Power grid rolling optimization scheduling control method and device and computer equipment

Abstract

The invention relates to a power grid rolling optimization scheduling control method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: inputting current operation data and historical operation data of a power grid into a day-ahead power grid optimization scheduling model to obtain day-ahead load prediction information of each load node of the power grid; constructing an intra-day power grid optimization scheduling model, and solving the intra-day power grid optimization scheduling model based on the day-ahead load prediction information of each load node to obtain intra-day load prediction information of each load node; determining an intra-day scheduling scheme for the power grid according to the intra-day load prediction information of each load node; and obtaining real-time load real information of each load node after the intra-day scheduling scheme is executed, and adjusting the day-ahead power grid optimization scheduling model based on the real-time load real information of each load node. By adopting the method, power grid optimization scheduling control can be accurately carried out.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device, computer equipment, storage medium and computer program product for controlling rolling optimization dispatching of a power grid. Background Art

[0002] Grid optimization dispatching and control is a strategy for dynamic adjustment and optimization management of distribution networks. It monitors the grid status in real time and dynamically adjusts the distribution plan to achieve the optimal balance between grid stability and economic benefits. However, the existing grid optimization dispatching and control methods do not fully consider the operation of each load during the operation of the grid, which leads to inaccurate and unreasonable grid optimization dispatching and control.

[0003] Therefore, there is a problem in traditional technologies that they cannot accurately perform grid optimization dispatching and control. Summary of the invention

[0004] Based on this, it is necessary to provide a power grid rolling optimization dispatching control method, device, computer equipment, computer-readable storage medium and computer program product that can accurately perform power grid optimization dispatching control in response to the above-mentioned technical problems.

[0005] A power grid rolling optimization dispatching control method, comprising:

[0006] Obtain current and historical operation data of the power grid;

[0007] Input the current operation data and historical operation data into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node in the power grid;

[0008] Construct an intraday power grid optimization dispatching model, and solve the intraday power grid optimization dispatching model based on the day-ahead load forecasting information of each load node to obtain the intraday load forecasting information of each load node; the intraday load forecasting information of the load node includes the load forecasting information of the load node in each period of the day;

[0009] Determine the daily dispatching plan for the power grid based on the daily load forecast information of each load node;

[0010] The real-time load information of each load node after executing the intraday dispatch plan is obtained, and based on the real-time load information of each load node, the day-ahead power grid optimization dispatch model is adjusted, and the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization dispatch model is returned to obtain the day-ahead load forecast information of each load node of the power grid, so as to optimize the intraday dispatch plan of the power grid in real time.

[0011] In one embodiment, based on the real-time load information of each load node, adjusting the day-ahead power grid optimization dispatching model includes:

[0012] Determine the error based on the real-time load information of each load node and the intraday load forecast information;

[0013] Based on the error, the day-ahead power grid optimization dispatching model is adjusted.

[0014] In one embodiment, the load node of the power grid includes a wind power generation system in the power grid; the constraint conditions of the intraday power grid optimization scheduling model include a first constraint condition for the wind power generation system; the first constraint condition is used to constrain the power generation power of the wind power generation system at each moment.

[0015] In one embodiment, the load nodes of the power grid include electric vehicle charging stations in the power grid; the constraints of the intraday power grid optimization scheduling model include a second constraint for the electric vehicle charging stations; and the second constraint is used to constrain user satisfaction with the electric vehicle charging stations.

[0016] In one embodiment, the load nodes of the power grid include energy storage devices in the power grid; the constraints of the intraday power grid optimization scheduling model include a third constraint for the energy storage devices; the third constraint is used to constrain the charge state of the energy storage devices at the moment of charging and discharging.

[0017] In one embodiment, the dispatching target of the intraday power grid optimization dispatching model is expressed as:

[0018] ;

[0019] in, Minimize characterization errors, For the prediction time domain, for Load forecast information at each moment, For Reference information of load forecast information at the moment, , is the weight coefficient, for Error information at the moment.

[0020] A power grid rolling optimization dispatching control device, comprising:

[0021] An acquisition module is used to acquire current and historical operation data of the power grid;

[0022] An input module is used to input current operation data and historical operation data into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node of the power grid;

[0023] A construction module is used to construct an intraday power grid optimization dispatching model, and solve the intraday power grid optimization dispatching model based on the day-ahead load forecasting information of each load node to obtain the intraday load forecasting information of each load node; the intraday load forecasting information of the load node includes the load forecasting information of the load node in each period of the day;

[0024] A determination module is used to determine the intraday dispatching plan for the power grid according to the intraday load forecast information of each load node;

[0025] The adjustment module is used to obtain the real-time load information of each load node after the execution of the intraday dispatch plan, and based on the real-time load information of each load node, adjust the day-ahead power grid optimization dispatch model, and return to the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization dispatch model to obtain the day-ahead load forecast information of each load node of the power grid, so as to optimize the intraday dispatch plan of the power grid in real time.

