Method and system for evaluating toughness of intelligent power distribution network in extreme weather

Abstract

The invention discloses a toughness evaluation method and system for an intelligent power distribution network in extreme weather, and the method comprises the steps: analyzing the fault condition of the power distribution network in extreme weather, and building a fault model in extreme weather; simulating an extreme weather scene, reducing the extreme weather scene through a clustering algorithm, obtaining the time-varying failure rate of each branch of the power distribution network, and extracting load and renewable energy output by adopting a sampling method; and constructing a power supply recovery strategy model with the maximum power failure load capacity as a target and a toughness evaluation index, and performing toughness evaluation on the power distribution network. The influence of extreme weather on the power distribution network is quantified through the fault model, the power distribution network toughness evaluation index system is established, the disaster resistance of the power distribution network can be quantitatively evaluated, power distribution network planning is guided, and the extreme weather handling capacity of the power distribution network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a method and system for evaluating the resilience of a smart distribution network under extreme weather conditions. Background Art

[0002] Under extreme weather conditions, distribution networks often experience multiple failures. At this time, considering only the N-1 safety criterion of the distribution network is not enough to ensure the safe operation of the distribution network, and the resilience of the distribution network must also be considered.

[0003] At present, some scholars have proposed to use the missing area of ​​the system function curve to evaluate the resilience of the distribution network, but have not established fault scenarios to evaluate the resilience of the distribution network; in terms of fault scenario screening, some scholars have used the missing area of ​​the system function curve during the fault duration to evaluate the resilience of the distribution network, and screened the fault scenarios through the information entropy method, making the evaluation indicators more reasonable, but it did not reflect the randomness of the disaster in the fault scenario screening process. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method for evaluating the resilience of a smart distribution network under extreme weather conditions to solve the problem of how to better evaluate the resilience of a distribution network to natural disasters.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for assessing the resilience of a smart distribution network under extreme weather conditions, comprising: analyzing the fault conditions of the distribution network under extreme weather conditions, and establishing a fault model under extreme weather conditions;

[0008] Based on the fault model, extreme weather scenarios are simulated and reduced by clustering algorithms to obtain the time-varying fault rate of each branch of the distribution network, and load and renewable energy output are extracted by sampling method;

[0009] Based on the load, renewable energy output and distribution network failure scenarios, a power supply restoration strategy model with the maximum power outage load as the target and resilience assessment indicators are constructed to conduct a resilience assessment of the distribution network.

[0010] As a preferred solution of the method for evaluating the resilience of a smart distribution network under extreme weather conditions described in the present invention, the establishment of a fault model under extreme weather conditions includes:

[0011] Establish component failure rate model and failure duration model;

[0012] The branch failure rate model is equivalent to a series model of the line model and the pole model, and the component failure rate model is established, which is expressed as:

[0013]

[0014] Among them, P h (s) represents the failure rate of the hth branch, m k and k represent the number of lines and poles of the h-th branch respectively;

[0015] The fault duration model is expressed as:

[0016] T=T sd +T fc -T re

[0017] Among them, T re represents the distribution network defense time, T sd represents the duration of extreme weather, T fc Indicates the fault repair time.

[0018] As a preferred solution of the method for evaluating the resilience of the smart distribution network under extreme weather conditions described in the present invention, the simulation of extreme weather scenarios and reductions includes:

[0019] M extreme weather scenes are extracted by Monte Carlo method, and K-means algorithm is used to reduce the extreme weather scenes to obtain K extreme weather scenes. The evaluation function and cluster center definition of the cth iteration are expressed as:

[0020]

[0021] Among them, f c Represents the sum of the distances from each node to its corresponding cluster center. represents the distance from the e-th node to its corresponding cluster center, represents the kth cluster center at the yth iteration, represents the kth cluster at the yth iteration, h e express The e-th node in express The number of nodes in .

[0022] As a preferred solution of the method for evaluating the resilience of the smart distribution network under extreme weather conditions described in the present invention, the sampling method is used to extract the load and renewable energy output, including:

[0023] Latin hypercube sampling is used to extract load and renewable energy output, which can be expressed as:

[0024]

[0025] in, and They represent the predicted values ​​of renewable energy output power and load at time t, P RES,t and P L,t They represent the actual values ​​of renewable energy output power and load at time t respectively.

