Multi-element power distribution network planning method for coping with network attack

Abstract

The invention discloses a multi-element power distribution network planning method for coping with a network attack, and relates to the technical field of power distribution network safety planning, and the method comprises the steps: S1, analyzing the vulnerability index of a power distribution network under the network attack; s2, generating a corresponding power network attack scene according to the vulnerability index in combination with historical network attack data; s3, establishing a power distribution network defense-attack-defense planning model based on the power network attack scene; s4, performing layout optimization on the power distribution network defense-attack-defense planning model based on the optimal energy storage configuration; s5, planning the power distribution network by adopting the optimized power distribution network defense-attack-defense planning model; the problem that the power distribution network is difficult to effectively prevent network attacks due to lack of pre-layout of network attack defense measures in a power distribution network planning technology is solved, the power distribution network is ensured to have effective and reliable network attack prevention capability, the defense efficiency of the power distribution network to the network attacks is remarkably improved, and safe and stable operation of the power distribution network is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network security planning, and in particular to a multi-element distribution network planning method for coping with network attacks. Background Art

[0002] With the rapid development of computer and communication technology, the traditional power system is increasingly integrated with the information and communication system, forming a typical power cyber-physical system (CPS), which makes the power system almost exposed to the communication network. With the continuous escalation of network attack methods, the security issues of power networks are becoming increasingly prominent. There are two directions in the research on power network security in the prior art: on the one hand, the research on CPS: establishing a CPS dependency model to characterize the coupling influence mechanism between the two, and then analyzing the vulnerability factors of the distribution network under network attacks; on the other hand, establishing a network attack resistance mechanism in the distribution network planning model: network modeling is carried out through the communication process between the power system and the information system, and the relationship between the system loss degree and the proportion of faulty nodes in the network is verified, so as to provide network security protection for the communication process between the power system and the information system; however, the above modeling process is mostly based on the perspective of communication topology, and there is a certain difference between the final model and the actual power system characteristics, which cannot accurately understand the operating conditions of the actual power system, and the analysis of the relationship between the system loss degree and the proportion of faulty nodes involves complex data analysis and calculation, which reduces the efficiency of the model; in addition, the above model lacks the consideration of the role of the defense measures of energy storage pre-layout in the distribution network configuration planning in affecting the power network attack, so that the distribution network planning does not have the ability to prevent network attacks, which leads to the fact that the power network after the planning is completed does not have the ability to effectively and reliably prevent network attacks.

[0003] Chinese Patent, Publication Number: CN110048884B, Publication Date: November 5, 2021, discloses a communication network planning method for a smart distribution network that can resist random attacks and intentional network attacks; based on the dynamic coupling relationship between the communication network and the power network, a communication network planning model in the smart distribution network under network attacks is established, and this model is solved by a scale-free network construction method; in the model, each edge of the communication network is determined based on the characteristic path length of the communication network and the maximum number of connected subgraphs of the physical network after the communication network is attacked and a chain failure occurs in the communication-power network, and in the physical network, the invisible faults of the power network are judged by power flow calculation, so as to prevent malicious attacks on the power communication network from Internet hackers from causing large-scale chain failures in the power communication network. However, this invention mainly calculates and determines the faulty nodes in the power network, removes them and then re-plans the mapping relationship between the communication and physical networks. Although this invention can minimize the scope of chain failures and the scope of power outages in the physical network after the communication network is attacked intentionally or randomly, this invention does not dynamically configure the defense information of network attacks during the initial power network planning, so as to achieve the ability to prevent network attacks during the power network planning process. Summary of the invention

[0004] The purpose of the present invention is to address the problem that in the prior art, the distribution network is difficult to effectively prevent network attacks due to the lack of consideration of the pre-layout of network attack defense measures when planning the distribution network; a multi-element distribution network planning method for responding to network attacks is proposed, by analyzing the vulnerability indicators of the distribution network under network attacks, combining historical network attack data to generate corresponding power network attack scenarios, and establishing a distribution network defense-attack-defense planning model that can adaptively and dynamically adjust the charging and discharging of the energy storage system according to different power grid attack scenarios. The distribution network is planned by the model, so that the distribution network has the ability to prevent and defend against power network attacks, and can flexibly and effectively respond to various power network attack scenarios, overcoming the problem that the existing distribution network planning technology lacks the pre-layout of network attack defense measures, resulting in the distribution network being difficult to effectively prevent network attacks, and significantly improving the effective and reliable network attack defense capability of the distribution network.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a multi-distribution network planning method for coping with network attacks, comprising the following steps: S1. Analyze the vulnerability indicators of distribution network under cyber attacks; S2. Generate a corresponding power network attack scenario based on the vulnerability index and historical network attack data; S3. Establishing a distribution network defense-attack-defense planning model based on the power network attack scenario; S4. Optimizing the layout of the distribution network defense-attack-defense planning model based on the optimal energy storage configuration; S5. Plan the distribution network using the optimized distribution network defense-attack-defense planning model.

