A distribution network reliability assessment method, device, equipment and storage medium

Abstract

The present invention discloses a distribution network reliability assessment method, device, equipment and storage medium. The method includes determining the output levels corresponding to multiple uncertain power access devices involved in the target grid of the distribution network to be assessed at different times of a typical day; using a random expectation model to determine the worst combination from different output level combinations; determining the expected number of hours of power outage for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid; determining the average power supply availability based on the number of hours of power consumption by users and the preset number of hours of power supply required by users; and using the average power supply availability as an assessment indicator to assess the reliability of the distribution network to be assessed. Using this method, typical scenarios can be combined with static assessments to better reflect the actual level. This method can become an effective tool to assist planners in improving the lean level from the perspective of reliability assessment.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of power grid technology, and in particular to a distribution network reliability assessment method, apparatus, device, and storage medium. Background Art

[0002] On the one hand, the effective medium-voltage distribution network reliability assessment theory and analysis model can adapt to the needs of high-quality development of urban medium-voltage distribution networks and meet the basic needs of safe and reliable power supply; on the other hand, it is conducive to improving the pertinence and economy of planning, and providing a feasible basis and main reference for evaluating the investment effectiveness of distribution networks.

[0003] Classic distribution network reliability assessment methods are based on probability and mathematical statistics. However, their offline evaluation characteristics make it difficult to reflect the conditions and status of the system in real-time operating environments. To overcome these shortcomings, existing research has primarily modeled the uncertainties of power access devices or deterministic parameters that affect system reliability, proposing theoretically feasible reliability assessment models and processes. However, reliability analysis based on specific grid architectures and assessment scenarios, as well as analysis of their impact on practical planning, is relatively lacking. Summary of the Invention

[0004] The embodiments of the present invention provide a distribution network reliability assessment method, apparatus, device, and storage medium, which can serve as an effective tool for assisting planners in improving the lean level from the perspective of reliability assessment.

[0005] In a first aspect, an embodiment of the present invention provides a distribution network reliability assessment method, comprising:

[0006] Determine the output levels of multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times of a typical day;

[0007] Using a random expectation model, the worst combination is determined from different power level combinations. The worst combination is the combination that results in the longest power outage duration. An output level combination consists of an uncertain power access device and its corresponding output levels at different times during a typical day.

[0008] Determine the expected number of hours of power outage for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid, wherein the expected number of hours of power outage for users is the sum of the expected number of hours of power outage for users under the worst combinations at different fault locations;

[0009] Determining the average power supply availability based on the number of hours of power usage by the user and the preset number of hours of power supply required by the user, wherein the number of hours of power usage by the user is the difference between the preset number of hours of power supply required by the user and the expected number of hours of power outage for the user;

[0010] The average power supply availability is used as an evaluation index to evaluate the reliability of the distribution network to be evaluated.

[0011] In a second aspect, an embodiment of the present invention further provides a distribution network reliability assessment device, comprising:

[0012] The first determination module is used to determine the output levels corresponding to multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times of a typical day;

[0013] a second determination module, configured to determine a worst-case combination from different power level combinations using a random expectation model, wherein the worst-case combination is a combination that results in the longest power outage duration, wherein an output level combination is composed of a certain uncertain power access device and the output levels corresponding to the certain uncertain power access device at different times during a typical day;

[0014] a third determining module, configured to determine an expected number of power outage hours for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid, wherein the expected number of power outage hours for users is the sum of the expected number of power outage hours for users under the worst combinations at different fault locations;

[0015] a fourth determining module, configured to determine an average power supply availability based on the number of power usage hours of the user and a preset number of power supply hours required by the user, wherein the number of power usage hours of the user is the difference between the preset number of power supply hours required by the user and the expected number of power outage hours of the user;

[0016] The evaluation module is used to evaluate the reliability of the distribution network to be evaluated by taking the average power supply availability as an evaluation indicator.

[0017] In a third aspect, an embodiment of the present invention further provides a computer device, including:

[0018] one or more processors;

[0019] a storage device for storing one or more programs;

[0020] The one or more programs are executed by the one or more processors, so that the one or more processors are used to implement the distribution network reliability assessment method described in any embodiment of the present invention.

[0021] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the distribution network reliability assessment method provided by any embodiment of the present invention.

