Virtual power plant side end cooperative voltage regulation control method and system, terminal and medium

Abstract

The invention relates to the field of virtual power plant regulation and control, and particularly discloses a virtual power plant side-end coordinated voltage regulation control method and system, a terminal and a medium, and the method comprises the steps: distributing a voltage regulation amount for each node cluster according to a voltage sensitivity coefficient and scheduling cost of the node cluster; constructing a cluster internal autonomous optimization model for each adjustable resource in each node cluster according to the optimization target; according to the distributed voltage regulation amount of each node cluster, solving the cluster internal autonomous optimization model of each node cluster through a distributed average consistency algorithm to obtain an output optimal value of each adjustable resource in each node cluster; and performing voltage regulation operation on the node cluster according to the output optimal value of each adjustable resource of the node cluster. According to the method, the output optimal value of each adjustable resource is obtained in the node cluster according to the distributed average consistency algorithm, side-end coordinated voltage regulation control is realized, the voltage regulation control efficiency and the regulation effect are improved, and the voltage regulation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant regulation and control, and in particular to a virtual power plant edge-end coordinated voltage regulation control method, system, terminal and medium. Background Art

[0002] With the rapid development of power systems and the widespread access to distributed energy, the voltage stability and regulation issues of power systems have become increasingly prominent. Traditional power system voltage regulation methods mainly rely on centralized control of large power plants and substations, but when faced with the access of distributed power sources (such as photovoltaics, wind power, etc.) and rapid changes in loads, this centralized control mode is often difficult to respond quickly and effectively regulate voltage.

[0003] As an emerging power system management technology, virtual power plants achieve efficient resource utilization and flexible regulation of power systems by integrating distributed energy, energy storage systems, and controllable loads. However, there are many node clusters in virtual power plants, and the voltage regulation capabilities and costs of each node are different. How to achieve voltage regulation efficiently and economically has become an urgent problem to be solved. Traditional voltage regulation methods are usually based on centralized control, but due to the differences in voltage regulation capabilities and costs of each node cluster, a single centralized control strategy is often difficult to achieve the global optimal voltage regulation effect. Summary of the invention

[0004] To solve the above problems, the present invention provides a virtual power plant edge collaborative voltage regulation control method, system, terminal and medium, which obtains the optimal output value of each adjustable resource within the node cluster according to the distributed average consistency algorithm, realizes edge collaborative voltage regulation control, improves voltage regulation control efficiency and regulation effect, and reduces voltage regulation cost.

[0005] In a first aspect, the technical solution of the present invention provides a method for controlling voltage regulation at the edge of a virtual power plant, comprising the following steps: Allocate voltage regulation to each node cluster based on the voltage sensitivity coefficient and scheduling cost of the node cluster; Constructing an internal cluster autonomous optimization model for each adjustable resource in each node cluster according to the optimization goal; According to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved through the distributed average consistency algorithm to obtain the optimal output value of each adjustable resource in each node cluster; The node cluster is regulated according to the optimal output value of each adjustable resource of the node cluster.

[0006] In an optional implementation, the voltage regulation amount is allocated to each node cluster according to the voltage sensitivity coefficient and the scheduling cost of the node cluster, specifically including: Arrange the scheduling priority of the first node according to the voltage sensitivity coefficient of the node cluster; Calculate the normalized scheduling cost of each node cluster; Arrange the scheduling priority of the second node according to the normalized scheduling cost of the node cluster; Obtaining a final scheduling priority of the node cluster according to the scheduling weights pre-configured for the first node scheduling priority and the second node scheduling priority; Allocate voltage regulation to each node cluster based on the final scheduling priority of the node cluster.

[0007] In an optional implementation, calculating the normalized scheduling cost of each node cluster specifically includes: Filter the adjustable resources in the node cluster based on the scheduling cost, response speed to control instructions, and stable control duration; Obtaining a voltage sensitivity coefficient of each screened adjustable resource; Obtaining a reference voltage adjustment amount for each screened adjustable resource; Calculate the normalized scheduling cost of each adjustable resource = scheduling cost / (voltage sensitivity coefficient*reference voltage adjustment amount); The normalized scheduling cost of each adjustable resource in the node cluster is added together to obtain the normalized scheduling cost of the node cluster.

