Power flow optimization method, device and equipment for power distribution network and medium

Abstract

The invention provides a power distribution network power flow optimization method and device, equipment and a medium, and relates to the technical field of power distribution networks, and the method comprises the steps: obtaining an initial state vector corresponding to each type of flexible resources accessed to a power distribution network; wherein the initial state vector comprises a plurality of state parameters; performing multi-source data integration based on each initial state vector to obtain a data integration result; determining a load flow calculation result according to the data integration result; and inputting the power flow calculation result into a genetic algorithm, and outputting a power flow optimization result of the power distribution network through the genetic algorithm based on a preset multi-target power flow optimization constraint condition. According to the method and the device, the problem that power flow optimization is difficult to carry out on flexible resources with different characteristics at the same time under a multi-target demand when various flexible resources are accessed to an existing power distribution network can be effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a distribution network power flow optimization method, a distribution network power flow optimization device, an electronic device, and a machine-readable storage medium. Background Art

[0002] By connecting to a variety of flexibility resources to optimize power flow, the distribution network can improve the operating efficiency, stability and flexibility of the power system, promote the effective use of renewable energy, reduce dependence on traditional fossil energy, and achieve both economic and environmental benefits.

[0003] With the access of flexible resources, distribution networks face new challenges in achieving power flow optimization under multi-objective demands. Current power flow optimization methods are difficult to simultaneously optimize power flow for different types of connected flexibility resources when facing multi-objective demands. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a distribution network flow optimization method, distribution network flow optimization device, equipment and medium to solve the problem in the prior art that when a distribution network is connected to a variety of flexibility resources, it is difficult to simultaneously optimize the flow of flexibility resources with different characteristics under multi-objective requirements.

[0005] In order to achieve the above-mentioned object, the present application provides a first aspect of a distribution network power flow optimization method, the method comprising:

[0006] Obtaining an initial state vector corresponding to each type of flexibility resource connected to the distribution network; wherein the initial state vector includes a plurality of state parameters;

[0007] Perform multi-source data integration based on each initial state vector to obtain a data integration result;

[0008] Determine the power flow calculation result according to the data integration result;

[0009] The power flow calculation results are input into a genetic algorithm, and based on preset multi-objective power flow optimization constraints, the power flow optimization results of the distribution network are output through the genetic algorithm.

[0010] In the embodiment of the present application, multi-source data integration is performed based on each initial state vector to obtain a data integration result, including:

[0011] For each initial state vector, the initial state vector is serialized to obtain an initial sequence corresponding to the initial state vector, and the initial sequence is dimensionless based on a preset reference sequence to obtain a target sequence corresponding to the initial state vector;

[0012] According to each target sequence, a plurality of data integration matrices are generated; wherein each data integration matrix corresponds to a resource type;

[0013] According to each data integration matrix, a data integration result is generated.

[0014] In the embodiment of the present application, the initial sequence is dimensionless based on a preset reference sequence to obtain a target sequence corresponding to the initial state vector, including:

[0015] Divide the preset benchmark sequence by the initial sequence to obtain a dimensionless sequence;

[0016] Determine a scaling factor required to adjust the maximum value in the dimensionless sequence to 1, adjust the maximum value in the dimensionless sequence to 1, and divide all values ​​in the dimensionless sequence except the maximum value by the scaling factor to obtain a target sequence corresponding to the initial state vector.

[0017] In the embodiment of the present application, determining the power flow calculation result according to the data integration result includes:

[0018] Construct the nonlinear form of Newton-Raphson method power flow calculation equation for distribution network;

[0019] Substituting the data integration result into the Newton-Raphson method power flow calculation equation, performing Taylor expansion on the Newton-Raphson method power flow calculation equation and taking only the first-order term to obtain a linearized equation; wherein the linearized equation includes a Jacobian matrix and a correction amount;

[0020] According to the iterative update method of the Jacobian matrix and the correction amount, the linearized equation is iterated to solve the correction amount until the latest solved correction amount meets the preset convergence condition, and the output result of the linearized equation when the solved correction amount meets the preset convergence condition is determined as the power flow calculation result.

