Space-time double-layer optimization method for power-traffic coupling system at extreme high temperature

Abstract

The invention relates to a space-time double-layer optimization method for a power-traffic coupling system at an extreme high temperature, and the method comprises the steps: building a space-time double-layer scheduling model, enabling an upper model to take the minimization of the loss load of a system as a target, and employing a power system considering the second-order cone constraint to reconstruct an optimal power flow model; the lower-layer model depicts the interaction behavior of the power grid and the electric vehicles from the coupling perspective of the power grid and the road network, and the number and the positions of the electric vehicles participating in response under the normal condition are input into the upper-layer model as initial values by taking maximization of the income of a power grid operator and an electric vehicle user as a target and obtaining related data under an extreme high-temperature scene; and carrying out loop iteration solution on the upper and lower layer models to finally obtain an optimized scheduling result so as to improve the power supply guarantee capability of the power system at an extreme high temperature and realize efficient collaborative configuration of resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system and transportation system coupling scheduling, and particularly relates to a time-space double-layer optimization method for power-transportation coupling system under extreme high temperature. BACKGROUND

[0002] With global warming, extreme high temperature weather is becoming increasingly frequent, bringing severe challenges to urban infrastructure, especially power systems and transportation systems. Under the scenario of extreme high temperature, the stable operation of the power system faces many difficulties. High temperature can cause a sharp increase in power load, and a substantial increase in residential and commercial electricity demand, such as long-term high-load operation of air conditioning and other refrigeration equipment, which puts a huge pressure on the power system and easily causes overload, equipment failure and other problems, thereby causing power outages and seriously affecting the normal conduct of residents' lives and social and economic activities.

[0003] At the same time, with the popularity of electric vehicles, their interaction with the power system is becoming increasingly frequent. As a mobile energy storage unit, electric vehicles have the potential to provide power support for the power system during peak periods. During the load peak period caused by extreme high temperature, if electric vehicles can be reasonably guided to participate in power grid interaction, it will help to alleviate the power supply pressure of the power system and improve the power supply reliability and resilience of the power system. However, current power system scheduling methods often fail to fully consider the coupling relationship between electric vehicles and the power system, and lack effective mechanisms to guide electric vehicles to provide power support for the power system during peak periods, resulting in the potential of this resource not being fully utilized.

[0004] Therefore, there is an urgent need for an optimization method that can comprehensively consider the coupling relationship between the power system and the transportation system, especially the interaction between electric vehicles and the power system. SUMMARY

[0005] In view of the problems and deficiencies in the prior art, the present application aims to provide a time-space double-layer optimization method for power-transportation coupling system under extreme high temperature, and the technical solution adopted is as follows:

[0006] A time-space double-layer optimization method for power-transportation coupling system under extreme high temperature, comprising:

[0007] building an upper time scale optimization model and a lower space scale optimization model;

[0008] inputting the number and location of electric vehicles participating in response under normal circumstances as initial values to the upper time scale optimization model;

[0009] solving the upper time scale optimization model to obtain power system node operation data and electric vehicle power information as the optimization results of the upper time scale optimization model;

[0010] Based on the optimization results of the upper-level time-scale optimization model, the return power value of electric vehicles is calculated and used as the input of the lower-level spatial-scale optimization model. The lower-level spatial-scale optimization model is solved to obtain the number and location of electric vehicles participating in the response, which is used as the optimization result of the lower-level spatial-scale optimization model.

[0011] The optimization results of the lower-level spatial-scale optimization model are returned to the upper-level temporal-scale optimization model to update the input of the upper-level temporal-scale optimization model. The iteration is repeated until the objective function values ​​of the upper and lower levels remain unchanged, at which point the iteration ends.

[0012] Furthermore, the upper-level time-scale optimization model aims to minimize the system load shedding. It adopts a power system reconfiguration optimal power flow model that considers second-order cone constraints and sets power constraints, voltage constraints, current constraints, radial constraints, and second-order cone constraints.