[0008] In an optional implementation, obtaining the final scheduling priority of the node cluster according to the scheduling weights pre-configured for the first node scheduling priority and the second node scheduling priority specifically includes: The priority parameters of each node cluster are calculated using the following formula: R i =λ1*R 1i +λ2*R 2i ; In the formula, R i is the priority parameter of the i-th node cluster, R 1i is the priority of the i-th node cluster in the first node scheduling priority, R 2i is the priority of the i-th node cluster in the second node scheduling priority; λ1 is the first weight coefficient, and λ2 is the second weight coefficient; Arrange the final scheduling priority of the node cluster according to the priority parameter size.

[0009] In an optional implementation, a cluster internal autonomous optimization model is constructed for each adjustable resource in each node cluster according to the optimization target, specifically including: Taking the minimum operation and dispatching cost as the objective function, the cluster internal autonomous optimization model is constructed with power system flow constraints, voltage upper and lower limit constraints, and line transmission constraints as constraints.

[0010] In an optional implementation, according to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved by a distributed average consistency algorithm to obtain the optimal output value of each adjustable resource in each node cluster, specifically including: Step 1: configure initial output for each adjustable resource in the node cluster according to the voltage regulation amount allocated by the node cluster; Step 2: Using a local optimal algorithm to solve the local optimal output of each adjustable resource according to the current state of the adjustable resources; Step 3: Through the communication network, the various adjustable resources in the node cluster exchange the local optimal output; Step 4: Use the distributed average consistency algorithm to update the local optimal output of the adjustable resources to obtain the global optimal output; Step 5: Check whether the global optimal output of each adjustable resource meets the convergence condition; Step 6: If not, continue to step 2; if satisfied, the current global optimal output can adjust the optimal output value of the resource.

[0011] In a second aspect, the technical solution of the present invention provides a virtual power plant edge coordinated voltage regulation control system, comprising: Voltage regulation allocation module: allocates voltage regulation to each node cluster according to the voltage sensitivity coefficient and scheduling cost of the node cluster; Cluster internal autonomous optimization model construction module: constructs a cluster internal autonomous optimization model for each adjustable resource in each node cluster according to the optimization target; Output optimal value calculation module: According to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved through the distributed average consistency algorithm to obtain the output optimal value of each adjustable resource in each node cluster; Voltage regulation execution module: performs voltage regulation operations on the node cluster according to the optimal output value of each adjustable resource of the node cluster.

[0012] In a third aspect, the technical solution of the present invention provides a terminal, including: A memory, used for storing a virtual power plant edge coordinated voltage regulation control program; A processor is used to implement the steps of the virtual power plant edge collaborative voltage regulation control method as described in any of the above items when executing the virtual power plant edge collaborative voltage regulation control program.

[0013] In a fourth aspect, the technical solution of the present invention provides a computer-readable storage medium, on which a virtual power plant edge collaborative voltage regulation control program is stored. When the virtual power plant edge collaborative voltage regulation control program is executed by a processor, the steps of the virtual power plant edge collaborative voltage regulation control method as described in any one of the above items are implemented.

[0014] The virtual power plant edge collaborative voltage regulation control method, system, terminal and medium provided by the present invention have the following beneficial effects compared with the prior art: firstly, by combining the voltage sensitivity coefficient and dispatching cost of the node cluster, a reasonable voltage regulation amount is allocated to each node cluster, thus avoiding unnecessary resource waste and reducing the overall dispatching cost, and constructing the cluster internal autonomous optimization model for solution, thus realizing the decentralization, autonomy and optimization of voltage regulation, so that each node cluster can autonomously adjust the voltage output according to its own situation and the allocated voltage regulation amount without waiting for centralized control instructions, thereby significantly improving the response speed of voltage regulation. At the same time, through the construction and solution of the cluster internal autonomous optimization model, each node cluster can optimize the voltage regulation strategy according to its own situation, thus improving the flexibility and stability of the system, and solving the cluster internal autonomous optimization model of each node cluster through the distributed average consistency algorithm, obtaining the globally optimal voltage regulation scheme, and realizing the collaborative regulation between each node cluster and the globally optimal voltage regulation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a method for coordinated voltage regulation control at the edge of a virtual power plant provided in an embodiment of the present invention.

[0017] Figure 2 It is a schematic block diagram of the structure of a virtual power plant edge collaborative voltage regulation control system provided by an embodiment of the present invention.

