Power distribution network multi-element distributed resource synchronous collaborative control method and device based on dynamic weight optimization, equipment, medium and product

Abstract

This application relates to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for synchronous and coordinated control of multiple distributed resources in a power distribution network based on dynamic weight optimization. It is applied in the field of big data technology. The method includes: acquiring control parameter information of each distributed resource in the power grid system; constructing an initial particle swarm based on the control parameter information of each distributed resource in the power grid system; evaluating the fitness of each particle in the initial particle swarm to obtain particle fitness, wherein the particle fitness is used to characterize the degree of control coordination among the distributed resources in the power grid system; and iteratively updating the control parameter information of each distributed resource in the power grid system based on the particle fitness to obtain target control parameter information. This method can improve the accuracy of coordinated control of distributed resources.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a method, device, computer equipment, computer-readable storage medium, and computer program product for synchronous and coordinated control of multi-distributed resources in a distribution network based on dynamic weight optimization. Background Technology

[0002] With the deepening of the global energy transition, distributed energy systems (such as solar and wind power) are gradually becoming an important part of modern power distribution networks. However, distributed energy is characterized by volatility and intermittency, especially wind and solar power output, which is greatly affected by weather and environmental factors, posing challenges to the operational stability and security of the power grid. Against this backdrop, how to achieve coordinated control of various distributed resources within the power grid system has become a critical issue that urgently needs to be addressed in modern power distribution network management. Therefore, there is a pressing need for a method to accurately generate control parameter information for distributed resources. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for synchronous and coordinated control of multiple distributed resources in a distribution network based on dynamic weight optimization, which can improve the accuracy of coordinated control of distributed resources and address the aforementioned technical problems.

[0004] Firstly, this application provides a method for synchronous and coordinated control of multiple distributed resources in a distribution network based on dynamic weight optimization, including:

[0005] Obtain control parameter information for each distributed resource in the power grid system;

[0006] Based on the control parameter information of each distributed resource in the power grid system, an initial particle swarm is constructed.

[0007] The fitness of each particle in the initial particle swarm is evaluated to obtain the particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system;

[0008] Based on the particle fitness, the control parameter information of each distributed resource in the power grid system is iteratively updated to obtain the target control parameter information.

[0009] In one embodiment, evaluating the fitness of each particle in the initialized particle swarm to obtain the particle fitness includes:

[0010] For each particle in the initial particle swarm, the operating status of the power grid system is evaluated based on the particle to obtain power grid operating status information;

[0011] The fitness of the particle is evaluated based on at least one of the component operation synchronization deviation, grid energy consumption information, and grid operation stability in the grid operation status information, thereby obtaining the particle fitness.

[0012] In one embodiment, the step of evaluating the fitness of the particle based on at least one of the component operation synchronization deviation, grid energy consumption information, and grid operation stability in the grid operation status information to obtain the particle fitness includes:

[0013] Obtain the first weight, second weight, and third weight;

[0014] Based on the first weight, the component operation synchronization deviation in the power grid operation status information is weighted to obtain the operation synchronization weighted deviation.

[0015] Based on the second weight, the power grid energy consumption information in the power grid operation status information is weighted to obtain the power grid energy consumption weighted information.

[0016] Based on the third weight, the power grid operation stability in the power grid operation status information is weighted to obtain the power grid operation weighted stability.

[0017] The fitness of the particle is evaluated based on the weighted deviation of the operation synchronization, the weighted information of the power grid energy consumption, and the weighted stability of the power grid operation, and the particle fitness of the particle is obtained.

[0018] In one embodiment, the method further includes:

[0019] If the synchronization deviation of the component operation is greater than the preset deviation threshold, then the first weight is increased;

[0020] The synchronization deviation of the components in the power grid system is continuously monitored until the synchronization deviation is no greater than a preset deviation threshold. Then, the first weight is reduced and the second weight is increased.

[0021] In one embodiment, after iteratively updating the control parameter information of each distributed resource in the power grid system based on the particle fitness to obtain the target control parameter information, the method further includes:

[0022] According to the target control parameter information, the distributed resources in the power grid system are scheduled and controlled respectively.

