An attack method, medium and device for urban electric traffic coupling system

Abstract

This invention provides an attack method, medium, and device for an urban electric-transportation coupling system, belonging to the technical field of electric-transportation system reliability assessment. The method comprises the following steps: S1. Coupling a power system model and a transportation system model via charging facilities to obtain an electric-transportation coupling model; S2. Developing three attack strategies for the electric-transportation coupling model: a cyber attack strategy, a physical attack strategy, and a cyber-physical coordinated attack strategy; and S3. Based on the electric-transportation coupling model and the three attack strategies, constructing a two-layer optimization model for cyber-physical attacks against the electric-transportation coupling system. This invention utilizes the aforementioned attack method, medium, and device for an urban electric-transportation coupling system. Through a systematic analysis of different attack strategies, the method explores the impact of these attack strategies on system operating costs and their ripple effects on the socioeconomic environment, ultimately ensuring the safe, stable, and economical operation of the coupling system in complex network environments.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability assessment of electric traffic systems, and in particular to an attack method, medium and device for an urban electric traffic coupling system. Background Art

[0002] The increasing complexity of urban infrastructure, particularly the close coupling of power and transportation networks, makes them potential targets for attack. This coupling increases the vulnerability of systems to attacks, as attackers can propagate interference from one system to the other. Physical damage and cyberattacks can also synergize, further exacerbating system damage. Furthermore, most existing research fails to fully consider the interaction between the two systems, resulting in strategies that may be ineffective in practice.

[0003] Although existing research has made significant progress in cyber attacks against independent power systems and transportation systems, there are significant deficiencies in research on coupled power and transportation systems:

[0004] Focus on a single system: Most studies focus on the security of a single system (power or transportation), ignoring the coupling between the power and transportation systems. This limitation prevents a comprehensive understanding of cross-system attack strategies, especially the complex interactions that can occur in coupled systems.

[0005] Insufficient attack stealth: Traditional cyberattack methods are generally easy to identify and prevent by modern security monitoring systems because they usually involve sudden and large-scale resource changes, which reduces the persistence and effectiveness of the attack.

[0006] Limited attack intensity: The impact of a single cyber attack is limited. Summary of the Invention

[0007] The purpose of this invention is to provide an attack method, medium, and device for urban electric-transportation coupling systems. By systematically analyzing different attack strategies, the authors explore the impact of attack strategies on system operating costs and their chain reactions on the social economy, ultimately ensuring the safe, stable, and economical operation of the coupling system in a complex network environment.

[0008] To achieve the above objectives, the present invention provides an attack method for an urban electric-transportation coupling system, comprising the following steps:

[0009] S1. Couple the power system model and the traffic system model through charging facilities to obtain a power-traffic coupling model;

[0010] S2. Set three attack strategies for the electric-traffic coupling model: network attack strategy, physical attack strategy, and network-physical coordinated attack strategy;

[0011] S3. Based on the power-transport coupling model and three attack strategies, a two-layer optimization model for cyber-physical attacks against the power-transport coupling system is constructed.

[0012] Preferably, in step S1, the objective function of the power system model is as follows:

[0013]

[0014] Among them, C E represents the power system operating cost; N represents the set of nodes in the power network; i represents the index of the node in the power network; b i represents fixed cost; a i represents the cost coefficient; P i G Indicates the output power of the generator.

[0015] Preferably, in step S1, the constraints of the power system model include:

[0016] Active power balance constraint of node i:

[0017]

[0018] Among them, P i L represents the active load of node i; ΔP i L represents the false load data injected into the power network node i; P ij represents the active power on the transmission line between node i and node j; P i CF represents the charging load fed back to the power grid node i by the traffic flow through the charging facilities; S i Indicates the load reduction corresponding to the i-th node; Ω i represents the set of all nodes directly connected to node i in the power network;

[0019] P ij The calculation formula is as follows:

[0020]

[0021] Among them, θ i and θ j Respectively represent the voltage phase angles corresponding to node i and node j; X ij represents the reactance of the line between node i and node j; L represents the set of power transmission lines in the power network;

[0022] P i CF The calculation formula is as follows:

[0023]

[0024] Among them, p EV represents the average charging power of each vehicle; rs represents the starting and ending points of the corresponding path; f k rs represents the traffic flow on the kth path from the starting point r to the end point s; K rs represents the set of all paths from the starting point r to the end point s in the transportation network;

[0025] Generator output power constraints:

[0026]

