Virtual power plant distributed scheduling method and system, terminal and storage medium

Abstract

The invention relates to the technical field of virtual power plants, and particularly provides a virtual power plant distributed scheduling method and system, a terminal and a storage medium, and the method comprises the steps: generating an electric quantity scheduling scheme based on a cost objective function of a virtual power plant; obtaining state parameters of the energy storage equipment, and decomposing the electric quantity scheduling scheme into predicted charging and discharging parameters of the energy storage equipment based on the state parameters; actual charging and discharging parameters of the energy storage equipment are monitored; and correcting the predicted charging and discharging parameters based on the actual charging and discharging parameters by using a feedback control strategy, and outputting the corrected charging and discharging parameters as execution parameters of the corresponding energy storage equipment. According to the invention, accurate control of the energy storage equipment is realized, so that accurate execution of the scheduling scheme generated by the virtual power plant is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and in particular relates to a distributed scheduling method, system, terminal and storage medium for a virtual power plant. Background Art

[0002] Shared energy storage equipment is the current energy storage trend in multi-source power grids, and the current dispatching methods of virtual power plants are mostly to generate total energy storage power or total discharge power. ‌‌Virtual power plant is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed energy resources such as distributed power sources, energy storage systems, controllable loads, electric vehicles, etc., so as to participate in the power market and power grid operation as a special power plant. ‌The core concepts of virtual power plants can be summarized as "communication" and "aggregation", and their key technologies include coordinated control technology, smart metering technology, and information and communication technology. Virtual power plants can aggregate these distributed energy resources to participate in the operation of the power market and ancillary service market, and provide management and ancillary services for distribution networks and transmission networks.

[0003] When there are many shared energy storage devices, it is a very complicated task to decompose the scheduling plan into the charging and discharging parameters of each energy storage device. How to reduce the complexity of the task and improve the precise control of each energy storage device is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a virtual power plant distributed scheduling method, system, terminal and storage medium to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a virtual power plant distributed scheduling method, comprising: Generate power dispatch plan based on the cost objective function of virtual power plant; Acquire state parameters of the energy storage device, and decompose the power dispatching scheme into predicted charging and discharging parameters of the energy storage device based on the state parameters; Monitor the actual charging and discharging parameters of energy storage equipment; The predicted charging and discharging parameters are corrected based on the actual charging and discharging parameters using a feedback control strategy, and the corrected charging and discharging parameters are output as execution parameters of the corresponding energy storage device.

[0006] In an optional implementation, obtaining state parameters of the energy storage device, and decomposing the power dispatching scheme into predicted charging and discharging parameters of the energy storage device based on the state parameters, includes: Acquire status parameters of each energy storage device, wherein the status parameters include rated capacity, used capacity and rated power; Based on the state parameters, the total charge and discharge parameters in the power dispatching scheme are decomposed into predicted charge and discharge parameters of each energy storage device, and each energy storage device joins the shared energy storage cluster.

[0007] In an optional implementation, monitoring actual charging and discharging parameters of the energy storage device includes: Collecting actual charge and discharge parameters of each energy storage device, and generating a timestamp for the actual charge and discharge parameters based on the collection time of the actual charge and discharge parameters, wherein the actual charge and discharge parameters include charge and discharge power; The actual charging and discharging parameters collected within a specified time period are sorted in chronological order according to timestamps to obtain a monitoring data sequence.

[0008] In an optional implementation, the predicted charge and discharge parameters are corrected based on the actual charge and discharge parameters using a feedback control strategy, and the corrected charge and discharge parameters are output as execution parameters of the corresponding energy storage device, including: Construct a feedback control function: ; in, is the actual charging and discharging power of the i-th energy storage device at time k, is the corrected charging and discharging power of the i-th energy storage device at time k+1, is the predicted charging and discharging power of the i-th energy storage device at time k; is the correction factor.