[0026] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0027] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0028] A computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0029] The above-mentioned power grid rolling optimization dispatch control method, device, computer equipment, storage medium and computer program product obtain the current operation data and historical operation data of the power grid; input the current operation data and historical operation data into the day-ahead power grid optimization dispatch model to obtain the day-ahead load forecast information of each load node of the power grid; construct an intraday power grid optimization dispatch model, and solve the intraday power grid optimization dispatch model based on the day-ahead load forecast information of each load node to obtain the intraday load forecast information of each load node; the intraday load forecast information of the load node includes the load forecast information of the load node in each time period of the day; according to the intraday load forecast information of each load node, determine the intraday dispatch plan for the power grid; obtain The real-time load information of each load node after executing the intraday dispatching plan is obtained, and based on the real-time load information of each load node, the day-ahead power grid optimization dispatching model is adjusted, and the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization dispatching model is returned to obtain the day-ahead load forecast information of each load node of the power grid, so as to optimize the intraday dispatching plan of the power grid in real time; in this way, it can be close to the actual operation state of the power grid, and the day-ahead power grid optimization dispatching model can be continuously updated based on the real-time operation state feedback, which is conducive to improving the accuracy of the day-ahead power grid optimization dispatching model, thereby facilitating the generation of a more accurate intraday dispatching plan, and realizing accurate rolling optimization dispatching of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] FIG1 is an application environment diagram of a power grid rolling optimization dispatch control method in one embodiment;

[0032] Figure 2 A schematic flow chart of a power grid rolling optimization dispatch control method in one embodiment;

[0033] Figure 3 A schematic diagram of a power grid rolling optimization dispatch control process in one embodiment;

[0034] Figure 4 A schematic diagram of a node of a power grid in one embodiment;

[0035] Figure 5 A schematic diagram of a node load prediction result in one embodiment;

[0036] Figure 6A load and dispatch result comparison diagram in an embodiment;

[0037] Figure 7 A schematic diagram of a scheduling result in an embodiment;

[0038] Figure 8 A schematic flow chart of a power grid rolling optimization dispatch control method in another embodiment;

[0039] Fig. 9 It is a structural block diagram of a power grid rolling optimization dispatch control device in one embodiment;

[0040] FIG. 10 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] The power grid rolling optimization dispatch control method provided in the embodiment of the present application can be applied in the application environment shown in FIG1. ​​The terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the current operation data and historical operation data of the power grid; the server 104 inputs the current operation data and the historical operation data into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node of the power grid; the server 104 constructs an intraday power grid optimization dispatching model, and solves the intraday power grid optimization dispatching model based on the day-ahead load forecast information of each load node to obtain the intraday load forecast information of each load node; the intraday load forecast information of the load node includes the load forecast information of the load node in each time period of the day; the server 104 determines the intraday dispatching plan for the power grid according to the intraday load forecast information of each load node; the server 104 obtains the real real load information of each load node after executing the intraday dispatching plan, and adjusts the day-ahead power grid optimization dispatching model based on the real real load information of each load node, and returns to the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node of the power grid, so as to optimize the intraday dispatching plan of the power grid in real time. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0043] In an exemplary embodiment, Figure 2 As shown, a power grid rolling optimization dispatch control method is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and includes the following steps S202 to S210. Among them:

[0044] Step S202, obtaining current operation data and historical operation data of the power grid.

[0045] The current operation data may include the operation status data of each load node of the power grid at the current moment.

[0046] The historical operation data may include the operation status data of each load node of the power grid at a historical moment.

[0047] Optionally, the server obtains the operating status data of each load node of the power grid at the current moment and the operating status data of each load node of the power grid at historical moments.

[0048] Step S204, inputting the current operation data and the historical operation data into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node of the power grid.

[0049] Among them, the day-ahead power grid optimization dispatching model can be a prediction model used to predict the load value and power generation on the day-ahead. In practical applications, the prediction model can be a neural network model trained by a training data set.

