Power distribution network resource scheduling method, apparatus and device, and storage medium

Abstract

The invention discloses a power distribution network resource scheduling method and device, equipment and a storage medium. The power distribution network resource scheduling method comprises the following steps: constructing a power distribution network resource scheduling model, wherein the power distribution network resource scheduling model comprises a resource scheduling function and a scheduling constraint condition set; constructing a hierarchical division problem of the scheduling constraint condition set, and solving the hierarchical division problem to obtain a hierarchical division result of the scheduling constraint condition set; performing hierarchical division on the scheduling constraint condition set according to a hierarchical division result to obtain a plurality of scheduling constraint condition subsets; obtaining current power data information, and performing parallel solving on the resource scheduling function according to each scheduling constraint condition subset and the current power data information to obtain power distribution network resource scheduling information; and scheduling the schedulable resources of the power distribution network according to the power distribution network resource scheduling information. According to the method, the solving speed of the power distribution network resource scheduling problem is increased, the time for obtaining a better scheduling scheme is shortened, and the real-time scheduling requirement of a power distribution network system is met.

Description

Technical Field

[0001] The present invention relates to the technical field of power applications, and in particular, to a method, device, equipment and storage medium for dispatching resources in a distribution network. Background Art

[0002] With the sustainable development of society, building a new distribution network system with clean energy as the main body is the future development trend. The power industry is actively transforming the energy production and consumption modes, optimizing the energy structure, and increasing the development intensity of renewable energy. However, the power generation of renewable energy (such as wind power and photovoltaic power) is greatly affected by weather and has the characteristics of intermittency, volatility and randomness. When a large amount of renewable energy is connected to the distribution network system, the demand for the dispatching ability of the distribution network system increases significantly.

[0003] When facing a large-scale power grid and a complex network structure, the traditional power generation resource dispatching method has a slow solution speed, making it impossible to obtain a better dispatching plan in time to meet the real-time dispatching requirements of the distribution network system. Summary of the Invention

[0004] The present invention provides a method, device, equipment and storage medium for dispatching resources in a distribution network to solve the problem that the traditional power generation resource dispatching method has a slow solution speed and cannot meet the real-time dispatching requirements when facing a large-scale power grid and a complex network structure.

[0005] In a first aspect, an embodiment of the present invention provides a method for dispatching resources in a distribution network, the method comprising:

[0006] Constructing a distribution network resource dispatching model, the distribution network resource dispatching model including: a resource dispatching function and a set of dispatching constraint conditions;

[0007] Constructing a hierarchical partitioning problem of the set of dispatching constraint conditions, and solving the hierarchical partitioning problem to obtain a hierarchical partitioning result of the set of dispatching constraint conditions;

[0008] Hierarchically partitioning the set of dispatching constraint conditions according to the hierarchical partitioning result to obtain a plurality of subsets of dispatching constraint conditions;

[0009] Obtaining current power data information, and parallelly solving the resource dispatching function according to each subset of dispatching constraint conditions and the current power data information to obtain distribution network resource dispatching information;

[0010] Dispatching the schedulable resources of the distribution network according to the distribution network resource dispatching information.

[0011] In a second aspect, an embodiment of the present invention provides a device for dispatching resources in a distribution network, the device comprising:

[0012] A model construction module for constructing a distribution network resource scheduling model, where the distribution network resource scheduling model includes: a resource scheduling function and a set of scheduling constraint conditions;

[0013] A problem construction and solution module for constructing a hierarchical partitioning problem of the set of scheduling constraint conditions and solving the hierarchical partitioning problem to obtain a hierarchical partitioning result of the set of scheduling constraint conditions;

[0014] A hierarchical partitioning module for hierarchically partitioning the set of scheduling constraint conditions according to the hierarchical partitioning result to obtain multiple subsets of scheduling constraint conditions;

[0015] An information determination module for obtaining current power data information and parallelly solving the resource scheduling function according to each subset of scheduling constraint conditions and the current power data information to obtain distribution network resource scheduling information;

[0016] A resource scheduling module for scheduling the schedulable resources of the distribution network according to the distribution network resource scheduling information.

[0017] In a third aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes:

[0018] At least one processor;

[0019] And a memory communicatively connected to the at least one processor;

[0020] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distribution network resource scheduling method according to any embodiment of the present invention.

[0021] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions for causing a processor to implement the distribution network resource scheduling method according to any embodiment of the present invention when executed.

[0022] In the technical solution of the embodiment of the present invention, by constructing a distribution network resource scheduling model, the distribution network resource scheduling model includes: a resource scheduling function and a set of scheduling constraint conditions; constructing a hierarchical partitioning problem of the set of scheduling constraint conditions, solving the hierarchical partitioning problem to obtain the hierarchical partitioning result of the set of scheduling constraint conditions; hierarchically partitioning the set of scheduling constraint conditions according to the hierarchical partitioning result to obtain multiple subsets of scheduling constraint conditions; obtaining current power data information, and parallelly solving the resource scheduling function according to each subset of scheduling constraint conditions and the current power data information to obtain distribution network resource scheduling information; scheduling the schedulable resources of the distribution network according to the distribution network resource scheduling information. This method hierarchically partitions the set of scheduling constraint conditions into multiple subsets of scheduling constraints by solving the hierarchical partitioning problem, and parallelly solves the resource scheduling function according to each subset of scheduling constraint conditions, realizing the decomposition of the complex distribution network resource scheduling problem into multiple simple sub-problems for parallel solution, thereby improving the solution speed of the distribution network resource scheduling problem, shortening the time to obtain a better scheduling plan, meeting the real-time scheduling requirements of the distribution network system, and providing strong support for the subsequent realization of connecting large-scale renewable energy to the distribution network system.

[0023] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 It is a flowchart of a distribution network resource scheduling method provided by an embodiment of the present invention;

[0026] Figure 2 It is a schematic diagram of the resource configuration of an example power node system;

[0027] Figure 3 It is a schematic structural diagram of a distribution network resource scheduling device provided by an embodiment of the present invention;

[0028] Figure 4 It shows a schematic structural diagram of an electronic device that can be used to implement the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0031] It should be noted that with the sustainable development of society, building a new distribution network system with renewable energy (clean energy) as the main body is the future development trend. Renewable energy has the characteristics of intermittency, volatility, and randomness. When a large amount of renewable energy is connected to the distribution network system, due to the slow solution speed of traditional power grid dispatching methods when facing large-scale power grids and complex network structures, it is impossible to obtain a better dispatching plan in time to meet the needs of real-time dispatching, thus bringing potential risks to the reliability and safety of power grid operation.

