Power supply recovery method for power distribution system to cope with network-physical collaborative threat based on R-SOP

Abstract

The invention discloses a reconfigurable intelligent soft switch-based power supply recovery method for a power distribution system to cope with a network-physical collaborative threat, and the method comprises the steps: 1, building a network-physical collaborative threat quantitative model, so as to analyze the influence characteristics of the quantitative model on the power distribution system; 2, establishing a reconfigurable intelligent soft switch model; 3, establishing a power supply recovery model of the power distribution system containing the reconfigurable intelligent soft switch to cope with the network-physical collaborative threat, and introducing power flow balance as a key target; and 4, converting the network-physical collaborative threat power supply recovery model coped with the power distribution system based on the reconfigurable intelligent soft switch into a mixed integer second-order cone programming constraint, and solving to obtain a power distribution system operation scheme including reconfigurable soft switch action, energy storage system action and a controllable switch. According to the invention, the load recovery rate is improved by using the reconfigurable intelligent soft switch, the line power flow out-of-limit risk is reduced, and the influence of the network-physical cooperative threat on the power distribution system is effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the field of power distribution system operation optimization, and specifically provides a power supply restoration method for a power distribution system based on a reconfigurable intelligent soft switch R-SOP to cope with network-physical collaborative threats. Background Art

[0002] In recent years, the increasing coupling between the information and physical layers of distribution systems has made them vulnerable to coordinated cyber-physical attacks. In defending distribution systems against coordinated cyber-physical threats, the synergistic effects of false data injection and line damage significantly increase the difficulty of load restoration and carry the risk of line current exceeding limits. Currently, the impact characteristics of coordinated cyber-physical threats on distribution systems have not been thoroughly analyzed, and the synergistic logic and impact mechanisms between cyber and physical attacks remain unclear.

[0003] However, due to the injection of false data, in cyber-physical collaborative threat scenarios, dispatchers optimize scheduling based on false measurement data, resulting in the received line power flow data appearing to be within safety limits. However, due to data tampering, the actual line power flow may have exceeded the safety threshold, resulting in the risk of line power exceeding the limit. Existing research often ignores the impact of false data on line power flow data and fails to consider the possibility of line power exceeding the limit. Summary of the Invention

[0004] In order to address the deficiencies of the above-mentioned prior art, the present invention proposes a power supply restoration method for a distribution system based on R-SOP to cope with network-physical collaborative threats. The method aims to resist network-physical collaborative threats by coordinating reconfigurable intelligent soft switches, controllable switches and energy storage devices, thereby achieving effective recovery under network-physical collaborative threats and improving the system's recovery capability under extreme conditions.

[0005] In order to achieve the above-mentioned object, the present invention adopts the following technical solutions:

[0006] The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats is characterized by comprising the following steps:

[0007] Step 1: Build a quantitative model of cyber-physical collaborative threats;

[0008] Step 2: Construct an operation model of the energy storage system in the power distribution system;

[0009] Step 3: Construct an operation model of the reconfigurable intelligent soft switch R-SOP including the energy storage system;

[0010] Step 4: Construct a virtual power flow model for power distribution system reconstruction;

[0011] Step 5: Construct a power supply restoration model for the distribution system based on R-SOP to cope with cyber-physical collaborative threats;

[0012] Step 6: Construct a power restoration model for the distribution system considering power flow balance under cyber-physical collaborative threats.

[0013] Step 7: The power supply restoration model of the distribution system considering power flow balance under the threat of network-physical collaboration is converted into a mixed integer second-order cone programming constraint and then solved to obtain the operation plan of the distribution system including the reconfigurable soft switch R-SOP action, the energy storage system action, and the controllable switch action.

[0014] The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats according to the present invention is also characterized in that, in step 1, a quantitative model of cyber-physical collaborative threats is constructed using equations (1) to (8):

[0015] (1)

[0016] (2)

[0017] (3)

[0018] (4)

[0019] (5)

[0020] (6)

[0021] (7)

[0022] (8)

[0023] In formula (1) to formula (8), represent Time Node The amount of load tampering; Represents the load tampering amplitude, ranging from 0 to 1; is the total number of moments; represents the power transfer distribution factor matrix; represents the power flow measurement attack vector; represents the load measurement attack vector; Represents the set of all nodes in the power distribution system; for Time Node Active power consumed by the load; represent Time Node The 0-1 integer variable corresponding to the load node; represent Time Node With node Branch road between The corresponding 0-1 integer variable; The number of attackable resources is set; represent Time branch Transmission power tampering amount; represent Time branch A 0-1 integer variable indicating whether the attack occurred; Represents physical attack resources; represent Time branch A 0-1 integer variable indicating whether the connection is established.

