Active power distribution network two-stage planning method and device considering network attack

Abstract

The invention provides an active power distribution network two-stage planning method and device considering network attacks, and the method comprises the steps: endowing a corresponding punishment cost coefficient according to the importance degree of a node, and building a target function; according to a preset expenditure budget, performing first-order planning of line reinforcement and remote control switch deployment on the target function, and constructing a first-order planning decision constraint; according to the line fault and the propagation of the line fault in the attack scene, based on the first-order planning decision constraint, performing second-order planning of a defense combat strategy on the target function to obtain a second-order planning decision constraint; solving the target function according to the first-order planning decision constraint and the second-order planning decision constraint to obtain an optimal defense combat strategy and an optimal planning strategy vector corresponding to the worst attack scene, so as to regulate and control the active power distribution network according to the optimal defense combat strategy and the optimal planning strategy vector; according to the invention, the power distribution planning flexibility of the active power distribution network under limited funds can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of active distribution network system planning, and in particular to a two-stage planning method and device for an active distribution network taking network attacks into account. Background Art

[0002] Severe power outages and corresponding economic losses caused by high-impact, low-frequency events such as extreme weather and cyber attacks have attracted widespread attention in the construction of resilient power systems. Compared with traditional transmission systems, active distribution networks are generally more vulnerable to high-impact, low-frequency events, which makes the planning and design of distribution network systems with the purpose of enhancing the resilience of distribution network systems more important. Line reinforcement and remote-controlled segmented switch deployment have been verified as two effective measures to enhance the resilience of distribution network systems in active distribution networks. Due to budget constraints, it is not possible to reinforce all lines in active distribution networks and install bilateral remote-controlled switches in each line. Unlike line reinforcement, which usually requires cost / benefit analysis, mathematical modeling of the optimal deployment of remote-controlled switches is considered to be a rather complex problem.

[0003] However, the propagation of potential line faults, such as tie line faults, was not fully considered, so the degradation of distribution network system performance could not be accurately assessed. The reinforcement plan for the distribution network system was based on the performance evaluation of the distribution network system, and the impact of funding issues on the planning of the distribution network system was not considered, resulting in the planning plan being unsuitable for actual conditions and having low practicality.

[0004] Therefore, how to propose a distribution network planning scheme under limited funds to improve the flexibility of the active distribution network is a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned existing related technologies and propose a two-stage planning method and device for an active distribution network taking into account network attacks, which can provide flexibility in the distribution planning of the active distribution network under limited funds.

[0006] In a first aspect, the present invention provides a two-stage planning method for an active distribution network taking into account network attacks, comprising:

[0007] According to the importance of the node, the corresponding penalty cost coefficient is assigned, and the objective function is established with the defense combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable;

[0008] According to the preset budget, the first-order planning of line reinforcement and remote switch deployment is performed for the objective function, and the first-order planning decision constraints are constructed;

[0009] According to the line fault in the attack scenario and the propagation of the line fault, based on the first-order planning decision constraint, the second-order planning of the defense operation strategy is performed on the objective function to obtain the second-order planning decision constraint;

[0010] According to the first-order planning decision constraints and the second-order planning decision constraints, the objective function is solved to obtain the optimal defense combat strategy and the optimal planning strategy vector corresponding to the worst attack scenario, so that the active distribution network can be regulated according to the optimal defense combat strategy and the optimal planning strategy vector.

[0011] The present invention adopts a penalty cost coefficient for nodes, measures the nodes where faults occur, and constructs an objective function through operating variables, uncertainty variables and planning variables. For the three variables, the present invention adopts line faults and the propagation of line faults, constructs a first-order planning decision constraint under the budget, and constructs a second-order planning decision constraint under the first-order planning decision constraint, so that the propagation after potential line faults can be fully considered based on the actual funding situation, the three variable values ​​can be accurately calculated, and then the distribution network planning is planned based on the three solution results, thereby improving the flexibility of the distribution network planning.

[0012] Furthermore, the corresponding penalty cost coefficient is assigned according to the importance of the node, and the defensive combat strategy is used as the operating variable, the attack scenario is used as the uncertainty variable, and the planning decision vector is used as the planning variable to establish the objective function, including:

[0013] A corresponding penalty cost coefficient is assigned according to the importance of the node. After a fault occurs, the product of the cumulative fault duration and the penalty cost coefficient of the node is obtained to obtain the total penalty cost. According to the order of the defensive combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable, an objective function of the corresponding min-max-min structure is established for the total penalty cost.

[0014] The present invention takes the total penalty cost of the penalty cost coefficient after each node fails as the target, and respectively obtains three variables with a min-max-min structure, which can ensure that the failure cost generated after the failure occurs is the lowest. It is more suitable for actual distribution network planning and has stronger practicality.

[0015] Further, according to the line fault in the attack scenario and the propagation of the line fault, based on the first-order planning decision constraint, the second-order planning of the defense operation strategy is performed on the objective function to obtain the second-order planning decision constraint, including:

[0016] Based on the first-order planning decision constraints, if a line fault occurs in an attack scenario, a fault propagation constraint is established according to the two end nodes directly affected by the line fault and the fault state indirectly propagated through the connected lines, and the minimum power outage area divided by the interconnection lines and switch deployment locations is obtained. According to the minimum power outage area, the second-order planning of the defense combat strategy is performed on the objective function to obtain the second-order planning decision constraints.

[0017] The present invention takes into account the deployment of line bilateral switches and the propagation impact of tie-line faults when designing distribution network planning problems. It can quickly obtain the minimum power outage area divided by tie-line and switch deployment locations under the complex operation constraints of the distribution network system, thereby accurately obtaining the distribution network planning scheme and improving the flexibility of distribution network planning.

