Scheduling method and device of power grid and storage medium

Abstract

The invention discloses a power grid scheduling method and device and a storage medium, and the method comprises the steps: collecting the original state data of a plurality of dimensions for a power grid in the operation process of the power grid; respectively constructing a target state matrix on each level of the power grid according to the original state data; calculating uncertainty of each level of the power grid according to the target state matrix of each level; performing singular value decomposition on the uncertainty of each level to obtain a state sub-matrix; according to the state sub-matrix of each level, identifying nodes with risks in each level of the power grid; and executing regulation and control operation on the nodes in the power grid. According to the embodiment, a dynamic scheduling mechanism is provided, multi-level and multi-dimensional uncertainty analysis is carried out on the power grid, the accuracy of uncertainty analysis of the power grid state in a complex environment is effectively improved, the scheduling efficiency of the power grid is improved, and therefore the stability and the operation efficiency of the power grid are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a scheduling method, equipment and storage medium for power grids. Background Art

[0002] As the scale of the power grid continues to expand, the uncertainty of dynamic load changes, power generation fluctuations and market-based transactions in the power grid has increased significantly, increasing the difficulty of power grid dispatch and placing higher demands on the power grid's rapid response and real-time adaptability.

[0003] Existing power grid dispatch optimization methods usually adopt a static dispatch model, which performs dispatch control through pre-set parameters and conditions. The static dispatch model mainly relies on single-dimensional uncertainty analysis. The accuracy of uncertainty analysis of power grid status in complex environments is low, which makes the dispatch efficiency of the power grid low, affecting the stability and operation efficiency of the power grid. Summary of the invention

[0004] In view of this, the present invention provides a dispatching method, device and storage medium for a power grid, so as to improve the accuracy of uncertainty analysis of the power grid state, thereby improving the dispatching efficiency of the power grid.

[0005] A first aspect of the present invention provides a scheduling method for a power grid, comprising:

[0006] During the operation of the power grid, collecting original status data of multiple dimensions of the power grid;

[0007] constructing target state matrices at each level of the power grid according to the original state data;

[0008] Calculating uncertainty for each level of the power grid according to the target state matrix of each level;

[0009] Performing singular value decomposition on the uncertainty at each level to obtain a state sub-matrix;

[0010] Identifying nodes at risk at each level of the power grid according to the state submatrices at each level;

[0011] A regulation operation is performed on the node in the power grid.

[0012] A second aspect of the present invention provides a dispatching device for a power grid, comprising:

[0013] The original state data acquisition module is used to collect original state data of multiple dimensions from the power grid during the operation of the power grid;

[0014] A target state matrix construction module, used to construct a target state matrix at each level of the power grid according to the original state data;

[0015] An uncertainty calculation module, used to calculate the uncertainty of each level of the power grid according to the target state matrix of each level;

[0016] A state submatrix decomposition module, used for performing singular value decomposition on the uncertainty at each level to obtain a state submatrix;

[0017] A node identification module, used for identifying nodes with risks at each level of the power grid according to the state sub-matrices at each level;

[0018] A control operation execution module is used to execute a control operation on the node in the power grid.

[0019] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the scheduling method of the power grid as described in the first aspect above.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the scheduling method for a power grid as described in the first aspect above.

[0024] A fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the scheduling method of the power grid as described in the first aspect above is implemented.

[0025] In this embodiment, during the operation of the power grid, original state data of various dimensions are collected from the power grid; target state matrices are constructed at each level of the power grid based on the original state data; uncertainties of each level of the power grid are calculated based on the target state matrices of each level; singular value decomposition is performed on the uncertainties of each level to obtain state sub-matrices; nodes with risks are identified at each level of the power grid based on the state sub-matrices of each level; and control operations are performed on nodes in the power grid. This embodiment provides a dynamic scheduling mechanism to perform multi-level and multi-dimensional uncertainty analysis on the power grid, effectively improving the accuracy of uncertainty analysis of the power grid state in a complex environment, improving the scheduling efficiency of the power grid, and thus ensuring the stability and operation efficiency of the power grid.

