Method for determining power supply recovery demand of key power facilities required for post-disaster traffic network evacuation

Abstract

The application provides a method for determining the power supply recovery demand of key power facilities required for post-disaster traffic network evacuation, and belongs to the technical field of power distribution network failure recovery, comprising: determining the relationship between the flow of charging stations and internal vehicles; determining the relationship between the power supply of charging stations and functions; based on the relationship between the flow of charging stations and internal vehicles and the relationship between the power supply of charging stations and functions, constructing an evacuation model considering post-disaster power distribution network recovery decision; solving the evacuation model considering post-disaster power distribution network recovery decision to obtain the power demand decision result of traffic network power facilities. The application constructs a dynamic traffic evacuation model of traffic network considering the influence of key power facilities, and considers the dynamic traffic flow constraint, evacuation curve characteristics, charging station dynamic traffic flow characteristic constraint and the like, determines the personnel evacuation path within the estimated evacuation period, realizes the shortest overall evacuation time, and provides the traffic evacuation power demand reference basis for the power distribution network recovery decision.

Description

Methods for Determining the Power Supply Restoration Needs of Critical Power Facilities for Post-Disaster Transportation Network Evacuation Technical Field

[0001] This invention relates to the field of power distribution network fault recovery technology, specifically to a method for determining the power supply recovery needs of key power facilities required for post-disaster transportation network evacuation. Background Technology

[0002] The continuous increase in the number of electric vehicles has promoted the deep integration of urban power grids and transportation networks. After sudden disasters, transportation faces evacuation tasks. Considering the energy needs of evacuation, utilizing electric vehicles and electric buses with V2G capabilities to quickly restore critical transportation loads is one of the important means to improve evacuation efficiency, reduce casualties, and mitigate disaster losses. Electric vehicles and electric buses have charging needs; however, critical transportation power facilities (such as charging stations) may face power outages under unforeseen events, thus preventing the timely and smooth implementation of evacuation strategies.

[0003] Regarding the coordinated emergency recovery of power distribution networks and transportation networks, scholars both domestically and internationally have conducted relevant research. For example, considering the supporting role of electric buses in urban areas for emergency recovery, methods for electric bus scheduling and dynamic recovery of critical loads have been proposed; simultaneously, considering the information between the power distribution network and the transportation network, optimization methods for power distribution network repair strategies after typhoons have been proposed; and with the goal of fair and balanced recovery of loads with equal weight during power outages, an active power distribution network fault balancing strategy considering the scheduling of emergency power vehicles in the transportation network has been proposed. However, research in the transportation field typically assumes the availability of critical transportation power facilities and does not deeply consider the potential power outages under unconventional events, which may lead to the failure to implement traffic evacuation strategies on time and smoothly.

[0004] Therefore, based on the interaction between charging stations and dynamic traffic flow, a cellular transmission model for charging stations is established to accurately characterize the impact of whether or not the power supply of charging stations is restored in emergency scenarios on traffic evacuation, so as to quickly and efficiently restore important loads in subsequent distribution network restoration strategies and improve the resilience of the distribution-transportation network. Summary of the Invention

[0005] The purpose of this invention is to provide a method for determining the power supply restoration requirements of key power facilities required for post-disaster transportation network evacuation, taking into account the impact of critical transportation power facilities on the transportation network, traffic flow constraints, evacuation curve characteristics, and dynamic traffic flow characteristics constraints of charging stations, so as to achieve the shortest overall evacuation time and provide a reference for the power demand of transportation evacuation for power distribution network restoration decisions. This method aims to solve at least one of the technical problems existing in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] On the one hand, the present invention provides a method for determining the power supply restoration needs of critical electrical facilities required for post-disaster transportation network evacuation, including:

[0008] Determine the relationship between charging station traffic and the number of vehicles inside the station;

[0009] Determine the relationship between the power supply and functions of the charging station;

[0010] Based on the relationship between charging station traffic and internal vehicles, as well as the relationship between charging station power supply and function, an evacuation model considering post-disaster power distribution network recovery decisions is constructed.

[0011] Solve the evacuation model that considers the post-disaster power distribution network restoration decision, and obtain the decision results of the power demand of the transportation network power facilities.

[0012] Preferably, determining the relationship between charging station traffic and internal vehicles includes:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] in, A value of 1 indicates that charging station c is in a non-queueing state at time τ. A value of 0 indicates that charging station c is in a queuing state at time τ; This represents the flow of vehicles without charging demand from charging station c to adjacent cell b at time τ. Let τ represent the flow of vehicles with charging needs flowing from cell a to charging station c at time τ; ε is a positive number that tends to infinity; M is a positive real number.

