Power supply network load balancing scheduling method and device, electronic equipment and medium

Abstract

The application discloses a power grid load balancing scheduling method and device, electronic equipment and medium. The method comprises the following steps: based on a first preset time period, determining the node load balancing degree of each generator node in the power grid according to the load information in the power grid; determining the target load balancing degree of each first preset time period within a second preset time period of the power grid according to the node load balancing degree of each generator node; wherein, the second preset time period is greater than the first preset time period; predicting the load of the power grid according to the target load balancing degree of each first preset time period within the second preset time period of the power grid, and performing load balancing scheduling on the power grid according to the prediction result. The technical scheme solves the problem that after a certain scale of renewable energy such as wind power is incorporated into the power grid, the load rate of part of the power transmission line is too high, and realizes the balanced scheduling of the load of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid load balancing, and in particular to a power grid load balancing scheduling method and device, electronic equipment and a medium. BACKGROUND

[0002] With the increase of renewable energy such as photovoltaic and wind power connected to the power grid, the load of the power grid has also increased. For the power industry, power source planning and grid planning are very important, which is a necessary step in the early planning of the power system.

[0003] However, for the power grid in China, after incorporating a certain scale of renewable energy such as wind power into the power grid, some transmission lines will have a phenomenon of excessively high load rate, which will affect the safe and reliable power supply, so it is very important to predict the load of the power grid and to reasonably plan the power source and the grid. SUMMARY

[0004] The present application provides a power grid load balancing scheduling method and device, electronic equipment and a medium to solve the problem of excessively high load rate of some transmission lines after incorporating a certain scale of renewable energy such as wind power into the power grid.

[0005] According to one aspect of the present application, a power grid load balancing scheduling method is provided, comprising:

[0006] Based on a first predetermined time period, the node load balancing degree of each generator node in the power grid is determined according to the load information in the power grid;

[0007] The target load balancing degree of each first predetermined time period within a second predetermined time period of the power grid is determined according to the node load balancing degree of each generator node; wherein the second predetermined time period is greater than the first predetermined time period;

[0008] The load of the power grid is predicted according to the target load balancing degree of each first predetermined time period within a second predetermined time period of the power grid, and the load balancing scheduling of the power grid is performed according to the prediction result.

[0009] According to another aspect of the present application, a power grid load balancing scheduling device is provided, comprising:

[0010] The first balancing degree determination module is configured to determine the node load balancing degree of each generator node in the power grid based on a first predetermined time period according to the load information in the power grid;

[0011] a second balance degree determining module, configured to determine a target load balance degree of the power supply network in each first preset time period within a second preset time period according to the node load balance degree of each generator node, wherein the second preset time period is longer than the first preset time period;

[0012] a scheduling module, configured to predict the load of the power supply network according to the target load balance degree of the power supply network in each first preset time period within a second preset time period, and perform load balance scheduling on the power supply network according to the prediction result.

[0013] According to another aspect of the present application, an electronic device is provided, which comprises:

[0014] at least one processor; and

[0015] a memory connected to the at least one processor in communication; wherein

[0016] 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 to enable the at least one processor to perform the power supply network load balance scheduling method according to any one of the embodiments of the present application.

[0017] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the power supply network load balance scheduling method according to any one of the embodiments of the present application when executed.

[0018] The technical solution of the embodiments of the present application is based on a first preset time period, determines the node load balance degree of each generator node in the power supply network according to the load information in the power supply network, determines the target load balance degree of the power supply network in each first preset time period within a second preset time period according to the node load balance degree of each generator node, wherein the second preset time period is longer than the first preset time period, predicts the load of the power supply network according to the target load balance degree of the power supply network in each first preset time period within a second preset time period, and performs load balance scheduling on the power supply network according to the prediction result, thereby solving the problem of excessively high load rate of some transmission lines after a certain scale of renewable energy such as wind power is integrated into the power grid, and realizing the balanced scheduling of the load of the power supply network.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to make the technical solution in the embodiments of the present application clearer, the accompanying drawings needed in the embodiment description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of the accompanying drawings.