[0026] As a preferred solution of the resilience assessment method of the smart distribution network under extreme weather conditions described in the present invention, constructing a power supply restoration strategy model with the maximum power outage load as the goal includes:

[0027] The objective function is established by restoring the maximum power outage load, which can be expressed as:

[0028]

[0029] Where D represents the number of power outage areas, Z d represents the dth power outage area, x i represents the load level of load i, is the weight coefficient of load i, u i,t A 0-1 variable indicating whether load i has restored power supply at time t, represents the active power of load i at time t;

[0030] Based on the power supply restoration strategy model, the constraints of the model are set, the equality constraints are node power balance constraints, and the inequality constraints include branch transmission power constraints, node voltage constraints, and energy storage charging and discharging power constraints.

[0031] As a preferred solution of the resilience assessment method of the smart distribution network under extreme weather conditions described in the present invention, a resilience assessment index including the distribution network defense time, resilience recovery coefficient, island duration coverage rate and average interruption time of important loads is constructed, which is expressed as:

[0032]

[0033] Among them, K represents the number of extreme weather scenarios, θ k represents the probability of occurrence of scene k, H DNDT,k , H RRC,k , H IDCO,k , HMITLC,k They represent the distribution network defense time, resilience recovery coefficient, island duration coverage rate and average interruption time of important loads in the kth extreme weather scenario, respectively. DNDT , H RRC , H IDCO , H MITLC They respectively represent the distribution network defense time, resilience recovery coefficient, island duration coverage rate and average interruption time of important loads.

[0034] As a preferred solution of the method for evaluating the resilience of a smart distribution network under extreme weather conditions described in the present invention, the resilience evaluation of the distribution network includes:

[0035] Step 1: Input the load curve, line length and switch in the distribution network and the renewable energy output curve data;

[0036] Step 2: Extract M extreme weather scenarios through the Monte Carlo method and reduce them to K through the K-means clustering method, with the initial scenario k = 1;

[0037] Step 3: Establish a fault scenario set. Use the Latin hypercube sampling method to establish a fault scenario set containing load, renewable energy output and branch faults, which contains N fault scenarios.

[0038] Step 4: Obtain the load equivalent loss rate of N fault scenarios, determine the power outage scope of the distribution network, supply power through the tie line, determine whether each feeder area forms an island, and if so, restore it through the power supply restoration strategy model, obtain the fault repair time according to the fault scenario, obtain the fault duration and calculate the power outage power of each time point and load;

[0039] Step 5: Determine whether the simulation of N fault scenarios is completed. If not, repeat step 4 until the calculation of N fault scenarios is completed, and calculate the distribution network resilience evaluation index of the kth sampling sample;

[0040] Step 6: Determine whether the extreme weather scenario simulation is completed. If not, add 1 to the scenario and return to step 3. If the simulation is completed, calculate the distribution network resilience assessment index.

[0041] In a second aspect, the present invention provides a resilience assessment system for a smart distribution network under extreme weather conditions, including: a construction module for analyzing fault conditions of a distribution network under extreme weather conditions and establishing a fault model under extreme weather conditions;

[0042] A processing module, used to simulate extreme weather scenarios based on the fault model, and reduce the extreme weather scenarios through a clustering algorithm, obtain the time-varying fault rate of each branch of the distribution network, and extract load and renewable energy output by a sampling method;

[0043] The evaluation module is used to construct a power supply restoration strategy model and resilience evaluation indicators with the maximum power outage load as the target based on the load, renewable energy output and distribution network failure scenario, and to conduct resilience evaluation on the distribution network.

[0044] In a third aspect, the present invention provides an electronic device, comprising:

[0045] Memory and processor;

[0046] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for assessing the resilience of the smart distribution network under extreme weather conditions are implemented.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for assessing the resilience of a smart distribution network under extreme weather conditions.