[0021] In an embodiment of the present application, the power flow calculation result is input into a genetic algorithm, and based on preset multi-objective power flow optimization constraints, the power flow optimization result of the distribution network is output through the genetic algorithm, including:

[0022] Under the preset multi-objective power flow optimization constraint conditions, the power flow calculation results are encoded to generate an initial population, and the initial population is used as the current population; wherein the initial population includes a plurality of individuals, each of which represents a power flow optimization scheme of the distribution network, and each of which includes power flow data of each resource type;

[0023] Determine the fitness value corresponding to each individual in the current population according to the preset fitness function;

[0024] According to the individuals whose fitness values ​​meet the preset fitness conditions, new individuals are generated by encoding, and the new individuals meet the preset multi-objective power flow optimization constraint conditions; the population composed of each new individual is updated as the current population, and the step of determining the fitness value corresponding to each individual in the current population according to the preset fitness function is returned to execute until the total number of times the fitness value determination step is executed meets the preset loop condition;

[0025] The individual corresponding to the maximum fitness value is determined as the distribution network power flow optimization result.

[0026] In an embodiment of the present application, the preset multi-objective flow optimization constraint conditions include that the economic index value, safety index value, and green index value calculated based on the flow data of various types of flexibility resources are all greater than the preset multi-objective index threshold value.

[0027] In the embodiment of the present application, the preset fitness function is the sum of the flow data of each type of flexibility resource and its corresponding preset comprehensive weight value multiplied together.

[0028] A second aspect of the present application provides a power distribution network flow optimization device, the device comprising:

[0029] A state vector acquisition module is used to obtain the initial state vector corresponding to each type of flexibility resource connected to the distribution network; wherein the initial state vector includes multiple state parameters;

[0030] A data integration module is used to integrate multi-source data based on each initial state vector to obtain a data integration result;

[0031] A power flow calculation module, used to determine a power flow calculation result according to the data integration result;

[0032] The power flow optimization module is used to input the power flow calculation results into the genetic algorithm, and output the distribution network power flow optimization results through the genetic algorithm based on the preset multi-objective power flow optimization constraints.

[0033] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the distribution network flow optimization method described in the first aspect when executing the computer program.

[0034] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the distribution network flow optimization method described in the first aspect above.

[0035] The distribution network flow optimization method, device, equipment and medium provided in this application can fully reflect the actual situation of the system by obtaining the initial state vector of each type of flexibility resource, ensure the accuracy of data integration, and use the Newton-Raphson method for flow calculation, which can efficiently handle complex power grid structures and provide fast and accurate flow calculation results. Finally, under multi-objective requirements (i.e., preset multi-objective flow optimization constraints), genetic algorithms are used to continuously adjust and optimize resource allocation to explore the optimal solution for distribution network flow optimization, i.e., determine the distribution network flow optimization results, and achieve simultaneous flow optimization of multiple flexibility resources, thereby improving the overall performance and flexibility of the distribution network. In addition, this method can simultaneously consider multiple optimization objectives, such as economy, reliability and environmental impact, and ultimately maximize the comprehensive benefits.

[0036] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0038] Figure 1 The schematic diagram shows a flow chart of a method for optimizing power flow in a power distribution network according to an embodiment of the present application;

[0039] Figure 2 The structural block diagram of the power distribution network flow optimization device according to the embodiment of the present application is schematically shown;

[0040] Figure 3 The internal structure diagram of the computer device according to the embodiment of the present application is schematically shown.

[0041] Description of Reference Numerals

[0042] A01-processor; A02-network interface; A03-internal memory; A04-display screen; A05-input device; A06-non-volatile storage medium; B01-operating system; B02-computer program. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0044] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0045] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0046] In view of the problem in the related art that when a distribution network is connected to a variety of flexibility resources, it is difficult to simultaneously optimize the flow of flexibility resources with different characteristics under multi-objective requirements, the present application provides a distribution network flow optimization method, device, equipment and medium. The distribution network flow optimization method, device, equipment and medium provided by the present application are described in detail below in conjunction with the accompanying drawings through specific examples and implementation methods.

[0047] Figure 1 The flow chart of the power distribution network optimization method according to the embodiment of the present application is schematically shown. Figure 1 As shown, in one embodiment of the present application, a method for optimizing power flow in a distribution network is provided, and the method may include the following steps.

