Power distribution network island division and reliability evaluation method considering typical scene load deviation, medium and equipment

Abstract

The invention provides a power distribution network island division and reliability evaluation method considering typical scene load deviation, a medium and equipment. The power distribution network island division and reliability evaluation method comprises the following steps: 1) establishing an island division model considering the typical scene load deviation; 2) establishing an island division strategy based on robust optimization solution and typical scene load deviation; and 3) based on the island division strategy considering the typical scene load deviation, establishing the power distribution network reliability evaluation method considering the typical scene load deviation. According to the method, the fault downstream division strategy can be formulated under the condition of considering the load deviation of the typical scene, and the power distribution network island division process considering the load deviation of the typical scene is considered in the reliability evaluation process, so that the reliability index considering the power supply capacity of the distributed power supply of the power distribution network is calculated; and the accuracy of reliability evaluation is improved.

Description

Technical Field

[0001] The present invention belongs to the field of distribution network optimization operation and reliability analysis, and specifically relates to a distribution network islanding division and reliability assessment method, medium and equipment taking into account typical scenario load deviations. Background Art

[0002] In recent years, with the continuous increase in the scale of distributed power access, the power supply capacity of distributed power has received more and more attention. When a fault occurs, the distributed power supply in the downstream area of ​​the fault supplies power to some important loads to form an island operation, which can effectively improve the fault recovery capability of the distribution network, thereby improving the reliability of the distribution network. At the same time, in the process of island division, it is necessary to consider the load deviation of typical scenarios to ensure the feasibility of the island division scheme. If the power supply capacity of distributed power is ignored in the process of reliability assessment, a more conservative reliability index conclusion will be obtained, which cannot reflect the beneficial effect of distributed power on improving reliability. Therefore, new technical solutions are needed to solve the above problems. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a distribution network islanding division and reliability assessment method, medium and equipment taking into account load deviations in typical scenarios.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a distribution network islanding division and reliability assessment method taking into account load deviation in typical scenarios, comprising the following steps:

[0006] S1: Establish an island partitioning model taking into account load deviation in typical scenarios;

[0007] S2: Perform robust optimization on the island partitioning model and establish an island partitioning strategy that takes into account load deviations in typical scenarios;

[0008] S3: Distribution network reliability assessment taking into account load deviation in typical scenarios based on island partitioning strategy.

[0009] Optionally, in step S1, the objective function of the island partitioning model is to maximize the total load power restored during the island operation period, and the specific expression is as follows:

[0010] Formula 1:

[0011] In the formula, f obj is the objective function, υ is the set of nodes; t represents the time period of island operation; b j represents the disconnection state of node j, b j =1 means the node is included in the island, b j=0 means the node is not included in the island; is the expected value of active power of node j in period t.

[0012] Optionally, in step S1, the constraint conditions of the island partition model include distributed power output constraints, power balance constraints within the island, and topology constraints;

[0013] The distributed generation output constraint is expressed as:

[0014] Formula 2: P DG，j，t ≤e j，t S DG，j，t , j∈{DG};

[0015] Formula 3:

[0016] Where P DG，j，t represents the active power output of node j in time period t; Q DG，j，t represents the reactive power output of node j in time period t; S DG，j,t represents the capacity of distributed generation at node j in time period t; e ，j，t is the ratio of active output to capacity of distributed generation at node j in time period t; {DG} is the set of distributed generation;

[0017] The power balance constraint within the island is expressed as:

[0018] Formula 4:

[0019] Formula 5:

[0020] Formula 6:

[0021] Formula 7:

[0022] Formula 8:

[0023] Formula 9:

[0024] Formula 10:

[0025] In the formula, H js，t , G js，t They represent the active and reactive power flowing from node j to node s during time period t; Q SVC，j，t represents the reactive power output by SVC at node j during period t; is the load active power of node j in period t in a typical scenario, ΔP L，j，t is the load deviation of node j in period t in a typical scenario; Q L，j，tis the reactive power demand of the load on node j in period t in a typical scenario; δ(j) and π(j) represent the child node set and parent node set of node j, respectively; represents the maximum capacity of line ij; c ij Indicates the disconnection status of line ij, c ij =1, the circuit is closed, c ij = 0 means the line is disconnected; ε is the set of lines; r ij 、x ij Represent the resistance and reactance of line ij respectively; U j，t is the voltage of node j in period t; M is a sufficiently large positive number; Ω is the uncertainty set of typical scenario load deviation, R j The covariance matrix representing the node load deviation in a typical scenario; C conf is the uncertainty;