[0023] Obtain scheduling feedback information from each distributed resource in the power grid system;

[0024] Based on the scheduling feedback information, the scheduling control of each distributed resource in the power grid system is verified, and the verification result is obtained.

[0025] In one embodiment, the step of verifying the scheduling control of each distributed resource in the power grid system based on the scheduling feedback information to obtain the verification result includes:

[0026] For each of the distributed resources, the control execution status of the distributed resource is evaluated based on the control feedback information in the scheduling feedback information to obtain component control execution status information;

[0027] If the component control execution status information indicates that the component control execution is successful, the timeliness of the control execution of the distributed resource is evaluated based on the synchronization pulse reception information in the scheduling feedback information to obtain the component control execution timeliness, and a verification result is generated based on the component control execution timeliness.

[0028] If the component control execution status information indicates that the component control execution has failed, the component control execution status information will be determined as the verification result.

[0029] Secondly, this application also provides a multi-source distributed resource synchronization and coordination control device for distribution networks based on dynamic weight optimization, comprising:

[0030] The acquisition module is used to acquire control parameter information of various distributed resources in the power grid system;

[0031] A construction module is used to construct an initial particle swarm based on the control parameter information of each distributed resource in the power grid system;

[0032] An evaluation module is used to evaluate the fitness of each particle in the initial particle swarm to obtain the particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system.

[0033] The update module is used to iteratively update the control parameter information of each distributed resource in the power grid system according to the particle fitness, so as to obtain the target control parameter information.

[0034] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0035] Obtain control parameter information for each distributed resource in the power grid system;

[0036] Based on the control parameter information of each distributed resource in the power grid system, an initial particle swarm is constructed.

[0037] The fitness of each particle in the initial particle swarm is evaluated to obtain the particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system;

[0038] Based on the particle fitness, the control parameter information of each distributed resource in the power grid system is iteratively updated to obtain the target control parameter information.

[0039] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0040] Obtain control parameter information for each distributed resource in the power grid system;

[0041] Based on the control parameter information of each distributed resource in the power grid system, an initial particle swarm is constructed.

[0042] The fitness of each particle in the initial particle swarm is evaluated to obtain the particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system;

[0043] Based on the particle fitness, the control parameter information of each distributed resource in the power grid system is iteratively updated to obtain the target control parameter information.

[0044] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0045] Obtain control parameter information for each distributed resource in the power grid system;

[0046] Based on the control parameter information of each distributed resource in the power grid system, an initial particle swarm is constructed.

[0047] The fitness of each particle in the initial particle swarm is evaluated to obtain the particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system;

[0048] Based on the particle fitness, the control parameter information of each distributed resource in the power grid system is iteratively updated to obtain the target control parameter information.

[0049] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for synchronous and coordinated control of multiple distributed resources in a power grid based on dynamic weight optimization acquire control parameter information of each distributed resource in the power grid system; construct an initial particle swarm based on the control parameter information of each distributed resource in the power grid system; evaluate the fitness of each particle in the initial particle swarm to obtain particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system; and iteratively update the control parameter information of each distributed resource in the power grid system based on the particle fitness to obtain target control parameter information.

[0050] In this way, the particle fitness, which can characterize the degree of control coordination among distributed resources in the power grid system, can be used as the basis for updating the control parameter information of each distributed resource. Thus, even when the power grid coordination under the control parameter information of each distributed resource obtained during training is poor, the accuracy of coordinated control of distributed resources can be improved by iteratively updating the control parameter information. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is an application environment diagram of a multi-distributed resource synchronization and collaborative control method for distribution networks based on dynamic weight optimization in one embodiment.

[0053] Figure 2 This is a flowchart illustrating a method for synchronous and coordinated control of multiple distributed resources in a distribution network based on dynamic weight optimization in one embodiment.

[0054] Figure 3 This is a flowchart illustrating the process of evaluating the fitness of each particle in the initial particle swarm in one embodiment.