[0022] An embodiment of the present invention provides a distribution network reliability assessment method, apparatus, device and storage medium. First, the output levels corresponding to multiple uncertain power access devices involved in a target grid of the distribution network to be assessed at different times of a typical day are determined; secondly, a random expectation value model is used to determine the worst combination from different output level combinations, wherein the worst combination is a combination that achieves the longest power outage duration. An output level combination consists of a certain uncertain power access device and the output levels corresponding to the certain uncertain power access device at different times of a typical day; then, based on the worst combination and the electrical topology connection relationship in the target grid, the expected number of hours of power outage for users after a fault occurs is determined, wherein the expected number of hours of power outage for users is the sum of the expected number of hours of power outage for users under the worst combinations at different fault locations; then, the average power supply availability is determined based on the user's power usage hours and the preset number of hours of power supply required by users, wherein the user's power usage hours is the difference between the preset number of hours of power supply required by users and the expected number of hours of power outage for users; finally, the average power supply availability is used as an evaluation indicator to evaluate the reliability of the distribution network to be assessed. Using the above technical solution, typical scenarios can be combined with static evaluation to better reflect the actual level. This method can become an effective tool to assist planners in improving the level of leanness from the perspective of reliability assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic flow chart of a distribution network reliability assessment method provided in the first embodiment of the present invention;

[0024] Figure 2 A schematic structural diagram of a distribution network to be evaluated in a distribution network reliability evaluation method provided in the first embodiment of the present invention;

[0025] Figure 3 A schematic diagram of a neural network model construction framework in a distribution network reliability assessment method provided in the first embodiment of the present invention;

[0026] Figure 4 A schematic flow chart of a distribution network reliability assessment method provided in the second embodiment of the present invention;

[0027] Figure 5 A schematic structural diagram of a distribution network reliability assessment device provided in a third embodiment of the present invention;

[0028] Figure 6 This is a structural diagram of a computer device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0030] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. When its operation is completed, the process can be terminated, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the features in the embodiments of the present invention and the embodiments can be combined with each other without conflict.

[0031] The term "including" and its variations used in the present invention are open inclusions, that is, "including but not limited to." The term "based on" means "based at least in part on." The term "one embodiment" means "at least one embodiment."

[0032] Example 1

[0033] Figure 1 This is a flow chart of a distribution network reliability assessment method provided in Example 1 of the present invention. The method is applicable to the situation where the grid structure of the distribution network is assessed. The method can be performed by a distribution network reliability assessment device, wherein the device can be implemented by software and / or hardware and is generally integrated on a computer device.

[0034] like Figure 1 As shown, a distribution network reliability assessment method provided by the first embodiment of the present invention includes the following steps:

[0035] S110: Determine output levels corresponding to a plurality of uncertain power access devices involved in a target grid of the distribution network to be evaluated at different times during a typical day.

[0036] In this embodiment, the parameters of the distribution network to be evaluated may include basic information about the current grid sites of the distribution network, the connection relationships between sites, the number, length, model, and failure rate of lines between connected sites, the current load status of each site, the access location, scale, historical output characteristics, operating characteristics, and switch combinations between and within grid sites under normal operation. These parameters require in-depth pre-assessment fundraising and research, in-depth on-site site investigations of current and planned sites, and the collection and acquisition of relevant information and data. Sites can primarily include switch stations on the primary network and ring main units on the secondary network. Uncertain power access equipment can include distributed power sources, energy storage units, and electric vehicles.

[0037] Figure 2 This is a schematic diagram of the structure of the distribution network to be evaluated in the distribution network reliability evaluation method provided by the first embodiment of the present invention. Figure 2 The distribution transformers connected to each load point are represented by Ta, Tb, Tc, Td, and Te, respectively, and the fuses are represented by Fa, Fb, Fc, Fd, Fe, and F5, respectively. In this example, both photovoltaic and energy storage are equivalently connected at source 0.

[0038] For example, Figure 2 The capacity of the backup power supply 7 can be 1500kW, the overhead line model is LGJ-240, the cable line model is YJV-300, and the distribution transformer model is S9. There are 5 load nodes in total, and the load conditions at the busbars numbered as nodes a to e are 800kW, 200kW, 50kW, 100kW, and 315kW, respectively. Table 1 is the first parameter table of the distribution network provided in Example 1 of the present invention, and Table 2 is the second parameter table of the distribution network provided in Example 1 of the present invention. When performing the reliability assessment, the following simplifications were made, and the influence of the upper power grid and the influence of the failure of the disconnectors on both sides adjacent to the circuit breaker were not considered.

[0039]

[0040]

[0041] Table 1

[0042]

[0043] Table 2

[0044] In this embodiment, a neural network model simulating output characteristics can be trained using historical output data on different typical days for multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated. Inputting the time of a typical day into the model automatically outputs the output level at that time. The time of a typical day is the hour, and the time of a typical day can be measured in hours, meaning that the time of a typical day can be any integer between 1 and 24 hours. For example, a typical day time can be 8:00 AM.

[0045] Furthermore, the determining of the output levels corresponding to the multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times in a typical day includes: obtaining historical output data of the multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated on different typical days; training based on the historical output data to obtain a target neural network model; inputting each moment included in each typical day into the target neural network model in sequence to obtain the output levels corresponding to the different moments in the typical day of each of the uncertain power access devices.