[0027] in, Indicates the minimum output power of the generator; Indicates the maximum output power of the generator;

[0028] Power constraints on transmission lines:

[0029]

[0030] Among them, P ijmin represents the minimum active power allowed on the transmission line between node i and node j; P ijmax represents the maximum active power allowed on the transmission line between node i and node j;

[0031] Load reduction constraint for node i:

[0032]

[0033] Preferably, in step S1, the objective function of the traffic system model is as follows:

[0034]

[0035] Among them, C T represents the operating cost of the transportation system; ω represents the monetary value of travel time, which converts the time cost in transportation into economic cost; t a represents the travel time of traffic flow on road section a; x a represents the traffic flow on road section a; T A represents the set of all road segments in the traffic network; θ is the integration variable.

[0036] Preferably, in step S1, the constraints of the traffic system model include:

[0037] Path-segment flow distribution constraints:

[0038]

[0039] in, Represents the (a,k)th element of the path-section association matrix, which is a binary decision variable. When the kth path passes through the ath section, is 1, otherwise 0;

[0040] Public Road Bureau function constraints:

[0041]

[0042] Among them, t a (x a ) represents the traffic flow x a The travel time t of the traffic flow on this road section is determined a ; represents the travel time of traffic flow without congestion on road section a; c a represents the capacity of road section a;

[0043] Origin-destination traffic demand balance constraints:

[0044]

[0045] Among them, d rs Indicates the actual traffic demand between the starting and ending points rs; Δd rs represents the false traffic demand data injected into the starting and ending points rs in the traffic network;

[0046] Traffic flow non-negativity constraint:

[0047]

[0048] Preferably, in step S1, the objective function of the electric-transport coupling model is as follows:

[0049] C OP =C E +C T +C shed (13);

[0050] Among them, C OP represents the total operating cost of the electric transportation coupling system; C shed represents the load shedding cost, which is calculated as follows:

[0051]

[0052] Where d represents the index of the load shedding node in the power network; cs d represents the cost coefficient of load shedding; Sd Indicates the load reduction of the dth node.

[0053] Preferably, in step S2, the network attack strategy is as follows:

[0054] The network attack strategy injects false load data ΔP into the power network nodes. i L and injecting false traffic flow demand Δd between the starting and ending points in the traffic network rs accomplish;

[0055] The constraints of the network attack strategy are as follows:

[0056]

[0057] Among them, δ P Indicates the maximum value of the ratio of the allowed load false data injection to the load prediction data; δ T Indicates the maximum value of the ratio of false demand data allowed to be injected into demand forecast data;

[0058] The constraints of the physical attack strategy are as follows:

[0059]

[0060] Where, l represents the index of the transmission line in the power network; P l represents the power on the lth transmission line in the power network; α l represents the physical attack vector on the power network; β a Represents a physical attack vector on transportation networks.

[0061] Preferably, in step S3, the two-layer optimization model for the cyber-physical attack on the power-transport coupling system is as follows:

[0062]

[0063] Among them, F A represents the attacker's objective function; F ED represents the objective function of the control center.

[0064] The present invention also provides a computer-readable storage medium having computer program instructions stored thereon; when the computer program instructions are executed by a processor, the above-mentioned attack method for the urban electric-transportation coupling system is implemented.

[0065] The present invention also provides an attack device for an urban electric traffic coupling system, comprising: the computer-readable storage medium as described above.

[0066] Therefore, the present invention adopts the above-mentioned attack method against the urban electric traffic coupling system, and the beneficial technical effects are as follows:

[0067] (1) Traffic demand redistribution attack technique: This invention proposes to redistribute local traffic demand while maintaining the stability of overall traffic demand, increasing the concealment of the attack and making it difficult for existing monitoring systems to detect it in a timely manner. This technique effectively increases the persistence and complexity of the attack.

[0068] (2) Targeted Design for Coupled Network Systems: This invention specifically targets the coupled relationship between the power and transportation systems, designing an attack strategy based on the interaction between the two systems. This strategy effectively exploits the interdependence between the power and transportation systems, inducing cross-system resource imbalances and chain reactions. While existing technologies mostly focus on a single system, this invention innovates at the coupled system level. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flow chart of an attack method for an urban electric traffic coupling system according to the present invention;

[0070] Figure 2 Schematic diagram of a two-layer model for the coordinated attack strategy against coupled systems. DETAILED DESCRIPTION

[0071] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0072] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0073] Example 1

[0074] like Figure 1 FIG. 1 is a flow chart of an attack method for an urban electric traffic coupling system according to the present invention, which specifically includes the following steps:

[0075] S1. Couple the power system model and the traffic system model through charging facilities to obtain a power-traffic coupling model;

[0076] The power system model is constructed using a DC power flow model, which considers the generation capacity, load distribution, and physical characteristics of power flow at each node. In this model, the operating cost of the power network is the power generation cost of all generators.