[0050] Optionally, the server models and analyzes the operating status data of each load node at the current moment and the operating status data at the historical moment, and models each load node of the power grid based on the operating status data of each load node at the current moment and the operating status data at the historical moment through the day-ahead power grid optimization dispatching model to predict the day-ahead load forecast value of each load node.

[0051] Step S206, construct an intraday power grid optimization dispatching model, and solve the intraday power grid optimization dispatching model based on the day-ahead load forecast information of each load node to obtain the intraday load forecast information of each load node; the intraday load forecast information of the load node includes the load forecast information of the load node in each time period of the day.

[0052] The intraday power grid optimization dispatch model can be a model for predicting the load value of each load node of the power grid within a day. In practical applications, the intraday power grid optimization dispatch model is a time domain dispatch model, which can predict the load prediction value of each load node of the power grid in each time zone.

[0053] Optionally, the server constructs an intraday power grid optimization dispatching model for the power grid, and then solves the intraday power grid optimization dispatching model based on the day-ahead load forecast information of each load node to obtain the load forecast value of each load node in each time period of the day.

[0054] Step S208, determining an intraday dispatching plan for the power grid based on the intraday load forecast information of each load node.

[0055] Among them, the intraday dispatch plan can be a plan for intraday dispatching of the power grid.

[0056] Optionally, the server determines a daily dispatching plan for the power grid based on the load forecast value of each load node in each time period during the day.

[0057] Step S210, obtain the real-time load information of each load node after executing the intraday scheduling plan, and adjust the day-ahead power grid optimization scheduling model based on the real-time load information of each load node, return to the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization scheduling model, and obtain the day-ahead load forecast information of each load node of the power grid, so as to optimize the intraday scheduling plan of the power grid in real time.

[0058] The real-time load information may be the real load value of each load node obtained by real-time measurement after executing the intraday scheduling plan.

[0059] Optionally, the server obtains the real-time load true value of each load node after executing the intraday scheduling plan, and uses the real-time load true value of each load node as feedback to update the day-ahead power grid optimization scheduling model. Then, the server re-inputs the current operating data and historical operating data into the day-ahead power grid optimization scheduling model to obtain the day-ahead load forecast value of each load node of the power grid. Then, the server re-solves the intraday power grid optimization scheduling model based on the day-ahead load forecast value of each load node of the power grid to obtain new intraday load forecast information of each load node. Then, the server determines a new intraday scheduling plan for the power grid based on the new intraday load forecast information of each load node. In this way, the intraday scheduling plan of the power grid is optimized in real time, thereby realizing rolling optimization scheduling of the power grid.

[0060] In the above-mentioned power grid rolling optimization dispatching control method, the current operation data and historical operation data of the power grid are obtained; the current operation data and historical operation data are input into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node of the power grid; an intraday power grid optimization dispatching model is constructed, and based on the day-ahead load forecast information of each load node, the intraday power grid optimization dispatching model is solved to obtain the intraday load forecast information of each load node; the intraday load forecast information of the load node includes the load forecast information of the load node in each time period of the day; according to the intraday load forecast information of each load node, the intraday dispatching plan for the power grid is determined; and the execution of the intraday dispatching plan is obtained. The real-time load information of each load node is obtained, and based on the real-time load information of each load node, the day-ahead power grid optimization dispatching model is adjusted, and the step of returning to inputting the current operation data and the historical operation data into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node of the power grid is performed to optimize the intraday dispatching plan of the power grid in real time; in this way, it can be close to the actual operation status of the power grid, and the day-ahead power grid optimization dispatching model can be continuously updated based on the real-time operation status feedback, which is conducive to improving the accuracy of the day-ahead power grid optimization dispatching model, thereby facilitating the generation of a more accurate intraday dispatching plan and realizing accurate optimization dispatching of the power grid.

[0061] In an exemplary embodiment, based on the real-time load information of each load node, the day-ahead power grid optimization dispatching model is adjusted, including: determining the error according to the real-time load information of each load node and the intraday load forecast information; and adjusting the day-ahead power grid optimization dispatching model based on the error.

[0062] The error may be determined based on the measured real-time load actual value of each load node and the predicted intraday load forecast value of each load node.

[0063] Optionally, the server determines an error based on the real-time load information of each load node and the intraday load forecast information, and uses the error as feedback to update the day-ahead power grid optimization dispatching model.

[0064] In this embodiment, the error is determined based on the real-time load information of each load node and the intraday load forecast information; based on the error, the day-ahead power grid optimization dispatching model is adjusted; in this way, the real-time results after executing the intraday dispatching plan can be fed back, and the day-ahead power grid dispatching optimization model can be adjusted, which can be closer to the actual operation of the power grid and is conducive to improving the accuracy of power grid dispatching.