[0024] Furthermore, in step 2, equations (9) to (12) are used to construct an operation model of the energy storage system in the power distribution system:

[0025] (9)

[0026] (10)

[0027] (11)

[0028] (12)

[0029] In formula (9) to formula (12), 、 are the minimum and maximum values ​​of the energy storage system charging power respectively; 、 are the minimum and maximum discharge power of the energy storage system, respectively. 、 For energy storage systems Charge and discharge power at all times; For energy storage systems Auxiliary variables at the moment, the energy storage system When charging, =1, the energy storage system is When discharging at any time, =0; 、 、 are the minimum, maximum and initial values ​​of the state of charge of the energy storage system respectively; 、 are the total capacity of the energy storage system and capacity of the moment; 、 are the charging and discharging efficiency of the energy storage system, respectively.

[0030] Furthermore, in step 3, equations (13) to (18) are used to construct an operation model of a reconfigurable intelligent soft switch R-SOP including an energy storage system:

[0031] (13)

[0032] (14)

[0033] (15)

[0034] (16)

[0035] (17)

[0036] (18)

[0037] In formula (13) to formula (18), 、 They represent the set of nodes connected to the converter and the set of all converters respectively; is the number of nodes connected to the converter VSC; 、 、 Node All connected converters are Total loss, DC side power and AC side power at the moment; For nodes All connected converters are Reactive power at the moment; For nodes All connected converters VSC Apparent power at the moment; For the inverter capacity; is the loss coefficient of R-SOP; Is a binary variable representing the converter exist Whether the node is connected to the voltage source converter at any time connected.

[0038] Furthermore, in step 4, equations (19) to (22) are used to construct a virtual power flow model for distribution system reconstruction:

[0039] (19)

[0040] (20)

[0041] (twenty one)

[0042] (twenty two)

[0043] In formula (19) to formula (22), is a node The port is in R-SOP open state; It is a branch road Virtual trends; is a node The power flow from the virtual power source; is a node Virtual needs; is the number of nodes in the distribution system; M is a constant.

[0044] Furthermore, in step 5, Equations (23) to (33) are used to construct a power supply restoration model for the distribution system based on R-SOP to cope with cyber-physical collaborative threats:

[0045] (twenty three)

[0046] (twenty four)

[0047] (25)

[0048] (26)

[0049] (27)

[0050] (28)

[0051] (29)

[0052] (30)

[0053] (31)

[0054] (32)

[0055] (33)

[0056] In formula (23) to formula (33), and They are Time branch Active and reactive power transmitted on the and Branch resistance and reactance; and They are Time Node The injected active and reactive power; and for Time Node Active and reactive load reduction; for Time Node voltage; for Time branch The current transmitted on 、 They are Time Node Active power output and active power reduction of photovoltaic PV; for Time Node The reactive power output of photovoltaic PV; and They are Time Node Active and reactive power consumed by the load; and for Time Node Active and reactive power injected at R-SOP; and are the upper and lower limits of node voltage in the distribution system respectively; It is the upper limit of branch current in the distribution system; For branch circuits in the distribution system Upper limit of power flow.

[0057] Furthermore, in step 6, the objective function of the power supply restoration model of the distribution system considering power flow balance under the threat of network-physical collaboration is constructed using equations (34) and (35): , and Equations (1) to (33) are used as constraints for the power supply restoration model of the distribution system considering power flow balance under the threat of network-physical collaboration;

[0058] (34)

[0059] (35)

[0060] In formula (34)-formula (35), , , They represent the loss, load reduction and power flow balance items of the distribution system respectively; Represents the coefficient of the power flow equilibrium term.

