Power control and task scheduling method and device for computing and power collaboration of smart power grid

Abstract

The invention provides a power control and task scheduling method and device for intelligent power grid power calculation cooperation, and relates to the technical field of intelligent power grid power calculation cooperation. The method comprises the following steps: determining task information of a power control task constructed for each intelligent sub-power grid in the intelligent power grid; the power control task is used for calculating controllable input power of a generator set in the intelligent sub-power grid, so that power generation parameters of the intelligent sub-power grid meet a supply-demand balance condition; establishing a resource scheduling optimization model by taking the total operation cost of each intelligent sub-grid and the execution condition of the power control task as optimization targets; and solving the resource scheduling optimization model according to the task information and the resource occupation state of each intelligent sub-grid to obtain a task allocation strategy, wherein the task allocation strategy represents the allocation condition of the power control task. The real-time changing supply-demand relationship of the smart grid is met, and the dynamic response performance of the smart grid system is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field related to smart grid, in particular to the technical field of smart grid computing power collaboration, and specifically relates to a power control and task scheduling method and device for smart grid computing power collaboration. BACKGROUND

[0002] With the accelerated development of smart grid and the continuous deepening of digital transformation, the computing power integrated scheduling technology for smart grid is gradually becoming a research hotspot in the academic and industrial fields at home and abroad.

[0003] The deep coupling of power systems and computing power systems puts forward higher requirements for intelligent scheduling mechanism. Realizing computing power collaborative scheduling not only helps to improve energy utilization efficiency and resource allocation level, and enhance the stability and reliability of the power grid, but also is a key support for promoting the sustainable development of smart grid.

[0004] The related technology proposes to establish a joint planning model of computing power center and power grid system, and uses a related algorithm to solve, so as to obtain the optimization result of power grid scheduling. Although this method has achieved certain results in improving the utilization efficiency of computing power resources, it mainly focuses on the collaborative optimization of load type and power side, and does not consider the influence of system dynamic characteristics, especially tracking error, on control performance in the process of smart grid operation.

[0005] Considering the complex structure of smart grid system and the highly heterogeneous computing tasks, higher requirements are put forward for computing power collaborative scheduling. Therefore, it is urgent to build an intelligent control and scheduling strategy with real-time response and efficient resource coordination capability, so as to ensure the stable operation of the system while realizing the overall economy and operation efficiency. This is also one of the core problems faced by the current computing power integrated scheduling technology. SUMMARY

[0006] In order to solve the problems in the prior art, the embodiments of the present application provide a power control and task scheduling method and device for smart grid computing power collaboration, which is beneficial to meet the real-time change of supply and demand relationship of smart grid, and effectively improve the dynamic response performance of smart grid system.

[0007] In a first aspect, the present application provides a power control and task scheduling method for smart grid computing power collaboration, which comprises:

[0008] determining task information of a power control task constructed for each intelligent sub-grid in the smart grid; the power control task is used to calculate the controllable input power of the generator set in the intelligent sub-grid, so that the power generation parameters of the intelligent sub-grid meet the supply and demand balance condition;

[0009] The total operation cost of each intelligent sub-grid and the execution of the power control task are taken as optimization objectives to establish a resource scheduling optimization model;

[0010] A task allocation strategy is obtained by solving the resource scheduling optimization model according to the task information and the resource occupation state of each intelligent sub-grid, and the task allocation strategy represents the allocation of the power control task.

[0011] In some embodiments of the present application, the following steps are adopted to perform the power control task constructed for the first intelligent sub-grid:

[0012] A first deviation between the total power generation and the power generation demand of the first intelligent sub-grid is determined;

[0013] The first deviation is converted into a frequency deviation of the first intelligent sub-grid;

[0014] A joint fractional order model is determined according to the fractional order model corresponding to each power generation system; the joint fractional order model includes a first correlation between the controllable input power and the power generation parameter;

[0015] A sliding mode controller is established based on the first correlation, taking a second deviation as a sliding mode control variable; the second deviation is the deviation between the power generation parameter and its corresponding expected power generation parameter; the second correlation between the controllable input power, the power generation parameter and the second deviation is defined in the sliding mode controller;

[0016] The controllable input power is calculated based on the sliding mode controller.

[0017] In some embodiments of the present application, the power generation system includes at least one of a solar photovoltaic system, a wind power generation system, an electric vehicle discharge system, a battery energy storage system and a diesel generator set; the controllable input power includes the input control power of the diesel generator set.

[0018] In some embodiments of the present application, the joint fractional order model is represented by the following formula:

[0019]

[0020] Wherein, , represents the power generation parameter of the i th intelligent sub-grid, represents the frequency deviation of the i th intelligent sub-grid, represents the output power of the solar photovoltaic system in the i th intelligent sub-grid, represents the output power of the wind power generation system in the i th intelligent sub-grid, represents the output power of the electric vehicle discharge system in the i th intelligent sub-grid, represents the output power of the battery energy storage system in the i-th smart sub-grid, represents the output power of the diesel generator set in the i-th smart sub-grid, represents the output power of the governor of the diesel generator set in the i-th smart sub-grid; represents a matrix of order fractional differential operators, is a system matrix, is a control matrix, is a nonlinear parameter matrix, is a disturbance term, and t represents time.

[0021] In some embodiments of the present application, the sliding mode control variable is expressed by the following formula:

[0022]

[0023] wherein, represents the sliding mode control variable of the i-th smart sub-grid, represents a matrix of order -1 fractional differential operators, represents the second deviation, and k represents a preset coefficient;

[0024] The sliding mode controller is expressed by the following formula:

[0025]

[0026] wherein, represents the controllable input power, , , , , , , , represents a preset control coefficient, represents a sign function, represents a hyperbolic secant function.

