Power distribution network power supply capacity estimation method and device, computer equipment and storage medium

Abstract

The application relates to a power distribution network power supply capacity estimation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring comprehensive characteristic parameters of a power distribution network, wherein the comprehensive characteristic parameters comprise a topological structure, operation data and main transformer capacity data; constructing a first estimation model for estimating the power supply capacity of the power distribution network according to the comprehensive characteristic parameters; acquiring flexible load data in the power distribution network, and constructing a second estimation model according to the flexible load data and the first estimation model; and solving the second estimation model according to an improved binary particle swarm algorithm, so as to acquire the power supply capacity of the power distribution network according to a solving result when each particle passes the N-1 test of the power distribution network, wherein N is the total number of load points of the power distribution network. The method can consider flexible load interaction, improve the estimation accuracy of the power supply capacity of the power distribution network, is conducive to better adapting the power supply capacity of the power distribution network in China to the load growth demand, and is conducive to forming an effective load transfer scheme when a main transformer fails.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method and apparatus for estimating the power supply capacity of a distribution network, computer equipment, and storage medium. Background Technology

[0002] With the development of power system technology, renewable energy sources such as wind turbines, photovoltaic cells, and electric vehicles are being widely integrated into medium-voltage distribution networks. Furthermore, electric vehicles are concentrated in the charging and discharging of medium- and low-voltage distribution networks, bringing numerous new challenges to the distribution network. These challenges mainly include: distributed generation bringing bidirectional power flow, increasing the uncertainty of distribution network operation; loads' dual role of both consuming and generating electricity leading to insufficient distribution network regulation capabilities; the large-scale integration of distributed power sources causing problems with the configuration of backup capacity on the distribution network side; and low utilization rates of distribution network equipment resulting in resource waste. Currently, the power supply capacity of my country's distribution network cannot adequately match the growing load demand. One key reason is that existing planning methods provide relatively crude assessments of the distribution network, with low accuracy in estimating the power supply capacity of distribution networks incorporating flexible loads. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for estimating the power supply capacity of a distribution network that can take into account flexible load interaction and improve the accuracy of estimating the power supply capacity of the distribution network, in order to address the above-mentioned technical problems.

[0004] Firstly, this application provides a method for estimating the power supply capacity of a distribution network. The method includes:

[0005] Obtain comprehensive characteristic parameters of the distribution network, including topology, operation data, and main transformer capacity data;

[0006] A first estimation model for estimating the power supply capacity of the distribution network is constructed based on comprehensive characteristic parameters;

[0007] Acquire flexible load data in the distribution network, and construct a second estimation model based on the flexible load data and the first estimation model;

[0008] The second estimation model is solved using an improved binary particle swarm optimization algorithm. When each particle passes the N-1 test of the distribution network, the power supply capacity of the distribution network is obtained based on the solution results.

[0009] Where N is the total number of load points in the distribution network.

[0010] In one embodiment, a first estimation model for estimating the power supply capacity of the distribution network is constructed based on comprehensive characteristic parameters, including:

[0011] The first objective function for the operation of the distribution network is constructed based on the comprehensive characteristic parameters. The first objective function is used to obtain the maximum value of the sum of the load values ​​of each main transformer in the distribution network.

[0012] The first constraint condition for the operation of the distribution network is constructed based on the comprehensive characteristic parameters;

[0013] The first objective function and the first constraint condition are used together to form the first estimation model for estimating the power supply capacity of the distribution network.

[0014] In one embodiment, the first constraint condition for the operation of the distribution network is constructed based on comprehensive characteristic parameters, including:

[0015] Based on comprehensive characteristic parameters, the first power supply capacity constraint, branch current carrying capacity constraint, radial power supply constraint, and first N-1 security constraint are constructed for the operation of the distribution network.

[0016] Among them, the first power supply capacity constraint, the branch current carrying capacity constraint, the radial power supply constraint, and the first N-1 safety constraint are used together to constitute the first constraint.

[0017] In one embodiment, a second estimation model is constructed based on flexible load data and a first estimation model, including:

[0018] Based on the flexible load data and the first objective function, a second objective function for the operation of the distribution network is constructed. The second objective function is used to obtain the maximum value of the sum of the load values ​​of each main transformer in the distribution network with added flexible load.

[0019] Construct a second set of constraints for the operation of the distribution network based on flexible load data and the first constraint.

[0020] The second objective function and the second constraint are used together to form the second estimation model.

[0021] In one embodiment, a second constraint condition for the operation of the distribution network is constructed based on flexible load data and the first constraint condition, including:

[0022] Construct a second power capacity constraint for the operation of the distribution network based on flexible load data and the first power capacity constraint.

[0023] The second N-1 safety constraints for distribution network operation are constructed based on flexible load data, the first N-1 safety constraints, and the second power supply capacity constraints.

[0024] Among them, the second power supply capacity constraint, the branch current carrying capacity constraint, the radial power supply constraint, and the second N-1 safety constraint are used together to constitute the second constraint.

[0025] In one embodiment, the second estimation model is solved using an improved binary particle swarm optimization algorithm. When each particle passes the N-1 test of the distribution network, the power supply capacity of the distribution network is obtained based on the solution results, including:

[0026] Initialize the particle swarm parameters, the velocity and position of each particle, including the maximum number of iterations;

[0027] The initial fitness value of each particle and the initial optimal individual position and the initial optimal group position associated with the fitness value are obtained based on the initial velocity and position, the second objective function and the comprehensive characteristic parameters of the distribution network.