[0026] The topological constraint is expressed as:

[0027] Formula 11:

[0028] Formula 12:

[0029] Formula 13: b j ≤H j ≤M·b j , j∈{DG};

[0030] Formula 14:

[0031] Formula 15:

[0032] In the formula, F ij represents the virtual power on branch ij; H j represents the power provided by the virtual source point j; N represents the number of islands.

[0033] Optionally, in step S2, the establishment of a distribution network islanding strategy that takes into account load deviations in typical scenarios is to transform the islanding model into a deterministic mixed integer second-order cone robust optimization problem, and solve it through a solving software to obtain the islanding strategy that takes into account load deviations in typical scenarios.

[0034] Optionally, the island partitioning model is converted into a deterministic mixed integer second-order cone robust optimization problem, specifically comprising the following steps:

[0035] S201: Convert the operation constraint equation 4 containing the typical scenario load deviation into:

[0036] Formula 16:

[0037] Equation 16 indicates that when the load deviation fluctuates to the maximum, the island power balance constraint is still satisfied;

[0038] S202: Convert the ellipsoidal uncertainty set formula 10 into:

[0039] Formula 17:

[0040] In the formula, ξ is ΔP L，j，t Equivalent substitution variables of ; || ||2 represents the 2-norm;

[0041] S203: Replace the load forecast error ΔPL in equation 16 with the form of an uncertainty set ，j，t , transform equation 16 into:

[0042] Formula 18:

[0043] In the formula, the uncertainty C conf Preset in advance, the covariance matrix R j Obtained from historical data, b j is the decision quantity in the first stage of the robust optimization model; in the second stage of the robust optimization model, ξ is the only decision quantity with uncertainty;

[0044] S204: Formula 18 is further transformed into:

[0045] Formula 19:

[0046] Since ||ξ||2≤1, Equation 19 is transformed into the corresponding second-order cone robust equivalent form:

[0047] Formula 20:

[0048] Optionally, in step S3, the distribution network reliability assessment taking into account the load deviation in typical scenarios based on the island division strategy specifically includes the following steps:

[0049] S301: inputting original data related to the distribution network, including parameters of distribution network components, topological structure of the distribution network, load timing requirements and distributed power generation timing output;

[0050] S302: According to the topological structure of the distribution network, traverse each possible component failure, traverse the impact of each possible component failure, and establish a FMEA table;

[0051] S303: Setting the simulation period of the Monte Carlo simulation method to N_Year, and setting the initial time T=0, the initial state of all components in the distribution network is normal operation;

[0052] S304: Generate a random number between [0, 1] and perform state sampling on all lines in the distribution network; obtain the trouble-free operation time TTF of each component according to formula 21 i , find the moment ttf with the shortest trouble-free operation time among all components as the fault time of this simulation, and the corresponding component with the shortest trouble-free operation time is recorded as component k;

[0053] Formula 21: TTF i =-lnR(t) / λ i ;

[0054] Formula 22: ttf = min{TTF i};

[0055] Where R(t) is a random number uniformly distributed between [0, 1]; i is the failure rate of each component i;

[0056] S305: Determine the fault repair time ttr according to the parameters of the fault component k k , read [ttf, ttf+ttr k ] The load timing demand and distributed power generation timing output within the time period are calculated, and the stable operation time is 5 hours as the time limit. The downstream area of ​​the fault is divided into islands according to the island division strategy;

[0057] S306: Determine the load set that can restore power supply according to the island division result {L G}, combined with the FMEA table to record and accumulate the number of power outages and power outage duration of each load node, update the simulation time T = T + ttf + ttr k ;

[0058] S307: If the simulation time T is less than the simulation period N_Year, continue to execute S304 to S306; otherwise, count the number of power outages and power outage time of each load node, and calculate the reliability index of the load point and the system side.