[0065] In order to facilitate the understanding of those skilled in the art, Figure 3 A schematic diagram of a power grid rolling optimization dispatching control process is provided as an example, and the process includes three stages: day-ahead dispatching, intraday dispatching, and real-time dispatching. Specifically, in the day-ahead dispatching stage, according to the real-time operating status of the current power grid, the goal of the active distribution network optimization dispatching system is formulated, and a time domain dispatching model is established; in the intraday dispatching stage, according to the formulated goal of the active distribution network optimization dispatching system, a rolling optimization objective function is performed with 2 hours as the maximum scale; in the real-time dispatching stage, according to real-time feedback, the day-ahead power grid optimization dispatching model is updated.

[0066] Figure 4 A schematic diagram of a node of a power grid is provided as an example. The data in Table 1 below are combined with Figure 4 The IEEE33 nodes are used, and the data in Table 1 are used in the simulation experiment. The data in Table 1 represent the relationship between the source load prediction and different influencing factors. The following Table 2 is the line data between each node in the IEEE33 node simulation model, which is used in the simulation experiment. The data in Table 2 are the node network line setting values. Figure 5 It is a schematic diagram of the load prediction results of the load node. Figure 6 This is a comparison chart of load and dispatch results before and after the technical solution of the present application is adopted. Figure 7This is a schematic diagram of the corresponding dispatching results after adopting the technical solution of the present application. It can be seen that after adopting the technical solution of the present application, the power distribution quota of the power grid is close to the total load, and distributed generation (DG) and power purchase are the load values ​​that need to be regulated.

[0067] Table 1

[0068]

[0069] Table 2

[0070]

[0071] In an exemplary embodiment, the load nodes of the power grid include a wind power generation system in the power grid; the constraints of the intraday power grid optimization scheduling model include a first constraint for the wind power generation system; the first constraint is used to constrain the power generation power of the wind power generation system at each moment.

[0072] The first constraint can be expressed as: , is the power generated by the wind power generation system at time t, It is the maximum instantaneous power generation of the wind power generation system.

[0073] In this embodiment, the constraints on the power generation capacity of the wind power generation system can be used as constraints of the intraday power grid optimization scheduling model, which can take into account the actual operation of the power grid and is conducive to achieving accurate optimization scheduling control of the power grid.

[0074] In an exemplary embodiment, the load nodes of the power grid include electric vehicle charging stations in the power grid; the constraints of the intraday power grid optimization scheduling model include second constraints for the electric vehicle charging stations; and the second constraints are used to constrain user satisfaction with the electric vehicle charging stations.

[0075] The second constraint can be expressed as:

[0076] ;

[0077] in, For user satisfaction, EV stands for electric vehicles. The average charge required for all electric vehicles to be charged, is the maximum required charge value.

[0078] In practical applications, it is necessary to model the electric vehicle charging station, which can be expressed as ;in: is the power of the charging station, is the power of the electric vehicle. By modeling the electric vehicle charging station, information such as the power consumption of the electric vehicle charging station in each period can be determined to facilitate real-time feedback within the day and in the future.

[0079] In this embodiment, the user's satisfaction with the electric vehicle charging station can be used as a constraint condition of the intraday power grid optimization scheduling model, which can take into account the actual operation of the power grid and is conducive to the accurate optimization scheduling control of the power grid.

[0080] In an exemplary embodiment, the load nodes of the power grid include energy storage devices in the power grid; the constraints of the intraday power grid optimization scheduling model include a third constraint for the energy storage device; the third constraint is used to constrain the charge state of the energy storage device at the moment of charging and discharging.

[0081] Among them, the third constraint can be expressed as:

[0082] ;

[0083] in, is the upper limit of the charge state of the energy storage device, is the lower limit of the charge state of the energy storage device, It is the charge state of the energy storage device at the moment of charging and discharging.

[0084] In this embodiment, the constraint on the charge state of the energy storage device can be used as a constraint condition of the intraday power grid optimization scheduling model, which can take into account the actual operation of the power grid and is conducive to achieving accurate optimization scheduling control of the power grid.

[0085] In an exemplary embodiment, the dispatch target of the intraday power grid optimization dispatch model is expressed as:

[0086] .

[0087] in, Characterization error minimization, objective function The role of is to make the predicted output power and the power used by each load node as close as possible. For the prediction time domain, Can be The load forecast value at the time, Can be for The reference value of the load forecast value at the moment, , is the weight coefficient, The error sequence at time.