[0032] Based on this, the embodiments of the present invention provide a method for dispatching distribution network resources. Figure 1 As shown in the flowchart of a method for dispatching distribution network resources provided by the embodiments of the present invention, the embodiments of the present invention are applicable to scenarios where various complex situations and changes occur in the power grid, and a better dispatching plan can be quickly obtained. This method can be executed by a distribution network resource dispatching device, which can be implemented in the form of software and / or hardware. Optionally, it can be implemented by an electronic device, which is preferably a mobile terminal, a desktop computer, a laptop computer, a server, etc.

[0033] As Figure 1 shown, the method for dispatching distribution network resources provided by the embodiments of the present invention may specifically include:

[0034] S101. Build a distribution network resource scheduling model, where the distribution network resource scheduling model includes: a resource scheduling function and a set of scheduling constraint conditions.

[0035] Among them, the distribution network resource scheduling model can be understood as a model for analyzing and scheduling the output of each power generation device in the distribution network. The resource scheduling function can be understood as a function for determining the optimal resource scheduling scheme, and the function can be guided by one or more optimization objectives, such as minimizing the operating cost, maximizing the resource utilization rate, minimizing the voltage deviation, and / or minimizing the active power loss, etc. The set of scheduling constraint conditions can be understood as a series of restrictive conditions that the scheduling scheme must satisfy to ensure the feasibility, safety, and / or economy of the scheduling scheme. For example, it can include line power flow constraints, voltage constraints, power constraints of power generation devices, etc.

[0036] In this embodiment, by determining the optimization objectives of the current scenario, the decision variables involved constitute the resource scheduling function, and a suitable set of scheduling constraint conditions is set. Based on the resource scheduling function and the set of scheduling constraint conditions, a distribution network resource scheduling model is constructed. Exemplarily, to ensure the safe operation of the distribution network system, the optimization objectives can be defined as the minimum voltage deviation, the maximum output of distributed power sources, and the minimum active power loss. The decision variables can be defined as the output of distributed power sources, the output of stable power sources, and the size of reactive power compensation. The set of scheduling constraint conditions includes voltage constraints, power flow constraints, security constraints, and adjustable resource output constraints.

[0037] It can be understood that the distributed power source can adopt new energy such as photovoltaic and wind power, and the reactive power compensation device can adopt a static var compensator (SVC), switched capacitor bank (CB), etc.

[0038] S102. Construct the hierarchical partitioning problem of the set of scheduling constraint conditions, and solve the hierarchical partitioning problem to obtain the hierarchical partitioning result of the set of scheduling constraint conditions.

[0039] Among them, the hierarchical partitioning problem can be understood as a problem for reasonably partitioning the set of scheduling constraint conditions to achieve a specific goal. The specific goal can be the shortest execution time of the set of scheduling constraint conditions, the least execution resource consumption, and / or the lowest execution complexity. The hierarchical partitioning result can be understood as a scheme for hierarchically partitioning the set of scheduling constraint conditions. For example, the constraint conditions including parameter upper and lower limits are partitioned into one layer, and the constraint conditions including square term calculations are partitioned into one layer, etc.

[0040] In this embodiment, the specific implementation of the hierarchical partitioning problem of constructing a scheduling constraint condition set may be to determine the objective function corresponding to the scheduling constraint condition set according to the optimization objectives corresponding to the scheduling constraint condition set, such as the shortest execution time, the least execution resource consumption, and / or the lowest execution complexity, etc. According to the objective function and the set constraint information (such as the upper and lower limits of execution time, the upper and lower limits of execution resource consumption, the upper and lower limits of execution complexity, and the limitations of the performance requirements of the distribution network system), construct the hierarchical partitioning problem of the scheduling constraint condition set, and obtain the hierarchical partitioning result by solving the hierarchical partitioning problem. In an alternative embodiment, the hierarchical partitioning problem may be solved by an optimization algorithm.

[0041] S103. Hierarchically partition the scheduling constraint condition set according to the hierarchical partitioning result to obtain multiple scheduling constraint condition subsets.

[0042] Among them, the scheduling constraint condition subset can be understood as the result obtained after partitioning the scheduling constraint condition set. The sum of all scheduling constraint condition subsets can constitute the complete scheduling constraint condition set, and each scheduling constraint condition subset contains at least one constraint condition.

[0043] In this embodiment, the scheduling constraint condition set is partitioned according to the hierarchical partitioning result to obtain multiple scheduling constraint condition subsets.

[0044] S104. Obtain the current power data information, and perform parallel solution on the resource scheduling function according to each scheduling constraint condition subset and the current power data information to obtain the distribution network resource scheduling information.

[0045] Among them, the distribution network resource scheduling information can be understood as the information used to provide a reference for scheduling the output of each schedulable resource in the distribution network.

[0046] In this embodiment, the current power data information is obtained. The current power data information may include the number of distributed power sources in the distribution network, the number of power nodes, the voltage of each power node, the active power injected by the distributed power sources into each power node, the voltage phase angle difference between each power node, and the conductance, etc. According to the current power data information and each scheduling constraint condition subset, perform parallel solution on the resource scheduling function, so as to obtain the distribution network resource scheduling information corresponding to the current power data information.

[0047] S105. Schedule the schedulable resources of the distribution network according to the distribution network resource scheduling information.

[0048] In this embodiment, determine the schedulable resources and the reasonable output of the schedulable resources according to the distribution network resource scheduling information, so as to schedule the actual output of the schedulable resources. The schedulable resources may include distributed power sources, stable power sources, and reactive power compensation devices.