[0026] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 This is a flow chart of a power grid scheduling method provided in Example 1 of the present invention.

[0029] Figure 2 It is a structural diagram of a dispatching device for a power grid provided in Embodiment 2 of the present invention.

[0030] Figure 3 It is a structural schematic diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] See also Figure 1 , shows a flow chart of a power grid dispatching method provided by the first embodiment of the present invention, the method can be executed by a power grid dispatching device, the power grid dispatching device can be implemented in the form of hardware and / or software, and the power grid dispatching device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] Step 101: During the operation of the power grid, original status data of multiple dimensions are collected from the power grid.

[0036] In this embodiment, during the operation of the power grid, original status data of various dimensions can be collected from the power grid to reflect the operation status of the power grid from various aspects.

[0037] In the specific implementation, the voltage, current, power generation and electricity price of the power grid are collected at each moment, and the voltage, current, power generation and electricity price at the same moment are combined into a state data matrix.

[0038] At this time, the state data matrix can be expressed as: D i =(V i ,I i ,P i ,M i ), where D i is the state data matrix at time i, V i is the voltage collected at time i, I i is the current collected at time i, P i is the power generated at time i, M i is the electricity price collected at time i.

[0039] The multiple state data matrices are sorted in the order of each moment to obtain a state data set. At this time, the state data set D can be expressed as: D = {D1, D2, ..., D n}.

[0040] Preprocess the state data set, such as data cleaning and filtering, to obtain the original state data D pretreatment , to eliminate abnormal data and noise and improve data quality.

[0041] Step 102: construct a target state matrix at each level of the power grid according to the original state data.

[0042] In this embodiment, the power grid can be divided into multiple levels according to the geographical distribution and function of the power grid, so that the original state data can be classified according to each level to construct a hierarchical target state matrix.

[0043] Exemplarily, the levels of the power grid include regions, subgrids and microgrids, wherein a region contains one or more subgrids, and a subgrid contains one or more microgrids, that is, a region represents the state of a power grid within a larger geographical area, a subgrid represents the state of a power grid subdivided within a region, and a microgrid represents the state of a power grid subdivided within a subgrid.

[0044] Furthermore, the target state matrix of the region is:

[0045]

[0046] Among them, D 区域 is the target state matrix of the region, γ j is the weight of the region, indicating the importance of the region in the original state data, δ j is the regional influencing factor, reflecting the operating characteristics of the regional power grid, ρ j represents the geographical distance between regions, V j is the voltage of the region, I j is the current in the area, P j is the regional power generation, M j is the electricity price of the region, and m is the number of regions.

[0047] The target state matrix of the subnet is:

[0048]

[0049] Among them, D 子网 is the target state matrix of the subnet, θ k is the weight of the subnet, indicating the importance of the subnet in the original state data, λ k is the impact factor of the subnet, reflecting the dynamic characteristics of the subnet, σ k is the load difference between subnets, V k is the voltage of the subnet, I k is the current of the subnet, P k is the power generation of the subgrid, Mk is the electricity price of the subnet, and l is the number of subnets.

[0050] The target state matrix of the microgrid is:

[0051]

[0052] Among them, D 微网 is the target state matrix of the microgrid, η p is the weight of the microgrid, indicating the importance of the microgrid in the original state data, μ p is the sensitivity factor of the microgrid, κ p is the state difference between microgrids, V p is the voltage of the microgrid, I p is the current of the microgrid, P p is the power generation of the microgrid, M p is the electricity price of the microgrid, and q is the number of microgrids.

[0053] Step 103: Calculate the uncertainty of each level of the power grid according to the target state matrix of each level.

[0054] In this embodiment, a multi-level multi-dimensional entropy matrix model can be established based on the hierarchical target state matrix, and the multi-level multi-dimensional entropy matrix model is used to represent the uncertainty of the power grid state at different levels, different geographical locations and different functional units.

[0055] When the levels of the power grid include regions, subgrids and microgrids, a unified entropy measurement function can be constructed for regions, subgrids and microgrids in a multi-level and multi-dimensional entropy matrix model to represent the state uncertainty of different levels of the power grid.