[0088] The objective function of the intraday power grid optimization dispatch model in this embodiment is the same as the objective function of the day-ahead power grid optimization dispatch model. It should be noted that the constraints of the day-ahead power grid optimization dispatch model include:

[0089] ;

[0090] in, Allocate total power to the distribution network, Enter the total power for the distribution network.

[0091] In another embodiment, Figure 8 As shown, a power grid rolling optimization dispatch control method is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and includes the following steps:

[0092] Step S802, obtaining current operation data and historical operation data of the power grid.

[0093] Step S804, input the current operation data and the historical operation data into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node of the power grid.

[0094] Step S806, construct an intraday power grid optimization dispatching model, and solve the intraday power grid optimization dispatching model based on the day-ahead load forecast information of each load node to obtain the intraday load forecast information of each load node; the intraday load forecast information of the load node includes the load forecast information of the load node in each time period of the day.

[0095] Step S808, determining an intraday dispatching plan for the power grid based on the intraday load forecast information of each load node.

[0096] Step S810, obtaining the real-time load information of each load node after executing the intraday scheduling plan, and determining the error based on the real-time load information of each load node and the intraday load forecast information.

[0097] Step S812: adjusting the day-ahead power grid optimization dispatching model based on the error.

[0098] Step S814, returns to the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization dispatching model to obtain the day-ahead load forecast information of each load node of the power grid, so as to optimize the intra-day dispatching plan of the power grid in real time.

[0099] It should be noted that the specific limitations of the above steps can refer to the specific limitations of a power grid rolling optimization dispatching control method mentioned above.

[0100] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0101] Based on the same inventive concept, the embodiment of the present application also provides a power grid rolling optimization dispatching control device for implementing the power grid rolling optimization dispatching control method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power grid rolling optimization dispatching control device embodiments provided below can refer to the limitations of the power grid rolling optimization dispatching control method above, and will not be repeated here.

[0102] In an exemplary embodiment, Fig. 9 As shown, a power grid rolling optimization dispatch control device is provided, including: an acquisition module 902, an input module 904, a construction module 906, a determination module 908 and an adjustment module 910, wherein:

[0103] An acquisition module 902 is used to acquire current operation data and historical operation data of the power grid;

[0104] An input module 904 is used to input current operation data and historical operation data into a day-ahead power grid optimization dispatching model to obtain day-ahead load forecast information of each load node of the power grid;

[0105] The construction module 906 is used to construct an intraday power grid optimization dispatching model, and solve the intraday power grid optimization dispatching model based on the day-ahead load forecasting information of each load node to obtain the intraday load forecasting information of each load node; the intraday load forecasting information of the load node includes the load forecasting information of the load node in each period of the day;

[0106] A determination module 908 is used to determine an intraday dispatching plan for the power grid according to the intraday load forecast information of each load node;

[0107] Adjustment module 910 is used to obtain the real-time load information of each load node after executing the intraday scheduling plan, and adjust the day-ahead power grid optimization scheduling model based on the real-time load information of each load node, and return to the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization scheduling model to obtain the day-ahead load forecast information of each load node of the power grid, so as to optimize the intraday scheduling plan of the power grid in real time.

[0108] In an exemplary embodiment, the adjustment module 910 is specifically configured to determine an error based on the real-time load information of each load node and the intraday load forecast information; and adjust the day-ahead power grid optimization dispatching model based on the error.

[0109] In an exemplary embodiment, the load nodes of the power grid include a wind power generation system in the power grid; the constraints of the intraday power grid optimization scheduling model include a first constraint for the wind power generation system; the first constraint is used to constrain the power generation of the wind power generation system at each moment.

[0110] In an exemplary embodiment, the load nodes of the power grid include electric vehicle charging stations in the power grid; the constraints of the intraday power grid optimization scheduling model include a second constraint for the electric vehicle charging stations; and the second constraint is used to constrain user satisfaction with the electric vehicle charging stations.

[0111] In an exemplary embodiment, the load nodes of the power grid include energy storage devices in the power grid; the constraints of the intraday power grid optimization scheduling model include a third constraint for the energy storage device; the third constraint is used to constrain the charge state of the energy storage device at the moment of charging and discharging.

[0112] In an exemplary embodiment, the dispatch target of the intraday power grid optimization dispatch model is expressed as:

[0113] ;

[0114] in, Minimize characterization errors, For the prediction time domain, for Load forecast information at each moment, For Reference information of load forecast information at the moment, , is the weight coefficient, for Error information at the moment.

