Power distribution network operation state determination method and device, electronic equipment and storage medium

Abstract

The invention provides a power distribution network operation state determination method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining environmental data, a power distribution network fault model and a current operation state of a power distribution network; according to the environmental data and a power distribution network fault model, determining fault conditions corresponding to the plurality of nodes and the plurality of branches at the plurality of moments; based on the current operation state of the power distribution network, the fault conditions corresponding to the multiple nodes and the multiple branches at the multiple moments and the corresponding condition constraints, a power distribution network elasticity improvement model is constructed, and solving targets comprise the maximum value of the sum of load recovery degrees at the multiple moments and the minimum value of the number of switching times of the switch states at the multiple moments; and solving the power distribution network elasticity improvement model, and determining the operation state of the power distribution network in a preset time period. Based on this, a multi-target long-time dynamic power distribution network reconstruction scheme for improving the elasticity of the power distribution network can be obtained, and the load recovery amount of the power distribution network after a disaster evolves for a period of time can be greatly improved.

Description

Technical Field

[0001] The present application relates to the field of automation technology, and in particular to a method, device, electronic device and storage medium for determining the operating status of a distribution network. Background Art

[0002] The normal operation of modern society is highly dependent on a stable and reliable power supply. The ability of a power system to prevent, resist and quickly restore loads from extreme events is called power system resilience. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change of the United Nations pointed out that global climate change is intensifying, and the risk of extreme meteorological disasters such as typhoons and rainstorms is increasing rapidly. In addition, due to the special terrain and environment of some cities and insufficient flood drainage capacity, urban flooding disasters occur frequently. Urban flooding disasters are characterized by strong imbalance and uncertainty, great damage to circuit components due to immersion, and difficulty in repairing components due to accumulated water, which poses a serious threat to the power system. Therefore, in response to wind and waterlogging disasters, how to dynamically improve the resilience of the distribution network is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0003] The present application provides a method, device, electronic device and storage medium for determining the operating status of a distribution network. By proposing a multi-objective, long-term dynamic distribution network reconstruction scheme to improve the elasticity of the distribution network, it can greatly improve the load recovery of the distribution network after a period of disaster evolution. It is especially suitable for disaster scenarios such as wind and flood disasters that last a long time and have complex impacts on the distribution system, thereby improving the elasticity of the distribution network against wind and flood disasters.

[0004] In a first aspect, the present application provides a method for determining an operating state of a distribution network, the method comprising:

[0005] Obtaining environmental data, a distribution network fault model, and a current operating state of the distribution network. The environmental data includes wind speed and water depth corresponding to multiple moments in a preset time period. The distribution network fault model is used to predict the failure probability of multiple nodes and multiple branches included in the distribution network. The current operating state of the distribution network includes the load power of multiple nodes and the current switch state of multiple branches.

[0006] According to the environmental data and the distribution network fault model, the fault probabilities corresponding to multiple nodes and multiple branches at multiple times are determined;

[0007] Determine the fault conditions corresponding to the multiple nodes and the multiple branches at multiple times according to the fault probabilities corresponding to the multiple nodes and the multiple branches at multiple times;

[0008] A distribution network resilience improvement model is constructed based on the current operating state of the distribution network, the fault conditions corresponding to multiple nodes and multiple branches at multiple times, and target condition constraints. The solution objectives of the distribution network resilience improvement model include the maximum value of the sum of load recovery degrees at multiple times and the minimum value of the number of switch state switching at multiple times. The target condition constraints include power flow balance constraints.

[0009] The distribution network flexibility improvement model is solved, and the operating status of the distribution network within a preset time period is determined based on the solution results.

[0010] It can be seen that in this application, firstly, according to the wind speed and water depth within a period of time and the distribution network fault model, the fault conditions of multiple nodes and multiple branches in the distribution network within a period of time are determined, and then the distribution network elasticity improvement model is constructed according to the fault conditions of multiple nodes and multiple branches within a period of time, the current operating state of the distribution network and the corresponding target condition constraints, and the maximum value of the sum of the load recovery degrees within multiple moments and the minimum value of the number of switch state switching within multiple moments are taken as the target, and the distribution network elasticity improvement model is solved to obtain the operating state of the distribution network within a period of time. In this way, the dynamic reconstruction of the distribution network can be realized based on the operating state of the distribution network within a period of time. In this way, a method for quantifying the probability of power grid failure in wind and flood scenarios is given, and a multi-objective long-term dynamic distribution network reconstruction scheme for improving the elasticity of the distribution network is proposed, which can greatly improve the load recovery of the distribution network after the disaster evolves for a period of time. It is especially suitable for disaster scenarios such as wind and flood disasters that last for a long time and have complex impacts on the distribution system, and improves the elasticity of the distribution network against waterlogging disasters.

[0011] In a feasible example, the fault probabilities corresponding to multiple nodes and multiple branches at multiple times are determined, including: when the target parameter is less than a first threshold, the fault probability is the target probability, the target parameter is the wind speed or the water depth, and the target probability is the failure probability when the node or branch is not affected by the target parameter; when the target parameter is not less than the first threshold and less than the second threshold, the fault probability is determined according to the distribution function of the first value, the first value is determined according to the ratio of the first parameter to the second parameter, the second parameter is the standard deviation of the natural logarithm of the target parameter that causes the fault, the first parameter is the logarithm of the third parameter, and the third parameter is the ratio between the target parameter and the median of the target parameter at the time of the fault; when the target parameter is not less than the second threshold, the fault probability is one.

[0012] In a feasible example, the distribution network fault model includes a tower fault model, an overhead line fault model and a substation fault model; the tower fault model and the overhead line fault model determine the fault probability according to the wind speed; the substation fault model determines the fault probability according to the water depth.

[0013] In the present application, corresponding fault models are established based on poles, overhead lines and substations, respectively, so that different distribution network equipment can be adapted, thereby improving the accuracy of the distribution network fault model.

[0014] In a feasible example, the fault conditions corresponding to the multiple nodes and the multiple branches at the multiple moments are determined according to the fault probabilities corresponding to the multiple nodes and the multiple branches at the multiple moments, including: for each moment in the multiple moments, generating multiple first uniform random numbers corresponding to the multiple nodes and multiple second uniform random numbers corresponding to the multiple branches, the first uniform random numbers and the second uniform random numbers being between zero and one; when the first uniform random number corresponding to each of the multiple nodes is not greater than the fault probability corresponding to each node at each moment, the fault condition of each node is determined to be a fault; when the first uniform random number corresponding to each of the multiple nodes is greater than the fault probability corresponding to each node at each moment, the fault condition of each node is determined to be no fault; when the second uniform random number corresponding to each of the multiple branches is not greater than the fault probability corresponding to each branch at each moment, the fault condition of each branch is determined to be a fault; when the second uniform random number corresponding to each of the multiple branches is greater than the fault probability corresponding to each branch at each moment, the fault condition of each branch is determined to be no fault.

