Attack method, medium and device for urban electric power traffic coupling system

Abstract

The invention provides an attack method, medium and device for an urban electric power traffic coupling system, and belongs to the technical field of reliability evaluation of an electric power traffic system.The attack method comprises the following steps that S1, an electric power system model and a traffic system model are coupled through a charging facility, and an electric power traffic coupling model is obtained; s2, setting three attack strategies, namely a network attack strategy, a physical attack strategy and a network physical cooperative attack strategy, for the power traffic coupling model; and S3, on the basis of the electric power traffic coupling model and the three attack strategies, constructing a double-layer optimization model aiming at the network-physical attack of the electric power traffic coupling system. According to the attack method, the medium and the device for the urban electric power traffic coupling system, through systematic analysis of different attack strategies, the increase of the attack strategies to the system operation cost and the chain reaction of the attack strategies to the social economy are explored; and finally, safe, stable and economical operation of the coupling system in a complex network environment is ensured.

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 current urban infrastructure, especially the close coupling of power grids and transportation networks, makes them potential targets for attacks. This coupling increases the vulnerability of the system to attacks, because attackers can propagate interference from one system to another, and physical damage and cyber attacks may be carried out in synergy, further exacerbating the degree of damage to the system. In addition, most existing studies fail to fully consider the interaction between the two systems, resulting in poor results in the formulation of strategies in actual operations.

[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 only on the security of a single system (power or transportation), ignoring the coupling between the power system and the transportation system. This limitation of research makes it impossible to fully understand cross-system attack strategies, especially the complex interactions that may occur in coupled systems.

[0005] Insufficient attack concealment: Traditional network attack methods are usually easy to be identified and prevented 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 the present invention is to provide an attack method, medium and device for an urban electric traffic coupling system, and through a systematic analysis of different attack strategies, explore the increase in system operating costs caused by attack strategies and their chain reactions on the social economy, and ultimately ensure the safe, stable and economical operation of the coupling system in a complex network environment.

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

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

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

[0011] S3. Based on the power-transportation coupling model and three attack strategies, a two-layer optimization model for cyber-physical attacks against the power-transportation 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 operating cost of the power system; 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 integral 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 it is 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 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 It represents the false traffic demand data injected into the starting and ending points rs in the traffic network;

[0046] Traffic flow non-negative constraints:

[0047]

[0048] Preferably, in step S1, the objective function of the electric-traffic 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, and the calculation formula is 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 a false traffic demand Δd between the start and end 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 It indicates the maximum value of the ratio of false demand data to demand forecast data allowed;

[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 the transportation network.

[0061] Preferably, in step S3, the two-layer optimization model of the cyber-physical attack on the electric-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, on which computer program instructions are stored; 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 for the urban electric traffic coupling system, and the beneficial technical effects are as follows:

[0067] (1) Traffic demand redistribution attack technology: This invention proposes to redistribute local demand while maintaining the stability of the total traffic demand, which increases the concealment of the attack and makes it difficult for the existing monitoring system to detect it in time. This technology effectively improves the persistence and complexity of the attack.

[0068] (2) Targeted design for coupled network systems: This invention specifically targets the mutual coupling relationship between the power and transportation systems, and designs an attack strategy based on the interaction between the two systems. This can effectively utilize the dependency between the power and transportation systems to induce resource imbalance and chain reactions across systems. Most existing technologies only focus on a single system, and this invention innovates at the coupling system level. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A flowchart of an attack method for an urban electric power transportation 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 through the accompanying drawings and embodiments.

[0072] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0073] Embodiment 1

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

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

[0076] Among them, the power system model is constructed: the power system is described by the DC power flow model, taking into account the power generation of each node, load distribution and the physical characteristics of power flow. 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 operating cost of the power system; 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 model construction: The mathematical model of the traffic system is constructed based on the user equilibrium theory, with the aim of describing the stable traffic flow distribution under a given starting point to end point demand. In this model, the operating cost of the traffic network is mainly 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 integral 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 it is 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 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 It represents the false traffic demand data injected into the starting and ending points rs in the traffic network;

[0113] Traffic flow non-negative constraints:

[0114]

[0115] Construction of power-transportation coupling model: First, by analyzing the coupling mechanism of the power system and the transportation system, a coupling model of the urban power-transportation system is constructed. The two independent systems are coupled through charging facilities to form an overall coupling network with interactive relationships. In this model, the operation of the transportation system directly affects the load state of the power system.

[0116] The cost of the power-transport coupling system mainly includes operating cost and load shedding cost. Load shedding cost refers to the loss caused to the system by the load that is forced to be shelved 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, and the calculation formula is 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, namely, network attack strategy, physical attack strategy and network-physical coordinated attack strategy, for the power-traffic coupling model;

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

[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 a false traffic 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 forecast demand 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 loads or traffic flows is affected, resulting in increased operating costs and reduced system efficiency, directly weakening the operating capacity at the physical level.

[0130] Physical attacks directly affect the normal operation of the system by destroying key infrastructure (such as power transmission lines and traffic roads). This embodiment uses power transmission lines and traffic roads as the targets of physical attacks. Assume that in one attack, the number of damaged power transmission lines does not exceed 1, and the number of damaged traffic roads does not exceed 1. Therefore, there are the following constraints:

[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 the transportation network.

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

[0134] S3. Based on the power-traffic coupling model and three attack strategies, a two-layer optimization model for cyber-physical attacks on the power-traffic 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] A power-traffic coupling model building module is used to couple the power system model and the traffic system model through charging facilities to obtain a power-traffic coupling model;

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

[0140] The two-layer optimization model building module is used to build a two-layer optimization model for cyber-physical attacks against the power-transportation coupling system based on the power-transportation 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, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0142] Therefore, the present invention adopts the above-mentioned attack method, medium and device for the urban power transportation coupling system, and through systematic analysis of different attack strategies, explores the increase in system operating costs and its chain reaction on social economy caused by attack strategies, and ultimately ensures the safe, stable and economical 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 solution of the present invention rather than to limit it. 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 solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for attacking an urban electric traffic coupling system, characterized in that: The following steps are involved: S1. The power system model and the traffic system model are coupled through charging facilities to obtain a power-traffic coupling model; S2. Set three attack strategies, namely, network attack strategy, physical attack strategy and network-physical coordinated attack strategy, for the power-traffic coupling model; S3. Based on the power-transportation coupling model and three attack strategies, a two-layer optimization model for cyber-physical attacks against the power-transportation coupling system is constructed.

2. According to claim 1, a method for attacking an urban electric traffic coupling system is characterized in that: In step S1, the objective function of the power system model is as follows: Among them, C E represents the operating cost of the power system; 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.

3. The attack method for the 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: 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; 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 the 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 integral variable.

5. The attack method for the 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 it is 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 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 It represents the false traffic demand data injected into the starting and ending points rs in the traffic network; Traffic flow non-negative constraints:

6. The attack method for the urban electric traffic coupling system according to claim 5 is characterized in that: 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 shed represents the load shedding cost, and the calculation formula is 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 Indicates the load reduction of the dth node.

7. The attack method for the urban electric traffic coupling system according to claim 6 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 a false traffic demand Δd between the start and end points in the traffic network rs accomplish; The constraints of the network attack strategy are as follows: in, δ P Indicates the maximum value of the ratio of the allowed load false data injection to the load prediction data; δ T It indicates the maximum value of the ratio of false demand data to demand forecast data allowed; 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 the transportation network.

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

9. 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 8 is implemented.

10. An attack device for an urban electric traffic coupling system, characterized in that: include: The computer readable storage medium of claim 9.

Images