[0006] In this scheme, by analyzing the vulnerability indicators of the distribution network under network attacks, the impact of network attacks on the distribution network can be evaluated based on the quantitative results, and the weaknesses of the distribution network in the face of network attacks can be clearly exposed, which is helpful to consider and construct more comprehensive possible power network attack scenarios based on these weaknesses combined with historical network attack data. Based on these power network attack scenarios, a clear improvement direction is provided for the reasonable planning of the distribution network, including the specific forms and paths of network attacks, etc., to ensure that the planning scheme can improve the network security protection capability of the distribution network in a targeted manner, and improve the prevention and effectiveness of the distribution network in dealing with network attacks; the energy storage situation is considered in the distribution network defense-attack-defense planning model through the optimal energy storage configuration, to ensure that the energy storage system can provide emergency power support when the power grid is attacked, to reduce the impact of network attacks on the power grid, and to maintain the stable operation of the power grid; finally, the distribution network is planned through the distribution network defense-attack-defense planning model that integrates the power network attack scenarios, energy storage configuration and the defense strategy of the distribution network, to provide scientific decision-making support for the distribution network planning, to ensure that the distribution network can effectively prevent network attacks, and at the same time achieve efficient and stable operation under network attacks.

[0007] Preferably, S1 comprises the following sub-steps: Establish a cyber attack process for the distribution network based on cyber attack scenarios; Based on the network attack process, the vulnerability of the distribution network under network attacks is analyzed, and the corresponding vulnerability indicators of the distribution network under network attacks are obtained.

[0008] Preferably, the vulnerability index includes at least a node importance index, a node failure rate index, a redundancy index, an attack response time index and a distribution network recovery capability index.

[0009] In this scheme, by calculating the node importance index and the node failure rate index, it can reflect the degree of damage to the power grid when facing a network attack, as well as the degree of damage to the power communication business and the proximity of the power grid collapse, determine which areas or businesses are more vulnerable to the attack, and provide a clear improvement direction for subsequent planning to ensure that key businesses can be better protected during attacks; by calculating the communication branch vulnerability index to identify the key communication branches in the power grid, the protection of these key branches can be strengthened during the planning process to improve their anti-attack capabilities; by calculating the redundancy index, the attack response time index and the distribution network recovery capacity index, it is possible to simulate the degree and scope of the impact of different attack scenarios on the power grid according to the changes in the overall operating status of the power grid after each node in the distribution network is attacked, and more accurately construct all possible power network attack scenarios, which helps to more realistically reflect the operating status and response of the power grid under network attacks, thereby improving the effectiveness and pertinence of the planning scheme.

[0010] Preferably, S2 comprises the following sub-steps: S21. Establish a power communication coupling network model based on the distribution network topology and the communication network topology; S22. Performing a vulnerability analysis on the power communication coupling network model based on the vulnerability index to obtain a risk scenario of the distribution network under power network attacks; S23. Using a cumulative score function algorithm to calculate the probability distribution of the risk scenario based on historical network attack data, so as to simplify the risk scenario and obtain a typical power network attack scenario of the distribution network.

[0011] Preferably, S3 includes the following sub-steps: S31. Formulate corresponding distribution network defense measures based on the power network attack scenario; S32, determining the extreme attack scenario that causes the greatest loss to the distribution network in the power network attack scenario based on the attack path and attack target corresponding to the power network attack scenario; S33, formulating corresponding recovery measures according to the extreme attack scenario; S34. Constructing the distribution network defense-attack-defense planning model based on the integration of the distribution network defense measures, the extreme attack scenarios and the recovery measures.

[0012] Preferably, the S31 includes the following sub-steps: S311, analyzing all the power network attack scenarios to obtain attack characteristics of the power network attack scenarios, where the attack characteristics at least include an attack target and an attack method; S312, classifying the power network attack scenarios based on the attack characteristics, and formulating corresponding distribution network defense measures according to the classification results; Among them, the distribution network defense measures at least include: strengthening the power equipment reinforcement targets, adding backup power equipment and intelligent equipment.

[0013] Preferably, the recovery measures at least include: load shedding, equipment commissioning and equipment isolation.