[0013] Furthermore, the upper-level time-scale optimization model aims to minimize the system load loss, and its objective function expression is as follows:

[0014]

[0015] In the formula, z y Let y be the iteration number, α be the target weight factor, and β be the target value. i Let i be the load importance coefficient. Let B be the load shedding amount of node i during time period t, B be the set of power system nodes, and T be the set of time periods within a day;

[0016] The power constraint is expressed as:

[0017]

[0018] In the formula: P i,t and Q i,t Let represent the actual injected active power and reactive power of node i during time period t, respectively; and Let represent the active power output and reactive power output of the generator at node i during time period t, respectively. and Let represent the active power demand and reactive power demand of node i during time period t, respectively. Let P be the electric vehicle return power at node i during time period t; E is the set of branches; P is the return power of electric vehicles at node i during time period t. ij,t and Q ij,t Let r represent the active power and reactive power flowing from node i to node j during time period t; ij and x ij Let i and j represent the resistance and reactance of the branch (i,j), respectively. Let be the square of the magnitude of the transmission current of branch (i,j) during time period t;

[0019] The voltage constraint is expressed as:

[0020]

[0021] In the formula: and V i V represents the upper and lower limits of the voltage magnitude at node i, respectively; i,t Let z be the voltage magnitude of node i during time period t; ij,t The variable is 0-1, reflecting the power flow direction of the branch. A value of 1 indicates that the positive power flow direction is from node i to node j; K is a very large positive real number.

[0022] The current constraint is expressed as:

[0023]

[0024] In the formula: and I i j These represent the upper and lower limits of the magnitude of the current transmitted in branch (i,j), respectively.

[0025] The radial constraint is expressed as follows:

[0026]

[0027] The expression for the second-order cone constraint is:

[0028]

[0029] Furthermore, the lower-level spatial scale optimization model aims to maximize the benefits for grid operators and electric vehicle users. It constructs the coupling relationship between the grid and road network nodes, and uses a travel chain model to simulate electric vehicle travel behavior. It considers the willingness of electric vehicle users to accept guidance from nearby interaction nodes, divides the actual corresponding road network area in the urban power system, and introduces a driving state set to record the real-time status information of each electric vehicle as input parameters for interaction between interaction nodes and the grid.

[0030] Furthermore, the topology of the road network consists of traffic nodes and a set of roads, expressed as:

[0031] G=(N,S) (10)

[0032] In the formula: G is an undirected graph describing the topology of the road network; N is the set of road network nodes; S is the set of connecting lines between road network nodes;

[0033] The coupling relationship between the power grid and road network nodes is expressed as follows:

[0034] ξ={L cou ∈B×N} (11)

[0035]

[0036] In the formula: ξ is the set of all interactive nodes connecting the power grid and the transportation network; L cou For elements in set ξ; A 0-1 variable used to represent whether road network nodes and power grid nodes are coupled. This indicates that the location can transmit electrical energy to the power grid through the road network node;

[0037] Furthermore, the lower-level spatial scale optimization model aims to maximize the revenue of the power grid operator, and its objective function expression is as follows:

[0038]

[0039] Where: g y For the lower-level optimization objective, c shed For unit power loss, To compensate electric vehicle users for their electricity price.

[0040] Furthermore, the process of dividing the actual corresponding road network area in the urban power system includes the following steps:

[0041] The actual road network area corresponding to the urban power system is divided into residential areas, work areas, and commercial areas, and the corresponding sets of road network nodes are denoted as R, W, and C, respectively, and the expression is:

[0042] L={B0,B f W of ,L 0f ,T0,T f ,T 0f ,T p} (1)

[0043] In the formula: L represents the set of states of all electric vehicles in the travel chain, including the following variables: the starting point B0 of the travel chain path; the ending point B0 of the travel chain path. f The overall route W in the travel chain of The travel distance L required for the travel link 0f Departure time of the travel chain: T0; Arrival time of the travel chain: T f Travel time T 0f Parking time T p ;

[0044] Each travel chain is broken down into multiple "travel segments", and the starting time of each travel segment is t. s N(μ,σ) follows a normal distribution2 The expression is:

[0045]

[0046] In the formula: μ is the average value at the departure time, σ 2 The variance of the departure time;

[0047] The consideration of electric vehicle users' willingness to accept guidance from nearby interaction nodes includes the following steps:

[0048] In extreme heat, the travel intentions of electric vehicle users change due to temperature. Based on the human comfort index standard, the travel intentions of electric vehicle users are defined as follows:

[0049] σ tra =exp(-λ tra PPD) (3)

[0050] In the formula, λ tra The travel chain correction factor is PPD, which is the predicted proportion of dissatisfaction in the human comfort index standard. The value of this index varies with temperature.