[0077] The objective function of the power system model is as follows:

[0078]

[0079] Among them, CE represents the power system operating cost; N represents the set of nodes in the power network; i represents the index of the node in the power network; b i represents fixed cost; a i represents the cost coefficient; P i G Indicates the output power of the generator.

[0080] The constraints of the power system model include:

[0081] Active power balance constraint of node i:

[0082]

[0083] Among them, P i L represents the active load of the connection node i; ΔP i L represents the false load data injected into the power network node i; P ij represents the active power on the transmission line between node i and node j; P i CF represents the charging load fed back to the power grid node i by the traffic flow through the charging facilities; S i Indicates the load reduction corresponding to the i-th node; Ω i represents the set of all nodes directly connected to node i in the power network;

[0084] P ij The calculation formula is as follows:

[0085]

[0086] Among them, θ i and θ j Respectively represent the voltage phase angles corresponding to node i and node j; X ij represents the reactance of the line between node i and node j; L represents the set of power transmission lines in the power network;

[0087] P i CF The calculation formula is as follows:

[0088]

[0089] Among them, p EV represents the average charging power of each vehicle; rs represents the starting and ending points of the corresponding path; represents the traffic flow on the kth path from the starting point r to the end point s; K rs It represents the set of all paths from the starting point r to the end point s in the transportation network;

[0090] Formula (4) reflects the coupling relationship between the power system and the transportation system, that is, the traffic flow is converted into the load of the power grid through wireless charging technology, and the road will charge the vehicle while the vehicle is driving on the wireless charging road.

[0091] Generator output power constraints:

[0092]

[0093] in, is the minimum output power of the generator; is the maximum output power of the generator;

[0094] Power constraints on transmission lines:

[0095]

[0096] Among them, P ijmin represents the minimum active power allowed on the transmission line between node i and node j; P ijmax represents the maximum active power allowed on the transmission line between node i and node j;

[0097] Load reduction constraint for node i:

[0098]

[0099] Traffic system modeling: The mathematical model of the traffic system is based on user equilibrium theory, aiming to describe the stable distribution of traffic flows under a given origin-to-destination demand. In this model, the operating cost of the traffic network is primarily reflected in the time cost of vehicles on the road.

[0100] The objective function of the traffic system model is as follows:

[0101]

[0102] Among them, C T represents the operating cost of the transportation system; ω represents the monetary value of travel time, which converts the time cost in transportation into economic cost; t a represents the travel time of traffic flow on road section a; x a represents the traffic flow on road section a; T A represents the set of all road segments in the traffic network; θ is the integration variable.

[0103] Preferably, in step S1, the constraints of the traffic system model include:

[0104] Path-segment flow distribution constraints:

[0105]

[0106] in, Represents the (a,k)th element of the path-section association matrix, which is a binary decision variable. When the kth path passes through the ath section, is 1, otherwise 0;

[0107] Public Road Bureau function constraints:

[0108]

[0109] Among them, t a (x a ) represents the traffic flow x a The travel time t of the traffic flow on this road section is determined a ; represents the travel time of traffic flow without congestion on road section a; c a represents the capacity of road section a;

[0110] Origin-destination traffic demand balance constraints:

[0111]

[0112] Among them, d rs Indicates the actual traffic demand between the starting and ending points rs; Δd rs represents the false traffic demand data injected into the starting and ending points rs in the traffic network;

[0113] Traffic flow non-negativity constraint:

[0114]

[0115] Construction of a Power-Transportation Coupling Model: First, by analyzing the coupling mechanisms between the power and transportation systems, a coupling model of the urban power-transportation system was constructed. Charging facilities were used to couple the two independent systems, forming a holistic coupled network with interactive relationships. In this model, the operation of the transportation system directly affects the load status of the power system.