[0049] A distribution network resource scheduling method provided by an embodiment of the present invention constructs a distribution network resource scheduling model, which includes: a resource scheduling function and a set of scheduling constraint conditions; constructs a hierarchical partitioning problem of the set of scheduling constraint conditions, solves the hierarchical partitioning problem to obtain a hierarchical partitioning result of the set of scheduling constraint conditions; hierarchically partitions the set of scheduling constraint conditions according to the hierarchical partitioning result to obtain multiple subsets of scheduling constraint conditions; obtains current power data information, and parallelly solves the resource scheduling function according to each subset of scheduling constraint conditions and the current power data information to obtain distribution network resource scheduling information; schedules the schedulable resources of the distribution network according to the distribution network resource scheduling information. This method hierarchically partitions the set of scheduling constraint conditions into multiple subsets of scheduling constraints by solving the hierarchical partitioning problem, and parallelly solves the resource scheduling function according to each subset of scheduling constraint conditions, realizing the decomposition of the complex distribution network resource scheduling problem into multiple simple sub-problems for parallel solution, thereby improving the solution speed of the distribution network resource scheduling problem, shortening the time to obtain a better scheduling scheme, meeting the real-time scheduling requirements of the distribution network system, and providing strong support for the subsequent realization of connecting large-scale renewable energy to the distribution network system.

[0050] As a first alternative embodiment of this embodiment, on the basis of the above embodiment, the constructing of the distribution network resource scheduling model can be specifically implemented as the following steps:

[0051] a1) Obtain the historical electrical data of the distribution network, and construct the resource scheduling function according to the historical electrical data.

[0052] In this embodiment, obtain the historical electrical data of the distribution network within a certain past time or period, and construct the resource scheduling function according to the historical electrical data.

[0053] As one implementation manner, the historical electrical data includes the current voltage of each power node in the distribution network, the active power injected by the power source at each power node within the current period, the voltage phase angle difference between power nodes, and conductance;

[0054] Correspondingly, this alternative embodiment can further specifically implement the constructing of the resource scheduling function according to the historical electrical data as the following content:

[0055] a11) Construct a first resource scheduling sub-function with the goal of minimizing voltage deviation according to the current voltage of each power node and the corresponding ideal voltage.

[0056] a12) Construct a second resource scheduling sub-function with the goal of maximizing the output of distributed power sources according to the active power injected by the power source at each power node within the current period.

[0057] a13) Construct a third resource scheduling sub - function with the goal of minimizing the active power loss of the distribution network according to the voltage phase - angle difference, conductance, voltage amplitude of the power nodes, and time interval.

[0058] a14) Based on the first resource scheduling sub - function, the second resource scheduling sub - function, and the third resource scheduling sub - function, construct the resource scheduling function.

[0059] In this embodiment, the resource scheduling function formed based on the first resource scheduling sub - function, the second resource scheduling sub - function, and the third resource scheduling sub - function can be specifically expressed as:

[0060]

[0061] In the formula, f1, f2, and f3 are the first resource scheduling sub - function, the second resource scheduling sub - function, and the third resource scheduling sub - function respectively; i is the power node number; N is the number of remaining power nodes except the power nodes to which the dispatchable resources are connected; U i,t is the actual voltage of power node i at time t; U i,ref is the ideal voltage of voltage node i at time t; N G is the number of distributed power sources in the distribution network; is the active power injected by the distributed power source at power node i at time t; T is the number of cycles; θ ij and G ij are the voltage phase - angle difference and conductance between power node i and power node j respectively; n is the total number of power nodes; U i and U j are the voltage amplitudes of node i and node j respectively; Δt represents the time interval.

[0062] Through the above - mentioned technical solution of this embodiment, by constructing the first resource scheduling sub - function with the goal of minimizing voltage deviation, the second resource scheduling sub - function with the goal of maximizing the output of distributed power sources, and the third resource scheduling sub - function with the goal of minimizing the active power loss of the distribution network, and constructing the resource scheduling function based on the first resource scheduling sub - function, the second resource scheduling sub - function, and the third resource scheduling sub - function, it realizes better optimization of the energy structure of the distribution network on the basis of ensuring the safe operation of the distribution network system, achieves diversified energy supply, and reduces the active power loss of the distribution network.

[0063] b1) Based on the resource scheduling function and the set of scheduling constraint conditions, the distribution network resource scheduling model is formed, where the set of scheduling constraint conditions includes constraints on the branch current amplitude, constraints on the power node voltage amplitude, constraints on the branch active power, constraints on the branch apparent power, constraints on the balance between the active power injected by the power node and the active power consumed by the load, and constraints on the balance between the reactive power injected by the power node and the reactive power consumed by the load.

[0064] In this embodiment, the set of scheduling constraint conditions can be specifically expressed as:

[0065]

[0066] In the formula, I ij represents the current amplitude of branch ij from power node i to power node j, and I ij,max is the upper limit value of the current amplitude of branch ij; U i is the voltage amplitude of power node i, and U i,max and U i,min are the upper and lower limits of the power node voltage amplitude; P ij and Q ij are the active power and reactive power of branch ij; P ij,min , P ij,max are the upper and lower limits of the active power of branch ij respectively; S ij,max is the maximum value of the apparent power; P g,i is the active power injected by the power source at power node i, and P L,i is the active power consumed by the load at power node i; Q g,i is the reactive power injected by the power source at power node i, and Q L,i is the reactive power consumed by the load at power node i.

[0067] Through the above technical solution of this embodiment, by forming the distribution network resource scheduling model based on the resource scheduling function and the set of scheduling constraint conditions, a scheduling scheme that can better optimize the energy structure of the distribution network while ensuring the safe operation of the distribution network system is obtained.

[0068] As the second alternative embodiment of this embodiment, on the basis of the above embodiment, the problem of hierarchical partitioning of constructing the set of scheduling constraint conditions can be specifically optimized into the following steps:

[0069] a2) According to the execution time and the first weight of each constraint condition in the set of scheduling constraint conditions, a first partitioning function with the goal of minimizing the total execution time of the set of scheduling constraint conditions is constructed.

[0070] Among them, the first weight can be understood as the weight relative to each constraint condition, which is used to represent the importance or execution frequency of the constraint condition.

[0071] In this embodiment, according to the execution time and the first weight of each constraint condition in the scheduling constraint condition set, the first partitioning function f4(x) constructed with the goal of minimizing the total execution time of the scheduling constraint condition set can be specifically expressed as:

[0072]

[0073] In the formula, w a is the first weight of the a-th constraint condition in the scheduling constraint condition set; x a is the execution time of the a-th constraint condition; sum is the total number of constraint conditions in the scheduling constraint condition set.