[0015] In the present application, the fault scenarios are simulated by the aforementioned method, which can cover various possible fault combinations, thereby being more conducive to the subsequent improvement of the resilience of the distribution network.

[0016] In a feasible example, the distribution network resilience improvement model is expressed as:

[0017]

[0018] Among them, α and β are used to represent weights, and the sum of α and β is 1; ω k Used to represent the weight corresponding to node k among multiple nodes; y k,t It is used to represent the fault condition of node k at time t, and y k,t It is a 0-1 variable, which is 1 when the node fails and 0 when the node is not failed; P k,t It is used to indicate the load power size corresponding to node k at time t; T is used to indicate the preset time period, B is used to indicate the set of multiple nodes, and E is used to indicate the set of multiple branches; a l,t It is used to indicate the fault condition of branch l at time t, a l,t-1 It is used to indicate the fault condition of branch l at time t-1, and also a l,t It is a 0-1 variable, which is 1 when the branch fails and 0 when the branch is not failed.

[0019] In the present application, based on the solution of the aforementioned distribution network resilience improvement model, a distribution network reconstruction solution can be obtained that can significantly improve the load recovery amount of the distribution network after the disaster evolves for a period of time.

[0020] In a feasible example, the current operating state of the distribution network also includes nodes in which distributed power sources are deployed among multiple nodes, and the target condition constraint also includes an output power constraint of the distributed power source, which is used to constrain the output power of the distributed power source to be within a preset threshold.

[0021] In a feasible example, the target condition constraints also include closed branch constraints and single commodity flow constraints; the closed branch constraint is used to constrain the number of closed branches among multiple branches, which is equal to the number of multiple nodes minus the number of root nodes among multiple nodes; the single commodity flow constraint includes flow constraint, root node quantity constraint and branch constraint; the flow constraint is used to constrain the sum of the inflow power of non-root nodes among multiple nodes to be equal to the sum of the outflow power plus the load of the node, and / or the sum of the inflow power of the root nodes among multiple nodes to be less than the sum of the outflow power plus the load of the node; the root node quantity constraint is used to constrain the number of root nodes among multiple nodes to be less than the number of faulty branches among multiple branches plus one; the branch constraint is used to constrain the flow power of faulty branches among multiple branches to be zero.

[0022] In a second aspect, the present application provides a device for determining an operating state of a distribution network, the device comprising:

[0023] an acquisition unit, used to acquire environmental data, a distribution network fault model, and a current operating state of the distribution network, wherein the environmental data includes wind speed and water depth corresponding to multiple moments in a preset time period, the distribution network fault model is used to predict the failure probability of multiple nodes and multiple branches included in the distribution network, and the current operating state of the distribution network includes load power of multiple nodes and current switch states of multiple branches;

[0024] A processing unit, used to determine the fault probabilities corresponding to a plurality of nodes and a plurality of branches at a plurality of times respectively according to the environmental data and the distribution network fault model;

[0025] The processing unit is further used to determine the fault conditions corresponding to the multiple nodes and the multiple branches at multiple times according to the fault probabilities corresponding to the multiple nodes and the multiple branches at multiple times;

[0026] The processing unit is further used to construct a distribution network elasticity improvement model based on the current operating state of the distribution network, the fault conditions corresponding to multiple nodes and multiple branches at multiple times, and the target condition constraints. The solution objectives of the distribution network elasticity improvement model include the maximum value of the sum of load recovery degrees at multiple times and the minimum value of the number of switch state switching at multiple times. The target condition constraints include power flow balance constraints;

[0027] The processing unit is also used to solve the distribution network elasticity improvement model and determine the operating status of the distribution network within a preset time period based on the solution result.

[0028] In a third aspect, the present application provides an electronic device comprising a processor, a memory, and a communication interface. The processor, the memory, and the communication interface are interconnected and perform communication with each other. The memory stores executable program code, the communication interface is used for wireless communication, and the processor is used to call the executable program code stored in the memory to execute, for example, some or all of the steps described in any method of the first aspect.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium, in which electronic data is stored. When the electronic data is executed by a processor, it is used to execute the electronic data to implement some or all of the steps described in the first aspect of the present application.

[0030] In a fifth aspect, the present application provides a computer program product, including a computer program, which is operable to cause a computer to execute some or all of the steps described in the first aspect of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A schematic diagram of the structure of a distribution network elasticity improvement system provided in an embodiment of the present application;

[0033] Figure 2 A schematic diagram of a flow chart of a method for determining the operating status of a distribution network provided in an embodiment of the present application;

[0034] Figure 3 A schematic diagram of a vulnerability curve of a tower and a line to wind speed provided in an embodiment of the present application;

[0035] Figure 4 A schematic diagram of a damage curve when a substation is flooded provided in an embodiment of the present application;

[0036] Figure 5 A schematic diagram of a vulnerability curve of substation flooding provided in an embodiment of the present application;

[0037] Figure 6A schematic diagram of a substation waterlogging vulnerability curve provided in an embodiment of the present application;

[0038] Figure 7 A block diagram of the functional units of a device for determining the operating status of a distribution network provided in an embodiment of the present application;

[0039] Figure 8 A block diagram of the functional units of another device for determining the operating status of a distribution network provided in an embodiment of the present application;

[0040] Fig. 9 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps is not limited to the listed steps, but optionally includes steps that are not listed, or optionally includes other steps inherent to these processes, methods, products or devices.

[0043] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0044] See also Figure 1 , Figure 1 A schematic diagram of a distribution network elasticity enhancement system provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the distribution network resilience enhancement system 100 includes a server 101 and a distribution network 102 .

[0045] The server 101 is a server, a server cluster, a cloud server, a cloud computing service center, or other forms of equipment with computing capabilities.

[0046] The distribution network 102 includes poles, overhead lines, substations, buried cables, etc. The server 101 is used to obtain devices in the distribution network 102 and divide the devices in the distribution network 102 into multiple nodes and multiple branches based on the radial topology of the distribution network.