[0009] In a second aspect, the present invention provides a virtual power plant distributed dispatching system, comprising: A scheme generation module is used to generate a power dispatch scheme based on the cost objective function of the virtual power plant; A scheme decomposition module, used to obtain state parameters of the energy storage device, and decompose the power dispatch scheme into predicted charging and discharging parameters of the energy storage device based on the state parameters; Parameter monitoring module, used to monitor the actual charging and discharging parameters of energy storage equipment; The parameter correction module is used to correct the predicted charge and discharge parameters based on the actual charge and discharge parameters using a feedback control strategy, and output the corrected charge and discharge parameters as execution parameters of the corresponding energy storage device.

[0010] In an optional implementation, the solution decomposition module includes: A state acquisition unit, used to acquire state parameters of each energy storage device, wherein the state parameters include rated capacity, used capacity and rated power; A scheme decomposition unit is used to decompose the total charging and discharging parameters in the power scheduling scheme into predicted charging and discharging parameters of each energy storage device based on the state parameters, and each energy storage device joins the shared energy storage cluster.

[0011] In an optional embodiment, the parameter monitoring module includes: A parameter collection unit, used to collect actual charge and discharge parameters of each energy storage device, and generate a timestamp for the actual charge and discharge parameters based on the collection time of the actual charge and discharge parameters, wherein the actual charge and discharge parameters include charge and discharge power; The sequence generation unit is used to sort the actual charge and discharge parameters collected within a specified time period in chronological order according to timestamps to obtain a monitoring data sequence.

[0012] In an optional implementation, the parameter correction module includes: Construct a feedback control function: ; in, is the actual charging and discharging power of the i-th energy storage device at time k, is the corrected charging and discharging power of the i-th energy storage device at time k+1, is the predicted charging and discharging power of the i-th energy storage device at time k; is the correction factor.

[0013] In a third aspect, a terminal is provided, including: processor, memory, wherein: The memory is used to store computer programs. The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0014] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0015] The beneficial effect of the present invention is that the virtual power plant distributed scheduling method, system, terminal and storage medium provided by the present invention realize accurate control of energy storage equipment to achieve accurate execution of the scheduling plan generated by the virtual power plant.

[0016] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1is a schematic flow chart of a method according to an embodiment of the present invention.

[0019] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0020] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] 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.

[0023] The virtual power plant distributed scheduling method provided in the embodiment of the present invention is executed by a computer device, and accordingly, the virtual power plant distributed scheduling system runs in the computer device.

[0024] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a virtual power plant distributed dispatching system. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0025] like Figure 1 As shown, the method includes: Step 110, generating an electricity dispatch plan based on the cost objective function of the virtual power plant; Step 120, obtaining state parameters of the energy storage device, and decomposing the power dispatching scheme into predicted charging and discharging parameters of the energy storage device based on the state parameters; Step 130, monitoring actual charging and discharging parameters of the energy storage device; Step 140: Correct the predicted charge and discharge parameters based on the actual charge and discharge parameters using a feedback control strategy, and output the corrected charge and discharge parameters as execution parameters of the corresponding energy storage device.

[0026] To facilitate understanding of the present invention, the distributed scheduling method for a virtual power plant provided by the present invention is further described below based on the principle of the distributed scheduling method for a virtual power plant of the present invention and in combination with the process of distributed scheduling based on a virtual power plant in an embodiment.

[0027] Specifically, the distributed scheduling method of the virtual power plant includes: S1. Generate a power dispatch plan based on the cost objective function of the virtual power plant.

[0028] In the operation and management of virtual power plants, it is first necessary to build an optimization model with the goal of minimizing costs based on factors such as market electricity price fluctuations, the production costs of various distributed energy sources, and the load demand of the power grid. The model will comprehensively consider the cost of power generation, transmission losses, efficiency losses of energy storage equipment, and possible subsidies and fines, etc., to generate an economical and efficient power dispatch plan. The plan not only includes the matching of power generation and demand of various energy sources in different time periods, but also clarifies the allocation strategy of power resources in different time periods, aiming to ensure that while meeting the balance between supply and demand, the operating cost is minimized.