[0027] In some embodiments of the present application, the total operation cost includes: total operation and maintenance cost and computing resource occupation cost; the total operation and maintenance cost includes operation and maintenance cost of each power generation system and energy consumption cost of the computing center in the smart sub-grid;

[0028] Taking the total operation cost of each smart sub-grid and the execution of the power control task as the optimization target, the resource scheduling optimization model further includes:

[0029] According to an execution condition of a power control task distributed to each smart sub-grid in a previous control period, a weighted weight of an operation and maintenance total cost and / or an algorithm resource occupation cost of each smart sub-grid in a current control period in a resource scheduling optimization model is adjusted, wherein the execution condition is determined by a size of a sliding mode variable in the sliding mode controller.

[0030] In a second aspect, the application provides a power control and task scheduling device for smart grid algorithm electricity cooperation, the device comprising:

[0031] A determination module is configured to determine task information of a power control task constructed for each smart sub-grid in a smart grid; the power control task is used to calculate a controllable input power of a generator set in the smart sub-grid, so that a power generation parameter of the smart sub-grid meets a supply-demand balance condition;

[0032] An establishment module is configured to establish a resource scheduling optimization model by taking a total operation cost of each smart sub-grid and an execution condition of the power control task as an optimization target;

[0033] A distribution module is configured to solve the resource scheduling optimization model according to the task information and a resource occupation state of each smart sub-grid to obtain a task distribution strategy, wherein the task distribution strategy represents an allocation condition of the power control task.

[0034] In some embodiments of the application, the device further comprises an execution module configured to execute the power control task constructed for a first smart sub-grid by using the following steps:

[0035] A first deviation between a total power generation power and a power generation demand power of the first smart sub-grid is determined;

[0036] The first deviation is converted into a frequency deviation of the first smart sub-grid;

[0037] A joint fractional order model is determined according to a corresponding fractional order model of each power generation system; the joint fractional order model includes a first correlation between the controllable input power and the power generation parameter;

[0038] A sliding mode controller taking a second deviation as a sliding mode control variable is established based on the first correlation; the second deviation is a deviation between the power generation parameter and an expected power generation parameter corresponding to the power generation parameter; a second correlation between the controllable input power, the power generation parameter and the second deviation is defined in the sliding mode controller;

[0039] The controllable input power is calculated based on the sliding mode controller.

[0040] In some embodiments of the present application, the power generation system comprises at least one of a solar photovoltaic system, a wind power generation system, an electric vehicle discharging system, a battery energy storage system, and a diesel generator set; the controllable input power comprises an input control power of the diesel generator set.

[0041] In some embodiments of the present application, the joint fractional order model is expressed by the following formula:

[0042]

[0043] wherein, , represents a power generation parameter of the i-th smart sub-grid, represents a frequency deviation of the i-th smart sub-grid, represents an output power of a solar photovoltaic system in the i-th smart sub-grid, represents an output power of a wind power generation system in the i-th smart sub-grid, represents an output power of an electric vehicle discharging system in the i-th smart sub-grid, represents an output power of a battery energy storage system in the i-th smart sub-grid, represents an output power of a diesel generator set in the i-th smart sub-grid, represents an output power of a governor of the diesel generator set in the i-th smart sub-grid; represents a matrix of fractional order differential operators, is a system matrix, is a control matrix, is a nonlinear parameter matrix, is a disturbance term, and t represents time.

[0044] In some embodiments of the present application, the sliding mode control variable is expressed by the following formula:

[0045]

[0046] wherein, represents a sliding mode control variable of the i-th smart sub-grid, represents a matrix of fractional order differential operators, represents the second deviation, and k represents a preset coefficient;

[0047] The sliding mode controller is expressed by the following formula:

[0048]

[0049] wherein, represents the controllable input power, , 、 、 、 、 、 、 denotes a preset control coefficient, sech denotes a hyperbolic secant function, and tanh denotes a hyperbolic tangent function.

[0050] In some embodiments of the present application, the total operation cost includes: total operation and maintenance cost and computing resource occupation cost; the total operation and maintenance cost includes operation and maintenance cost of each power generation system and energy consumption cost of a computing center in the intelligent sub-power grid;

[0051] The establishing module is specifically configured to: according to an execution condition of a power control task distributed to each intelligent sub-power grid in a previous control period, adjust a weighted weight of the total operation and maintenance cost and / or the computing resource occupation cost of each intelligent sub-power grid in a current control period in the resource scheduling optimization model; wherein the execution condition is determined by a size of a sliding mode variable in the sliding mode controller.

[0052] In a third aspect, the present application provides a computer device, including a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to realize the steps of the power control and task scheduling method of the smart grid computing power collaboration.

[0053] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program / instruction, and the computer program / instruction is executed by a processor to realize the steps of the power control and task scheduling method of the smart grid computing power collaboration.

[0054] In a fifth aspect, the present application provides a computer program product, which includes a computer program / instruction, and the computer program / instruction is executed by a processor to realize the steps of the power control and task scheduling method of the smart grid computing power collaboration.

[0055] From the above description, it can be seen that power control tasks are constructed for each intelligent sub-power grid in the smart grid, which are used to calculate the controllable input power of the generator set in the intelligent sub-power grid, so that the power generation parameters of the intelligent sub-power grid meet the supply and demand balance condition, thereby facilitating to meet the real-time changing supply and demand relationship of the smart grid and effectively improving the dynamic response performance of the smart grid system.

[0056] Further, the computing resources of each intelligent sub-power grid in the smart grid are uniformly scheduled, a multi-objective optimization model of the smart grid computing power collaboration is constructed, the computing resources are optimally allocated, and finally an efficient multi-agent computing power collaboration scheduling scheme is formed.

[0057] In addition, in view of the problem of insufficient control accuracy, a fractional order exponential convergence sliding mode tracking controller is proposed, which significantly improves the tracking accuracy and dynamic response performance of the system; in view of the problems of low utilization efficiency of intelligent power grid computing resources and high energy consumption, a multi-objective optimization model of intelligent power grid task scheduling is constructed, and a whale optimization algorithm with adaptive inertia weight is designed, realizing efficient allocation of computing resources and effective control of operation energy consumption, thereby improving the operation efficiency and energy efficiency of the intelligent power grid as a whole.