[0021] Figure 1 is a flow chart of a power grid load balance scheduling method according to the first embodiment of the present application;

[0022] Figure 2 is a structural schematic diagram of a remote dispatch master station module according to the present application;

[0023] Figure 3 is a structural schematic diagram of a power grid load balance scheduling device according to the third embodiment of the present application;

[0024] Figure 4 is a structural schematic diagram of an electronic device for implementing the power grid load balance scheduling method according to the present application. DETAILED DESCRIPTION

[0025] In order to make the technical solution in the embodiments of the present application clearer, the accompanying drawings needed in the embodiment description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of the accompanying drawings.

[0026] It should be noted that the terms "first", "second", and "target" and the like in the specification and claims of the present application and the above-mentioned accompanying 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, system, product or device including a series of steps or units does not necessarily have to include only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0027] Embodiment One

[0028] Figure 1For a flow chart of a power grid load balancing scheduling method according to an embodiment of the present application, the embodiment can be applicable to the case of predicting the balance degree of the power grid and planning the power supply and the power grid on the basis of ensuring safe and reliable power supply to realize the balanced scheduling of the power grid load, the method can be executed by a power grid load balancing scheduling device, the power grid load balancing scheduling device can be realized in the form of hardware and / or software, and the power grid load balancing scheduling device can be configured in a power grid load balancing scheduling equipment. As shown in Figure 1 , the method comprises:

[0029] S110, based on a first preset time period, determining the node load balance degree of each generator node in the power grid according to the load information in the power grid.

[0030] The load information in the power grid is used to describe the power consumption of each generator set in the power grid, and the first preset time period can be a minimum time period for recording the load information in the power grid, i.e. the load information in the power grid is recorded periodically according to the first preset time period. The first preset time period can be set according to the actual recording time required, which is not limited here, for example, it can be 1 hour. The generator node can refer to a load system in the power grid that can supply power to the load. A load system can be determined as a generator node, so the load information of each load system needs to be recorded to ensure that the node load balance degree of each generator node can be obtained, and the stability of the power grid load balancing scheduling is avoided to be reduced due to missing information. The load system can be a fixed load system and an adjustable load system. The fixed load system can be a load amount in the system that has been fixed, so the node load balance degree of the fixed load system under normal operation is also unchanged. The adjustable load system can be a load amount in the system that can be adjusted, so the node load balance degree of the adjustable load system will change constantly, so it is necessary to accurately obtain the node load balance degree of the load system to ensure that the power grid load can be effectively balanced and scheduled.

[0031] In a feasible embodiment, determining the node load balance degree of each generator node in the power grid according to the load information in the power grid can comprise:

[0032] The node load balance degree of each generator node is determined according to the following formula:

[0033]

[0034] , wherein w i is the node load balance degree of the i th generator node, ΔE i is the total load balance degree of the power grid; and ΔI iis the injection current increment corresponding to the i-th generator node; E(i) refers to the corresponding load balance degree of the i-th generator node after the injection current is increased; E1 refers to the corresponding load balance degree of the power supply network under the initial condition.

[0035] Specifically, after the load information in the power supply network is acquired, the total load balance degree of the power supply network and the injection current increment corresponding to each generator node are determined from the load information, the node load balance degree of each generator node is accurately obtained through calculation, and the accuracy of the load balance degree is determined by ΔP. ΔP is set according to the actual situation, which is not specifically limited here. For example, ΔP is 0.001, that is, the load balance degree is accurate to the third digit after the decimal point. Optionally, in the process of calculating the load balance degree, only the generator set whose capacity does not exceed the preset limit is adjusted in load.

[0036] In the technical solution, the node load balance degree of each generator node in the first preset time period is accurately calculated through a formula, and the node load balance degree of each generator node is recorded, so that the data amount of the node load balance degree is not missed, which is beneficial to the confirmation of the load balance degree of the first preset time period in the power supply network in the later period.