[0027] Preferably, equations (1) and (2) indicate whether charging station c is in a queuing state at time τ; equations (3) and (4) indicate whether charging station c is in a non-queuing state at both time τ and time τ+1. Equations (5) and (6) indicate that if charging station c is in a non-queuing state at time τ and in a queuing state at time τ+1, then Equations (7) and (8) indicate that if charging station c is in a queuing state at time τ and in a non-queuing state at time τ+1, then Equations (9) and (10) indicate that if charging station c is in a queuing state at both time τ and time τ+1, then Equation (11) indicates that all vehicles flowing into the charging station have charging needs, while those flowing out have no charging needs; Equation (12) indicates that it is impossible for a vehicle with a charging need to depart to appear in the terminating cell; Equation (13) indicates that the final number of vehicles in the terminating cell should be equal to the total number of vehicles departing from the source cell.

[0028] Preferably, the relationship between the power supply and functions of the charging station is determined, including:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] in, Let Q be the load state of the distribution network node corresponding to cell c of the charging station at time τ. A value of 1 indicates that the charging station has resumed power supply, while a value of 0 indicates that the charging station has lost power. c,max δ represents the maximum outflow or inflow rate of cell c in the charging station; cN is the backpropagation coefficient; c,max This represents the maximum vehicle flow rate for cell c of the charging station.

[0035] Preferably, an evacuation model considering post-disaster power distribution network restoration decisions is established, including:

[0036] The objective function expression is as follows:

[0037]

[0038] Where f1 represents the traffic evacuation objective function; d represents whether there are vehicles that need charging, which is 1 if they need charging and 0 if they do not. Let be the number of vehicles with / without charging needs within cell a in time period τ.

[0039] Preferably, the evacuation model that considers post-disaster power grid restoration decisions also includes traffic flow balance constraints and constraints on vehicle flow volume and density:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] in, Let τ represent the traffic flow in cell i at time τ, where there are vehicles with charging needs (k=1) and vehicles without charging needs (k=0). Let Ω be the traffic flow from cell j to adjacent cell i at time τ; -1 (i) is the upstream cell of cell i; Ω(i) is the downstream cell of cell i; Q i,max δ represents the maximum traffic flow limit for cell i within a given time period; i N is the congestion coefficient of cell i; i,max The upper limit of the total traffic flow for cell i; Let i be the initial traffic flow distribution of resource k within cell i; Let $\frac{i}{j}$ be the initial transfer flow of resource $k from cell $i$ to cell $j$. It is a set of cell connection relationships.

[0048] Secondly, the present invention provides a system for determining the power supply restoration needs of critical power facilities required for post-disaster transportation network evacuation, comprising:

[0049] The first determining module is used to determine the relationship between the charging station's traffic flow and the vehicles inside the station.

[0050] The second determining module is used to determine the relationship between the power supply and functions of the charging station;

[0051] The module is used to build an evacuation model that takes into account the decision-making process for the recovery of the power distribution network after a disaster, based on the relationship between the charging station traffic and the vehicles inside, as well as the relationship between the charging station's power supply and functions.

[0052] The solution module is used to solve the evacuation model that considers the post-disaster power distribution network restoration decision, and obtain the decision results of the power demand of transportation network power facilities.

[0053] Thirdly, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the method described above for determining the power supply restoration requirements of key power facilities needed for post-disaster transportation network evacuation.

[0054] Fourthly, the present invention provides a computer program product, including a computer program that, when run on one or more processors, is used to implement the method described above for determining the power supply restoration requirements of critical power facilities needed for post-disaster transportation network evacuation.

[0055] Fifthly, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the method for determining the power supply restoration requirements of critical power facilities required for post-disaster transportation network evacuation as described above.

[0056] Beneficial effects of this invention:

[0057] A dynamic traffic evacuation model for the transportation network was constructed, taking into account the impact of key power facilities in the transportation sector. The model considers dynamic traffic flow constraints, evacuation curve characteristics, and dynamic traffic flow characteristics constraints of charging stations. It determines the evacuation routes within the estimated evacuation period to achieve the shortest overall evacuation time, thus providing a reference for the power demand of traffic evacuation for power distribution network restoration decisions.

[0058] The advantages of additional aspects of the invention will be set forth more clearly in the following description or will be learned by practice of the invention. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 is a schematic diagram of the power distribution network-transportation network coupling system after an extreme event, as described in an embodiment of the present invention.

[0061] Figure 2 is a schematic diagram of a transportation network cell according to an embodiment of the present invention.

[0062] Figure 3 is a schematic diagram of the evacuation demand response curve according to an embodiment of the present invention.

[0063] Figure 4 is a schematic diagram of the traffic flow at the charging station according to an embodiment of the present invention.