[0028] Determine whether each particle meets the second constraint condition based on the individual optimal position and the group optimal position;

[0029] If not, the velocity and position of each particle are updated according to the initialized particle swarm parameters, and the fitness value, individual optimal position and swarm optimal position of each particle are repeatedly obtained until each particle meets the second constraint condition in order to pass the N-1 safety test of the distribution network.

[0030] If the number of iterations is less than the maximum number of iterations, the velocity and position of each particle are updated repeatedly, and the fitness value, individual optimal position and group optimal position of each particle are obtained repeatedly, until each particle meets the second constraint and the number of iterations is equal to the maximum number of iterations.

[0031] The load values ​​of each load point under the optimal load distribution are obtained by determining the individual optimal position and the group optimal position of each particle, so as to obtain the power supply capacity of the distribution network.

[0032] In one embodiment, the particle swarm parameters further include inertia weights, learning coefficients, and the total dimension of the system; the velocity and position of each particle are updated according to the following formulas, including:

[0033]

[0034]

[0035] In the formula, ω represents the inertia weight indicating how much inertia the particle retains with respect to its current velocity; c1 and c2 are learning coefficients representing the degree to which the particle learns its individual optimal position and its group optimal position, respectively; r1, r2, and r3 are randomly generated numbers in each iteration; D is the total dimension of the system, which is equal to the total number of load points N in the distribution network; and d is the dimension updated for the current velocity. Let be the d-th dimension coordinate of the position of particle i after the t-th iteration. Let d be the dimensional coordinate of the optimal position of particle i after the t-th iteration; Let d be the dimensional coordinate of the optimal position of the population after the t-th iteration. Let be the d-th dimension velocity of particle i after the t-th iteration. Let be the d-th dimension velocity of particle i after the (t+1)th iteration. Let d be the d-th dimension coordinate of the position of particle i after the (t+1)th iteration.

[0036] Secondly, this application also provides a power distribution network power supply capacity estimation device, which includes:

[0037] The parameter acquisition module is used to acquire the comprehensive characteristic parameters of the distribution network, including topology, operating data and main transformer capacity data.

[0038] The first model construction module is used to construct a first estimation model for estimating the power supply capacity of the distribution network based on comprehensive characteristic parameters.

[0039] The second model building module is used to acquire flexible load data in the distribution network and build a second estimation model based on the flexible load data and the first estimation model.

[0040] The power supply capacity estimation module is used to solve the second estimation model based on the improved binary particle swarm algorithm, so that when each particle passes the N-1 test of the distribution network, the power supply capacity of the distribution network can be obtained based on the solution results.

[0041] Where N is the total number of load points in the distribution network.

[0042] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0043] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0044] The aforementioned method, apparatus, computer equipment, and storage medium for estimating the power supply capacity of a distribution network acquire comprehensive characteristic parameters such as the distribution network's topology, operational data, and main transformer capacity data. Based on these comprehensive characteristic parameters, a first estimation model is constructed to estimate the distribution network's power supply capacity. This first estimation model meticulously considers the impact of feeder connections and main transformer distribution on the distribution network's power supply capacity. Furthermore, flexible load data in the distribution network is acquired, and a second estimation model is constructed based on this data and the first estimation model. This second estimation model further considers the interaction of flexible loads, fully taking into account load power transfer scenarios during distribution network faults, thereby calculating the maximum power supply capacity of the distribution network. Finally, the second estimation model is solved using an improved binary particle swarm optimization algorithm. When each particle passes the N-1 test of the distribution network, it indicates that the solution fully considers all possible multi-layer power transfer methods during distribution network faults. Therefore, a relatively accurate distribution network power supply capacity can be obtained based on the solution results. The power supply capacity estimation method of the distribution network proposed in this application can take into account the interaction of flexible loads, improve the accuracy of the power supply capacity estimation of the distribution network, help to better match the power supply capacity of my country's distribution network with the demand for load growth, and facilitate the formation of an effective load transfer scheme when the main transformer fails. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating a method for estimating the power supply capacity of a distribution network in one embodiment;

[0046] Figure 2 This is a flowchart illustrating the process of constructing a first estimation model for estimating the power supply capacity of a distribution network based on comprehensive characteristic parameters in one embodiment.

[0047] Figure 3 This is a flowchart illustrating the process of constructing a second estimation model based on flexible load data and a first estimation model in one embodiment.

[0048] Figure 4 This is a flowchart illustrating the process of constructing a second constraint for the operation of a distribution network based on flexible load data and a first constraint, as shown in one embodiment.

[0049] Figure 5 This is a schematic diagram illustrating the process of solving a second estimation model using an improved binary particle swarm optimization algorithm in one embodiment, so as to obtain the power supply capacity of the distribution network based on the solution results when each particle passes the N-1 test of the distribution network.

[0050] Figure 6 This is a graph showing the convergence iteration of the improved binary particle swarm optimization algorithm in one embodiment.

[0051] Figure 7 This is a structural block diagram of a power distribution network power supply capacity estimation device in one embodiment;

[0052] Figure 8 This is an internal structural diagram of a computer device in one embodiment.