[0046] In this embodiment, for multiple uncertain power access devices, their historical output data on different typical days are counted, and the output levels corresponding to different times of the day are recorded as random variables ξ t ,ξ t ={ξ1,ξ2,ξ3,...,ξ t}, t can represent the time of a typical day. According to ξ t The target neural network model can be trained to approximate historical output data and describe the output level of typical units.

[0047] For example, taking the establishment of a neural network model through a program as an example, the process includes the following:

[0048] Figure 3 This is a schematic diagram of a neural network model construction framework in a distribution network reliability assessment method provided in the first embodiment of the present invention. Figure 3 As shown, run the programming software, open the main interface, select New > Function, and use the newff command in the newly popped-up window to create a feedforward neural network with one input neuron, 20 hidden neurons, and one output neuron. Use several typical daily historical output data as training samples to train the model feedforward neural network. The input data in the training samples is the time of day on a typical day, and the output data is the historical output level of a typical day. This training generates a target neural network model that simulates the output characteristics. When the time of day is input, the trained target neural network model automatically outputs the output level at that time.

[0049] Furthermore, before determining the worst combination from different output level combinations using a random expectation model, it also includes: determining the charging or discharging power corresponding to the energy storage unit in the uncertain power access device at each time of a typical day, and the charging or discharging power satisfies the boundary constraint function.

[0050] In this embodiment, after determining the output levels of multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times during a typical day, it is necessary to describe the output operating characteristics of the energy storage unit. The power level of the energy storage unit at each time during a typical day must meet the constraints required by the operating characteristics, that is, the charging or discharging power must meet the boundary constraint function. The formula for the boundary constraint function is as follows:

[0051]

[0052] |P s (t)|≤γ (2)

[0053] Q s,min ≤Q s (t)≤Q s,max (3)

[0054] Q s (t) = Q s (t-1)+η s P s (t)△t (4)

[0055] Among them, P s (t) represents the charge / discharge power of the energy storage unit at time t. When P s (t) indicates charging when it is a positive value, and discharging when it is a negative value; △t indicates the time interval, which can be 1 hour; γ indicates the upper limit of the charge / discharge power of the energy storage unit at each time; Q s (t) represents the amount of electricity in the energy storage unit at time t; Q s,min and Q s,max Respectively represent the lower and upper limits of the energy storage unit capacity; η s It indicates the charge / discharge efficiency of the energy storage unit and can be determined based on the specific capital situation.

[0056] It should be noted that the boundary constraint function can be written by any programming software. In this embodiment, it can be written by programming software. The specific process is as follows:

[0057] Open the programming software, select New > Function, and a blank function programming interface will appear. Write the expression for the bound constraint function. Bound constraint functions can include both inequality constraints and equality constraints. Therefore, when writing a bound constraint function, set two arrays, one for the results of the constraint equations and one for the results of the constraint inequalities. The elements in the arrays corresponding to constraint equations are all 0, and the elements in the arrays corresponding to constraint inequalities are all less than 0.

[0058] For example, create 24 optimization variables for a typical day, denoted as y1, y2…, y24 as the objects to be optimized. Use a programming language to write the boundary constraint function as follows:

[0059] For formula (1): y1+y2+…+y24=0;

[0060] For formula (2): the absolute values of y1 to y24 must all be less than the upper limit of the permitted energy storage unit power, which is 100kW in this example;

[0061] For formulas (3) to (4): Create a process variable S to calculate the capacity of the energy storage unit. The initial value of S is recorded as 1 / 2 of the energy storage unit capacity, which is 400kWh in this embodiment. In addition to energy storage, node 0 can provide a maximum downstream capacity of 1200kW. The value level of the process variable at each time must be calculated according to the relationship described in formula (4), that is, S2 = S1 + y1; S3 = S2 + y2; ..., S24 = S23 + y23. The process variables S1, S2, ..., S24 at each time must be less than 400kWh.

[0062] S120. Determine the worst combination from different power level combinations using a random expectation model.

[0063] The worst combination is the combination with the longest power outage duration, and an output level combination is composed of a certain uncertain power access device and the output levels corresponding to the certain uncertain power access device at different times in a typical day.

[0064] In this embodiment, in order to describe the reliability level of the target grid under extreme conditions as much as possible, the power supply capacity of the target grid should be evaluated as close as possible to the extreme conditions of the fault level. The random expectation value model is used to calculate the worst combination scenario that may occur under various output level combinations, and this is used as the boundary to evaluate the reliability level of the distribution network to be evaluated.

[0065] Furthermore, a random expectation value model is used to determine the worst combination from different output level combinations, including: screening out multiple output level combinations that satisfy the boundary constraint function from different output level combinations through the random expectation value model; determining the charge / discharge power corresponding to the output level combination that satisfies the boundary constraint function through the random expectation value model; determining the difference between the output level corresponding to the output level combination that satisfies the boundary constraint function at different times and the charge / discharge power through the random expectation value model; calculating the expected value of each of the differences through the random expectation value model; selecting the minimum expected value from each of the expected values through the random expectation value model; and using the output level combination corresponding to the minimum expected value as the worst combination through the random expectation value model.