[0051] The travel chain, taking into account user travel intentions, is obtained. A driving state set is introduced to record the real-time state of each electric vehicle at each time t, serving as the input parameter for interaction between the interactive nodes and the power grid. The expression is:

[0052]

[0053] In the formula: V t x This represents the real-time status information of the x-th electric vehicle at time t; The driving state represents the position of the x-th electric vehicle in the road network at time t. Let x be the amount of hydrogen stored in the x-th electric vehicle at time t. The node number that is closest to the x-th electric vehicle at time t;

[0054] The willingness of electric vehicle users to participate in the response changes with the change in the compensation electricity price. The willingness of electric vehicle users to participate in the response is defined by the following expression:

[0055]

[0056] In the formula, Let λ be the proportion of electric vehicle users participating in the response at time t. EV The electric vehicle user willingness coefficient For the unit power compensation electricity price of electric vehicle users at time t, c EVThe expected electricity price per unit of power for electric vehicle users Let N be the number of electric vehicles actually participating in the scheduling at time t. all The total number of electric vehicles within the power system area;

[0057] The process of introducing a driving status set to record the real-time status information of each electric vehicle, which serves as the input parameter for interaction between the interaction node and the power grid, includes the following steps:

[0058] Based on the real-time status information of each electric vehicle and the willingness of electric vehicle users to participate in the response, the power injected by each interactive node into the power system node at time t is calculated, and the expression is:

[0059]

[0060] In the formula, Let be the power injected into the power system by the interactive node at time t under node i. Let be the number of electric vehicles connected to the interactive node at node i at time t. Let T be the output power of the k-th electric vehicle at time t. e For the time set in which electric vehicles participate in the response.

[0061] Furthermore, the condition that the objective function values ​​of the upper and lower layers remain unchanged is expressed as follows:

[0062] |z y+1 -z y |≤ε1 (9)

[0063] |g y+1 -g y |≤ε2 。 (10)

[0064] A computer device includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the steps of a spatiotemporal two-layer optimization method for a power-transportation coupled system under extreme high temperatures.

[0065] A computer storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of a spatiotemporal two-layer optimization method for a power-transportation coupled system under extreme high temperatures are disclosed.

[0066] Compared with existing technologies, its advantages are as follows:

[0067] This invention establishes a spatiotemporal dual-layer scheduling model, starting from the upper time scale and the lower spatial scale respectively, seeking to minimize the system load loss and maximize the benefits of each entity. By acquiring relevant data under extreme high temperature scenarios, the number and location of electric vehicles participating in the response under normal conditions are used as initial values ​​and input into the upper time scale optimization model. Through iterative solving of the upper and lower models, the optimized scheduling result is finally obtained, thereby improving the power supply guarantee capability of the power system under extreme high temperatures and realizing efficient collaborative allocation of resources. Attached Figure Description

[0068] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0069] Figure 1 This is a diagram of the model's solution process. Detailed Implementation

[0070] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0071] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0072] The following reference Figure 1 The technical solutions of some embodiments of the present invention are described below.

[0073] Example 1

[0074] This invention establishes a spatiotemporal two-layer scheduling model, starting from the upper time scale and the lower spatial scale respectively, to seek the goal of minimizing the system load loss and maximizing the benefits of each subject.

[0075] The optimization objective of the upper-level time-scale optimization model is to minimize the system load shedding. Equation (1) is the objective function of the upper-level time-scale optimization model, and its expression is:

[0076]

[0077] Where z y Let y be the iteration number, α be the target weight factor, and β be the target value. i Let i be the load importance coefficient. Let t represent the load shedding of node i during time period t, B represent the set of nodes in the power system, and T represent the set of time periods within a day.