[0061] Furthermore, the step 7 includes the following steps:

[0062] Step 7.1: and Replaced by two linear variables and , and use the big-M method to relax Equations (19)-(21) and (25)-(26) to transform them into linear constraints, and then use Equations (36)-(40) to construct the transformed constraints:

[0063] (36)

[0064] (37)

[0065] (38)

[0066] (39)

[0067] (40)

[0068] In formula (36) to formula (40), and respectively Time Node The square of the voltage at Time Node To Node Intermediate branch The square of the current;

[0069] Step 7.2: Use equation (41) to transform equation (22) into a second-order cone constraint:

[0070] (41)

[0071] In formula (41), T represents transposition;

[0072] Step 7.3: Use the big-M method to decouple the power and current transmitted by the branch, and then use Equations (42) to (44) to construct the transformed constraints:

[0073] (42)

[0074] (43)

[0075] (44)

[0076] Step 7.4: Use equation (45) to transform equation (18) into a rotating cone constraint:

[0077] (45).

[0078] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the power supply recovery method, and the processor is configured to execute the program stored in the memory.

[0079] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium. The characteristic of the computer program is that when the computer program is executed by a processor, the steps of the power supply recovery method are executed.

[0080] Compared with the prior art, the present invention has the following beneficial effects:

[0081] 1. This paper establishes a quantitative model of cyber-physical collaborative threats to deeply analyze their impact characteristics on the distribution system. By comparing the load reduction of the distribution system under various cyber-physical collaborative threat scenarios, the collaborative logic and impact mechanism between cyber attacks and physical attacks are explored. This solves the problem of the difficulty in accurately assessing the impact of collaborative attacks on the distribution system, quantifies the impact of different types of collaborative attacks on the stability of the distribution system, and reveals their synergistic mechanism, thereby providing a scientific basis for the defense strategy of the distribution system and providing technical support for improving the security and resilience of the distribution system.

[0082] 2. This invention incorporates a reconfigurable smart soft switch (R-SOP) into the distribution system power restoration model under cyber-physical collaborative threats to effectively improve load recovery. By reconfiguring feeders and ports, and ports and VSCs, R-SOP significantly improves load recovery capabilities compared to traditional smart soft switches (SOPs) while maintaining the same total port capacity.

[0083] 3. This invention innovatively introduces power flow balancing as a key objective and formulates an optimal recovery strategy, thereby effectively mitigating the impact of cyber-physical collaborative threats on the distribution system and significantly reducing the probability of line power exceeding the limit. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is an improved IEEE33-node power distribution system topology diagram;

[0085] Figure 2 This is a graph showing the results of a cyber-physical collaborative threat effect test;

[0086] Figure 3 It is the load recovery rate and composition diagram of the recovery process;

[0087] Figure 4 It is the transmission power and energy storage power diagram of each feeder of R-SOP;

[0088] Figure 5 It is the capacity allocation diagram of each port of R-SOP;

[0089] Figure 6 It is the line transmission power diagram with and without power flow equalization term;

[0090] Figure 7 It is the line power flow exceeding limit distribution probability diagram. DETAILED DESCRIPTION

[0091] In this embodiment, a method for restoring power supply to a power distribution system based on a reconfigurable intelligent soft switch in response to cyber-physical collaborative threats is provided. The specific steps are as follows:

[0092] Step 1: Build a cyber-physical collaborative threat quantification model:

[0093] Step 1-1: Construct a cyber-physical collaborative threat quantification model using equations (1) to (8):

[0094] (1)

[0095] (2)

[0096] (3)

[0097] (4)

[0098] (5)

[0099] (6)

[0100] (7)

[0101] (8)

[0102] In formula (1) to formula (8), represent Time Node The amount of load tampering; Represents the load tampering amplitude, ranging from 0 to 1; is the total number of moments; is the number of nodes in the power distribution system; represents the power transfer distribution factor matrix; represents the power flow measurement attack vector; represents the load measurement attack vector; Represents the set of all nodes in the power distribution system; for Time Node Active power consumed by the load; represent Time Node The 0-1 integer variable corresponding to the load node; represent Time Node With node Branch road between The corresponding 0-1 integer variable; The number of attackable resources is set, which is an integer constant; represent Time branch Transmission power tampering amount; represent Time branch A 0-1 integer variable indicating whether the attack occurred. Represents physical attack resources, an integer constant; represent Time branch A 0-1 integer variable indicating whether the connection is established.