[0074] b2) According to the hierarchical complexity and the second weight after hierarchical partitioning of the scheduling constraint condition set, construct a second partitioning function with the goal of minimizing the total hierarchical complexity of the scheduling constraint condition set.

[0075] Among them, the hierarchical complexity can be understood as the complexity of each layer after hierarchical partitioning (metrics such as the number of code lines and the depth of function calls). The second weight can be understood as the weight relative to the complexity of each divided layer, which is used to represent the importance of this layer in the distribution network system.

[0076] In this embodiment, according to the hierarchical complexity and the second weight after hierarchical partitioning of the scheduling constraint condition set, the second partitioning function f5(y) constructed with the goal of minimizing the total hierarchical complexity of the scheduling constraint condition set can be specifically expressed as:

[0077]

[0078] In the formula, c b is the second weight of the b-th layer; y b is the hierarchical complexity of the b-th layer; m is the total number of layers.

[0079] c2) According to the first partitioning function, the second partitioning function, and the hierarchical partitioning constraint conditions, determine a hierarchical partitioning problem with the goal of minimizing the sum of the total execution time and the total hierarchical complexity, where the hierarchical partitioning constraint conditions include constraints on the execution time of each constraint condition, constraints on each hierarchical complexity, and constraints on the performance of the distribution network system.

[0080] In this embodiment, in order to make the execution time of the divided constraint conditions meet the requirements of the distribution network system, the number of divided layers and the hierarchical complexity of each layer meet the actual situation of the distribution network, and at the same time make the performance under the current constraint condition division and hierarchical structure meet the performance requirements of the distribution network system, the hierarchical division constraint conditions can be specifically expressed as:

[0081]

[0082] D(x,y)≥D req ;

[0083] In the formula, x max , x min are respectively the maximum value and the minimum value of the execution time of the a-th constraint condition, y max , y min are respectively the maximum value and the minimum value of the hierarchical complexity of the b-th layer, D(x,y) is the performance of the distribution network system under the current constraint condition division and hierarchical structure, and D req is the minimum performance required by the system.

[0084] Continuing with the above description, according to the first division function, the second division function and the hierarchical division constraint conditions, the hierarchical division problem with the goal of minimizing the sum of the total execution time and the total hierarchical complexity can be specifically expressed as:

[0085] min[λ1f4(x)+λ2f5(y)];

[0086]

[0087] D(x,y)≥D req ;

[0088] In the formula, λ1 and λ2 are the weights corresponding to the first division function and the second division function determined by the analytic hierarchy process, where λ1 + λ2 = 1.

[0089] Through the above technical solution of this embodiment, by determining the hierarchical division problem with the goal of minimizing the sum of the total execution time and the total hierarchical complexity according to the first division function, the second division function and the hierarchical division constraint conditions, it is possible to divide the complex set of scheduling constraint conditions in the distribution network into multiple smaller and more easily processed subsets of scheduling constraint conditions, thereby realizing the acceleration of the solution speed of the distribution network resource scheduling model on the basis of ensuring the safe operation of the distribution network system, and providing strong support for the promotion of related services such as the access of distributed power sources.

[0090] As the third alternative embodiment of this embodiment, based on the above embodiment, the hierarchical division result obtained by solving the hierarchical division problem to obtain the scheduling constraint condition set can be specifically optimized into the following steps:

[0091] a3) Initialize the parameters and initialize the population using the chaotic map Bernoulli method. The parameters include the population size, the maximum number of iterations, the size of the external archive, and the access table. The population is a set of hierarchical division results for the hierarchical division problem.

[0092] In this embodiment, the chaotic map Bernoulli method is used to initialize the population to generate a more diverse and uniform population, thereby improving the ability to solve complex optimization models. The Bernoulli equation can be specifically expressed as:

[0093]

[0094] In the formula, x(t) is the state variable at time t, and x(t + 1) is the state variable at time t + 1; λ3 is a constant parameter, where 0 ≤ λ3 ≤ 1, and is used for the threshold and scale factor of the piecewise function.

[0095] b3) Adjust the weight according to the current iteration number and the maximum iteration number.

[0096] It should be noted that while adjusting the weight, the initial fitness can be calculated according to the position of each individual in the population after initialization. The individual can be a hummingbird.

[0097] c3) Execute the guided foraging process, the territorial foraging process, and the migration search process. Among them, in the guided foraging process, a target individual is selected for each individual in the population based on the access table, and the current position is updated according to the non-linearly increasing adaptive weight and the position of the target individual, and the fitness is updated.

[0098] Among them, the position of the target individual can be understood as the position corresponding to the current optimal solution.

[0099] In this embodiment, the non-linearly increasing adaptive weight can be specifically expressed as:

[0100]

[0101] In the formula, w is the current weight value; w max 、w min are the maximum and minimum values of the weight; T' max is the maximum number of iterations; t' is the current iteration number; e is the natural constant, approximately equal to 2.718; k is the adjustment exponent, which controls the rate of exponential growth.

[0102] d3) Perform non - dominated sorting on the population, and filter out non - dominated solutions; store the non - dominated solutions in an external archive and adjust the crowding distance.

[0103] e3) If the maximum number of iterations is reached or the fitness no longer improves, stop the iteration to obtain the hierarchical partitioning result of the set of scheduling constraints.

[0104] In this embodiment, if the maximum number of iterations is reached or the fitness no longer improves, stop the iteration and output the current optimal solution as the hierarchical partitioning result of the set of scheduling constraints.

[0105] With the above - mentioned technical solution of this embodiment, the hierarchical partitioning problem is solved through an optimization algorithm, which speeds up the solution of the hierarchical partitioning problem, and the obtained hierarchical partitioning result is better and more in line with the actual operation requirements. It can more flexibly handle various complex situations and changes that may occur in the distribution network (such as load fluctuations, access of distributed power sources, etc.), thereby improving the stability and reliability of the distribution network operation. At the same time, using the chaotic - mapping Bernoulli method to initialize the population can generate a more diverse and uniform population, and introducing the non - linear increasing adaptive weight method to balance the global and local optimization capabilities of the algorithm, improving the solution ability of the optimization algorithm for complex hierarchical partitioning problems, so that a better hierarchical partitioning result can be obtained faster.