[0056] Exemplarily, the uncertainty is:

[0057]

[0058] Among them, H L is the uncertainty of the level L in the grid, where level L represents a region, subgrid or microgrid, and N L represents the number of levels L (the number of regions is m, the number of subnets is l, and the number of microgrids is q), α L,i is the weight of the i-th region in level L, D L,i is the parameter of the i-th region in the target state matrix of level L.

[0059] The entropy measurement function (i.e. uncertainty) of the region H 区域 , the entropy measurement function (i.e. uncertainty) of the subnetwork H 子网 and the entropy measurement function (i.e. uncertainty) of the microgrid H 微网 Combining to form a multi-level multi-dimensional entropy matrix model H 总, which is used to comprehensively represent the uncertainty of power grid status at different levels, different geographical locations and functional units: H 总 =H 区域 +H 子网 +H 微网 .

[0060] Step 104: Perform singular value decomposition on the uncertainty of each level to obtain a state sub-matrix.

[0061] In this embodiment, the uncertainties at different levels in the multi-level multi-dimensional entropy matrix model are subjected to singular value decomposition to obtain a state sub-matrix related to the power grid state at the level.

[0062] On the one hand, the entropy measurement function (i.e., uncertainty) of the region is subjected to matrix decomposition to extract the characteristic state variables and their associated uncertainty indicators in the region and generate the state submatrix of the region.

[0063] Exemplarily, the state submatrix of a region is:

[0064]

[0065] Among them, S 区域 is the state submatrix of the region, m is the number of regions, λ 区域,i is the singular value of the region, indicating the importance of the grid state feature i in the region, u 区域,i is the left eigenvector of the region, representing the projection of the grid state characteristics in the input space, v 区域,i is the right eigenvector of the region, representing the projection of the grid state characteristics in the output space, represents the tensor outer product operation, δ 区域,i is the weight factor of regional uncertainty, reflecting the influence of the grid status characteristics in the same region on the overall uncertainty, φ 区域,i It is the geographical characteristic factor of the region.

[0066] On the other hand, the entropy measurement function (i.e., uncertainty) of the subnetwork is matrix decomposed to extract the characteristic state variables and their associated uncertainty indicators within the subnetwork, and generate the state submatrix of the subnetwork.

[0067] Exemplarily, the state sub-matrix of the subnet is:

[0068]

[0069] Among them, S 子网 is the state sub-matrix of the subnet, l is the number of subnets, λ 子网,k is the singular value of the subnet, indicating the importance of the grid state feature k in the subnet, u 子网,k is the left eigenvector of the subnet, v 子网,kis the right eigenvector of the subnet, β 子网,k is the dynamic characteristic influencing factor of the subnet, θ 子网,k is the state transfer impact factor of the subnet, reflecting the transfer effect of the state between subnets, σ 子网,k Indicates the load difference between subnets.

[0070] On the other hand, the entropy measurement function (ie, uncertainty) of the microgrid is matrix decomposed to extract characteristic state variables and their associated uncertainty indicators within the microgrid, and generate a state submatrix of the microgrid.

[0071] Exemplarily, the state sub-matrix of the microgrid is:

[0072]

[0073] Among them, S 微网 is the state sub-matrix of the microgrid, q is the number of microgrids, λ 微网,p is the singular value of the microgrid, indicating the importance of the grid state feature p in the region, u 微网,p is the left eigenvector of the microgrid, v 微网,p is the right eigenvector of the microgrid, γ 微网,p is the sensitivity factor of the microgrid, reflecting the uncertainty impact of the fluctuation son on the microgrid state, μ 微网,p is the fluctuation characteristic of the microgrid, κ 微网,p is the state difference of the microgrid, ψ 微网,p is the phase angle inside the microgrid, indicating the correlation phase between the grid state characteristics.

[0074] Step 105 : identifying nodes with risks at each level of the power grid according to the state sub-matrices at each level.