[0059] In a second aspect, the present invention proposes a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the distribution network islanding division and reliability assessment method taking into account load deviations in typical scenarios as described in the first aspect.

[0060] In a third aspect, the present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the distribution network island division and reliability assessment method taking into account load deviations in typical scenarios as described in the first aspect is implemented.

[0061] The beneficial effects of the present invention are: compared with traditional reliability analysis methods, the present invention takes into account the recovery effect of distributed power sources on loads in typical scenarios, and takes into account the load deviation of typical scenarios in the process of island division, ensuring the feasibility of the island recovery plan after a fault, objectively and fully reflecting the beneficial effects of distributed power sources on improving reliability, and improving the accuracy of reliability evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flow chart of a distribution network islanding division and reliability assessment method taking into account load deviation in typical scenarios proposed by the present invention;

[0063] Figure 2 Schematic diagram of the power distribution network used in this embodiment;

[0064] Figure 3 It is the load power timing curve of typical scenario;

[0065] Figure 4 This is the power timing curve of distributed power supply in a typical scenario. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0067] In one embodiment, the present invention proposes a distribution network islanding division and reliability assessment method taking into account load deviation in typical scenarios, such as Figure 1 As shown, the following steps are included:

[0068] S1: Establish an island partitioning model that takes into account load deviations in typical scenarios.

[0069] The objective function of the island partitioning model taking into account the load deviation in typical scenarios is to maximize the total load power restored during the island operation period. The specific expression is as follows:

[0070]

[0071] Where: f obj is the objective function, υ is the set of nodes; t represents the time period of island operation; b j represents the disconnection state of node j, b j =1 means the node is included in the island, b j =0 means the node is not included in the island; is the expected value of active power of node j in period t.

[0072] The constraint expressions of the island partitioning model taking into account load deviations in typical scenarios are as follows.

[0073] (1) Distributed power generation output constraints

[0074] After a partial failure of the distribution network, part of the load downstream of the fault is supplied by distributed power sources to form an island. The constraint on the output of distributed power sources is expressed as:

[0075] P DG，j，t ≤e j，t S DG，j，t , j∈{DG} Formula (2)

[0076]

[0077] Where: P DG，j，t represents the active power output of node j in time period t; Q DG，j，t represents the reactive power output of node j in time period t; S DG，j，t represents the capacity of distributed generation at node j in time period t; e j，t is the ratio of active output to capacity of distributed generation at node j in time period t; {DG} is the set of distributed generation.

[0078] (2) Power balance constraints within an island

[0079] The power balance within the island is the basic condition to ensure the safe operation of the island. The operation constraints based on the linear distribution network power flow model are expressed as:

[0080]

[0081] Where: H js，t , G js，t They represent the active and reactive power flowing from node j to node s in time period t; Q SVC，j，t represents the reactive power output by SVC (static VAR compensation device) at node j during period t; is the load active power of node j in period t in a typical scenario, ΔP L，j，t is the load deviation of node j in period t in a typical scenario; Q L，j，t is the reactive power demand of the load on node j in the period t in a typical scenario; δ(j) and π(j) represent the child node set and parent node set of node j respectively; represents the maximum capacity of line ij; c ij Indicates the disconnection status of line ij, c ij =1, the circuit is closed, c ij = 0 means the line is disconnected; ε is the set of lines; r ij 、x ij Represent the resistance and reactance of line ij respectively; U j，tis the voltage of node j in period t; in order to meet the condition that the voltage difference between the two ends of the line is not restricted after the line is disconnected, M is a sufficiently large positive number, and the default value is 1000; Ω is the uncertainty set of typical scenario load deviation, R j The covariance matrix representing the node load deviation in a typical scenario; C conf is the uncertainty.

[0082] (3) Topological constraints

[0083] The islands divided by the distribution network are radial in structure and need to meet the following two conditions at the same time:

[0084] 1) Each subgraph is a connected graph;

[0085] 2) The number of branches in the distribution network is equal to the number of nodes minus the number of subgraphs.