[0014] Preferably, the S34 includes the following sub-steps: S341, establishing an objective function based on the distribution network defense measures and the extreme attack scenarios to maximize the extreme attack scenarios and minimize the distribution network attack losses; S342, establishing constraints of the objective function according to the extreme attack scenario and the energy storage configuration, wherein the constraints include grid operation constraints, energy storage system constraints, and attack scenario constraints; S343. Solve the objective function based on the constraint conditions to obtain the optimal attack scenario set under the defense strategy and the recovery strategy set of the distribution network after the attack, so as to construct the distribution network defense-attack-defense planning model.

[0015] Preferably, in S343, solving the objective function based on the constraint condition to obtain the optimal attack scenario set under the defense strategy and the recovery strategy set of the distribution network after the attack includes the following sub-steps: Dividing the objective function into a first problem and a second problem based on the Bender algorithm; The first problem represents the selection of the optimal defense decision according to the fault status of the distribution line, and the second problem represents the solution of the optimal restoration strategy that satisfies various constraints according to the optimal defense strategy in the first problem; The first problem and the second problem are iteratively solved until convergence, to obtain an optimal attack scenario set under the defense strategy corresponding to the first problem and a recovery strategy set of the distribution network after the attack corresponding to the second problem.

[0016] Preferably, S4 includes the following sub-steps: S41. Calculate the normal distribution of the load at each operating time of the distribution network based on the operating power parameters of the distribution network, and use the expectation of the normal distribution of the load as the target load value of the distribution network under the power network attack scenario to build a charging and discharging model of the energy storage system; S42. Optimize the charging and discharging model with the goal of minimizing the operating cost of the energy storage system to obtain the optimal energy storage configuration for the distribution network to cope with the extreme attack scenario; S43. Optimizing the defense measures and recovery measures of the distribution network defense-attack-defense planning model based on the optimal energy storage configuration.

[0017] In this scheme, by using the expectation of the normal distribution of load as the load value, it can more accurately reflect the operating status of the distribution network in most cases, thereby providing a more reliable basis for formulating defense plans; the optimal energy storage configuration for the distribution network to cope with extreme attack scenarios is carried out with the minimization of the operating cost of the energy storage system as the optimization goal, taking into account the uncertainty of the load, ensuring that the defense plan is more comprehensive and flexible, and can adapt to load changes in different scenarios.

[0018] Beneficial effects of the present invention: 1. By analyzing the vulnerability indicators of the distribution network under cyber attacks, the impact of cyber attacks on the distribution network can be clearly and specifically quantified, which helps to establish all possible power network attack scenarios and provide targeted improvement directions for subsequent distribution network planning; 2. Optimizing the layout of the distribution network defense-attack-defense planning model through optimal energy storage configuration can improve the adaptability of the energy storage system and the distribution network's defense capabilities, flexibly adjust the charging and discharging mode of the energy storage system, ensure that the energy storage system can provide emergency power support when the power grid is attacked, maintain the stable operation of the power grid, enhance the elasticity and resilience of the distribution network, reduce the impact of cyber attacks on the operation of the power grid, and ensure the continuity and stability of power supply; 3. The distribution network defense-attack-defense planning model simulates and analyzes the distribution network response under different attack scenarios, integrates and analyzes the data of previous network attacks, and introduces network attacks into the planning problem of multiple distribution networks, thereby reducing the interference and impact of network attacks on the distribution network, making the planning scheme closer to actual threats and needs, and significantly improving the scientific nature and pertinence of distribution network planning, comprehensively improving the network security protection level of the distribution network, reducing the impact of network attacks on the operation of the power grid, and ensuring the stable operation of the power grid in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0020] Figure 1 This is a flow chart of a multi-distribution network planning method for coping with network attacks according to this embodiment.

[0021] Figure 2 This is a schematic diagram of a 33-node system of this embodiment.

[0022] Figure 3 This is a schematic diagram of a distribution network fault wiring situation using a mobile energy storage device pre-configured in this embodiment.

[0023] Figure 4 This is a schematic diagram of a distribution network fault wiring situation in this embodiment without using mobile energy storage equipment pre-configuration.

[0024] Figure 5 This is a fault wiring situation after a pre-layout deterministic optimization method of this embodiment.

[0025] Figure 6 This is a fault wiring situation after the deterministic optimization method without pre-layout in this embodiment.

[0026] Figure 7 This is a schematic diagram comparing the economic performance of a distribution network pre-configured network attack and an unconfigured network attack in this embodiment. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Example 1: Figure 1 As shown, a multi-distribution network planning method for coping with network attacks includes steps S1-S4, wherein: S1. Analyze the vulnerability indicators of distribution network under cyber attacks.