[0066] Theoretically, the scenario where the distributed power generation output is insufficient and the remaining energy storage capacity is the minimum at a certain time in a typical day is the scenario corresponding to the worst possible combination. The charge / discharge power of the energy storage unit at time t is recorded as the decision variable x t , the worst combination is calculated using the random expectation model as follows:

[0067]

[0068] Where E[·] represents the random variable ξ t The expected value of the function f calculation result under different output level combinations. In the present invention, the expression of function f is as follows:

[0069] f=ξ t -x t (6)

[0070] The optimization goal is to find the output level combination with the minimum photovoltaic output and the maximum energy storage charging power. In this objective function, the photovoltaic output level ξ t The smaller the energy storage charging power x t The larger the value, the smaller the computational level of function f. g(·) describes the boundary constraint function that the energy storage device must satisfy when operating, with the subscript i for different output level combinations.

[0071] For example, the objective function expression shown in formula (6) can be written by software. The built-in optimization toolbox of the programming can be called to perform calculations, that is, to solve the optimization model including the objective function described by formula (6), the boundary constraint function as the boundary, and y as the optimization variable. In this embodiment, based on the sampling of the photovoltaic output combination at each moment, it is also possible to verify by exhaustive enumeration whether there is a moment when the photovoltaic output level is at a low value and the energy storage is charged at the maximum power allowed, and to correct the maximum power that node 0 can theoretically provide under this boundary.

[0072] S130: Determine the expected number of hours of power outage for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid.

[0073] The expected number of hours of power outage for users is the sum of the expected number of hours of power outage for users under the worst combinations at different fault locations.

[0074] In this embodiment, the power outage of the distribution network after the fault occurs is analyzed according to the worst combination situation and the electrical topology connection relationship. The connection relationship and topology structure between the nodes of the target grid are described using a connection relationship matrix. Furthermore, the electrical topology connection relationship is determined according to the number of the connection relationship matrix corresponding to each node in the target grid; wherein the number of the connection relationship matrix is determined according to a numbering rule, and the numbering rule includes: if there is a line relationship between the nodes and the switch devices on the lines between the nodes are in a closed state, then the number of the connection relationship matrix corresponding to the node is a first value; if there is no line relationship between the nodes, then the number of the connection relationship matrix corresponding to the node is a second value.

[0075] The specific processing method is as follows: each node in the target grid is uniformly numbered. For two nodes that have a line connection relationship and the switch devices on the line are closed, the elements at the row and column positions of the connection relationship matrix corresponding to the two nodes are marked as a first value. For example, the first value can be 1; for nodes that do not have a connection relationship, the elements at the row and column positions corresponding to the number of the connection relationship matrix are marked as a second value. For example, the second value can be 0.

[0076] In this embodiment, the expected number of hours of power outage for a user after a fault occurs can be determined by correcting the elements of the connectivity matrix of the node where the fault occurs to a second value, indicating a lack of connectivity. Simultaneously, the connectivity between the load points and power points in the connectivity matrix is determined. If a load point is connected to a power point, the load loss at that load point is calculated by subtracting the available power supply from the load. A value of 0 or a negative value indicates no load loss. The failure rate and corresponding load loss level in the current fault scenario are recorded, and the sum of the products of the failure rates and corresponding load losses at different fault locations is accumulated to obtain the expected number of hours of power outage for the user.

[0077] S140: Determine average power supply availability according to the user's power usage hours and a preset number of power supply hours required by the user.

[0078] The number of hours of electricity used by the user is the difference between the preset number of hours of power supply required by the user and the expected number of hours of power outage for the user.

[0079] In this embodiment, the calculation formula for the average power supply availability is as follows:

[0080]

[0081] The number of power supply hours required by the user can be set according to the actual situation. Generally, the number of power supply hours required by the user can be set to 8760 hours. The number of power hours used by the user is the difference between the number of power supply hours required by the user and the number of power outage hours expected by the user.

[0082] S150: Use the average power supply availability as an evaluation indicator to evaluate the reliability of the distribution network to be evaluated.

[0083] In this embodiment, there are various ways to evaluate the reliability of the distribution network to be evaluated based on the evaluation indicators, which are not limited here. For example, if the average power supply availability of a distribution network to be evaluated is greater than a set value, it can be indicated that the reliability of the distribution network to be evaluated is high.