[0116] The costs of the power-transport coupling system mainly include operating costs and load shedding costs. Load shedding costs refer to the losses caused to the system by the load shedding forced after the power system is attacked. Therefore, the objective function of the power-transport coupling model is as follows:

[0117] C OP =C E +C T +C shed (13);

[0118] Among them, C OP represents the total operating cost of the electric transportation coupling system; C shedrepresents the load shedding cost, which is calculated as follows:

[0119]

[0120] Where d represents the index of the load shedding node in the power network; cs d represents the cost coefficient of load shedding; S d Indicates the load reduction of the dth node.

[0121] S2. Set three attack strategies for the electric-traffic coupling model: network attack strategy, physical attack strategy, and network-physical coordinated attack strategy;

[0122] Cyber attacks: By injecting false data into the load and demand of power and transportation systems, local data can be slightly adjusted without changing the overall load / demand of the system, resulting in an imbalance in resource allocation and gradually disrupting system operations.

[0123] The network attack strategy is as follows:

[0124] The network attack strategy injects false load data ΔP into the power network nodes. i L and injecting false traffic flow demand Δd between the starting and ending points in the traffic network rs accomplish;

[0125] The constraints of the network attack strategy are as follows:

[0126]

[0127] Among them, δ P Indicates the maximum value of the ratio of the allowed load false data injection to the load prediction data; δ T Indicates the maximum value of the ratio of false demand data to demand forecast data allowed.

[0128] Formula (15) and Formula (17) ensure that the total load and total demand of the power network and transportation network remain unchanged before and after the attack. In order to successfully launch the network attack, the injected false load data is limited to a certain range.

[0129] Physical attacks: By damaging transmission lines or traffic roads, the distribution of load or traffic flow is affected, resulting in increased operating costs and reduced system efficiency, directly weakening the operational capabilities at the physical level.

[0130] Physical attacks directly affect the normal operation of the system by damaging critical infrastructure (such as power transmission lines and traffic roads). This embodiment uses power transmission lines and traffic roads as the targets of physical attacks. It is assumed that in an attack, the number of damaged power transmission lines and traffic roads does not exceed one. Therefore, the following constraints apply:

[0131]

[0132] Where, l represents the index of the transmission line in the power network; P l represents the power on the lth transmission line in the power network; α l represents the physical attack vector on the power network; β a Represents a physical attack vector on transportation networks.

[0133] Cyber-physical coordinated attacks: Combining cyber attacks with physical attacks, increasing load pressure through cyber attacks while amplifying the impact of attacks by physically destroying transmission lines or transportation facilities, making the coupled system operate at high costs.

[0134] S3. Based on the power-transport coupling model and three attack strategies, a two-layer optimization model for cyber-physical attacks on the power-transport coupling system is constructed (e.g. Figure 2 As shown), as follows:

[0135]

[0136] Among them, F A represents the attacker's objective function, which aims to maximize the operating cost of the electric-transportation coupling system; F ED represents the objective function of the control center, which aims to minimize the operating cost of the electric-transportation coupling system.

[0137] The present invention also provides an attack device for an urban electric traffic coupling system, comprising:

[0138] The power-transport coupling model building module is used to couple the power system model and the transportation system model through charging facilities to obtain the power-transport coupling model;

[0139] The attack strategy design module is used to set three attack strategies for the power-traffic coupling model: network attack strategy, physical attack strategy, and network-physical coordinated attack strategy;

[0140] A two-layer optimization model construction module is used to construct a two-layer optimization model for cyber-physical attacks against the power-transport coupling system based on the power-transport coupling model and three attack strategies.

[0141] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0142] Therefore, the present invention adopts the above-mentioned attack method, medium and device for the urban power-transportation coupling system. Through systematic analysis of different attack strategies, it explores the increase in system operating costs caused by attack strategies and their chain reactions on the social economy, and ultimately ensures the safe, stable and economic operation of the coupling system in a complex network environment.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An attack method for urban power-transportation coupling system, used for power-transportation system reliability assessment, characterized by: The following steps are involved: S1. Couple the power system model and the traffic system model through charging facilities to obtain a power-traffic coupling model; S2. Set three attack strategies for the electric-traffic coupling model: network attack strategy, physical attack strategy, and network-physical coordinated attack strategy; S3. Based on the power-transport coupling model and three attack strategies, a two-layer optimization model for cyber-physical attacks against the power-transport coupling system is constructed; In step S1, the objective function of the electric-transport coupling model is as follows: C OP =C E +C T +C shed (13); Among them, C OP represents the total operating cost of the electric transportation coupling system; C E represents the operating cost of the power system; C T represents the operating cost of the transportation system; C shed represents the load shedding cost, which is calculated as follows: Where d represents the index of the load shedding node in the power network; cs d represents the cost coefficient of load shedding; S d represents the load reduction of the dth node; N represents the set of nodes in the power network; In step S3, the two-layer optimization model for the cyber-physical attack on the power-transportation coupled system is as follows: F A =maxC OP F ED =minC OP (23); Among them, F A represents the attacker's objective function; F ED represents the objective function of the control center.