[0048] Compared with the prior art, the present invention has the following beneficial effects: the present invention adopts Monte Carlo simulation to simulate the randomness of extreme weather, and quantifies the impact of extreme weather on the distribution network through a fault model; in terms of evaluation indicators, the disaster suffered by the distribution network is divided into two stages, and a distribution network resilience evaluation index system is established, which can quantitatively evaluate the disaster resistance of the distribution network, and can guide the distribution network planning according to the resilience evaluation method, such as improving the resilience of the distribution network by reinforcing and transforming the distribution network grid or optimizing the configuration of controllable power sources; the optimal distribution network repair strategy can also be further studied and formulated according to the resilience indicators: including the reasonable allocation of repair personnel, the reasonable layout and scheduling of emergency power generation vehicles and mobile energy storage power supplies, so as to improve the ability of the distribution network to cope with extreme weather. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0050] Figure 1 It is a schematic diagram of the overall process of a method for assessing the resilience of a smart distribution network under extreme weather conditions according to an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a distribution network resilience assessment process of a method for assessing the resilience of a smart distribution network under extreme weather conditions described in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0055] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0056] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0057] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] Example 1

[0059] Reference Figure 1-Figure 2, is an embodiment of the present invention, and provides a method for evaluating the resilience of a smart distribution network under extreme weather conditions, such as Figure 1 ,include:

[0060] S101, analyzing the fault conditions of the distribution network under extreme weather conditions and establishing a fault model under extreme weather conditions.

[0061] Preferably, in order to quantify the impact of extreme weather on the distribution network, a fault model under extreme weather is established, including:

[0062] Establish component failure rate model and failure duration model;

[0063] The branch can only operate normally when both the line and the pole remain in normal operation. The branch failure rate model is equivalent to a series model of the line model and the pole model, and the component failure rate model is established, which is expressed as:

[0064]

[0065] Among them, P h (s) represents the failure rate of the hth branch, m k and k represent the number of lines and poles of the h-th branch respectively;

[0066] The fault duration model is expressed as:

[0067] T=T sd +T fc -T re

[0068] Among them, T re represents the distribution network defense time, T sd represents the duration of extreme weather, T fc Indicates the fault repair time;

[0069] Specifically, the normal distribution is used to define the duration of extreme weather T sd , expressed as:

[0070] T sd ~N(μ,σ 2 )

[0071] Where μ and σ represent the expectation and standard deviation of extreme weather duration, respectively.

[0072] Fault repair time T fc It is expressed as:

[0073] T fc =λ sce λ num T norm

[0074] Among them, λ sce represents the severe fault coefficient of the distribution network, λ num represents the repair efficiency coefficient of the repair team, T norm Indicates the standard repair time of a fault.

[0075] S102, based on the fault model, simulate extreme weather scenarios and reduce them through clustering algorithms to obtain the time-varying fault rate of each branch of the distribution network, and use sampling method to extract load and renewable energy output.

[0076] Preferably, simulation of extreme weather scenarios and reductions include:

[0077] M extreme weather scenes are extracted by Monte Carlo method, and K-means algorithm is used to reduce the extreme weather scenes to obtain K extreme weather scenes. The evaluation function and cluster center definition of the cth iteration are expressed as:

[0078]

[0079] Among them, f c Represents the sum of the distances from each node to its corresponding cluster center. represents the distance from the e-th node to its corresponding cluster center, represents the kth cluster center at the yth iteration, represents the kth cluster at the yth iteration, h e express The e-th node in express The number of nodes in .

[0080] Preferably, the sampling method is used to extract load and renewable energy output, including:

[0081] Latin hypercube sampling is used to extract load and renewable energy output, which can be expressed as:

[0082]

[0083] in, and They represent the predicted values ​​of renewable energy output power and load at time t, P RES,t and P L,t They represent the actual values ​​of renewable energy output power and load at time t respectively.

[0084] It should be noted that it is assumed that the forecast errors of load and renewable energy output follow a normal distribution, with the forecast value as the mean and the standard deviation as 10% of the forecast value.

[0085] By simulating extreme weather scenarios, a large number of scenarios can be simulated and analyzed more efficiently to predict possible situations. Clustering algorithms can reduce scenarios to reduce computational complexity and improve evaluation efficiency. Obtaining time-varying failure rates helps understand the possibility of failures at different time points. Sampling loads and renewable energy output can more realistically reflect the uncertainty in actual operations and provide more accurate failure rate data for evaluating the resilience of distribution networks.

[0086] S103, based on the load, renewable energy output and distribution network fault scenarios, build a power supply restoration strategy model with the maximum power outage load as the target and resilience assessment indicators to conduct resilience assessment on the distribution network.