[0055] Figure 4 This is a schematic diagram of the verification steps after scheduling distributed resources according to target control parameter information in one embodiment;

[0056] Figure 5 This is a structural block diagram of a multi-distributed resource synchronization and coordination control device for a distribution network based on dynamic weight optimization in one embodiment.

[0057] Figure 6This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that all information (including but not limited to model weight information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved in this application are information and data authorized by the user or fully authorized by all parties, and the acquisition, transmission, storage, use, and processing of the relevant data comply with the relevant provisions of national laws and regulations. Users can refuse or easily refuse content pushed to them (e.g., target control parameter information). In the embodiments of this application, certain existing solutions in the industry, such as software, components, and models, may be mentioned. These should be considered exemplary, and their purpose is merely to illustrate the feasibility of implementing the technical solution of this application, but does not mean that the applicant has or necessarily used such a solution.

[0060] The method for synchronous and coordinated control of multi-source distributed resources in distribution networks based on dynamic weight optimization provided in this application can be applied to, for example... Figure 1 In the application environment shown, the power grid system 102 communicates with the server 104 via a network. A data storage system can store the data that the server 104 needs to process. The data storage system can be integrated onto the server 104, or it can be located in the cloud or on another network server. The server 104 obtains the control parameter information of each distributed resource in the power grid system 102; based on the control parameter information of each distributed resource in the power grid system, it constructs an initial particle swarm; it evaluates the fitness of each particle in the initial particle swarm to obtain the particle fitness, whereby the particle fitness characterizes the degree of control coordination among the distributed resources in the power grid system 102; based on the particle fitness, iteratively updates the control parameter information of each distributed resource in the power grid system 102 to obtain the target control parameter information. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0061] In one exemplary embodiment, such as Figure 2 As shown, a method for synchronous and coordinated control of multi-element distributed resources in a distribution network based on dynamic weight optimization is presented, and this method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 202 to 208. Wherein:

[0062] Step 202: Obtain control parameter information for each distributed resource in the power grid system.

[0063] The distributed resources in step 202 include, but are not limited to, energy storage components and new energy components, including, but not limited to, solar energy components, wind energy components, and photovoltaic components.

[0064] As an example, step 202 includes: querying a preset database to obtain control parameter information of each distributed resource in the power grid system.

[0065] The preset database typically contains control parameter information developed based on control experience for each distributed resource.

[0066] In another embodiment, step 202 includes: randomly generating control parameter information for each distributed resource in the power grid system.

[0067] Step 204: Construct an initial particle swarm based on the control parameter information of each distributed resource in the power grid system.

[0068] As an example, step 204 includes: performing feature encoding on the control parameter information of each distributed resource in the power grid system to obtain a parameter encoding matrix; determining the particle swarm information for initializing the particle swarm; and constructing the initial particle swarm based on the parameter encoding matrix and the particle swarm information, wherein the particle swarm information includes particle velocity.

[0069] As one embodiment, an initial particle swarm is constructed based on the parameter encoding matrix and particle swarm information, including: determining each control parameter information in the parameter encoding matrix as each particle in the initial particle swarm, and determining the particle velocity as the motion velocity of each particle in the initial particle swarm.

[0070] Optionally, the control parameter information of each distributed resource in the power grid system is feature-encoded to obtain a parameter encoding matrix, which can be expressed by the formula:

[0071]

[0072] in, For the parameter encoding matrix, This refers to the first control parameter information for the first distributed resource belonging to the first particle swarm. For the first particle group The first distributed resource Information on control parameters, For belonging to the first The first control parameter information of the first distributed resource in the particle swarm. For belonging to the first The first in the particle swarm The first pooling layer Information on control parameters.

[0073] Step 206: Evaluate the fitness of each particle in the initial particle swarm to obtain the particle fitness, whereby the particle fitness is used to characterize the degree of control coordination among distributed resources in the power grid system.

[0074] For example, step 206 includes: for each particle in the initialized particle swarm, evaluating the particle's fitness based on the degree of control coordination of each distributed resource in the power grid system corresponding to the particle, and obtaining the particle's fitness.

[0075] Step 208: Based on the particle fitness, iteratively update the control parameter information of each distributed resource in the power grid system to obtain the target control parameter information.