[0075] In this embodiment, the state sub-matrix of each level is subjected to hierarchical uncertainty analysis, the entropy value in the state sub-matrix of each level is calculated, the uncertainty of the power grid state in each level is quantified, the degree of chaos is characterized, and high-risk nodes are identified in the multi-level structure of the power grid based on the degree of chaos.

[0076] In the specific implementation, hierarchical uncertainty analysis can be performed based on the state sub-matrix of each level, and the entropy value in the state sub-matrix of each level can be calculated to update the uncertainty of each level of the power grid and quantify the degree of chaos of the power grid status at different levels.

[0077] For example, the updated uncertainty is:

[0078]

[0079] Among them, H′ L represents the updated uncertainty level L. Level L represents the region, subnetwork or microgrid, N Lrepresents the number of levels L, π L,i is the weight coefficient of the i-th node or sub-region in the L-th level, S L,i is the parameter of the i-th region in the state submatrix of level L.

[0080] If the updated uncertainty is greater than or equal to a preset threshold, then the node with the highest updated uncertainty in each level is determined to be at risk, that is, the node is at high risk.

[0081] Then, the nodes at risk can be expressed as:

[0082]

[0083] Among them, R 高风险 is the set of nodes at risk, X is the node at risk, H′ 区域,i is the updated uncertainty of region i, H′ 子网,j is the uncertainty of subnet j after update, H′ 微网,k is the updated uncertainty of microgrid k, and the nodes whose updated uncertainty is greater than or equal to the threshold ∈ have the highest uncertainty risk in the current state.

[0084] Step 106: Perform control operations on nodes in the power grid.

[0085] In this embodiment, a dynamically optimized scheduling scheme can be designed based on the uncertainty of the node (especially the uncertainty after update), and the scheduling scheme can be executed separately in multiple levels of the power grid for regulation to improve the stability of the power grid.

[0086] During the operation of the power grid, the external environmental data and the state changes within the system are monitored in real time, and the multi-level and multi-dimensional entropy matrix model is dynamically updated and reconstructed. The update includes real-time correction of the state sub-matrices at different levels, so that the multi-level entropy matrix model can timely reflect the changes in the operating status of the power grid.

[0087] The adjusted dynamically optimized dispatching plan will be synchronized to the grid control center and dispatching departments at all levels to achieve coordinated control of regions, subgrids and microgrids, and optimize the overall stability and operating efficiency of the grid.

[0088] In the specific implementation, the nodes can be sorted according to uncertainty (especially the updated uncertainty) to obtain the priority of the nodes, and the nodes with high-risk uncertainty in the power grid can be identified first to build a control priority set, determine the high-risk nodes that should be regulated first in the region, subgrid and microgrid, and guide the priority allocation of control resources.

[0089] According to the priority, a dynamic management strategy for the power grid load is formulated in combination with the real-time load demand fluctuations of the power grid and the market price fluctuation trends, and a first regulation strategy is generated for the load of the nodes in the power grid, that is, the first regulation strategy is used to dynamically adjust the load distribution of each high-risk node in the power grid.

[0090] According to the priority, the second regulation strategy is generated for the power generation power of the nodes in the power grid according to the power generation capacity of each level of nodes in the current power grid and the market fluctuation situation. That is, the second regulation strategy is used to enable the power generation power to be adaptively adjusted at different nodes to cope with the impact of market price fluctuations and ensure the cost-effectiveness and stability of power generation.

[0091] According to the priority, the third regulation strategy is generated for the operation of node trading electricity in the power grid by real-time monitoring of market price fluctuations. That is, the third regulation strategy is a response strategy for market transactions, which enables the market trading behavior of nodes at all levels to be adaptively adjusted according to price fluctuations, thereby optimizing the economy and response speed of power transactions.

[0092] A dispatching plan is generated according to the first control strategy, the second control strategy and the third control strategy, and coordinated control is implemented at each level of the power grid according to the dispatching plan to improve the stability and operation efficiency of the power grid under high uncertainty conditions.