[0029] Specifically, S1 includes the following sub-steps: Establish a cyber attack process for the distribution network based on cyber attack scenarios; Based on the network attack process, the vulnerability of the distribution network under network attacks is analyzed, and the corresponding vulnerability indicators of the distribution network under network attacks are obtained.

[0030] Specifically, the vulnerability index includes at least a node importance index, a node failure rate index, a redundancy index, an attack response time index, and a distribution network recovery capability index.

[0031] As an implementation method, the calculation formula of the node importance index CI is as follows: Where, L i is the importance measure of node i, which can usually be expressed as the number of edges connected to the node, the load capacity of the node, or the traffic borne by the node in the network; L total It is the overall importance measure of the entire network; the node importance index CI characterizes the influence of key nodes on network functions.

[0032] The calculation formula of the node failure rate index P is as follows: P = α / (α+β) (2) Where α is the failure rate and β is the repair rate.

[0033] The calculation formula of the redundancy index R is as follows: Where P backup is the number of backup paths and devices; P total It is the total number of all paths and devices in the network.

[0034] The calculation formula of attack response time indicator RT is as follows: RT=T detect +T respond (4) Where, T detect The time required to detect an attack, T respond The time required to respond to an attack.

[0035] The calculation formula of the distribution network restoration capacity index RC is as follows: Where, T normal is the time for the distribution network to resume normal operation, T total is the total downtime of the distribution network.

[0036] In this embodiment, the vulnerability index fully considers the various threats and challenges that the power grid may face in the future, so as to formulate a planning scheme that better meets actual needs. According to the analysis of the node importance index, the resource allocation of the distribution network can be reasonably optimized. When planning the distribution network, more resources can be invested in the protection of key nodes and communication branches to improve the overall anti-attack capability of the power grid; by strengthening the protection of key nodes and communication branches, and optimizing energy storage configuration and other measures, the resilience of the power grid can be significantly improved. Even in the case of a network attack, the distribution network can quickly resume stable operation and reduce the time and scope of power outages.

[0037] S2. Generate corresponding power network attack scenarios based on vulnerability indicators and historical network attack data.

[0038] Specifically, S2 includes the following sub-steps: S21. Establish a power communication coupling network model based on the distribution network topology and the communication network topology; S22. Perform vulnerability analysis on the power communication coupling network model based on vulnerability indicators to obtain risk scenarios of the distribution network under power network attacks; S23. Use the cumulative score function algorithm to calculate the probability distribution of risk scenarios based on historical network attack data to simplify the risk scenarios and obtain typical power network attack scenarios for the distribution network.

[0039] In this embodiment, since the existing distribution network is formed by coupling the power grid and the communication network, the components are diverse and the coupling relationship is complex, complex network theory can be used for modeling and analysis; the busbars and lines in the power grid are modeled as nodes and edges; the intelligent devices and their connections in the communication network can also be represented as nodes and edges, so as to construct a two-layer network power communication coupling network model, which closely combines energy transmission and communication systems, provides theoretical support for in-depth analysis of the interaction mechanism between the two, and provides a research tool for improving the operating efficiency of the distribution network. The model clearly shows that there are two operating states for the two types of nodes, power nodes and communication nodes: normal and faulty. Their operating states can be represented by a joint state function.

[0040] As an implementation method, the joint state function can be implemented by the state variable x i and i Define the joint state of the entire two-layer network. For example, the overall state of a system operation can be defined as: ij =x i ·y i , where x i =1 and y i =1, z ij =1, indicating that the system is in normal operation; otherwise, z ij =0, indicating that the system is in a fault state.

[0041] Furthermore, in order to reduce the complexity of the attack scenario, the cumulative distribution function algorithm can be used to simplify the attack scenario, and its cumulative probability distribution F(a) is shown as follows: Where P is the probability distribution that the attack loss corresponding to the attack scenario X is less than the set threshold a.

[0042] S3. Establish a distribution network defense-attack-defense planning model based on power network attack scenarios.

[0043] Specifically, S3 includes the following sub-steps: S31. Formulate corresponding distribution network defense measures based on power network attack scenarios; S32, determining the extreme attack scenario that causes the greatest loss to the distribution network in the power network attack scenario based on the attack paths and attack targets corresponding to the power network attack scenario; S33. Develop corresponding recovery measures based on extreme attack scenarios; S34. Construct a distribution network defense-attack-defense planning model based on the integration of distribution network defense measures, extreme attack scenarios and recovery measures.

