Power calculation cooperative scheduling method and device for smart power grid

Abstract

The invention provides an electricity calculation cooperative scheduling method and device for an intelligent power grid, and relates to the technical field of resource scheduling of the intelligent power grid, and the method comprises the steps: determining the task information of a sliding mode control calculation task constructed for each intelligent sub-power grid in the intelligent power grid; the sliding mode control calculation task is used for solving a control variable so that the voltage state and the current state of the intelligent sub-grid tend to a voltage control sliding mode surface and a current control sliding mode surface respectively; establishing a resource scheduling optimization model by taking the total calculation cost of each intelligent sub-grid and the task execution condition of the sliding mode control calculation task as optimization targets; and solving the resource scheduling optimization model to obtain a task allocation strategy, wherein the task allocation strategy represents the allocation mode of the sliding mode control calculation task of each intelligent sub-grid. On the premise of effectively inhibiting voltage deviation and current deviation in the operation process of the intelligent power grid, the problems of low computing power resource utilization efficiency, high energy consumption and the like are solved.

Description

Technical Field

[0001] This application belongs to the field of power generation and power grid related technology, especially the field of resource scheduling technology of smart grid, specifically a computing-based collaborative scheduling method and device for smart grid. Background Technology

[0002] Against the backdrop of rapid development of smart grids and comprehensive advancement of digital transformation, integrated computing and power dispatching technology for smart grids has become a core issue of widespread concern. The deep integration of power systems and computing systems in smart grids creates an urgent need for integrated computing and power dispatching.

[0003] In related technologies, a two-layer multi-agent deep reinforcement learning framework, comprising a global layer and local layers, has been constructed for intelligent scheduling integrating computing and power systems. In this framework, the global layer allocates computational tasks through centralized deep reinforcement learning, while the local layer optimizes internal energy scheduling through distributed deep reinforcement learning. This is combined with a hierarchical multi-agent dual-delay deep deterministic policy gradient algorithm to achieve cross-layer interaction and rapid convergence. However, this approach, due to its two-layer multi-agent structure and the integration of complex deep reinforcement learning algorithms, results in a complex overall architecture, long training time, and high computational resource consumption. This makes it particularly difficult to achieve rapid deployment and application in power grid scheduling scenarios with high real-time requirements.

[0004] Furthermore, voltage and current deviations typically occur during smart grid operation, impacting overall system performance. Given the complexity of smart grids and the diversity of computational tasks, there is an urgent need for advanced real-time, efficient power control methods and computing power dispatch strategies to achieve both stable operation and economic efficiency in smart grids. This has become a key challenge in the field of integrated computing and power dispatching technology for smart grids. Summary of the Invention

[0005] To address the problems existing in the prior art, this application provides a computing-power collaborative scheduling method and device for smart grids, which solves the problems of low computing power resource utilization efficiency and high energy consumption while effectively suppressing voltage and current deviations during the operation of smart grids.

[0006] Firstly, this application provides a computational-coordinated scheduling method for smart grids, the method comprising:

[0007] The task information for the sliding mode control calculation task for each smart subgrid in the smart grid is determined; the sliding mode control calculation task is used to solve the control variables so that the voltage state and current state of the smart subgrid tend to the voltage control sliding surface and the current control sliding surface, respectively.

[0008] A resource scheduling optimization model is established by taking the total computational cost of each smart sub-grid and the task execution status of the sliding mode control computation task as optimization objectives.

[0009] Based on the task information and the resource occupancy status of each smart subgrid, the resource scheduling optimization model is solved to obtain the task allocation strategy, which represents the allocation method of sliding mode control calculation tasks for each smart subgrid.

[0010] In some embodiments of this application, the sliding mode control computation task for constructing the first smart subgrid is performed using the following steps:

[0011] Based on the output voltage and output current of the first smart subgrid and the multi-agent cooperative control parameters of the second smart subgrid, the multi-agent cooperative control parameters of the first smart subgrid are calculated; the multi-agent cooperative control parameters include: multi-agent average voltage, multi-agent average current, multi-agent secondary controller voltage, and multi-agent secondary controller current.

[0012] Calculate the voltage correction value based on the average voltage and nominal voltage of the multi-agents in the first smart sub-grid;

[0013] Calculate the current correction value based on the average current and output current of the multi-agents in the first smart sub-grid.

[0014] The voltage reference value is calculated based on the nominal voltage, the voltage correction value, the current correction value, and the product of the virtual droop impedance and the output current of the first smart subgrid.

[0015] A second-order sliding mode voltage-current controller is established based on the voltage reference value and the first deviation of the output voltage. The second-order sliding mode voltage-current controller defines the correlation between the filter current reference value and the first deviation, and the correlation between the control variable and the second deviation. The second deviation is the deviation between the filter current reference value and the actual filter current.

[0016] The control variables are calculated based on the second-order sliding mode voltage and current controller.

[0017] In some embodiments of this application, the output voltage, output current, and multi-agent cooperative control parameters of the first smart subgrid, and the multi-agent cooperative control parameters of the second smart subgrid, satisfy a consistency control relationship, which is expressed by the following formula:

[0018]

[0019] in, This represents the output voltage of the i-th smart subgrid. Let represent the average voltage across multiple agents in the i-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the current of the multi-agent secondary controller in the i-th smart subgrid. This represents the output current of the i-th smart subgrid. This represents the output voltage of the j-th smart subgrid. Let represent the multi-agent average voltage of the j-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the j-th smart subgrid. This represents the average current across multiple agents in the j-th smart subgrid. This represents the current of the multi-agent secondary controller in the j-th smart subgrid. This represents the communication weight between smart subgrids. If the j-th smart subgrid and the i-th smart subgrid can exchange information, then... If there is no communication, then ; Here, N represents the gain coefficient, and N represents the total number of smart subgrids.

[0020] In some embodiments of this application, a voltage correction value is calculated based on the average voltage and nominal voltage of the multi-agents in the first smart subgrid, including calculating the voltage correction value based on the following formula:

[0021]

[0022] in, This represents the voltage deviation of the i-th smart subgrid. Indicates the nominal voltage. This represents the output voltage of the i-th smart subgrid. This represents the voltage deviation sliding mode of the i-th smart subgrid. This represents the voltage correction value for the i-th smart subgrid. represents the positive gain coefficient; sech represents the hyperbolic secant function, and tanh represents the hyperbolic tangent function.

