Scenarized sample editing method and related device

Abstract

The invention belongs to the technical field of power system dispatching and control, and particularly discloses a scenarized sample editing method and a related device. The scenarized sample editing method comprises the following steps: determining a boundary constraint condition, and carrying out optimization adjustment on the determined boundary constraint condition by utilizing an optimization algorithm to obtain an optimized constraint condition; modifying and adjusting a pre-obtained original historical data sample to obtain an edited sample; and carrying out calculation arrangement and simulation operation on the obtained edited sample, carrying out comparison verification on a result and boundary constraint conditions, if all the boundary constraint conditions are met, outputting the edited sample as a scenarized sample of a selected scene, and if the boundary constraint conditions are not met, outputting the edited sample as a scenarized sample of the selected scene. Otherwise, determining the boundary constraint condition again according to the actual operation condition of the power grid and the selected scene demand. According to the method, effective construction and editing of the scenarized sample are realized, and the efficiency and the accuracy are relatively high.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching and control, and relates to power grid regulation operation simulation and experimental verification, and in particular to a scenario-based sample editing method and related devices. Background Art

[0002] In the power market environment, the objects of new power system regulation are becoming increasingly complex, the uncertainty of power grid operation is increasing, and the difficulty of power grid regulation continues to increase. The demand for improving technical support capabilities and improving market rules verification in the field of power grid regulation is unprecedentedly urgent. The power grid regulation digital service platform constructed in the existing technology can support scientific and technological innovation and digital research and development in the field of regulation, create a control technology test and verification environment and a multi-level spot market simulation digital sandbox, provide multi-dimensional panoramic control data and full-process simulation of power grid operation for units at all levels, and support business needs such as control technology test verification, spot market operation simulation, intelligent body training, control operation simulation, and software quality management; among them, scenario-based sample construction is a key link in the application of the power grid regulation digital service platform. To further explain, technological innovation, verification of electricity market rules and software development in the field of power grid control require not only the use of simple standard examples to verify the correctness of theoretical methods in the early stages of research, but also massive actual power grid data samples to verify the adaptability, practicality, rationality, robustness and accuracy of technologies, methods, rules and software. Among them, the more comprehensive the data samples used for experimental verification, the higher the maturity of the technologies, methods, rules and software; the more comprehensive the data samples used for training, the stronger the practicality of the intelligent agents obtained through training.

[0003] At present, due to the different data collection accuracy of the actual power grid and the inconsistency caused by the untimely data upload and download, as well as the data missing caused by data maintenance or system abnormalities, it is very difficult to extract scenario data samples; in addition, the "warning, fault, emergency, recovery" power grid operation scenarios are relatively scarce, and it is difficult to obtain the corresponding scenario data sample set based on the actual power grid data, but these scarce scenarios are the key scenarios for technology, methods, rules, and software test verification. In summary, given the scarcity of sample data for key power grid scenarios such as boundary scenarios, fault scenarios, and extreme scenarios outside of normal scenarios, it is impossible to effectively support the panoramic simulation of power grid operation modes and the test verification of new technologies in such scenarios. It is urgent to develop a new solution that generates specific scenario samples based on actual power grid cases. Summary of the invention

[0004] The purpose of the present invention is to provide a scenario sample editing method and related devices to solve one or more of the above-mentioned technical problems. In the technical solution disclosed in the present invention, historical data is used as the sample basis, and parameter settings are adjusted according to the application scenario to generate a scenario model that better meets the actual needs, thereby realizing the effective construction and editing of scenario samples with high efficiency and accuracy; further, the generated scenario samples can provide reliable support for the simulation of the entire process of power grid regulation and operation and the experimental verification of new technologies in the field of regulation, which can effectively promote the development of new technologies.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a scenario-based sample editing method, comprising the following steps: Determine boundary constraints based on actual grid operation conditions and selected scenario requirements, and use optimization algorithms to optimize and adjust the determined boundary constraints to obtain optimized constraints; Based on the obtained optimized constraint conditions, the pre-acquired original historical data samples are modified and adjusted to obtain edited samples; The obtained edited samples are calculated and simulated by using power grid operation simulation software or model, and the results of the calculation and simulation are compared and verified with the boundary constraints. If all boundary constraints are met, the edited samples are output as scenario samples of the selected scenario, otherwise the boundary constraints are re-determined according to the actual power grid operation situation and the requirements of the selected scenario.