[0076] As one embodiment, step 208 includes: traversing the relationship between the particle fitness of each particle in the particle swarm and the particle fitness of the best particle in the particle swarm; for each particle in the particle swarm, if the particle fitness of a particle is greater than the particle fitness of the best particle in the particle swarm, then the particle fitness of that particle is determined as the particle fitness of the best particle in the particle swarm; after traversal, if the particle fitness of the best particle in the particle swarm is greater than the particle fitness of the best particle in the particle swarm in the previous iteration, then the particle fitness of the best particle in the particle swarm is determined as the particle fitness of the best particle in the particle swarm in the current iteration; if the particle... If the fitness of the best particle in the swarm is not greater than the fitness of the best particle in the previous iteration, then the fitness of the best particle in the previous iteration is determined as the fitness of the best particle in the current iteration. If the fitness of the particle in the current iteration does not meet the preset fitness condition, then the control parameter information of the current iteration is adjusted, and the process returns to the step of constructing an initial particle swarm based on the control parameter information of each distributed resource in the power grid system. If the fitness of the particle in the current iteration meets the preset fitness condition, then the control parameter information of the current iteration is determined as the target control parameter information.

[0077] As one embodiment, adjusting the control parameter information for the current iteration includes: obtaining a random number within a preset value range, and adjusting the control parameter information for the current iteration based on the random number.

[0078] Furthermore, based on the random number, the control parameter information for the current iteration is adjusted, including: obtaining the iterative design parameters and determining the global particle information, wherein the iterative design parameters are parameters set as needed, or can be the contraction and expansion coefficients; and the control parameter information for the current iteration is adjusted based on the global particle information, the random number, and the iterative design parameters.

[0079] As one embodiment, determining global particle information includes: determining the average value of the best particles in all iterative particle swarms as global particle information.

[0080] Furthermore, the average value of the best particles in all iterative particle swarms is determined as global particle information, including the ratio between the sum of the best particles in all iterative particle swarms and the number of iterations.

[0081] Here, the value of a particle is its corresponding weight value, for example, This is the value of a particle belonging to the first particle swarm.

[0082] In the aforementioned method for synchronous and coordinated control of multiple distributed resources in a distribution network based on dynamic weight optimization, the control parameter information of each distributed resource in the power grid system is obtained. An initial particle swarm is constructed based on this information. The fitness of each particle in the initial particle swarm is evaluated to obtain the particle fitness, which characterizes the degree of control coordination among the distributed resources in the power grid system. Based on the particle fitness, the control parameter information of each distributed resource in the power grid system is iteratively updated to obtain the target control parameter information. Thus, the particle fitness, which characterizes the degree of control coordination among the distributed resources in the power grid system, serves as the basis for updating the control parameter information of each distributed resource. This allows for improved accuracy of coordinated control of distributed resources, even when the grid coordination under the control parameter information obtained from training is poor.

[0083] In one exemplary embodiment, such as Figure 3 As shown, step 206 includes steps 302 to 304. Wherein:

[0084] Step 302: For each particle in the initialized particle swarm, evaluate the operating status of the power grid system based on the particles to obtain power grid operating status information.

[0085] The grid operation status information in step 302 includes at least one of component operation synchronization deviation, grid energy absorption information, and grid operation stability. Grid energy absorption information is used to characterize the proportion of active power from new energy sources (photovoltaics, wind power, etc.) actually absorbed by the grid to its theoretical maximum active power.

[0086] As an embodiment, step 302 includes: determining the operating parameter information of each distributed resource in the power grid system that operates according to the control parameter information corresponding to the particle; determining the operating deviation information between each pair of distributed resources based on the operating parameter information of each distributed resource; and determining the component operation synchronization deviation based on the operating deviation information between each pair of distributed resources.

[0087] The operational deviation information includes, but is not limited to, phase difference, phase angle difference, and frequency difference. The component operational synchronization deviation can be obtained by weighted fusion of the operational deviation information between each pair of distributed resources.