[0023] The current correction value is calculated based on the average current and output current of the multi-agents in the first smart sub-grid, including the calculation of the current correction value based on the following formula:

[0024]

[0025] in, This represents the current deviation of the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the output current of the i-th smart subgrid. This represents the sliding mode of the current deviation in the i-th smart subgrid. Indicates the positive gain coefficient; This represents the current correction value for the i-th smart subgrid.

[0026] In some embodiments of this application, the second-order sliding mode voltage-current controller is represented by the following formula:

[0027]

[0028] in, Indicates voltage-controlled sliding surface. This represents the voltage reference value of the i-th smart subgrid. This represents the output voltage of the i-th smart subgrid. This represents the reference value of the filter current for the i-th smart subgrid. This represents the actual filtered current of the i-th smart subgrid. This represents the i-th current-controlled sliding surface. Indicates the gain coefficient. denoted by , sign represents the input duty cycle of the boost converter in the i-th smart subgrid, and sign represents the sign function.

[0029] In some embodiments of this application, the resource scheduling optimization model is further defined by taking the total computational energy consumption, total task processing time, voltage control sliding mode variables, and current control sliding mode variables of each smart sub-grid as optimization objectives.

[0030] Based on the execution status of the sliding mode control calculation tasks allocated to each of the smart subgrids in the previous control cycle, the weighting of the total computational energy consumption of each of the smart subgrids in the current control cycle in the resource scheduling optimization model is adjusted; wherein, the execution status is determined by the magnitude of the voltage control sliding mode variable and / or the current control sliding mode variable.

[0031] Secondly, this application provides a computing-based collaborative scheduling device for smart grids, the device comprising:

[0032] The determination module is used to determine the task information of the sliding mode control calculation task for each smart subgrid in the smart grid; the sliding mode control calculation task is used to solve the control variables so that the voltage state and current state of the smart subgrid tend to the voltage control sliding surface and the current control sliding surface, respectively.

[0033] A module is established to use the total computational cost of each smart sub-grid and the task execution status of the sliding mode control computation task as optimization objectives to establish a resource scheduling optimization model.

[0034] The allocation module is used to solve the resource scheduling optimization model based on the task information and the resource occupancy status of each of the smart subgrids to obtain a task allocation strategy. The task allocation strategy represents the allocation method of the sliding mode control calculation tasks of each of the smart subgrids.

[0035] In some embodiments, an execution module is further included, configured to perform the sliding mode control computation task for constructing the first smart subgrid using the following steps:

[0036] Based on the output voltage and output current of the first smart subgrid and the multi-agent cooperative control parameters of the second smart subgrid, the multi-agent cooperative control parameters of the first smart subgrid are calculated; the multi-agent cooperative control parameters include: multi-agent average voltage, multi-agent average current, multi-agent secondary controller voltage, and multi-agent secondary controller current.

[0037] Calculate the voltage correction value based on the average voltage and nominal voltage of the multi-agents in the first smart sub-grid;

[0038] Calculate the current correction value based on the average current and output current of the multi-agents in the first smart sub-grid.

[0039] The voltage reference value is calculated based on the nominal voltage, the voltage correction value, the current correction value, and the product of the virtual droop impedance and the output current of the first smart subgrid.

[0040] A second-order sliding mode voltage-current controller is established based on the voltage reference value and the first deviation of the output voltage. The second-order sliding mode voltage-current controller defines the correlation between the filter current reference value and the first deviation, and the correlation between the control variable and the second deviation. The second deviation is the deviation between the filter current reference value and the actual filter current.

[0041] The control variables are calculated based on the second-order sliding mode voltage and current controller.

[0042] In some embodiments, the output voltage, output current, and multi-agent cooperative control parameters of the first smart subgrid, and the multi-agent cooperative control parameters of the second smart subgrid, satisfy a consistency control relationship, which is expressed by the following formula:

[0043]

[0044] in, This represents the output voltage of the i-th smart subgrid. Let represent the average voltage across multiple agents in the i-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the current of the multi-agent secondary controller in the i-th smart subgrid. This represents the output current of the i-th smart subgrid. This represents the output voltage of the j-th smart subgrid. Let represent the multi-agent average voltage of the j-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the j-th smart subgrid. This represents the average current across multiple agents in the j-th smart subgrid. This represents the current of the multi-agent secondary controller in the j-th smart subgrid. This represents the communication weight between smart subgrids. If the j-th smart subgrid and the i-th smart subgrid can exchange information, then... If there is no communication, then ; Here, N represents the gain coefficient, and N represents the total number of smart subgrids.

[0045] In some embodiments, the voltage correction value is calculated based on the following formula:

[0046]

[0047] in, This represents the voltage deviation of the i-th smart subgrid. Indicates the nominal voltage. This represents the output voltage of the i-th smart subgrid. This represents the voltage deviation sliding mode of the i-th smart subgrid. This represents the voltage correction value for the i-th smart subgrid. represents the positive gain coefficient; sech represents the hyperbolic secant function, and tanh represents the hyperbolic tangent function.

[0048] The current correction value is calculated based on the average current and output current of the multi-agents in the first smart sub-grid, including the calculation of the current correction value based on the following formula:

[0049]

[0050] in, This represents the current deviation of the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the output current of the i-th smart subgrid. This represents the sliding mode of the current deviation in the i-th smart subgrid. Indicates the positive gain coefficient; This represents the current correction value for the i-th smart subgrid.

[0051] In some embodiments, the second-order sliding mode voltage-current controller is represented by the following formula:

[0052]

[0053] in, Indicates voltage-controlled sliding surface. This represents the voltage reference value of the i-th smart subgrid. This represents the output voltage of the i-th smart subgrid. This represents the reference value of the filter current for the i-th smart subgrid. This represents the actual filtered current of the i-th smart subgrid. This represents the i-th current-controlled sliding surface. Indicates the gain coefficient. denoted by , sign represents the input duty cycle of the boost converter in the i-th smart subgrid, and sign represents the sign function.

[0054] In some embodiments, the establishment module further includes:

[0055] The adjustment unit is used to adjust the weighting of the total computational energy consumption of each smart subgrid in the current control cycle in the resource scheduling optimization model based on the execution status of the sliding mode control computation tasks allocated to each smart subgrid in the previous control cycle; wherein the execution status is determined by the magnitude of the voltage control sliding mode variable and / or the current control sliding mode variable.

[0056] Thirdly, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a computer-based power grid collaborative scheduling method for smart grids.

[0057] Fourthly, this application provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of a computer-powered collaborative scheduling method for smart grids.