[0037] S120, determining the target load balance degree of each first preset time period in a second preset time period of the power supply network according to the node load balance degree of each generator node; wherein the second preset time period is greater than the first preset time period.

[0038] The second preset time period refers to the time for predicting the load balance degree, which can be set according to the actual situation, which is not specifically limited here. For example, 24 hours.

[0039] In a feasible embodiment, determining the target load balance degree of each first preset time period in a second preset time period of the power supply network according to the node load balance degree of each generator node can include:

[0040] sequentially determining the generator node load balance degree set in the power supply network in each first preset time period in the second preset time period;

[0041] determining the target load balance degree of each first preset time period according to the generator node load balance degree set.

[0042] Specifically, after determining the generator node load balancing degree of each first preset time period in the second preset time period, the generator node load balancing degrees of each first preset time period are collected together, and the target load balancing degree of each first preset time period is determined according to the generator node load balancing degree collection. Only when the target load balancing degree of each first preset time period is confirmed, the generator node load balancing degree in the power supply network is not missed, so as to ensure the accuracy of the load prediction of the power supply network.

[0043] The technical scheme determines the target load balancing degree of each first preset time period through the generator node load balancing degree collection in the power supply network in each first preset time period in the second preset time period, guarantees the accuracy of the target load balancing degree, and is beneficial to subsequent load prediction of the power supply network, so as to facilitate load balancing scheduling of the power supply network.

[0044] In a feasible embodiment, determining the target load balancing degree of each first preset time period according to the generator node load balancing degree collection can include:

[0045] sorting the node load balancing degrees in the generator node load balancing degree collection in descending order;

[0046] if the generator node load balancing degree collection includes at least one node load balancing degree less than zero, taking the first node load balancing degree less than zero in the sorting result as the target load balancing degree of the corresponding first preset time period;

[0047] otherwise, taking the node load balancing degree at the end of the sorting as the target load balancing degree of the corresponding first preset time period.

[0048] Specifically, the generator node load balancing degree collection needs to be used to determine the target load balancing degree of the corresponding first preset time period, so the determination method of the generator node load balancing degree collection is very important. The method can be to sort the node load balancing degrees in the generator node load balancing degree collection in descending order, and then determine the target load balancing degree of the corresponding first preset time period according to each descendingly arranged generator node load balancing degree collection. If the generator node load balancing degree collection includes at least one node load balancing degree less than zero, the first node load balancing degree less than zero in the sorting result is taken as the target load balancing degree of the corresponding first preset time period; otherwise, the node load balancing degree at the end of the sorting is taken as the target load balancing degree of the corresponding first preset time period.

[0049] The technical solution sorts the node load balancing degrees in the generator node load balancing degree set in descending order, so as to facilitate determining the target load balancing degree of each first preset time period by using the judgment method that the generator node load balancing degree set includes at least one node load balancing degree less than zero, and realizes accurate determination of the target load balancing degree of each first preset time period.

[0050] In one feasible embodiment, the method for predicting the load of the power supply network according to the target load balancing degree of each first preset time period in the second preset time period includes:

[0051] Specifically, only when the accuracy of the load prediction of the power supply network is ensured, the error of the load balancing scheduling of the power supply network can be reduced, and the stability of the load balancing scheduling of the power supply network is ensured.

[0052] In one feasible embodiment, the method for predicting the load of the power supply network according to the target load balancing degree of each first preset time period in the second preset time period includes:

[0053] Wavelet decomposition is performed on the target load balancing degree to obtain a decomposition result.

[0054] The load of the power supply network is predicted by using an SVR model according to the decomposition result and historical load data of the power supply network.

[0055] The historical load data can be all data related to the load of the power supply network before the current second preset period. Adding the historical load data for analysis makes it easier to predict the load of the power supply network, avoids accidental errors in the current data, avoids misjudgment of the prediction, and realizes the accuracy of the load prediction of the power supply network.