[0053] Explanation of icon numbers:

[0054] Distribution network power supply capacity estimation device: 10; Parameter acquisition module: 11; First model construction module: 12; Second model construction module: 13; Power supply capacity estimation module: 14. Detailed Implementation

[0055] To facilitate understanding of the embodiments of this application, a more comprehensive description of the embodiments of this application will be provided below with reference to the accompanying drawings. The drawings illustrate preferred embodiments of the embodiments of this application. However, the embodiments of this application can be implemented in many different forms and are not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the embodiments of this application more thorough and complete.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of this application belong. The terminology used herein in the description of embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of this application.

[0057] It is understood that the term "comprising / including" specifies the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but does not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. The terms "first," "second," etc., may be used herein to describe various parameters, but these parameters are not limited by these terms. These terms are used only to distinguish one parameter from another. For example, without departing from the scope of this application, a first estimation model may be referred to as a second estimation model, and vice versa.

[0058] Example 1

[0059] Distribution network power supply capacity refers to the maximum power supply capacity of the distribution network. It describes the maximum power supply capacity that the distribution network can meet the electricity demand of users within a certain power supply area under the condition of satisfying the N-1 test. The N-1 test means that under normal operating conditions, if any component in the power system, such as a line, generator, transformer, or DC monopole, is fault-free or disconnected due to a fault, the power system should be able to maintain stable operation and normal power supply, and other components should not exceed their rated load and the voltage and frequency should be within the allowable range.

[0060] Currently, the power supply capacity of my country's distribution network cannot adequately match the growing load demand, mainly due to two reasons: First, the current distribution network construction is relatively weak, leading to bottlenecks in the face of rapidly growing loads and causing supply-demand imbalances; second, existing planning methods provide relatively crude assessments of the distribution network, only offering a general reference indicator from a global perspective based on statistical data, without a detailed consideration of the impact of specific distribution network feeder connections and main transformer distribution on the system's power supply capacity. To develop a more accurate and comprehensive method for calculating the power supply capacity of the distribution network, some scholars have proposed the concept of total supply capability (TSC). The first scholar used the network maximum flow method to perform simple graph theory modeling of the distribution network, transforming the problem into an operations research optimization maximum flow problem or linear programming form, and solved it using existing linear programming solvers, but without considering the N-1 fault scenario. The second scholar adopted a linear programming modeling approach, using the load that needs to be transferred by the faulty main transformer as the variable. The model uses the capacity constraints of the connections between main transformers as a simple approximation of the main transformer outlet feeder capacity constraints, but errors due to simplification still exist. The third scholar proposed a method for calculating the maximum power supply capacity of the distribution network based on the interconnection relationship of the main transformers. However, when considering the N-1 safety constraint, the load rate of the main transformer is obtained by equal distribution, which is too idealistic, and the load transfer is limited to adjacent feeders. The fourth scholar solved the problem of secondary transfer of power supply under the short-term overload of the main transformer under the N-1 fault, but the transfer range is still limited to the case of primary or secondary transfer, and the operation sequence of transfer is limited, failing to consider exploring the deeper potential of the distribution network's power supply.

[0061] This application provides a method for estimating the power supply capacity of a distribution network, taking into account the interaction of flexible loads in the distribution network, and can calculate the power supply capacity of the distribution network including flexible loads. For example... Figure 1 As shown, the method for estimating the power supply capacity of the distribution network includes steps 110 to 140.

[0062] Step 110: Obtain the comprehensive characteristic parameters of the distribution network, including topology, operating data, and main transformer capacity data. In this embodiment, the feeder connection status can be obtained through the topology. In the distribution network, one main transformer includes multiple loads, and one feeder includes multiple loads.

[0063] Step 120: Construct a first estimation model for estimating the power supply capacity of the distribution network based on comprehensive characteristic parameters.

[0064] Preferably, such as Figure 2 As shown, step 120 above, which constructs a first estimation model for estimating the power supply capacity of the distribution network based on comprehensive characteristic parameters, includes steps 210 to 220.

[0065] Step 210: Construct a first objective function for the operation of the distribution network based on the comprehensive characteristic parameters. The first objective function is used to obtain the maximum sum of the load values ​​carried by each main transformer in the distribution network. In this embodiment, the first objective function can be expressed by the following formula:

[0066] maxT SC =∑L i

[0067] In the formula, T SC It is the maximum sum of the load values ​​of all main transformers in the distribution network, L. i It is the load value carried by the i-th main transformer.

[0068] Step 220: Construct the first constraint conditions for the operation of the distribution network based on the comprehensive characteristic parameters. The first objective function and the first constraint conditions together constitute a first estimation model for estimating the power supply capacity of the distribution network. In this embodiment, the first estimation model includes the first objective function and the first constraint conditions.