[0058] Fifthly, this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of a computer-based power grid collaborative scheduling method.

[0059] As described above, since sliding mode control computational tasks are constructed for each smart subgrid in the smart grid to calculate control variables so that the voltage and current states of the smart subgrid tend to the voltage control sliding surface and the current control sliding surface, respectively, the voltage and current deviations of the smart subgrids are effectively suppressed, reducing their impact on system performance. Furthermore, the computational resources of each smart subgrid in the smart grid are uniformly scheduled, a multi-objective optimization model for smart grid computation-power coordination is constructed, and computational resources are optimally allocated, ultimately forming an efficient multi-agent computation-power coordination scheduling scheme. Compared with existing technologies, the positive effects of this application include the following:

[0060] First, to address the voltage and current deviation problem, a multi-agent sliding mode consensus control strategy is proposed. Second, to address the low computational efficiency and high energy consumption of smart grids, a multi-agent computation-power collaborative scheduling strategy is designed, thereby comprehensively improving the operational performance and energy efficiency of smart grids. This effectively solves the problems of voltage and current deviation, low utilization efficiency of computing resources, and high energy consumption existing in the operation of smart grids.

[0061] Second, the multi-agent sliding mode consensus control strategy designed in this application first calculates the average voltage, secondary controller voltage, average current, and secondary controller current of the multi-agents. Based on this, a multi-agent voltage deviation sliding mode controller and a multi-agent current deviation sliding mode controller are designed to achieve dynamic compensation for the smart grid and provide voltage and current correction values. Furthermore, a second-order sliding mode control algorithm is used to design a second-order sliding mode voltage and current controller for the multi-agents to generate control signals and adjust the output of the distributed generator in real time through a boost converter. This effectively suppresses voltage deviations in the smart grid, ensures load current sharing, promotes rapid convergence of the system state, and effectively enhances the operational stability of the smart grid.

[0062] To make the above and other objects, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating a computing-based collaborative scheduling method for smart grids, as described in an embodiment of this application.

[0065] Figure 2 This is a flowchart illustrating the execution process of the sliding mode control calculation task in an embodiment of this application;

[0066] Figure 3 This is a schematic diagram of the structure of a computing-coordinated scheduling device for smart grids, as described in an embodiment of this application.

[0067] Figure 4 This is a schematic diagram of the structure of the computer device in the embodiments of this application.

[0068] Explanation of symbols in the attached drawings:

[0069] 301. Determine the module;

[0070] 302. Create a module;

[0071] 303. Allocation Module;

[0072] 402. Computer equipment;

[0073] 404, Processor;

[0074] 406. Memory;

[0075] 408. Drive mechanism;

[0076] 410. Input / output module;

[0077] 412. Input devices;

[0078] 414. Output devices;

[0079] 416. Presentation equipment;

[0080] 418. Graphical User Interface;

[0081] 420. Network interface;

[0082] 422. Communication link;

[0083] 424. Communication bus. Detailed Implementation

[0084] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0085] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0086] This application provides the method operation steps as described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.

[0087] This application provides a computational-coordinated scheduling method for smart grids. The method includes: determining task information for sliding mode control computation tasks constructed for each smart subgrid in the smart grid; the sliding mode control computation tasks are used to solve control variables to make the voltage state and current state of the smart subgrid tend towards the voltage control sliding surface and the current control sliding surface, respectively; establishing a resource scheduling optimization model using the computational cost of each smart subgrid and the task execution status of the sliding mode control computation tasks as optimization objectives; and obtaining a task allocation strategy by solving the resource scheduling optimization model based on the task information and the resource occupancy status of each smart subgrid, wherein the task allocation strategy characterizes the allocation of sliding mode control tasks for each smart subgrid.

[0088] As described above, since sliding mode control computational tasks are constructed for each smart subgrid in the smart grid to calculate control variables so that the voltage and current states of the smart subgrid tend to approach the voltage control sliding surface and the current control sliding surface, respectively, the voltage and current deviations of the smart subgrid are effectively suppressed, reducing their impact on system performance. Furthermore, the computational resources of each smart subgrid in the smart grid are uniformly scheduled, a multi-objective optimization model for smart grid computation-power coordination is constructed, and computational resources are optimally allocated, ultimately forming an efficient multi-agent computation-power coordination scheduling scheme.

[0089] The computational-coordinated scheduling method for smart grids provided in this application will be further described below with reference to the accompanying drawings. See also... Figure 1 The method includes the following steps:

[0090] S101: Determine the task information for the sliding mode control calculation task constructed for each smart subgrid in the smart grid; the sliding mode control calculation task is used to solve the control variables so that the voltage state and current state of the smart subgrid tend to the voltage control sliding surface and the current control sliding surface, respectively.

[0091] The computing-based collaborative scheduling method for smart grids provided in this application can be applied to the dispatch center of a smart grid. The smart grid also includes smart subgrids interconnected by distribution lines, and the dispatch center establishes communication connections with each smart subgrid.

[0092] In this embodiment, considering the impact of voltage and current deviations on system performance during smart grid operation, a sliding mode control calculation task is constructed for each smart subgrid. This task is used to calculate control variables to make the voltage and current states tend towards the set voltage control sliding mode surface and current control sliding mode surface. Specifically, the voltage control sliding mode surface introduces the deviation between the voltage reference value and the actual output voltage, and the current control sliding mode surface introduces the deviation between the filter current reference value and the actual filter current.

[0093] As an example, the control variable could be the input duty cycle of the boost converter. The input duty cycle of the boost converter can be adjusted in real time based on the calculation results to effectively suppress voltage deviations in the smart grid.

[0094] In this embodiment of the application, the sliding mode control calculation task can be constructed by each smart sub-grid itself. In the construction process, a boost converter model and a power distribution line dynamic model are required.

[0095] To facilitate understanding, the following describes the composition of the smart grid and the construction of related models of smart subgrids.

[0096] For example, a smart grid includes N smart subgrids interconnected by m distribution lines, wherein each smart subgrid may include a generator, a step-up converter, and a supercomputing center.

[0097] To ensure the stable operation of the entire smart grid, the various smart subgrids need to work collaboratively through information exchange. For example, each smart subgrid can be configured with an intelligent agent, which can be understood as a software / hardware entity with a certain degree of autonomy, or a combination of software and hardware. The intelligent agents of different smart subgrids can exchange their respective information to achieve collaborative work.