[0044] Specifically, S31 includes the following sub-steps: S311. Analyze all power network attack scenarios to obtain attack characteristics of the power network attack scenarios, where the attack characteristics at least include attack targets and attack methods; S312, classifying the power network attack scenarios based on the attack characteristics, and formulating corresponding distribution network defense measures according to the classification results; Among them, the distribution network defense measures include at least: strengthening the power equipment reinforcement targets, adding backup power equipment and intelligent equipment.

[0045] Specifically, the recovery measures include at least: load shedding, equipment commissioning, and equipment isolation.

[0046] Specifically, S34 includes the following sub-steps: S341. Establish an objective function based on distribution network defense measures and extreme attack scenarios to maximize extreme attack scenarios and minimize distribution network attack losses; S342. Establishing constraint conditions of the objective function according to the extreme attack scenario and the energy storage configuration, wherein the constraint conditions include grid operation constraints, energy storage system constraints, and attack scenario constraints; S343. Solve the objective function based on the constraint conditions to obtain the optimal attack scenario set under the defense strategy and the recovery strategy set of the distribution network after the attack, so as to construct a distribution network defense-attack-defense planning model.

[0047] Specifically, in S343, the objective function is solved based on the constraint conditions to obtain the optimal attack scenario set under the defense strategy and the recovery strategy set of the distribution network after the attack, including the following sub-steps: Based on Bender algorithm, the objective function is divided into the first problem and the second problem; The first problem represents the selection of the optimal defense decision according to the fault status of the distribution line, and the second problem represents the solution of the optimal restoration strategy that meets all constraints based on the optimal defense strategy in the first problem; The first and second problems are solved iteratively until convergence, and the optimal attack scenario set under the defense strategy corresponding to the first problem and the recovery strategy set of the distribution network after the attack corresponding to the second problem are obtained.

[0048] S4. Optimize the layout of the distribution network defense-attack-defense planning model based on the optimal energy storage configuration.

[0049] Specifically, S4 includes the following sub-steps: S41. Calculate the normal distribution of the load at each operating time of the distribution network based on the operating power parameters of the distribution network, and use the expectation of the normal distribution of the load as the target load value of the distribution network in the power network attack scenario to build a charging and discharging model of the energy storage system; S42. Taking minimizing the operating cost of the energy storage system as the optimization goal, the charging and discharging model is optimized to obtain the optimal energy storage configuration for the distribution network to cope with extreme attack scenarios; S43. Optimize the defense measures and recovery measures of the distribution network defense-attack-defense planning model based on the optimal energy storage configuration.

[0050] In this embodiment, by using the expectation of the normal distribution of load as the load value, it is possible to more accurately reflect the operating status of the distribution network in most cases, thereby providing a more reliable basis for formulating defense plans; the optimal energy storage configuration for the distribution network to cope with extreme attack scenarios is carried out with the minimization of the operating cost of the energy storage system as the optimization goal, taking into account the uncertainty of the load, ensuring that the defense plan is more comprehensive and flexible, and can adapt to load changes in different scenarios.

[0051] Specifically, we can build wind power and photovoltaic models based on Webuil distribution and Beta distribution respectively, and use a large amount of grid load data to fit the load data at the same time on different days into a normal distribution. Finally, the expectation of the normal distribution at each time is used as the load value at that time on a typical day, and the load value is used as the target load value to build a charging and discharging model of the energy storage system. Among them, Webuil distribution can accurately describe the intermittent, random and volatile nature of wind power. By detecting historical wind energy data, we can get a clear relationship between the output power of wind turbines and wind speed, as shown in the following formula: Where P r Indicates the rated power of the wind turbine, v r Indicates rated wind speed; Beta distribution can be used to model solar irradiance to obtain the relationship between the output power of solar photovoltaic units and solar irradiance, as shown below: Where A represents the area of ​​the solar panel and η represents the efficiency of converting solar energy into electrical energy. In this embodiment, the energy storage system can be used as a backup power source when a grid failure or attack occurs, providing power support for key loads, reducing the duration and scope of power outages. Through the reasonable configuration and operation of the energy storage system, the resilience of the distribution network when it is attacked by a network is improved, such as the ability to resist attacks, restore power supply, and maintain normal operation. At the same time, the energy storage system can also smooth load fluctuations, improve the voltage stability and frequency stability of the grid, and further improve the quality of power supply.