[0029] Generating an electricity dispatch plan for energy storage equipment is a native function of the virtual power plant. For example, the virtual power plant guides user aggregators to store surplus photovoltaic power in shared energy storage while meeting user-side electricity demand through electricity price subsidies.

[0030] S2. Acquire state parameters of the energy storage device, and decompose the power dispatch plan into predicted charging and discharging parameters of the energy storage device based on the state parameters.

[0031] The state parameters of each energy storage device are obtained, wherein the state parameters include rated capacity, used capacity and rated power; based on the state parameters, the total charge and discharge parameters in the power dispatching scheme are decomposed into predicted charge and discharge parameters of each energy storage device, and each energy storage device is added to the shared energy storage cluster.

[0032] Obtain detailed status parameters of each energy storage device, including but not limited to rated capacity, currently used capacity, rated power, and possible health status or remaining life. Based on these detailed parameters, the system intelligently decomposes the total charge and discharge demand in the power scheduling plan into specific predicted charge and discharge parameters for each energy storage device. This process not only takes into account the physical limitations of energy storage devices (such as capacity and power limitations), but also ensures load balancing and efficient utilization among energy storage devices, enabling them to work together as part of a shared energy storage cluster. During the calculation process, the system first calculates the available capacity of each energy storage device (rated capacity minus used capacity), and then reasonably allocates the charge and discharge tasks based on the total charge and discharge demand and factors such as the efficiency and response time of the energy storage device.

[0033] Specifically, the difference between the rated capacity and the used capacity of the energy storage device is calculated to obtain the available capacity of the energy storage device. The energy storage device, the available capacity and the rated power are converted into the comprehensive charging score of the energy storage device, and the energy storage devices are selected in descending order according to the comprehensive charging score until the available capacity of the selected energy storage device reaches the total charging capacity in the power scheduling plan. The used capacity and rated power of the energy storage device are converted into the comprehensive discharge score, and the energy storage devices are selected in descending order according to the comprehensive discharge score until the used capacity of the selected energy storage device reaches the total discharge capacity in the power scheduling plan.

[0034] S3. Monitor the actual charging and discharging parameters of the energy storage device.

[0035] The actual charge and discharge parameters of each energy storage device are collected, and a timestamp is generated for the actual charge and discharge parameters based on the collection time of the actual charge and discharge parameters, wherein the actual charge and discharge parameters include the charge and discharge power; the actual charge and discharge parameters collected within a specified time limit are sorted in order according to the timestamps to obtain a monitoring data sequence.

[0036] In order to ensure the accurate execution of the power dispatch plan and the safe operation of the energy storage equipment, the system needs to continuously monitor the actual charging and discharging parameters of each energy storage device. These parameters mainly include real-time charging and discharging power, temperature, voltage, current and other key indicators. At the same time, the system will add a timestamp to each piece of actual charging and discharging data collected to accurately record the time point when it occurred. Afterwards, the system will arrange these timestamped data in chronological order to form an orderly monitoring data sequence, providing a solid foundation for subsequent data analysis, fault warning and performance optimization.

[0037] S4. Using a feedback control strategy to correct the predicted charge and discharge parameters based on the actual charge and discharge parameters, and outputting the corrected charge and discharge parameters as execution parameters of the corresponding energy storage device.

[0038] In one embodiment of the present invention, the feedback mechanism includes constructing a feedback control function: ; in, is the actual charging and discharging power of the i-th energy storage device at time k, is the corrected charging and discharging power of the i-th energy storage device at time k+1, is the predicted charging and discharging power of the i-th energy storage device at time k; is the correction factor.

[0039] The neural network model is used to train the correction coefficient based on the continuously input charging and discharging power.

