehicles at charging stations. When it is a negative value, represents the discharge benefit; Represents the cost of recharging electric vehicles at battery swap stations; in, Where, represents the electric vehicle load at charging station f at time t, represents the electric vehicle load at battery swap station b at time t; The objective function of the upper optimization model is: objective function = min(Ψ t +Ψ e ).

8. The electric vehicle traffic-power flow coordinated optimization method according to claim 7, characterized in that: The lower-level optimization model includes the AC optimal power flow model of the distribution network including electric vehicle loads. The AC optimal power flow model of the distribution network is composed of the following operating constraints, which are specifically expressed as follows: Where m and n are the distribution network node numbers; π(n) represents the set of distribution network nodes connected to distribution network node n; M is the set of distribution network nodes; and is the power generation cost coefficient; P represents the active power generated by the distributed generator at the distribution network node n at time t; mn,t represents the active power on line (m,n) at time t; represents the active power generated by the renewable energy power supply unit at the distribution network node n at time t; R mn Indicates the resistance value of the line (m,n); I mn,t represents the current on line (m,n) at time t; represents the fixed active load of distribution network node n at time t; Q mn,t represents the reactive power on line (m,n) at time t; represents the reactive power generated by the distributed generator at the distribution network node n at time t; X mn Indicates the reactance value of line (m,n); represents the fixed reactive load of distribution network node n at time t; U n,t represents the square of the voltage of the distribution network node n at time t; is the upper limit of the current transmitted by line (m,n); P mn and are the upper limits of active power transmitted by line (m,n); Q mn and are the upper limits of reactive power transmitted by line (m,n); U n and are the upper and lower limits of the voltage allowed at node n in the distribution network, respectively; and are the upper and lower limits of the active power generated by the distributed generator at the distribution network node n; and are the upper and lower limits of the reactive power generated by the distributed generator at the distribution network node n; It represents the electric vehicle load on the distribution network node n at time t. When the distribution network node is not connected to the energy replenishment station, is 0; The upper limit of the active power generated by the renewable energy power supply unit at the distribution network node n; μ n,t , are all Lagrange multipliers corresponding to the operating constraints, where is the marginal electricity price at the distribution network node.

9. The electric vehicle traffic-power flow coordinated optimization method according to claim 8, characterized in that: Based on the second-order cone relaxation and polyhedron approximation method, the underlying optimization model is transformed into the Karush-Kuhn-Tucker condition, which includes the following steps: The lower optimization model is convexified using second-order cone relaxation. Specifically, the following expressions in the running constraints are relaxed into second-order cone constraints. The required relaxed operating constraints are as follows: The expression of the second-order cone constraint is as follows: The polyhedron approximation method is to convert the second-order cone constraint into the following two sets of linear constraints: Where, Auxiliary variables used for polyhedron approximation; k = 1,...v, where v is the parameter of polyhedron approximation; are all Lagrange multipliers corresponding to linear constraints; Construct the Lagrangian function L of the lower optimization model. The specific expression is as follows: L=f(x,y)+λ·h(x,y)+τ·g(x,y) Then the Karush-Kuhn-Tucker conditions of the lower optimization model are as follows: h(x,y)=0 τ·g(x,y)=0 τ≥0 Where x represents the variables of the upper optimization model; y represents the variables of the lower optimization model; f(x,y) represents the objective function of the lower optimization model; h(x,y) represents the equality constraint in the lower optimization model; g(x,y) represents the inequality constraint in the lower optimization model; λ and τ are the Lagrange multipliers of the corresponding constraints; The nonlinear complementary relaxation constraint in the Karush-Kuhn-Tucker condition is: τ·g(x,y)=0; The nonlinear complementary slack constraint in the Karush-Kuhn-Tucker condition is converted into a linear constraint. The specific expression is as follows: 0≤τ≤M·(1-ε) 0≤g(x,y)≤M·ε Where ε represents an auxiliary variable with a value of 0 or 1; M is a constant.

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