[0056] The technical solution performs wavelet decomposition on the target load balancing degree to obtain a decomposition result, and then predicts the load of the power supply network by using an SVR model according to the decomposition result and historical load data of the power supply network, thereby ensuring the accuracy of the load prediction of the power supply network and ensuring the stability of the load balancing scheduling of the power supply network.

[0057] In one feasible embodiment, the wavelet decomposition of the target load balancing degree to obtain a decomposition result can include:

[0058] The decomposition result T of the wavelet decomposition of the target load balancing degree is represented as:

[0059] T = {(t n ,r n ),(t n-1 ,r n-1},...,(t1,r1)};

[0060] wherein, t n refers to the power supply network load value of the nth decomposition; r n refers to the load threshold value of the nth decomposition, and t n and r n satisfy the following formula:

[0061] Specifically, the target load balancing degree is wavelet-decomposed to obtain each power supply network load value t n and load threshold value r n , and respectively satisfy to ensure the accuracy of the decomposition.

[0062] The technical solution, by wavelet-decomposing the target load balancing degree and obtaining accurate decomposition results, is more conducive to subsequent load prediction of the power supply network through the model.

[0063] In a feasible embodiment, according to the decomposition results and the historical load data of the power supply network, the load of the power supply network is predicted through the SVR model, which can include:

[0064] The power supply network load prediction model is constructed based on the following formula:

[0065]

[0066] wherein, f(·) refers to the outside model function of the power supply network load;

[0067] Based on the historical load data of the power supply network, the outside model function is fitted through the self-learning of the SVR model to obtain the power supply network load prediction result.

[0068] Specifically, the power supply network load prediction model is determined according to the decomposition results, and then based on the historical load data of the power supply network, the outside model function is fitted through the self-learning of the SVR model to obtain the power supply network load prediction result, and the load balancing scheduling of the power supply network is performed.

[0069] The technical solution accurately obtains the power grid load prediction model, and fits the outside model function of the power grid load through the self-learning of the SVR model, thereby realizing accurate prediction of the load of the power supply network.

[0070] The technical scheme of the embodiment of the present application is based on a first preset time period, determines the node load balancing degree of each generator node in the power supply network according to the load information in the power supply network, determines the target load balancing degree of each first preset time period within a second preset time period of the power supply network according to the node load balancing degree of each generator node, wherein the second preset time period is greater than the first preset time period, predicts the load of the power supply network according to the target load balancing degree of each first preset time period within the second preset time period of the power supply network, and performs load balancing scheduling on the power supply network according to the prediction result, thereby solving the problem of excessively high load rate of some power transmission lines after a certain scale of renewable energy such as wind power is integrated into the power grid, and realizing load balancing scheduling of the power supply network.

[0071] Embodiment two

[0072] The power supply network load balancing scheduling method of the embodiment of the present application can be realized by a remote dispatch master station module, a communication module and a processor module.

[0073] Figure 2 The structure diagram of the remote dispatch master station module provided by the embodiment of the present application is shown.

[0074] In the embodiment, the remote dispatch master station module is controlled in the process of power supply network load balancing scheduling, and the power supply network load balancing scheduling method of the embodiment of the present application is configured in the remote dispatch master station module. In order to meet the basic requirements of stability, safety, practicality and advancement, two networks and two servers are configured for the remote dispatch master station. At the same time, considering the future expansion demand and the current system capacity, two historical servers and SCADA data servers and two front-end machines are arranged. The front-end subnetwork is arranged separately, and two FIS servers, four double-screen workstations and one WEB server are configured for the front-end subnetwork to implement dispatch monitoring and other operations. In the design of the remote dispatch master station module, one maintenance workstation is configured for implementing system maintenance, one report workstation and one engineer station are configured for printing and making reports. Two FIS workstations are configured for analyzing and browsing the fault information of the power supply network. The main equipment is installed in a rack mode, and the equipment is centrally managed to facilitate maintenance. The background center network of the remote dispatch master station module and the hot standby mode of the front-end acquisition network are both double-network redundant hot standby modes. Under normal circumstances, the two networks can achieve load balancing, and when one of the networks fails, the other network can completely replace all communication loads. In addition, the background center network and the front-end acquisition network are isolated to ensure the reliability and real-time performance of data.