[0018] In a second aspect, the present invention provides a two-stage planning device for an active distribution network taking into account network attacks, comprising: a target function building module, a first-order planning module, a second-order planning module and a solution module; wherein,

[0019] The objective function construction module is used to assign corresponding penalty cost coefficients according to the importance of nodes, and to establish an objective function with the defense combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable;

[0020] The first-order planning module is used to perform first-order planning of line reinforcement and remote switch deployment for the objective function according to a preset budget, and to construct first-order planning decision constraints;

[0021] The second-order planning module is used to perform second-order planning of the defense operation strategy on the objective function according to the line fault in the attack scenario and the propagation of the line fault, based on the first-order planning decision constraints, to obtain the second-order planning decision constraints;

[0022] The solving module is used to solve the objective function according to the first-order planning decision constraints and the second-order planning decision constraints, and obtain the optimal defense combat strategy and the optimal planning strategy vector corresponding to the worst attack scenario, so as to regulate the active distribution network according to the optimal defense combat strategy and the optimal planning strategy vector. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a two-stage planning method for an active distribution network taking into account network attacks provided in this embodiment;

[0024] Figure 2 is a schematic diagram of deploying a bilateral remote control switch in a line provided by this embodiment;

[0025] Figure 3is a schematic diagram of two fault propagation modes provided in this embodiment;

[0026] Figure 4 is a schematic diagram of the topological structure of the IEEE 33-node distribution network system provided in this embodiment;

[0027] Figure 5 is a schematic diagram of fault identification and reconstruction with different budgets provided in this embodiment;

[0028] Figure 6 is a schematic diagram of a fault detection result with a lower budget provided by this embodiment;

[0029] Figure 7 is a schematic diagram of a fault detection result with a higher budget provided by this embodiment;

[0030] Figure 8 It is a structural diagram of a two-stage planning device for an active distribution network taking network attacks into consideration provided in this embodiment. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] It is worth noting that the purpose of the present invention is to address the problem of incomplete consideration of factors in the planning modeling of improving the resilience of active distribution networks under high-impact low-frequency events such as cyber attacks, and propose a two-stage robust planning model that considers random interconnection line failures and related fault propagation to improve the resilience of active distribution networks. The first stage involves decisions on line reinforcement and remote switch deployment, and the second stage applies the worst-case scenario to determine defensive operations in active distribution networks. In order to better illustrate the technical solution of the present invention, it will be described in detail from the following embodiments.

[0033] Example 1

[0034] See also Figure 1 , is a flow chart of a two-stage planning method for an active distribution network taking into account network attacks provided in this embodiment, including: steps S11 to S14, specifically:

[0035] Step S11, assign corresponding penalty cost coefficients according to the importance of nodes, and establish an objective function with the defense combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable.

[0036] In some embodiments, a corresponding penalty cost coefficient is assigned according to the importance of the node, and an objective function is established with the defensive combat strategy as the operating variable, the attack scenario as the uncertainty variable, and the planning decision vector as the planning variable, including: assigning a corresponding penalty cost coefficient according to the importance of the node, after a fault occurs, accumulating the product of the fault duration and the penalty cost coefficient of the node to obtain the total penalty cost, and establishing an objective function of a corresponding min-max-min structure for the total penalty cost in the order of the defensive combat strategy as the operating variable, the attack scenario as the uncertainty variable, and the planning decision vector as the planning variable.

[0037] It is worth noting that the expected power shortage is usually used as a quantitative standard for evaluating the performance of the distribution network system. In this embodiment, different penalty cost coefficients are assigned according to the importance of each node in the distribution network system. When the distribution network system is attacked by a network, the line failure will cause some nodes to lose load. The load reduction of the node is multiplied by the fault duration and the penalty cost coefficient of the node to obtain the failure cost of the node. After the distribution network system fails, the failure costs of the nodes with load reduction are added to obtain the total penalty cost of the distribution network system to evaluate the consequences of its power outage.

[0038] In some embodiments, the objective function of the min-max-min structure is expressed as:

[0039]

[0040] Among them, Z, U and Y represent the sets of planning variables, uncertainty variables and operation variables respectively; is the penalty cost coefficient of node i; is the load shedding of node i at time t; Δt is the fault duration. The above variables will be iteratively calculated in the two-stage robust optimization model to obtain the final planning solution.

[0041] In some embodiments, the expected energy not served (EENS) can be used as a quantitative indicator to evaluate the performance of the distribution network system, and different weights are assigned to each node load to evaluate the consequences of power outages, thereby establishing an objective function.

[0042] Step S12: According to the preset budget, first-order planning of line reinforcement and remote switch deployment is performed on the objective function to construct first-order planning decision constraints.

[0043] In some embodiments, according to a preset budget, a first-order planning of line reinforcement and remote switch deployment is performed on the objective function, and a first-order planning decision constraint is constructed, including: setting K remote switches in the line, and reinforcing the line and deploying at least one switch on any line, and according to the investment cost of deploying remote switches and line reinforcement, establishing budget constraints, candidate planning position constraints for remote switches, line reinforcement constraints, and at least one switch constraint for deploying any line to obtain a first-order planning decision constraint; wherein K is a positive integer.

[0044] In some embodiments, a planning scheme for line reinforcement and remote switch deployment in an active distribution network is designed to obtain first-order planning decision constraints, including steps S12.1 to S12.4, specifically:

[0045] Step S12.1: The power supply company sets a limited budget for the distribution network system and controls it with a budget constraint. The budget constraint can be expressed as:

[0046]

[0047] In the formula, C Inv Represents the total investment cost; C DG represents the installation cost of distributed generator sets; C RCS Represents the investment cost of the remote switch; C H represents the investment cost of line reinforcement; Indicates the upper limit of investment budget.