[0098] To accurately describe the dynamic characteristics of the smart grid, a boost converter model and a dynamic model of the distribution lines between smart subgrids are established.

[0099] For example, based on Kirchhoff's laws, the boost converter model for the i-th smart subgrid is established as follows:

[0100]

[0101] Where (i=1,2,…,N),L fi R fi and C fi These are the inductor, resistor, and capacitor of the boost converter in the i-th smart subgrid, respectively. dci i fi i Li d i These represent the input voltage, filter current, load current, and input duty cycle of the boost converter for the i-th smart subgrid, respectively. i Let i be the output voltage of the i-th smart subgrid. ij This represents the current in the distribution line between the i-th smart subgrid and the j-th smart subgrid (j=1,2,…,N).

[0102] Furthermore, based on Kirchhoff's laws, a dynamic model of the distribution lines between smart sub-grids is established:

[0103]

[0104] Among them, R ij L ij Let represent the resistance and inductance of the power distribution line between the i-th smart subgrid and the j-th smart subgrid, respectively.

[0105] In power distribution lines, R ij Much larger than L ij ,therefore Furthermore, we can obtain:

[0106]

[0107] Therefore, combining Equation 1, the model of the boost converter for the i-th smart subgrid can be obtained as follows:

[0108]

[0109] Based on the aforementioned boost converter model and the dynamic model of distribution lines between smart subgrids, a sliding mode control calculation task can be constructed. The sliding mode control calculation task involves setting the voltage control sliding surface and the current control sliding surface. Further description of the sliding mode control calculation task is provided below.

[0110] In this embodiment of the application, the control of voltage and current states is continuous. That is, for each smart subgrid, a new sliding mode control calculation task is generated based on the current state in each control cycle.

[0111] Within a control cycle, after each smart grid subgrid constructs its sliding mode control computation task, it can send the relevant task information to the scheduling center. The scheduling center then allocates the computing resources of the entire smart grid to determine how to distribute the sliding mode control computation tasks among the smart grid subgrids. For example, the task information may include resource occupancy information and task completion deadlines. The resource occupancy information may include the computational load of the task.

[0112] S102: Using the total computational cost of each smart sub-grid and the task execution status of the sliding mode control computation task as optimization objectives, establish a resource scheduling optimization model.

[0113] In this embodiment, computational cost may include computational energy consumption, task execution status may include task processing time, and may also include the magnitude of the sliding mode variable in the sliding mode control computation task. It is understood that the smaller the sliding mode variable, the closer the system is to the sliding mode control surface, i.e., the better the sliding mode control effect.

[0114] The dispatch center can comprehensively consider the computing energy consumption and task processing time of each smart subgrid to establish a multi-objective optimization model.

[0115] S103: Based on the task information and the resource occupancy status of each smart subgrid, the resource scheduling optimization model is solved to obtain the task allocation strategy, which represents the allocation of sliding mode control calculation tasks for each smart subgrid.

[0116] In this embodiment, the scheduling center uses a multi-objective optimization algorithm to solve the resource scheduling optimization model and obtain a task allocation strategy. Subsequently, the scheduling center allocates the sliding mode control calculation tasks of each smart subgrid to the target smart subgrid based on the task allocation strategy. For example, for the i-th smart subgrid, the corresponding sliding mode control calculation task may be allocated to that i-th smart subgrid for calculation, or it may be allocated to the j-th smart subgrid.

[0117] For an introduction to the establishment of the resource scheduling optimization model and the multi-objective optimization algorithm, please refer to the following text.

[0118] In some embodiments of this application, the control variable for the sliding mode calculation control task is the input duty cycle of the boost converter in the smart subgrid. That is, by adjusting the input duty cycle of the boost converter, the voltage and current states of the smart subgrid can be regulated.

[0119] To suppress voltage and current deviations, a deviation between the voltage reference value and the actual output voltage is introduced into the designed voltage control sliding surface, and a deviation between the filter current reference value and the actual filter current is introduced into the current control sliding surface.

[0120] Sliding mode control drives the system's state trajectory to reach and maintain on a preset surface, known as the sliding surface. When the state trajectory lies outside the sliding surface, the control quantity calculated by the sliding mode control algorithm drives the state trajectory to quickly point towards the sliding surface.

[0121] The following section, combining the aforementioned boost converter model and power distribution line dynamic model, introduces the specific design of the voltage-controlled sliding surface and the current-controlled sliding surface, as well as the execution process of the sliding mode control calculation task.

[0122] See Figure 2 The sliding mode control calculation task for constructing the first smart subgrid can be performed based on the following steps:

[0123] S201: Calculate the multi-agent collaborative control parameters of the first intelligent subgrid based on the output voltage and output current of the first intelligent subgrid and the multi-agent collaborative control parameters of the second intelligent subgrid; the multi-agent collaborative control parameters include: multi-agent average voltage, multi-agent average current, multi-agent secondary controller voltage and multi-agent secondary controller current.

[0124] In this embodiment, the first smart subgrid and its neighboring smart subgrids satisfy a consensus control relationship. Consistency control refers to the process by which multiple agents communicate only with their neighbors, ultimately causing a certain state variable of all agents to approach or reach a common value.

[0125] In one embodiment of this application, the above-mentioned consistency control relationship is expressed by the following formula:

[0126]

[0127] in, This represents the output voltage of the i-th smart subgrid. Let represent the average voltage across multiple agents in the i-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the current of the multi-agent secondary controller in the i-th smart subgrid. This represents the output current of the i-th smart subgrid. This represents the output voltage of the j-th smart subgrid. Let represent the multi-agent average voltage of the j-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the j-th smart subgrid. This represents the average current across multiple agents in the j-th smart subgrid. This represents the current of the multi-agent secondary controller in the j-th smart subgrid. This represents the communication weight between smart subgrids. If the j-th smart subgrid and the i-th smart subgrid can exchange information, then... If there is no communication, then ; Here, N represents the gain coefficient, and N represents the total number of smart subgrids.

[0128] It is understood that in this application, the "." above a symbol represents the derivative with respect to that symbol, that is, the derivative with respect to time.

[0129] It is evident that in the process of calculating the multi-agent collaborative control parameters of the i-th smart subgrid, the output voltage and output current of the i-th smart subgrid and the multi-agent collaborative control parameters of other smart subgrids with which it interacts with information are required. For the multi-agent collaborative control parameters of other smart subgrids, the latest data can be used for calculation.

[0130] S202: Calculate the voltage correction value based on the average voltage and nominal voltage of the multi-agents in the first smart sub-grid.