2. The attack method for urban electric traffic coupling system according to claim 1 is characterized in that: In step S1, the objective function of the power system model is as follows: Among them, C E represents the power system operating cost; N represents the set of nodes in the power network; i represents the index of the node in the power network; b i represents fixed cost; a i represents the cost coefficient; Indicates the output power of the generator.

3. The attack method for urban electric traffic coupling system according to claim 2 is characterized in that: In step S1, the constraints of the power system model include: Active power balance constraint of node i: in, represents the active load of node i; represents the false load data injected into the power network node i; P ij represents the active power on the transmission line between node i and node j; represents the charging load fed back to the power grid node i by the traffic flow through the charging facilities; S i Indicates the load reduction corresponding to the i-th node; Ω i represents the set of all nodes directly connected to node i in the power network; P ij The calculation formula is as follows: Among them, θ i and θ j Respectively represent the voltage phase angles corresponding to node i and node j; X ij represents the reactance of the line between node i and node j; L represents the set of power transmission lines in the power network; P i CF The calculation formula is as follows: Among them, p EV represents the average charging power of each vehicle; rs represents the starting and ending points of the corresponding path; represents the traffic flow on the kth path from the starting point r to the end point s; K rs represents the set of all paths from the starting point r to the end point s in the transportation network; Generator output power constraints: in, Indicates the minimum output power of the generator; Indicates the maximum output power of the generator; Power constraints on transmission lines: Among them, P ijmin represents the minimum active power allowed on the transmission line between node i and node j; P ijmax represents the maximum active power allowed on the transmission line between node i and node j; Load reduction constraint for node i:

4. The attack method for urban electric traffic coupling system according to claim 3 is characterized in that: In step S1, the objective function of the traffic system model is as follows: Among them, C T represents the operating cost of the transportation system; ω represents the monetary value of travel time, which converts the time cost in transportation into economic cost; t a represents the travel time of traffic flow on road section a; x a represents the traffic flow on road section a; T A represents the set of all road segments in the traffic network; θ is the integration variable.

5. The attack method for urban electric traffic coupling system according to claim 4 is characterized in that: In step S1, the constraints of the traffic system model include: Path-segment flow distribution constraints: in, Represents the (a,k)th element of the path-section association matrix, which is a binary decision variable. When the kth path passes through the ath section, is 1, otherwise 0; Public Road Bureau function constraints: Among them, t a (x a ) represents the traffic flow x a The travel time t of the traffic flow on this road section is determined a ; represents the travel time of traffic flow without congestion on road section a; c a represents the capacity of road section a; Origin-destination traffic demand balance constraints: Among them, d rs Indicates the actual traffic demand between the starting and ending points rs; Δd rs represents the false traffic demand data injected into the starting and ending points rs in the traffic network; Traffic flow non-negativity constraint:

6. The attack method for urban electric traffic coupling system according to claim 5 is characterized in that: In step S2, the network attack strategy is as follows: The network attack strategy injects false load data ΔP into the power network nodes. i L and injecting false traffic flow demand Δd between the starting and ending points in the traffic network rs accomplish; The constraints of the network attack strategy are as follows: Among them, δ P Indicates the maximum value of the ratio of the allowed load false data injection to the load prediction data; δ T Indicates the maximum value of the ratio of false demand data allowed to be injected into demand forecast data; The constraints of the physical attack strategy are as follows: Where, l represents the index of the transmission line in the power network; P l represents the power on the lth transmission line in the power network; α l represents the physical attack vector on the power network; β a Represents a physical attack vector on transportation networks.

7. The attack method for urban electric traffic coupling system according to claim 6 is characterized in that: In step S3, the two-layer optimization model for the cyber-physical attack on the power-transportation coupled system is as follows: Among them, F A represents the attacker's objective function; F ED represents the objective function of the control center.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the attack method for the urban electric transportation coupling system according to any one of claims 1 to 7 is implemented.

9. An attack device targeting an urban electric traffic coupling system, used for electric traffic system reliability assessment, characterized in that: include: The computer-readable storage medium of claim 8.

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