[0047] Specifically, the server 101 obtains environmental data, a distribution network fault model, and the current operating status of the distribution network. The environmental data includes wind speeds and water depths corresponding to multiple moments in a preset time period. The distribution network fault model is used to predict the failure probabilities of multiple nodes and multiple branches included in the distribution network 102. The current operating status of the distribution network includes the load power of multiple nodes and the current switch status of multiple branches. According to the environmental data and the distribution network fault model, the failure probabilities corresponding to multiple nodes and multiple branches at multiple moments are determined. According to the failure probabilities corresponding to multiple nodes and multiple branches at multiple moments, the failure conditions corresponding to multiple nodes and multiple branches at multiple moments are determined. Based on the current operating status of the distribution network, the failure conditions corresponding to multiple nodes and multiple branches at multiple moments, and the target condition constraints, a distribution network elasticity improvement model is constructed. The solution target of the distribution network elasticity improvement model includes the maximum value of the sum of load recovery degrees in multiple moments and the minimum value of the number of switch state switching in multiple moments. The target condition constraints include flow balance constraints. Finally, the distribution network elasticity improvement model is solved, and the operating status of the distribution network in the preset time period is determined based on the solution results. Subsequently, the multiple nodes and multiple branches included in the distribution network can be configured based on the operating status of the distribution network within a preset time period, thereby greatly improving the load recovery of the distribution network after the disaster has evolved for a period of time, increasing the life of the branch switches, and improving the resilience of the distribution network against urban flooding disasters.

[0048] Based on this, an embodiment of the present application provides a method for determining the operating status of a distribution network. The embodiment of the present application is described in detail below in conjunction with the accompanying drawings.

[0049] See also Figure 2 , Figure 2 A flow chart of a method for determining the operating status of a distribution network provided in an embodiment of the present application, the method is applied to the above-mentioned server, such as Figure 2 As shown, the method comprises the following steps:

[0050] Step S201, obtaining environmental data, a distribution network fault model, and a current operating status of the distribution network.

[0051] The environmental data includes wind speed and water depth corresponding to multiple moments in a preset time period. The distribution network fault model is used to predict the failure probability of multiple nodes and multiple branches included in the distribution network. The current operating status of the distribution network includes the load power of multiple nodes and the current switch status of multiple branches.

[0052] Among them, the distribution network fault model is mainly obtained by modeling the impact of the power system under typhoon and urban waterlogging scenarios. Typhoons, heavy rains and floods will further cause urban waterlogging. Urban waterlogging refers to heavy or continuous precipitation far exceeding the interception and drainage capacity of a region, resulting in water accumulation in underground spaces or low-lying areas. The substation flooding caused by urban waterlogging and the damage to the distribution network lines and towers caused by typhoons are the main failure modes of the power grid under typhoon weather. After weather parameters (such as wind speed and precipitation depth) can be obtained from the Meteorological Bureau or other database sources, the vulnerability curves of vulnerable components such as distribution network lines, towers, substations, and buried cables under urban waterlogging and typhoon scenarios can be established. Combining the vulnerability curves with weather conditions, the failure probability of power system components can be obtained, and then disaster scenarios can be generated, and targeted strategies for improving the resilience of distribution networks can be given. This part first models the vulnerability curves of each component in the distribution network under wind and waterlogging disasters.

[0053] The fragility function describes the probability of failure of a component or component part in the distribution network, and its condition depends on the load related to the potential danger intensity (such as the depth of waterlogging in the substation, typhoon wind speed, etc.). The establishment of the fragility curve is based on: a) statistical analysis of a large amount of historical data; b) experiments; c) model analysis; d) professional judgment. This method combines historical data from open source data sets with existing fragility curve models to establish fragility curves for each component of the distribution network to determine the failure probability of each component of the distribution network under specific disaster scenarios.

[0054] The strength characteristics of various engineering structures are log-normally distributed. For a given device, the risk factor strength S when it is damaged is d (such as immersion depth) can be described by a lognormal distribution, that is:

[0055] in, is the median of the risk threshold at damage (water depth at damage), β ds is the standard deviation of the natural logarithm of the equipment damage threshold. Then the probability of equipment damage at this risk intensity POS (Probability of an Outage Start) can be expressed as:

[0056] The distribution network towers, distribution network lines and substations are discussed separately below.

[0057] Specifically, in a feasible embodiment, the distribution network fault model includes a tower fault model, an overhead line fault model and a substation fault model; the tower fault model and the overhead line fault model determine the fault probability according to the wind speed; the substation fault model determines the fault probability according to the water depth.

[0058] It can be understood that in the present application, poles and substations can correspond to nodes in the distribution network, overhead lines can correspond to branches in the distribution network, or towers and overhead lines can also correspond to branches in the distribution network.

[0059] At the same time, in a feasible embodiment, the fault probabilities corresponding to multiple nodes and multiple branches at multiple times are determined, including: when the target parameter is less than the first threshold, the fault probability is the target probability, the target parameter is the wind speed or the water depth, and the target probability is the fault probability when the node or branch is not affected by the target parameter; when the target parameter is not less than the first threshold and less than the second threshold, the fault probability is determined according to the distribution function of the first value, the first value is determined according to the ratio of the first parameter to the second parameter, the second parameter is the standard deviation of the natural logarithm of the target parameter that causes the fault, the first parameter is the logarithm of the third parameter, and the third parameter is the ratio between the target parameter and the median of the target parameter at the time of the fault; when the target parameter is not less than the second threshold, the fault probability is one.

[0060] The tower fault model, overhead line fault model and substation fault model are described below respectively.

[0061] For tower failure model:

[0062] Assuming that the weather conditions are good (i.e., when the wind speed is low), the distribution network towers will not be damaged. The vulnerability curve for the distribution network towers is constructed as follows. The probability that a distribution network tower is in a fault state at time T can be simplified as a function of the wind speed passing through the tower, which can be expressed as:

[0063] Where v is the wind speed passing through the tower, v critical represents the wind speed when the probability of tower failure is about 1, v collapse is the wind speed at which the probability of the tower not failing is negligible. T,Tow,hv is the failure probability of the tower when the wind speed is high, which can be expressed as: Here it can be linearized, where β and It depends on the tower condition. Here, β is expressed as the standard deviation of the natural logarithm of the wind speed that causes tower failure obtained from statistical analysis; It is the median wind speed that causes tower failure obtained by statistical analysis. Where Φ is the distribution function of the standard normal distribution. For example, if all tower parameters are considered to be the same, it can be concluded that β≈0.3268,

[0064] For overhead line fault model:

[0065] The line may also be affected by storm weather. It is assumed that the damage to the line is independent of the damage to the tower, so their vulnerability curves are different. The vulnerability of the line should also be related to the wind speed. The function corresponding to the overhead line fault model can be expressed as:

[0066] in, is the failure rate of the line in “good weather” (when the wind speed is low). It is understandable that since the wind speed may be different in the area where the line passes, it can be considered that the line break is only related to the maximum wind speed on the line. Therefore, the wind speed v here is considered to be the highest wind speed in the area where the line passes, v critical represents the wind speed when the line failure probability is about 1, v collapse Indicates the wind speed at which the probability of a line failure based on wind speed is negligible.