[0058] In order to make the above and other objects, features and advantages of the present application more apparent, the following will describe a preferred embodiment in detail, and the accompanying drawings will be described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0060] Figure 1 A flowchart of a power control and task scheduling method for intelligent power grid computing power cooperation in an embodiment of the present application;

[0061] Figure 2 A flowchart of the execution process of the power control task in an embodiment of the present application;

[0062] Figure 3 A structural diagram of a power control and task scheduling device for intelligent power grid computing power cooperation in an embodiment of the present application;

[0063] Figure 4 A structural diagram of a computer device in an embodiment of the present application.

[0064] Explanation of drawing symbols:

[0065] 301, determination module;

[0066] 302, establishment module;

[0067] 303, distribution module;

[0068] 402, computer device;

[0069] 404, processor;

[0070] 406, memory;

[0071] 408, driving mechanism;

[0072] 410, input / output module;

[0073] 412, input device;

[0074] 414, output device;

[0075] 416, presentation device;

[0076] 418, graphical user interface;

[0077] 420, network interface;

[0078] 422, communication link;

[0079] 424, communication bus. DETAILED DESCRIPTION

[0080] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0081] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, device, product, or apparatus that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or apparatus.

[0082] The present application provides method operation steps as described in the embodiments or flowcharts, but more or fewer operation steps can be included based on routine or non-creative work. The order of steps listed in the embodiments is only one of the many execution orders of the steps, and does not represent the only execution order. In actual system or device product execution, the method order shown in the embodiments or drawings can be executed in sequence or in parallel.

[0083] The core inventive concept of the present application is that: a power control task is constructed for each smart sub-grid in the smart grid, which is used to calculate the controllable input power of the generator set in the smart sub-grid, so that the power generation parameters of the smart sub-grid meet the supply-demand balance condition, thereby facilitating to meet the real-time changing supply-demand relationship of the smart grid and effectively improving the dynamic response performance of the smart grid system.

[0084] Further, the computing resources of each smart sub-grid in the smart grid are uniformly scheduled, a multi-objective optimization model of smart grid computing power collaboration is constructed, the computing resources are optimally allocated, and finally an efficient multi-agent computing power collaboration scheduling scheme is formed.

[0085] The smart grid computing power collaboration power control and task scheduling method provided by the present application will be further introduced below in combination with the drawings. Referring to Figure 1 , the method comprises the following steps:

[0086] S101: Determine the task information of the power control task constructed for each smart sub-grid in the smart grid; the power control task is used to calculate the controllable input power of the generator set in the smart sub-grid, so that the power generation parameters of the smart sub-grid meet the supply-demand balance condition.

[0087] The smart grid computing power collaboration power control and task scheduling method provided by the present application can be applied to the dispatching center of the smart grid, and the smart grid further comprises a plurality of smart sub-grids interconnected by power distribution lines. Each smart sub-grid can comprise a plurality of power generation systems.

[0088] For example, each smart sub-grid comprises a solar photovoltaic system, a wind power generation system, an electric vehicle discharge system, a battery energy storage system and a diesel generator set. In addition, a supercomputing center is also included, which is used to execute various computing tasks of the smart sub-grid.

[0089] Among them, the input power of part of the power generation system is uncontrollable, for example, wind power generation system, solar photovoltaic system, etc. The input power of these power generation systems is related to the current state of the environment. In addition, the input power of some power generation systems is controllable, for example, diesel generator set, by adjusting the input power of such power generation systems, so that the power generation parameters of the smart sub-grid meet the supply-demand balance condition.

[0090] For example, in the present application, the controllable input power of the generator set includes the input control power of the diesel generator set.

[0091] In the embodiments of the present application, different power generation systems are modeled by fractional order modeling according to the characteristics of the power generation systems, then a joint fractional order model of the intelligent sub-power grid is established, a fractional order exponential convergence sliding mode controller is constructed based on the joint fractional order model, and the power generation of the intelligent sub-power grid is tracked and controlled to adapt to the power supply demand of the current power grid.

[0092] The description of the fractional order modeling and the sliding mode controller can be found below.

[0093] In the embodiments of the present application, the control of the output power of the power generation system of the intelligent sub-power grid is continuously performed, that is, for each intelligent sub-power grid, a new power control task is generated according to the current state in each control period.

[0094] In one control period, after each intelligent sub-power grid constructs a power control task, the related task information can be sent to the scheduling center, and the computing resources of the entire intelligent power grid are scheduled by the scheduling center to determine how to allocate the power control tasks of the intelligent sub-power grid. For example, the task information can include resource occupation information and task completion deadline. The resource occupation information can include the size of the task computing amount.

[0095] S102: The total operating cost of each intelligent sub-power grid and the execution of the power control task are taken as optimization objectives, and a resource scheduling optimization model is established.

[0096] For example, the total operating cost includes a total operating and maintenance cost and an algorithm resource occupation cost, and the total operating and maintenance cost includes the operating and maintenance cost of each power generation system and the energy consumption cost of the computing center in the intelligent sub-power grid.

[0097] The scheduling center can comprehensively consider the computing energy consumption, algorithm resource occupation, and the like of each intelligent sub-power grid to establish a multi-objective optimization model.

[0098] S103: The task allocation strategy is obtained by solving the resource scheduling optimization model according to the task information and the resource occupation state of each intelligent sub-power grid, and the task allocation strategy represents the allocation of the power control task.

[0099] In the embodiments of the present application, the scheduling center solves the resource scheduling optimization model by using a multi-objective optimization algorithm to obtain the task allocation strategy. Then, the scheduling center allocates the power control tasks of each intelligent sub-power grid to the target intelligent sub-power grid based on the task allocation strategy. For example, for the i-th intelligent sub-power grid, the corresponding power control task can be allocated to the i-th intelligent sub-power grid for calculation, or can be allocated to the j-th intelligent sub-power grid.

[0100] The establishment of the resource scheduling optimization model and the multi-objective optimization algorithm are described below.

[0101] In the embodiments of the present application, since the fractional order model corresponding to each power generation system and the joint fractional order model are needed for constructing the sliding mode controller, in order to facilitate understanding, firstly, the construction of the fractional order model of each power generation system in the intelligent sub-grid is described.