[0103] Step 2: Construct a reconfigurable intelligent soft switch model with energy storage:

[0104] Step 2-1: Construct the operation model of the energy storage system using equations (9) to (12):

[0105] (9)

[0106] (10)

[0107] (11)

[0108] (12)

[0109] In formula (9) to formula (12), 、 are the minimum and maximum values ​​of the energy storage system charging power respectively; 、 are the minimum and maximum discharge power of the energy storage system, respectively. 、 For energy storage systems Charge and discharge power at all times; For energy storage systems Auxiliary variables at the moment, the energy storage system When charging, =1, the energy storage system is When discharging at any time, =0; 、 、 are the minimum, maximum and initial values ​​of the state of charge of the energy storage system respectively; 、 are the total capacity of the energy storage system and capacity of the moment; 、 are the charging and discharging efficiency of the energy storage system, respectively.

[0110] Step 2-2: Operation model of reconfigurable intelligent soft switch R-SOP with energy storage:

[0111] (13)

[0112] (14)

[0113] (15)

[0114] (16)

[0115] (17)

[0116] (18)

[0117] In formula (13) to formula (18), 、 They represent the set of nodes connected to the converter VSC and the set of all converters VSC respectively; is the number of nodes connected to the converter VSC; is the number of converters VSC; 、 、 Node All connected converters VSC Total loss, DC side power and AC side power at the moment; For nodes All connected converters VSC Reactive power at the moment; For nodes All connected converters VSC Apparent power at the moment; For the inverter capacity; is the loss coefficient of R-SOP; Is a binary variable representing the converter exist Whether the node is connected to the voltage source converter at any time connected.

[0118] Step 3: Construct a virtual power flow model for power distribution system reconstruction:

[0119] Step 3-1: Construct a virtual power flow model for distribution system reconstruction using equations (19) to (22):

[0120] (19)

[0121] (20)

[0122] (twenty one)

[0123] (twenty two)

[0124] In formula (19) to formula (22), is a node The port is in R-SOP open state; It is a branch road Virtual Trends; is a node The power flow from the virtual power source; is a node Virtual demand; is the number of nodes in the distribution system; M is a constant.

[0125] Step 4: Construct a power restoration model for the distribution system based on R-SOP to cope with cyber-physical collaborative threats:

[0126] Step 4-1: Construct the fault distribution system power flow model including R-SOP using equations (23) to (33):

[0127] (twenty three)

[0128] (twenty four)

[0129] (25)

[0130] (26)

[0131] (27)

[0132] (28)

[0133] (29)

[0134] (30)

[0135] (31)

[0136] (32)

[0137] (33)

[0138] In formula (23) to formula (33), and They are Time branch Active and reactive power transmitted on the and Branch resistance and reactance; and They are Time Node The injected active and reactive power; and for Time Node Active and reactive load reduction; for Time Node voltage; for Time branch The current transmitted on 、 They are Time Node Active power output and active power reduction of photovoltaic PV; for Time Node The reactive power output of photovoltaic PV; and They are Time Node Active and reactive power consumed by the load; and for Time Node Active and reactive power injected at R-SOP; and are the upper and lower limits of node voltage in the distribution system respectively; It is the upper limit of branch current in the distribution system; For branch circuits in the distribution system Upper limit of power flow.

[0139] Step 4-2: Construct the objective function of the distribution system power supply restoration model considering power flow balance under network-physical collaborative threats from (34) to (35): :

[0140] (34)

[0141] (35)

[0142] In formula (34)-formula (35), , , They represent the loss, load reduction and power flow balance items of the distribution system respectively; represents the coefficient of the power flow equilibrium term;

[0143] Step 4: The proposed R-SOP-based power restoration model for the distribution system in response to cyber-physical collaborative threats is converted into a mixed integer second-order cone programming constraint and then solved to obtain an operation plan for the distribution system that includes the reconfigurable soft switching action, the energy storage system action, and the controllable switches.