[0088] Step 304: Evaluate the fitness of the particle based on at least one of the following in the power grid operation status information: component operation synchronization deviation, power grid energy consumption information, and power grid operation stability, and obtain the particle fitness.

[0089] For example, step 304 includes: obtaining a first weight, a second weight, and a third weight; weighting the component operation synchronization deviation in the power grid operation status information according to the first weight to obtain the operation synchronization weighted deviation; weighting the power grid energy consumption information in the power grid operation status information according to the second weight to obtain the power grid energy consumption weighted information; weighting the power grid operation stability in the power grid operation status information according to the third weight to obtain the power grid operation weighted stability; and evaluating the fitness of the particle according to the operation synchronization weighted deviation, the power grid energy consumption weighted information, and the power grid operation weighted stability to obtain the particle fitness.

[0090] The aforementioned weights (including but not limited to the first, second, and third weights) can be set by the user as needed, specifically corresponding to the user's preferred grid control objectives. When the user's preferred grid control objective is grid revenue, the second weight will be set higher than the first and third weights; for example, the second weight can be set to 0.4, and the first and third weights to 0.3. When the user's preferred grid control objective is safe grid operation, the second weight will be set lower than the first and third weights; for example, the second weight can be set to 0.2, and the first and third weights to 0.4. These weights can also be empirical values, and no restrictions are placed here.

[0091] It is understandable that, since the distributed resources in the power grid system are controlled in real time, there may be a moment when the power grid system is operating stably, and the next moment when the power grid system is not operating stably. That is to say, the power grid control objectives for the power grid system may be different at different times. If the above situation is not considered, it is possible that when the power grid system is operating stably, the focus is not on the power grid benefits; or when the power grid is operating unstablely, the focus is not on the power grid safe operation, resulting in the inability to take into account both the benefits and safety of power grid operation.

[0092] As an example, to ensure that the benefits and safety of power grid operation can be balanced, the above method further includes: if the synchronization deviation of component operation is greater than a preset deviation threshold, then increase the first weight; continuously detect the synchronization deviation of component operation in the power grid system until the synchronization deviation of component operation is not greater than the preset deviation threshold, then decrease the first weight, and increase the second weight.

[0093] The preset deviation threshold can be an empirical value, such as 0.3. When increasing the first weight, the following situations may occur: Situation 1: Decrease the second weight while keeping the third weight unchanged; Situation 2: Decrease the second weight while increasing the third weight. When decreasing the first weight or increasing the second weight, the third weight can be decreased or kept unchanged.

[0094] Thus, by continuously monitoring the synchronization deviation of the power grid system's components, indirect monitoring of the power grid's operational stability can be achieved. This allows for adjustments: when the synchronization deviation is large, the first weight can be increased to focus distributed resource control on grid operational safety; conversely, when the synchronization deviation is small, the second weight can be increased to focus distributed resource control on grid profitability. Therefore, this approach achieves a balance between grid operational profitability and operational safety.

[0095] In this embodiment, for each particle in the initialized particle swarm, the operating status of the power grid system is evaluated based on the particle to obtain power grid operating status information. The fitness of the particle is evaluated based on at least one of the component operating synchronization deviation, power grid energy absorption information, and power grid operating stability in the power grid operating status information to obtain the particle fitness. Using at least one of the component operating synchronization deviation, power grid energy absorption information, and power grid operating stability as the evaluation basis for particle fitness, the objective of particle optimization is composed of at least one of the component operating synchronization deviation, power grid energy absorption information, and power grid operating stability, thereby improving the accuracy of the synchronous and coordinated control of multi-distributed resources in the distribution network based on dynamic weight optimization.

[0096] In one exemplary embodiment, such as Figure 4As shown, after step 208, steps 402 to 406 are also included. Wherein:

[0097] Step 402: According to the target control parameter information, perform scheduling control on each distributed resource in the power grid system.

[0098] In step 402, the target control parameter information is used to characterize the target control parameters of each distributed resource.

[0099] Step 404: Obtain scheduling feedback information from each distributed resource in the power grid system.

[0100] The scheduling feedback information in step 404 includes at least one of synchronization pulse reception information and control feedback information.