[0064] Figure 5 is a schematic diagram of the power supply requirements of key electrical equipment in the transportation network according to an embodiment of the present invention. Detailed Implementation

[0065] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0066] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0067] It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as here.

[0068] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0069] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0070] To facilitate understanding of the present invention, the present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0071] Those skilled in the art should understand that the accompanying drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0072] Example 1

[0073] This embodiment 1 provides a system for determining the power supply restoration needs of critical power facilities required for post-disaster transportation network evacuation. The system includes:

[0074] The first determining module is used to determine the relationship between the charging station's traffic flow and the vehicles inside the station.

[0075] The second determining module is used to determine the relationship between the power supply and functions of the charging station;

[0076] The module is used to build an evacuation model that takes into account the decision-making process for the recovery of the power distribution network after a disaster, based on the relationship between the charging station traffic and the vehicles inside, as well as the relationship between the charging station's power supply and functions.

[0077] The solution module is used to solve the evacuation model that considers the post-disaster power distribution network restoration decision, and obtain the decision results of the power demand of transportation network power facilities.

[0078] In this embodiment 1, the above-described system is used to implement a method for determining the power supply restoration needs of critical electrical facilities required for post-disaster transportation network evacuation, including:

[0079] The first determining module is used to determine the relationship between the charging station's flow rate and the vehicles inside the station.

[0080] The second determining module is used to determine the relationship between the power supply and function of the charging station;

[0081] Using building blocks, an evacuation model is constructed that takes into account the relationship between charging station traffic and internal vehicles, as well as the relationship between charging station power supply and function.

[0082] The solution module is used to solve the evacuation model that considers the decision-making of the post-disaster power distribution network restoration, and the decision results of the power demand of the transportation network power facilities are obtained.

[0083] Determine the relationship between charging station traffic and the number of vehicles inside the station, including:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] in, A value of 1 indicates that charging station c is in a non-queueing state at time τ. A value of 0 indicates that charging station c is in a queuing state at time τ; This represents the flow of vehicles without charging demand from charging station c to adjacent cell b at time τ. Let τ represent the flow of vehicles with charging needs flowing from cell a to charging station c at time τ; ε is a positive number that tends to infinity; M is a positive real number.

[0098] Equations (1) and (2) indicate whether charging station c is in a queuing state at time τ; Equations (3) and (4) indicate whether charging station c is in a non-queuing state at both time τ and time τ+1. Equations (5) and (6) indicate that if charging station c is in a non-queuing state at time τ and in a queuing state at time τ+1, then Equations (7) and (8) indicate that if charging station c is in a queuing state at time τ and in a non-queuing state at time τ+1, then Equations (9) and (10) indicate that if charging station c is in a queuing state at both time τ and time τ+1, then Equation (11) indicates that all vehicles flowing into the charging station have charging needs, while those flowing out have no charging needs; Equation (12) indicates that it is impossible for a vehicle with a charging need to depart to appear in the terminating cell; Equation (13) indicates that the final number of vehicles in the terminating cell should be equal to the total number of vehicles departing from the source cell.

[0099] Determine the relationship between the power supply and functions of the charging station, including:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] in, Let Q be the load state of the distribution network node corresponding to cell c of the charging station at time τ. A value of 1 indicates that the charging station has resumed power supply, while a value of 0 indicates that the charging station has lost power. c,max δ represents the maximum outflow or inflow rate of cell c in the charging station; c N is the backpropagation coefficient; c,max This represents the maximum vehicle flow rate for cell c of the charging station.

[0106] Establish an evacuation model that considers post-disaster power distribution network restoration decisions, including:

[0107] The objective function expression is as follows:

[0108]

[0109] Where f1 represents the traffic evacuation objective function; d represents whether there are vehicles that need charging, which is 1 if they need charging and 0 if they do not. Let be the number of vehicles with / without charging needs within cell a in time period τ.

[0110] The evacuation model that considers post-disaster power grid restoration decisions also includes traffic flow balance constraints and constraints on vehicle flow volume and density.

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] in, Let τ represent the traffic flow in cell i at time τ, where there are vehicles with charging needs (k=1) and vehicles without charging needs (k=0). Let Ω be the traffic flow from cell j to adjacent cell i at time τ; -1 (i) is the upstream cell of cell i; Ω(i) is the downstream cell of cell i; Q i,max δ represents the maximum traffic flow limit for cell i within a given time period; i N is the congestion coefficient of cell i; i,max The upper limit of the total traffic flow for cell i; Let i be the initial traffic flow distribution of resource k within cell i; Let $\frac{i}{j}$ be the initial transfer flow of resource $k from cell $i$ to cell $j$. It is a set of cell connection relationships.