[0040] Based on the actual charge and discharge parameters obtained through real-time monitoring, the system will further construct and execute a feedback control strategy to correct and adjust the initial predicted charge and discharge parameters. The feedback control function is designed to reduce the deviation between the predicted value and the actual value and improve the response speed and stability of the system. Specifically, the function calculates the corrected charge and discharge power at the next moment (moment k+1) based on the actual charge and discharge power at the previous moment (moment k), the predicted charge and discharge power, and the preset correction coefficient. In this way, the system can dynamically adjust the charge and discharge behavior of the energy storage device to make it closer to actual needs, thereby improving the flexibility and reliability of the overall energy system. The choice of correction coefficient depends on the specific design goals of the system, such as the pursuit of fast response, stable output, or maximum energy efficiency.

[0041] In another embodiment of the present invention, after the dispatch control instruction is issued, there is inevitably an error between the actual power system output result and the prediction model output result. To solve this problem, MPC introduces a feedback control strategy to continuously correct the current control instruction. This is a closed-loop control idea. Optimization and feedback control are carried out together to continuously obtain the "optimal" instruction at the current moment, which is exactly what is needed for the optimal dispatch of the power system.

[0042] Model predictive control is a model-based algorithm that requires a model that describes the dynamic behavior of the object, which is called a prediction model. For the model predictive control algorithm, the prediction model only emphasizes the "prediction" function of the model, rather than the form of the model. That is to say, the model used should have the function of predicting the future output of the controlled object based on the current information and future input. In addition, in model predictive control, the length of time that the model exercises the prediction function can be set. Generally, a multi-step prediction method based on the prediction model is adopted to reflect the change trend of the system in multiple time domains in the future.

[0043] Assume the input variable is u , the output variable is y ,by k The controlled system is j The input and output sequence before time periods is and thereafter m The input sequence for each period is , predicting the future of the system p Response of time period ,in m To control the time domain, p is the prediction time domain, all of which are finite values ​​and are generally taken as m ≤ p As mentioned earlier, one of the functions of predictive models is to control the “future inputs” (variables) of the system. u ) to adjust the system's predicted output (variable y) to make it as close as possible to the expected value of the system output optimization variable. The prediction model can provide prediction information to the model predictive control algorithm, find the optimal control rate, and obtain the optimal control sequence that the system needs to input, and then control the system output so that the future change trend of the system is consistent with the expected value.

[0044] The optimization control in the MPC algorithm, after determining the optimization target, does not perform one or more global optimizations to obtain control instructions, but adopts a rolling finite-time local optimization strategy. The actual value of the system is measured at each sampling moment, and then the optimal control rate for a finite period of time from that moment is solved according to the objective function. The calculated control action sequence only has the current value that is actually executed. At the next sampling moment, the optimal control rate is obtained by referring to the previous step again, and the local optimization is repeatedly pushed forward to the future time domain. This process is called a rolling optimization process. The optimization control in the model predictive control algorithm, after determining the optimization target, does not perform one or more global optimizations to obtain control instructions, but adopts a rolling finite-time local optimization strategy. The actual value of the system is measured at each sampling moment, and then the optimal control rate for a finite period of time from that moment is solved according to the objective function. The calculated control action sequence only has the current value that is actually executed. At the next sampling moment, the optimal control rate is obtained by referring to the previous step again, and the local optimization is repeatedly pushed forward to the future time domain. This process is called a rolling optimization process.