[0018] Figure 3 It is a schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0021] Figure 1 This is a flow chart of a method for coordinated voltage regulation control at the edge of a virtual power plant provided by an embodiment of the present invention. Figure 1 The execution subject may be a virtual power plant edge coordinated voltage regulation control system. The virtual power plant edge coordinated voltage regulation control method provided by the embodiment of the present invention is executed by a computer device, and accordingly, the virtual power plant edge coordinated voltage regulation control system runs in the computer device. According to different requirements, the order of the steps in the flowchart can be changed, and some can be omitted.

[0022] like Figure 1 As shown, the method includes the following steps.

[0023] S1, allocates voltage regulation to each node cluster according to the voltage sensitivity coefficient and scheduling cost of the node cluster.

[0024] This embodiment uses the size of the voltage sensitivity coefficient to arrange the node scheduling priority, and after selecting the adjustable load within each node resource according to the scheduling cost, response speed and duration, the scheduling cost of each node resource is converted into a uniform scheduling cost corresponding to the unit voltage adjustment amount according to the sensitivity coefficient, so that the resources of different nodes can be compared with each other in cost, and then the scheduling priority is sorted based on the cost, and the final scheduling priority is formulated in combination with the voltage sensitivity coefficient and the scheduling priority of the cost, and then the voltage adjustment amount is allocated. Specifically, the following steps are included.

[0025] S1.1, arranging the scheduling priority of the first node according to the voltage sensitivity coefficient of the node cluster.

[0026] The voltage sensitivity system of each node cluster is obtained, and the scheduling priority is sorted in descending order according to the voltage sensitivity coefficient to obtain the first node scheduling priority.

[0027] S1.2, calculate the normalized scheduling cost of each node cluster.

[0028] This embodiment performs normalized scheduling cost calculation so that different node clusters can compare costs with each other. Specifically, after selecting the adjustable loads according to the scheduling cost, response speed and duration within each node resource, the scheduling cost of each node resource is converted into a normalized scheduling cost corresponding to the unit voltage adjustment amount according to the sensitivity coefficient. Specifically, the following steps are included.

[0029] S1.2.1, select the adjustable resources in the node cluster according to the scheduling cost, the speed of responding to the control instructions and the stable adjustment duration.

[0030] This embodiment excludes resources with excessively high scheduling costs to ensure overall economy, and selects resources that can quickly respond to scheduling instructions to improve system response speed. At the same time, the continuous adjustment capability of resources is taken into consideration to ensure that the required adjustment amount can be stably provided during the scheduling period, and finally the adjustable resources within the node cluster are selected.

[0031] S1.2.2, obtaining the voltage sensitivity coefficient of each screened adjustable resource.

[0032] The voltage sensitivity coefficient is used to describe the impact of unit voltage adjustment on resource scheduling cost.

[0033] S1.2.3, obtaining the reference voltage adjustment amount of each selected adjustable resource.

[0034] In this embodiment, a reference voltage adjustment amount, ie, a unit voltage adjustment amount, is selected as a reference value for converting the scheduling cost of each node resource into a uniform form.

[0035] S1.2.4, calculate the normalized dispatch cost of each adjustable resource = dispatch cost / (voltage sensitivity coefficient*reference voltage adjustment amount).

[0036] Based on the above formula, the scheduling cost of each adjustable resource is converted into the cost corresponding to each unit voltage adjustment amount using the voltage sensitivity coefficient.

[0037] S1.2.5, adding up the normalized scheduling costs of each adjustable resource in the node cluster to obtain the normalized scheduling cost of the node cluster.

[0038] The normalized scheduling costs of the adjustable resources in the node cluster are added together and summed, and the summed result is used as the normalized scheduling cost of the node cluster. It should be noted that the above operation is performed for each node cluster to obtain the normalized scheduling cost of each node cluster.

[0039] S1.3, arranging the second node scheduling priority according to the normalized scheduling cost of the node cluster.

[0040] The scheduling priorities are sorted in ascending order according to the normalized scheduling costs of the node cluster to obtain the second node scheduling priority.

[0041] S1.4, obtaining a final scheduling priority of the node cluster according to the scheduling weights pre-configured for the first node scheduling priority and the second node scheduling priority.

[0042] This embodiment pre-configures weight coefficients of the first node scheduling priority and the second node scheduling priority, and calculates the priority parameters of each node cluster using the following formula.