[0119] Example 2

[0120] In disaster scenarios, the primary task is to rapidly evacuate people from disaster-stricken areas to safe locations. First, assuming the availability of critical power infrastructure in the transportation network, the electric bus fleet used for evacuation and auxiliary power restoration is identified, and an optimal evacuation plan is decided. The power demand of each critical power facility in the transportation network at different times is determined. A cellular transport model is used to model the dynamic traffic flow, where the cellular constraints of charging stations need to reflect the vehicle holding and charging waiting characteristics of the charging stations.

[0121] The cellular transmission model for charging stations needs to consider the relationship between the inflow / outflow of traffic and the number of vehicles within a cell under different states, as well as the relationship between the power supply status of the charging station and its functions, to ensure an accurate depiction of the impact of charging station power demand on traffic evacuation under extreme disasters. Therefore, a mixed-integer linear programming model can be established with the objective of minimizing the overall evacuation time. This model can then be solved using mature commercial optimization software to obtain the power demand strategy for critical transportation infrastructure.

[0122] Based on the above theory, this embodiment 2 provides a method for determining the power supply restoration needs of critical power facilities required for post-disaster transportation network evacuation, including the following steps:

[0123] 1) Consider the relationship between charging station traffic and internal vehicles;

[0124] 2) Consider the relationship between the power supply and function of the charging station;

[0125] 3) Establish an evacuation model that considers post-disaster power distribution network restoration decisions;

[0126] 4) Determining the power supply needs of key electrical facilities required for post-disaster transportation network evacuation.

[0127] The modeling that considers the relationship between charging station traffic and internal vehicles is as follows:

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[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

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[0141] In the formula: A value of 1 indicates that charging station c is in a non-queueing state at time τ. A value of 0 indicates that charging station c is in a queuing state at time τ; This represents the flow of vehicles without charging demand from charging station c to adjacent cell b at time τ. Let τ represent the flow of vehicles with charging needs flowing from cell a to charging station c at time τ; ε is a positive number that tends to infinity; and M is a very large positive real number.

[0142] Equations (1) and (2) indicate whether charging station c is in a queuing state at time τ; Equations (3) and (4) indicate whether charging station c is in a non-queuing state at both time τ and time τ+1. Equations (5) and (6) indicate that if charging station c is in a non-queuing state at time τ and in a queuing state at time τ+1, then Equations (7) and (8) indicate that if charging station c is in a queuing state at time τ and in a non-queuing state at time τ+1, then Equations (9) and (10) indicate that if charging station c is in a queuing state at both time τ and time τ+1, then Equation (11) indicates that all vehicles flowing into the charging station have charging needs, while those flowing out have no charging needs. Equation (12) indicates that it is impossible for a vehicle with a charging need to depart to appear in the terminating cell; Equation (13) indicates that the final number of vehicles in the terminating cell should be equal to the total number of vehicles departing from the source cell.

[0143] The modeling considering the relationship between power supply and function of the charging station is as follows:

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] In the formula: Let Q be the load state of the distribution network node corresponding to cell c of the charging station at time τ. A value of 1 indicates that the charging station has resumed power supply, while a value of 0 indicates that the charging station has lost power. c,max δ represents the maximum outflow or inflow rate of cell c in the charging station; c N is the backpropagation coefficient; c,max This represents the maximum vehicle flow rate for cell c of the charging station.

[0150] Equation (14) represents the traffic flow conservation constraint of charging station c; Equation (15) represents that the number of vehicles leaving c at time τ is less than the number of vehicles in c at time τ; Equations (16) and (17) represent that both the outflow and inflow flows are less than the maximum value. At the same time, if the charging station loses power, the number of vehicles leaving the charging station is 0, indicating that the vehicles are waiting to be charged inside; Equation (18) represents the inflow vehicle restriction constraint, that is, if the charging station reaches the maximum vehicle capacity limit, the traffic flow entering the station is restricted.

[0151] The establishment of the evacuation model that considers post-disaster power distribution network recovery decisions is detailed below:

[0152] 1) Objective function

[0153] This embodiment establishes a dynamic traffic evacuation model for the transportation network that considers the impact of critical power-consuming facilities, providing a basis for traffic network evacuation decisions. The evacuation problem aims to minimize the overall evacuation time, taking into account dynamic traffic flow constraints, evacuation curve characteristics, and dynamic traffic flow characteristics of charging stations, to determine evacuation routes within the estimated evacuation period. The objective function expression is as follows:

[0154]

[0155] In the formula: f1 represents the traffic evacuation objective function; d represents whether there are vehicles that need charging, which is 1 if they need charging and 0 if they do not. Let be the number of vehicles with / without charging needs within cell a in time period τ.

[0156] 2) Constraints

[0157] (1) Transportation network constraints

[0158] Traffic-related constraints include the traffic flow balance constraints of cells and the constraints on vehicle flow and density.