[0093] In this embodiment, during the operation of the power grid, original state data of various dimensions are collected from the power grid; target state matrices are constructed at each level of the power grid based on the original state data; uncertainties of each level of the power grid are calculated based on the target state matrices of each level; singular value decomposition is performed on the uncertainties of each level to obtain state sub-matrices; nodes with risks are identified at each level of the power grid based on the state sub-matrices of each level; and control operations are performed on nodes in the power grid. This embodiment provides a dynamic scheduling mechanism to perform multi-level and multi-dimensional uncertainty analysis on the power grid, effectively improving the accuracy of uncertainty analysis of the power grid state in a complex environment, improving the scheduling efficiency of the power grid, and thus ensuring the stability and operation efficiency of the power grid.

[0094] Embodiment 2

[0095] See also Figure 2 , shows a schematic diagram of the structure of a dispatching device for a power grid provided by Embodiment 2 of the present invention. Figure 2 As shown, the device comprises:

[0096] The original state data collection module 201 is used to collect original state data of multiple dimensions from the power grid during the operation of the power grid;

[0097] A target state matrix construction module 202 is used to construct a target state matrix at each level of the power grid according to the original state data;

[0098] An uncertainty calculation module 203, configured to calculate the uncertainty of each level of the power grid according to the target state matrix of each level;

[0099] A state sub-matrix decomposition module 204 is used to perform singular value decomposition on the uncertainty of each level to obtain a state sub-matrix;

[0100] A node identification module 205, configured to identify nodes at risk at each level of the power grid according to the state submatrices at each level;

[0101] The control operation execution module 206 is used to execute a control operation on the node in the power grid.

[0102] In one embodiment of the present invention, the original state data acquisition module 201 is further used for:

[0103] Collecting voltage, current, power generation and electricity price of the power grid at each time;

[0104] The voltage, the current, the generated power and the electricity price at the same time form a state data matrix;

[0105] Sorting the plurality of state data matrices according to the order of each moment to obtain a state data set;

[0106] The state data set is preprocessed to obtain original state data.

[0107] In one embodiment of the present invention, the levels of the power grid include region, subnet and microgrid; the region includes one or more subnets, and the subnet includes one or more microgrids;

[0108] The target state matrix of the region is:

[0109]

[0110] Among them, D 区域 is the target state matrix of the region, γ j is the weight of the region, δ j is the impact factor of the region, ρ j represents the geographical distance between the regions, V j is the voltage in the region, I j is the current in the region, P j is the power generation capacity of the area, M j is the electricity price of the area, m is the number of areas;

[0111] The target state matrix of the subnet is:

[0112]

[0113] Among them, D 子网 is the target state matrix of the subnet, θ k is the weight of the subnet, λ k is the influence factor of the subnet, σ k is the load difference between the subnets, V k is the voltage of the subnet, I k is the current of the subnet, P k is the power generation of the subgrid, M k is the electricity price of the subnet, l is the number of the subnets;

[0114] The target state matrix of the microgrid is:

[0115]

[0116] Among them, D 微网 is the target state matrix of the microgrid, η p is the weight of the microgrid, μ p is the sensitivity factor of the microgrid, κ p is the state difference between the microgrids, V p is the voltage of the microgrid, I p is the current of the microgrid, P p is the power generation of the microgrid, M p is the electricity price of the microgrid, and q is the number of microgrids.

[0117] In one embodiment of the present invention, the uncertainty is:

[0118]

[0119] Among them, H L is the uncertainty of level L in the power grid, where level L represents the region, the subgrid or the microgrid, N L represents the number of levels L, α L,i is the weight of the i-th region in level L, D L,i is the parameter of the i-th region in the target state matrix of level L.