[0067] Among them, P T,Line,hv The failure probability of the line when the wind speed is high but still within the tolerance range of the line can be further simplified:

[0068] Here, it is also linearized, where β and It also depends on the line conditions, and β here is expressed as the standard deviation of the natural logarithm of the wind speed that causes line failure obtained from statistical analysis; It is the median value of the wind speed that causes line failure obtained by statistical analysis. For example, if the parameters of all lines are considered to be the same, it can be concluded that β≈0.1926,

[0069] For example, see Figure 3 , Figure 3 A schematic diagram of a vulnerability curve of a tower and a line to wind speed provided in an embodiment of the present application. Figure 3 As shown, the horizontal axis is wind speed, the vertical axis is fault probability, the dotted line and solid line images on the left are the linearization results of the line to wind speed and the original log-normal distribution curve, respectively, and the dotted line and solid line images on the right are the linearization results of the tower to wind speed and the original log-normal distribution curve, respectively.

[0070] At a certain time T, it can be considered that the failures of the tower and the line are independent of each other, and the probability of failure of branch e due to excessive wind speed can be expressed as: Where P T,Tow With P T,Line are the failure probabilities of a single tower or line at time T, which are obtained by mapping the (maximum) wind speed at the location of the tower (line) to the tower (line) vulnerability curve. T and L represent the collection of towers and lines in branch e, respectively.

[0071] For substation fault model:

[0072] When the storm develops further and urban waterlogging occurs, the outdoor switchgear, circuit breaker equipment, transformers, and buried cables of the ground substation may be submerged, further affecting the power system. Since the failure of such equipment is related to the depth of waterlogging at its location, its vulnerability model is similar, with only some parameters being different. Here, the substation is used as an example to establish the model.

[0073] For example, Figure 4 As shown, Figure 4 A schematic diagram of a damage curve when a substation is flooded is provided in an embodiment of the present application, such as Figure 4 As shown, the horizontal axis is the water depth and the vertical axis is the damage ratio. When the water depth is D, the damage percentage u of the substation can be expressed as: u = (4.68D + 0.77)%.

[0074] Considering the substation as a whole, when the components in the substation are flooded and the damaged components exceed 3%, the substation stops serving and the node trips. The water level depth D can be used as the risk threshold for the substation to stop serving. For example, Figure 5 A schematic diagram of a vulnerability curve of substation flooding provided in an embodiment of the present application is shown in FIG. Figure 5 As shown in the figure, from left to right are the softened fragility curve, the original fragility curve and the sharpened fragility curve, the horizontal axis is the water depth and the vertical axis is the failure probability. It should be pointed out that different regions may have different design standards and protection measures, and parameters such as substation topology are also different, but the curve form can be used as a reference.

[0075] In the substation flooding failure model established in this paper, the failure probability P of a substation at time T is T,Sub It can be simplified as a piecewise linear function of the substation flooding depth D: Where D is the substation flooding depth, D collapse is the flooding depth at which the probability of no failure of the substation can be ignored (here 0.52m is taken, and the curve can be reconstructed according to the historical data of the research object to adapt to the actual situation). Similarly, the probability of substation damage when the water level is high can be calculated. T,Sub,hD (D) is simplified as follows:

[0076] For example, see Figure 6 , Figure 6 A schematic diagram of a substation waterlogging vulnerability curve provided in an embodiment of the present application. Figure 6 As shown, it includes the waterlogging vulnerability curve of a single substation, with the horizontal axis being the water depth and the vertical axis being the failure probability. The solid line corresponds to the logarithmic distribution function, and the dotted line corresponds to the linearized result.

[0077] Understandably, after obtaining more historical data on the failure of power system components and the connection with real-time weather conditions, the existing model can be modified to establish a more accurate vulnerability curve and improve the confidence of the model.

[0078] In the present application, corresponding fault models are established based on poles, overhead lines and substations, respectively, so that different distribution network equipment can be adapted, thereby improving the accuracy of the distribution network fault model.

[0079] Step S202: determining the fault probabilities corresponding to a plurality of nodes and a plurality of branches at a plurality of times respectively according to the environmental data and the distribution network fault model.

[0080] Among them, based on the distribution network fault model shown in the above embodiment and the predicted environmental data, the fault probability corresponding to each node and branch at multiple times can be determined.

[0081] Step S203, determining the fault conditions corresponding to the multiple nodes and the multiple branches at multiple times according to the fault probabilities corresponding to the multiple nodes and the multiple branches at multiple times.

[0082] Specifically, in a feasible embodiment, according to the fault probabilities corresponding to the multiple nodes and the multiple branches at the multiple moments, the fault conditions corresponding to the multiple nodes and the multiple branches at the multiple moments are determined, including: for each moment in the multiple moments, generating multiple first uniform random numbers corresponding to the multiple nodes, and multiple second uniform random numbers corresponding to the multiple branches, the first uniform random numbers and the second uniform random numbers are between zero and one; when the first uniform random number corresponding to each node in the multiple nodes is not greater than the fault probability corresponding to each node at each moment, the fault condition of each node is determined to be a fault; when the first uniform random number corresponding to each node in the multiple nodes is greater than the fault probability corresponding to each node at each moment, the fault condition of each node is determined to be no fault; when the second uniform random number corresponding to each branch in the multiple branches is not greater than the fault probability corresponding to each branch at each moment, the fault condition of each branch is determined to be a fault; when the second uniform random number corresponding to each branch in the multiple branches is greater than the fault probability corresponding to each branch at each moment, the fault condition of each branch is determined to be no fault.

[0083] Among them, in the power system, the failure probabilities of multiple nodes and branches are usually derived from historical data or empirical statistics. In order to simulate the failure behavior of the entire system under different conditions, the Monte Carlo method can be used to generate failure scenarios. The Monte Carlo method can effectively simulate the interaction between multiple random variables (such as node and branch failure probabilities) through random sampling and statistical analysis.

[0084] First, suppose there are N n nodes and N b Each node and branch has an independent failure probability and And the fault probability is determined based on the distribution network fault model shown in the above embodiment. Wherein, i = 1, 2, ..., N n ,j=1,2,…,N b Using the Monte Carlo method, we first perform a random simulation of the fault state of each node and branch. i , if the generated random number U i satisfy If the node is faulty, then the node is considered normal. j , if the random number V j ≤P bj , then it is determined that the branch is faulty.

[0085] Repeat the above steps N s times (i.e., N s The simulation data is used to calculate the overall failure probability of the system.

[0086] Node failure judgment is expressed as: Among them, U i is a uniform random number on [0,1], Indicates the fault status of node ni (1 indicates fault, 0 indicates normal).