[0075] In the communication module, a communication network structure of ring network is designed, which has high security and reliability, and network expansion is also very convenient. The ring network structure uses the Ethernet switch network with optical fiber as the communication skeleton, which is mainly used for communication of remote dispatching master station. In the remote dispatching master station module, the switch cabinet, optical fiber screen and communication switching station are installed, and the optical cable is laid. This structure can save investment and facilitate communication maintenance.

[0076] In the remote dispatching master station module, multiple communication servers are erected. The communication server is designed as follows: the server adopts symmetrical cluster architecture, which is composed of dynamic server cluster, static server cluster and bus, and can realize functions such as log maintenance, notification system generation and topic control. The dynamic server cluster is composed of chat room setting server, contact avatar server, user interest server, mail server, BOS server and authentication server; the static server cluster is composed of conversion server, password server, notification server and distribution server.

[0077] In the design of the processor module, a data processing chip is used to process the related data of power grid load dispatching. The data processing chip used is an embedded data encryption processing chip, which can realize encryption processing of the related data of power grid load dispatching, and enhance the security of the system.

[0078] The chip is composed of the following modules: random number management module, QKD coprocessor module, network offload engine module, external memory controller module, DMA controller module, SRAM module, BootROM module and CPU core module. Among them, the random number management module mainly manages the random numbers input from the outside of the chip through the random number manager, and distributes them to the QKD coprocessor module as needed. The QKD coprocessor module mainly realizes data processing algorithm through the QKD_SOC chip, and can also generate a secure key through the chip to encrypt the processed data. The network offload engine module uses the TCP / IP offload engine to provide communication services for each module of the chip, including corresponding network communication services based on UDP protocol and TCP protocol. The external memory controller module uses an external memory controller model MIMXRT1011DAE5A, which can realize the management of SDRAM large-capacity external storage devices, and the specific functions include refresh operation, read-write control and initialization, etc., which can provide an interconnection bus and an AHB interface, so that other modules can access the external storage devices together. The DMA controller module can realize data transfer between memory and peripherals, as well as between memories through the DMA controller. The SRAM module uses SDRAM large-capacity external storage devices as the medium for data storage and program running, and its size is 32KB.

[0079] The technical scheme has the advantages that the double-network double-server configuration in the remote dispatching master station module improves the stability and increases the working efficiency and protection of the device by adding multiple devices compared with the original device, and the remote dispatching master station module also provides a double-network redundant hot backup mode, so that when a network fails, the other network can take over the work in time to ensure the efficiency and stability of the work. In the communication module, a ring network communication network structure is designed, which can provide daily maintenance by adding multiple communication servers to the remote dispatching master station to ensure the communication of the remote dispatching master station, thereby ensuring the daily communication maintenance and saving the investment cost. In the processor module, a data processing chip is used to process the related data of the power supply network load scheduling to realize the encryption processing of the power supply network load data and enhance the security of the system.

[0080] Embodiment three

[0081] Figure 3 A structure diagram of a power supply network load balancing scheduling device according to embodiment three of the present application is provided. As shown in the figure, Figure 3 the device comprises:

[0082] A first balancing degree determination module 210 is configured to determine the node load balancing degree of each generator node in the power supply network based on a first preset time period according to the load information in the power supply network.

[0083] A second balancing degree determination module 220 is configured to determine the target load balancing degree of the power supply network in each first preset time period within a second preset time period according to the node load balancing degree of each generator node; wherein the second preset time period is greater than the first preset time period.

[0084] A scheduling module 230 is configured to predict the load of the power supply network according to the target load balancing degree of the power supply network in each first preset time period within a second preset time period, and to perform load balancing scheduling on the power supply network according to the prediction result.