[0078] Constraints:

[0079] This invention employs a power system reconfiguration optimal power flow model considering second-order cone constraints. Equation (2-4) represents the power constraint, and its expression is:

[0080]

[0081] In the formula: P i,t and Q i,t Let represent the actual injected active power and reactive power of node i during time period t, respectively; and Let represent the active power output and reactive power output of the generator at node i during time period t, respectively. and Let represent the active power demand and reactive power demand of node i during time period t, respectively. Let P be the electric vehicle return power at node i during time period t; E is the set of branches; P is the return power of electric vehicles at node i during time period t. ij,t and Q ij,t Let r represent the active power and reactive power flowing from node i to node j during time period t; ij and x ij Let i and j represent the resistance and reactance of the branch (i,j), respectively. Let be the square of the magnitude of the transmission current of branch (i,j) during time period t.

[0082] Equation (5-7) represents the voltage and current constraints, and its expression is:

[0083]

[0084] In the formula: and V i V represents the upper and lower limits of the voltage magnitude at node i, respectively; i,t and V j,t Let z be the voltage magnitude of node i and node j during time period t; ij,t The variables are 0-1, reflecting the power flow direction of the branch; a value of 1 indicates that the positive power flow direction is from node i to node j; K is a large positive real number, expressed as:

[0085]

[0086] In the formula: and I i j These represent the upper and lower limits of the magnitude of the current transmitted in branch (i,j), respectively.

[0087] Equation (8) is the radial constraint, and its expression is:

[0088]

[0089] Equation (9) is a second-order cone constraint, expressed as:

[0090]

[0091] Example 2

[0092] The lower-level spatial scale optimization model characterizes the interaction behavior between the power grid and electric vehicles under extreme high temperatures from the perspective of the coupling between the power grid and the road network. Equation (10) indicates that the topology of the road network consists of traffic nodes and road sets, and its expression is:

[0093] G=(N,S) (20)

[0094] In the formula: G is an undirected graph describing the topology of the road network; N is the set of road network nodes; S is the set of connecting lines between road network nodes.

[0095] Interactive nodes, while functioning as nodes in the road network, also possess electrical characteristics. Specifically, electric vehicles transmit electrical energy to the power grid at these interactive nodes. Equations (11-12) establish the coupling relationship between the power grid and road network nodes, expressed as follows:

[0096] ξ={L cou ∈B×N} (21)

[0097]

[0098] In the formula: ξ is the set of all interactive nodes connecting the power grid and the transportation network; L cou For elements in set ξ; A 0-1 variable used to represent whether road network nodes and power grid nodes are coupled. This indicates that the location can transmit electrical energy to the power grid through the road network node.

[0099] The lower-level spatial scale optimization model aims to maximize the revenue of the power grid operator. Equation (13) is the objective function of the lower-level spatial scale optimization model, and its expression is:

[0100]

[0101] Where: g y For the lower-level optimization objective, c shed For unit power loss, To compensate electric vehicle users for their electricity price, Let be the electric vehicle return power at node i during time period t.

[0102] Constraints:

[0103] Since the scheduling and status of electric vehicles need to take into account information from the power grid and road network, this invention uses a travel chain model to simulate the travel behavior of electric vehicles. It takes into account the willingness of electric vehicle users to accept guidance from the nearest interaction node, divides the actual corresponding road network area in the urban power system, and introduces a driving status set to record the real-time status information of each electric vehicle as an input parameter for the interaction between the interaction node and the power grid.

[0104] This invention divides the actual road network area in the urban power system into residential areas, work areas, and commercial areas, with the corresponding road network node sets denoted as R, W, and C, respectively, and expressed as:

[0105] L={B0,B f W of ,L 0f ,T0,T f ,T 0f ,T p} (twenty four)

[0106] In the formula: L represents the set of states of all electric vehicles in the travel chain, including the following variables: the starting point B0 of the travel chain path; the ending point B0 of the travel chain path. f The overall route W in the travel chain of The travel distance L required for the travel link 0f Departure time of the travel chain: T0; Arrival time of the travel chain: T f Travel time T 0f Parking time T p .

[0107] Each travel chain can be broken down into multiple "travel segments", with each segment starting at time t. s N(μ,σ) follows a normal distribution 2 The expression is:

[0108]

[0109] In the formula, f(t) s (t) represents the departure time. s The probability distribution function, μ is the average value at the departure time, σ 2 This represents the variance of the departure time.