[0106] As the fourth alternative embodiment of this embodiment, it further includes:

[0107] a4) Construct a directed graph of the distribution network resource scheduling model. The graph nodes of the directed graph represent the power nodes in the distribution network resource scheduling model, and the directed edges represent the power transmission relationships between the power nodes.

[0108] In this embodiment, the specific implementation of constructing the directed graph of the distribution network resource scheduling model can be to abstract each component in the distribution network as a power node in the directed graph; and abstract the power transmission relationship between the components as a directed edge in the directed graph.

[0109] The directed graph can be represented by (V, E), where V = {v1, v2, …, v k} represents all power nodes, and E = {e ij} represents all directed edges connecting the power nodes. It can be understood that according to the actual operation situation and scheduling objectives of the distribution network, the attributes of each power node and the weights of each directed edge are set.

[0110] b4) Convert the directed graph into a directed acyclic graph.

[0111] In this embodiment, the specific implementation process of converting the directed graph into a directed acyclic graph can be through a delay link, and the calculation result of the previous moment is used as feedback to convert the directed graph into a directed acyclic graph. The update rule of the delay variable in the delay link can be specifically expressed as:

[0112] d η (t + 1)= f η (d η (t), R(η, t));

[0113] In the formula, the delay variables d η (t) and d η (t + 1) respectively represent the delay outputs of the power node η at time steps t and t + 1, and f η is the calculation function of the power node η; R(η, t) is all the directly related input data of the power node η at time step t, including the real-time measurement data of the distribution network resources (such as distributed power sources, energy storage devices, loads, etc.) represented by the power node, such as power, voltage, current, etc. These input data reflect the current state and operation conditions of the distribution network resources.

[0114] It should be noted that it is ensured that there is no closed loop in any form in the directed graph, that is, for any power node η, there is no path that starts from η and finally returns to η after passing through a series of directed edges.

[0115] c4) Determine the sub-flowcharts corresponding to each of the scheduling constraint condition subsets in the directed acyclic graph according to the hierarchical division result.

[0116] In this embodiment, the corresponding scheduling constraint subsets are determined according to the hierarchical division result, and the sub-flowcharts (i.e., partial directed acyclic graphs) respectively corresponding to each scheduling constraint subset are determined according to the directed acyclic graph.

[0117] With the above technical solution of this embodiment, the distribution network resource scheduling model is represented by a directed acyclic graph, which can more clearly show the association and dependence relationships among the nodes in the distribution network; determining the sub-flowcharts corresponding to each of the scheduling constraint condition subsets in the directed acyclic graph according to the hierarchical division result provides strong support for subsequent thread allocation.

[0118] As the fifth alternative embodiment of this embodiment, on the basis of the above alternative embodiment, the parallel solution of the resource scheduling function according to each of the scheduling constraint condition subsets and the current power data information to obtain the distribution network resource scheduling information can be specifically optimized into the following steps:

[0119] a5) Determine the thread allocation information according to the attribute information of the sub-flowchart corresponding to the scheduling constraint condition subset; the thread allocation information includes: the number of thread blocks and the thread number.

[0120] Among them, the thread allocation information can be understood as the guiding information for allocating threads to each subset of scheduling constraint conditions. The attribute information of the sub-flowcharts includes the total number of sub-flowcharts, the numbers of the sub-flowcharts, and the number of constraint conditions divided in the sub-flowcharts.

[0121] In this embodiment, the specific implementation manner of determining the number of thread blocks and thread numbers according to the attribute information of the sub-flowcharts corresponding to the subsets of scheduling constraint conditions can be expressed as:

[0122]

[0123] In the formula, N bq is the number of thread blocks required for calculation; q is the total number of sub-flowcharts; G is the number of the sub-flowchart; s is the number of multi-scenarios; N ag is the number of core array groups required for processing related operations; N k is the number of constraint conditions divided in the sub-flowchart.

[0124]

[0125] In the formula, bid represents the thread block number; tid represents the thread number within the block; mod(A,B) represents the remainder of the integer A divided by the integer B; σ is the maximum number of threads in a single thread block; r is the number of divided layers; z is the number of constraint conditions that can be run in the thread.

[0126] b5) Allocate threads to the solution tasks of each of the resource scheduling functions according to the thread allocation information; the solution tasks of the resource scheduling functions include: solving the resource scheduling function according to the subset of scheduling constraint conditions and the current power data information.

[0127] In this embodiment, threads are allocated to the solution tasks of each of the resource scheduling functions corresponding to each subset of scheduling constraint conditions on a processor with parallel computing capabilities such as a GPU, so that the resource scheduling function can be solved in parallel according to each subset of scheduling constraint conditions and the current power data information.

[0128] c5) Solve the solution tasks of the resource scheduling function based on the allocated threads to obtain alternative resource scheduling information that satisfies the subset of scheduling constraint conditions.

[0129] Among them, the alternative resource scheduling information can be understood as data information that satisfies the subset of scheduling constraint conditions and can be used as a possible solution of the resource scheduling function, and is used as an alternative for the distribution network resource scheduling information.

[0130] In this embodiment, based on the allocated threads, the resource scheduling function is solved respectively by using the data in the current power data information that meet each subset of scheduling constraint conditions, and each alternative resource scheduling information corresponding to each subset of scheduling constraint conditions is obtained.

[0131] d5) Determine the distribution network resource scheduling information according to the alternative resource scheduling information that respectively meets each subset of the scheduling constraint conditions.

[0132] In this embodiment, the specific manner of determining the distribution network resource scheduling information according to the alternative resource scheduling information that respectively meets each subset of the scheduling constraint conditions may be to take the intersection of each alternative resource scheduling information to obtain the alternative resource scheduling information that meets all subsets of the scheduling constraint conditions as the distribution network resource scheduling information; it may also be to input each alternative resource scheduling information into other subsets of the scheduling constraint conditions except the subset of the scheduling constraint conditions corresponding to the alternative resource scheduling information for verification, and obtain the alternative resource scheduling information that meets all subsets of the scheduling constraint conditions as the distribution network resource scheduling information. It can be understood that if there is no alternative resource scheduling information that can meet all subsets of the scheduling constraint conditions, new alternative resource scheduling information that meets each subset of the scheduling constraint conditions is re-determined and verified again until the alternative resource scheduling information that meets all subsets of the scheduling constraint conditions is obtained, and the alternative resource scheduling information is used as the distribution network resource scheduling information; it may also be to first take the intersection of each alternative resource scheduling information. If there is no alternative resource scheduling information in the intersection, then each alternative resource scheduling information is input into other subsets of the scheduling constraint conditions except the subset of the scheduling constraint conditions corresponding to the alternative resource scheduling information for verification, and obtain the alternative resource scheduling information that meets all subsets of the scheduling constraint conditions as the distribution network resource scheduling information.