[0069] Preferably, step 220, which constructs the first constraint condition for distribution network operation based on comprehensive characteristic parameters, includes: constructing a first power supply capacity constraint condition, a branch current carrying capacity constraint condition, a radial power supply constraint condition, and a first N-1 security constraint condition for distribution network operation based on comprehensive characteristic parameters. The first power supply capacity constraint condition, branch current carrying capacity constraint condition, radial power supply constraint condition, and first N-1 security constraint condition are used together to constitute the first constraint condition. In this embodiment, the first constraint condition includes the first power supply capacity constraint condition, the branch current carrying capacity constraint condition, the radial power supply constraint condition, and the first N-1 security constraint condition, and each constraint condition can be expressed by the following formula:

[0070] (a) First power supply capacity constraint

[0071] Power point s j The sum of the supplied loads should be less than the capacity of the power source.

[0072]

[0073] In the formula, L i For load point l i The load value, S j For power point s j The capacity value, For load point l i The k-th path leading to the destination. Let the starting point be the power source point s j The set of all paths, A state variable that is either 0 or 1, representing the on / off state of a path, where 0 indicates the path is open or closed. A value of 1 indicates the path is disconnected, while 1 indicates the path is connected. Path A path is connected if and only if all its branches are connected.

[0074] (b) Branch line current carrying capacity constraints

[0075] Flowing through branch road b m The power value should be less than its maximum current carrying capacity, i.e.

[0076]

[0077] In the formula, B m For branch road b m Maximum capacity, For passing through branch road b i The set of all paths.

[0078] (c) Radial power supply constraint conditions

[0079] The operation of the distribution network should meet the radial constraint, and the power supply path set at any load point should be... In each case, only one power supply path is connected, that is...

[0080]

[0081] In the formula, For the composite point l i The set of all paths ending at a given destination.

[0082] At the same time, if the path If it is a path, then it is included in the path. any path within It is also a pathway, that is

[0083]

[0084] (d) First N-1 security constraint

[0085] Let set I S Let set D represent the set of branches containing the tie switches under the original operating mode. S This represents the set of branches containing the sectionalizing switch. When the main transformer t (t=1,2,…,N) S When exiting the runtime due to a fault, the N-1 check under this fault condition can be represented by the following 0-1 type integer programming problem:

[0086]

[0087]

[0088] In the formula, For load point l iThe destination is via branch road b. l The set of all paths, For load point l j The destination is via branch road b. l The set of all paths. If a solution exists, it means that the failure condition passes the safety check.

[0089] Step 130: Obtain flexible load data in the distribution network, and construct a second estimation model based on the flexible load data and the first estimation model. In this embodiment, the flexible load data includes not only specific data such as the output of electric vehicles and the output of energy storage components, but also the numerical changes reflected by the corresponding impact of the flexible load on some comprehensive characteristic parameters of the original distribution network, such as the operating data.

[0090] Preferably, such as Figure 3 As shown, step 130 above, which involves constructing the second estimation model based on the flexible load data and the first estimation model, includes steps 310 to 320.

[0091] Step 310: Construct a second objective function for the distribution network operation based on the flexible load data and the first objective function. The second objective function is used to obtain the maximum sum of the load values ​​of each main transformer in the distribution network with added flexible loads. In this embodiment, the second objective function can be expressed by the following formula:

[0092] maxT SC =∑(L i -P EV -P ES )

[0093] In the formula, P EV P represents the power output of the electric vehicle. ES This indicates the output of the energy storage element. When the output is negative, it indicates that the element is a load.

[0094] Step 320: Construct second constraints for distribution network operation based on flexible load data and the first constraint. The second objective function and the second constraint together constitute the second estimation model. Similarly, in this embodiment, the second estimation model includes the second objective function and the second constraint.

[0095] Preferably, such as Figure 4 As shown, step 320 above, which constructs the second constraint condition for the operation of the distribution network based on the flexible load data and the first constraint condition, includes steps 410 to 420.

[0096] Step 410: Construct the second power supply capacity constraint for the operation of the distribution network based on the flexible load data and the first power supply capacity constraint.

[0097] Step 420: Construct the second N-1 security constraints for distribution network operation based on flexible load data, the first N-1 security constraints, and the second power supply capacity constraints. The second power supply capacity constraints, branch current carrying capacity constraints, radial power supply constraints, and the second N-1 security constraints collectively constitute the second constraint condition. Similarly, in this embodiment, the second constraint condition includes the second power supply capacity constraints, branch current carrying capacity constraints, radial power supply constraints, and the second N-1 security constraints.

[0098] Since introducing flexible loads does not change the topology of the distribution network, the branch current carrying capacity constraint and radial power supply constraint in the second constraint remain consistent with those in the first constraint and do not require modification. However, the first power supply capacity constraint and the first N-1 security constraint need to be modified accordingly to construct the second power supply capacity constraint and the second N-1 security constraint in the second constraint, respectively. The second power supply capacity constraint can be expressed by the following formula:

[0099]

[0100] In the formula, P PV P represents the output of photovoltaic power generation. H P represents the output of hydropower. Wind This represents wind power output. Based on the second power supply capacity constraint, a corresponding second N-1 security constraint can be constructed.

[0101] Step 140: Solve the second estimation model using the improved binary particle swarm optimization algorithm. When each particle passes the N-1 test of the distribution network, obtain the power supply capacity of the distribution network based on the solution results. Here, N is the total number of load points in the distribution network. In this embodiment, the improved binary particle swarm optimization algorithm, based on the flexible loads with known output conditions, works together with other loads in the distribution network to find the optimal output strategy for the flexible loads, thus effectively identifying them.