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166] In the formula: Let τ represent the traffic flow in cell i at time τ, where there are vehicles with charging needs (k=1) and vehicles without charging needs (k=0). Let Ω be the traffic flow from cell j to adjacent cell i at time τ; -1 (i) is the upstream cell of cell i; Ω(i) is the downstream cell of cell i; Q i,max δ represents the maximum traffic flow limit for cell i within a given time period; i N is the congestion coefficient of cell i; i,max The upper limit of the total traffic flow for cell i; Let i be the initial traffic flow distribution of resource k within cell i; Let $\frac{i}{j}$ be the initial transfer flow of resource $k from cell $i$ to cell $j$. It is a set of cell connection relationships.

[0167] Equation (20) represents the traffic flow balance of each cell. Equation (21) represents that the traffic flow of vehicle k from cell i to adjacent cells is not greater than the traffic flow of that cell. Equation (22) represents that the total traffic flow of vehicles from cell i to adjacent cells is not greater than the traffic flow transfer limit of that cell. Equation (23) represents that the total traffic flow of vehicles from adjacent cells to cell i is not greater than the traffic flow transfer limit of that cell. Equation (24) represents that when traffic congestion occurs in that cell, the total inflow of cell i does not exceed the remaining capacity of cell i. Equations (25) and (26) represent the initial and final traffic flow states in each cell, respectively.

[0168] (2) Cell constraints of charging stations

[0169] The charging station cell has a vehicle holding characteristic, meaning that vehicles entering the charging station must remain for at least a period of time to wait for charging to complete, and there is also a queuing waiting time when the charging piles are saturated. Based on the first-in-first-out principle, the characteristics of the charging station cell transmission model are analyzed, describing the relationship between the inflow and outflow of the charging station and the number of vehicles in the cell. In addition, vehicles with charging needs become vehicles without charging needs after entering and leaving the cell. The constraints are shown in equations (1)-(13).

[0170] (3) Constraints on the impact of power restoration at charging stations

[0171] In addition to modeling the queuing effect at charging stations, whether or not power is restored will affect charging and queuing. Constraints are shown in equations (14)-(18).

[0172] In summary, the power supply restoration needs of critical electrical facilities required for post-disaster transportation network evacuation are determined as follows:

[0173] 1) Objective function: Equation (19).

[0174] 2) Constraints: Equations (1)-(18), Equations (20)-(26).

[0175] The model is solved using the Convex optimization modeling toolkit and the Mosek solver in the Julia language. The solution yields the decision results of a dynamic traffic dispersal model for the transportation network that considers the impact of critical transportation power facilities, including traffic flow at charging stations and power demand for critical transportation power facilities.

[0176] Example 3

[0177] This embodiment 3 provides a method for determining the power supply restoration needs of critical electrical facilities required for post-disaster transportation network evacuation. It analyzes the characteristics of the charging station cellular transmission model using a first-in, first-out (FIFO) principle, while also considering the impact of whether the charging station's power supply function is restored. To minimize evacuation time, the power demand of critical facilities is derived. This method includes three aspects: first, considering the relationship between the inflow / outflow of charging stations and the number of vehicles within a cell under different states; second, recognizing that the charging station's power supply is closely related to its functional implementation and its impact on traffic evacuation needs to be accurately characterized; and finally, integrating the above methods to establish a charging station cellular transmission modeling method for post-disaster power distribution network restoration decisions. This method can determine the power demand of critical transportation facilities under major power outages, providing a strong basis for power distribution network restoration decisions and ensuring the accurate and efficient implementation of restoration strategies.

[0178] Includes the following steps:

[0179] Step 1: Consider the relationship between the charging station's traffic flow and the number of vehicles inside.

[0180] Charging station cells exhibit vehicle holding characteristics, meaning that vehicles entering the charging station must remain for at least a certain period to complete charging, and there is also a queuing time required when charging piles are saturated. Based on the first-in, first-out (FIFO) principle, the characteristics of the charging station cell transmission model are analyzed, describing the relationship between the inflow and outflow of the charging station and the number of vehicles within the cell. Furthermore, vehicles with charging needs become vehicles without charging needs after entering and exiting the cell. The mathematical model is as follows:

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[0194] In the formula: A value of 1 indicates that charging station c is in a non-queueing state at time τ. A value of 0 indicates that charging station c is in a queuing state at time τ; This represents the flow of vehicles without charging demand from charging station c to adjacent cell b at time τ. Let τ represent the flow of vehicles with charging needs flowing from cell a to charging station c at time τ; ε is a positive number that tends to infinity; and M is a very large positive real number.