[0133] The above technical solution of this embodiment realizes the rapid solution of the distribution network resource scheduling model by determining the thread allocation information corresponding to each subset of the scheduling constraint conditions according to the attribute information of the sub-flowcharts corresponding to each subset of the scheduling constraint conditions, and allocating threads for the solving tasks of the resource scheduling function according to the thread allocation information. Based on the allocated threads, the solving tasks of the resource scheduling function are solved, making full use of the parallel computing capabilities of processors such as GPUs, reducing the computing cost and resource consumption, and being able to obtain better distribution network resource scheduling information in a shorter time to meet the real-time scheduling requirements of the distribution network system, providing strong support for the promotion of subsequent related services.

[0134] To better understand a distribution network resource scheduling method provided by an embodiment of the present invention, a specific example is given here.

[0135] Figure 2 It is a schematic diagram of the resource configuration of a power node system for an example. As Figure 2As shown in the figure, the IEEE 33 power node system is used for simulation analysis. The voltage is selected as 12.66 kV, the total active power of the load is 3715 kW, and the total reactive power is 2547 kvar. The base capacity of the example is set to 10 MVA, and the voltage constraint of the power node is 0.98 p.u. - 1.02 p.u. The total access capacity of distributed power sources is 3 MW. Photovoltaic cells with a capacity of 0.5 MW are connected to power nodes 10, 13, and 24 respectively, and wind turbines with a capacity of 0.5 MW are connected to power nodes 16, 17, and 32 respectively.

[0136] A static var compensator is added to the system. The connection point is power node 25, and the capacity is 1 Mvar. There are 10 capacitor banks CB, installed at power node 22, with a capacity of 0.5 Mvar. There are 2 battery energy storages (ESS), and the grid connection positions are power nodes 16 and 32, with an active power of 0.5 MW and a reactive power of 0.5 Mvar.

[0137] With the high - proportion penetration of distributed power sources, the risk of voltage over - limit in the distribution network increases sharply. The voltage deviation of the traditional method is large, and there is a risk of over - limit. The average voltage deviations of the traditional method and the method of the present invention are 0.4998 and 0.4403 respectively. Compared with the traditional method, the average voltage deviation of the method of the present invention is reduced by about 11.90%. Therefore, the method proposed by the present invention can minimize the voltage deviation on the premise of ensuring the safe operation of the distribution network.

[0138] The optimization effect is particularly obvious from 14:00 to 22:00. At 20:00, the network loss is reduced by 18.69 kW. The network losses of the traditional method and the method of the present invention are 542.24 kW·h and 452.58 kW·h respectively. Compared with the traditional method, the network loss of the method of the present invention is reduced by about 16.53%. Therefore, the method of the present invention performs better in reducing network losses.

[0139] In order to study the utilization rates of photovoltaic and wind power under different methods respectively, the DG utilization rates of the traditional method and the method of the present invention are 32.19% and 36.28% respectively. Compared with the traditional method, the DG utilization rate of the method of the present invention is increased by about 4.09%. The method of the present invention is more conservative in ensuring the safe and reliable operation of the distribution network for the output of new energy.

[0140] Figure 3 It is a schematic structural diagram of a distribution network resource scheduling device provided by an embodiment of the present invention. As Figure 3 shown, the device includes: a model construction module 21, a problem construction and solution module 22, a hierarchical division module 23, an information determination module 24, and a resource scheduling module 25. Among them,

[0141] A model construction module 21 is configured to construct a distribution network resource scheduling model, where the distribution network resource scheduling model includes: a resource scheduling function and a set of scheduling constraint conditions;

[0142] A problem construction and solution module 22 is configured to construct a hierarchical partitioning problem of the set of scheduling constraint conditions, and solve the hierarchical partitioning problem to obtain a hierarchical partitioning result of the set of scheduling constraint conditions;

[0143] A hierarchical partitioning module 23 is configured to hierarchically partition the set of scheduling constraint conditions according to the hierarchical partitioning result to obtain multiple subsets of scheduling constraint conditions;

[0144] An information determination module 24 is configured to obtain current power data information, and perform parallel solution on the resource scheduling function according to each subset of scheduling constraint conditions and the current power data information to obtain distribution network resource scheduling information;

[0145] A resource scheduling module 25 is configured to schedule the schedulable resources of the distribution network according to the distribution network resource scheduling information.

[0146] A distribution network resource scheduling device provided by an embodiment of the present invention constructs a distribution network resource scheduling model, where the distribution network resource scheduling model includes: a resource scheduling function and a set of scheduling constraint conditions; constructs a hierarchical partitioning problem of the set of scheduling constraint conditions, and solves the hierarchical partitioning problem to obtain a hierarchical partitioning result of the set of scheduling constraint conditions; hierarchically partitions the set of scheduling constraint conditions according to the hierarchical partitioning result to obtain multiple subsets of scheduling constraint conditions; obtains current power data information, and performs parallel solution on the resource scheduling function according to each subset of scheduling constraint conditions and the current power data information to obtain distribution network resource scheduling information; schedules the schedulable resources of the distribution network according to the distribution network resource scheduling information. This method hierarchically partitions the set of scheduling constraint conditions into multiple scheduling constraint subsets by solving the hierarchical partitioning problem, and performs parallel solution on the resource scheduling function according to each subset of scheduling constraint conditions, realizing the decomposition of complex distribution network resource scheduling problems into multiple simple sub-problems for parallel solution, thereby improving the solution speed of distribution network resource scheduling problems, shortening the time to obtain a better scheduling scheme, meeting the real-time scheduling requirements of the distribution network system, and providing strong support for the subsequent realization of integrating large-scale renewable energy into the distribution network system.