[0084] A distribution network reliability assessment method provided in a first embodiment of the present invention first determines the output levels corresponding to multiple uncertain power access devices involved in a target grid of the distribution network to be assessed at different times of a typical day; secondly, a random expectation value model is used to determine the worst combination from different output level combinations, wherein the worst combination is a combination that achieves the longest power outage duration, and an output level combination consists of a certain uncertain power access device and the output levels corresponding to the certain uncertain power access device at different times of a typical day; then, based on the worst combination and the electrical topology connection relationship in the target grid, the expected number of hours of power outage for users after a fault occurs is determined, wherein the expected number of hours of power outage for users is the sum of the expected number of hours of power outage for users under the worst combinations at different fault locations; then, based on the number of hours of power consumption by users and a preset number of hours of power supply required by users, the average power supply availability is determined, wherein the number of hours of power consumption by users is the difference between the preset number of hours of power supply required by users and the expected number of hours of power outage for users; finally, the average power supply availability is used as an evaluation indicator to evaluate the reliability of the distribution network to be assessed. Using the above method, typical scenarios can be combined with static evaluation to better reflect the actual level. This method can become an effective tool to assist planners in improving the level of leanness from the perspective of reliability assessment.

[0085] Example 2

[0086] Figure 5This is a flow chart of a distribution network reliability assessment method provided by the second embodiment of the present invention. This second embodiment is optimized based on the above embodiments. In this embodiment, the expected number of hours of power outage for users after a fault occurs is determined based on the worst combination and the electrical topology connection relationship in the target grid. It is further specified as follows: the number of the connection relationship matrix corresponding to the node at the fault point is corrected to a second value; the connection relationship between the load point and the power supply point under the connection relationship matrix is determined by a preset function; if the connection relationship between the load point and the power supply point is connected, the difference between the load corresponding to the worst combination and the supplyable power corresponding to the worst combination is used as the loss load corresponding to the fault point; the failure rate corresponding to different fault points in the target grid of the distribution network to be assessed is multiplied by the loss load corresponding to the fault point; the results obtained after the multiplication are accumulated to obtain the expected number of hours of power outage for users. For details not yet provided in this embodiment, please refer to the first embodiment.

[0087] like Figure 4 As shown, a distribution network reliability assessment method provided by the second embodiment of the present invention includes the following steps:

[0088] S210: Determine output levels corresponding to a plurality of uncertain power access devices involved in a target grid of the distribution network to be evaluated at different times during a typical day.

[0089] S220. Determine the worst combination from different power level combinations using a random expectation model.

[0090] S230: Correct the serial number of the connection relationship matrix corresponding to the node at the fault point to a second value.

[0091] S240: Determine the connection relationship between the load points and the power points in the connection relationship matrix using a preset function.

[0092] In this embodiment, the preset function can describe and implement the following functions: taking the location of the fault as input, taking the total output level of the power supply connected to each load point after the fault occurs as output, selecting the fault point at different locations of the target grid, and recording the probability of the fault occurring at the fault location and the amount of load loss after the fault occurs, thereby calculating the expected number of hours of power outage for users of the target grid.

[0093] The preset function can be pre-programmed using a programming program. The specific code for creating a connection relationship is as follows:

[0094]

[0095]

[0096] S250: If the connection relationship between the load point and the power supply point is connected, the difference between the load corresponding to the worst combination and the supplyable power corresponding to the worst combination is used as the loss load corresponding to the fault point.

[0097] If the result of the difference is 0 or a negative value, it means that there will be no load loss.

[0098] In this embodiment, after a fault occurs, the first circuit breaker upstream of the fault point can be tripped first. At this point, the power outage time of the load point has begun to accumulate according to the breaking action time; then the disconnector closest to the upstream of the fault point is tripped to restore the circuit breaker to close, reducing the impact of the power outage; at the same time, further analysis can be conducted to determine whether the load downstream of the fault point has other dedicated load supply paths. If so, the normally open switch is closed, and the backup power supply is used to supply power to this part of the load, thereby reducing the impact and duration of the power outage. For example, the code to implement the above functions is as follows:

[0099]

[0100]

[0101] S260: Multiply the failure rates corresponding to different fault points in the target grid of the distribution network to be evaluated by the loss load corresponding to the fault points.

[0102] Among them, the failure rate of a fault point and the loss load corresponding to the fault can be used as a combination. The target grid of the distribution network to be evaluated can include multiple different above combinations. Multiplying the failure rate and loss load in each combination can obtain multiple multiplied results.

[0103] S270. Accumulate the multiplication results to obtain the expected number of power outage hours for the user.

[0104] The expected number of hours of power outage for the user can be obtained by summing the multiplied results.

[0105] S280: Determine the average power supply availability according to the user's power usage hours and the preset number of power supply hours required by the user.

[0106] The number of hours of electricity used by the user is the difference between the preset number of hours of power supply required by the user and the expected number of hours of power outage for the user.

[0107] S290: Use the average power supply availability as an evaluation indicator to evaluate the reliability of the distribution network to be evaluated.