[0087] Branch fault judgment indicates: Among them, V j is a uniform random number on [0,1], Represents node b j fault status.

[0088] Total failure scenario matrix: The result of each round of simulation can be expressed as a vector Represents, where t=1,2,…,N s Through the above steps, a large number of fault scenarios can be generated in the simulation, which can be used to subsequently build a distribution network resilience improvement model.

[0089] Understandably, the simulation needs to be repeated multiple times for each moment, and the fault scenarios of each round need to be recorded, including which nodes and branches have failed. Finally, the fault scenarios with the greatest impact on the distribution network can be screened out. The impact here can be determined by the number of node failures and branch failures. The more node failures and branch failures there are, the greater the impact.

[0090] In the present application, the fault scenarios are simulated by the aforementioned method, which can cover various possible fault combinations, thereby being more conducive to the subsequent improvement of the resilience of the distribution network.

[0091] Step S204, constructing a distribution network resilience improvement model based on the current operating state of the distribution network, the fault conditions corresponding to multiple nodes and multiple branches at multiple times, and the target condition constraints.

[0092] Among them, the solution objectives of the distribution network resilience improvement model include the maximum value of the sum of load recovery degrees at multiple times and the minimum value of the number of switch state switching at multiple times, and the target condition constraints include power flow balance constraints.

[0093] In a feasible embodiment, the distribution network resilience improvement model is expressed as:

[0094]

[0095] Among them, α and β are used to represent weights, and the sum of α and β is 1; ω k Used to represent the weight corresponding to node k among multiple nodes; y k,t It is used to represent the fault condition of node k at time t, and y k,t It is a 0-1 variable, which is 1 when the node fails and 0 when the node is not failed; P k,t It is used to indicate the load power size corresponding to node k at time t; T is used to indicate the preset time period, B is used to indicate the set of multiple nodes, and E is used to indicate the set of multiple branches; a l,t It is used to indicate the fault condition of branch l at time t, a l,t-1 It is used to indicate the fault condition of branch l at time t-1, and also a l,t It is a 0-1 variable, which is 1 when the branch fails and 0 when the branch is not failed.

[0096] Among them, ω k It can be set according to the importance of the load at node k. For example, according to the importance of the load at node k, it is divided into class 1, class 2 and class 3, and the weight ω is set respectively. k =ω i (k∈B,i=1,2,3), and ω1≥ω2≥ω3.

[0097] It can be understood that if the initial switch state of branch 1 is closed and branch 1 fails, branch 1 will switch the switch from closed to open based on fault isolation. If node 1 fails, the load of node 1 cannot be powered normally. At this time, the switches of the branches connected upstream and downstream of node 1 need to be switched to closed, so as to switch the load of node 1 to other normal power supply paths.

[0098] In addition, in a feasible embodiment, the current operating state of the distribution network also includes nodes in which distributed power sources are deployed among the multiple nodes, and the target condition constraint also includes an output power constraint of the distributed power source, and the output power constraint of the distributed power source is used to constrain the output power of the distributed power source to be within a preset threshold.

[0099] Furthermore, in a feasible embodiment, the target condition constraints also include closed branch constraints and single commodity flow constraints; the closed branch constraint is used to constrain the number of closed branches among multiple branches, which is equal to the number of multiple nodes minus the number of root nodes among multiple nodes; the single commodity flow constraint includes flow constraint, root node quantity constraint and branch constraint; the flow constraint is used to constrain the sum of the inflow power of non-root nodes among multiple nodes, which is equal to the sum of the outflow power plus the load of the node, and / or the sum of the inflow power of the root nodes among multiple nodes, which is less than the sum of the outflow power plus the load of the node; the root node quantity constraint is used to constrain the number of root nodes among multiple nodes to be less than the number of faulty branches among multiple branches plus one; the branch constraint is used to constrain the flow power of faulty branches among multiple branches to be zero.

[0100] The following is a detailed description of the target condition constraints:

[0101] First, for the distribution network operation constraints, including power flow balance constraints and safety constraints, the formula is as follows:

[0102]

[0103] Among them, δ j is the set of all branches out of node j, π j is the set of all branches flowing into node j, B is the set of nodes except the substation node, and B + Represents all nodes in the system. Contains variables: node injected power (p, q), branch flow (P, Q), node voltage (V), and branch current (I). The injected active power p of node j is j Equal to the sum of the active power generated by the generators at node j Subtract the sum of active loads of node j The injected reactive power q of node j is j Equal to the sum of the reactive powers generated by the generators at node j Subtract the sum of reactive loads at node j P jk It is used to represent the active power flowing from node j to node k, P ij It is used to represent the active power flowing from node i to node j. jk It is used to represent the reactive power flowing from node j to node k, Q ij It is used to represent the reactive power flowing from node i to node j.ij It is used to represent the admittance of the line between node i and node j. ij Used to represent the impedance of the line between node i and node j. j Used to represent the ground impedance at node j. j Used to represent the ground admittance at node j. I ij Used to indicate the minimum allowable current value between node i and node j; Used to indicate the maximum allowable current between node i and node j. V j It is used to indicate the minimum allowable voltage value of node j; Used to indicate the maximum allowable voltage value of node j.

[0104] make The above-mentioned power flow balance constraint and safety constraint formula can be transformed into SOCR to obtain:

[0105]

[0106] in, Used to represent the square of the current between node i and node j; Used to represent the square of the voltage at node j.

[0107] Secondly, in order to better realize the post-disaster recovery of important loads, distributed generation (DG) is added to some nodes in the proposed model. This part of DG is not affected by the disaster or is less affected. It is considered that it can still work normally during the evolution of the disaster. For the DG at node j, its output satisfies:

[0108] Among them, B DG represents the set of all nodes equipped with distributed generation; the above formula is used to represent the active output of DG at node j at time t Reactive power It should be between the upper and lower limits of output. It is used to represent the minimum allowable value of the active output of DG at node j; It is used to indicate the maximum allowable value of the active output of DG at node j. It is used to represent the minimum allowable value of the reactive power output of DG at node j; It is used to indicate the maximum allowable value of reactive power output of DG at node j.