[0048] Step 100, obtaining the initial state vector corresponding to each type of flexibility resource connected to the distribution network.

[0049] The initial state vector includes multiple state parameters.

[0050] In an embodiment of the present application, source data is acquired from PIMS (Power Information Management System) once every preset time period. The source data includes state vectors of all types of flexibility resources in the power system network. The state vector of each type of flexibility resource includes three state parameters: voltage (U), current (I), and phase (G). Flexibility resources mainly include renewable energy, energy storage systems, and demand-side management resources. The following is an explanation of each type of flexibility resource.

[0051] Renewable energy (such as solar energy and wind energy) is random and intermittent. By optimizing the access and power flow of renewable energy, the flexibility and reliability of the distribution network can be improved. For example, by installing photovoltaic power stations and wind turbines and taking advantage of their output characteristics, the power supply can be increased during peak load periods to reduce the pressure on the distribution network.

[0052] Energy storage systems can store electricity when there is an oversupply of electricity and release it when there is a peak in electricity demand, thereby regulating the load on the distribution network. Energy storage systems can be divided into battery storage, pumped storage, compressed air storage, etc. According to the actual situation of the distribution network, a suitable energy storage system can be selected. For example, by building energy storage power stations near photovoltaic power stations and wind farms, the utilization rate of renewable energy and the flexibility of the distribution network can be effectively improved.

[0053] Demand-side management resources refer to achieving the balance and optimization of the distribution network load by adjusting the user's electricity consumption behavior and pattern. For example, guiding users to use electricity during low-load periods through economic means such as time-of-use electricity prices and peak-valley electricity prices can reduce the pressure on the distribution network during peak hours. In addition, the refined management of user electricity consumption behavior can be achieved through smart home systems, remote control and other methods, further improving the flexibility of the distribution network.

[0054] Step 200 , performing multi-source data integration based on each initial state vector to obtain a data integration result.

[0055] In a specific implementation, step 200 includes the following steps:

[0056] Step 210, for each initial state vector, serialize the initial state vector to obtain an initial sequence corresponding to the initial state vector, and non-dimensionalize the initial sequence based on a preset reference sequence to obtain a target sequence corresponding to the initial state vector.

[0057] Serializing the initial state vector refers to arranging the values ​​in the initial state vector in a predetermined order, such as arranging them in the order of voltage, current, and phase.

[0058] In this specific implementation, the preset reference sequence may be derived from a previously acquired initial state vector, an initial sequence formed after serialization processing, or a sequence determined by relevant personnel based on experience or a computer program. This application does not limit the method for determining the preset reference sequence.

[0059] In this specific implementation, the specific implementation method of non-dimensionalizing the initial sequence based on the preset reference sequence to obtain the target sequence corresponding to the initial state vector is:

[0060] Divide the preset benchmark sequence by the initial sequence to obtain a dimensionless sequence;

[0061] Determine a scaling factor required to adjust the maximum value in the dimensionless sequence to 1, adjust the maximum value in the dimensionless sequence to 1, and divide all values ​​in the dimensionless sequence except the maximum value by the scaling factor to obtain a target sequence corresponding to the initial state vector.

[0062] The initial state vector is serialized and dimensionless in sequence to facilitate subsequent calculation and analysis.

[0063] Step 220, generating multiple data integration matrices according to each target sequence.

[0064] Each data integration matrix corresponds to a resource type.

[0065] In one example, the data integration matrix A corresponding to renewable energy can be expressed as: A = [U1, I1, G1], where U1, I1, and G1 represent the dimensionless values ​​of voltage, current, and phase, respectively. Similarly, the data integration matrix B corresponding to the energy storage system can be expressed as: B = [U2, I2, G2], and the data integration matrix C corresponding to the demand-side management resource can be expressed as: C = [U3, I3, G3].

[0066] Step 230: Generate data integration results according to each data integration matrix.

[0067] Specifically, the data integration result is in a matrix form, which includes various data integration matrices.

[0068] Based on the above example, the data integration result X generated according to each data integration matrix can be expressed as: X=[A, B, C].

[0069] Step 300: Determine the power flow calculation result according to the data integration result.

[0070] In a specific implementation, step 300 includes the following steps:

[0071] Step 310: construct a nonlinear Newton-Raphson method power flow calculation equation for the distribution network.