[0043] R i =λ1*R 1i +λ2*R 2i In the formula, R i is the priority parameter of the i-th node cluster, R 1i is the priority of the i-th node cluster in the first node scheduling priority, R 2i is the priority of the i-th node cluster in the second node scheduling priority; λ1 is the first weight coefficient, and λ2 is the second weight coefficient.

[0044] Then, the final scheduling priority of the node cluster is arranged according to the size of the priority parameter, and the final scheduling priority is obtained by sorting the priority parameters in descending order.

[0045] S1.5, allocating voltage regulation amount to each node cluster according to the final scheduling priority of the node cluster.

[0046] The voltage regulation amount is allocated according to the rule that the higher the scheduling priority, the larger the voltage regulation amount is allocated, so as to ensure the efficiency of voltage regulation control and reduce costs.

[0047] S2, constructs a cluster internal autonomous optimization model for each adjustable resource in each node cluster according to the optimization target.

[0048] This embodiment takes the lowest operation and dispatching cost as the objective function, and constructs a cluster internal autonomous optimization model with power system flow constraints, voltage upper and lower limit constraints, and line transmission constraints as constraints. Then, the cluster internal autonomous optimization model is solved through a distributed average consistency algorithm to obtain the optimal value of the output of various adjustable resources within the cluster.

[0049] S3, according to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved through the distributed average consistency algorithm to obtain the optimal output value of each adjustable resource in each node cluster.

[0050] The dispatchable resources within the node cluster can be regarded as an intelligent agent. Each intelligent agent coordinates with each other externally, coordinates the grid dispatch information and internal distributed resources internally, and makes the best decision for a certain goal. Each intelligent agent communicates with its neighbors around it, and adjusts its own behavior through communication and learning. This strategy will greatly reduce the communication scale, dispatch calculation amount and response time required for the active power control of the formed virtual power plant, so that rapid voltage adjustment can be performed. This embodiment realizes decentralized collaborative control through a distributed average consensus algorithm to ensure the economic allocation of adjustable resources within the cluster, and specifically includes the following steps.

[0051] S3.1, configuring initial output for each adjustable resource in the node cluster according to the voltage regulation amount allocated by the node cluster.

[0052] Before solving, initial configuration is performed, including configuring the initial output of each adjustable resource in the node cluster as needed.

[0053] S3.2, using a local optimal algorithm to solve the local optimal output of each adjustable resource according to the current state of the adjustable resources.

[0054] The purpose of this step is to perform local optimization. For each adjustable resource, a local optimal algorithm is used to solve the local optimal output according to the current state.

[0055] S3.3, through the communication network, the local optimal output is exchanged between the adjustable resources in the node cluster.

[0056] The purpose of this step is to exchange information. Through the communication network, the optimization results, that is, the local optimal output, are exchanged between the adjustable resources.

[0057] S3.4, using the distributed average consistency algorithm to update the local optimal output of the adjustable resources to obtain the global optimal output.

[0058] The purpose of this step is to update the state. According to the information received from other adjustable resources, the consistency algorithm is used to update the state variables of the adjustable resource, including adjusting the resource output, so as to gradually reach global consistency and obtain the current global optimal output.

[0059] S3.5, detecting whether the global optimal output of each adjustable resource meets the convergence condition.

[0060] S3.6, if not satisfied, continue to execute S3.2, if satisfied, the current global optimal output can adjust the optimal output value of the resource.

[0061] Convergence judgment is performed to detect whether the convergence conditions are met. If not, the iterative solution step is continued. If convergence has occurred, the current global optimal output is the optimal output value of the adjustable resource to obtain the optimal value of the output of each adjustable resource in the node cluster.

[0062] S4: Perform voltage adjustment operations on the node cluster according to the optimal output values ​​of each adjustable resource of the node cluster.

[0063] This embodiment achieves rapid response, low cost, high stability and global optimization of voltage regulation through edge collaboration, autonomous optimization and global coordinated regulation.

[0064] The above describes in detail an embodiment of a virtual power plant edge collaborative voltage regulation control method. Based on the virtual power plant edge collaborative voltage regulation control method described in the above embodiment, an embodiment of the present invention also provides a virtual power plant edge collaborative voltage regulation control system corresponding to the method.