[0086] In order to ensure that each subgraph is a connected graph, an auxiliary virtual power flow constraint is introduced. If each node except the source node is contained in the island, the virtual power demand of the node is set to 1; otherwise, the virtual power demand of the node is set to 0. The mathematical model of the auxiliary virtual power flow constraint is as follows:

[0087]

[0088] b j ≤H j ≤M·b j , j∈{DG} (Equation 13)

[0089]

[0090] Where: F ij represents the virtual power on branch ij; H j represents the power provided by the virtual source point j; N represents the number of islands.

[0091] S2: Establish an island partitioning strategy based on robust optimization solution and typical scenario load deviation.

[0092] The specific steps of the island partitioning strategy based on robust optimization solution and typical scenario load deviation are as follows.

[0093] S201: Transform the operation constraint formula (4) containing the typical scenario load deviation into:

[0094]

[0095] Formula (16) indicates that when the load deviation fluctuation is the largest, the island power balance constraint is still satisfied.

[0096] S202: Convert the ellipsoidal uncertainty set formula (10) into:

[0097]

[0098] Where: ξ is ΔP L，j，t An equivalent substitution variable of ; || ||2 represents the 2-norm.

[0099] S203: Replace the load forecast error ΔP in equation (16) with the form of uncertainty set L，j，t , formula (16) can be transformed into:

[0100]

[0101] Where: Uncertainty C conf The covariance matrix R can be preset in advance j Obtained from historical data, b j is the decision quantity in the first stage of the robust optimization model; in the second stage of the robust optimization model, ξ is the only decision quantity with uncertainty.

[0102] S204: Formula (18) can be further transformed into:

[0103]

[0104] Since ||ξ||2≤1, equation (19) can be transformed into the corresponding second-order cone robust equivalent form:

[0105]

[0106] After the above transformation process, the island partitioning model is transformed into a deterministic mixed integer second-order cone robust optimization problem. The commercial solving software is used to solve the island partitioning strategy that takes into account the load deviation of typical scenarios.

[0107] S3: Based on the island division strategy taking into account the load deviation in typical scenarios, a distribution network reliability assessment method taking into account the load deviation in typical scenarios is established.

[0108] The specific steps for establishing a distribution network reliability assessment method taking into account load deviations in typical scenarios are as follows.

[0109] S301: Input component parameters, distribution network topology, load timing requirements, distributed generation timing output and other related raw data.

[0110] S302: According to the topological structure of the distribution network, traverse each possible component failure, traverse the impact of each possible component failure, and establish a FMEA table.

[0111] S303: Setting the simulation period of the Monte Carlo simulation method to N_Year, and setting the initial time T=0, the initial states of all components in the distribution network are normal operation.

[0112] S304: Generate a random number between [0, 1] and perform state sampling on all lines in the distribution network. According to formula (21), the fault-free operation time TTF of each component is obtained: i , find the moment ttf with the shortest trouble-free operation time among all components as the fault time of this simulation, and the corresponding component with the shortest trouble-free operation time is recorded as component k.

[0113] TTF i =-lnR(t) / λ i Formula (21)

[0114] ttf=min{TTF i Formula (22)

[0115] Where: R(t) is a random number that follows a uniform distribution between [0, 1]; i is the failure rate of each component i.

[0116] S305: Determine the fault repair time ttr according to the parameters of the fault component k k , read [ttf, ttf+ttr k ] The time series values ​​of load demand and distributed generation output within the time period. Taking 5 hours of stable operation as the time limit, the downstream area of ​​the fault is divided into islands according to the island division method taking into account the load deviation of typical scenarios.

[0117] S306: Determine the load set that can restore power supply according to the island division result {L G}, combined with the FMEA table to record and accumulate the number of power outages and power outage duration of each load node, update the simulation time T = T + ttf + ttr k .

[0118] S307: If the simulation time T is less than the simulation period N_Year, continue to execute steps S304 to S306; otherwise, count the number of power outages and the power outage time of each load node, and calculate the reliability index of the load point and the distribution network side.

[0119] Based on the above scheme, this embodiment applies and analyzes the above method, as follows:

[0120] In this embodiment, the IEEE 33-node test distribution network is used as an example. Two photovoltaic power generation distribution networks and two wind power generation distribution networks are installed at nodes 2, 17, 5, and 15 respectively. The capacity of the distributed power source is 6MW. In order to provide instantaneous power support, a storage distribution network is installed on each distributed power source node. The island aims to operate stably for 5 hours. The typical scenario load and distributed power generation timing curve are shown in the attached Figure 3 and attached Figure 4 Table 1 shows the data of each IEEE 33 line, including the length of the line, the duration of the line switching fault, and the duration of the line repair-switching fault, assuming that the failure rate of all lines is 0.1 times / (year·km).