[0110] Under extreme high temperatures, the travel intentions of electric vehicle users will change due to temperature. Based on the human comfort index standard, equation (16) defines the travel intentions of electric vehicle users as follows:

[0111] σ tra =exp(-λ tra PPD) (26)

[0112] In the formula, σ tra To meet the travel needs of electric vehicle users, λ tra The travel chain correction factor is PPD, which is the predicted proportion of dissatisfaction in the human comfort index standard. The value of this index varies with temperature.

[0113] The travel chain that takes into account users' travel intentions is shown in Table 1:

[0114] Table 1. Travel chain list considering user travel intentions

[0115] Serial number Travel chain Proportion ​ R→R 0.2 - 0.1 σ tra ]] ​ R→C→R 0.1 + 0.1σ tra ]] <L2> R→W→R 0.7 - 0.2σ tra ]] ​ R→W→C→R 0.2σ tra ]]

[0116] Equation (17) introduces a driving state set to record the real-time state of each electric vehicle at each time t, which serves as the input parameter for the interaction between the interactive node and the power grid, and is expressed as:

[0117]

[0118] In the formula, V t x This represents the real-time status information of the x-th electric vehicle at time t; The driving state represents the position of the x-th electric vehicle in the road network at time t. Let x be the amount of hydrogen stored in the x-th electric vehicle at time t. Let be the number of the interaction node that is closest to the x-th electric vehicle at time t.

[0119] The willingness of electric vehicle users to participate in the response changes with the change in the compensation electricity price. The willingness of electric vehicle users to participate in the response is defined by the following expression:

[0120]

[0121] In the formula, Let λ be the proportion of electric vehicle users participating in the response at time t. EV The electric vehicle user willingness coefficient For the unit power compensation electricity price of electric vehicle users at time t, c EV The expected electricity price per unit of power to compensate electric vehicle users. N represents the actual number of electric vehicles participating in the scheduling at time t. all This represents the total number of electric vehicles within the power system area.

[0122] Based on the real-time status information of each electric vehicle and the willingness of electric vehicle users to participate in the response, the power injected by each interactive node into the power system node at time t can be calculated:

[0123]

[0124] In the formula, Let be the power injected into the power system by the interactive node at time t under node i. Let be the number of electric vehicles connected to the interactive node at node i at time t. Let T be the output power of the k-th electric vehicle at time t. e For the time set in which electric vehicles participate in the response.

[0125] Example 3

[0126] To further illustrate the solution process of the spatiotemporal two-layer optimization model of the power-transportation coupled system under extreme high temperatures, see the attached document. Figure 1 As shown, the supply guarantee strategy proposed in this invention is explained from the model solution process:

[0127] Acquire daily temperature data and typical power system load data under extreme high temperature scenarios as inputs to the upper-level time-scale optimization model;

[0128] The number and location of electric vehicles participating in the response under normal circumstances are used as initial values ​​input into the upper-level time-scale optimization model;

[0129] The solution yields the operating data of each node in the power system and the power information of the electric vehicles.

[0130] Based on the optimization results of the upper-level time-scale optimization model, the return power value of electric vehicles is calculated and used as the input of the lower-level spatial-scale optimization model to solve for the number and location of electric vehicles participating in the response.

[0131] The optimization results of the lower spatial scale optimization model are returned to the upper time scale optimization model to update the input of the upper time scale optimization model. The iteration is repeated until equation (22-23) is satisfied and the objective function values ​​of the upper and lower levels remain unchanged, at which point the iteration ends.

[0132] |z y+1 -z y |≤ε1 (32)

[0133] |g y+1 -g y |≤ε2 (33).

[0134] Example 4

[0135] Based on the same inventive concept, embodiments of the present invention also provide a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the spatiotemporal two-layer optimization methods for an electric-transportation coupled system under extreme high temperatures described in embodiments 1-3.

[0136] Example 5

[0137] Based on the same inventive concept, this embodiment of the invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of any one of the spatiotemporal dual-layer optimization methods for an electric-transportation coupled system under extreme high temperatures described in embodiments 1-3.