[0144] Step 4-1: Combine two nonlinear variables and Replaced by two linear variables and The big-M method is used to relax the power flow constraints (19)-(21) and (25)-(26) to transform them into linear constraints, and then the transformed constraints are constructed using (36)-(40):

[0145] (36)

[0146] (37)

[0147] (38)

[0148] (39)

[0149] (40)

[0150] In formula (36) to formula (40), and respectively Time Node The square of the voltage, Time Node To Node The square of the branch current between them;

[0151] Step 4-2: Use equation (41) to transform equation (22) into a second-order cone constraint:

[0152] (41)

[0153] In formula (41), T represents transposition;

[0154] Step 4-3: Use the big-M method to decouple the power and current transmitted by the line, and then use equations (42) to (44) to construct the converted constraints:

[0155] (42)

[0156] (43)

[0157] (44)

[0158] Step 4-4: Use equation (45) to transform the capacity constraint equation (18) of R-SOP into a rotating cone constraint:

[0159] (45)

[0160] The above method is used to transform the power supply restoration model constraints of the distribution system based on reconfigurable intelligent soft switches to cope with network-physical collaborative threats into mixed integer second-order cone programming constraints and then solve them. The operation plan of the distribution system including the reconfigurable soft switch action, the energy storage system action, and the controllable switch is obtained.

[0161] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0162] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

[0163] To help those skilled in the art better understand the present invention, the example analysis includes the following components:

[0164] 1. Case description and simulation results analysis:

[0165] In order to verify its effectiveness, the present invention adopts Figure 1 The improved IEEE 33-node system with 6-port R-SOP is shown as a test system, and a case analysis is performed. Figure 1In the test system shown, controllable section switches are at 3, 20, 6, 25, 27, 30, and 14, and controllable tie switches are at 33 and 34. Nodes connected to the photovoltaic system are at 7, 15, 33, 22, and 27. The system base voltage is 12.66 kV, with a voltage safety range of 0.95-1.05 pu. Energy storage is deployed on the DC side of the R-SOP, with a maximum charge and discharge power of 0.5 MW and a capacity of 1.5 MW·h. The initial state of charge is 60%, the maximum and minimum states of charge are 90% and 40%, and the charge and discharge efficiency is 95%. The R-SOP uses a six-feeder, four-converter system, connected to nodes 33, 9, 12, 15, 18, and 22, for a total capacity of 1.5 MW. The capacities of the four converters are set according to the golden ratio: 0.75 MVA, 0.4635 MVA, 0.1770 MVA, and 0.1095 MVA, respectively. The load demand data of nodes attacked by LR in the power outage area increases by 50%, while the load demand data of nodes attacked by LR in the non-power outage area decreases, keeping the total load demand data modification amount at 0MW.

[0166] At the same time, in order to measure the balance of the system power flow, the equation is defined to calculate the power flow balance value:

[0167]

[0168] In order to fully demonstrate the effectiveness of the power supply restoration method for the distribution system based on reconfigurable intelligent soft switches in response to network-physical collaborative threats, four schemes, Case 1, Case 2, Case 3, and Case 4, are set up for comparison in the example part.

[0169] Case 1 includes a distribution system fault recovery method with a four-port SOP, but ignores the impact of network attacks.

[0170] Case 2: Fault recovery method for distribution system with four-port SOP to deal with cyber-physical collaborative threats.

[0171] Case 3: Distribution system fault recovery method including R-SOP, which uses R-SOP to cooperate with fault reconstruction and recovery, but ignores the impact of network attacks.

[0172] Case 4: The proposed distribution system power restoration method with R-SOP addresses cyber-physical collaborative threats. All numerical simulations in this case study were performed in MATLAB 2021a and solved using the YALMIP toolbox and Gurobi solver in a 64-bit Windows environment.

[0173] exist Figure 3In the IEEE 33-node test system shown in Figure 1, the above four solutions were executed simultaneously. The recovery performance of Case 1, Case 2, Case 3, and Case 4 was obtained, as shown in Table 1.

[0174] Table 1

[0175]

[0176] Table 1 shows that the load recovery rate in Case 3 is higher than that in Case 1, and that in Case 4 is higher than that in Case 2, demonstrating the advantages of R-SOP over SOP. The average voltage deviation in all four cases is around 2%, demonstrating the voltage regulation capabilities of both R-SOP and SOP. Without the power flow balancing term, the power flow balance values ​​in all four cases are higher than with it. The presence of the power flow balancing term brings the power flow of each line closer to the mean. A comparison of the four cases reveals that while the power flow balance values ​​in Cases 1, 2, and 3 are lower, this is at the expense of a significant reduction in load recovery. Overall, Case 4 demonstrates the best recovery performance of the four cases, maintaining a high load recovery rate while also balancing voltage deviation and power flow balance.