[0085] Optionally, the first balancing degree determination module is specifically configured to:

[0086] determine the node load balancing degree of each generator node according to the following formula:

[0087]

[0088] wherein w i is the node load balancing degree of the i th generator node, ΔE i is the total load balancing degree of the power supply network; and ΔI iE(i) refers to the incremental injection current corresponding to the i-th generator node; E(i) refers to the load balance degree corresponding to the i-th generator node after the injection current is increased; E1 refers to the load balance degree corresponding to the power grid under the initial condition.

[0089] Optional, the second equilibrium determination module is specifically used for:

[0090] The set of generator node load balance in the power supply network for each of the first preset time periods within the second preset time period is determined sequentially.

[0091] The target load balance for each first preset time period is determined based on the set of generator node load balances.

[0092] Optionally, the second load balancing determination module includes a target load balancing determination unit, specifically used for:

[0093] Determining the target load balancing degree for each first preset time period based on the generator node load balancing degree set includes:

[0094] Sort the node load balance in the generator node load balance set in descending order;

[0095] If the set of generator node load balancing degrees includes at least one node load balancing degree less than zero, then the first node load balancing degree less than zero in the sorting result shall be taken as the target load balancing degree for the corresponding first preset time period.

[0096] Otherwise, the load balancing degree of the node ranked last will be used as the target load balancing degree for the corresponding first preset time period.

[0097] Optional, the scheduling module, specifically used for:

[0098] The target load balancing degree is decomposed using wavelet decomposition to obtain the decomposition result;

[0099] Based on the decomposition results and historical load data of the power grid, the load of the power grid is predicted using the SVR model.

[0100] Optionally, the scheduling module includes a result determination unit, specifically used for:

[0101] The wavelet decomposition result T of the target load balancing degree is expressed as:

[0102] T = {(t n ,r n ),(t n-1 ,r n-1 ),...,(t1,r1)};

[0103] Among them, t nRefers to the power supply network load value of the nth decomposition;r n Refers to the load threshold value of the nth decomposition, and t n And r n Satisfy the following formula:

[0104] Optionally, the scheduling module comprises a prediction unit, specifically configured to:

[0105] The power supply network load prediction model is constructed based on the following formula:

[0106]

[0107] Wherein, f(·) refers to the outside model function of the power supply network load;

[0108] Based on the historical load data of the power supply network, the outside model function is fitted through the self-learning of the SVR model, and the power supply network load prediction result is obtained.

[0109] The power supply network load balancing scheduling device provided by the embodiment of the application can execute the power supply network load balancing scheduling method provided by any embodiment of the application, has the function modules and beneficial effects corresponding to the execution method.

[0110] In the technical solution of the application, the acquisition, storage, use, processing and the like of data comply with relevant provisions of national laws and regulations, and do not violate public order and good customs.

[0111] Embodiment four

[0112] According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0113] Figure 4 The structure schematic diagram of the electronic device which can be used to realize the power supply network load balancing scheduling method of the embodiment of the application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, 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 application described and / or claimed herein.

[0114] As Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. 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.

[0115] Various 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, a speaker, etc., a storage unit 18, such as a magnetic 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.

[0116] The processor 11 can be various general and / or special-purpose 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-purpose 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 performs various methods and processes described above, such as the power grid load balancing scheduling method.

[0117] In some embodiments, the power grid load balancing scheduling method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto 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 method of the power grid load balancing scheduling described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the power grid load balancing scheduling method by any other appropriate means, such as by means of firmware.

[0118] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0119] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented on the computer or other programmable apparatus. The computer programs can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0120] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0121] To provide for interaction with a user, the systems and techniques described here can 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 a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0122] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can 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.

[0123] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can 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 large management difficulty and weak business scalability in traditional physical host and VPS service.