[0072] Step 320, substituting the data integration result into the Newton-Raphson method power flow calculation equation, performing Taylor expansion on the Newton-Raphson method power flow calculation equation and taking only the first-order terms to obtain a linearized equation.

[0073] The linearized equation includes the Jacobian matrix and the correction value.

[0074] Specifically, the Jacobian matrix is ​​a matrix composed of partial derivatives of the objective function, which is used to describe the local linear approximation of the function at a certain point. By calculating the Jacobian matrix, we can get the rate of change information of the objective function, which helps us understand how the function value changes near the current estimated value, thus providing direction for iteration.

[0075] The correction is obtained by multiplying the inverse of the Jacobian matrix by the residual (i.e., the value of the objective function at the current estimate) and is used to update the current estimate. The purpose is to gradually approach the root of the equation by adding the correction to the current estimate. The size and direction of the correction reflects the degree of adjustment towards the solution.

[0076] That is to say, the Jacobian matrix is ​​used to describe the local linear approximation of the objective function at the current estimated point. By calculating the partial derivatives of the function, it provides the direction and rate of function change. The correction amount is calculated based on the inverse of the Jacobian matrix and the residual (i.e., the function value) of the current estimated point, and is used to update the current estimate to help it gradually approach the solution to the equation.

[0077] Step 330, iterate the linearized equation according to the iterative update method of the Jacobian matrix and the correction amount, solve the correction amount, until the latest solved correction amount meets the preset convergence condition, and determine the output result of the linearized equation when the solved correction amount meets the preset convergence condition as the power flow calculation result.

[0078] In this specific implementation, the preset convergence condition can be set as the correction amount is less than 1, that is, when the latest solved correction amount is less than 1, the solution of the equation is output, and the solution is the flow calculation result, which includes the flow distribution of different types of flexibility resources.

[0079] Step 400: input the power flow calculation result into a genetic algorithm, and output the distribution network power flow optimization result through the genetic algorithm based on preset multi-objective power flow optimization constraints.

[0080] In a specific implementation, step 400 includes the following steps:

[0081] Step 410: Under the preset multi-objective power flow optimization constraint conditions, the power flow calculation results are encoded to generate an initial population, and the initial population is used as the current population.

[0082] The initial population includes a plurality of individuals, each of which represents a power flow optimization scheme of the distribution network, and each of which includes power flow data of each resource type.

[0083] In this specific implementation, the preset multi-objective power flow optimization constraint condition includes that the economic index value, safety index value, and green index value calculated based on the power flow data of various types of flexible resources are all greater than the preset multi-objective index threshold value. Among them, the preset multi-objective index threshold value can be selected as 0.

[0084] Among them, economy, safety and green are the optimization goals of the embodiment of this application. For different optimization goals, there will be quantitative influence weights between different types of flexibility resources, and these weights will guide the resource allocation in the optimization process.

[0085] Specifically, for each optimization target, one method for determining its index value is to determine the sum of the products of the flow data of each type of flexibility resource and its corresponding impact weight under the optimization target as the index value of the optimization target. It is worth mentioning that under different optimization targets, the impact weights corresponding to each type of flexibility resource can be set manually or determined by other methods.

[0086] Step 420: determine the fitness value corresponding to each individual in the current population according to a preset fitness function.

[0087] In this specific implementation, the preset fitness function is the sum of the flow data of each type of flexibility resource and its corresponding preset comprehensive weight value multiplied together.

[0088] Among them, the preset comprehensive weight value corresponding to the flexibility resource can be set manually or determined by other means. The size of the preset comprehensive weight value corresponding to the flexibility resource reflects the influence of this type of flexibility resource in the distribution network flow optimization, that is, the importance of this type of flexibility resource in realizing the distribution network flow optimization.

[0089] Step 430, according to the individuals whose fitness values ​​meet the preset fitness conditions, encode and generate new individuals, and the new individuals meet the preset multi-objective power flow optimization constraints; update the population composed of each new individual to the current population, and return to execute step 420 until the total number of executions of step 420 meets the preset loop condition.