[0065] Figure 2 is a schematic block diagram of a virtual power plant edge coordinated voltage regulation control system structure provided by an embodiment of the present invention. In this embodiment, the virtual power plant edge coordinated voltage regulation control system 200 can be divided into multiple functional modules according to the functions it performs, such as Figure 2 The functional modules may include: a voltage regulation allocation module 210, a cluster internal autonomous optimization model construction module 220, an output optimal value calculation module 230, and a voltage regulation execution module 240. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory.

[0066] The voltage regulation amount allocation module 210 allocates the voltage regulation amount to each node cluster according to the voltage sensitivity coefficient and the scheduling cost of the node cluster.

[0067] The cluster internal autonomous optimization model construction module 220 constructs a cluster internal autonomous optimization model for each adjustable resource in each node cluster according to the optimization target.

[0068] Output optimal value calculation module 230: According to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved by a distributed average consistency algorithm to obtain the output optimal value of each adjustable resource in each node cluster.

[0069] The voltage regulation execution module 240 performs voltage regulation operations on the node cluster according to the optimal output value of each adjustable resource of the node cluster.

[0070] The virtual power plant edge collaborative voltage regulation control system of this embodiment is used to implement the aforementioned virtual power plant edge collaborative voltage regulation control method. Therefore, the specific implementation method of this system can be seen in the embodiment part of the virtual power plant edge collaborative voltage regulation control method in the previous text. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part, which will not be elaborated here.

[0071] In addition, since the virtual power plant edge collaborative voltage regulation control system of this embodiment is used to implement the aforementioned virtual power plant edge collaborative voltage regulation control method, its function corresponds to that of the above-mentioned method and will not be repeated here.

[0072] Figure 3 A schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention includes: a processor 310, a memory 320 and a communication unit 330. The processor 310 is used to implement the following steps when implementing the virtual power plant edge coordinated voltage regulation control program stored in the memory 320: Allocate voltage regulation to each node cluster based on the voltage sensitivity coefficient and scheduling cost of the node cluster; Constructing an internal cluster autonomous optimization model for each adjustable resource in each node cluster according to the optimization goal; According to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved through the distributed average consistency algorithm to obtain the optimal output value of each adjustable resource in each node cluster; The node cluster is regulated according to the optimal output value of each adjustable resource of the node cluster. The terminal 300 includes a processor 310, a memory 320 and a communication unit 330. These components communicate via one or more buses. It can be understood by those skilled in the art that the structure of the server shown in the figure does not constitute a limitation of the present invention, and it can be a bus structure or a star structure, and can also include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0073] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0074] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0075] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0076] The present invention also provides a computer storage medium, wherein the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0077] The computer storage medium stores a virtual power plant edge coordinated voltage regulation control program, and when the virtual power plant edge coordinated voltage regulation control program is executed by the processor, the following steps are implemented: Allocate voltage regulation to each node cluster based on the voltage sensitivity coefficient and scheduling cost of the node cluster; Constructing an internal cluster autonomous optimization model for each adjustable resource in each node cluster according to the optimization goal; According to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved through the distributed average consistency algorithm to obtain the optimal output value of each adjustable resource in each node cluster; The node cluster is regulated according to the optimal output value of each adjustable resource of the node cluster. Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0078] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0081] The above disclosure is only a preferred embodiment of the present invention, but the present invention is not limited thereto. Any non-creative changes that can be thought of by a person skilled in the art, as well as several improvements and modifications made without departing from the principle of the present invention, should fall within the protection scope of the present invention.

Claims

1. A method for coordinated voltage regulation control at the edge of a virtual power plant. It is characterized in that The following steps are involved: Allocate voltage regulation to each node cluster based on the voltage sensitivity coefficient and scheduling cost of the node cluster; Constructing an internal cluster autonomous optimization model for each adjustable resource in each node cluster according to the optimization goal; According to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved through the distributed average consistency algorithm to obtain the optimal output value of each adjustable resource in each node cluster; The node cluster is regulated according to the optimal output value of each adjustable resource of the node cluster.