[0087] Preferably, constructing a power supply restoration strategy model with the maximum power outage load as the goal includes:

[0088] During the power supply restoration process, the network loss is ignored, and the objective function is established to maximize the restored power outage load, which is expressed as:

[0089]

[0090] Where D represents the number of power outage areas, Z d represents the dth power outage area, x i represents the load level of load i, is the weight coefficient of load i, u i,t A 0-1 variable indicating whether load i has restored power supply at time t, represents the active power of load i at time t;

[0091] Based on the power supply restoration strategy model, the constraints of the model are set. The equality constraints are node power balance constraints, and the inequality constraints include branch transmission power constraints, node voltage constraints, and energy storage charging and discharging power constraints.

[0092] Exemplarily, the equality constraint is expressed as:

[0093]

[0094] Among them, P i,t and Q i,t are the active and reactive injected power of node i at time t, U i,t and U j,t are the voltage amplitudes of nodes i and j at time t, G ij and B ij are the conductance and susceptance between nodes i and j, respectively, θ ij is the phase angle difference between nodes i and j;

[0095] The inequality constraints are expressed as:

[0096]

[0097] in, is the maximum power constraint of the ij branch, x ij,t is the state variable of the operation status of branch j at time t, x=1 indicates normal operation of the branch, and x=0 indicates a branch failure. and are the upper and lower limits of the voltage at node i, and are the initial capacity of energy storage at node i and the energy storage capacity at time z, and are the minimum and maximum capacity of energy storage of node i, and are the charging and discharging active power of energy storage at node i at time t, δ c and δ dis are the charging and discharging efficiency of energy storage, and are the maximum charging and discharging active powers of energy storage at node i, and is the charging and discharging status of the energy storage at node i at time t.

[0098] Preferably, a resilience evaluation index including the defense time of the distribution network, the resilience recovery coefficient, the coverage rate of the island duration, and the average interruption time of important loads is constructed, which is expressed as:

[0099]

[0100] Among them, K represents the number of extreme weather scenarios, θ k represents the probability of occurrence of scene k, H DNDT,k , H RRC,k , H IDCO,k , H MITLC,k They represent the distribution network defense time, resilience recovery coefficient, island duration coverage rate and average interruption time of important loads in the kth extreme weather scenario, respectively. DNDT , H RRC , H IDCO , H MITLC They respectively represent the distribution network defense time, resilience recovery coefficient, island duration coverage rate and average interruption time of important loads.

[0101] Exemplarily, the resilience assessment indicators are as follows:

[0102] a. Distribution network defense time

[0103] The distribution network defense time is defined as the time from when the distribution network can maintain normal operation after extreme weather occurs to when a fault occurs, expressed as:

[0104]

[0105] Among them, θ n is the probability of occurrence of failure scenario n, H DNDT,n is the distribution network defense time under fault scenario n, and N is the number of fault scenarios extracted.

[0106] b. Toughness recovery coefficient

[0107] A resilience recovery coefficient is constructed to characterize the impact of load resilience of different properties on the overall resilience level of the entire network, and multiple fault scenarios and their occurrence probabilities are taken into account at the same time. The resilience recovery coefficient is between 0 and 1. The larger the resilience recovery coefficient, the higher the resilience of the distribution network. The resilience recovery coefficient H RRC Expressed as:

[0108]

[0109] Among them, B n is the load equivalent loss rate under the fault scenario, T n is the fault duration under fault scenario n, λ cr and λ co are the weight coefficients of important loads and general loads respectively, and are the original power requirements of important loads and general loads at time t under fault scenario n, and are the missing power of important load and general load at time t under fault scenario n, and are respectively the set of important loads and general loads in the power outage area under fault scenario n, and They are respectively the collection of important loads and general loads during normal operation of the distribution network.