[0131] In this embodiment of the application, the nominal voltage can be preset, and the voltage deviation can be calculated by combining the average voltage of the multi-agent system calculated in step S201.

[0132] Furthermore, a multi-agent voltage deviation sliding mode controller is designed to generate voltage correction values ​​for the smart subgrid based on the voltage deviation.

[0133] In some embodiments of this application, the voltage correction value can be calculated based on the following formula:

[0134]

[0135] in, This represents the voltage deviation of the i-th smart subgrid. Indicates the nominal voltage. This represents the output voltage of the i-th smart subgrid. This represents the voltage deviation sliding mode of the i-th smart subgrid. This represents the voltage correction value for the i-th smart subgrid. represents the positive gain coefficient; sech represents the hyperbolic secant function, and tanh represents the hyperbolic tangent function.

[0136] S203: Calculate the current correction value based on the average current and output current of the multi-agents in the first smart sub-grid.

[0137] Accordingly, a multi-agent current deviation sliding mode controller is designed to calculate the current deviation based on the average current and output current of the multiple agents in the smart subgrid. Further design of the multi-agent current deviation sliding mode controller is then undertaken to generate current correction values ​​based on the current deviation.

[0138] In some embodiments of this application, the current correction value can be calculated based on the following formula:

[0139]

[0140] in, This represents the current deviation of the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the output current of the i-th smart subgrid. This represents the sliding mode of the current deviation in the i-th smart subgrid. Indicates the positive gain coefficient; This represents the current correction value for the i-th smart subgrid.

[0141] As can be seen, in this embodiment, a voltage deviation sliding mode quantity including voltage deviation, an integral term of voltage deviation, and a derivative term of voltage deviation are set, as well as a current deviation sliding mode quantity including current deviation, an integral term of current deviation, and a derivative term of current deviation. Therefore, during the sliding mode control process, the influence of voltage deviation and current deviation is fully considered. When controlling the system state based on the sliding mode controller, control efficiency can be improved, which helps the power grid system state to quickly stabilize.

[0142] S204: Calculate the voltage reference value based on the nominal voltage, the voltage correction value, the current correction value, and the product of the virtual droop impedance and the output current of the first smart subgrid.

[0143] This step essentially describes the process of calculating the voltage reference value in droop control. Droop control in smart grids is a method used to control generator and inverter resources in the power grid to achieve uniform power distribution and system stability.

[0144] In droop control, a virtual droop impedance is preset as a calculation parameter to calculate the voltage drop caused by the current passing through it, and then the voltage reference value is calculated in combination with the nominal voltage.

[0145] In this embodiment, during the calculation of the voltage reference value, the voltage correction value and current correction value calculated above are further introduced into the droop control algorithm.

[0146] In some embodiments of this application, the voltage reference value is calculated based on the following formula:

[0147]

[0148] in, This represents the voltage reference value of the i-th smart subgrid. This represents the virtual droop impedance of the i-th smart subgrid.

[0149] S205: Establish a second-order sliding mode voltage and current controller based on the voltage reference value and the first deviation of the output voltage. The second-order sliding mode voltage and current controller defines the correlation between the filter current reference value and the first deviation, and the correlation between the control variable and the second deviation. The second deviation is the deviation between the filter current reference value and the actual filter current.

[0150] In this embodiment, a second-order sliding mode voltage and current controller is further established based on the first deviation between the voltage reference value and the output voltage.

[0151] Among them, second-order sliding mode belongs to high-order sliding mode control technology. By raising the discontinuity of the control quantity to the higher-order derivative of the sliding mode variable, a continuous control signal can be generated, thereby suppressing chattering problems while retaining the strong robustness of traditional sliding mode control.

[0152] For example, a superspiral second-order sliding mode control algorithm can be used.

[0153] In some embodiments of this application, the second-order sliding mode voltage-current controller is represented by the following formula:

[0154]

[0155] in, Indicates voltage-controlled sliding surface. This represents the voltage reference value of the i-th smart subgrid. This represents the output voltage of the i-th smart subgrid. This represents the reference value of the filter current for the i-th smart subgrid. This represents the actual filtered current of the i-th smart subgrid. This represents the i-th current-controlled sliding surface. Indicates the gain coefficient. denoted by , sign represents the input duty cycle of the boost converter in the i-th smart subgrid, and sign represents the sign function.

[0156] S206: Calculate the control variables based on the second-order sliding mode voltage and current controller.

[0157] Based on the above formula, the control variables in the current control cycle can be obtained, namely the input duty cycle of the boost converter in the smart subgrid.

[0158] After completing the calculation, the smart subgrid responsible for calculating the sliding mode control calculation task of the first smart subgrid can send the calculation result directly to the first smart subgrid, or send the calculation result to the first smart subgrid through the dispatch center.

[0159] In this embodiment, the first smart subgrid controls the boost converter based on the real-time calculated input duty cycle of the boost converter, thereby enabling real-time adjustment of the output of the distributed generator through the boost converter. This effectively suppresses voltage deviation in the smart grid, ensures load current sharing, promotes rapid system state convergence, and effectively enhances the operational stability of the smart grid.

[0160] In some embodiments of this application, it is assumed that each smart subgrid constructs a total of H sliding mode control computing tasks within each control cycle. The scheduling center integrates and schedules the computing and storage resources of the N supercomputing centers included in the smart grid, and rationally allocates the computing tasks through a multi-agent computing-grid collaborative scheduling scheme.

[0161] To effectively assess computing energy consumption, an energy consumption model for supercomputing centers is established:

[0162]

[0163] in, Let be the total energy consumption of the i-th smart subgrid supercomputing center at time t; Let be the number of servers active in the supercomputing center of the i-th smart subgrid at time t; The energy utilization efficiency of the supercomputing center for the i-th smart subgrid; , Let represent the energy consumption of the supercomputing center of the i-th smart subgrid when idle and when fully loaded, respectively. This represents the energy consumption of the supercomputing center infrastructure of the i-th smart subgrid; Let be the batch processing workload that the supercomputing center of the i-th smart subgrid needs to handle at time t; Let be the amount of interactive computing tasks processed by the supercomputing center of the i-th smart subgrid at time t. This represents the amount of tasks that the supercomputing center of the i-th smart subgrid can process per unit time.

[0164] Further establish a smart grid computing resource scheduling model, and determine the computation time of task m in the supercomputing center of the i-th smart grid subsystem. :

[0165]

[0166] The computing power of each supercomputing center Let m be the computational workload of task m, where m = 1, 2, ..., M.