[0120] In one embodiment of the present invention, the state sub-matrix of the region is:

[0121]

[0122] Among them, S 区域 is the state submatrix of the region, m is the number of regions, λ区域,i are the singular values ​​of the region, u 区域,i is the left eigenvector of the region, v 区域,i is the right eigenvector of the region, represents the tensor outer product operation, δ 区域,i is the weight factor for the uncertainty in the region, φ 区域,i is the geographical characteristic factor of the area;

[0123] The state sub-matrix of the subnet is:

[0124]

[0125] Among them, S 子网 is the state sub-matrix of the sub-network, l is the number of the sub-network, λ 子网,k is the singular value of the subnet, u 子网,k is the left eigenvector of the subnet, v 子网,k is the right eigenvector of the subnet, β 子网,k is the dynamic characteristic influencing factor of the subnet, θ 子网,k is the state transfer impact factor of the subnet, σ 子网,k Indicates the load difference between the subnets;

[0126] The state sub-matrix of the microgrid is:

[0127]

[0128] Among them, S 微网 is the state submatrix of the microgrid, q is the number of microgrids, λ 微网,p is the singular value of the microgrid, u 微网,p is the left eigenvector of the microgrid, v 微网,p is the right eigenvector of the microgrid, γ 微网,p is the sensitivity factor of the microgrid, μ 微网,p is the fluctuation characteristic of the microgrid, κ 微网,p is the state difference of the microgrid, ψ 微网,p is the phase angle inside the microgrid.

[0129] In one embodiment of the present invention, the node identification module 205 includes:

[0130] An uncertainty updating module, configured to update the uncertainty of each level of the power grid according to the state sub-matrix of each level;

[0131] The risk determination module is used to determine that the node with the highest uncertainty after the update is at risk in each level if the uncertainty after the update is greater than or equal to a preset threshold.

[0132] In one embodiment of the present invention, the updated uncertainty is:

[0133]

[0134] Among them, H′ L The uncertainty level L after the level L is updated represents the region, the subnet or the micronet, N L represents the number of levels L, π L,i is the weight coefficient of the i-th node or sub-region in the L-th level, S L,i is the parameter of the i-th region in the state submatrix of level L.

[0135] In one embodiment of the present invention, the control operation execution module 206 is further used to:

[0136] Sort the nodes according to the uncertainty to obtain the priority of the nodes;

[0137] generating a first control strategy for the load of the node in the power grid according to the priority;

[0138] generating a second control strategy for the power generation power of the node in the power grid according to the priority;

[0139] generating a third regulation strategy for the operation of trading power of the nodes in the power grid according to the priority;

[0140] Generate a scheduling plan according to the first control strategy, the second control strategy and the third control strategy;

[0141] Coordinated control is achieved at each level of the power grid according to the dispatching plan.

[0142] The dispatching device for a power grid provided in the embodiment of the present invention can execute the dispatching method for a power grid provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the dispatching method for a power grid.

[0143] Embodiment 3

[0144] See also Figure 3, showing a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0145] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0146] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0147] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the dispatching method of the power grid.

[0148] In some embodiments, the dispatching method of the power grid may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the dispatching method of the power grid described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the dispatching method of the power grid in any other appropriate manner (e.g., by means of firmware).

[0149] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0150] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0151] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0152] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0153] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0154] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0155] Embodiment 4

[0156] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the scheduling method of the power grid provided in any embodiment of the present invention is implemented.

[0157] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0158] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0159] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A dispatching method for a power grid, characterized in that: include: During the operation of the power grid, collecting original status data of multiple dimensions of the power grid; constructing target state matrices at each level of the power grid according to the original state data; Calculating uncertainty for each level of the power grid according to the target state matrix of each level; Performing singular value decomposition on the uncertainty at each level to obtain a state sub-matrix; Identifying nodes at risk at each level of the power grid according to the state submatrices at each level; A regulation operation is performed on the node in the power grid.

2. The method according to claim 1, characterized in that: The collecting of original status data of multiple dimensions from the power grid includes: Collecting voltage, current, power generation and electricity price of the power grid at each time; The voltage, the current, the generated power and the electricity price at the same time form a state data matrix; Sorting the plurality of state data matrices according to the order of each moment to obtain a state data set; The state data set is preprocessed to obtain original state data.