[0101] Step 406: Based on the scheduling feedback information, verify the scheduling control of each distributed resource in the power grid system and obtain the verification results.

[0102] As an embodiment, step 406 includes: for each distributed resource, evaluating the control execution status of the distributed resource based on the control feedback information in the scheduling feedback information to obtain component control execution status information; if the component control execution status information indicates that the component control execution is successful, evaluating the timeliness of the distributed resource control execution based on the synchronization pulse reception information in the scheduling feedback information to obtain component control execution timeliness, and generating a verification result based on the component control execution timeliness; if the component control execution status information indicates that the component control execution has failed, determining the component control execution status information as the verification result.

[0103] Furthermore, based on the timeliness of component control execution, a verification result is generated, including: if the timeliness of component control execution is lower than a preset execution timeliness threshold, a verification result indicating that the component is not synchronized is generated; if the timeliness of component control execution is not lower than the preset execution timeliness threshold, a verification result indicating that the component is synchronized is generated.

[0104] Optionally, the above method further includes: debugging the distributed resources in the power grid system when the verification result indicates that the components are not synchronized or the component control execution fails.

[0105] Therefore, considering that distributed resources may inherently have delays or faults, even if the optimal target control parameters have been obtained through particle swarm optimization, the distributed resources may still fail to achieve the expected results. Thus, a verification process is needed to check for these inherent problems in the distributed resources themselves, ensuring the accuracy of distributed resource control in the power grid system.

[0106] In this embodiment, after updating the target control parameter information, the distributed resources in the power grid system are scheduled and controlled according to the target control parameter information; the scheduling feedback information of each distributed resource in the power grid system is obtained; the scheduling control of each distributed resource in the power grid system is verified based on the scheduling feedback information, and the verification result is obtained. Considering that the distributed resources may have problems, even if the optimal target control parameter information has been obtained through the particle swarm optimization algorithm, the distributed resources may still not achieve the expected results. Therefore, after scheduling and controlling the distributed resources, verification is performed from the dimension of scheduling feedback to ensure that repair processing can be carried out in a timely manner when there are problems with the distributed resources, thus ensuring the accuracy of the control of the distributed resources.

[0107] As a detailed embodiment, control parameter information of each distributed resource in the power grid system is obtained; an initial particle swarm is constructed based on the control parameter information of each distributed resource in the power grid system; for each particle in the initial particle swarm, the operating status of the power grid system is evaluated based on the particle to obtain power grid operating status information; a first weight, a second weight, and a third weight are obtained; based on the first weight, the component operating synchronization deviation in the power grid operating status information is weighted to obtain operating synchronization weighted deviation; based on the second weight, the power grid energy consumption information in the power grid operating status information is weighted to obtain power grid energy consumption weighted information; based on the third weight, the power grid operating stability in the power grid operating status information is weighted to obtain power grid operating weighted stability; based on the operating synchronization weighted deviation, the power grid energy consumption weighted information, and the power grid operating weighted stability, the fitness of the particles is evaluated to obtain the particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system; based on the particle fitness, the control parameter information of each distributed resource in the power grid system is iteratively updated to obtain target control parameter information.

[0108] Furthermore, according to the target control parameter information, scheduling control is performed on each distributed resource in the power grid system; scheduling feedback information of each distributed resource in the power grid system is obtained; for each distributed resource, the control execution status of the distributed resource is evaluated based on the control feedback information in the scheduling feedback information to obtain component control execution status information; if the component control execution status information indicates successful component control execution, the timeliness of the distributed resource control execution is evaluated based on the synchronization pulse reception information in the scheduling feedback information to obtain component control execution timeliness, and a verification result is generated based on the component control execution timeliness; if the component control execution status information indicates failed component control execution, the component control execution status information is determined as the verification result.