[0167] When task m arrives at the supercomputing center of the i-th smart grid subsystem, its total computation time consists of two parts: waiting time and actual computation time. If there are no other tasks at the supercomputing center, task m can start computation directly, and its total computation time is equal to its own computation time. For subsequent tasks, they must wait for the previous task in the center to complete its computation before they can begin execution; therefore, their waiting time is the sum of the computation times of all preceding tasks. Thus, the overall computation time of the supercomputing center of the i-th smart grid subsystem is... The sum of the computation time for all tasks assigned to this computing center, i.e.:

[0168]

[0169] The completion time D of task m executed on the supercomputing center of the i-th smart grid subsystem. im All should satisfy the following constraints:

[0170]

[0171] in, Let be the deadline for calculating task m.

[0172] When the i-th smart subgrid supercomputing center performs task m, it should satisfy the center's storage resource constraints. Therefore, the storage resource constraints should satisfy:

[0173]

[0174] For the storage capacity of the i-th smart subgrid supercomputing center, Let m be the storage size for the m-th task.

[0175] Based on the above formula, a multi-objective optimization model for smart grid computing-electricity coordination, namely a resource scheduling optimization model, can be established as follows:

[0176]

[0177] in, They are respectively The upper and lower limits.

[0178] The following section introduces the methods for solving resource scheduling optimization models.

[0179] In some embodiments of this application, a mutant particle swarm genetic algorithm can be used to optimize the scheduling of computing resources, ultimately obtaining a multi-agent computing-power collaborative scheduling scheme, thereby improving resource utilization efficiency and system performance.

[0180] For example, the speed and position of the mutated particle swarm genetic algorithm are updated according to the following formula:

[0181]

[0182]

[0183] in, Let be the velocity of the i-th particle after the (t+1)-th cycle in the j-th dimension; The inertia weight coefficient of the particle after the t-th cycle; Let be the velocity of the i-th particle after the t-th cycle in the j-th dimension; and For learning factors; This represents the best historical position of the i-th particle after the t-th cycle in the j-th dimension. The globally optimal position after the t-th iteration in the j-th dimension; A random number between 0 and 1; A random number between 0 and 1; Let be the position of the i-th particle after the t-th cycle in the j-th dimension. Let σ be the position of the i-th particle after the (t-1)-th cycle in the j-th dimension. m As an intermediate variable, r is a random number between 0 and 1, m i Let be the mutation index of the i-th particle.

[0184] The particle motion inertia weight coefficient update function is:

[0185]

[0186] in, The maximum weight; Minimum weight; The maximum number of iterations, Let be the particle motion inertia weight coefficients for the t-th iteration. is the particle motion inertia weight coefficient for the (t+1)th iteration, where t is the iteration number of the mutated particle swarm genetic algorithm.

[0187] Determined according to the following formula:

[0188]

[0189] in, Here, r is the network error vector; r is a random number between 0 and 1; d is the distribution factor; d i Let be the cross-index of the i-th particle.

[0190] The calculation is completed based on the above formula to obtain the position of the optimal particle, which represents the final determined task allocation strategy, that is, the allocation method of sliding mode control calculation tasks for each smart sub-grid.

[0191] In this embodiment, a multi-agent computing-electricity collaborative scheduling scheme is obtained by optimizing the scheduling of computing resources for the smart grid through a mutated particle swarm genetic algorithm, thereby solving the problems of voltage and current deviations, inefficient allocation of computing resources, and high energy consumption during the operation of the smart grid.

[0192] The computing and storage resources of the supercomputing center of the smart grid are uniformly scheduled, a multi-objective optimization model of computing and power coordination in the smart grid is constructed, and a mutant particle swarm genetic algorithm is introduced to optimize the allocation of computing resources. Finally, an efficient multi-agent computing and power coordination scheduling scheme is formed, which can effectively reduce the impact of voltage and current deviations on the system during the operation of the smart grid, improve computing efficiency, and reduce overall energy consumption.

[0193] In some embodiments of this application, the total computational cost of each smart subgrid and the execution status of the sliding mode control computation task are used as optimization objectives to establish a resource scheduling optimization model. This further includes: adjusting the weighting of the computational energy consumption of each smart subgrid in the current control period in the resource scheduling optimization model based on the execution status of the sliding mode control computation task allocated to each smart subgrid in the previous control period; wherein the execution status is determined by the magnitude of the voltage control sliding mode variable and / or current control sliding mode variable of the sliding mode control computation task.

[0194] In this embodiment, the dispatch center can statistically analyze the execution status of sliding mode control calculation tasks in each smart subgrid, and then adjust the weighting coefficients included in the resource scheduling optimization model appropriately based on the execution status to improve the rationality of allocating sliding mode control calculation tasks.

[0195] For example, the execution status is determined by the magnitude of the voltage-controlled sliding mode variables and / or current-controlled sliding mode variables of the sliding mode control calculation task, as mentioned above. and Voltage-controlled sliding mode variable The smaller the value, the closer the voltage state of the smart grid system tends to the voltage control sliding surface, meaning the better the control effect; correspondingly, the current control sliding variable... The smaller the value, the closer the current state of the smart subgrid system is to the current control sliding surface, meaning the better the control effect.

[0196] For a smart subgrid performing sliding mode control calculations, the dispatch center can evaluate the performance of that smart subgrid in performing the sliding mode control calculations based on the magnitudes of the voltage control sliding mode variables and / or current control sliding mode variables from the previous control cycle or several previous control cycles. For example, a scoring algorithm is used, and the score is negatively correlated with the magnitudes of the voltage control sliding mode variables and current control sliding mode variables. That is, the score indicates the quality of the smart subgrid's performance in performing the sliding mode control calculations within the historical control cycles; a higher score indicates a better performance of the smart subgrid in performing the sliding mode control calculations in the previous control cycle or several previous control cycles.

[0197] Furthermore, the weighting of the computing energy consumption of each smart subgrid within the current control cycle is adjusted. For example, for a smart subgrid with a higher score, when allocating resources by comprehensively considering the computing energy consumption of each smart subgrid, the weighting of its computing energy consumption is reduced, thereby reducing the restriction on its computing energy consumption and allowing it to operate with higher computing energy consumption, thus undertaking more computing tasks.