[0177] Figure 2 The attack effects of different CCPT scenarios are shown, where the darker the color of the table, the better the CCPT attack effect. The test results show the synergistic logic between cyber attacks and physical attacks: 1. Select nodes close to the end of the feeder as attack targets. By increasing the load data of these nodes, the dispatcher receives incorrect load information, thereby performing unnecessary load reduction. When the power outage area contains multiple nodes, the node closest to the end of the feeder is preferentially attacked. The closer to the end of the feeder, the more significant the attack effect. 2. First select the end node of the feeder that is not connected to the distributed power supply as the attack target. If the node is connected to a distributed power supply, the power supply will provide a certain amount of power support for the node, thereby weakening the attack effect.

[0178] Figure 3 The load recovery rate and composition during the recovery process are shown; Figure 4 Transmit power and energy storage power for each feeder of R-SOP; Figure 5 The figure shows the capacity allocation of each R-SOP port. The figure shows that during the recovery process, R-SOP flexibly switches the port capacity and the direction of power transfer at different times to adapt to the load demand of different power outage areas, effectively increasing the load recovery rate.

[0179] Figure 6 The line transmission power diagram with and without power flow balance term is shown in Figure 2. Figure 6It turns out that using power flow balancing increases the transmission power of line 18 (the line between nodes 2-19), reducing the power transmitted by line 2-5. At this point, R-SOP allocates capacity to feeder 22, transferring the increased power from line 18 to other nodes. Therefore, the R-SOP's power transfer reduces the transmission power of some lines, effectively preventing the injection of false data.

[0180] Figure 7 is the line flow over-limit distribution probability diagram, from Figure 7 It is found that the power flow balance item reduces the probability of power flow exceeding the limit for most lines. The reduction is particularly significant for the first 10 lines with relatively high power.

[0181] In this specification, the schematic descriptions of the present invention do not necessarily refer to the same embodiment or example. Those skilled in the art may combine and combine the different embodiments or examples described in this specification. In addition, the contents of the embodiments in this specification are merely an enumeration of the implementation forms of the inventive concept. The scope of protection of the present invention should not be considered limited to the specific forms described in the implementation cases. The scope of protection of the present invention also includes equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats, characterized in that: The following steps are involved: Step 1: Build a quantitative model of cyber-physical collaborative threats; Step 2: Construct an operation model of the energy storage system in the power distribution system; Step 3: Construct an operation model of the reconfigurable intelligent soft switch R-SOP including the energy storage system; Step 4: Construct a virtual power flow model for power distribution system reconstruction; Step 5: Construct a power supply restoration model for the distribution system based on R-SOP to cope with cyber-physical collaborative threats; Step 6: Construct a power restoration model for the distribution system considering power flow balance under cyber-physical collaborative threats. Step 7: The power supply restoration model of the distribution system considering power flow balance under the threat of network-physical collaboration is converted into a mixed integer second-order cone programming constraint and then solved to obtain the operation plan of the distribution system including the reconfigurable soft switch R-SOP action, the energy storage system action, and the controllable switch action.

2. The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats according to claim 1, characterized in that: In step 1, a quantitative model of cyber-physical collaborative threats is constructed using equations (1) to (8): (1) (2) (3) (4) (5) (6) (7) (8) In formula (1) to formula (8), represent Time Node The amount of load tampering; Represents the load tampering amplitude, ranging from 0 to 1; is the total number of moments; represents the power transfer distribution factor matrix; represents the power flow measurement attack vector; represents the load measurement attack vector; Represents the set of all nodes in the power distribution system; for Time Node Active power consumed by the load; represent Time Node The 0-1 integer variable corresponding to the load node; represent Time Node With node Branch road between The corresponding 0-1 integer variable; The number of attackable resources is set; represent Time branch Transmission power tampering amount; represent Time branch A 0-1 integer variable indicating whether the attack occurred; Represents physical attack resources; represent Time branch A 0-1 integer variable indicating whether the connection is established.