[0048] In some embodiments, K is 2, which can ensure that the dual switches in the line perform better than a single switch in extreme events; or K can be 1 to meet a lower budget; or K can be 3, or K can be 4, or other more remote control switches to meet a higher budget, in order to achieve faster and stronger defense when extreme events occur, so as to achieve higher resilience of the active distribution network.

[0049] Step S12.2: In this embodiment, two remote control switches are set in the line, and the candidate planning position constraints can be expressed as:

[0050]

[0051] In the formula, c RCS Indicates the unit remote switch cost; A 0 / 1 variable indicating whether a switch is deployed. If the sender / receiver of line ij has a remote switch deployed, the value is 1; otherwise, the value is 0.

[0052] Step S12.3: Reinforce the conventional lines / interconnection lines in the distribution network system. The line reinforcement constraint can be expressed as:

[0053]

[0054] In the formula, Indicates the line reinforcement cost per unit distance; l ij represents the length of line ij; A 0 / 1 variable indicating whether the line is reinforced. If line ij is reinforced, it is 1, otherwise it is 0.

[0055] Step S12.4: At least one switch needs to be deployed on any tie line in the distribution network system to maintain the radial operation of the distribution network. The line reinforcement constraint and the constraint of deploying at least one switch on any line can be expressed as:

[0056]

[0057] In some embodiments, see Figure 2 , is a schematic diagram of deploying bilateral remote control switches in a line provided by this embodiment, according to Figure 2 In the upper part of the diagram, when line LS2 fails, when only one switch is deployed on the line, the load of node 1 can be restored normally, but node 2 cannot. The power supply can only be restored after the fault is repaired, which will increase the EENS of the distribution network system and reduce the power supply reliability of the distribution network system. When two section switches are installed on the line, the load of node 2 can be powered by the distributed power supply connected on the right. Figure 2 In the lower part of the figure, when the tie line and the normal line fail, if there is only one switch on the tie line, nodes 3 and 4 cannot be restored to power supply, while if the remote switches are deployed on both sides, nodes 3 and 4 can be powered normally. It can be seen that the bilateral remote switches are conducive to reducing the area where the fault propagates and can restore the load of more normal nodes. Therefore, the deployment of bilateral remote switches is better than unilateral deployment.

[0058] Step S13: According to the line faults and the propagation of the line faults in the attack scenario, based on the first-order planning decision constraints, the second-order planning of the defense operation strategy is performed on the objective function to obtain the second-order planning decision constraints.

[0059] It is worth noting that in the second stage, the active distribution network system will follow the corresponding operating constraints to mitigate the harmful effects of high-impact low-frequency event attack strategies, such as power flow adjustment and microgrid formation, and the feasible area of ​​​​the operation of the distribution network system is described by multiple sets of constraints to describe the second-order planning constraints.

[0060] In some embodiments, according to the line faults and the propagation of the line faults in the attack scenario, based on the first-order planning decision constraints, the second-order planning of the defensive combat strategy is performed on the objective function to obtain the second-order planning decision constraints, including: based on the first-order planning decision constraints, if a line fault occurs in the attack scenario, a fault propagation constraint is established according to the two end nodes directly affected by the line fault and the fault state indirectly propagated through the connected lines, and the minimum power outage area divided by the interconnection lines and switch deployment locations is obtained; according to the minimum power outage area, the second-order planning of the defensive combat strategy is performed on the objective function to obtain the second-order planning decision constraints.

[0061] In some embodiments, the planned islanding in the active distribution network is to adjust the network topology and restore the node load not affected by the fault by operating the remote control switch. Considering the uncertainty of the deployment location of the tie line and the switch, this example implements a fast detection mechanism for identifying the minimum power outage area through fault propagation constraints.

[0062] In some embodiments, when the remote switch is deployed on a line, that is, or The switch operation state of the sending or receiving end of the line can be turned on, that is, or

[0063] In some embodiments, the fault propagation constraint corresponding to the minimum power outage area is expressed as:

[0064]

[0065] Among them, the 0 / 1 variable represents the on / off state of the switch, 1 for on and 0 for off.

[0066] In some embodiments, the fault propagation constraint for identifying the minimum outage area can be expressed as:

[0067]

[0068] in, It means that if a conventional line or tie line ij fails, the fault can be directly propagated to node i, which is 1, otherwise it is 0; represents the number of lines connected to node i; h i,t,1 It is 0 if node i is identified as faulty after the first detection, otherwise it is 1; T is the total duration of the fault;

[0069]

[0070] If the conventional line RL and the tie line TL ij are in the outage state at time t, it is 0, otherwise it is 1; taking the conventional line (RL) as an example, only when the line ij is out of service And the switch deployed on line ij near node i is closed hour,

[0071]

[0072] h i,t,p It means that if node i is identified as faulty after the pth detection at time t, it is 0, otherwise it is 1; It means that if the faulty node j can indirectly propagate the fault to node i, it is 1, otherwise it is 0; Represents the number of lines connected to node i.

[0073] It is worth noting that the fault propagation constraint includes three constraints. The first two constraints indicate that any faulty node j connected to a normal node i can indirectly propagate the fault through the connected line; the third constraint ensures that once node i is confirmed as a faulty node in a certain detection, this faulty state will be inherited in subsequent detections.

[0074] In some embodiments, see Figure 3 , is a schematic diagram of two fault propagation modes provided in this embodiment, including: direct fault propagation (Direct propagation), that is, directly affecting the nodes at both ends of the attacked line after the fault occurs to reduce the load; and indirect fault propagation (Indirect propagation), that is, propagating from the faulty node to its external nodes. Figure 2 In the example, since line 23 is affected by a low-frequency, high-impact event such as a network attack and no sectionalizing switch is deployed, nodes 2 and 3 are directly affected and load is reduced, becoming faulty nodes, which is direct fault propagation. Since no sectionalizing switch is deployed in lines 34 and 36, nodes 4 and 6 are also affected by the fault, which is indirect fault propagation.