[0102] The diesel generator set is composed of a generator and an internal combustion engine, which are connected by coaxial coupling mode to form a complete power and power generation unit. Among them, the speed regulator of the generator directly determines the power output characteristics of the diesel generator set. In view of its obvious nonlinear and memory dynamic behavior in the running process, the fractional order modeling of the diesel generator set is carried out to more accurately depict its dynamic response characteristics, which can be described based on the following formula:

[0103]

[0104]

[0105] 、 is a fractional order differential operator, is a frequency deviation, is the control input of the diesel generator set, is the output power of the diesel generator set, is the output power of the speed regulator, and are the time constants of the speed regulator and the diesel generator, respectively, R i is the speed regulating coefficient of the diesel engine set, the subscript i=1, 2, …, N represents the i-th intelligent sub-grid.

[0106] wherein, can be calculated according to the deviation between the total power generation and the power generation demand, which can be seen in the following.

[0107] The solar photovoltaic system converts the received solar radiation energy directly into electrical energy based on the principle of photoelectric effect. Considering the complex influence of external disturbances such as light intensity and temperature change on the output current and voltage characteristics of the system, as well as the non-integer order characteristics existing in its dynamic response, in order to more accurately describe its power generation behavior, the fractional order modeling of the solar photovoltaic system is carried out, which can be described based on the following formula:

[0108]

[0109] is a fractional order differential operator, is the output power of the solar photovoltaic system, is the input power of the solar photovoltaic system, is the time constant of the solar photovoltaic system.

[0110] Wind turbine utilizes wind energy to drive blade rotation and generate electricity, whose dynamic characteristics are greatly affected by wind speed fluctuation and system inertia. To more accurately describe its dynamic response behavior, a fractional order model is established for wind turbine, which can be described based on the following formula:

[0111]

[0112] is a fractional order differential operator, is the output power of the wind turbine, is the time constant of the wind turbine, is the input power of the wind turbine, is the characteristic parameter of the wind turbine.

[0113] The discharging system of electric vehicle and the battery energy storage system are important power consumption and energy storage units in smart grid, which have significant nonlinearity and memory characteristics. To accurately reflect the dynamic behavior in the charging and discharging process, a fractional order model is established for the discharging system of electric vehicle and the battery energy storage system, which can be described based on the following formula:

[0114]

[0115]

[0116] is a fractional order differential operator, is the output power of the electric vehicle, is the time constant of the electric vehicle, is the coefficient characteristic of the electric vehicle, is the output power of the battery energy storage system, is the time constant of the battery energy storage system, is the coefficient characteristic of the battery energy storage system, is the control input of the battery energy storage system.

[0117] Referring to Figure 2 The power control task of the first smart sub-grid construction can be performed based on the following steps:

[0118] S201: Determine the first deviation of the total power generation and power generation demand of the first smart sub-grid.

[0119] In the embodiments of the present application, the total power generation can be adjusted to meet the real-time power demand, realizing the stable operation of the smart sub-grid i, and the corresponding power deviation is:

[0120]

[0121] is the power deviation of the smart sub-grid i, a power demand of the smart sub-grid i, a total power generation of the smart sub-grid i.

[0122] S202: convert the first deviation into a frequency deviation of the first smart sub-grid.

[0123] It can be understood that the smart grid meets the supply-demand balance condition that the total power generation is substantially equal to the power load in real time. A sudden change (such as a load surge) in the smart grid breaks this balance, resulting in a frequency deviation.

[0124] For the controllable input power generation system of the smart sub-grid, the huge rotor of the generator set stores kinetic energy. When there is a power shortage, in order to make up for the energy gap, the rotating kinetic energy is automatically consumed, causing the grid frequency to decrease. Conversely, power surplus will cause the frequency to rise. Therefore, there is a correlation between power deviation and frequency deviation.

[0125] In some embodiments of the present application, the frequency deviation can be calculated based on the following formula:

[0126]

[0127] is a fractional order differential operator, is an equivalent inertia constant of the smart sub-grid i, is a damping constant of the smart sub-grid i.

[0128] The equivalent inertia constant is a key dynamic characteristic parameter used to quantify the overall resistance to frequency change of the power system, which represents the inertia time constant when the total rotational inertia of the entire power system is equivalent to a whole.

[0129] The damping constant is a key parameter used to quantify the ability to suppress power and frequency oscillation in the dynamic response process of the system, which represents the inherent or introduced positive effect of the system through control, which can consume oscillation energy and make the system return to steady state.

[0130] S203: determine a joint fractional order model according to the fractional order model corresponding to each power generation system; the joint fractional order model includes a first correlation between the controllable input power and the power generation parameter.

[0131] In an embodiment of the present application, in combination with the fractional order module of each power generation system, the fractional order model of the frequency fluctuation of the smart sub-grid i can be calculated as:

[0132]

[0133] The combined fractional order model of the intelligent sub-grid i is derived from the above formula as follows:

[0134]

[0135]

[0136] wherein, , represents the power generation parameter of the i-th intelligent sub-grid, represents the frequency deviation of the i-th intelligent sub-grid, represents the output power of the solar photovoltaic system in the i-th intelligent sub-grid, represents the output power of the wind power generation system in the i-th intelligent sub-grid, represents the output power of the electric vehicle discharging system in the i-th intelligent sub-grid, represents the output power of the battery energy storage system in the i-th intelligent sub-grid, represents the output power of the diesel generator set in the i-th intelligent sub-grid, represents the output power of the governor of the diesel generator set in the i-th intelligent sub-grid; represents a fractional order differential operator matrix, a system matrix, a control matrix, a nonlinear parameter matrix, a disturbance term, and t represents time.

[0137] wherein each matrix is represented by the following formula:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] It can be seen that the combined fractional order model defines the first correlation between the controllable input power and the power generation parameter .