[0109] Finally, the distribution network also has a radial topology constraint. The necessary and sufficient conditions for satisfying the radial constraint are: the connectivity and the node-branch number relationship must be satisfied at the same time. From the perspective of virtual power flow, this paper proposes a single commodity flow constraint to ensure the connectivity of the graph, which together with the node-branch number relationship constitutes a radial constraint. The formula is as follows:

[0110]

[0111] Among them, π i and δ i They are the sets of branches with end node i (inflow node i) and branches with first node i (outflow node i); the number of nodes in the network is N; R is the set of root nodes in each subnet, and one root node is taken from each subnet, generally a substation node or a distributed power source node, and the number of root nodes is equal to the number of subnets, which is N. R ; F ij represents the virtual power flow through line i→j, F ki represents the virtual power flow through line k→i; D i is the root node flag of each node, which is a 0-1 variable. i =1, then node i is the root node; N fault is the number of faulty branches at the current moment; M is a large positive real number, generally set to the number of nodes; a ij Indicates the line disconnection status, which is a 0-1 variable, where the line is disconnected and the line is closed. B is used to represent a collection of multiple nodes; E is used to represent a collection of multiple branches.

[0112] The first formula above indicates that the number of all closed circuits is equal to the number of all nodes minus the number of subnets, ensuring that all generated networks are trees. The following three formulas are single commodity flow constraints to ensure the connectivity of each subnet: 1) Ensure the conservation of virtual flow at each node. Each node has 1 unit of virtual demand. The sum of the virtual flows out of all nodes plus the sum of the virtual load minus the virtual flows in is the injected power of the node. If the node is not a root node, the injected power is 0. If the node is a root node, the injected power is not 0 and is less than or equal to M; 2) Ensure the number of subnets. The number of root nodes in the topology is less than the number of fault branches plus one; 3) Ensure branch connectivity. Use the "big M method" to determine the line disconnection: if the line is closed, it is equivalent to no constraint, and if the line is disconnected, the virtual flow of the branch is 0.

[0113] Step S205, solving the distribution network flexibility improvement model, and determining the operating status of the distribution network within a preset time period based on the solution result.

[0114] The distribution network elasticity improvement model includes the multiple constraints shown in the above embodiments, and the optimization variables may further include branch disconnection variables a ij , Distributed Generation Output Variable Node injection power (p, q), branch power flow (P, Q), node voltage (V), and branch current (I). The above model is a mixed integer second-order cone optimization problem, which can be solved directly using the gurobi solver or other solvers.

[0115] Based on this, the operation status of the distribution network in the preset time period includes the branch disconnection status corresponding to each moment, the node distributed power output, the node injection power, the branch flow, the node voltage and the branch current. Based on this, the distribution network can be reconstructed according to the operation status of the distribution network in the preset time period. For example, according to the above results, the lines and nodes that need to be repaired can be determined in time, so that the repair personnel can repair the distribution network nodes and distribution network lines.

[0116] It can be seen that in the embodiment of the present application, firstly, according to the wind speed and water depth within a period of time and the distribution network fault model, the fault conditions of multiple nodes and multiple branches in the distribution network within a period of time are determined, and then the distribution network elasticity improvement model is constructed according to the fault conditions of multiple nodes and multiple branches within a period of time, the current operating state of the distribution network and the corresponding target condition constraints, and the maximum value of the sum of the load recovery degrees within multiple moments and the minimum value of the number of switch state switching within multiple moments are taken as the target, and the distribution network elasticity improvement model is solved to obtain the operating state of the distribution network within a period of time. In this way, the dynamic reconstruction of the distribution network can be realized based on the operating state of the distribution network within a period of time. In this way, a method for quantifying the probability of power grid failure in wind and flood scenarios is given, and a multi-objective long-term dynamic distribution network reconstruction scheme for improving the elasticity of the distribution network is proposed, which can greatly improve the load recovery of the distribution network after the disaster evolves for a period of time. It is especially suitable for disaster scenarios such as wind and flood disasters that last for a long time and have complex impacts on the distribution system, and improves the elasticity of the distribution network against waterlogging disasters.

[0117] In accordance with the above-mentioned embodiments, please refer to Figure 7 , Figure 7 This is a functional unit block diagram of a device for determining the operating status of a distribution network provided in an embodiment of the present application. The device for determining the operating status of a distribution network may be the above-mentioned server or a part of the server. Figure 7 As shown, the distribution network operation status determination device 70 includes:

[0118] An acquisition unit 701 is used to acquire environmental data, a distribution network fault model, and a current operating state of the distribution network. The environmental data includes wind speed and water depth corresponding to multiple moments in a preset time period. The distribution network fault model is used to predict the failure probability of multiple nodes and multiple branches included in the distribution network. The current operating state of the distribution network includes the load power of multiple nodes and the current switch state of multiple branches.

[0119] A processing unit 702 is used to determine the fault probabilities corresponding to a plurality of nodes and a plurality of branches at a plurality of times according to the environmental data and the distribution network fault model;

[0120] The processing unit 702 is further configured to determine the fault conditions corresponding to the multiple nodes and the multiple branches at multiple times according to the fault probabilities corresponding to the multiple nodes and the multiple branches at multiple times;

[0121] The processing unit 702 is further used to construct a distribution network resilience improvement model based on the current operating state of the distribution network, the fault conditions corresponding to multiple nodes and multiple branches at multiple times, and the target condition constraints. The solution objectives of the distribution network resilience improvement model include the maximum value of the sum of load recovery degrees at multiple times and the minimum value of the number of switch state switching at multiple times. The target condition constraints include power flow balance constraints.

[0122] The processing unit 702 is further used to solve the distribution network flexibility improvement model, and determine the operating status of the distribution network within a preset time period based on the solution result.

[0123] In a feasible embodiment, in terms of determining the fault probabilities corresponding to multiple nodes and multiple branches at multiple times, the processing unit 702 is specifically used to: when the target parameter is less than a first threshold, the fault probability is the target probability, the target parameter is wind speed or water depth, and the target probability is the fault probability when the node or branch is not affected by the target parameter; when the target parameter is not less than the first threshold and less than the second threshold, the fault probability is determined according to the distribution function of the first value, the first value is determined according to the ratio of the first parameter to the second parameter, the second parameter is the standard deviation of the natural logarithm of the target parameter that causes the fault, the first parameter is the logarithm of the third parameter, and the third parameter is the ratio between the target parameter and the median of the target parameter at the time of the fault; when the target parameter is not less than the second threshold, the fault probability is one.

[0124] In a feasible embodiment, the distribution network fault model includes a tower fault model, an overhead line fault model and a substation fault model; the tower fault model and the overhead line fault model determine the fault probability according to the wind speed; the substation fault model determines the fault probability according to the water depth.