[0195] Equations (1) and (2) indicate whether charging station c is in a queuing state at time τ; Equations (3) and (4) indicate whether charging station c is in a non-queuing state at both time τ and time τ+1. Equations (5) and (6) indicate that if charging station c is in a non-queuing state at time τ and in a queuing state at time τ+1, then Equations (7) and (8) indicate that if charging station c is in a queuing state at time τ and in a non-queuing state at time τ+1, then Equations (9) and (10) indicate that if charging station c is in a queuing state at both time τ and time τ+1, then Equation (11) indicates that all vehicles flowing into the charging station have charging needs, while those flowing out have no charging needs. Equation (13) indicates that it is impossible for a vehicle with a charging need to depart to appear in the terminating cell; Equation (14) indicates that the final number of vehicles in the terminating cell should be equal to the total number of vehicles departing from the source cell.

[0196] Step 2: Consider the relationship between the power supply and function of the charging station.

[0197] Besides the queuing effect at charging stations, which needs to be modeled, the restoration of power supply will also affect charging and queuing. The mathematical model is as follows:

[0198]

[0199]

[0200]

[0201]

[0202]

[0203] In the formula: Let Q be the load state of the distribution network node corresponding to cell c of the charging station at time τ. A value of 1 indicates that the charging station has resumed power supply, while a value of 0 indicates that the charging station has lost power. c,max δ represents the maximum outflow or inflow rate of cell c in the charging station; c N is the backpropagation coefficient; c,max This represents the maximum vehicle flow rate for cell c of the charging station.

[0204] Equation (14) represents the traffic flow conservation constraint of charging station c; Equation (15) represents that the number of vehicles leaving c at time τ is less than the number of vehicles in c at time τ; Equations (16) and (17) represent that both the outflow and inflow flows are less than the maximum value. At the same time, if the charging station loses power, the number of vehicles leaving the charging station is 0, indicating that the vehicles are waiting to be charged inside; Equation (18) represents the inflow vehicle restriction constraint, that is, if the charging station reaches the maximum vehicle capacity limit, the traffic flow entering the station is restricted.

[0205] Step 3: Establish a dynamic traffic dispersal model for the transportation network that takes into account the impact of key power facilities in the transportation sector.

[0206] 1) Objective function

[0207] The evacuation problem aims to minimize the overall evacuation time, taking into account dynamic traffic flow constraints, evacuation curve characteristics, and dynamic traffic flow characteristics constraints of charging stations, and determines the evacuation routes within the estimated evacuation period.

[0208] The objective function expression is as follows:

[0209]

[0210] In the formula: f1 represents the traffic evacuation objective function; d represents whether there are vehicles that need charging, which is 1 if they need charging and 0 if they do not. Let be the number of vehicles with / without charging needs within cell a in time period τ.

[0211] 2) Constraints

[0212] (1) Transportation network constraints

[0213] Traffic-related constraints include the traffic flow balance constraints of cells and the constraints on vehicle flow and density.

[0214]

[0215]

[0216]

[0217]

[0218]

[0219]

[0220]

[0221] In the formula: Let τ represent the traffic flow in cell i at time τ, where there are vehicles with charging needs (k=1) and vehicles without charging needs (k=0). Let Ω be the traffic flow from cell j to adjacent cell i at time τ; -1 (i) is the upstream cell of cell i; Ω(i) is the downstream cell of cell i; Q i,max δ represents the maximum traffic flow limit for cell i within a given time period; i N is the congestion coefficient of cell i; i,max The upper limit of the total traffic flow for cell i; Let i be the initial traffic flow distribution of resource k within cell i; Let $\frac{i}{j}$ be the initial transfer flow of resource $k from cell $i$ to cell $j$. It is a set of cell connection relationships.

[0222] Equation (20) represents the traffic flow balance of each cell. Equation (21) represents that the traffic flow of vehicle k from cell i to adjacent cells is not greater than the traffic flow of that cell. Equation (22) represents that the total traffic flow of vehicles from cell i to adjacent cells is not greater than the traffic flow transfer limit of that cell. Equation (23) represents that the total traffic flow of vehicles from adjacent cells to cell i is not greater than the traffic flow transfer limit of that cell. Equation (24) represents that when traffic congestion occurs in that cell, the total inflow of cell i does not exceed the remaining capacity of cell i. Equations (25) and (26) represent the initial and final traffic flow states in each cell, respectively.

[0223] (2) Cell constraints of charging stations

[0224] The constraints are shown in equations (1)-(13).

[0225] (3) Constraints on the impact of power restoration at charging stations

[0226] The constraints are shown in equations (14)-(18).

[0227] In summary, the dynamic traffic dispersal model for the transportation network, considering the impact of critical power facilities in transportation, is as follows:

[0228] 1) Objective function: Equation (19).

[0229] 2) Constraints: Equations (1)-(18), Equations (20)-(26).