[0121] Table 1 IEEE 33-bus distribution network line parameters

[0122]

[0123] Table 2 lists the distribution network reliability indicators before and after considering the island division.

[0124] Table 2 Distribution network reliability indicators before and after considering island division

[0125] No islands considered Robust Optimization Model SAIFI (times / year) 5.16 5.16 SAIDI (h / year) 3.535 3.181 ASAI (%) 99.9597％ 99.9637％ ENSI (MWh / year) 21.87 19.68

[0126] It can be seen from Table 2 that compared with the reliability data of the distribution network without considering the island, when the island is divided based on the robust optimization model, the SAIDI index of the distribution network decreases by 10.01%; the ENSI index decreases by 10.01%; and the ASAI index increases by 0.004% on the original basis. Considering the island division effectively improves the reliability index of the distribution network.

[0127] In another embodiment, the present invention proposes a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the distribution network islanding division and reliability assessment method taking into account load deviations in typical scenarios as described in the aforementioned embodiment.

[0128] In another embodiment, the present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the distribution network island division and reliability assessment method taking into account load deviations in typical scenarios as described in the aforementioned embodiment is implemented.

[0129] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0130] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0131] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A distribution network islanding division and reliability assessment method taking into account typical scenario load deviation, characterized in that: The steps include: S1: Establish an island partitioning model taking into account load deviation in typical scenarios; S2: Perform robust optimization on the island partitioning model and establish an island partitioning strategy that takes into account load deviations in typical scenarios; S3: Distribution network reliability assessment taking into account load deviation in typical scenarios based on island partitioning strategy.

2. A distribution network islanding division and reliability assessment method taking into account typical scenario load deviations as claimed in claim 1, characterized in that: In step S1, the objective function of the island partitioning model is to maximize the total load power restored during the island operation period, and the specific expression is as follows: Formula 1: In the formula, f obj is the objective function, υ is the set of nodes; t represents the time period of island operation; b j represents the disconnection state of node j, b j =1 means the node is included in the island, b j =0 means the node is not included in the island; is the expected value of active power of node j in period t.

3. A distribution network islanding division and reliability assessment method taking into account typical scenario load deviations as claimed in claim 2, characterized in that: In step S1, the constraints of the island partition model include distributed power output constraints, power balance constraints within the island, and topology constraints; The distributed generation output constraint is expressed as: Formula 2: P DG，j，t ≤e j，t S DG，j，t , j∈{DG}; Formula 3: Where P DG，j，t represents the active power output of node j in time period t; Q DG，j，t represents the reactive power output of node j in time period t; S DG，j，t represents the capacity of distributed generation at node j in time period t; e j，t is the ratio of active output to capacity of distributed generation at node j, time period t; {DG} is the collection of distributed generation; The power balance constraint within the island is expressed as: Formula 4: Formula 5: Formula 6: Formula 7: Formula 8: -M(1 - c ij ) + 2(r ij H ij，t + x ij G ij，t ) ≤ U j，t -U i，t ≤ M(1 - c ij ) + 2(r ij H ij，t + x ij G j，t ), Formula 9: Formula 10: In the formula, H js，t , G js，t They represent the active and reactive power flowing from node j to node s during time period t; Q SVC，j，t represents the reactive power output by SVC at node j during period t; is the load active power of node j in period t in a typical scenario, ΔP L，j，t is the load deviation of node j in a period in a typical scenario; Q L，j，t is the reactive power demand of the load on node j in period t in a typical scenario; δ(j) and π(j) represent the child node set and parent node set of node j, respectively; represents the maximum capacity of line ij; c ij Indicates the disconnection status of line ij, c ij =1, the circuit is closed, c ij =0 means the line is disconnected; ε is the set of lines; r ij 、x ij Respectively represent the resistance and reactance of line ij; U j，t is the voltage of node j during period t; M is a sufficiently large positive number; Ω is the uncertainty set of typical scenario load deviations, R j The covariance matrix representing the node load deviation in a typical scenario; C conf is the uncertainty; The topological constraint is expressed as: Formula 11: Formula 12: Formula 13: b j ≤H j ≤M·b j , j∈{DG}; Formula 14: -M·c ij ≤F ij ≤M·c ij , Formula 15: In the formula, F ij represents the virtual power on branch ij; H j represents the power provided by the virtual source point j; N represents the number of islands.