[0109] Thus, by acquiring the control parameter information of each distributed resource in the power grid system; constructing an initial particle swarm based on this information; evaluating the fitness of each particle in the initial particle swarm to obtain particle fitness, which characterizes the degree of control coordination among the distributed resources in the power grid system; and iteratively updating the control parameter information of each distributed resource based on the particle fitness to obtain target control parameter information. In this way, the particle fitness, which characterizes the degree of control coordination among the distributed resources in the power grid system, serves as the basis for updating the control parameter information of each distributed resource. Therefore, even when the power grid coordination under the control parameter information of the distributed resources obtained during training is poor, iterative updates of the control parameter information can improve the accuracy of coordinated control of distributed resources.

[0110] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0111] Based on the same inventive concept, this application also provides a device for implementing the above-mentioned method for synchronous and coordinated control of multiple distributed resources in a distribution network based on dynamic weight optimization. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for synchronous and coordinated control of multiple distributed resources in a distribution network based on dynamic weight optimization provided below can be found in the limitations of the method for synchronous and coordinated control of multiple distributed resources in a distribution network based on dynamic weight optimization described above, and will not be repeated here.

[0112] In one exemplary embodiment, such as Figure 5 As shown, a multi-element distributed resource synchronization and coordination control device 500 for distribution networks based on dynamic weight optimization is provided, including: an acquisition module 502, a construction module 504, an evaluation module 506, and an update module 508, wherein:

[0113] The acquisition module 502 is used to acquire control parameter information of each distributed resource in the power grid system.

[0114] Module 504 is used to construct an initial particle swarm based on the control parameter information of each distributed resource in the power grid system.

[0115] Evaluation module 506 is used to evaluate the fitness of each particle in the initial particle swarm to obtain the particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system.

[0116] The update module 508 is used to iteratively update the control parameter information of each distributed resource in the power grid system according to the particle fitness, so as to obtain the target control parameter information.

[0117] In one embodiment, the evaluation module 506 is further configured to evaluate the operating status of the power grid system for each particle in the initialized particle swarm, based on the particle, to obtain power grid operating status information; and evaluate the fitness of the particle based on at least one of the component operation synchronization deviation, power grid energy consumption information, and power grid operation stability in the power grid operating status information, to obtain the particle fitness of the particle.

[0118] In one embodiment, the evaluation module 506 is further configured to obtain a first weight, a second weight, and a third weight; to perform weighted processing on the component operation synchronization deviation in the power grid operation status information according to the first weight, thereby obtaining an operation synchronization weighted deviation; to perform weighted processing on the power grid energy consumption information in the power grid operation status information according to the second weight, thereby obtaining power grid energy consumption weighted information; to perform weighted processing on the power grid operation stability in the power grid operation status information according to the third weight, thereby obtaining power grid operation weighted stability; and to evaluate the fitness of the particle based on the operation synchronization weighted deviation, the power grid energy consumption weighted information, and the power grid operation weighted stability, thereby obtaining the particle fitness.

[0119] In one embodiment, the evaluation module 506 is further configured to increase the first weight if the component operation synchronization deviation is greater than a preset deviation threshold; continuously detect the component operation synchronization deviation of the power grid system until the component operation synchronization deviation is not greater than the preset deviation threshold, then decrease the first weight, and increase the second weight.

[0120] In one embodiment, after iteratively updating the control parameter information of each distributed resource in the power grid system according to particle fitness to obtain target control parameter information, the above-mentioned distribution network multi-distributed resource synchronous and coordinated control device 500 based on dynamic weight optimization further includes: a verification module, used to perform scheduling control on each distributed resource in the power grid system according to the target control parameter information; obtain scheduling feedback information of each distributed resource in the power grid system; and verify the scheduling control of each distributed resource in the power grid system according to the scheduling feedback information to obtain the verification result.

[0121] In one embodiment, the verification module is further configured to, for each distributed resource, evaluate the control execution status of the distributed resource based on the control feedback information in the scheduling feedback information to obtain component control execution status information; if the component control execution status information indicates that the component control execution is successful, evaluate the timeliness of the distributed resource control execution based on the synchronization pulse reception information in the scheduling feedback information to obtain component control execution timeliness, and generate a verification result based on the component control execution timeliness; if the component control execution status information indicates that the component control execution has failed, determine the component control execution status information as the verification result.