[0198] As can be seen, in this embodiment, considering the differences in task execution effects caused by the differences in computing power and supported computing accuracy among different smart subgrids, the weighting of the computing energy consumption of each smart subgrid in the current control cycle is appropriately adjusted based on the execution status of the sliding mode control computing tasks of the smart subgrids in historical periods. More computing tasks are allocated to smart subgrids with better task execution, thereby increasing the energy consumption of some smart subgrids in exchange for more stable control effects, which is conducive to improving the stability of the overall smart grid system.

[0199] Based on the same inventive concept, this application also provides a computing-based collaborative scheduling device for smart grids, such as... Figure 3 As shown, the device includes:

[0200] The determination module 301 is used to determine the task information of the sliding mode control calculation task for each smart subgrid in the smart grid; the sliding mode control calculation task is used to solve the control variables so that the voltage state and current state of the smart subgrid tend to the voltage control sliding surface and the current control sliding surface, respectively.

[0201] Module 302 is established to use the total computational cost of each smart sub-grid and the task execution status of the sliding mode control computation task as optimization objectives to establish a resource scheduling optimization model.

[0202] The allocation module 303 is used to solve the resource scheduling optimization model based on the task information and the resource occupancy status of each of the smart sub-grids to obtain a task allocation strategy. The task allocation strategy represents the allocation method of the sliding mode control calculation tasks of each of the smart sub-grids.

[0203] Furthermore, the device also includes an execution module for performing the sliding mode control calculation task for constructing the first smart subgrid using the following steps:

[0204] Based on the output voltage and output current of the first smart subgrid and the multi-agent cooperative control parameters of the second smart subgrid, the multi-agent cooperative control parameters of the first smart subgrid are calculated; the multi-agent cooperative control parameters include: multi-agent average voltage, multi-agent average current, multi-agent secondary controller voltage, and multi-agent secondary controller current.

[0205] Calculate the voltage correction value based on the average voltage and nominal voltage of the multi-agents in the first smart sub-grid;

[0206] Calculate the current correction value based on the average current and output current of the multi-agents in the first smart sub-grid.

[0207] The voltage reference value is calculated based on the nominal voltage, the voltage correction value, the current correction value, and the product of the virtual droop impedance and the output current of the first smart subgrid.

[0208] A second-order sliding mode voltage-current controller is established based on the voltage reference value and the first deviation of the output voltage. The second-order sliding mode voltage-current controller defines the correlation between the filter current reference value and the first deviation, and the correlation between the control variable and the second deviation. The second deviation is the deviation between the filter current reference value and the actual filter current.

[0209] The control variables are calculated based on the second-order sliding mode voltage and current controller.

[0210] Furthermore, the output voltage, output current, and multi-agent cooperative control parameters of the first smart subgrid, and the multi-agent cooperative control parameters of the second smart subgrid, satisfy a consistency control relationship, which is expressed by the following formula:

[0211]

[0212] in, This represents the output voltage of the i-th smart subgrid. Let represent the average voltage across multiple agents in the i-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the current of the multi-agent secondary controller in the i-th smart subgrid. This represents the output current of the i-th smart subgrid. This represents the output voltage of the j-th smart subgrid. Let represent the multi-agent average voltage of the j-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the j-th smart subgrid. This represents the average current across multiple agents in the j-th smart subgrid. This represents the current of the multi-agent secondary controller in the j-th smart subgrid. This represents the communication weight between smart subgrids. If the j-th smart subgrid and the i-th smart subgrid can exchange information, then... If there is no communication, then ; Here, N represents the gain coefficient, and N represents the total number of smart subgrids.

[0213] Furthermore, the voltage correction value is calculated based on the following formula:

[0214]

[0215] in, This represents the voltage deviation of the i-th smart subgrid. Indicates the nominal voltage. This represents the output voltage of the i-th smart subgrid. This represents the voltage deviation sliding mode of the i-th smart subgrid. This represents the voltage correction value for the i-th smart subgrid. represents the positive gain coefficient; sech represents the hyperbolic secant function, and tanh represents the hyperbolic tangent function.

[0216] The current correction value is calculated based on the average current and output current of the multi-agents in the first smart sub-grid, including the calculation of the current correction value based on the following formula:

[0217]

[0218] in, This represents the current deviation of the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the output current of the i-th smart subgrid. This represents the sliding mode of the current deviation in the i-th smart subgrid. Indicates the positive gain coefficient; This represents the current correction value for the i-th smart subgrid.

[0219] Furthermore, the second-order sliding mode voltage-current controller is expressed by the following formula:

[0220]

[0221] in, Indicates voltage-controlled sliding surface. This represents the voltage reference value of the i-th smart subgrid. This represents the output voltage of the i-th smart subgrid. This represents the reference value of the filter current for the i-th smart subgrid. This represents the actual filtered current of the i-th smart subgrid. This represents the i-th current-controlled sliding surface. Indicates the gain coefficient. denoted by , sign represents the input duty cycle of the boost converter in the i-th smart subgrid, and sign represents the sign function.

[0222] Furthermore, the module further includes:

[0223] The adjustment unit is used to adjust the weighting of the total computational energy consumption of each smart subgrid in the current control cycle in the resource scheduling optimization model based on the execution status of the sliding mode control computation tasks allocated to each smart subgrid in the previous control cycle; wherein the execution status is determined by the magnitude of the voltage control sliding mode variable and / or the current control sliding mode variable.

[0224] Since the principle of the above-mentioned device in solving the problem is similar to that of the above-mentioned method, the implementation of the above-mentioned device can refer to the implementation of the above-mentioned method, and the repeated parts will not be described again.

[0225] This application also provides a computer device, such as Figure 4 The diagram shown is a structural schematic of a computer device provided in an embodiment of this application. The computing-electricity collaborative scheduling device for smart grids can be the computer device in the embodiment of this application, which executes the computing-electricity collaborative scheduling method for smart grids described above.

[0226] Computer device 402 may include one or more processors 404, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 402 may also include any memory 406 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, memory 406 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Furthermore, any memory may provide volatile or non-volatile retention of information. Furthermore, any memory may represent a fixed or removable component of computer device 402. In one case, when processor 404 executes associated instructions stored in any memory or combination of memories, computer device 402 may perform any operation of the associated instructions. Computer device 402 also includes one or more drive mechanisms 408 for interacting with any memory, such as hard disk drive mechanisms, optical disk drive mechanisms, etc.