[0144] According to the joint fractional order model of the intelligent sub-power grid i, the generation parameters include the frequency deviation and the deviation between the output power of each generation system and the corresponding expected power. Among them, for the uncontrollable input generation system, the corresponding expected power can be determined in multiple ways. For example, the expected power can be set in advance, and can be associated with the environment, time, etc.

[0145] S204: Based on the first association relationship, a sliding mode controller taking the second deviation as a sliding mode control variable is established; the second deviation is the deviation between the generation parameter and the corresponding expected generation parameter; the second association relationship between the controllable input power, the generation parameter and the second deviation is defined in the sliding mode controller.

[0146] In the embodiments of the present application, based on the above joint fractional order model, a sliding mode controller is further constructed for controlling the output power of the intelligent sub-power grid i.

[0147] Firstly, the tracking error of each generation parameter of the intelligent sub-power grid i is defined as

[0148]

[0149] Among them, represents the expected generation parameter corresponding to each generation parameter of the intelligent sub-power grid i, and can also be understood as the expected trajectory. It can be known from the above that the expected generation parameter includes the expected frequency deviation and the expected power of each generation system. For the expected frequency deviation, it can be determined according to the control target, for example, the expected frequency deviation in the expected generation parameter is set to 0 if it is expected to eliminate the frequency deviation.

[0150] In some embodiments of the present application, the joint fractional order model of the intelligent sub-power grid i and the second deviation defined above are combined to construct a sliding mode controller, wherein the fractional order sliding mode surface is expressed as follows, which can also be understood as the sliding mode surface of the sliding mode controller.

[0151]

[0152] Among them, represents the sliding mode control variable of the i-th intelligent sub-power grid, represents the fractional order differential operator matrix, represents the second deviation, and k represents a preset coefficient;

[0153] Correspondingly, the sliding mode controller can be expressed by the following formula:

[0154] ​

[0155] wherein, represents the controllable input power, , , , , , , , represents a preset control coefficient, sech represents a hyperbolic secant function, and tanh represents a hyperbolic tangent function.

[0156] It can be seen that the controllable input power , the power generation parameter , and the second deviation are associated.

[0157] S205: Calculate the controllable input power based on the sliding mode controller.

[0158] In the process of performing the power control task, the control variable in the current control period, that is, the controllable input power, can be obtained by calculating based on the above formula.

[0159] After the calculation is completed, the intelligent sub-grid responsible for calculating the power control task of the first intelligent sub-grid can directly send the calculation result to the first intelligent sub-grid, or send the calculation result to the first intelligent sub-grid through the dispatching center.

[0160] In this embodiment, the first intelligent sub-grid controls the input power of the diesel generator set according to the controllable input power calculated in real time, so as to control the intelligent sub-grid to approach the condition of meeting the supply-demand balance as much as possible.

[0161] In some embodiments of the present application, in each control period, it is assumed that each intelligent sub-grid constructs H power control tasks, the dispatching center unifies and integrates the computing power resources and storage resources of the N supercomputing centers included in the smart grid, and through a multi-agent computing power collaborative scheduling scheme, the computing tasks are reasonably distributed.

[0162] In order to effectively evaluate the computing energy consumption, the operation cost of each power generation system is defined.

[0163] The operation cost of the diesel generator can be represented as:

[0164]

[0165] wherein, , is the operation and maintenance cost and fuel cost of the diesel generator, is the operation and maintenance parameter of the diesel generator, , , is the diesel generator coefficient, T is the total running time.

[0166] The operating cost of a solar photovoltaic system can be represented as:

[0167]

[0168] wherein, is the operating and maintenance parameter of the diesel generator.

[0169] The operating cost of a wind turbine can be represented as:

[0170]

[0171] wherein, is the operating and maintenance parameter of the wind turbine.

[0172] The operating cost of an electric vehicle can be represented as:

[0173]

[0174] wherein, is the operating and maintenance parameter of the electric vehicle.

[0175] The operating cost of a battery energy storage system can be represented as:

[0176]

[0177] wherein, is the operating and maintenance parameter of the battery energy storage system.

[0178] To effectively improve the economy of the smart grid system and reduce energy consumption, an energy consumption model of the supercomputing center is established:

[0179]

[0180]

[0181] wherein, represents the total energy consumption of the i-th smart sub-grid supercomputing center; O i (t) is the number of servers in the i-th smart sub-grid supercomputing center in an active state; PUE i is the energy utilization efficiency of the i-th smart sub-grid supercomputing center; , respectively represent the energy consumption of the i-th smart sub-grid supercomputing center server in idle state and full load state; is the amount of batch processing computing tasks that the i-th smart sub-grid supercomputing center needs to process; is the amount of interactive computing tasks that the i-th smart sub-grid supercomputing center needs to process, represents the amount of tasks that the i-th intelligent sub-grid supercomputing center server can handle in a unit of time; represents the energy consumption of the i-th intelligent sub-grid supercomputing center infrastructure, is the operation cost of the intelligent sub-grid supercomputing center, is the operation and maintenance parameter of the intelligent sub-grid supercomputing center.

[0182] To ensure the smooth execution of computing tasks and avoid assigning them to supercomputing centers with insufficient resources, the scheduling process needs to consider the multi-dimensional computing power resources of each node, such as CPU, memory, disk I / O, and network bandwidth. The resources required by the computing task application should not exceed the current available resources of the node. The specific resource constraint conditions are as follows:

[0183]

[0184] wherein d n represents the demand of computing task n for resources, H represents the total number of computing tasks, a i represents the total amount of resources of the intelligent sub-grid supercomputing center i; y n,i is a decision variable, if computing task n is assigned to intelligent sub-grid supercomputing center i, then y n,i = 1, otherwise y n,i = 0.

[0185] In the process of executing computing task scheduling, to prevent the same task from being assigned to multiple intelligent sub-grid supercomputing centers for repeated execution, causing waste of computing resources, it is necessary to ensure that each computing task can only be scheduled to one node. This scheduling uniqueness requirement can be guaranteed by the following constraint conditions:

[0186]

[0187] The computing time of computing task n in the i-th intelligent grid subsystem supercomputing center is time i :

[0188]

[0189] wherein c i is the computing power value of the i-th supercomputing center, and size n is the size of the computing amount of task n.