[0102] Preferably, such as Figure 5 As shown, step 140 above solves the second estimation model according to the improved binary particle swarm algorithm, so that when each particle passes the N-1 test of the distribution network, the power supply capacity of the distribution network is obtained according to the solution result, including steps 510 to 560.

[0103] Step 510: Initialize the particle swarm parameters, the velocity and position of each particle. The particle swarm parameters include the maximum number of iterations.

[0104] Step 520: Based on the initialized velocity and position, the second objective function, and the comprehensive characteristic parameters of the distribution network, obtain the initial fitness value of each particle, as well as the initial individual optimal position and the initial group optimal position associated with the fitness value.

[0105] Step 530: Determine whether each particle meets the second constraint condition based on the individual optimal position and the group optimal position.

[0106] Step 540: If the condition is not met, update the velocity and position of each particle according to the initialized particle swarm parameters, and repeatedly obtain the fitness value, individual optimal position and swarm optimal position of each particle until each particle meets the second constraint condition in order to pass the N-1 safety test of the distribution network.

[0107] Preferably, the particle swarm parameters also include inertia weights, learning coefficients, and the total dimension of the system; the velocity and position of each particle are updated according to the following formulas, including:

[0108]

[0109]

[0110] In the formula, ω represents the inertia weight indicating how much inertia the particle retains with respect to its current velocity; c1 and c2 are learning coefficients representing the degree to which the particle learns its individual optimal position and its group optimal position, respectively; r1, r2, and r3 are randomly generated numbers in each iteration; D is the total dimension of the system, which is equal to the total number of load points N in the distribution network; and d is the dimension updated for the current velocity. Let be the d-th dimension coordinate of the position of particle i after the t-th iteration. Let d be the dimensional coordinate of the optimal position of particle i after the t-th iteration; Let d be the dimensional coordinate of the optimal position of the population after the t-th iteration. Let be the d-th dimension velocity of particle i after the t-th iteration. Let be the d-th dimension velocity of particle i after the (t+1)th iteration. Let be the d-th dimension coordinate of the position of particle i after the (t+1)th iteration. Here, c1 and c2 are non-negative real numbers, and r1, r2, and r3 are all randomly generated numbers between (0,1).

[0111] In this embodiment, for a distribution network system with B branches, the total number of load points in the distribution network, i.e., the total dimension of the particles, is defined as B. The coordinates of each dimension of the particle are either 0 or 1, representing the opening or closing of the corresponding branch switch, respectively. For a distribution system with L loops, according to the meaning of graph theory trees, to ensure that the radial power supply constraint condition of the network is satisfied, the position of each particle should have L dimensions with coordinates of 0, i.e., the switches of the corresponding L branches are open. The total dimension of the particles in the improved binary particle swarm optimization algorithm is set to N, the total number of load points in the distribution network system. LEach coordinate dimension represents the load magnitude at a load point, while the particle's coordinate vector represents the overall load distribution. Then, an N-1 test for various main transformer faults is performed on the load distribution corresponding to each particle. If particle i passes all N-1 tests, its fitness value is set to the sum of all loads in the system, i.e., the sum of all its coordinates. Conversely, if particle i fails all N-1 tests, a penalty function is applied, and its fitness value is set to 0. The fitness value can be expressed by the following formula:

[0112]

[0113] Step 550: If the number of iterations is less than the maximum number of iterations, then repeatedly update the velocity and position of each particle and repeatedly obtain the fitness value, individual optimal position, and group optimal position of each particle until each particle meets the second constraint condition and the number of iterations equals the maximum number of iterations. In this embodiment, as... Figure 6 To improve the convergence iteration curve of the binary particle swarm optimization algorithm, the vertical value tends to stabilize as the number of iterations approaches the maximum number of iterations.

[0114] Step 560: Obtain the load value of each load point under the optimal load distribution based on the individual optimal position and the group optimal position of each particle, so as to obtain the power supply capacity of the distribution network.

[0115] In this embodiment, the improved binary particle swarm optimization algorithm records the optimal position of the individual and the optimal position of the group in each iteration. If the result of a subsequent iteration is better than the historical record, it replaces the historical optimal record. The coordinate vector of the optimal particle output by the algorithm is the load value of each load point under the optimal load distribution of the system. The on / off state vector of the path set obtained by the optimal particle after the N-1 fault test of the main transformer is the transfer scheme under the corresponding main transformer fault condition. Since the second N-1 safety constraint in this embodiment does not specify the load transfer relationship, this embodiment can fully consider all possible multi-layer transfer methods under fault conditions, effectively overcoming the limitations of traditional methods. Moreover, after solving for the maximum power supply capacity of the distribution network and its distribution on each feeder, for different main transformer faults, according to the second N-1 safety constraint, a corresponding power outage load transfer scheme can be formed, represented by the on / off state vector of the path set.