[0138] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0139] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0140] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0141] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0142] In the description of this specification, the terms "one embodiment," "some embodiments," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A time-space double-layer optimization method for power-traffic coupling systems under extreme high temperature, characterized in that, The application relates to a method for optimizing the charging and discharging of electric vehicles in a power grid. The method comprises the following steps: a normal number of electric vehicles participating in response and positions of the electric vehicles are taken as initial values and input into an upper time-scale optimization model; the upper time-scale optimization model is solved to obtain operation data of each node of the power grid and power information of the electric vehicles, which are taken as optimization results of the upper time-scale optimization model; the optimization results of the upper time-scale optimization model are used to calculate return power values of the electric vehicles, which are taken as inputs of a lower space-scale optimization model, and the lower space-scale optimization model is solved to obtain the number of the electric vehicles participating in response and positions of the electric vehicles, which are taken as optimization results of the lower space-scale optimization model; the optimization results of the lower space-scale optimization model are returned to the upper time-scale optimization model, and the inputs of the upper time-scale optimization model are updated, and the iteration is circular until a condition that target function values of upper and lower layers are constant is met, and the iteration is ended.

2. The time-space double-layer optimization method for power-traffic coupling systems under extreme high temperature according to claim 1, characterized in that, The upper time-scale optimization model takes minimization of system load loss as a target, adopts a power system reconstruction optimal power flow model considering second-order cone constraints, and sets active power constraints, voltage constraints, current constraints, radial constraints and second-order cone constraints.

3. The time-space double-layer optimization method for power-traffic coupling systems under extreme high temperature according to claim 2, characterized in that, The upper time-scale optimization model takes minimization of system load loss as a target, and a target function expression is as follows: In the formula, z y is the upper layer optimization target, y is the iteration number, a is the target weight factor, β i is the load importance coefficient of node i, is the load loss of node i in period t, B is the node set of the power system, and T is the period set in a day. The active power constraints have an expression as follows: where P i,t and Q i,t denote the actual injected active and reactive power of node i at time period t, respectively; and denote the active and reactive generation of generator at node i at time period t, respectively; and denote the active and reactive demand of node i at time period t, respectively; is the EV return power of node i at time period t; E is the set of branches; P ij,t and Q ij,t denote the active and reactive power flowing from node i to node j at time period t, respectively; r ij and x ij denote the resistance and reactance of branch (i, j), respectively; is the square of the transmission current modulus of branch (i, j) at time period t. The voltage constraints have an expression as follows: where: and V i Vmaxiand Vminirepresent the upper and lower bounds of the voltage magnitude at node i, respectively; V i,t Vitdenotes the voltage magnitude at node i at time t; z ij,t is a 0-1 variable reflecting the power flow direction, taking 1 if the power flow is in the positive direction from node i to node j; K is a large positive real number; The current constraints have an expression as follows: wherein: and I i j Ii,jmaxand Ii,jminrepresent the upper and lower limits of the current module value transmitted by branch (i,j), respectively; The radial constraints have an expression as follows: The second-order cone constraints have an expression as follows:

4. The time-space double-layer optimization method for power-traffic coupling systems under extreme high temperature according to claim 1, characterized in that, The lower space-scale optimization model takes maximization of the benefits of a power grid operator and electric vehicle users as a target, constructs a coupling relationship between the power grid and road network nodes, adopts a trip chain model to simulate electric vehicle travel behaviors, considers the willingness of the electric vehicle users to accept nearby interactive node guidance, divides actual corresponding road network areas in the urban power system, and introduces a driving state set to record real-time state information of each electric vehicle as input parameters of the interaction between the interactive nodes and the power grid.

5. The time-space dual-layer optimization method for power-traffic coupling systems at extreme high temperatures according to claim 4, characterized in that, The topological structure of the road network is composed of traffic nodes and a road set, and an expression is as follows: G=(N,S) (10) In the formula, G is an undirected graph for describing the topological structure of the road network, N is a road network node set, and S is a road set connecting the road network nodes; The coupling relationship between the power grid and the road network nodes is constructed, and an expression is as follows: ξ = {L cou ∈ B x N} (11) where: ξ is the set of all interaction nodes connecting the power grid and the transportation network; L cou is an element of the set ξ; is a 0-1 variable indicating whether a road network node and a power grid node are coupled, if then it indicates that power can be transmitted from the road network node to the power grid at this location.