[0090] In this specific implementation, the preset fitness condition is that the fitness value is greater than or equal to the preset fitness threshold, wherein the preset fitness threshold can be set manually. That is to say, for each individual, when its fitness value is greater than or equal to the preset fitness threshold, the individual is retained, and optimization encoding can be performed based on the individual later to obtain a new individual.

[0091] In this specific implementation, the preset loop condition is that the total number of executions of step 420 is greater than or equal to the preset number of cycles, wherein the preset number of cycles can be manually set. That is, when the total number of executions of step 420 reaches the preset number of cycles, the iteration is stopped, and the optimal solution for power flow optimization of the distribution network is determined according to the fitness value obtained in the evolution process.

[0092] Step 440: determine the individual corresponding to the maximum value in the fitness value as the distribution network power flow optimization result.

[0093] Specifically, the individual corresponding to the maximum value in fitness value is selected as the distribution network power flow optimization result, with the aim of ensuring that the selected scheme can best meet multiple optimization objectives under comprehensive evaluation.

[0094] It can be seen that the distribution network flow optimization method provided in the embodiment of the present application can fully reflect the actual situation of the system and ensure the accuracy of data integration by obtaining the initial state vector of each type of flexibility resource. By using the Newton-Raphson method for flow calculation, it can efficiently handle complex power grid structures and provide fast and accurate flow calculation results. Finally, under multi-objective requirements (i.e., preset multi-objective flow optimization constraints), the genetic algorithm is used to continuously adjust and optimize resource allocation to explore the optimal solution for distribution network flow optimization, that is, to determine the distribution network flow optimization results, and realize the simultaneous flow optimization of multiple flexibility resources, improve the overall performance and flexibility of the distribution network, and effectively solve the problem of how to simultaneously optimize the flow of flexibility resources with different characteristics under multi-objective requirements. In addition, this method can simultaneously consider multiple optimization objectives, such as economy, reliability, and environmental impact, and ultimately maximize the comprehensive benefits.

[0095] Figure 1 FIG. 1 is a flow chart of a method for optimizing power flow in a power distribution network according to an embodiment of the present invention. Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0096] Figure 2 The structural block diagram of the power distribution network flow optimization device according to the embodiment of the present application is schematically shown. Figure 2As shown, in one embodiment of the present application, a distribution network flow optimization device is provided, and the distribution network flow optimization device may include the following functional modules.

[0097] The state vector acquisition module is used to acquire the initial state vector corresponding to each type of flexibility resource connected to the distribution network, wherein the initial state vector includes multiple state parameters.

[0098] The data integration module is used to integrate multi-source data based on each initial state vector to obtain a data integration result.

[0099] The power flow calculation module is used to determine the power flow calculation result according to the data integration result.

[0100] The power flow optimization module is used to input the power flow calculation results into the genetic algorithm, and output the distribution network power flow optimization results through the genetic algorithm based on the preset multi-objective power flow optimization constraints.

[0101] In the embodiment of the present application, the data integration module may include:

[0102] The vector matrix processing unit is used to serialize each initial state vector to obtain an initial sequence corresponding to the initial state vector, and to dimensionlessly convert the initial sequence based on a preset reference sequence to obtain a target sequence corresponding to the initial state vector.

[0103] The first data integration unit is used to generate a plurality of data integration matrices according to each target sequence, wherein each data integration matrix corresponds to a resource type.

[0104] The second data integration unit is used to generate data integration results according to each data integration matrix.

[0105] In an embodiment of the present application, the vector matrix processing unit may include:

[0106] The first data processing subunit is used to divide the preset reference sequence by the initial sequence to obtain a dimensionless sequence.

[0107] The second data processing subunit is used to determine the proportional factor required to adjust the maximum value in the dimensionless sequence to 1, adjust the maximum value in the dimensionless sequence to 1, and divide all other values ​​in the dimensionless sequence except the maximum value by the proportional factor to obtain a target sequence corresponding to the initial state vector.

[0108] In an embodiment of the present application, the power flow calculation module may include:

[0109] The nonlinear equation construction unit is used to construct the nonlinear form of the Newton-Raphson method power flow calculation equation for the distribution network.

[0110] The equation linearization unit is used to substitute the data integration result into the Newton-Raphson method power flow calculation equation, perform Taylor expansion on the Newton-Raphson method power flow calculation equation, and take only the first-order term to obtain a linearized equation. The linearized equation includes a Jacobian matrix and a correction amount.