[0116] In this embodiment, comprehensive characteristic parameters such as the topology, operation data, and main transformer capacity data of the distribution network are obtained. A first estimation model is constructed based on these parameters to estimate the power supply capacity of the distribution network. This first estimation model meticulously considers the impact of feeder connections and main transformer distribution on the power supply capacity of the distribution network. Furthermore, flexible load data in the distribution network is obtained, and a second estimation model is constructed based on this data and the first estimation model. This second estimation model further considers the interaction of flexible loads, fully considering load power transfer during distribution network faults, thereby calculating the maximum power supply capacity of the distribution network. Finally, the second estimation model is solved using an improved binary particle swarm optimization algorithm. When each particle passes the N-1 test of the distribution network, it indicates that the solution fully considers all possible multi-layer power transfer methods during distribution network faults. Therefore, a more accurate power supply capacity of the distribution network can be obtained based on the solution results. The power supply capacity estimation method of the distribution network adopted in this application can take into account the interaction of flexible loads, improve the estimation accuracy of the power supply capacity of the distribution network, and help to better match the power supply capacity of my country's distribution network with the demand for load growth. It is also conducive to forming an effective load transfer scheme when the main transformer fails.

[0117] It should be understood that, although the flowcharts involved in the embodiments described above are... Figures 1-5 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, the flowcharts involved in the embodiments described above... Figures 1-5 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0118] Example 2

[0119] Based on the same inventive concept, this application also provides a distribution network power supply capacity estimation device for implementing the above-mentioned distribution network power supply capacity estimation method. The solution provided by this device is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more distribution network power supply capacity estimation device embodiments provided below can be found in the limitations of the distribution network power supply capacity estimation method above, and will not be repeated here.

[0120] This application also provides a power distribution network supply capacity estimation device 10. For example... Figure 7 As shown, the power supply capacity estimation device 10 for a distribution network includes a parameter acquisition module 11, a first model construction module 12, a second model construction module 13, and a power supply capacity estimation module 14. The parameter acquisition module 11 is used to acquire comprehensive characteristic parameters of the distribution network, including topology, operating data, and main transformer capacity data. The first model construction module 12 is used to construct a first estimation model for estimating the power supply capacity of the distribution network based on the comprehensive characteristic parameters. The second model construction module 13 is used to acquire flexible load data in the distribution network and construct a second estimation model based on the flexible load data and the first estimation model. The power supply capacity estimation module 14 is used to solve the second estimation model using an improved binary particle swarm optimization algorithm. When each particle passes the N-1 test of the distribution network, the power supply capacity of the distribution network is obtained based on the solution results. Here, N is the total number of load points in the distribution network.

[0121] Preferably, the first model construction module 12 includes a first objective function construction unit and a first constraint condition construction unit. The first objective function construction unit is used to construct a first objective function for the operation of the distribution network based on comprehensive characteristic parameters. The first objective function is used to obtain the maximum value of the sum of the load values ​​carried by each main transformer in the distribution network. The first constraint condition construction unit is used to construct first constraint conditions for the operation of the distribution network based on comprehensive characteristic parameters. The first objective function and the first constraint conditions together constitute a first estimation model for estimating the power supply capacity of the distribution network.

[0122] Preferably, the aforementioned first constraint construction unit is further configured to construct a first power supply capacity constraint, a branch current carrying capacity constraint, a radial power supply constraint, and a first N-1 security constraint for the distribution network operation based on comprehensive characteristic parameters. The power supply capacity constraint, branch current carrying capacity constraint, radial power supply constraint, and N-1 security constraint are used together to constitute the first constraint.

[0123] Preferably, the second model construction module 13 includes a second objective function construction unit and a second constraint condition construction unit. The second objective function construction unit is used to construct a second objective function for the distribution network operation based on the flexible load data and the first objective function. The second objective function is used to obtain the maximum sum of the load values ​​of each main transformer in the distribution network with added flexible loads. The second constraint condition construction unit is used to construct second constraints for the distribution network operation based on the flexible load data and the first constraint condition. The second objective function and the second constraint condition together constitute the second estimation model.

[0124] Preferably, the second constraint construction unit is further configured to construct a second power capacity constraint for distribution network operation based on flexible load data and the first power capacity constraint; and to construct a second N-1 safety constraint for distribution network operation based on flexible load data, the first N-1 safety constraint, and the second power capacity constraint. The second power capacity constraint, branch current carrying capacity constraint, radial power supply constraint, and the second N-1 safety constraint are used together to constitute the second constraint.

[0125] Preferably, the power supply capacity estimation module 14 includes an initialization unit, an optimal position acquisition unit, a constraint condition judgment unit, an update acquisition unit, an iterative acquisition unit, and a power supply capacity estimation unit. The initialization unit initializes the particle swarm parameters, the velocity and position of each particle, and the particle swarm parameters include the maximum number of iterations. The optimal position acquisition unit acquires the initial fitness value of each particle and the initial individual optimal position and initial group optimal position associated with the fitness value, based on the initialized velocity and position, the second objective function, and the comprehensive characteristic parameters of the distribution network. The constraint condition judgment unit determines whether each particle meets the second constraint condition based on the individual optimal position and the group optimal position. The update acquisition unit updates the velocity and position of each particle based on the initialized particle swarm parameters if the constraint condition is not met, and repeatedly acquires the fitness value, individual optimal position, and group optimal position of each particle until each particle meets the second constraint condition, thus passing the N-1 security check of the distribution network. The iterative acquisition unit is used to repeatedly update the velocity and position of each particle and repeatedly acquire the fitness value, individual optimal position, and group optimal position of each particle if the number of iterations is less than the maximum number of iterations, until each particle meets the second constraint condition and the number of iterations equals the maximum number of iterations. The power supply capacity estimation unit is used to obtain the load value of each load point under the optimal load distribution based on the individual optimal position and group optimal position of each particle, so as to obtain the power supply capacity of the distribution network.