3. The method according to claim 1, characterized in that The levels of the power grid include region, subnet and microgrid; the region includes one or more subnets, and the subnet includes one or more microgrids; The target state matrix of the region is: Among them, D 区域 is the target state matrix of the region, γ j is the weight of the region, δ j is the impact factor of the region, ρ j represents the geographical distance between the regions, V j is the voltage in the region, I j is the current in the region, P j is the power generation capacity of the area, M j is the electricity price of the area, m is the number of areas; The target state matrix of the subnet is: Among them, D 子网 is the target state matrix of the subnet, θ k is the weight of the subnet, λ k is the influence factor of the subnet, σ k is the load difference between the subnets, V k is the voltage of the subnet, I k is the current of the subnet, P k is the power generation of the subgrid, M k is the electricity price of the subnet, l is the number of the subnets; The target state matrix of the microgrid is: Among them, D 微网 is the target state matrix of the microgrid, η p is the weight of the microgrid, μ p is the sensitivity factor of the microgrid, κ p is the state difference between the microgrids, V p is the voltage of the microgrid, I p is the current of the microgrid, P p is the power generation of the microgrid, M p is the electricity price of the microgrid, and q is the number of microgrids.

4. The method according to claim 3, characterized in that The uncertainty is: Among them, H L is the uncertainty of level L in the power grid, where level L represents the region, the subgrid or the microgrid, N L represents the number of levels L, α L,i is the weight of the i-th region in level L, D L,i is the parameter of the i-th region in the target state matrix of level L.

5. The method according to claim 4, characterized in that The state submatrix of the region is: Among them, S 区域 is the state submatrix of the region, m is the number of regions, λ 区域,i are the singular values ​​of the region, u 区域,i is the left eigenvector of the region, v 区域,i is the right eigenvector of the region, represents the tensor outer product operation, δ 区域,i is the weight factor for the uncertainty in the region, φ 区域,i is the geographical characteristic factor of the area; The state sub-matrix of the subnet is: Among them, S 子网 is the state sub-matrix of the sub-network, l is the number of the sub-network, λ 子网,k is the singular value of the subnet, u 子网,k is the left eigenvector of the subnet, v 子网,k is the right eigenvector of the subnet, β 子网,k is the dynamic characteristic influencing factor of the subnet, θ 子网,k is the state transfer impact factor of the subnet, σ 子网,k Indicates the load difference between the subnets; The state sub-matrix of the microgrid is: Among them, S 微网 is the state submatrix of the microgrid, q is the number of microgrids, λ 微网,p is the singular value of the microgrid, u 微网,p is the left eigenvector of the microgrid, v 微网,p is the right eigenvector of the microgrid, γ 微网,p is the sensitivity factor of the microgrid, μ 微网,p is the fluctuation characteristic of the microgrid, κ 微网,p is the state difference of the microgrid, ψ 微网,p is the phase angle inside the microgrid.

6. The method according to claim 5, characterized in that The identifying nodes with risks at each level of the power grid according to the state sub-matrices at each level respectively includes: updating the uncertainty of each level of the power grid according to the state sub-matrix of each level; If the uncertainty after update is greater than or equal to a preset threshold, then it is determined in each level that the node with the highest uncertainty after update is at risk.

7. The method according to claim 6, characterized in that The updated uncertainty is: Among them, H′ L The uncertainty level L after the level L is updated represents the region, the subnet or the micronet, N L represents the number of levels L, π L,i is the weight coefficient of the i-th node or sub-region in the L-th level, S L,i is the parameter of the i-th region in the state submatrix of level L.

8. The method according to any one of claims 1 to 7, characterized in that The performing a control operation on the node in the power grid includes: Sort the nodes according to the uncertainty to obtain the priority of the nodes; generating a first control strategy for the load of the node in the power grid according to the priority; generating a second control strategy for the power generation power of the node in the power grid according to the priority; generating a third regulation strategy for the operation of trading power of the nodes in the power grid according to the priority; Generate a scheduling plan according to the first control strategy, the second control strategy and the third control strategy; Coordinated control is achieved at each level of the power grid according to the dispatching plan.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the dispatching method for a power grid according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the scheduling method for a power grid according to any one of claims 1 to 8 is implemented.

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