[0190] When the computing task n is scheduled to the supercomputing center of the i-th smart grid subsystem, its total computing time is composed of the waiting time and the actual computing time. If the center has no other computing task being executed at the moment, the computing task n can be directly run, and its total computing time is equal to its own computing time. For the computing task arrived later, it needs to wait for the completion of the previous computing task before it can be executed, and its waiting time is the sum of the computing time of all the previous computing tasks. Therefore, the total computing time D i of the supercomputing center of the i-th smart grid subsystem is the sum of the computing time of all the computing tasks allocated to the center, that is:

[0191]

[0192] wherein L is the number of computing tasks allocated to the supercomputing center of the i-th smart grid subsystem.

[0193] Meanwhile, the completion time D i,n of the task n executed on the supercomputing center of the i-th smart grid subsystem should satisfy the following constraints:

[0194]

[0195] wherein d n is the deadline of the computing task m.

[0196] Therefore, for the multi-dimensional computing resources such as CPU, memory, disk IO, and network bandwidth, the comprehensive resource utilization rate U j of the supercomputing center can be represented as:

[0197]

[0198] wherein , , , are the CPU, memory, disk IO, and network bandwidth respectively applied by the computing task; , , , are the total amount of CPU, memory, disk IO, and network bandwidth respectively of the supercomputing center j; , , , are the utilization rate of CPU, memory, disk IO, and network bandwidth respectively of the supercomputing center j.

[0199] Correspondingly, the average resource utilization rate U avg of all the supercomputing centers can be calculated as follows:

[0200]

[0201] To measure the balance of resource allocation in each supercomputing center in the global range, the embodiment can use the standard deviation S as a measurement index of global resource load balancing. The calculation formula is as follows:

[0202]

[0203] Further, an intelligent power grid task scheduling multi-objective optimization model integrating operation cost, energy consumption index, and system tracking performance is constructed:

[0204]

[0205] Among them: 、 、 are the weights of intelligent power grid energy consumption, comprehensive resource utilization rate, and tracking error, respectively.

[0206] The method for solving the resource scheduling optimization model is introduced as follows.

[0207] The core of the multi-objective optimization model is to seek a compromise solution that balances multiple objectives from all feasible scheduling schemes, and then obtain the optimal scheduling strategy. In view of the problems of insufficient control accuracy, low utilization rate of computing resources, and high energy consumption in the operation of intelligent power grids, an improved adaptive inertia weight whale optimization algorithm is proposed. The algorithm combines the adaptive inertia weight adjustment strategy based on fitness normalization and population entropy feedback mechanism, introduces the nonlinear coupling mechanism of spiral behavior and surrounding behavior, and superimposes local Gaussian disturbance modulation, effectively realizing the synergistic enhancement of global search ability and local fine optimization ability.

[0208] In some embodiments of the application, the improved adaptive inertia weight whale optimization algorithm used has the following process:

[0209] Initialize the position of the whale individual :

[0210]

[0211] D is the dimension, are the maximum and minimum whale individual positions, respectively, and t is the current iteration number.

[0212] Calculate the fitness of the whale individual , and select the individual with the best fitness as the initial global optimal solution :

[0213]

[0214] Calculate the fitness normalization :

[0215]

[0216] 、 are the current population optimal / worst fitness, respectively, is a minimum constant.

[0217] Calculate the population entropy:

[0218]

[0219] M represents the population size, H t is the population entropy of the tth generation, is the probability distribution of individual i in the tth generation.

[0220] Calculate the nonlinear inertia weight, which controls the convergence speed and search range, solves the problem of premature convergence and falling into local optimum:

[0221]

[0222] where, is the inertia weight of the tth generation, , are the minimum and maximum inertia weight values, respectively, is a nonlinear adjustment factor, is the maximum value of the entropy theory.

[0223] Calculate the local Gaussian disturbance:

[0224]

[0225] where, is the disturbance intensity coefficient.

[0226] Calculate the coupling offset factor, which is used to modulate the position change:

[0227]

[0228] where, is the offset factor adjustment coefficient, T max is the maximum number of iterations.

[0229] Generate control variables and update positions:

[0230]

[0231] A, C are control vectors used to adjust the surrounding update intensity; r is a random variable, , a is the convergence control factor.

[0232] Calculate the surrounding difference value:

[0233]

[0234] Fuse the spiral behavior and the surrounding behavior, and superimpose the local Gaussian disturbance, so as to update the position of the whale individual:

[0235]

[0236] b、 is a parameter for controlling the spiral path, wherein is a random number.

[0237] If the current individual is better than the historical optimum, update the global optimal solution:

[0238]

[0239] The convergence factor is updated, and a linear decreasing strategy is adopted to reduce a:

[0240]

[0241] Repeat the above steps until t = Tmax, the iteration is ended, and the optimal solution is output.

[0242] Based on the above formula, the optimal solution is obtained, which represents the finally determined task allocation strategy, that is, the allocation of power control tasks of each intelligent sub-grid.

[0243] In this embodiment, the improved adaptive inertia weight whale optimization algorithm is used to optimize the scheduling of computing resources of the smart grid, so as to realize the optimal allocation of computing resources and task scheduling, and form an algorithm-electricity collaborative control and task scheduling scheme with high performance and high energy efficiency.

[0244] In some embodiments of the present application, the total operating cost of each intelligent sub-grid and the execution of the power control task are taken as the optimization target, and the resource scheduling optimization model further includes:

[0245] According to the execution of the power control task allocated to each intelligent sub-grid in the last control period, the weighted weights of the total operating and maintenance cost and / or the computing resource occupation cost of each intelligent sub-grid in the current control period in the resource scheduling optimization model are adjusted; wherein, the execution is determined by the size of the sliding mode control variable in the sliding mode controller.

[0246] Specifically, in this embodiment, the scheduling center can count the execution of the power control task participated by each intelligent sub-grid, and then adjust the weighted coefficients contained in the resource scheduling optimization model based on the execution, so as to improve the rationality of the allocated power control task.