[0126] Each module in the aforementioned power distribution network capacity estimation device 10 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0127] Example 3

[0128] This application also provides a computer device, such as... Figure 8 As shown, it includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-mentioned method for estimating the power supply capacity of the distribution network.

[0129] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0130] Example 4

[0131] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for estimating the power supply capacity of a power distribution network.

[0132] Example 5

[0133] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for estimating the power supply capacity of a power distribution network.

[0134] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0135] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0136] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for estimating the power supply capacity of a distribution network, characterized in that, The method includes: Obtain comprehensive characteristic parameters of the distribution network, including topology, operating data, and main transformer capacity data; A first estimation model for estimating the power supply capacity of the distribution network is constructed based on the comprehensive characteristic parameters. Acquire flexible load data in the distribution network, and construct a second estimation model based on the flexible load data and the first estimation model; The second estimation model is solved using an improved binary particle swarm optimization algorithm. When each particle passes the N-1 test of the distribution network, the power supply capacity of the distribution network is obtained based on the solution results. Where N is the total number of load points in the distribution network; The first estimation model for estimating the power supply capacity of the distribution network based on the comprehensive characteristic parameters includes: Based on the comprehensive characteristic parameters, a first objective function for the operation of the distribution network is constructed. The first objective function is used to obtain the maximum value of the sum of the load values ​​of each main transformer in the distribution network. Based on the comprehensive characteristic parameters, the first power capacity constraint, branch current carrying capacity constraint, radial power supply constraint, and first N-1 security constraint are constructed for the operation of the distribution network. The first objective function and the first constraint condition are used together to form the first estimation model for estimating the power supply capacity of the distribution network; the first power supply capacity constraint condition, the branch current carrying capacity constraint condition, the radial power supply constraint condition, and the first N-1 security constraint condition are used together to form the first constraint condition. The first power capacity constraint characterizes the power point s j The sum of the supplied loads should be less than the capacity of the power source. In the formula, L i For load point l i The load value, S j For power point s j The capacity value, For load point l i The k-th path leading to the destination. Let the starting point be the power source point s j The set of all paths, A state variable that is either 0 or 1, representing the on / off state of a path, where 0 indicates the path is open or closed. Disconnected; 1 indicates the path is connected; path The necessary and sufficient condition for connectivity is that all branches in the path are connected. The branch current carrying capacity constraint condition characterizes the flow through branch b m The power value should be less than its maximum current carrying capacity, i.e. In the formula, B m For branch road b m Maximum capacity, For passing through branch road b i The set of all paths; The radial power supply constraint condition characterizes the requirement that the operation of the distribution network should satisfy the radial constraint, which is the set of power supply paths for any load point. In each case, only one power supply path is connected, that is... In the formula, For the composite point l i The set of all paths leading to the destination; At the same time, if the path If it is a path, then it is included in the path. any path within It is also a pathway, that is The first N-1 security constraint condition is set I. S Let set D represent the set of branches containing the tie switches under the original operating mode. S This represents the set of branches containing the sectionalizing switch; when the main transformer t (t=1,2,…,N) S When exiting the runtime due to a fault, the N-1 check under this fault condition can be represented by the following 0-1 type integer programming problem: In the formula, For load point l i The destination is via branch road b l The set of all paths, For load point l j The destination is via branch road b l The set of all paths; if the problem has a solution, it means that the failure condition can pass the safety check.

2. The method according to claim 1, characterized in that, The first objective function can be expressed by the following formula: maxT SC =∑L i In the formula, T SC It is the maximum sum of the load values ​​of all main transformers in the distribution network, L. i It is the load value carried by the i-th main transformer.

3. The method according to claim 1, characterized in that, The flexible load data includes the power output of electric vehicles and the power output of energy storage components.

4. The method according to claim 1, characterized in that, A second estimation model is constructed based on the flexible load data and the first estimation model, including: Based on the flexible load data and the first objective function, a second objective function for the operation of the distribution network is constructed. The second objective function is used to obtain the maximum value of the sum of the load values ​​carried by each main transformer in the distribution network with added flexible load. Based on the flexible load data and the first constraint, a second constraint for the operation of the distribution network is constructed. The second objective function and the second constraint are used together to form the second estimation model.

5. The method according to claim 4, characterized in that, The construction of the second constraint condition for distribution network operation based on the flexible load data and the first constraint condition includes: Based on the flexible load data and the first power capacity constraint, a second power capacity constraint for the operation of the distribution network is constructed. Based on the flexible load data, the first N-1 security constraint, and the second power capacity constraint, a second N-1 security constraint for the operation of the distribution network is constructed. The second power supply capacity constraint, the branch current carrying capacity constraint, the radial power supply constraint, and the second N-1 security constraint are used together to constitute the second constraint.