[0122] Each module in the aforementioned dynamic weight optimization-based multi-source distributed resource synchronous and coordinated control device for distribution networks can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0123] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for synchronous and coordinated control of multi-source distributed resources in a power distribution network based on dynamic weight optimization. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0124] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0125] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0126] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0127] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0128] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0129] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0130] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for synchronous and coordinated control of multiple distributed resources in a distribution network based on dynamic weight optimization, characterized in that, The method includes: Obtain control parameter information for each distributed resource in the power grid system; Based on the control parameter information of each distributed resource in the power grid system, an initial particle swarm is constructed. The fitness of each particle in the initial particle swarm is evaluated to obtain the particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system; Based on the particle fitness, the control parameter information of each distributed resource in the power grid system is iteratively updated to obtain the target control parameter information.

2. The method according to claim 1, characterized in that, The evaluation of the fitness of each particle in the initialized particle swarm to obtain the particle fitness includes: For each particle in the initial particle swarm, the operating status of the power grid system is evaluated based on the particle to obtain power grid operating status information; The fitness of the particle is evaluated based on at least one of the component operation synchronization deviation, grid energy consumption information, and grid operation stability in the grid operation status information, thereby obtaining the particle fitness.

3. The method according to claim 2, characterized in that, The step of evaluating the fitness of the particle based on at least one of the component operation synchronization deviation, grid energy consumption information, and grid operation stability in the grid operation status information to obtain the particle fitness includes: Obtain the first weight, second weight, and third weight; Based on the first weight, the component operation synchronization deviation in the power grid operation status information is weighted to obtain the operation synchronization weighted deviation. Based on the second weight, the power grid energy consumption information in the power grid operation status information is weighted to obtain the power grid energy consumption weighted information. Based on the third weight, the power grid operation stability in the power grid operation status information is weighted to obtain the power grid operation weighted stability. The fitness of the particle is evaluated based on the weighted deviation of the operation synchronization, the weighted information of the power grid energy consumption, and the weighted stability of the power grid operation, and the particle fitness of the particle is obtained.

4. The method according to claim 3, characterized in that, The method further includes: If the synchronization deviation of the component operation is greater than the preset deviation threshold, then the first weight is increased; The synchronization deviation of the components in the power grid system is continuously monitored until the synchronization deviation is no greater than a preset deviation threshold. Then, the first weight is reduced and the second weight is increased.

5. The method according to claim 1, characterized in that, After iteratively updating the control parameter information of each distributed resource in the power grid system based on the particle fitness to obtain the target control parameter information, the method further includes: According to the target control parameter information, the distributed resources in the power grid system are scheduled and controlled respectively. Obtain scheduling feedback information from each distributed resource in the power grid system; Based on the scheduling feedback information, the scheduling control of each distributed resource in the power grid system is verified, and the verification result is obtained.

6. The method according to claim 5, characterized in that, The step of verifying the scheduling control of each distributed resource in the power grid system based on the scheduling feedback information and obtaining the verification result includes: For each of the distributed resources, the control execution status of the distributed resource is evaluated based on the control feedback information in the scheduling feedback information to obtain component control execution status information; If the component control execution status information indicates that the component control execution is successful, the timeliness of the control execution of the distributed resource is evaluated based on the synchronization pulse reception information in the scheduling feedback information to obtain the component control execution timeliness, and a verification result is generated based on the component control execution timeliness. If the component control execution status information indicates that the component control execution has failed, the component control execution status information will be determined as the verification result.

7. A multi-source distributed resource synchronization and collaborative control device for distribution networks based on dynamic weight optimization, characterized in that, The device includes: The acquisition module is used to acquire control parameter information of various distributed resources in the power grid system; A construction module is used to construct an initial particle swarm based on the control parameter information of each distributed resource in the power grid system; An evaluation module is used to evaluate the fitness of each particle in the initial particle swarm to obtain particle fitness, wherein the particle fitness is used to characterize the degree of control coordination of each distributed resource in the power grid system. The update module is used to iteratively update the control parameter information of each distributed resource in the power grid system according to the particle fitness, so as to obtain the target control parameter information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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