[0110] c. Island duration coverage

[0111] The resilience level of the distribution network is evaluated by the island duration coverage ratio, which is expressed as:

[0112]

[0113] Considering the duration coverage of different islands and their weight coefficients, the duration coverage of the island under fault scenario n is H IDCO,n Expressed as:

[0114]

[0115] Among them, N ins is the number of islands under fault scenario n, HIDCO,nj is the duration coverage of island i under fault scenario n, λ j is the weight coefficient of island j;

[0116] When the maximum output of the power supply in the island is greater than the load demand power, the island system can withstand a certain load fluctuation and maintain the continuous operation of the island. The sustainable duration of the island can be obtained, and the sustainable duration coverage rate of island i can be obtained according to the fault duration, which is expressed as:

[0117]

[0118] in, is the duration of island j, T j (t) is the time that the power supply in island j can support, Z j is the range of the island, and are the discharge power of distributed generation and energy storage of load i in island j at time instant;

[0119]

[0120] in, is the average power of island j during the fault period, E j,cr The power supply for important loads in the island j is restored, E j,co Restore power to general loads within island i.

[0121] d. Average interruption time of important loads

[0122] In order to evaluate the power supply reliability of uninterrupted power supply to important loads, an important load average interruption time index is established. This index can more intuitively reflect the resilience of the distribution network and is expressed as:

[0123]

[0124] Among them, H MITCL,n is the average interruption time of important loads under fault scenario n, that is,

[0125]

[0126] Among them, T i out is the interruption time of important load i, Ω cr is the set of important loads, N cr is the number of important loads.

[0127] Preferably, Figure 2 , the resilience assessment of the distribution network includes:

[0128] Step 1: Input the load curve, line length and switch in the distribution network and the renewable energy output curve data;

[0129] Step 2: Extract M extreme weather scenarios through the Monte Carlo method and reduce them to K through the K-means clustering method, with the initial scenario k = 1;

[0130] Step 3: Establish a fault scenario set. Use the Latin hypercube sampling method to establish a fault scenario set containing load, renewable energy output and branch faults, which contains N fault scenarios.

[0131] Step 4: Obtain the load equivalent loss rate of N fault scenarios, determine the power outage scope of the distribution network, give priority to power supply through the tie line, determine whether each feeder area forms an island, and if so, restore it through the power supply restoration strategy model, obtain the fault repair time according to the fault scenario, obtain the fault duration and calculate the power outage power of each time point and load;

[0132] Step 5: Determine whether the simulation of N fault scenarios is completed. If not, repeat step 4 until the calculation of N fault scenarios is completed, and calculate the distribution network resilience evaluation index of the kth sampling sample;

[0133] Step 6: Determine whether the extreme weather scenario simulation is completed. If not, add 1 to the scenario and return to step 3. If the simulation is completed, calculate the distribution network resilience assessment index.

[0134] The present invention adopts Monte Carlo simulation to simulate the randomness of extreme weather, and quantifies the impact of extreme weather on the distribution network through a fault model; in terms of evaluation indicators, the disaster suffered by the distribution network is divided into two stages, and a distribution network resilience evaluation index system is established, which can quantitatively evaluate the disaster resistance of the distribution network, and can guide the distribution network planning according to the resilience evaluation method, such as improving the resilience of the distribution network by reinforcing and transforming the distribution network grid or optimizing the configuration of controllable power sources; the optimal distribution network repair strategy can also be further studied and formulated according to the resilience indicators: including the reasonable allocation of repair personnel, the reasonable layout and scheduling of emergency power generation vehicles and mobile energy storage power supplies, so as to improve the ability of the distribution network to cope with extreme weather.

[0135] The above is a schematic scheme of a method for assessing the resilience of a smart distribution network under extreme weather conditions in this embodiment. It should be noted that the technical scheme of the system for assessing the resilience of a smart distribution network under extreme weather conditions and the technical scheme of the method for assessing the resilience of a smart distribution network under extreme weather conditions described above belong to the same concept. For details not described in detail in the technical scheme of the system for assessing the resilience of a smart distribution network under extreme weather conditions in this embodiment, please refer to the description of the technical scheme of the method for assessing the resilience of a smart distribution network under extreme weather conditions described above.

[0136] The resilience assessment system of the smart distribution network under extreme weather conditions in this embodiment includes:

[0137] Building a module to analyze the fault conditions of the distribution network under extreme weather conditions and establish a fault model under extreme weather conditions;

[0138] The processing module is used to simulate extreme weather scenarios based on the fault model, reduce the extreme weather scenarios through clustering algorithms, obtain the time-varying fault rate of each branch of the distribution network, and extract the load and renewable energy output using a sampling method;

[0139] The evaluation module is used to build a power supply restoration strategy model and resilience evaluation indicators with the maximum power outage load as the target based on load, renewable energy output and distribution network fault scenarios, and to conduct resilience evaluation on the distribution network.