[0075] In some embodiments, the fault propagation constraints may also include:

[0076]

[0077] in, represents the maximum number of detections; the two constraints in the formula indicate that the indirect fault propagation from line ij to node i is effective in the p-th detection only when node i is confirmed to be faulty in the p-1-th detection and the remote control switch deployed on line ij is not turned on.

[0078] In some embodiments, according to the minimum power outage area, the objective function is subjected to second-order planning of the defensive combat strategy to obtain the second-order planning decision constraints, including: combining the fault propagation constraint corresponding to the minimum power outage area with the load reduction constraint of the fault node, the line operation status constraint, the node safety margin constraint, the output limit constraint corresponding to the substation and the distributed power source, the active and reactive power constraints of the line power-on, the linear power flow equation constraint, the active and reactive power constraints of the node, the operation time interval constraint of the switch, the load constraint of the microgrid formed in the degradation period to restore the normal node, the connection constraint of the radial distribution network and the radial operation constraint, and performing second-order planning of the defensive combat strategy for the objective function to obtain the combined constraint, and using the combined constraint as the second-order planning decision constraint.

[0079] It is worth noting that after a node fails, its load will be reduced, and the load of normal nodes can change continuously without exceeding local demand.

[0080] In some embodiments, the load shedding constraint for a failed node is expressed as:

[0081]

[0082] Among them, P i L represents the total load demand of node i; represents the load shedding on node i at time t.

[0083] In some embodiments, if there is at least one open switch on line ij Then the line is shut down (w ij,t =0).

[0084] In some embodiments, the line operation status constraint is expressed as:

[0085]

[0086] Among them, w ij,t It means that if line ij is powered at time t, it is 1, otherwise it is 0.

[0087] In some embodiments, the voltage per unit value of each node must satisfy the node safety margin constraint, which is expressed as:

[0088]

[0089] in, V i , Represents the voltage safety margin of node i.

[0090] In some embodiments, power can only be generated at normal nodes, and the output limits corresponding to the substation and the distributed power source are expressed as follows:

[0091]

[0092]

[0093] in, represents the active and reactive power generated by the power source at node i at time t; represents the active and reactive capacity of substation i; Represents the active and reactive capacity of distributed generation at node i.

[0094] In some embodiments, when the line is powered on, its active and reactive power should not exceed the specified line capacity limit. The active and reactive power constraints of the line power on are expressed as:

[0095]

[0096] Among them, w ij,t If line ij is powered at time t, it is 1, otherwise it is 0; P ij,t ,Q ij,t represents the active and reactive power on line ij at time t; Indicates the maximum allowable capacity of active and reactive power of line ij.

[0097] In some embodiments, this embodiment applies the verified linearized power flow equation to the radial distribution network system and solves the load recovery problem. The linear power flow equation constraint is expressed as:

[0098]

[0099] in, represent the upstream and downstream of node i respectively.

[0100] In some embodiments, when line ij is energized, the voltage limit between the two nodes remains unchanged, and the active and reactive loads on each node are set to decrease in equal proportions. The active and reactive constraints of the nodes are expressed as:

[0101]

[0102] Among them, w ij,t If line ij is powered at time t, it is 1, otherwise it is 0; r ij ,x ij Represents the resistance and reactance of line ij.

[0103] In some embodiments, frequent switching operations may shorten the service life of the remote switch and may cause high surge voltage, so the operating time interval of the switch is limited. The operating time interval constraint of the switch is expressed as:

[0104]

[0105] in, If the switch at the sending and receiving end of line ij is operated at time t+1, it is 1, otherwise it is 0; t0, t e Indicates the time points when the fault event starts and ends.

[0106] In some embodiments, in the degradation period, the load of the normal node is restored in time by forming a microgrid. The present invention defines the node containing the substation or distributed power source as the root node, and its distribution boundary needs to satisfy the load constraint of the microgrid formed in the degradation period to restore the normal node, which is expressed as:

[0107]

[0108] Among them, ξ i,t If node i is the root node at time t, it is 1, otherwise it is 0.

[0109] In some embodiments, in a radial distribution network taking fault propagation into account, connection constraints should be imposed only on normal nodes and the lines connected thereto. Therefore, the connection constraints of the radial distribution network are expressed as:

[0110]

[0111]

[0112] Among them, F ij,t represents the power flow on line ij at time t.

[0113] In some embodiments, the distribution network system satisfies radial operation through constraints, and thus the radial operation constraints are:

[0114]

[0115] The number of energized lines is equal to the number of nodes minus the number of microgrids.

[0116] Step S14: Solve the objective function according to the first-order planning decision constraints and the second-order planning decision constraints to obtain the optimal defense operation strategy and the optimal planning strategy vector corresponding to the worst attack scenario, so that the active distribution network can be regulated according to the optimal defense operation strategy and the optimal planning strategy vector.

[0117] It is worth mentioning that the two-stage planning problem is decomposed into the master problem (MP) and subproblem (SP), and a suitable algorithm is selected to solve the model.

[0118] In some embodiments, the objective function is solved according to the first-order planning decision constraints and the second-order planning decision constraints to obtain the optimal defense operation strategy and the optimal planning strategy vector corresponding to the worst attack scenario, including: decomposing the objective function into a main problem and a sub-problem, according to the first-order planning decision constraints and the second-order planning decision constraints, in the main problem, under a given worst attack scenario and a given optimal defense operation strategy, obtaining the optimal planning decision vector, substituting the optimal planning decision vector into the sub-problem, and solving the worst attack scenario and the optimal defense operation strategy.

[0119] In some embodiments, solving the objective function includes steps S14.1 to S14.3, specifically:

[0120] Step 4.1: In the main question In the above example, the given worst attack scenario u* and the given best defense strategy y* are used to calculate the current optimal planning decision vector z*, and the C&CG algorithm is used to solve it.