2. The virtual power plant edge coordinated voltage regulation control method according to claim 1, It is characterized in that The voltage regulation amount is allocated to each node cluster according to the voltage sensitivity coefficient and scheduling cost of the node cluster, including: Arrange the scheduling priority of the first node according to the voltage sensitivity coefficient of the node cluster; Calculate the normalized scheduling cost of each node cluster; Arrange the scheduling priority of the second node according to the normalized scheduling cost of the node cluster; Obtaining a final scheduling priority of the node cluster according to the scheduling weights pre-configured for the first node scheduling priority and the second node scheduling priority; Allocate voltage regulation to each node cluster based on the final scheduling priority of the node cluster.

3. The virtual power plant edge coordinated voltage regulation control method according to claim 2, It is characterized in that Calculate the normalized scheduling cost of each node cluster, including: Filter the adjustable resources in the node cluster based on the scheduling cost, response speed to control instructions, and stable control duration; Obtaining a voltage sensitivity coefficient of each screened adjustable resource; Obtaining a reference voltage adjustment amount for each screened adjustable resource; Calculate the normalized scheduling cost of each adjustable resource = scheduling cost / (voltage sensitivity coefficient*reference voltage adjustment amount); The normalized scheduling cost of each adjustable resource in the node cluster is added together to obtain the normalized scheduling cost of the node cluster.

4. The virtual power plant edge coordinated voltage regulation control method according to claim 3, It is characterized in that According to the scheduling weights pre-configured for the first node scheduling priority and the second node scheduling priority, the final scheduling priority of the node cluster is obtained, which specifically includes: The priority parameters of each node cluster are calculated using the following formula: R i =λ1*R 1i +λ2*R 2i ; In the formula, R i is the priority parameter of the i-th node cluster, R 1i is the priority of the i-th node cluster in the first node scheduling priority, R 2i is the priority of the i-th node cluster in the second node scheduling priority; λ1 is the first weight coefficient, and λ2 is the second weight coefficient; Arrange the final scheduling priority of the node cluster according to the priority parameter size.

5. The method for controlling the edge-end coordinated voltage regulation of a virtual power plant according to any one of claims 1 to 4, It is characterized in that According to the optimization goal, an internal cluster autonomous optimization model is constructed for each adjustable resource in each node cluster, including: Taking the minimum operation and dispatching cost as the objective function, the cluster internal autonomous optimization model is constructed with power system flow constraints, voltage upper and lower limit constraints, and line transmission constraints as constraints.

6. The virtual power plant edge coordinated voltage regulation control method according to claim 5, It is characterized in that According to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved through the distributed average consistency algorithm to obtain the optimal output value of each adjustable resource in each node cluster, including: Step 1: configure initial output for each adjustable resource in the node cluster according to the voltage regulation amount allocated by the node cluster; Step 2: Using a local optimal algorithm to solve the local optimal output of each adjustable resource according to the current state of the adjustable resources; Step 3: Through the communication network, the various adjustable resources in the node cluster exchange the local optimal output; Step 4: Use the distributed average consistency algorithm to update the local optimal output of the adjustable resources to obtain the global optimal output; Step 5: Check whether the global optimal output of each adjustable resource meets the convergence condition; Step 6: If not, continue to step 2; if satisfied, the current global optimal output can adjust the optimal output value of the resource.

7. A virtual power plant edge coordinated voltage regulation control system, It is characterized in that include, Voltage regulation allocation module: allocates voltage regulation to each node cluster according to the voltage sensitivity coefficient and scheduling cost of the node cluster; Cluster internal autonomous optimization model construction module: constructs a cluster internal autonomous optimization model for each adjustable resource in each node cluster according to the optimization target; Output optimal value calculation module: According to the voltage regulation amount allocated to each node cluster, the cluster internal autonomous optimization model of each node cluster is solved through the distributed average consistency algorithm to obtain the output optimal value of each adjustable resource in each node cluster; Voltage regulation execution module: performs voltage regulation operations on the node cluster according to the optimal output value of each adjustable resource of the node cluster.

8. A terminal, It is characterized in that include: A memory, used for storing a virtual power plant edge coordinated voltage regulation control program; A processor is used to implement the steps of the virtual power plant edge collaborative voltage regulation control method as described in any one of claims 1-6 when executing the virtual power plant edge collaborative voltage regulation control program.

9. A computer-readable storage medium, It is characterized in that The readable storage medium stores a virtual power plant edge collaborative voltage regulation control program, and when the virtual power plant edge collaborative voltage regulation control program is executed by the processor, the steps of the virtual power plant edge collaborative voltage regulation control method as described in any one of claims 1-6 are implemented.

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