[0140] This embodiment further provides an electronic device, which is applicable to the case of resilience assessment of a smart distribution network under extreme weather conditions, and includes:

[0141] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the resilience assessment method of the smart distribution network under extreme weather conditions as proposed in the above embodiment.

[0142] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for evaluating the resilience of a smart distribution network under extreme weather conditions proposed in the above embodiment is implemented.

[0143] The storage medium proposed in this embodiment and the method for realizing resilience assessment of smart distribution networks under extreme weather conditions proposed in the above-mentioned embodiments belong to the same inventive concept. For technical details not fully described in this embodiment, reference can be made to the above-mentioned embodiments, and this embodiment has the same beneficial effects as the above-mentioned embodiments.

[0144] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0145] Example 2

[0146] Referring to Table 1, an embodiment of the present invention provides a resilience assessment method for a smart distribution network under extreme weather conditions. In order to verify its beneficial effects, comparison results of two schemes are provided, which are scientifically demonstrated through economic benefit calculation and simulation experiments.

[0147] In order to explore the impact of power line type on the resilience of the distribution network, the IEEE-33 node example system is used for analysis. Lines 5-10 are replaced with cable lines, and the difference in distribution network resilience before and after the change of power line type is compared, as shown in Table 1.

[0148] Table 1: Impact of power line type on distribution network resilience

[0149] condition <![CDATA[H DNDT / h]]> <![CDATA[H RRC ]]> <![CDATA[H IDCO / %]]> <![CDATA[H MITCL / h]]> Before line replacement 5.60 0.6263 83.30 2.26 After line replacement 6.10 0.6725 83.45 1.45

[0150] As can be seen from Table 1, compared with before replacement, the distribution network resilience indicators after line replacement are as follows: the defense time of the distribution network increases, the resilience recovery coefficient increases, the coverage rate of the sustainable duration of the island increases slightly, and the average interruption time of important loads decreases, indicating that the evaluation indicators in the two stages of disaster prevention and disaster reduction are improved after line replacement. Therefore, replacing line 5-10 increases the resilience of the distribution network. This is because after the distribution network replaces line 5-10, when extreme weather comes, the failure of line 5-10 can be approximately ignored, which increases the defense time of the distribution network, reduces the power supply loss and important load interruption time of the distribution network, increases the resilience recovery coefficient, and slightly increases the coverage rate of the sustainable duration of the island.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the resilience of a smart distribution network under extreme weather conditions, characterized in that: include: Analyze the fault conditions of the distribution network under extreme weather conditions and establish a fault model under extreme weather conditions; Based on the fault model, extreme weather scenarios are simulated and reduced by clustering algorithms to obtain the time-varying fault rate of each branch of the distribution network, and load and renewable energy output are extracted by sampling method; Based on the load, renewable energy output and distribution network failure scenarios, a power supply restoration strategy model with the maximum power outage load as the target and resilience assessment indicators are constructed to conduct a resilience assessment of the distribution network.

2. The method for evaluating the resilience of a smart distribution network under extreme weather conditions according to claim 1, characterized in that: The establishment of the fault model under extreme weather conditions includes: Establish component failure rate model and failure duration model; The branch failure rate model is equivalent to a series model of the line model and the pole model, and the component failure rate model is established, which is expressed as: Among them, P h (s) represents the failure rate of the hth branch, m k and k represent the number of lines and poles of the h-th branch respectively; The fault duration model is expressed as: T=T sd +T fc -T re Among them, T re represents the distribution network defense time, T sd represents the duration of extreme weather, T fc Indicates the fault repair time.

3. The method for evaluating the resilience of a smart distribution network under extreme weather conditions according to claim 1 or 2, characterized in that: Simulation of extreme weather scenarios and reductions include: M extreme weather scenes are extracted by Monte Carlo method, and K-means algorithm is used to reduce the extreme weather scenes to obtain K extreme weather scenes. The evaluation function and cluster center definition of the cth iteration are expressed as: Among them, f c Represents the sum of the distances from each node to its corresponding cluster center. represents the distance from the e-th node to its corresponding cluster center, represents the kth cluster center at the yth iteration, represents the kth cluster at the yth iteration, h e express The e-th node in express The number of nodes in .