[0052] It can be understood that when the nodes in the distribution network include power nodes, load nodes and energy storage nodes, the attack scenario can be expressed as the destruction or control of some nodes or lines; in order to minimize the loss of the distribution network when it is attacked, assuming that the defender's strategy set is X, the objective function of the distribution network defense-attack-defense planning model can be expressed as: Among them, L(x,y,z) represents the loss function of the system at different stages, which depends on the defense measure x, attack measure y and recovery measure z. X is the feasible strategy set of the defender, Y(x) is the feasible attack set of the attacker under the defense strategy x, and Z(y) is the recovery strategy set of the defender after the attack.

[0053] After obtaining the objective function, constraints are added to it according to the extreme attack scenario and energy storage configuration. The constraints include at least: Grid operation constraints: Among them, G represents the power node set, L represents the load node set, S represents the energy storage node set, P i G represents the power generation of power node i, represents the power consumption of load node j, The charging and discharging power of energy storage node k; Energy storage system constraints: in, represents the minimum charging and discharging power of energy storage node k, represents the maximum charging and discharging power of energy storage node k, represents the minimum energy storage capacity of energy storage node k, represents the maximum energy storage capacity of energy storage node k; Attack scenario constraints: according to different power attack scenarios, node status x i And the objective function and various constraint parameters are adjusted.

[0054] Furthermore, according to the objective function, the sub-objective functions of the three layers of the defense-attack-defense model can be obtained, as follows: the attack scheme that maximizes the loss function value is used as the optimal attack strategy to cause the greatest destructive effect. The objective function is: After the attack occurs, the defender takes recovery measures to minimize the damage, and the objective function is: Let Y(x) be the attacker's feasible attack set under defense strategy x as the first stage (first problem), and let Z(y) be the defender's recovery strategy set after the attack as the second stage (second problem). Use Bender's algorithm to solve the two-stage problem. The steps are as follows: The first problem is transformed into the formulation of pre-disaster planning measures, that is, for a given distribution line fault situation u l , and get the optimal decision P ul :The first step is to simplify the initial problem: only consider the pre-disaster planning decision P ul , without considering the post-disaster scenario u, that is, it is simplified to the preliminary planning before the disaster. The initial solution can be a guessing solution, such as the simplest line reinforcement or energy storage configuration.

[0055] The second step is to solve the sub-problem (second problem) for the given P0, namely the operational decision Q after the disaster and the worst scenario u.

[0056] Specifically, the second problem can be expressed as an optimization problem, expressed as That is, given the decision P in the first problem, find the optimal operation decision Q * , ensuring that current, voltage, equipment capacity and reliability constraints are met, and operational decisions Q * The choice of security policy will determine the system's ability to recover after an attack.

[0057] If recovery is difficult, the system may not be able to resume normal operation for a long time. This scenario is the worst scenario. The second problem indirectly depicts the worst state of the distribution network after the attack by optimizing the loss of the recovery measure z under a given attack strategy y. If an operation decision Q that satisfies all constraints can be found * This means that the second problem is feasible, and we can calculate Z(Q * )=b T Q * The target value is obtained to evaluate the reliability or economic benefits of power supply and minimize the post-disaster recovery cost or loss, and the pre-disaster planning decision P will be adjusted according to the new information; this means introducing new attack defense measures in the first problem to improve the resilience of the system in specific disaster scenarios.

[0058] If no action decision Q can be found that satisfies all constraints *That is, the second problem is not feasible, and a Benders cut constraint is generated and added to the first problem; the cut constraint reflects the constraints under which the current pre-disaster decision P fails in a specific post-disaster scenario (that is, when the second problem is not feasible). These constraints may involve current, voltage, equipment operating capacity, etc. The generation of the cut constraint means that these conditions cannot be met under the current decision. For example, if the current of a certain line exceeds the allowable range in a certain disaster scenario, the cut constraint will limit the decision of the first problem through a mathematical expression, so that future decisions must take this into account.

[0059] After adding the Bender cut constraint to the first problem, the first problem is solved again. The updated first problem is: which contains all the Bender cuts generated from the second problem: Where G ≥ b T Q k , and contains all Bender cuts generated from the second problem:

[0060] The third step is to check the convergence conditions. If the second problem is currently feasible and the objective function of the first problem reaches the limit of minimization or maximization, and the number of added cut constraints has stabilized, the solution of the first problem no longer changes significantly under these constraints, then P is the global optimal solution and the algorithm ends. Otherwise, continue to iterate the above steps until convergence.