[0121] Step 4.2: In the subproblem In the solution, the values ​​of u* and y* will be found for the optimal planning decision vector z*. Since there are binary variables in the min problem, the problem is non-convex and does not satisfy the strong duality. It needs to be solved using the Nested C&CG algorithm.

[0122] Step 4.3: The optimization problem in the two-stage robust programming model of the active distribution network constructed in this embodiment is a mixed integer linear programming (MILP) problem, which can be solved quickly and effectively by a commercial solver (such as Gurobi).

[0123] This embodiment adopts a penalty cost coefficient for the node, measures the node where the fault occurs, and constructs an objective function through operating variables, uncertainty variables and planning variables. For the three variables, the present invention adopts line faults and the propagation of line faults, constructs a first-order planning decision constraint under the budget, and constructs a second-order planning decision constraint under the first-order planning decision constraint, so that based on the actual funding situation, the propagation after the potential line fault can be fully considered, the three variable values ​​can be accurately calculated, and then the distribution network planning is planned based on the three solution results, thereby improving the flexibility of the distribution network planning.

[0124] Example 2

[0125] In order to verify the effectiveness and superiority of the method proposed in this embodiment 1, a simulation is performed using an improved IEEE 33-node distribution network system, whose topology is shown in Figure 4 , is a schematic diagram of the topological structure of the IEEE 33-node distribution network system provided in this embodiment. In order to evaluate the maximum expected power shortage of the distribution network system under high-impact low-frequency time events such as network attacks, this embodiment assumes that each successful attack will directly cause a line failure; and after the line is reinforced, it will no longer be affected by the original fault. After the degradation period of the distribution network system, it will enter the recovery and maintenance period, during which the average service repair time of the node load is 2 hours. Nodes 16, 19, 24, 29, and 33 are regarded as critical loads in the distribution network system, and the load demand of each node is shown in Table 1. Three energy storage devices are installed at nodes 18, 22, and 25 as distributed energy sources, with active capacities of 80kW, 120kW, and 300kW, respectively. All simulations can be solved using the commercial solver Gurobi.

[0126] Table 1 Location and requirements of key loads

[0127]

[0128] In order to verify the effectiveness of the planning method proposed in Example 1, two distribution network system planning schemes with budgets of US$200,000 and US$500,000 are set respectively, and the fault identification and reconstruction of the distribution network system after high-impact low-frequency events such as network attacks are studied. Figure 5 , is a schematic diagram of fault identification and reconstruction with different budgets provided in this embodiment.

[0129] In this embodiment, two failure scenarios are considered, namely, a line failure caused by extreme weather and a line failure under a network multi-target attack, and it is assumed that the failure consequences caused by them are the same.

[0130] When a large number of lines fail, the substation will temporarily be unable to supply power to users at the node. Therefore, the active distribution network will generate planned islands to restore the load of key nodes, especially those with high load demand, such as node 24, to reduce the EENS of the distribution network system.

[0131] Example 1: When the budget is $200,000, the planning and design will focus on reinforcing the lines near important load nodes and installing sectionalizers. After the fault occurs, the distribution network system is reconstructed to form microgrid MG-1 to restore the critical loads of nodes 24 and 29 and microgrid MG-2 to restore the critical loads of node 19. When lines 23-24, 28-29, and 32-33 fail, the MG-1 island is formed thanks to the deployment of sectionalizers on both sides of nodes 24 and 29. When lines 2-19 and 12-22 fail, the MG-2 island is formed thanks to the deployment of sectionalizers on both sides of node 19 and the deployment of bilateral remote control switches on tie lines 12-22.

[0132] Example 2: When the budget is $500,000, compared with Example 1, the load at key node 33 can be restored, thanks to the reinforcement of line 31-32 and line 32-33, which will not be affected by the fault in Example 1, and bilateral section switches are deployed on tie line 18-33 to isolate the fault.

[0133] See also Figure 6 and Figure 7 , respectively, are schematic diagrams of fault detection results with a lower budget provided in this embodiment and schematic diagrams of fault detection results with a higher budget provided in this embodiment. By applying the fault detection method proposed in Embodiment 1, it can be concluded that Figure 5 Results of node fault detection with two different budgets, i.e., the whole process of detecting the node status at the end of the degradation phase of the distribution network system. Figure 6 and Figure 7 It can be seen that under the budget of $200,000 and $500,000, the node status of the distribution network system in the third round of testing is the same as the test results of the last round, verifying the accuracy and effectiveness of this method. Figure 6 and Figure 7 The node status recognition results are Figure 5 From the intuitive fault areas shown in , it can be found that the fault detection method proposed in Example 1 can accurately detect the health status of all nodes.

[0134] The purpose of the two-stage robust planning method designed in the present invention is to enhance the ability of active distribution networks to resist high-impact low-frequency events such as cyber attacks. Conventional line reinforcement, interconnection line reinforcement and deployment of remote-controlled segmented switches are used as three investment options for distribution network planning in the present invention. First, the minimum fault area search algorithm is used to model the propagation of power outage faults in conventional lines and interconnection lines in detail and linearize the operating constraints. Secondly, the Nested C&CG algorithm is used to solve the two-stage robust optimization model designed in the present invention. The calculation results show that the active distribution network resilience improvement planning model proposed in the present invention, through deployment and line reinforcement in conventional lines and interconnection lines, can quickly form a microgrid after a line failure, restore the load of key nodes, and effectively reduce the penalty cost of the distribution network system.

[0135] Example 3

[0136] See also Figure 8 , is a structural diagram of a two-stage planning device for an active distribution network taking into account network attacks provided in this embodiment, including: a target function construction module 31, a first-order planning module 32, a second-order planning module 33 and a solution module 34.