[0108] A distribution network reliability assessment method provided in Example 2 of the present invention embodies the process of determining average power supply availability. This method not only provides a reasonable quantitative basis for grid planning in distribution networks, but also effectively compensates for deficiencies caused by a certain degree of subjectivity. Using computer simulation software, this method rationally, scientifically, and accurately assesses the reliability of the target grid for urban medium-voltage distribution networks, which are in urgent need of optimization and investment refinement. This method considers various uncertainties and actual planning needs, providing an operational, referenceable, and theoretically based reliability assessment process.

[0109] Example 3

[0110] Figure 5 This is a structural diagram of a distribution network reliability assessment device provided in Example 3 of the present invention. The device can be used to assess the grid structure of the distribution network, wherein the device can be implemented by software and / or hardware and is generally integrated on a computer device.

[0111] like Figure 5 As shown, the apparatus includes: a first determination module 610 , a second determination module 620 , a third determination module 630 , a fourth determination module 640 and an evaluation module 650 .

[0112] The first determination module 610 is configured to determine the output levels corresponding to a plurality of uncertain power access devices involved in a target grid of the distribution network to be evaluated at different times during a typical day;

[0113] A second determination module 620 is configured to determine a worst-case combination from different power level combinations using a random expectation model, where the worst-case combination is the combination that results in the longest power outage duration. An output level combination consists of a certain uncertain power access device and the output levels corresponding to the certain uncertain power access device at different times during a typical day.

[0114] A third determining module 630 is configured to determine an expected number of power outage hours for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid, where the expected number of power outage hours for users is the sum of the expected number of power outage hours for users under the worst combinations at different fault locations;

[0115] A fourth determining module 640 is configured to determine an average power supply availability based on the number of power usage hours of the user and a preset number of power supply hours required by the user, wherein the number of power usage hours of the user is the difference between the preset number of power supply hours required by the user and the expected number of power outage hours of the user;

[0116] The evaluation module 650 is configured to evaluate the reliability of the distribution network to be evaluated by taking the average power supply availability as an evaluation indicator.

[0117] In this embodiment, the device first determines the output levels corresponding to different times of a typical day for multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated through the first determination module 610; secondly, the device determines the worst combination from different output level combinations using a random expectation model through the second determination module 620, where the worst combination is the combination that achieves the longest power outage duration, and an output level combination consists of a certain uncertain power access device and the output levels corresponding to different times of a typical day for the certain uncertain power access device; and then determines the worst combination based on the worst combination through the third determination module 630. The expected number of hours of power outage for users after the fault occurs is determined by combining the electrical topology connection relationship in the target grid, and the expected number of hours of power outage for users is the sum of the expected number of hours of power outage for users under the worst combination at different fault locations; then, the fourth determination module 640 is used to determine the average power supply availability according to the number of power hours used by users and the preset number of power supply hours required by users, and the average power supply availability is determined according to the preset number of power supply hours required by users and the expected number of power outage hours for users; finally, the evaluation module 650 uses the average power supply availability as an evaluation indicator to evaluate the reliability of the distribution network to be evaluated.

[0118] This embodiment provides a distribution network reliability assessment device that can combine typical scenarios with static assessment to better reflect the actual level. This method can become an effective tool to assist planners in improving the lean level from the perspective of reliability assessment.

[0119] Furthermore, the first determination module 610 is specifically used to: obtain historical output data of multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated on different typical days; train a target neural network model based on the historical output data; input each moment included in each typical day into the target neural network model in turn to obtain the output level corresponding to each of the uncertain power access devices at different moments in the typical day.

[0120] Furthermore, the device also includes a fifth determination module, which is used to determine the charging or discharging power corresponding to the energy storage unit in the uncertain power access device at each time of a typical day before determining the worst combination from different output level combinations using a random expectation value model, and the charging or discharging power satisfies the boundary constraint function.

[0121] Furthermore, the second determination module 620 is specifically used to: screen out multiple output level combinations that meet the boundary constraint function from different output level combinations through a random expectation value model; determine the charge / discharge power corresponding to the output level combination that meets the boundary constraint function through a random expectation value model; determine the difference between the output level corresponding to the output level combination that meets the boundary constraint function at different times and the charge / discharge power through a random expectation value model; calculate the expected value of each of the differences through a random expectation value model; select the minimum expected value from each of the expected values through a random expectation value model; and use the output level combination corresponding to the minimum expected value as the worst combination through a random expectation value model.

[0122] Furthermore, the electrical topology connection relationship is determined based on the numbering of the connection relationship matrix corresponding to each node in the target grid; wherein, the numbering of the connection relationship matrix is determined based on a numbering rule, and the numbering rule includes: if there is a line relationship between the nodes and the switching devices on the lines between the nodes are in a closed state, then the numbering of the connection relationship matrix corresponding to the node is a first value; if there is no line relationship between the nodes, then the numbering of the connection relationship matrix corresponding to the node is a second value.