[0061] In this embodiment, according to the distribution network defense-attack-defense planning model considering the participation of energy storage, the model is solved through a two-stage robust optimization and row and column generation algorithm to seek the distribution network fault state scenario with the greatest loss, that is, to obtain the largest power network attack scenario, so as to formulate corresponding recovery measures in a targeted manner to ensure that the distribution network has strong defense capabilities. It can be understood that when the distribution network can prevent the most serious power network attacks and has the ability to recover, then the distribution network can flexibly and effectively defend against attacks with a smaller degree than the worst attacks.

[0062] S5. Plan the distribution network using the optimized distribution network defense-attack-defense planning model.

[0063] As a specific embodiment, Figure 2 As shown, a multi-distribution network planning method for coping with network attacks provided by this embodiment is verified based on a 33-node system. The system has 5 DEG nodes; 12 important load nodes; 5 PV nodes; and the remaining part is ordinary load nodes. The specific steps are as follows: A1. Analyze the vulnerability index of distribution network under network attack based on equations (1)-(5); A2. The busbars and lines in the power grid are abstracted into nodes and edges; the relationship between the intelligent terminal devices and the communication nodes in the communication network is abstracted into nodes and edges, and a power communication coupling network model is established. The two types of nodes in the model - power nodes and communication nodes - have two operating states, namely normal and faulty; their operating states can be represented by setting state variables; A3. Based on Webuil distribution and Beta distribution, wind power and photovoltaic models are constructed respectively. A large amount of grid load data is used to fit the load data at the same time on different days into a normal distribution. Finally, the expectation of the normal distribution at each time is used as the load value at that time on a typical day to construct a charging and discharging model of the energy storage system. A5. Considering mobile energy storage and various power network attack scenarios, a distribution network defense-attack-defense planning model is constructed. The model is suitable for the formulation of defense plans for important infrastructure including power systems, and is solved using the Bender algorithm to obtain the distribution network fault state scenario with the greatest loss. A6. Plan the layout of the distribution network using the distribution network defense-attack-defense planning model. Plan the structures and parameters of the distribution network according to the distribution network fault state scenario with the greatest loss, strengthen the maintenance of each node, and enable the distribution network to have the ability to effectively and reliably prevent and defend against power network attacks; the fault wiring of the distribution network based on network attack defense pre-layout (i.e., using mobile energy storage equipment to pre-configure the distribution network) is as follows: Figure 3 As shown in the figure, the fault wiring situation after no pre-layout (without considering the pre-configuration of mobile energy storage) is as follows Figure 4 As shown in the figure, the fault wiring after the pre-layout deterministic optimization method is as follows Figure 5 As shown in the figure, the fault wiring situation after the deterministic optimization method without pre-layout is as follows Figure 6 As shown in the figure, the economic performance comparison of the system before and after the method of this embodiment is shown in Figure 7 As shown in the figure, it can be seen that the pre-layout distribution network defense attack planning method can effectively reduce the losses after network attacks and improve the robustness and economy of the overall power system.

[0064] In this embodiment, the distribution network is planned through the defense-attack-defense planning model to achieve targeted adjustments to the energy storage system, grid structure, and coupling relationship with the communication system of the distribution network. For example, key nodes and lines are strengthened accordingly, backup equipment is set, and the charging and discharging plan of the energy storage system is adaptively adjusted. For example, based on the analysis of vulnerability indicators, power attack scenarios are matched according to parameters such as voltage, so as to further match the recovery measures of the corresponding power attack scenarios, and dynamic adjustments are made according to the recovery measures. For example, if the loss of the distribution network is increasing, the discharge parameters of the energy storage system are increased to ensure stable power supply and reduce the duration of power outages, so as to make the distribution network planning scheme closer to actual threats and needs, improve the scientific nature and pertinence of the planning, and ensure that the distribution network can effectively and reliably respond to the impact of network attacks on the distribution network.

[0065] The beneficial effects of the implementation example are as follows: through comprehensive network attack analysis and targeted planning measures, the impact of network attacks on the distribution network is significantly reduced, ensuring that the power grid can quickly resume stable operation when attacked; reasonable planning of energy storage configuration and defense strategy enables the distribution network to have the ability to prevent power network attacks, and enables the distribution network to maintain a certain degree of self-recovery ability when facing emergencies, reducing the time and scope of power outages, and planning based on power network attack scenarios and defense-attack-defense planning models, so that the planning scheme is closer to actual threats and needs, and the defense and recovery capabilities of the distribution network are improved.

[0066] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A multi-distribution network planning method for coping with network attacks, characterized by: The steps include: S1. Analyze the vulnerability indicators of distribution network under cyber attacks; S2. Generate a corresponding power network attack scenario based on the vulnerability index and historical network attack data; S3. Establishing a distribution network defense-attack-defense planning model based on the power network attack scenario; S4. Optimizing the layout of the distribution network defense-attack-defense planning model based on the optimal energy storage configuration; S5. Plan the distribution network using the optimized distribution network defense-attack-defense planning model.