[0247] For example, the execution situation is determined by the size of the sliding mode control variable in the sliding mode controller, i.e., the above-mentioned The voltage control sliding mode variable The smaller the voltage control sliding mode variable is, the better the control effect of the power generation parameter of the intelligent sub-grid system is.

[0248] Therefore, for the intelligent sub-grid that executes the power control task, the dispatching center can evaluate the execution situation of the intelligent sub-grid in the previous control period or the previous several control periods according to the size of the sliding mode control variable. For example, a scoring algorithm is used for scoring, and the score of the scoring algorithm is negatively correlated with the size of the sliding mode control variable. That is, the score represents the degree of good or bad of the intelligent sub-grid in the historical control period in executing the power control task, and the higher the score is, the better the execution effect of the intelligent sub-grid in the previous control period or the previous several control periods in executing the power control task is.

[0249] Further, the weighted weights of the total operation and maintenance cost and / or the computing resource occupation cost of each intelligent sub-grid in the current control period are adjusted. For example, for the intelligent sub-grid with a higher score, when the resource allocation is comprehensively considered in terms of the calculation energy consumption of each intelligent sub-grid, the weighted weight of the total operation and maintenance cost and / or the computing resource occupation cost of the intelligent sub-grid is reduced, so as to reduce the limitation of the calculation energy consumption and the limitation of the maintenance cost of the intelligent sub-grid, and allow the intelligent sub-grid to operate at a higher calculation energy consumption or a higher maintenance cost, thereby undertaking more calculation tasks.

[0250] It can be seen that, in the embodiment, considering that different intelligent sub-grids have different task execution effects due to differences in calculation capability, supported calculation precision, and the like, the weighted weights of the total operation and maintenance cost and / or the computing resource occupation cost of each intelligent sub-grid in the current control period are adjusted according to the execution situation of the power control task of the intelligent sub-grid in the historical period, and more calculation tasks are allocated to the intelligent sub-grid with a better execution situation, so as to improve the stability of the control effect at the cost of the energy consumption of part of the intelligent sub-grids, which is beneficial to improving the stability of the overall system of the intelligent grid.

[0251] Based on the same inventive concept, the present application also provides a power control and task scheduling device for intelligent grid computing cooperation, as shown in Figure 3 The device comprises:

[0252] A determination module 301 is configured to determine task information of a power control task constructed for each intelligent sub-grid in an intelligent grid; the power control task is used to calculate a controllable input power of a generator set in the intelligent sub-grid, so that a power generation parameter of the intelligent sub-grid meets a supply-demand balance condition.

[0253] The establishing module 302 is configured to establish a resource scheduling optimization model, taking total operation cost of each smart sub-grid and execution of the power control task as an optimization target.

[0254] The allocating module 303 is configured to solve the resource scheduling optimization model according to the task information and resource occupation state of each smart sub-grid to obtain a task allocation strategy, which represents allocation of the power control task.

[0255] Further, the apparatus further comprises an executing module configured to execute the power control task constructed for the first smart sub-grid by using the following steps:

[0256] determining a first deviation between total power generation and power generation demand of the first smart sub-grid;

[0257] converting the first deviation into a frequency deviation of the first smart sub-grid;

[0258] determining a joint fractional order model according to a fractional order model corresponding to each power generation system; the joint fractional order model comprises a first correlation between the controllable input power and the power generation parameter;

[0259] establishing a sliding mode controller taking a second deviation as a sliding mode control variable based on the first correlation; the second deviation is a deviation between the power generation parameter and an expected power generation parameter corresponding to the power generation parameter; the sliding mode controller defines a second correlation between the controllable input power, the power generation parameter and the second deviation;

[0260] calculating the controllable input power based on the sliding mode controller.

[0261] Further, the power generation system comprises at least one of a solar photovoltaic system, a wind power generation system, an electric vehicle discharging system, a battery energy storage system and a diesel generator set; the controllable input power comprises input control power of the diesel generator set.

[0262] Further, the joint fractional order model is represented by the following formula:

[0263]

[0264] wherein, , represents a power generation parameter of the i th smart sub-grid, represents a frequency deviation of the i th smart sub-grid, represents output power of a solar photovoltaic system in the i th smart sub-grid, represents output power of a wind power generation system in the i th smart sub-grid, represents the output power of the electric vehicle discharging system in the i-th smart sub-grid, represents the output power of the battery energy storage system in the i-th smart sub-grid, represents the output power of the diesel generator set in the i-th smart sub-grid, represents the output power of the speed governor of the diesel generator set in the i-th smart sub-grid; a matrix of order fractional differential operators, is a system matrix, is a control matrix, is a nonlinear parameter matrix, is a disturbance term, and t represents time.

[0265] Further, the sliding mode control variable is represented by the following formula:

[0266]

[0267] wherein, represents the sliding mode control variable of the i-th smart sub-grid, represents a matrix of order fractional differential operators, represents the second deviation, and k represents a preset coefficient;

[0268] The sliding mode controller is represented by the following formula:

[0269]

[0270] wherein, represents the controllable input power, , , , , , , , represents a preset control coefficient, sech represents a hyperbolic secant function, and tanh represents a hyperbolic tangent function.

[0271] Further, the total operation cost includes: a total operation and maintenance cost and a computing resource occupation cost; the total operation and maintenance cost includes an operation and maintenance cost of each power generation system and an energy consumption cost of a computing center in the smart sub-grid;

[0272] ​The establishing module is specifically configured to: according to an execution condition of a power control task assigned to each smart sub-grid in a previous control period, adjust a weighted weight of an operation and maintenance total cost and / or an algorithm resource occupation cost of each smart sub-grid in a current control period in a resource scheduling optimization model; wherein the execution condition is determined by a size of a sliding mode variable in the sliding mode controller.

[0273] Since the principle of solving the problem of the above device is similar to the above method, the implementation of the above device can be referred to the implementation of the above method, and the repeated parts will not be repeated.