[0115] Each module in the above-mentioned power grid rolling optimization dispatch control device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0116] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG10. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store power grid rolling optimization dispatching control data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a power grid rolling optimization dispatching control method is implemented.

[0117] Those skilled in the art will understand that the structure shown in FIG. 10 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0118] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned method for controlling rolling optimization and dispatching of a power grid. The steps of the above-mentioned method for controlling rolling optimization and dispatching of a power grid may be the steps of the above-mentioned method for controlling rolling optimization and dispatching of a power grid in each of the above-mentioned embodiments.

[0119] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned power grid rolling optimization dispatching control method. The steps of the power grid rolling optimization dispatching control method here can be the steps of the power grid rolling optimization dispatching control method in each of the above-mentioned embodiments.

[0120] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned power grid rolling optimization dispatching control method. Here, the steps of the power grid rolling optimization dispatching control method can be the steps of the power grid rolling optimization dispatching control method in each of the above-mentioned embodiments.

[0121] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0122] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A power grid rolling optimization dispatching control method, characterized in that: The method comprises: Obtain current and historical operation data of the power grid; Inputting the current operation data and the historical operation data into a day-ahead power grid optimization dispatching model to obtain day-ahead load forecast information of each load node of the power grid; Constructing an intraday power grid optimization dispatching model, and solving the intraday power grid optimization dispatching model based on the day-ahead load forecasting information of each load node, to obtain the intraday load forecasting information of each load node; the intraday load forecasting information of the load node includes the load forecasting information of the load node in each time period of the day; Determining an intraday dispatching plan for the power grid according to the intraday load forecast information of each of the load nodes; The real-time load information of each load node after executing the intraday scheduling plan is obtained, and based on the real-time load information of each load node, the day-ahead power grid optimization scheduling model is adjusted, and the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization scheduling model to obtain the day-ahead load forecast information of each load node of the power grid is returned to perform real-time optimization of the intraday scheduling plan of the power grid.

2. The method according to claim 1, characterized in that The adjusting the day-ahead power grid optimization dispatching model based on the real-time load information of each load node includes: Determining the error based on the real-time load information and the intraday load forecast information of each load node; Based on the error, the day-ahead power grid optimization dispatching model is adjusted.

3. The method according to claim 1, characterized in that The load nodes of the power grid include the wind power generation system in the power grid; the constraint conditions of the intraday power grid optimization scheduling model include a first constraint condition for the wind power generation system; the first constraint condition is used to constrain the power generation power of the wind power generation system at each moment.

4. The method according to claim 1, characterized in that: The load nodes of the power grid include electric vehicle charging stations in the power grid; the constraints of the intraday power grid optimization scheduling model include second constraints for the electric vehicle charging stations; the second constraints are used to constrain user satisfaction with the electric vehicle charging stations.

5. The method according to claim 1, characterized in that The load nodes of the power grid include energy storage devices in the power grid; the constraints of the intraday power grid optimization scheduling model include a third constraint for the energy storage device; the third constraint is used to constrain the charge state of the energy storage device at the moment of charging and discharging.

6. The method according to claim 1, characterized in that The dispatching target of the intraday power grid optimization dispatching model is expressed as: ； in, Minimize characterization errors, For the prediction time domain, for Load forecast information at each moment, For Reference information of load forecast information at the moment, , is the weight coefficient, for Error information at the moment.

7. A power grid rolling optimization dispatching control device, characterized in that: The device comprises: An acquisition module is used to acquire current and historical operation data of the power grid; An input module, used to input the current operation data and the historical operation data into a day-ahead power grid optimization dispatching model to obtain day-ahead load forecast information of each load node of the power grid; A construction module is used to construct an intraday power grid optimization dispatching model, and based on the day-ahead load forecasting information of each load node, solve the intraday power grid optimization dispatching model to obtain the intraday load forecasting information of each load node; the intraday load forecasting information of the load node includes the load forecasting information of the load node in each time period of the day; A determination module, configured to determine an intraday dispatching plan for the power grid according to intraday load forecast information of each load node; An adjustment module is used to obtain the real-time load information of each load node after executing the intraday scheduling plan, and based on the real-time load information of each load node, adjust the day-ahead power grid optimization scheduling model, return to the step of inputting the current operation data and the historical operation data into the day-ahead power grid optimization scheduling model to obtain the day-ahead load forecast information of each load node of the power grid, so as to optimize the intraday scheduling plan of the power grid in real time.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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

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