[0045] Only the current value is executed because the prediction in model predictive control is limited in time domain, and in fact there are inevitably model errors and external interference, so the best control sequence for solving the optimization problem cannot be regarded as "optimal" and cannot be fully applied to the system. Therefore, only the first component of the optimization solution sequence at each sampling moment is applied to the system to reduce the prediction and cumulative errors. In other words, in k Always find the optimal solution as the input variable sequence , its first component u ( k | k ) acts on the system; k +1 moment, with actual measured value y ( k +1) Re-predict the future output of the system for the initial state y ( k +2| k +1) and solve the optimization problem, and then take the first component of the optimization solution sequence u ( k +1| k+1) acts on the system, and this is repeated continuously, rolling to the future moment for optimization control as the "current" sampling moment passes. Overall, in the ideal case where the model has no errors, MPC may not be able to achieve global optimization, but its rolling operation can take into account uncertainties such as model mismatch, time variation, and interference, and compensate for them in time. This method of participating in real-time optimization of the system with actual sampling values ​​can continuously correct the errors caused by system uncertainty, so that the control remains actually optimal. It has stronger anti-interference ability than global optimization, is more suitable for actual control conditions, and has stronger system robustness.

[0046] In some embodiments, the virtual power plant distributed dispatching system may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the virtual power plant distributed dispatching system may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) Functions of distributed scheduling of virtual power plants.

[0047] In this embodiment, the virtual power plant distributed dispatching system can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules of the system 200 may include: a solution generation module 210, a solution decomposition module 220, a parameter monitoring module 230 and a parameter correction 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. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0048] A scheme generation module is used to generate a power dispatch scheme based on the cost objective function of the virtual power plant; A scheme decomposition module, used to obtain state parameters of the energy storage device, and decompose the power dispatch scheme into predicted charging and discharging parameters of the energy storage device based on the state parameters; Parameter monitoring module, used to monitor the actual charging and discharging parameters of energy storage equipment; The parameter correction module is used to correct the predicted charge and discharge parameters based on the actual charge and discharge parameters using a feedback control strategy, and output the corrected charge and discharge parameters as execution parameters of the corresponding energy storage device.

[0049] Optionally, as an embodiment of the present invention, the solution decomposition module includes: A state acquisition unit, used to acquire state parameters of each energy storage device, wherein the state parameters include rated capacity, used capacity and rated power; A scheme decomposition unit is used to decompose the total charging and discharging parameters in the power scheduling scheme into predicted charging and discharging parameters of each energy storage device based on the state parameters, and each energy storage device joins the shared energy storage cluster.

[0050] Optionally, as an embodiment of the present invention, the parameter monitoring module includes: A parameter collection unit, used to collect actual charge and discharge parameters of each energy storage device, and generate a timestamp for the actual charge and discharge parameters based on the collection time of the actual charge and discharge parameters, wherein the actual charge and discharge parameters include charge and discharge power; The sequence generation unit is used to sort the actual charge and discharge parameters collected within a specified time period in chronological order according to timestamps to obtain a monitoring data sequence.

[0051] Optionally, as an embodiment of the present invention, the parameter correction module includes: Construct a feedback control function: ; in, is the actual charging and discharging power of the i-th energy storage device at time k, is the corrected charging and discharging power of the i-th energy storage device at time k+1, is the predicted charging and discharging power of the i-th energy storage device at time k; is the correction factor.

[0052] Figure 3 A structural schematic diagram of a terminal 300 provided in an embodiment of the present invention, wherein the terminal 300 can be used to execute the virtual power plant distributed scheduling method provided in an embodiment of the present invention.

[0053] The terminal 300 may include: a processor 310, a memory 320 and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0058] Therefore, the present invention realizes precise control of energy storage equipment to achieve precise execution of the scheduling plan generated by the virtual power plant. The technical effects that can be achieved by this embodiment can be found in the description above and will not be repeated here.

[0059] 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.

[0060] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0061] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 systems or modules, which can be electrical, mechanical or other forms.

[0062] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0063] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0064] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A distributed scheduling method for a virtual power plant, characterized in that: include: Generate power dispatch plan based on the cost objective function of virtual power plant; Acquire state parameters of the energy storage device, and decompose the power dispatching scheme into predicted charging and discharging parameters of the energy storage device based on the state parameters; Monitor the actual charging and discharging parameters of energy storage equipment; The predicted charging and discharging parameters are corrected based on the actual charging and discharging parameters using a feedback control strategy, and the corrected charging and discharging parameters are output as execution parameters of the corresponding energy storage device.