[0137] In some embodiments, the objective function construction module 31 outputs the objective function according to the penalty cost coefficient of the input node, and transmits the objective function to the first-order planning module 32 and the solution module 34; after receiving the objective function, the first-order planning module 32 outputs the first-order planning decision constraints, and transmits the first-order planning decision constraints to the second-order planning module 33 and the solution module 34; after the second-order planning module 33 inputs the first-order planning decision constraints, it outputs the second-order planning decision constraints to the solution module 34; after receiving the objective function, the first-order planning decision constraints and the second-order planning decision constraints, the solution module 34 outputs the planning scheme; the planning scheme includes: the optimal defense combat strategy and the optimal planning strategy vector corresponding to the worst attack scenario.

[0138] The objective function module 31 is constructed to assign corresponding penalty cost coefficients according to the importance of nodes, and to establish an objective function with the defense combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable.

[0139] In some embodiments, a corresponding penalty cost coefficient is assigned according to the importance of the node, and an objective function is established with the defensive combat strategy as the operating variable, the attack scenario as the uncertainty variable, and the planning decision vector as the planning variable, including: assigning a corresponding penalty cost coefficient according to the importance of the node, after a fault occurs, accumulating the product of the fault duration and the penalty cost coefficient of the node to obtain the total penalty cost, and establishing an objective function of a corresponding min-max-min structure for the total penalty cost in the order of the defensive combat strategy as the operating variable, the attack scenario as the uncertainty variable, and the planning decision vector as the planning variable.

[0140] In some embodiments, the objective function of the min-max-min structure is expressed as:

[0141]

[0142] Among them, Z, U and Y represent the sets of planning variables, uncertainty variables and operation variables respectively; is the penalty cost coefficient of node i; is the load shedding of node i at time t; Δt is the fault duration.

[0143] The first-order planning module 32 is used to perform first-order planning of line reinforcement and remote switch deployment for the objective function according to a preset budget and to construct first-order planning decision constraints.

[0144] In some embodiments, according to a preset budget, a first-order planning of line reinforcement and remote switch deployment is performed on the objective function, and a first-order planning decision constraint is constructed, including: setting K remote switches in the line, and reinforcing the line and deploying at least one switch on any line, and according to the investment cost of deploying remote switches and line reinforcement, establishing budget constraints, candidate planning position constraints for remote switches, line reinforcement constraints, and at least one switch constraint for deploying any line to obtain a first-order planning decision constraint; wherein K is a positive integer.

[0145] The second-order planning module 33 is used to perform second-order planning of the defense operation strategy for the objective function according to the line faults and the propagation of the line faults in the attack scenario and based on the first-order planning decision constraints to obtain the second-order planning decision constraints.

[0146] In some embodiments, according to the line faults and the propagation of the line faults in the attack scenario, based on the first-order planning decision constraints, the second-order planning of the defensive combat strategy is performed on the objective function to obtain the second-order planning decision constraints, including: based on the first-order planning decision constraints, if a line fault occurs in the attack scenario, a fault propagation constraint is established according to the two end nodes directly affected by the line fault and the fault state indirectly propagated through the connected lines, and the minimum power outage area divided by the interconnection lines and switch deployment locations is obtained; according to the minimum power outage area, the second-order planning of the defensive combat strategy is performed on the objective function to obtain the second-order planning decision constraints.

[0147] In some embodiments, the fault propagation constraint corresponding to the minimum power outage area is expressed as:

[0148]

[0149] Among them, the 0 / 1 variable represents the on / off state of the switch, 1 for on and 0 for off;

[0150]

[0151] in, It means that if a conventional line or tie line ij fails, the fault can be directly propagated to node i, which is 1, otherwise it is 0; represents the number of lines connected to node i; h i,t,1 It is 0 if node i is identified as faulty after the first detection, otherwise it is 1; T is the total duration of the fault;

[0152]

[0153] If the conventional line RL and the tie line TL ij are in the outage state at time t, it is 0, otherwise it is 1;

[0154]

[0155] h i,t,p It means that if node i is identified as faulty after the pth detection at time t, it is 0, otherwise it is 1; It means that if the faulty node j can indirectly propagate the fault to node i, it is 1, otherwise it is 0; represents the number of lines connected to node i;

[0156]

[0157] in, The two constraints in the formula indicate that the indirect fault propagation from line ij to node i is effective in the pth detection only when node i is confirmed as faulty in the p-1th detection and the remote switch deployed on line ij is not turned on.

[0158] In some embodiments, according to the minimum power outage area, the objective function is subjected to second-order planning of the defensive combat strategy to obtain the second-order planning decision constraints, including: combining the fault propagation constraint corresponding to the minimum power outage area with the load reduction constraint of the fault node, the line operation status constraint, the node safety margin constraint, the output limit constraint corresponding to the substation and the distributed power source, the active and reactive power constraints of the line power-on, the linear power flow equation constraint, the active and reactive power constraints of the node, the operation time interval constraint of the switch, the load constraint of the microgrid formed in the degradation period to restore the normal node, the connection constraint of the radial distribution network and the radial operation constraint, and performing second-order planning of the defensive combat strategy for the objective function to obtain the combined constraint, and using the combined constraint as the second-order planning decision constraint.

[0159] In some embodiments, the load shedding constraint for a failed node is expressed as:

[0160]

[0161] Among them, P i L represents the total load demand of node i; represents the load shedding on node i at time t.

[0162] In some embodiments, the line operation status constraint is expressed as:

[0163]

[0164] Among them, w ij,t It means that if line ij is powered at time t, it is 1, otherwise it is 0.

[0165] In some embodiments, the node safety margin constraint is expressed as:

[0166]

[0167] in, V i , Represents the voltage safety margin of node i.