[0111] The power flow calculation unit is used to iterate the linearized equation according to the iterative update method of the Jacobian matrix and the correction amount, and solve the correction amount until the latest solved correction amount meets the preset convergence condition, and the output result of the linearized equation when the solved correction amount meets the preset convergence condition is determined as the power flow calculation result.

[0112] In an embodiment of the present application, the power flow optimization module may include:

[0113] The initial population generation unit is used to encode the power flow calculation results under the preset multi-objective power flow optimization constraint conditions, generate an initial population, and use the initial population as the current population. The initial population includes a plurality of individuals, each of which represents a power flow optimization scheme of the distribution network, and each of which includes power flow data of each resource type.

[0114] The fitness calculation unit is used to determine the fitness value corresponding to each individual in the current population according to a preset fitness function.

[0115] The loop execution unit is used to encode and generate new individuals according to the individuals whose fitness values ​​meet the preset fitness conditions, and the new individuals meet the preset multi-objective power flow optimization constraints; the population composed of each new individual is updated to the current population, and the fitness calculation unit is returned to execute until the total number of executions of the fitness calculation unit meets the preset loop condition.

[0116] The power flow optimization determination unit is used to determine the individual corresponding to the maximum value in the fitness value as the power flow optimization result of the distribution network.

[0117] In an embodiment of the present application, the preset multi-objective flow optimization constraint conditions include that the economic index value, safety index value, and green index value calculated based on the flow data of various types of flexibility resources are all greater than the preset multi-objective index threshold value.

[0118] In the embodiment of the present application, the preset fitness function is the sum of the flow data of each type of flexibility resource and its corresponding preset comprehensive weight value multiplied together.

[0119] Since the distribution network flow optimization device provided in the embodiment of the present application is a virtual device corresponding to the distribution network flow optimization method of the above embodiment, it can also solve the problem in the prior art that when the distribution network is connected to a variety of flexibility resources, it is difficult to simultaneously optimize the flow of flexibility resources with different characteristics under multi-objective requirements.

[0120] An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the distribution network flow optimization method described in the above embodiment when executing the computer program.

[0121] The electronic device provided in the embodiment of the present application includes a processor capable of running the distribution network power flow optimization method of the aforementioned embodiment, and can therefore also solve the problem in the prior art that when a distribution network is connected to a variety of flexibility resources, it is difficult to simultaneously optimize the power flow of flexibility resources with different characteristics under multi-objective requirements.

[0122] An embodiment of the present application provides a machine-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the distribution network power flow optimization method described in the above embodiment.

[0123] The machine-readable storage medium provided in the embodiment of the present application stores instructions for enabling the machine to execute the distribution network power flow optimization method of the above embodiment. Therefore, it can also solve the problem in the prior art that when a distribution network is connected to a variety of flexibility resources, it is difficult to simultaneously optimize the power flow of flexibility resources with different characteristics under multi-objective requirements.

[0124] Figure 3 The internal structure diagram of the computer device of the embodiment of the present application is schematically shown. Figure 3 As shown, in one embodiment of the present application, a computer device is provided, which may be a terminal. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, a method for optimizing the power flow of a distribution network is implemented. The display screen A04 of the computer device may be a liquid crystal display or an electronic ink display, and the input device A05 of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0125] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0126] In one embodiment, the power distribution network flow optimization device provided by the present application can be implemented in the form of a computer program. The computer program can be Figure 3 The computer device is run on the computer device shown. The memory of the computer device can store various program modules constituting the distribution network flow optimization device, and the computer program composed of various program modules enables the processor to execute the steps of the distribution network flow optimization method of each embodiment of the present application described in this specification.

[0127] Figure 3 The computer device shown can be Figure 2 In the power distribution network flow optimization device shown, the state vector acquisition module executes step 100 , the data integration module executes step 200 , the power flow calculation module executes step 300 , and the power flow optimization module executes step 400 .