[0125] In a feasible embodiment, in terms of determining the fault conditions corresponding to the multiple nodes and the multiple branches at multiple moments according to the fault probabilities corresponding to the multiple nodes and the multiple branches at multiple moments, the processing unit 702 is specifically used to: for each moment in the multiple moments, generate multiple first uniform random numbers corresponding to the multiple nodes, and multiple second uniform random numbers corresponding to the multiple branches, the first uniform random number and the second uniform random number being between zero and one; when the first uniform random number corresponding to each of the multiple nodes is not greater than the fault probability corresponding to each node at each moment, the fault condition of each node is determined to be a fault; when the first uniform random number corresponding to each of the multiple nodes is greater than the fault probability corresponding to each node at each moment, the fault condition of each node is determined to be not a fault; when the second uniform random number corresponding to each of the multiple branches is not greater than the fault probability corresponding to each branch at each moment, the fault condition of each branch is determined to be a fault; when the second uniform random number corresponding to each of the multiple branches is greater than the fault probability corresponding to each branch at each moment, the fault condition of each branch is determined to be not a fault.

[0126] In a feasible embodiment, the distribution network resilience improvement model is expressed as:

[0127]

[0128] Among them, α and β are used to represent weights, and the sum of α and β is 1; ω k Used to represent the weight corresponding to node k among multiple nodes; y k,t It is used to represent the fault condition of node k at time t, and y k,t It is a 0-1 variable, which is 1 when the node fails and 0 when the node is not failed; P k,t It is used to indicate the load power size corresponding to node k at time t; T is used to indicate the preset time period, B is used to indicate the set of multiple nodes, and E is used to indicate the set of multiple branches; a l,t It is used to indicate the fault condition of branch l at time t, a l,t-1 It is used to indicate the fault condition of branch l at time t-1, and also a l,t It is a 0-1 variable, which is 1 when the branch fails and 0 when the branch is not failed.

[0129] In a feasible embodiment, the current operating state of the distribution network also includes nodes in which distributed power sources are deployed among the multiple nodes, and the target condition constraint also includes an output power constraint of the distributed power source, and the output power constraint of the distributed power source is used to constrain the output power of the distributed power source to be within a preset threshold.

[0130] In a feasible embodiment, the target condition constraints also include closed branch constraints and single commodity flow constraints; the closed branch constraint is used to constrain the number of closed branches in multiple branches, which is equal to the number of multiple nodes minus the number of root nodes in multiple nodes; the single commodity flow constraint includes flow constraint, root node quantity constraint and branch constraint; the flow constraint is used to constrain the sum of the inflow power of non-root nodes in multiple nodes, which is equal to the sum of the outflow power plus the load of the node, and / or the sum of the inflow power of the root nodes in multiple nodes, which is less than the sum of the outflow power plus the load of the node; the root node quantity constraint is used to constrain the number of root nodes in multiple nodes to be less than the number of faulty branches in multiple branches plus one; the branch constraint is used to constrain the flow power of faulty branches in multiple branches to be zero.

[0131] It can be understood that since the method embodiment and the device embodiment are different presentation forms of the same technical concept, the content of the method embodiment part in this application should be synchronously adapted to the device embodiment part and will not be repeated here.

[0132] In the case of an integrated unit, such as Figure 8 As shown, Figure 8 This is a block diagram of the functional units of another device 70 for determining the operating status of a distribution network provided in an embodiment of the present application. Figure 8 In the embodiment, the distribution network operation status determination device 70 includes: a processing module 812 and a communication module 811. The processing module 812 is used to control and manage the actions of the distribution network operation status determination device 70, for example, the steps of the acquisition unit 701 and the processing unit 702, and / or other processes for executing the technology described herein. The communication module 811 is used to support the interaction between the distribution network operation status determination device 70 and other devices. Figure 8 As shown, the distribution network operating state determining device 70 may further include a storage module 813 , and the storage module 813 is used to store program codes and data of the distribution network operating state determining device 70 .

[0133] Among them, the processing module 812 can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 811 can be a transceiver, an RF circuit or a communication interface, and the like. The storage module 813 can be a memory.

[0134] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here. The above distribution network operation status determination device 70 can execute the above Figure 2 The method for determining the operating status of the distribution network is shown.

[0135] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0136] Fig. 9 This is a structural block diagram of an electronic device provided in an embodiment of the present application. Fig. 9 As shown, the electronic device 900 may include one or more of the following components: a processor 901, a memory 902 and a communication interface 903. The processor 901, the memory 902 and the communication interface 903 are interconnected and perform communication work with each other, wherein the memory 902 may store one or more computer programs, and the one or more computer programs may be configured to implement the methods described in the above embodiments when executed by one or more processors 901.

[0137] The processor 901 may include one or more processing cores. The processor 901 uses various interfaces and lines to connect the various parts of the entire electronic device 900, and executes various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets or instruction sets stored in the memory 902, and calling data stored in the memory 902. Optionally, the processor 901 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 901 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU) and a modem. It is understandable that the above-mentioned modem may not be integrated into the processor 901, and may be implemented separately through a communication chip.

[0138] The memory 902 may include a random access memory (RAM) or a read-only memory (ROM). The memory 902 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the electronic device 900 during use, etc.

[0139] It is understandable that the electronic device 900 may include more or fewer structural elements than those in the above structural block diagram, for example, a power module, physical buttons, a WiFi (Wireless Fidelity) module, a speaker, a Bluetooth module, a sensor, etc., which are not limited here.

[0140] The electronic device 900 may be a server or a part of a server.

[0141] An embodiment of the present application provides a computer-readable storage medium, wherein program data is stored in the computer-readable storage medium, and when the program data is executed by a processor, the program data is used to execute part or all of the steps of any one of the methods for determining the operating status of a distribution network recorded in the above method embodiments.

[0142] The present application also provides a computer program product, including a computer program, which can be operated to enable a computer to execute some or all steps of any method for determining the operating status of a distribution network as described in the above method embodiment. The computer program product can be a software installation package.

[0143] It should be noted that, for any of the aforementioned method embodiments of the method for determining the operating status of a distribution network, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by this application.

[0144] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude multiple situations. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0145] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of any of the above-mentioned methods for determining the operating status of a distribution network can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a disk or an optical disk, etc.

[0146] The above is a detailed introduction to the embodiments of the present application. In this article, specific examples are used to illustrate the principles and implementation methods of a method, device, electronic device and storage medium for determining the operating status of a distribution network of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, based on the idea of ​​a method, device, electronic device and storage medium for determining the operating status of a distribution network of the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0147] The present application is described with reference to the flowcharts and / or block diagrams of the methods, hardware products, and computer program products of the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] It can be understood that any product that is controlled or configured to execute the processing method of the flowchart described in the method embodiment of a method for determining the operating status of a distribution network in the present application, such as the terminal and computer program product in the above flowchart, falls within the scope of the related products described in the present application.