2. The method according to claim 1, characterized in that Acquiring state parameters of the energy storage device, and decomposing the power dispatching scheme into predicted charging and discharging parameters of the energy storage device based on the state parameters, including: Acquire status parameters of each energy storage device, wherein the status parameters include rated capacity, used capacity and rated power; Based on the state parameters, the total charge and discharge parameters in the power dispatching scheme are decomposed into predicted charge and discharge parameters of each energy storage device, and each energy storage device joins the shared energy storage cluster.

3. The method according to claim 1, characterized in that Monitor the actual charging and discharging parameters of energy storage equipment, including: Collecting actual charge and discharge parameters of each energy storage device, and generating a timestamp for the actual charge and discharge parameters based on the collection time of the actual charge and discharge parameters, wherein the actual charge and discharge parameters include charge and discharge power; The actual charging and discharging parameters collected within a specified time period are sorted in chronological order according to timestamps to obtain a monitoring data sequence.

4. The method according to claim 3, characterized in that The predicted charging and discharging parameters are corrected based on the actual charging and discharging parameters using the feedback control strategy, and the corrected charging and discharging parameters are output as the execution parameters of the corresponding energy storage device, including: Construct a feedback control function: ； in, is the actual charging and discharging power of the i-th energy storage device at time k, is the corrected charging and discharging power of the i-th energy storage device at time k+1, is the predicted charging and discharging power of the i-th energy storage device at time k; is the correction factor.

5. A virtual power plant distributed dispatching system, characterized in that: include: A scheme generation module is used to generate a power dispatch scheme based on the cost objective function of the virtual power plant; A scheme decomposition module, used to obtain state parameters of the energy storage device, and decompose the power dispatch scheme into predicted charging and discharging parameters of the energy storage device based on the state parameters; Parameter monitoring module, used to monitor the actual charging and discharging parameters of energy storage equipment; The parameter correction module is used to correct the predicted charge and discharge parameters based on the actual charge and discharge parameters using a feedback control strategy, and output the corrected charge and discharge parameters as execution parameters of the corresponding energy storage device.

6. The system according to claim 5, characterized in that The solution decomposition module includes: A state acquisition unit, used to acquire state parameters of each energy storage device, wherein the state parameters include rated capacity, used capacity and rated power; A scheme decomposition unit is used to decompose the total charging and discharging parameters in the power scheduling scheme into predicted charging and discharging parameters of each energy storage device based on the state parameters, and each energy storage device joins the shared energy storage cluster.

7. The system according to claim 5, characterized in that The parameter monitoring module includes: A parameter collection unit, used to collect actual charge and discharge parameters of each energy storage device, and generate a timestamp for the actual charge and discharge parameters based on the collection time of the actual charge and discharge parameters, wherein the actual charge and discharge parameters include charge and discharge power; The sequence generation unit is used to sort the actual charge and discharge parameters collected within a specified time period in chronological order according to timestamps to obtain a monitoring data sequence.

8. The system according to claim 7, characterized in that The parameter correction module comprises: Construct a feedback control function: ； in, is the actual charging and discharging power of the i-th energy storage device at time k, is the corrected charging and discharging power of the i-th energy storage device at time k+1, is the predicted charging and discharging power of the i-th energy storage device at time k; is the correction factor.

9. A terminal, characterized in that: include: A memory, used for storing a distributed scheduling program of a virtual power plant; A processor is used to implement the steps of the virtual power plant distributed scheduling method as described in any one of claims 1 to 4 when executing the virtual power plant distributed scheduling program.

10. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a virtual power plant distributed scheduling program, which, when executed by a processor, implements the steps of the virtual power plant distributed scheduling method according to any one of claims 1 to 4.

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