4. The method for evaluating the resilience of a smart distribution network under extreme weather conditions as claimed in claim 3, characterized in that: The sampling method is used to extract load and renewable energy output, including: Latin hypercube sampling is used to extract load and renewable energy output, which can be expressed as: in, and They represent the predicted values ​​of renewable energy output power and load at time t, P RES,t and P L,t They represent the actual values ​​of renewable energy output power and load at time t respectively.

5. The method for evaluating the resilience of a smart distribution network under extreme weather conditions as claimed in claim 4, characterized in that: The power supply restoration strategy model with the maximum power outage load as the goal includes: The objective function is established by restoring the maximum power outage load, which can be expressed as: Where D represents the number of power outage areas, Z d represents the dth power outage area, x i represents the load level of load i, is the weight coefficient of load i, u i,t A 0-1 variable indicating whether load i has restored power supply at time t, represents the active power of load i at time t; Based on the power supply restoration strategy model, the constraints of the model are set, the equality constraints are node power balance constraints, and the inequality constraints include branch transmission power constraints, node voltage constraints, and energy storage charging and discharging power constraints.

6. The method for evaluating the resilience of a smart distribution network under extreme weather conditions as claimed in claim 5, characterized in that: The resilience evaluation index including the distribution network defense time, resilience recovery coefficient, island duration coverage and the average interruption time of important loads is constructed and expressed as: Among them, K represents the number of extreme weather scenarios, θ k represents the probability of occurrence of scene k, H DNDT,k , H RRC,k , H IDCO,k , H MITLC,k They represent the distribution network defense time, resilience recovery coefficient, island duration coverage rate and average interruption time of important loads in the kth extreme weather scenario, respectively. DNDT , H RRC , H IDCO , H MITLC They respectively represent the distribution network defense time, resilience recovery coefficient, island duration coverage rate and average interruption time of important loads.

7. The method for evaluating the resilience of a smart distribution network under extreme weather conditions as claimed in claim 6, characterized in that: The resilience assessment of the distribution network includes: Step 1: Input the load curve, line length and switch in the distribution network and the renewable energy output curve data; Step 2: Extract M extreme weather scenarios through the Monte Carlo method and reduce them to K through the K-means clustering method, with the initial scenario k = 1; Step 3: Establish a fault scenario set. Use the Latin hypercube sampling method to establish a fault scenario set containing load, renewable energy output and branch faults, which contains N fault scenarios. Step 4: Obtain the load equivalent loss rate of N fault scenarios, determine the power outage scope of the distribution network, supply power through the tie line, determine whether each feeder area forms an island, and if so, restore it through the power supply restoration strategy model, obtain the fault repair time according to the fault scenario, obtain the fault duration and calculate the power outage power of each time point and load; Step 5: Determine whether the simulation of N fault scenarios is completed. If not, repeat step 4 until the calculation of N fault scenarios is completed, and calculate the distribution network resilience evaluation index of the kth sampling sample; Step 6: Determine whether the extreme weather scenario simulation is completed. If not, add 1 to the scenario and return to step 3. If the simulation is completed, calculate the distribution network resilience assessment index.

8. A resilience assessment system for smart distribution networks under extreme weather conditions, characterized in that: include, Building a module to analyze the fault conditions of the distribution network under extreme weather conditions and establish a fault model under extreme weather conditions; A processing module, used to simulate extreme weather scenarios based on the fault model, and reduce the extreme weather scenarios through a clustering algorithm, obtain the time-varying fault rate of each branch of the distribution network, and extract load and renewable energy output by a sampling method; The evaluation module is used to construct a power supply restoration strategy model and resilience evaluation indicators with the maximum power outage load as the target based on the load, renewable energy output and distribution network failure scenario, and to evaluate the resilience of the distribution network.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for assessing the resilience of a smart distribution network under extreme weather conditions as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the steps of the method for assessing the resilience of a smart distribution network under extreme weather conditions as described in any one of claims 1 to 7.

Images