[0151] Obviously, those skilled in the art can make various changes and modifications to the method, device, electronic device and storage medium for determining the operating status of a distribution network provided by the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining the operating status of a distribution network, characterized in that: The method comprises: Acquire environmental data, a distribution network fault model, and a current operating state of the distribution network, wherein the environmental data includes wind speed and water depth corresponding to multiple moments in a preset time period, the distribution network fault model is used to predict the failure probability of multiple nodes and multiple branches included in the distribution network, and the current operating state of the distribution network includes load power of multiple nodes and current switch states of multiple branches; Determine, according to the environmental data and the distribution network fault model, the fault probabilities corresponding to the multiple nodes and the multiple branches at the multiple time points; Determine the fault conditions corresponding to the multiple nodes and the multiple branches at the multiple moments respectively according to the fault probabilities corresponding to the multiple nodes and the multiple branches at the multiple moments respectively; A distribution network elasticity improvement model is constructed based on the current operating state of the distribution network, the fault conditions corresponding to the multiple nodes and the multiple branches at the multiple moments, and the target condition constraints. The solution target of the distribution network elasticity improvement model includes the maximum value of the sum of the load recovery degrees at the multiple moments and the minimum value of the number of switch state switching at the multiple moments. The target condition constraints include power flow balance constraints. A distribution network elasticity improvement model is solved, and the operating status of the distribution network within the preset time period is determined based on the solution result.

2. The method according to claim 1, characterized in that The determining the failure probabilities corresponding to the plurality of nodes and the plurality of branches at the plurality of times respectively includes: When the target parameter is less than a first threshold, the failure probability is a target probability, the target parameter is the wind speed or the water depth, and the target probability is a failure probability when the node or the branch is not affected by the target parameter; When the target parameter is not less than the first threshold value and less than the second threshold value, the failure probability is determined according to the distribution function of the first value, the first value is determined according to the ratio of the first parameter to the second parameter, the second parameter is the standard deviation of the natural logarithm of the target parameter causing the failure, the first parameter is the logarithm of the third parameter, and the third parameter is the ratio between the target parameter and the median of the target parameter at the time of the failure; When the target parameter is not less than the second threshold, the failure probability is one.

3. The method according to claim 1, characterized in that The distribution network fault model includes a tower fault model, an overhead line fault model and a substation fault model; the tower fault model and the overhead line fault model determine the fault probability according to the wind speed; the substation fault model determines the fault probability according to the water depth.

4. The method according to claim 1, characterized in that: The determining, according to the failure probabilities that the multiple nodes and the multiple branches respectively correspond to at the multiple times, the failure conditions that the multiple nodes and the multiple branches respectively correspond to at the multiple times, comprises: For each of the multiple moments, generate a plurality of first uniform random numbers corresponding to the multiple nodes respectively, and a plurality of second uniform random numbers corresponding to the multiple branches respectively, wherein the first uniform random numbers and the second uniform random numbers are between zero and one; When the first uniform random number corresponding to each node among the multiple nodes is not greater than the failure probability corresponding to each node at each time, determining that the failure condition of each node is a failure; When the first uniform random number corresponding to each node among the multiple nodes is greater than the failure probability corresponding to each node at each time, it is determined that the failure status of each node is not a failure; When the second uniform random number corresponding to each branch of the multiple branches is not greater than the failure probability corresponding to each branch at each time, determining that the failure condition of each branch is a failure; When the second uniform random number corresponding to each branch of the multiple branches is greater than the failure probability corresponding to each branch at each moment, it is determined that the failure condition of each branch is not a failure.

5. The method according to any one of claims 1 to 3, characterized in that: The distribution network flexibility improvement model is expressed as: Among them, α and β are used to represent weights, and the sum of α and β is 1, ω k It is used to represent the weight corresponding to the node k among the multiple nodes, y k,t It is used to represent the fault condition of node k at time t, P k,t is used to represent the load power corresponding to node k at time t, T is used to represent the preset time period, B is used to represent the set of the multiple nodes, E is used to represent the set of the multiple branches, a l,t It is used to indicate the fault condition of branch l at time t, a l,t-1 Used to indicate the fault condition of branch l at time t-1.

6. The method according to any one of claims 1 to 5, characterized in that: The current operating state of the distribution network also includes nodes in which distributed power sources are deployed among the multiple nodes, and the target condition constraint also includes an output power constraint of the distributed power source, and the output power constraint of the distributed power source is used to constrain the output power of the distributed power source to be within a preset threshold.

7. The method according to any one of claims 1 to 6, characterized in that: The target condition constraints also include closed branch constraints and single commodity flow constraints; The closed branch constraint is used to constrain the number of closed branches in the multiple branches, which is equal to the number of the multiple nodes minus the number of root nodes in the multiple nodes; The single commodity flow constraints include flow constraints, root node quantity constraints and branch constraints; The flow constraint is used to constrain the sum of inflow powers of non-root nodes among the multiple nodes to be equal to the sum of outflow powers plus the load of the node, and / or the sum of inflow powers of the root node among the multiple nodes to be less than the sum of outflow powers plus the load of the node; The root node number constraint is used to constrain the number of root nodes in the multiple nodes to be less than the number of faulty branches in the multiple branches plus one; The branch constraint is used to constrain the flow power of a faulty branch among the multiple branches to be zero.

8. A device for determining the operating status of a distribution network, characterized in that: The device comprises: an acquisition unit, configured to acquire environmental data, a distribution network fault model, and a current operating state of the distribution network, wherein the environmental data includes wind speeds and water depths corresponding to multiple moments in a preset time period, the distribution network fault model is configured to predict the failure probabilities of multiple nodes and multiple branches included in the distribution network, and the current operating state of the distribution network includes load powers of multiple nodes and current switch states of multiple branches; A processing unit, configured to determine the fault probabilities corresponding to the plurality of nodes and the plurality of branches at the plurality of times respectively according to the environmental data and the distribution network fault model; The processing unit is further configured to determine the fault conditions corresponding to the multiple nodes and the multiple branches at the multiple moments respectively according to the fault probabilities corresponding to the multiple nodes and the multiple branches at the multiple moments respectively; The processing unit is further used to construct a distribution network elasticity improvement model based on the current operating state of the distribution network, the fault conditions corresponding to the multiple nodes and the multiple branches at the multiple moments, and the target condition constraints, wherein the solution target of the distribution network elasticity improvement model includes the maximum value of the sum of the load recovery degrees at multiple moments and the minimum value of the number of switch state switching at multiple moments, and the target condition constraints include power flow balance constraints; The processing unit is further used to solve the distribution network elasticity improvement model, and determine the operating status of the distribution network within the preset time period based on the solution result.

9. An electronic device, comprising a processor, a memory, and a computer program stored in the memory, characterized in that: The processor is used to call the executable program code computer program stored in the memory to execute the method according to any one of claims 1-7.

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 method according to any one of claims 1 to 7 is implemented.

Images