[0230] The model is solved using the Convex optimization modeling toolkit and the Mosek solver in the Julia language.

[0231] As shown in Figures 1 to 5, in this embodiment, a dynamic traffic evacuation model of the traffic network that considers the impact of key traffic power facilities is applied to the test system. The topology of the test system is shown in Figure 1. Combined with the basic information shown in Tables 1-2, the application of this method is illustrated in the cell partitioning diagram shown in Figure 2.

[0232] First, the key roads affecting vehicle evacuation were selected, namely roads 2→1, 1→4, 4→8, 5→4, 4→8, 5→9, 9→8, 6→5, 2→5, and 5→2 in Figure 1, and then divided into 81 cells at 1-minute intervals, i.e., Γ. int =1 minute, as shown in Figure 2. Source cells 1, 40, and 51 correspond to nodes 2, 5, and 6 in the traffic network of Figure 1; termination cell 29 corresponds to node 8, which is a refuge; charging station cells 78, 79, 80, and 81 correspond to charging stations 1, 2, 3, and 4. Assume that each of the three source cells has 800 vehicles to be evacuated, of which 10% require charging.

[0233] First, based on the first-in-first-out (FIFO) principle, the characteristics of the charging station cellular transmission model are analyzed, deriving a modeling method for the relationship between the inflow and outflow of charging stations and the number of vehicles within a cell. Then, considering the impact of whether or not power is restored at a charging station on charging and queuing, a modeling method characterizes the relationship between the restoration of power and the implementation of its functions. Finally, combining the above methods, a charging station cellular transmission model is established for post-disaster power distribution network restoration decisions. This model can accurately characterize the impact of the restoration of power supply at charging stations on traffic evacuation in emergency scenarios, providing a strong basis for subsequent power distribution network restoration decisions and achieving efficient coordinated restoration of the power distribution and transportation networks.

[0234] Step 1: Based on the information of the test system in Figure 1 and the cell division in Figure 2, the source cell, the termination cell, the charging station cell, and the distribution network node are matched one by one.

[0235] Step 2: The demand response of vehicles with charging needs is taken into account in the establishment of the evacuation model.

[0236] Step 3: Apply commercial modeling tools and solvers to solve the problem. This will yield the traffic flow changes at each charging station throughout the evacuation process, as well as the power demand of critical transportation infrastructure at different times. The method described in this embodiment can characterize the impact of charging station power demand on traffic evacuation under extreme scenarios, providing a strong basis for subsequent power distribution network restoration decisions.

[0237] Table 1. Transportation Network Parameters

[0238]

[0239] Table 2 Charging Station Parameters

[0240]

[0241] Example 4

[0242] Embodiment 4 of the present invention provides a non-transitory computer-readable storage medium for storing computer instructions. When executed by a processor, the computer instructions implement a method for determining the power supply restoration requirements of critical power facilities needed for post-disaster transportation network evacuation. The method includes:

[0243] Determine the relationship between charging station traffic and the number of vehicles inside the station;

[0244] Determine the relationship between the power supply and functions of the charging station;

[0245] Based on the relationship between charging station traffic and internal vehicles, as well as the relationship between charging station power supply and function, an evacuation model is established that takes into account post-disaster power distribution network recovery decisions.

[0246] Solve the evacuation model that considers the post-disaster power distribution network restoration decision, and obtain the decision results of the power demand of the transportation network power facilities.

[0247] Example 5

[0248] Embodiment 5 of the present invention provides a computer program (product), including a computer program that, when run on one or more processors, is used to implement a method for determining the power supply restoration needs of critical power facilities required for post-disaster transportation network evacuation. The method includes:

[0249] Determine the relationship between charging station traffic and the number of vehicles inside the station;

[0250] Determine the relationship between the power supply and functions of the charging station;

[0251] Based on the relationship between charging station traffic and internal vehicles, as well as the relationship between charging station power supply and function, an evacuation model is established that takes into account post-disaster power distribution network recovery decisions.

[0252] Solve the evacuation model that considers the post-disaster power distribution network restoration decision, and obtain the decision results of the power demand of the transportation network power facilities.

[0253] Example 6

[0254] Embodiment 6 of the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for determining the power supply restoration needs of critical power facilities required for post-disaster transportation network evacuation. The method includes:

[0255] Determine the relationship between charging station traffic and the number of vehicles inside the station;

[0256] Determine the relationship between the power supply and functions of the charging station;

[0257] Based on the relationship between charging station traffic and internal vehicles, as well as the relationship between charging station power supply and function, an evacuation model is established that takes into account post-disaster power distribution network recovery decisions.

[0258] Solve the evacuation model that considers the post-disaster power distribution network restoration decision, and obtain the decision results of the power demand of the transportation network power facilities.