6. The method according to claim 4, characterized in that, The step of solving the second estimation model using the improved binary particle swarm optimization algorithm, and obtaining the power supply capacity of the distribution network based on the solution results when each particle passes the N-1 test of the distribution network, includes: Initialize the particle swarm parameters, the velocity and position of each particle, whereby the particle swarm parameters include the maximum number of iterations; Based on the initialized velocity and position, the second objective function, and the comprehensive characteristic parameters of the power distribution network, the initial fitness value of each particle, as well as the initial individual optimal position and the initial group optimal position associated with the fitness value are obtained. Based on the individual optimal position and the group optimal position, determine whether each particle meets the second constraint condition; If not, the velocity and position of each particle are updated according to the initialized particle swarm parameters, and the fitness value, the individual optimal position and the swarm optimal position of each particle are repeatedly obtained until each particle meets the second constraint condition in order to pass the N-1 security test of the power distribution network. If the number of iterations is less than the maximum number of iterations, then the velocity and position of each particle are updated repeatedly, and the fitness value, the individual optimal position and the group optimal position of each particle are obtained repeatedly, until each particle meets the second constraint condition and the number of iterations is equal to the maximum number of iterations; Based on the individual optimal position and the group optimal position of each particle, the load value of each load point under the optimal load distribution is obtained to obtain the power supply capacity of the distribution network.

7. The method according to claim 6, characterized in that, The particle swarm parameters also include inertia weights, learning coefficients, and the total dimension of the system; the velocity and position of each particle are updated according to the following formulas, including: In the formula, ω is the inertial weight representing how much inertia a particle retains with respect to the current velocity; c1 and c2 are the learning coefficients representing the degree to which the particle learns its individual optimal position and its group optimal position, respectively; r1, r2, and r3 are all randomly generated numbers in each iteration; D is the total dimension of the system, which is equal to the total number of load points N in the distribution network; and d is the dimension of the current velocity update. Let be the d-th dimension coordinate of the position of particle i after the t-th iteration. Let d be the dimensional coordinate of the optimal position of particle i after the t-th iteration; Let be the d-th dimension coordinate of the optimal position of the population after the t-th iteration. Let be the d-th dimension velocity of particle i after the t-th iteration. Let be the d-th dimension velocity of particle i after the (t+1)th iteration. Let d be the d-th dimension coordinate of the position of particle i after the (t+1)th iteration.

8. A power supply capacity estimation device for a distribution network, characterized in that, The device includes: The parameter acquisition module is used to acquire comprehensive characteristic parameters of the distribution network, including topology, operating data and main transformer capacity data. The first model construction module is used to construct a first estimation model for estimating the power supply capacity of the distribution network based on the comprehensive feature parameters. The second model building module is used to acquire flexible load data in the distribution network and build a second estimation model based on the flexible load data and the first estimation model. The power supply capacity estimation module is used to solve the second estimation model according to the improved binary particle swarm algorithm, so as to obtain the power supply capacity of the distribution network based on the solution results when each particle passes the N-1 test of the distribution network. Where N is the total number of load points in the distribution network; The first model construction module includes a first objective function construction unit and a first constraint condition construction unit. The first objective function construction unit is used to construct a first objective function for the operation of the distribution network based on comprehensive characteristic parameters. The first objective function is used to obtain the maximum value of the sum of the load values ​​of each main transformer in the distribution network. The first constraint condition construction unit is used to construct a first constraint condition for the operation of the distribution network based on comprehensive characteristic parameters. The first objective function and the first constraint condition are used together to form a first estimation model for estimating the power supply capacity of the distribution network. The first constraint construction unit is also used to construct the first power supply capacity constraint, branch current carrying capacity constraint, radial power supply constraint, and first N-1 security constraint for the operation of the distribution network based on the comprehensive characteristic parameters; wherein, the power supply capacity constraint, branch current carrying capacity constraint, radial power supply constraint, and N-1 security constraint are used to jointly constitute the first constraint. The first power capacity constraint characterizes the power point s j The sum of the supplied loads should be less than the capacity of the power source. In the formula, L i For load point l i The load value, S j For power point s j The capacity value, For load point l i The k-th path leading to the destination. Let the starting point be the power source point s j The set of all paths, A state variable that is either 0 or 1, representing the on / off state of a path, where 0 indicates the path is open or closed. Disconnected; 1 indicates the path is connected; path The necessary and sufficient condition for connectivity is that all branches in the path are connected. The branch current carrying capacity constraint condition characterizes the flow through branch b m The power value should be less than its maximum current carrying capacity, i.e. In the formula, B m For branch road b m Maximum capacity, For passing through branch road b i The set of all paths; The radial power supply constraint condition characterizes the requirement that the operation of the distribution network should satisfy the radial constraint, which is the set of power supply paths for any load point. In each case, only one power supply path is connected, that is... In the formula, For the composite point l i The set of all paths leading to the destination; At the same time, if the path If it is a path, then it is included in the path. any path within It is also a pathway, that is The first N-1 security constraint condition is set I. S Let set D represent the set of branches containing the tie switches under the original operating mode. S This represents the set of branches containing the sectionalizing switch; when the main transformer t (t=1,2,…,N) S When exiting the runtime due to a fault, the N-1 check under this fault condition can be represented by the following 0-1 type integer programming problem: In the formula, For load point l i The destination is via branch road b l The set of all paths, For load point l j The destination is via branch road b l The set of all paths; if the problem has a solution, it means that the failure condition can pass the safety check.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 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, it implements the steps of the method according to any one of claims 1 to 7.

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