[0124] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0125] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A power grid load balancing scheduling method, characterized in that, The method comprises the following steps: determining the node load balance degree of each generator node in the power supply network according to the load information in the power supply network based on a first preset time period; determining the target load balance degree of each first preset time period within a second preset time period of the power supply network according to the node load balance degree of each generator node; wherein the second preset time period is greater than the first preset time period; predicting the load of the power supply network according to the target load balance degree of each first preset time period within the second preset time period of the power supply network, and performing load balance scheduling on the power supply network according to the prediction result; wherein determining the target load balance degree of each first preset time period within the second preset time period of the power supply network according to the node load balance degree of each generator node comprises: determining the generator node load balance degree set in the power supply network within each first preset time period in the second preset time period in turn; determining the target load balance degree of each first preset time period according to the generator node load balance degree set; wherein determining the target load balance degree of each first preset time period according to the generator node load balance degree set comprises: sorting the node load balance degrees in the generator node load balance degree set in descending order; if the generator node load balance degree set includes at least one node load balance degree less than zero, the first node load balance degree less than zero in the sorting result is taken as the target load balance degree of the corresponding first preset time period; otherwise, the node load balance degree at the end of the sorting is taken as the target load balance degree of the corresponding first preset time period.

2. The method of claim 1, wherein, determining the node load balance degree of each generator node in the power supply network according to the load information in the power supply network comprises: determining the node load balance degree of each generator node according to the following formula: ； wherein, is the load balancing degree of the i-th generator node, is the load balancing degree of the i-th generator node, is the total load balancing degree of the power supply network; is the incremental injected current of the i-th generator node, is the corresponding load balancing degree of the i-th generator node after increasing the injected current, is the corresponding load balancing degree of the i-th generator node after increasing the injected current, is the corresponding load balancing degree of the i-th generator node after increasing the injected current, is the corresponding load balancing degree of the power supply network in the initial situation.

3. The method of claim 1, wherein, predicting the load of the power supply network according to the target load balance degree of each first preset time period within the second preset time period of the power supply network comprises: wavelet decomposing the target load balance degree to obtain a decomposition result; predicting the load of the power supply network through an SVR model according to the decomposition result and historical load data of the power supply network.

4. The method of claim 3, wherein, wavelet decomposing the target load balance degree to obtain a decomposition result comprises: a decomposition result of wavelet decomposition on the target load balancing degree is represented as: ； wherein, refers to the power supply network load value of the first decomposition; refers to the load threshold value of the first decomposition, and and satisfy the following formula: ; .

5. The method of claim 4, wherein, predicting the load of the power supply network through an SVR model according to the decomposition result and historical load data of the power supply network comprises: constructing a power supply network load prediction model based on the following formula: ； wherein is a function of the external model of the supply grid load; fitting the outer model function through self-learning of an SVR model based on historical load data of the power supply network to obtain a power supply network load prediction result.

6. A power grid load balancing scheduling apparatus, characterized in that, The method comprises the following steps: a first balance degree determination module for determining the node load balance degree of each generator node in the power supply network according to the load information in the power supply network based on a first preset time period; a second balance degree determination module for determining the target load balance degree of each first preset time period within a second preset time period of the power supply network according to the node load balance degree of each generator node; wherein the second preset time period is greater than the first preset time period; The scheduling module is configured to predict the load of the power supply network according to a target load balance degree of the power supply network in each first preset time period within a second preset time period, and perform load balance scheduling on the power supply network according to the prediction result. The second balance degree determination module is specifically configured to: sequentially determine a set of generator node load balance degrees in the power supply network in each first preset time period within the second preset time period; and determine the target load balance degree of each first preset time period according to the set of generator node load balance degrees. The second balance degree determination module includes a target load balance degree determination unit, which is specifically configured to: sort the node load balance degrees in the set of generator node load balance degrees in descending order; if the set of generator node load balance degrees includes at least one node load balance degree less than zero, take the first node load balance degree less than zero in the sorted result as the target load balance degree of the corresponding first preset time period; otherwise, take the node load balance degree at the end of the sorting as the target load balance degree of the corresponding first preset time period.

7. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; 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 to enable the at least one processor to perform the power supply network load balance scheduling method of any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to perform the power supply network load balance scheduling method of any one of claims 1-5 when executed.

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