[0274] The present application also provides a computer device, as shown in Figure 4 The structure schematic diagram of the computer device provided by the embodiment of the present application, and the power control and task scheduling device of the smart grid and algorithm electricity cooperation can be the computer device in the embodiment of the present application, and execute the power control and task scheduling method of the smart grid and algorithm electricity cooperation in the present application.

[0275] The computer device 402 can include one or more processors 404, such as one or more central processing units (CPUs), each of which can implement one or more hardware threads. The computer device 402 can also include any memory 406 for storing any kind of information, such as code, settings, data, etc. Without limitation, for example, the memory 406 can include any one or a combination of more than one of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, etc. More generally, any memory can store information using any technology. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 402. In one case, the computer device 402 can perform any operation of the associated instructions when the processor 404 executes the associated instructions stored in any memory or combination of memories. The computer device 402 also includes one or more drive mechanisms 408 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0276] The computer device 402 can also include an input / output module 410 (I / O) for receiving input (via input device 412) and for providing output (via output device 414). One specific output mechanism can include a presentation device 416 and associated graphical user interface (GUI) 418. In other embodiments, the input / output module 410 (I / O), input device 412, and output device 414 can not be included, and the computer device 402 can be a standalone computer device in a networked environment. The computer device 402 can 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 above-described components so that each component can communicate with each other component.

[0277] The communication links 422 can be implemented in any manner, such as through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication links 422 can include any combination of hardwired links, wireless links, routers, gateway functionality, name servers, etc., governed by any protocol or combination of protocols.

[0278] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program, and the computer program is run by a processor to execute the steps of the above method.

[0279] The embodiments of the present application further provide a computer readable instruction, wherein when the processor executes the instruction, the program in the instruction makes the processor execute the steps of the above method.

[0280] It should be understood that the size of the sequence number of each process described above does not mean the order of execution in various embodiments of the present application. The execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0281] It should also be understood that in the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships. For example, A and / or B can represent three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in the present application generally represents that the front and rear associated objects are in an "or" relationship.

[0282] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0283] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0284] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can also be electrical, mechanical or other form of connection.

[0285] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0286] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically independently, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0287] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part of the prior art that contributes to the present application, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0288] The principles and implementation manners of the present application are described in the specific embodiments in the present application. The above embodiment description is only used to help understand the method and core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed, and the above is not understood as a limitation of the present application.

Claims

1. A power control and task scheduling method for smart grid computing-coordinated operation, characterized in that, The method includes: The task information for the power control task constructed for each smart subgrid in the smart grid is determined; the power control task is used to calculate the controllable input power of the generator sets in the smart subgrid so that the power generation parameters of the smart subgrid meet the supply and demand balance condition. A resource scheduling optimization model is established using the total operating cost of each smart sub-grid and the execution status of the power control 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 of the power control task.

2. The method according to claim 1, characterized in that, The power control task for the first smart subgrid is performed using the following steps: Determine the first deviation between the total power generation of the first smart subgrid and the power generation demand. The first deviation is converted into the frequency deviation of the first smart subgrid. Based on the fractional-order models corresponding to each power generation system, a joint fractional-order model is determined; the joint fractional-order model includes a first correlation between the controllable input power and the power generation parameters; Based on the first correlation, a sliding mode controller is established with the second deviation as the sliding mode control variable; The second deviation is the deviation between the power generation parameter and its corresponding expected power generation parameter; the sliding mode controller defines a second correlation between the controllable input power, the power generation parameter and the second deviation; The controllable input power is calculated based on the sliding mode controller.

3. The method according to claim 2, characterized in that, The power generation system includes at least one of a solar photovoltaic system, a wind power generation system, an electric vehicle discharge system, a battery energy storage system, and a diesel generator set; the controllable input power includes the input control power of the diesel generator set.

4. The method according to claim 3, characterized in that, The joint fractional-order model is expressed by the following formula: in, , This represents the generation parameters of the i-th smart subgrid. This represents the frequency deviation of the i-th smart subgrid. The output power of the solar photovoltaic system in the i-th smart subgrid This represents the output power of the wind power generation system in the i-th smart subgrid. This represents the output power of the electric vehicle discharge system in the i-th smart subgrid. This represents the output power of the battery energy storage system in the i-th smart subgrid. This represents the output power of the diesel generator set in the i-th smart subgrid. This represents the output power of the governor of the diesel generator set in the i-th smart subgrid; express Fractional differential operator matrix For the system matrix, For the control matrix, It is a nonlinear parameter matrix. Let t be the disturbance term, and t represent time.

5. The method according to claim 4, characterized in that, The sliding mode control variable is expressed by the following formula: in, Let i represent the sliding mode control variable of the i-th smart subgrid. express Fractional differential operator matrix This represents the second deviation, and k represents the preset coefficient; The sliding mode controller is represented by the following formula: in, This refers to the controllable input power. , , , , , , , represents the preset control coefficient, sech represents the hyperbolic secant function, and tanh represents the hyperbolic tangent function.

6. The method according to any one of claims 2-5, characterized in that, The total operating cost includes: total operation and maintenance cost and computing resource occupancy cost; the total operation and maintenance cost includes the operation and maintenance cost of each of the power generation systems and the energy consumption cost of the computing center in the smart sub-grid. The resource scheduling optimization model, which takes the total operating cost of each smart sub-grid and the execution status of the power control task as optimization objectives, further includes: Based on the execution status of the power control tasks allocated to each of the smart subgrids in the previous control cycle, the weighting of the total operation and maintenance cost and / or computing resource occupation cost 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 sliding mode variable in the sliding mode controller.

7. A power control and task scheduling device for smart grid computing-coordinated operation, characterized in that, The device includes: The determination module is used to determine the task information for the power control task constructed for each smart subgrid in the smart grid; the power control task is used to calculate the controllable input power of the generator sets in the smart subgrid so that the power generation parameters of the smart subgrid meet the supply and demand balance condition. A module is established to use the total operating cost of each smart sub-grid and the execution status of the power control 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 sub-grids to obtain a task allocation strategy, wherein the task allocation strategy represents the allocation status of the power control task.

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.

Images