4. A distribution network islanding division and reliability assessment method taking into account typical scenario load deviations as claimed in claim 3, characterized in that: In step S2, the establishment of a distribution network islanding strategy that takes into account load deviations in typical scenarios is to transform the islanding model into a deterministic mixed integer second-order cone robust optimization problem, and obtain the islanding strategy that takes into account load deviations in typical scenarios by solving it through solving software.

5. A distribution network islanding division and reliability assessment method taking into account typical scenario load deviations as claimed in claim 4, characterized in that: The island partitioning model is transformed into a deterministic mixed integer second-order cone robust optimization problem, which specifically includes the following steps: S201: Convert the operation constraint equation 4 containing the typical scenario load deviation into: Formula 16: Equation 16 indicates that when the load deviation fluctuates to the maximum, the island power balance constraint is still satisfied; S202: Convert the ellipsoidal uncertainty set formula 10 into: Formula 17: In the formula, ξ is ΔP L，j，t Equivalent substitution variables of ; || ||2 represents the 2-norm; S203: Replace the load forecast error ΔP in equation 16 with the form of an uncertain set L，j，t , transform equation 16 into: Formula 18: In the formula, the uncertainty C conf Preset in advance, the covariance matrix R j Obtained from historical data, b j is the decision quantity in the first stage of the robust optimization model; in the second stage of the robust optimization model, ξ is the only decision quantity with uncertainty; S204: Formula 18 is further transformed into: Formula 19: Since ‖ξ‖2≤1, Equation 19 is transformed into the corresponding second-order cone robust equivalent form: Formula 20:

6. The method for islanding and reliability assessment of a distribution network taking into account load deviation in typical scenarios as claimed in claim 1, characterized in that: In step S3, the distribution network reliability assessment taking into account the load deviation in typical scenarios based on the island division strategy specifically includes the following steps: S301: inputting original data related to the distribution network, including parameters of distribution network components, topological structure of the distribution network, load timing requirements and distributed power generation timing output; S302: According to the topological structure of the distribution network, traverse each possible component failure, traverse the impact of each possible component failure, and establish a FMEA table; S303: Setting the simulation period of the Monte Carlo simulation method to N_Year, and setting the initial time T=0, the initial state of all components in the distribution network is normal operation; S304: Generate a random number between [0, 1] and perform state sampling on all lines in the distribution network; According to formula 21, the trouble-free operation time TTF of each component is obtained i , find the moment ttf with the shortest trouble-free operation time among all components as the fault time of this simulation, and the corresponding component with the shortest trouble-free operation time is recorded as component k; Equation 21: TTF i = -lnR(t) / λ i ; Formula 22: ttf = min{TTF i }; Where R(t) is a random number uniformly distributed between [0, 1]; i is the failure rate of each component i; S305: Determine the fault repair time ttr according to the parameters of the fault component k k , read [ttf, ttf+ttr k ] The load timing demand and distributed power generation timing output within the time period are calculated, and the stable operation time is 5 hours as the time limit. The downstream area of ​​the fault is divided into islands according to the island division strategy; S306: Determine the load set that can restore power supply according to the island division result {L G }, combined with the FMEA table to record and accumulate the number of power outages and power outage duration of each load node, update the simulation time T = T + ttf + ttr k ; S307: If the simulation time T is less than the simulation period N_Year, continue to execute S304 to S306; otherwise, count the number of power outages and power outage time of each load node, and calculate the reliability index of the load point and the system side.

7. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables the computer to execute the distribution network islanding division and reliability assessment method taking into account load deviations in typical scenarios as described in any one of claims 1 to 6.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for islanding and reliability assessment of a distribution network taking into account load deviations in typical scenarios as described in any one of claims 1 to 6 is implemented.

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