2. A multi-distribution network planning method for coping with network attacks according to claim 1, characterized in that: The S1 comprises the following sub-steps: Establish a cyber attack process for the distribution network based on cyber attack scenarios; Based on the network attack process, the vulnerability of the distribution network under network attacks is analyzed, and the corresponding vulnerability indicators of the distribution network under network attacks are obtained.

3. A multi-distribution network planning method for coping with network attacks according to claim 2, characterized in that: The vulnerability index includes at least a node importance index, a node failure rate index, a redundancy index, an attack response time index and a distribution network recovery capability index.

4. A multi-distribution network planning method for coping with network attacks according to claim 2, characterized in that: The S2 comprises the following sub-steps: S21. Establish a power communication coupling network model based on the distribution network topology and the communication network topology; S22. Performing a vulnerability analysis on the power communication coupling network model based on the vulnerability index to obtain a risk scenario of the distribution network under power network attacks; S23. Using a cumulative score function algorithm to calculate the probability distribution of the risk scenario based on historical network attack data, so as to simplify the risk scenario and obtain a typical power network attack scenario of the distribution network.

5. The method for planning a multi-element distribution network to cope with network attacks according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31. Formulate corresponding distribution network defense measures based on the power network attack scenario; S32, determining the extreme attack scenario that causes the greatest loss to the distribution network in the power network attack scenario based on the attack path and attack target corresponding to the power network attack scenario; S33, formulating corresponding recovery measures according to the extreme attack scenario; S34. Constructing the distribution network defense-attack-defense planning model based on the integration of the distribution network defense measures, the extreme attack scenarios and the recovery measures.

6. A multi-distribution network planning method for coping with network attacks according to claim 5, characterized in that: The S31 includes the following sub-steps: S311, analyzing all the power network attack scenarios to obtain attack characteristics of the power network attack scenarios, where the attack characteristics at least include an attack target and an attack method; S312, classifying the power network attack scenarios based on the attack characteristics, and formulating corresponding distribution network defense measures according to the classification results; Among them, the distribution network defense measures at least include: strengthening the power equipment reinforcement targets, adding backup power equipment and intelligent equipment.

7. A multi-distribution network planning method for coping with network attacks according to claim 5, characterized in that: The recovery measures at least include: load shedding, equipment commissioning and equipment isolation.

8. The method for planning a multi-element distribution network to cope with network attacks according to claim 5, characterized in that: The S34 comprises the following sub-steps: S341, establishing an objective function based on the distribution network defense measures and the extreme attack scenarios to maximize the extreme attack scenarios and minimize the distribution network attack losses; S342, establishing constraints of the objective function according to the extreme attack scenario and the energy storage configuration, wherein the constraints include grid operation constraints, energy storage system constraints, and attack scenario constraints; S343. Solve the objective function based on the constraint conditions to obtain the optimal attack scenario set under the defense strategy and the recovery strategy set of the distribution network after the attack, so as to construct the distribution network defense-attack-defense planning model.

9. A multi-distribution network planning method for coping with network attacks according to claim 8, characterized in that: In S343, solving the objective function based on the constraint condition to obtain the optimal attack scenario set under the defense strategy and the recovery strategy set of the distribution network after the attack includes the following sub-steps: Dividing the objective function into a first problem and a second problem based on the Bender algorithm; The first problem represents the selection of the optimal defense decision according to the fault status of the distribution line, and the second problem represents the solution of the optimal restoration strategy that satisfies various constraints according to the optimal defense strategy in the first problem; The first problem and the second problem are iteratively solved until convergence, to obtain an optimal attack scenario set under the defense strategy corresponding to the first problem and a recovery strategy set of the distribution network after the attack corresponding to the second problem.

10. A multi-distribution network planning method for coping with network attacks according to claim 5, characterized in that: The S4 comprises the following sub-steps: S41, calculating the normal distribution of the load at each operating time of the distribution network based on the operating power parameters of the distribution network, and using the expectation of the normal distribution of the load as the target load value of the distribution network under the power network attack scenario to build a charging and discharging model of the energy storage system; S42, optimizing the charging and discharging model with minimizing the operating cost of the energy storage system as the optimization goal, and obtaining the optimal energy storage configuration for the distribution network to cope with the extreme attack scenario; S43. Optimizing the defense measures and recovery measures of the distribution network defense-attack-defense planning model based on the optimal energy storage configuration.

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