[0147] Further, the model construction module 21 may specifically include:

[0148] A resource scheduling function construction unit is configured to obtain historical electrical data of the distribution network, and construct the resource scheduling function according to the historical electrical data;

[0149] A resource scheduling model determination unit, configured to form the distribution network resource scheduling model based on the resource scheduling function and the set of scheduling constraint conditions;

[0150] Wherein, the set of scheduling constraint conditions includes constraints on the branch current amplitude, constraints on the power node voltage amplitude, constraints on the branch active power, constraints on the branch apparent power, constraints on the balance between the active power injected by the power node and the active power consumed by the load, and constraints on the balance between the reactive power injected by the power node and the reactive power consumed by the load.

[0151] Further, the historical electrical data includes the current voltage of each power node in the distribution network, the active power injected by the power source at each power node within the current period, the voltage phase angle difference between power nodes, and conductance;

[0152] Correspondingly, the resource scheduling function construction unit can specifically be used for:

[0153] Construct a first resource scheduling sub-function with the goal of minimizing voltage deviation according to the current voltage of each power node and the corresponding ideal voltage;

[0154] Construct a second resource scheduling sub-function with the goal of maximizing the output of distributed power sources according to the active power injected by the power source at each power node within the current period;

[0155] Construct a third resource scheduling sub-function with the goal of minimizing the active power loss of the distribution network according to the voltage phase angle difference between power nodes, conductance, power node voltage amplitude, and time interval;

[0156] The resource scheduling function is constituted based on the first resource scheduling sub-function, the second resource scheduling sub-function, and the third resource scheduling sub-function.

[0157] Further, the problem construction and solution module 22 can specifically include a problem construction unit and a problem solution unit. The problem construction unit can specifically be used for:

[0158] Construct a first partitioning function with the goal of minimizing the total execution time of the set of scheduling constraint conditions according to the execution time and the first weight of each constraint condition in the set of scheduling constraint conditions;

[0159] Construct a second partitioning function with the goal of minimizing the total hierarchical complexity of the set of scheduling constraint conditions according to the hierarchical complexity after hierarchical partitioning of the set of scheduling constraint conditions and the second weight;

[0160] Determine a hierarchical partitioning problem with the goal of minimizing the sum of the total execution time and the total hierarchical complexity according to the first partitioning function, the second partitioning function, and the hierarchical partitioning constraint conditions;

[0161] Among them, the hierarchical division constraint conditions include constraints on the execution time of each of the constraint conditions, constraints on the complexity of each layer, and constraints on the performance of the distribution network system.

[0162] Furthermore, the problem-solving unit can specifically be used for:

[0163] Initializing parameters and initializing the population using the chaotic map Bernoulli method, where the parameters include population size, maximum number of iterations, external archive size, and access table; the population is a set of hierarchical division results for the hierarchical division problem.

[0164] Adjusting the weight according to the current iteration number and the maximum number of iterations.

[0165] Executing the guided foraging process, territorial foraging process, and migration search process; among them, in the guided foraging process, a target individual is selected for each individual in the population based on the access table, and the current position is updated according to the non-linearly increasing adaptive weight and the position of the target individual, and the fitness is updated.

[0166] Performing non-dominated sorting on the population, screening out non-dominated solutions; storing the non-dominated solutions in the external archive, and adjusting the crowding distance.

[0167] If the maximum number of iterations is reached or the fitness no longer improves, stop the iteration to obtain the hierarchical division result of the scheduling constraint condition set.

[0168] Furthermore, the device further includes a graphical representation module, and the graphical representation module can specifically be used for:

[0169] Constructing a directed graph of the distribution network resource scheduling model, where the graph nodes of the directed graph represent the power nodes in the distribution network resource scheduling model, and the directed edges represent the power transmission relationship between the power nodes.

[0170] Converting the directed graph into a directed acyclic graph.

[0171] Determining the sub-flowcharts corresponding to each of the scheduling constraint condition subsets in the directed acyclic graph according to the hierarchical division result.

[0172] Furthermore, the information determination module 24 can specifically be used for:

[0173] Determining thread allocation information according to the attribute information of the sub-flowcharts corresponding to the scheduling constraint condition subsets; the thread allocation information includes: the number of thread blocks and the thread number.

[0174] Allocate threads to the solution tasks of each of the resource scheduling functions according to the thread allocation information; the solution tasks of the resource scheduling functions include: solving the resource scheduling functions according to the subset of scheduling constraint conditions and the current power data information.

[0175] Based on the allocated threads, solve the solution tasks of the resource scheduling functions to obtain alternative resource scheduling information that satisfies the subset of scheduling constraint conditions.

[0176] Determine the distribution network resource scheduling information according to the alternative resource scheduling information that respectively satisfies each subset of scheduling constraint conditions.

[0177] The distribution network resource scheduling device provided by the embodiments of the present invention can execute the distribution network resource scheduling method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0178] Figure 4 The structural schematic diagram of an electronic device 30 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0179] As Figure 4 shown, the electronic device 30 includes at least one processor 31, and a memory communicatively connected to at least one processor 31, such as a read-only memory (ROM) 32, a random access memory (RAM) 33, etc. Among them, the memory stores a computer program executable by at least one processor, and the processor 31 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 32 or the computer program loaded from the storage unit 38 into the random access memory (RAM) 33. In the RAM 33, various programs and data required for the operation of the electronic device 30 can also be stored. The processor 31, the ROM 32, and the RAM 33 are connected to each other through a bus 34. The input / output (I / O) interface 35 is also connected to the bus 34.

[0180] Multiple components in the electronic device 30 are connected to the I / O interface 35, including: an input unit 36, such as a keyboard, a mouse, etc.; an output unit 37, such as various types of displays, speakers, etc.; a storage unit 38, such as a magnetic disk, an optical disc, etc.; and a communication unit 39, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0181] The processor 31 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 31 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 31 executes the various methods and processes described above, such as the distribution network resource scheduling method.

[0182] In some embodiments, the distribution network resource scheduling method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 38. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 30 via the ROM 32 and / or the communication unit 39. When the computer program is loaded into the RAM 33 and executed by the processor 31, one or more steps of the distribution network resource scheduling method described above can be executed. Alternatively, in other embodiments, the processor 31 can be configured to execute the distribution network resource scheduling method in any other suitable manner (e.g., by means of firmware).