[0227] Computer device 402 may also include an input / output module 410 (I / O) for receiving various inputs (via input device 412) and providing various outputs (via output device 414). A specific output mechanism may include a presentation device 416 and an associated graphical user interface (GUI) 418. In other embodiments, the input / output module 410 (I / O), input device 412, and output device 414 may be omitted, and the device may function solely as a computer device within a network. Computer device 402 may also include one or more network interfaces 420 for exchanging data with other devices via one or more communication links 422. One or more communication buses 424 couple the components described above together.

[0228] Communication link 422 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 422 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0229] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described method.

[0230] This application also provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to perform the steps of the above-described method.

[0231] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0232] It should also be understood that, in the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this application generally indicates that the preceding and following related objects have an "or" relationship.

[0233] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0234] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0235] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0236] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0237] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0238] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0239] This application uses specific embodiments to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this application should not be construed as a limitation of this application.

Claims

1. A computational-coordinated scheduling method for smart grids, characterized in that, The method includes: The task information for the sliding mode control calculation task for each smart subgrid in the smart grid is determined; the sliding mode control calculation task is used to solve the control variables so that the voltage state and current state of the smart subgrid tend to the voltage control sliding surface and the current control sliding surface, respectively. A resource scheduling optimization model is established by taking the total computational cost of each smart sub-grid and the task execution status of the sliding mode control computation task as optimization objectives. Based on the task information and the resource occupancy status of each smart subgrid, the resource scheduling optimization model is solved to obtain the task allocation strategy, which represents the allocation method of sliding mode control calculation tasks for each smart subgrid.

2. The method according to claim 1, characterized in that, The sliding mode control computation task for the construction of the first smart subgrid is performed using the following steps: Based on the output voltage and output current of the first smart subgrid and the multi-agent cooperative control parameters of the second smart subgrid, calculate the multi-agent cooperative control parameters of the first smart subgrid. The multi-agent cooperative control parameters include: multi-agent average voltage, multi-agent average current, multi-agent secondary controller voltage, and multi-agent secondary controller current. Calculate the voltage correction value based on the average voltage and nominal voltage of the multi-agents in the first smart sub-grid; Calculate the current correction value based on the average current and output current of the multi-agents in the first smart sub-grid. The voltage reference value is calculated based on the nominal voltage, the voltage correction value, the current correction value, and the product of the virtual droop impedance and the output current of the first smart subgrid. A second-order sliding mode voltage-current controller is established based on the voltage reference value and the first deviation of the output voltage. The second-order sliding mode voltage-current controller defines the correlation between the filter current reference value and the first deviation, and the correlation between the control variable and the second deviation. The second deviation is the deviation between the filter current reference value and the actual filter current. The control variables are calculated based on the second-order sliding mode voltage and current controller.

3. The method according to claim 2, characterized in that, The output voltage, output current, and multi-agent collaborative control parameters of the first smart subgrid, and the multi-agent collaborative control parameters of the second smart subgrid, satisfy a consistency control relationship, which is expressed by the following formula: in, This represents the output voltage of the i-th smart subgrid. Let represent the average voltage across multiple agents in the i-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the current of the multi-agent secondary controller in the i-th smart subgrid. This represents the output current of the i-th smart subgrid. This represents the output voltage of the j-th smart subgrid. Let represent the multi-agent average voltage of the j-th smart subgrid. This represents the voltage of the multi-agent secondary controller in the j-th smart subgrid. This represents the average current across multiple agents in the j-th smart subgrid. This represents the current of the multi-agent secondary controller in the j-th smart subgrid. This represents the communication weight between smart subgrids. If the j-th smart subgrid and the i-th smart subgrid can exchange information, then... If there is no communication, then ; Here, N represents the gain coefficient, and N represents the total number of smart subgrids.

4. The method according to claim 2 or 3, characterized in that, The voltage correction value is calculated based on the average voltage and nominal voltage of the multi-agents in the first smart sub-grid, including the calculation of the voltage correction value based on the following formula: in, This represents the voltage deviation of the i-th smart subgrid. Indicates the nominal voltage. This represents the output voltage of the i-th smart subgrid. This represents the voltage deviation sliding mode of the i-th smart subgrid. This represents the voltage correction value for the i-th smart subgrid. represents the positive gain coefficient; sech represents the hyperbolic secant function, and tanh represents the hyperbolic tangent function. The current correction value is calculated based on the average current and output current of the multi-agents in the first smart sub-grid, including the calculation of the current correction value based on the following formula: in, This represents the current deviation of the i-th smart subgrid. Let represent the average current of the i-th smart subgrid across multiple agents. This represents the output current of the i-th smart subgrid. This represents the sliding mode of the current deviation in the i-th smart subgrid. Indicates the positive gain coefficient; This represents the current correction value for the i-th smart subgrid.

5. The method according to claim 4, characterized in that, The second-order sliding mode voltage-current controller is expressed by the following formula: in, Indicates voltage-controlled sliding surface. This represents the voltage reference value of the i-th smart subgrid. This represents the output voltage of the i-th smart subgrid. This represents the reference value of the filter current for the i-th smart subgrid. This represents the actual filtered current of the i-th smart subgrid. This represents the i-th current-controlled sliding surface. Indicates the gain coefficient. denoted by , sign represents the input duty cycle of the boost converter in the i-th smart subgrid, and sign represents the sign function.

6. The method according to claim 1, characterized in that, The resource scheduling optimization model, which takes the total computational cost of each smart subgrid and the task execution status of the sliding mode control computation task as optimization objectives, further includes: Based on the execution status of the sliding mode control calculation tasks allocated to each of the smart subgrids in the previous control cycle, the weighting of the computational energy consumption of each of the smart subgrids in the current control cycle in the resource scheduling optimization model is adjusted; wherein, the execution status is determined by the magnitude of the voltage control sliding mode variable and / or current control sliding mode variable of the sliding mode control calculation task.

7. A computing-based collaborative scheduling device for smart grids, characterized in that, The device includes: The determination module is used to determine the task information of the sliding mode control calculation task for each smart subgrid in the smart grid; the sliding mode control calculation task is used to solve the control variables so that the voltage state and current state of the smart subgrid tend to the voltage control sliding surface and the current control sliding surface, respectively. A module is established to use the total computational cost of each smart sub-grid and the task execution status of the sliding mode control computation task as optimization objectives to establish a resource scheduling optimization model. The allocation module is used to solve the resource scheduling optimization model based on the task information and the resource occupancy status of each of the smart subgrids to obtain a task allocation strategy. The task allocation strategy represents the allocation method of the sliding mode control calculation tasks of each of the smart subgrids.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

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

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-6.

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