3. The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats according to claim 2, characterized in that: In step 2, equations (9) to (12) are used to construct an operation model of the energy storage system in the power distribution system: (9) (10) (11) (12) In formula (9) to formula (12), 、 are the minimum and maximum values ​​of the energy storage system charging power respectively; 、 are the minimum and maximum discharge power of the energy storage system, respectively. 、 For energy storage systems Charge and discharge power at all times; For energy storage systems Auxiliary variables at the moment, the energy storage system When charging, =1, the energy storage system is When discharging at any time, =0; 、 、 are the minimum, maximum and initial values ​​of the state of charge of the energy storage system respectively; 、 are the total capacity of the energy storage system and capacity of the moment; 、 are the charging and discharging efficiency of the energy storage system, respectively.

4. The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats according to claim 3, characterized in that: In step 3, equations (13) to (18) are used to construct an operation model of a reconfigurable intelligent soft switch R-SOP including an energy storage system: (13) (14) (15) (16) (17) (18) In formula (13) to formula (18), 、 They represent the set of nodes connected to the converter and the set of all converters respectively; is the number of nodes connected to the converter VSC; 、 、 Node All connected converters are Total loss, DC side power and AC side power at the moment; For nodes All connected converters are Reactive power at the moment; For nodes All connected converters VSC Apparent power at the moment; For the inverter capacity; is the loss coefficient of R-SOP; Is a binary variable representing the converter exist Whether the node is connected to the voltage source converter at any time connected.

5. The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats according to claim 4, characterized in that: In step 4, equations (19) to (22) are used to construct a virtual power flow model for distribution system reconstruction: (19) (20) (21) (22) In formula (19) to formula (22), is a node The port is in R-SOP open state; It is a branch road Virtual trends; is a node The power flow from the virtual power source; is a node Virtual needs; is the number of nodes in the distribution system; M is a constant.

6. The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats according to claim 5, characterized in that: In step 5, Equations (23) to (33) are used to construct a power supply restoration model for the distribution system based on R-SOP to cope with cyber-physical collaborative threats: (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) In formula (23) to formula (33), and They are Time branch Active and reactive power transmitted on the and Branch resistance and reactance; and They are Time Node The injected active and reactive power; and for Time Node Active and reactive load reduction; for Time Node voltage; for Time branch The current transmitted on 、 They are Time Node Active power output and active power reduction of photovoltaic PV; for Time Node The reactive power output of photovoltaic PV; and They are Time Node Active and reactive power consumed by the load; and for Time Node Active and reactive power injected at R-SOP; and are the upper and lower limits of node voltage in the distribution system respectively; It is the upper limit of branch current in the distribution system; For branch circuits in the distribution system Upper limit of power flow.

7. The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats according to claim 6, characterized in that: In step 6, equations (34) and (35) are used to construct the objective function of the power supply restoration model of the distribution system considering power flow balance under the threat of network-physical collaboration: , and Equations (1) to (33) are used as constraints for the power supply restoration model of the distribution system considering power flow balance under the threat of network-physical collaboration; (34) (35) In formula (34)-formula (35), , , They represent the loss, load reduction and power flow balance items of the distribution system respectively; Represents the coefficient of the power flow equilibrium term.

8. The power supply restoration method for a power distribution system based on R-SOP to cope with cyber-physical collaborative threats according to claim 7, characterized in that: The step 7 comprises the following steps: Step 7.1: and Replaced by two linear variables and , and use the big-M method to relax Equations (19)-(21) and (25)-(26) to transform them into linear constraints, and then use Equations (36)-(40) to construct the transformed constraints: (36) (37) (38) (39) (40) In formula (36) to formula (40), and respectively Time Node The square of the voltage at Time Node To Node Intermediate branch The square of the current; Step 7.2: Use equation (41) to transform equation (22) into a second-order cone constraint: (41) In formula (41), T represents transposition; Step 7.3: Use the big-M method to decouple the power and current transmitted by the branch, and then use Equations (42) to (44) to construct the transformed constraints: (42) (43) (44) Step 7.4: Use equation (45) to transform equation (18) into a rotating cone constraint: (45)。 9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports a processor to execute the power supply restoration method according to any one of claims 1 to 8, and the processor is configured to execute the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power supply restoration method according to any one of claims 1 to 8 are executed.

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