[0123] Furthermore, the third determination module 630 is specifically used to: correct the number of the connection relationship matrix corresponding to the node at the fault point to a second value; determine the connection relationship between the load point and the power supply point under the connection relationship matrix through a preset function; if the connection relationship between the load point and the power supply point is connected, then the difference between the load corresponding to the worst combination and the supplyable power corresponding to the worst combination is used as the loss load corresponding to the fault point; multiply the failure rate corresponding to different fault points in the target grid of the distribution network to be evaluated by the loss load corresponding to the fault point; and accumulate the results after the multiplication to obtain the expected number of hours of power outage for the user.

[0124] Furthermore, the fourth determining module 640 is specifically configured to use the ratio of the user's electricity usage hours to the preset number of hours the user needs to supply electricity as the average power supply availability.

[0125] The above-mentioned distribution network reliability assessment device can execute the distribution network reliability assessment method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0126] Example 4

[0127] Figure 6 This is a schematic diagram of the structure of a computer device provided by the fourth embodiment of the present invention. Figure 6As shown, the computer device provided by the fourth embodiment of the present invention includes: one or more processors 71 and a storage device 72; the processor 71 in the computer device can be one or more, Figure 6 Take a processor 71 as an example; the storage device 72 is used to store one or more programs; the one or more programs are executed by the one or more processors 71, so that the one or more processors 71 implement the distribution network reliability assessment method as described in any one of the embodiments of the present invention.

[0128] The computer device may further include an input device 73 and an output device 74 .

[0129] The processor 71, storage device 72, input device 73 and output device 74 in the computer device can be connected through a bus or other means. Figure 6 The bus connection is taken as an example.

[0130] The storage device 72 in the computer device is a computer-readable storage medium that can be used to store one or more programs, which can be software programs, computer executable programs, and modules, such as the program instructions / modules corresponding to the distribution network reliability assessment method provided in the first or second embodiment of the present invention (for example, the attached Figure 6 The modules in the distribution network reliability assessment device shown include: a first determination module 610, a second determination module 620, a third determination module 630, a fourth determination module 640, and an assessment module 650. The processor 71 executes the software programs, instructions, and modules stored in the storage device 72 to perform various functional applications and data processing of the computer device, that is, to implement the distribution network reliability assessment method in the above method embodiment.

[0131] The storage device 72 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the computer device. Furthermore, the storage device 72 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some instances, the storage device 72 may further include memory remotely located relative to the processor 71, and such remote memory may be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0132] The input device 73 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the computer device. The output device 74 may include a display device such as a display screen.

[0133] Furthermore, when one or more programs included in the above-mentioned computer device are executed by the one or more processors 71, the program performs the following operations:

[0134] Determine the output levels of multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times of a typical day;

[0135] Using a random expectation model, the worst combination is determined from different power level combinations. The worst combination is the combination that results in the longest power outage duration. An output level combination consists of an uncertain power access device and its corresponding output levels at different times during a typical day.

[0136] Determine the expected number of hours of power outage for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid, wherein the expected number of hours of power outage for users is the sum of the expected number of hours of power outage for users under the worst combinations at different fault locations;

[0137] Determining the average power supply availability based on the number of hours of power usage by the user and the preset number of hours of power supply required by the user, wherein the number of hours of power usage by the user is the difference between the preset number of hours of power supply required by the user and the expected number of hours of power outage for the user;

[0138] The average power supply availability is used as an evaluation index to evaluate the reliability of the distribution network to be evaluated.

[0139] Example 5

[0140] A fifth embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the program is used to perform a distribution network reliability assessment method. The method includes:

[0141] Determine the output levels of multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times of a typical day;

[0142] Using a random expectation model, the worst combination is determined from different power level combinations. The worst combination is the combination that results in the longest power outage duration. An output level combination consists of an uncertain power access device and its corresponding output levels at different times during a typical day.

[0143] Determine the expected number of hours of power outage for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid, wherein the expected number of hours of power outage for users is the sum of the expected number of hours of power outage for users under the worst combinations at different fault locations;

[0144] Determining the average power supply availability based on the number of hours of power usage by the user and the preset number of hours of power supply required by the user, wherein the number of hours of power usage by the user is the difference between the preset number of hours of power supply required by the user and the expected number of hours of power outage for the user;

[0145] The average power supply availability is used as an evaluation index to evaluate the reliability of the distribution network to be evaluated.

[0146] Optionally, when the program is executed by a processor, it can also be used to execute the distribution network reliability assessment method provided by any embodiment of the present invention.