[0168] In some embodiments, the output limit constraints corresponding to the substation and the distributed generation are respectively expressed as:

[0169]

[0170]

[0171] in, represents the active and reactive power generated by the power source at node i at time t; represents the active and reactive capacity of substation i; Represents the active and reactive capacity of distributed generation at node i.

[0172] In some embodiments, the active and reactive constraints for line energization are expressed as:

[0173]

[0174] Among them, w ij,t If line ij is powered at time t, it is 1, otherwise it is 0; P ij,t ,Q ij,t represents the active and reactive power on line ij at time t; Indicates the maximum allowable capacity of active and reactive power of line ij.

[0175] In some embodiments, the linear power flow equation constraint is expressed as:

[0176]

[0177] in, represent the upstream and downstream of node i respectively.

[0178] In some embodiments, the active and reactive constraints of a node are expressed as:

[0179]

[0180] Among them, w ij,t If line ij is powered at time t, it is 1, otherwise it is 0; r ij ,x ij Represents the resistance and reactance of line ij.

[0181] In some embodiments, the operating time interval constraint of the switch is expressed as:

[0182]

[0183] in, If the switch at the sending and receiving end of line ij is operated at time t+1, it is 1, otherwise it is 0; t0, t e Indicates the time points when the fault event starts and ends.

[0184] In some embodiments, the load constraint of the microgrid restored to normal nodes formed in the degradation period is expressed as:

[0185]

[0186] Among them, ξ i,t If node i is the root node at time t, it is 1, otherwise it is 0.

[0187] In some embodiments, the connection constraints of the radial distribution network are expressed as:

[0188]

[0189]

[0190] Among them, F ij,t represents the power flow on line ij at time t.

[0191] In some embodiments, the radial operation constraints are:

[0192]

[0193] The number of energized lines is equal to the number of nodes minus the number of microgrids.

[0194] The solving module 34 is used to solve the objective function according to the first-order planning decision constraints and the second-order planning decision constraints, and obtain the optimal defense operation strategy and the optimal planning strategy vector corresponding to the worst attack scenario, so as to regulate the active distribution network according to the optimal defense operation strategy and the optimal planning strategy vector.

[0195] In some embodiments, the objective function is solved according to the first-order planning decision constraints and the second-order planning decision constraints to obtain the optimal defense operation strategy and the optimal planning strategy vector corresponding to the worst attack scenario, including: decomposing the objective function into a main problem and a sub-problem, according to the first-order planning decision constraints and the second-order planning decision constraints, in the main problem, under a given worst attack scenario and a given optimal defense operation strategy, obtaining the optimal planning decision vector, substituting the optimal planning decision vector into the sub-problem, and solving the worst attack scenario and the optimal defense operation strategy.

[0196] This embodiment adopts the objective function construction module 31 to give penalty cost coefficients to nodes, measure nodes with faults, and construct objective functions through operating variables, uncertainty variables and planning variables. For the three variables, this embodiment adopts the first-order planning module 32 to construct first-order planning decision constraints under the budget according to line faults and the propagation of line faults, and constructs second-order planning decision constraints through the second-order planning module 33 under the first-order planning decision constraints, so that based on the actual funding situation, the propagation after potential line faults can be fully considered, and the three variable values ​​can be accurately calculated through the solution module 34, and then the distribution network planning is planned based on the three solution results, thereby improving the flexibility of the distribution network planning.

[0197] Those skilled in the art will appreciate that the embodiments of the present application may also provide computer program products. Therefore, the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application may adopt the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes.

[0198] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0199] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0201] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A two-stage planning method for active distribution networks taking into account network attacks, characterized in that: include: According to the importance of the node, the corresponding penalty cost coefficient is assigned, and the objective function is established with the defense combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable; According to the preset budget, the first-order planning of line reinforcement and remote switch deployment is performed for the objective function, and the first-order planning decision constraints are constructed; According to the line fault in the attack scenario and the propagation of the line fault, based on the first-order planning decision constraint, the second-order planning of the defense operation strategy is performed on the objective function to obtain the second-order planning decision constraint; According to the first-order planning decision constraints and the second-order planning decision constraints, the objective function is solved to obtain the optimal defense combat strategy and the optimal planning strategy vector corresponding to the worst attack scenario, so that the active distribution network can be regulated according to the optimal defense combat strategy and the optimal planning strategy vector.

2. The two-stage planning method for active distribution network according to claim 1, characterized in that: The objective function is established by assigning a corresponding penalty cost coefficient according to the importance of the node, taking the defense combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable, including: A corresponding penalty cost coefficient is assigned according to the importance of the node. After a fault occurs, the product of the cumulative fault duration and the penalty cost coefficient of the node is obtained to obtain the total penalty cost. According to the order of the defensive combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable, an objective function of the corresponding min-max-min structure is established for the total penalty cost.

3. The two-stage planning method for active distribution network according to claim 2, characterized in that: The objective function of the min-max-min structure is expressed as: Among them, Z, U and Y represent the sets of planning variables, uncertainty variables and operation variables respectively; is the penalty cost coefficient of node i; is the load shedding of node i at time t; Δt is the fault duration.

4. The two-stage planning method for active distribution network according to claim 1, characterized in that: According to the line fault in the attack scenario and the propagation of the line fault, based on the first-order planning decision constraint, performing second-order planning of the defense operation strategy on the objective function to obtain the second-order planning decision constraint includes: Based on the first-order planning decision constraints, if a line fault occurs in an attack scenario, a fault propagation constraint is established according to the two end nodes directly affected by the line fault and the fault state indirectly propagated through the connected lines, and the minimum power outage area divided by the interconnection lines and switch deployment locations is obtained. According to the minimum power outage area, the second-order planning of the defense combat strategy is performed on the objective function to obtain the second-order planning decision constraints.