[0128] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0129] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0130] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0132] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0133] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0134] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0135] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0136] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for optimizing power flow in a distribution network, characterized in that: The method comprises: Obtaining an initial state vector corresponding to each type of flexibility resource connected to the distribution network; wherein the initial state vector includes a plurality of state parameters; Perform multi-source data integration based on each initial state vector to obtain a data integration result; Determine the power flow calculation result according to the data integration result; The power flow calculation results are input into a genetic algorithm, and based on preset multi-objective power flow optimization constraints, the power flow optimization results of the distribution network are output through the genetic algorithm.

2. The method according to claim 1, characterized in that Based on each initial state vector, multi-source data is integrated to obtain data integration results, including: For each initial state vector, the initial state vector is serialized to obtain an initial sequence corresponding to the initial state vector, and the initial sequence is dimensionless based on a preset reference sequence to obtain a target sequence corresponding to the initial state vector; According to each target sequence, a plurality of data integration matrices are generated; wherein each data integration matrix corresponds to a resource type; According to each data integration matrix, a data integration result is generated.

3. The method according to claim 2, characterized in that The initial sequence is dimensionless based on the preset reference sequence to obtain the target sequence corresponding to the initial state vector, including: Divide the preset benchmark sequence by the initial sequence to obtain a dimensionless sequence; Determine a scaling factor required to adjust the maximum value in the dimensionless sequence to 1, adjust the maximum value in the dimensionless sequence to 1, and divide all values ​​in the dimensionless sequence except the maximum value by the scaling factor to obtain a target sequence corresponding to the initial state vector.

4. The method according to claim 1, characterized in that According to the data integration results, the power flow calculation results are determined, including: Construct the nonlinear form of Newton-Raphson method power flow calculation equation for distribution network; Substituting the data integration result into the Newton-Raphson method power flow calculation equation, performing Taylor expansion on the Newton-Raphson method power flow calculation equation and taking only the first-order term to obtain a linearized equation; wherein the linearized equation includes a Jacobian matrix and a correction amount; According to the iterative update method of the Jacobian matrix and the correction amount, the linearized equation is iterated to solve the correction amount until the latest solved correction amount meets the preset convergence condition, and the output result of the linearized equation when the solved correction amount meets the preset convergence condition is determined as the power flow calculation result.

5. The method according to claim 1, characterized in that The power flow calculation results are input into the genetic algorithm, and based on the preset multi-objective power flow optimization constraints, the power flow optimization results of the distribution network are output through the genetic algorithm, including: Under the preset multi-objective power flow optimization constraint conditions, the power flow calculation results are encoded to generate an initial population, and the initial population is used as the current population; wherein the initial population includes a plurality of individuals, each of which represents a power flow optimization scheme of the distribution network, and each of which includes power flow data of each resource type; Determine the fitness value corresponding to each individual in the current population according to the preset fitness function; According to the individuals whose fitness values ​​meet the preset fitness conditions, new individuals are generated by encoding, and the new individuals meet the preset multi-objective power flow optimization constraint conditions; the population composed of each new individual is updated as the current population, and the step of determining the fitness value corresponding to each individual in the current population according to the preset fitness function is returned to execute until the total number of times the fitness value determination step is executed meets the preset loop condition; The individual corresponding to the maximum fitness value is determined as the distribution network power flow optimization result.

6. The method according to claim 5, characterized in that The preset multi-objective power flow optimization constraint conditions include that the economic index value, safety index value, and green index value calculated based on the power flow data of various types of flexibility resources are all greater than the preset multi-objective index threshold value.

7. The method according to claim 5, characterized in that The preset fitness function is the sum of the power flow data of various types of flexibility resources and their corresponding preset comprehensive weight values.

8. A power distribution network flow optimization device, characterized in that: The device comprises: A state vector acquisition module is used to obtain the initial state vector corresponding to each type of flexibility resource connected to the distribution network; wherein the initial state vector includes multiple state parameters; A data integration module is used to integrate multi-source data based on each initial state vector to obtain a data integration result; A power flow calculation module, used to determine a power flow calculation result according to the data integration result; The power flow optimization module is used to input the power flow calculation results into the genetic algorithm, and output the distribution network power flow optimization results through the genetic algorithm based on the preset multi-objective power flow optimization constraints.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the distribution network power flow optimization method described in any one of claims 1 to 7 is implemented.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the power flow optimization method for a power distribution network according to any one of claims 1 to 7.

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