[0147] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination thereof. The computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0148] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0149] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0150] The computer program code for performing the operations of the present invention can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0151] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A distribution network reliability assessment method, characterized in that: The method comprises: Determine the output levels of multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times of a typical day; Using a random expectation model, the worst combination is determined from different power level combinations. The worst combination is the combination that results in the longest power outage duration. An output level combination consists of an uncertain power access device and its corresponding output levels at different times during a typical day. Determine the expected number of hours of power outage for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid, wherein the expected number of hours of power outage for users is the sum of the expected number of hours of power outage for users under the worst combinations at different fault locations; Determining the average power supply availability based on the number of hours of power usage by the user and the preset number of hours of power supply required by the user, wherein the number of hours of power usage by the user is the difference between the preset number of hours of power supply required by the user and the expected number of hours of power outage for the user; The average power supply availability is used as an evaluation indicator to evaluate the reliability of the distribution network to be evaluated; Among them, the determining of the output levels corresponding to the multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times in a typical day includes: obtaining the historical output data of the multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated on different typical days; training based on the historical output data to obtain a target neural network model; inputting each moment included in each typical day into the target neural network model in sequence to obtain the output levels corresponding to the different moments in the typical day of each uncertain power access device.

2. The method according to claim 1, characterized in that Before using the random expectation model to determine the worst combination from different output level combinations, it also includes: The charging or discharging power corresponding to each time of a typical day of the energy storage unit in the uncertain power access device is determined, and the charging or discharging power satisfies a boundary constraint function.

3. The method according to claim 2, characterized in that The random expectation model is used to determine the worst combination from different power level combinations, including: Screening out a plurality of output level combinations that satisfy the boundary constraint function from different output level combinations through a random expectation model; Determining the charge / discharge power corresponding to the output level combination that satisfies the boundary constraint function through a random expectation model; Determine, by a random expectation model, the difference between the output level corresponding to the output level combination satisfying the boundary constraint function at different times and the charge / discharge power; Calculating the expected value of each of the differences using a random expected value model; Selecting the minimum expected value from the expected values using a random expected value model; The output level combination corresponding to the minimum expected value is taken as the worst combination through the random expected value model.

4. The method according to claim 1, wherein The electrical topology connection relationship is determined according to the number of the connection relationship matrix corresponding to each node in the target grid; The numbering of the connection relationship matrix is determined according to a numbering rule, and the numbering rule includes: if there is a line relationship between the nodes and the switching device on the line between the nodes is in a closed state, the number of the connection relationship matrix corresponding to the node is a first value; if there is no line relationship between the nodes, the number of the connection relationship matrix corresponding to the node is a second value.

5. The method according to claim 1, wherein Determining the expected number of power outage hours for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid includes: Correcting the number of the connection relationship matrix corresponding to the node at the fault point to a second value; Determine the connection relationship between the load points and the power points under the connection relationship matrix through a preset function; If the connection relationship between the load point and the power supply point is connected, the difference between the load amount corresponding to the worst combination and the available power amount corresponding to the worst combination is used as the loss load amount corresponding to the fault point; Multiplying the failure rates corresponding to different fault points in the target grid of the distribution network to be evaluated by the loss load corresponding to the fault points; The multiplied results are accumulated to obtain the expected number of hours of power outage for the user.

6. The method according to claim 1, characterized in that Determining the average power supply availability based on the user's power usage hours and the preset user's required power supply hours includes: The ratio of the user's electricity usage hours to the preset user's required power supply hours is taken as the average power supply availability.

7. A distribution network reliability assessment device, characterized in that: The device comprises: The first determination module is used to determine the output levels corresponding to multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated at different times of a typical day; a second determination module, configured to determine a worst-case combination from different power level combinations using a random expectation model, wherein the worst-case combination is a combination that results in the longest power outage duration, wherein an output level combination is composed of a certain uncertain power access device and the output levels corresponding to the certain uncertain power access device at different times during a typical day; a third determining module, configured to determine an expected number of power outage hours for users after a fault occurs based on the worst combination and the electrical topology connection relationship in the target grid, wherein the expected number of power outage hours for users is the sum of the expected number of power outage hours for users under the worst combinations at different fault locations; a fourth determining module, configured to determine an average power supply availability based on the number of power usage hours of the user and a preset number of power supply hours required by the user, wherein the number of power usage hours of the user is the difference between the preset number of power supply hours required by the user and the expected number of power outage hours of the user; An evaluation module, configured to evaluate the reliability of the distribution network to be evaluated by using the average power supply availability as an evaluation indicator; Among them, the first determination module is specifically used to: obtain the historical output data of multiple uncertain power access devices involved in the target grid of the distribution network to be evaluated on different typical days; train based on the historical output data to obtain a target neural network model; input each moment included in each typical day into the target neural network model in turn to obtain the output level corresponding to each of the uncertain power access devices at different moments in the typical day.

8. A computer device, characterized in that: include: one or more processors; a storage device for storing one or more programs; The one or more programs are executed by the one or more processors, so that the one or more processors are used to execute the distribution network reliability assessment method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the distribution network reliability assessment method according to any one of claims 1 to 6 is implemented.

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