5. The two-stage planning method for active distribution network according to claim 4, characterized in that: The fault propagation constraint corresponding to the minimum power outage area is expressed as: Among them, the 0 / 1 variable represents the on / off state of the switch, 1 for on and 0 for off; in, It means that if a conventional line or tie line ij fails, the fault can be directly propagated to node i, which is 1, otherwise it is 0; represents the number of lines connected to node i; h i,t,1 It is 0 if node i is identified as faulty after the first detection, otherwise it is 1; T is the total duration of the fault; If the conventional line RL and the tie line TL ij are in the outage state at time t, it is 0, otherwise it is 1; h i,t,p It means that if node i is identified as faulty after the pth detection at time t, it is 0, otherwise it is 1; It means that if the faulty node j can indirectly propagate the fault to node i, it is 1, otherwise it is 0; represents the number of lines connected to node i; in, represents the maximum number of detections; the two constraints are used to indicate that the indirect fault propagation from line ij to node i is effective in the p-th detection only when node i is confirmed to be faulty in the p-1-th detection and the remote control switch deployed on line ij is not turned on.

6. The two-stage planning method for active distribution network according to claim 4, characterized in that: The step of performing second-order planning of a defense strategy for the objective function according to the minimum power outage area to obtain second-order planning decision constraints includes: The fault propagation constraint corresponding to the minimum power outage area is combined with the load reduction constraint of the fault node, the line operation status constraint, the node safety margin constraint, the output limit constraint corresponding to the substation and the distributed power source, the active and reactive power constraints of the line energization, the linear power flow equation constraint, the active and reactive power constraints of the node, the operation time interval constraint of the switch, the load constraint of the microgrid formed in the degradation period to restore the normal node, the connection constraint of the radial distribution network and at least one of the radial operation constraints, and the second-order planning of the defense combat strategy of the objective function is performed to obtain the joint constraint, and the joint constraint is used as the second-order planning decision constraint.

7. The two-stage planning method for active distribution network according to claim 6, characterized in that: The load reduction constraint of the faulty node is expressed as: in, represents the total load demand of node i; represents the load shedding on node i at time t; The line operation status constraint is expressed as: Among them, w ij,t It means that if line ij is powered at time t, it is 1, otherwise it is 0; The node safety margin constraint is expressed as: Among them, V i , represents the voltage safety margin of node i; The output limit constraints corresponding to the substation and distributed generation are expressed as follows: in, represents the active and reactive power generated by the power source at node i at time t; represents the active and reactive capacity of substation i; represents the active and reactive capacity of the distributed generation at node i; The active and reactive constraints of the line energization are expressed as: Among them, w ij,t If line ij is powered at time t, it is 1, otherwise it is 0; P ij,t ,Q ij,t represents the active and reactive power on line ij at time t; Indicates the maximum permissible capacity of active and reactive power of line ij; The linear power flow equation constraint is expressed as: in, Represent the upstream and downstream of node i respectively; The active and reactive constraints of the node are expressed as: Among them, w ij,t If line ij is powered at time t, it is 1, otherwise it is 0; r ij ,x ij represents the resistance and reactance of line ij; The switch operation time interval constraint is expressed as: in, If the switch at the sending and receiving end of line ij is operated at time t+1, it is 1, otherwise it is 0; t0, t e Indicates the time points when the fault event starts and ends; The load constraint of the microgrid restored to normal nodes formed during the degradation period is expressed as: Among them, ξ i,t If node i is the root node at time t, it is 1, otherwise it is 0; The connection constraints of the radial distribution network are expressed as: Among them, F ij,t represents the power flow on line ij at time t; The radial operation constraints are: The number of energized lines is equal to the number of nodes minus the number of microgrids.

8. The two-stage planning method for active distribution network according to claim 1, characterized in that: The objective function is solved according to the first-order planning decision constraint and the second-order planning decision constraint to obtain the optimal defense combat strategy and the optimal planning strategy vector corresponding to the worst attack scenario, including: The objective function is decomposed into a main problem and sub-problems. According to the first-order planning decision constraints and the second-order planning decision constraints, in the main problem, under a given worst attack scenario and a given optimal defense operation strategy, an optimal planning decision vector is obtained. The optimal planning decision vector is substituted into the sub-problem to solve the worst attack scenario and the optimal defense operation strategy.

9. The two-stage planning method for active distribution network according to claim 1, characterized in that: According to the preset budget, the first-order planning of line reinforcement and remote switch deployment is performed on the objective function, and the first-order planning decision constraints are constructed, including: K remote control switches are set in the line, and the line is reinforced and at least one switch is deployed on any line. According to the investment cost of deploying the remote control switch and reinforcing the line, a budget constraint, a candidate planning position constraint of the remote control switch, a line reinforcement constraint and a constraint on deploying at least one switch on any line are established to obtain a first-order planning decision constraint; wherein K is a positive integer.

10. A two-stage planning device for active distribution network taking into account network attacks, characterized in that: include: Construct objective function module, first-order planning module, second-order planning module and solution module; among them, The objective function construction module is used to assign corresponding penalty cost coefficients according to the importance of nodes, and to establish an objective function with the defense combat strategy as the operating variable, the attack scenario as the uncertainty variable and the planning decision vector as the planning variable; The first-order planning module is used to perform first-order planning of line reinforcement and remote switch deployment for the objective function according to a preset budget, and to construct first-order planning decision constraints; The second-order planning module is used to perform second-order planning of the defense operation strategy on the objective function according to the line fault in the attack scenario and the propagation of the line fault, based on the first-order planning decision constraints, to obtain the second-order planning decision constraints; The solving module is used to solve the objective function according to the first-order planning decision constraints and the second-order planning decision constraints, and obtain the optimal defense combat strategy and the optimal planning strategy vector corresponding to the worst attack scenario, so as to regulate the active distribution network according to the optimal defense combat strategy and the optimal planning strategy vector.

Images