6. The time-space double-layer optimization method for power-traffic coupling systems under extreme high temperature according to claim 4, characterized in that, The lower space-scale optimization model takes maximization of the benefits of the power grid operator as a target, and a target function expression is as follows: where: g y c is the lower optimization target shed is the load loss per unit of electric quantity, is the unit compensation price for electric vehicle users.

7. The time-space double-layer optimization method for power-traffic coupling systems at extreme high temperatures according to claim 4, characterized in that, The actual corresponding road network areas in the urban power system are divided, and the following steps are included: The actual corresponding road network areas in the urban power system are divided into residential areas, working areas and commercial areas, and corresponding road network node sets are respectively denoted as R, W and C, and an expression is as follows: L = {B0, B f ,W of ,L 0f ,T0, T f ,T 0f ,T p} (1) In the formula, L represents the state set of all electric vehicles in the trip chain, including the following variables: the starting point B0 of the trip chain path; the end point B f of the trip chain path; the overall route W in the trip chain of ; the driving distance L required by the trip chain route 0f ; the departure time T0 of the trip chain; the arrival time T f of the trip chain; the travel time T 0f ; the parking time T p ; Each trip chain is decomposed into a number of "trip segments", each trip segment having a start time t s is normally distributed N(μ,σ 2 with expression: where: μ is the mean value of the departure time, σ 2 is the variance of the departure time; The willingness of the electric vehicle users to accept nearby interactive node guidance is considered, and the following steps are included: In extremely high-temperature weather, the travel willingness of the electric vehicle users will change due to the influence of temperature, and the travel willingness of the electric vehicle users is defined based on a human comfort index standard, and an expression is as follows: σ tra = exp(-λ tra PPD) (3) where λ tra is the trip chain correction factor, and PPD is the predicted percentage of dissatisfied index in the thermal comfort index standard, which varies with temperature. A travel chain considering the travel willingness of users is obtained, a driving state set is introduced to record the real-time state of each electric vehicle at each time t, as an input parameter for interaction between the interaction node and the power grid, and the expression is: wherein: V t x represents the real-time state information of the xth electric vehicle at time t; is the running state, i.e., represents the position of the xth electric vehicle in the road network at time t; is the hydrogen storage of the xth electric vehicle at time t; is the number of the interaction node closest to the xth electric vehicle at time t; The willingness of the electric vehicle user to participate in the response will change with the change of the compensation price, the willingness of the electric vehicle user to participate in the response is defined, and the expression is: In the formula, is the proportion coefficient of the electric vehicle user's participation response at time t, λ EV is the electric vehicle user's willingness coefficient, is the unit power compensation price of the electric vehicle user at time t, c EV is the expected unit power compensation price of the electric vehicle user, is the actual number of electric vehicles participating in dispatch at time t, N all is the total number of electric vehicles in the power system area; The driving state set is introduced to record the real-time state information of each electric vehicle, as an input parameter for interaction between the interaction node and the power grid, and the steps include the following steps: According to the real-time state information of each electric vehicle and the willingness of the electric vehicle user to participate in the response, the power injected by each interaction node into the power system node at time t is obtained, and the expression is: In the formula, Pi(t) is the power injected by the interactive node at node i at time t, Nk(t) is the number of electric vehicles connected to the power system at node i at time t, Pk(t) is the output power of the kth electric vehicle at time t, e T is the set of time when the electric vehicle participates in the response.

8. The time-space dual-layer optimization method for power-traffic coupling systems at extreme high temperatures according to claim 1, characterized in that, The condition for satisfying the unchanged value of the upper and lower layer objective functions is that the expression is: |z y+1 -z y |≤ε1 (9) |g y+1 -g y |≤ε2 (10).

9. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the time-space double-layer optimization method of the electric power-traffic coupling system under extreme high temperature according to any one of claims 1-8.

10. A computer storage medium, characterized in that, The computer storage medium has a computer program stored thereon, and the computer program is executed by the processor to realize the steps of the time-space double-layer optimization method of the electric power-traffic coupling system under extreme high temperature according to any one of claims 1-8.

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