[0183] Various embodiments of the systems and technologies described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, and the programmable processor can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0184] A computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing device, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0185] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0186] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0187] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected with each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0188] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0189] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0190] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for dispatching resources in a distribution network, characterized in that, Including: Construct a distribution network resource scheduling model, where the distribution network resource scheduling model includes: a resource scheduling function and a set of scheduling constraint conditions; Construct the hierarchical partitioning problem of the set of scheduling constraint conditions, and solve the hierarchical partitioning problem to obtain the hierarchical partitioning result of the set of scheduling constraint conditions; According to the hierarchical partitioning result, partition the set of scheduling constraint conditions to obtain multiple subsets of scheduling constraint conditions; Obtain the current power data information, and parallelly solve the resource scheduling function according to each subset of scheduling constraint conditions and the current power data information to obtain the distribution network resource scheduling information; Schedule the schedulable resources of the distribution network according to the distribution network resource scheduling information.

2. The method according to claim 1, wherein The construction of the distribution network resource scheduling model includes: Obtain the historical electrical data of the distribution network, and construct the resource scheduling function according to the historical electrical data; Based on the resource scheduling function and the set of scheduling constraint conditions, form the distribution network resource scheduling model; Among them, the set of scheduling constraint conditions includes constraints on the branch current amplitude, constraints on the power node voltage amplitude, constraints on the branch active power, constraints on the branch apparent power, constraints on the balance between the active power injected by the power node and the active power consumed by the load, and constraints on the balance between the reactive power injected by the power node and the reactive power consumed by the load.

3. The method according to claim 2, wherein The historical electrical data includes the current voltage of each power node in the distribution network, the active power injected by the power source at each power node during the current period, the voltage phase angle difference between power nodes, and conductance; Correspondingly, the construction of the resource scheduling function according to the historical electrical data includes: Construct a first resource scheduling sub-function with the goal of minimizing voltage deviation according to the current voltage of each power node and the corresponding ideal voltage; Construct a second resource scheduling sub-function with the goal of maximizing the output of distributed power sources according to the active power injected by the power source at each power node during the current period; Construct a third resource scheduling sub-function with the goal of minimizing the active power loss of the distribution network according to the voltage phase angle difference between power nodes, conductance, power node voltage amplitude, and time interval; Based on the first resource scheduling sub-function, the second resource scheduling sub-function, and the third resource scheduling sub-function, construct the resource scheduling function.

4. The method according to any one of claims 1 to 3, characterized in that, The construction of the hierarchical partitioning problem of the set of scheduling constraint conditions includes: Construct a first partitioning function with the goal of minimizing the total execution time of the set of scheduling constraint conditions according to the execution time and the first weight of each constraint condition in the set of scheduling constraint conditions; Construct a second partitioning function with the goal of minimizing the total hierarchical complexity of the set of scheduling constraint conditions according to the hierarchical complexity after partitioning the set of scheduling constraint conditions and the second weight; According to the first partitioning function, the second partitioning function, and the hierarchical partitioning constraint conditions, determine a hierarchical partitioning problem with the goal of minimizing the sum of the total execution time and the total hierarchical complexity; Among them, the hierarchical partitioning constraint conditions include constraints on the execution time of each constraint condition, constraints on each hierarchical complexity, and constraints on the performance of the distribution network system.

5. The method according to any one of claims 1 to 3, characterized in that The solution to the hierarchical partitioning problem to obtain the hierarchical partitioning result of the scheduling constraint condition set includes: Initializing parameters and initializing the population using the chaotic mapping Bernoulli method, where the parameters include the population size, the maximum number of iterations, the size of the external archive, and the access table; the population is a set of hierarchical partitioning results of the hierarchical partitioning problem. Adjusting the weight according to the current iteration number and the maximum number of iterations. Performing a guided foraging process, a territorial foraging process, and a migration search process; among them, in the guided foraging process, a target individual is selected for each individual in the population based on the access table, and the current position is updated according to the non-linearly increasing adaptive weight and the position of the target individual, and the fitness is updated. Performing non-dominated sorting on the population, screening out non-dominated solutions; storing the non-dominated solutions in the external archive, and adjusting the crowding distance. If the maximum number of iterations is reached or the fitness no longer improves, stop the iteration to obtain the hierarchical partitioning result of the scheduling constraint condition set.

6. The method according to claim 1, wherein It also includes: Constructing a directed graph of the distribution network resource scheduling model, where the graph nodes of the directed graph represent the power nodes in the distribution network resource scheduling model, and the directed edges represent the power transmission relationship between the power nodes. Converting the directed graph into a directed acyclic graph. Determining the sub-flowcharts corresponding to each scheduling constraint condition subset in the directed acyclic graph according to the hierarchical partitioning result.

7. The method according to claim 6, wherein Performing parallel solution of the resource scheduling function according to each scheduling constraint condition subset and the current power data information to obtain distribution network resource scheduling information, including: Determining thread allocation information according to the attribute information of the sub-flowchart corresponding to the scheduling constraint condition subset; the thread allocation information includes: the number of thread blocks and the thread number. Allocating threads to the solution tasks of each resource scheduling function according to the thread allocation information; the solution tasks of the resource scheduling function include: solving the resource scheduling function according to the scheduling constraint condition subset and the current power data information. Based on the allocated threads, solving the solution tasks of the resource scheduling function to obtain alternative resource scheduling information that satisfies the scheduling constraint condition subset. Determining the distribution network resource scheduling information according to the alternative resource scheduling information that respectively satisfies each scheduling constraint condition subset.

8. A distribution network resource scheduling device, characterized in that, It includes: A model construction module for constructing a distribution network resource scheduling model, where the distribution network resource scheduling model includes: a resource scheduling function and a scheduling constraint condition set. A problem construction and solution module for constructing the hierarchical partitioning problem of the scheduling constraint condition set and solving the hierarchical partitioning problem to obtain the hierarchical partitioning result of the scheduling constraint condition set. A hierarchical partitioning module for hierarchically partitioning the scheduling constraint condition set according to the hierarchical partitioning result to obtain multiple scheduling constraint condition subsets. An information determination module for obtaining the current power data information and performing parallel solution of the resource scheduling function according to each scheduling constraint condition subset and the current power data information to obtain distribution network resource scheduling information. A resource scheduling module for scheduling the schedulable resources of the distribution network according to the distribution network resource scheduling information.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distribution network resource scheduling method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for implementing the distribution network resource scheduling method according to any one of claims 1-7 when executed by a processor.

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