[0259] 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.

[0260] 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 that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0261] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to perform a series of operational steps on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0262] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or variations that can be made by those skilled in the art without creative effort should be included within the scope of protection of the present invention.

Claims

1. A method for determining the power supply restoration needs of critical electrical facilities required for post-disaster transportation network evacuation, characterized in that, include: Determine the relationship between charging station traffic and the number of vehicles inside the station; Determine the relationship between the power supply and functions of the charging station; Based on the relationship between charging station traffic and internal vehicles, as well as the relationship between charging station power supply and function, an evacuation model considering post-disaster power grid restoration decisions is established. Solving this evacuation model yields the power demand decision results for transportation network power facilities. Specifically, determining the relationship between charging station traffic and internal vehicles includes: （1）； （2）； （3）； （4）； （5）； （6）； （7）； （8）； （9）； （10）； （11）； （12）； (13); among which, A value of 1 indicates a charging station. exist Always in a non-queue state. A value of 0 indicates a charging station. exist Always in a queue; express Always by charging station Flowing out to adjacent cells Traffic flow with no need for charging; express Time by cell Flowing into charging stations Traffic flow with charging needs; It is a positive number that tends towards infinity; Let be a positive real number; where equations (1) and (2) represent the charging station. exist Whether it is in a queuing state at any time; Equations (3) and (4) indicate if the charging station exist Time and If it is always in a non-queue state, then Equations (5) and (6) indicate that if the charging station exist Always in a non-queue state. If you are always in a queue, then Equations (7) and (8) indicate that if the charging station exist Always in a queue If it is always in a non-queuing state, then Equations (9) and (10) indicate that if the charging station exist Time and If you are always in a queue, then Equation (11) indicates that all vehicles flowing into the charging station have charging needs, while those flowing out have no charging needs; Equation (12) indicates that it is impossible for a vehicle with a charging need to depart to appear in the terminating cell; Equation (13) indicates that the final number of vehicles in the terminating cell should be equal to the total number of vehicles departing from the source cell; Among these, determining the relationship between the power supply and function of the charging station includes: （14）； （15）； （16）； （17）； (18); among which, for Time Charging Station Cell The load status of the corresponding distribution network node: if it is 1, it means that the charging station has resumed power supply; if it is 0, it means that the charging station has lost power. For charging station cells The maximum value of outflow or inflow; This is the backpropagation coefficient; For charging station cells The upper limit of traffic flow; among which, an evacuation model considering post-disaster power distribution network restoration decisions is established, including: the objective function expression is as follows: (19); among which, Express the objective function for traffic evacuation; This indicates whether a vehicle needs charging. If it needs charging, the value is 1; otherwise, it is 0. For the first Time cell The number of vehicles with / without charging needs within the area; establishing an evacuation model that considers post-disaster power grid restoration decisions, including traffic flow balance constraints and the relationship between vehicle flow volume and density constraints: （20）； （21）； （22）； （23）； （24）； （25）； (26); among which, for The time cell i has a charging requirement ( ) and vehicles without charging needs ( Traffic flow; for -1 is the traffic flow from cell j to adjacent cell i at time -1; Let i be the upstream cell of cell i; This is the downstream cell of cell i; The maximum traffic flow for cell i within a given time period; Let be the congestion coefficient of cell i; The upper limit of the total traffic flow for cell i; Let i be the initial traffic flow distribution of resource k within cell i; Let $\frac{i}{j}$ be the initial transfer flow of resource $k from cell $i$ to cell $j$. It is a set of cell connection relationships.

2. A system for determining the power supply restoration needs of critical power facilities required for post-disaster transportation network evacuation based on the method described in claim 1, characterized in that, include: The first determining module is used to determine the relationship between the charging station's traffic flow and the vehicles inside the station. The second determining module is used to determine the relationship between the power supply and functions of the charging station; The construction module is used to establish an evacuation model that considers the decision-making process for the restoration of the power distribution network after a disaster, based on the relationship between the traffic flow of the charging station and the vehicles inside, as well as the relationship between the power supply and function of the charging station. The solution module is used to solve the evacuation model that considers the decision-making process for the restoration of the power distribution network after a disaster, and obtain the decision results of the power demand of the transportation network power facilities.

3. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the method for determining the power supply restoration requirements of critical power facilities needed for post-disaster transportation network evacuation as described in claim 1.

4. A computer program product, characterized in that, Includes a computer program, which, when run on one or more processors, is used to implement the method for determining the power supply restoration requirements of critical power facilities needed for post-disaster transportation network evacuation as described in claim 1.

5. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the method for determining the power supply restoration requirements of critical power facilities needed for post-disaster transportation network evacuation as described in claim 1.

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