[0006] A further improvement of the present invention is that The step of determining the boundary constraint conditions according to the actual operation of the power grid and the requirements of the selected scenario includes: intelligent editing of boundary data and verification of constraint boundary data; wherein, The intelligent editing of boundary data includes statistical analysis of power generation and consumption of original historical data samples, planning and forecasting constraint boundary management and safety verification constraint boundary management; wherein, the statistical analysis of power generation and consumption of original historical data samples includes statistical analysis of power generation and consumption based on original historical data samples, and modifying the system load curve within the limit value to obtain the system load forecast curve; the planning and forecasting constraint boundary management includes one or more of bus load forecast management, tie line plan management, new energy forecast plan management, generator unit boundary constraint management, unit group boundary constraint management, generator unit information management and power plant information management; the safety verification constraint boundary management includes one or more of unit constraint parameter management, section constraint management, branch constraint management, branch N-1 calibration and correction and equipment maintenance and shutdown management; The constraint boundary data verification includes one or more of power grid balance verification, power grid model data verification, unit bus load data verification, fixed output unit verification, unit power constraint verification and climbing constraint verification.

[0007] A further improvement of the present invention is that The bus load forecasting management includes: adjusting the forecasting data of all load devices in the area by adjusting the system load data based on the historical load curve; in the bus load forecasting management, the scheme adopted when allocating the load modification value and the deviation value is an equal proportion allocation scheme or a step-by-step equal proportion allocation scheme; The new energy forecast plan management includes: querying the total new energy, total wind power, and total photovoltaic data, querying the curve data of the new energy power plant according to the plant station, and modifying the planned curve of the new energy power plant; In the new energy forecast plan management, the way of allocating wind and solar power generation to the plant station includes: according to the historical power generation curve, by modifying the total wind and solar power, adjusting in proportion to the actual power generation; according to the historical typical curve, specifying the power generation of wind power and photovoltaic power plants to achieve the stretching generation of the power curve; The generator group boundary constraint management includes: querying and modifying the adjustable upper and lower limit curves of thermal power units, querying and setting the hydropower vibration zone, querying and setting the upper and lower limits and plan curves of pumped storage units, querying and setting the upper and lower limits and plan curves of energy storage units, querying and setting the unit status, modifying the fixed output, startup and shutdown information, and setting one or more of the upper and lower adjustment rates of the units; The unit group boundary constraint management includes: querying and modifying the adjustable upper and lower limit curves of the unit group, setting the unit group power constraint, and setting one or more of the power related verification functions.

[0008] A further improvement of the present invention is that The unit constraint parameter management includes: setting of planning curve and power constraint; The section constraint management includes: section data query and section limit modification; The branch constraint management includes: querying and setting the resistance, reactance and thermal stability information of the branch; The equipment maintenance and shutdown management includes: management query of branch information and query and setting of branch maintenance and shutdown.

[0009] A further improvement of the present invention is that The step of optimizing and adjusting the determined boundary constraint conditions by using an optimization algorithm to obtain the optimized constraint conditions comprises: Use SCUC or SCED for optimization calculation, use three-way or equal load rate method for debugging, obtain results that meet the preset constraint safety operation requirements, and obtain optimized constraint conditions; Among them, the preset constraints include one or more of system load balance constraints, system spinning reserve constraints, unit output upper and lower limit constraints, unit climbing constraints, branch flow constraints, section flow constraints, new energy unit output constraints and unit group constraints.

[0010] A further improvement of the present invention is that The generator group constraints include one or more of generator group power constraints, generator group electricity constraints and generator group spinning reserve constraints.

[0011] A further improvement of the present invention is that In the step of using the power grid operation simulation software or model to perform calculation arrangement and simulation operation on the obtained edited samples, and comparing and verifying the results obtained by the calculation arrangement and simulation operation with the boundary constraint conditions, When conducting comparative verification, one or more of the following methods shall be used: power grid balance analysis, unit calculation result analysis and safety verification result analysis.

[0012] A second aspect of the present invention provides a scenario-based sample editing system, comprising: The constraint condition acquisition module is used to determine the boundary constraint conditions according to the actual operation of the power grid and the requirements of the selected scenario, and optimize and adjust the determined boundary constraint conditions using the optimization algorithm to obtain the optimized constraint conditions; A sample editing module is used to modify and adjust the pre-acquired original historical data samples based on the obtained optimized constraint conditions to obtain edited samples; The verification module is used to use the power grid operation simulation software or model to perform calculation arrangement and simulation operation on the edited samples obtained, and compare and verify the results obtained from the calculation arrangement and simulation operation with the boundary constraints. If all boundary constraints are met, the edited samples are output as scenario samples of the selected scenario, otherwise the boundary constraints are re-determined according to the actual power grid operation situation and the requirements of the selected scenario.

[0013] According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the scenario-based sample editing method as described in any one of the first aspects of the present invention is implemented.

[0014] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the scenario-based sample editing method as described in any one of the first aspects of the present invention is implemented.

[0015] Compared with the prior art, the present invention has the following beneficial effects: In the technical solution disclosed in the present invention, based on the historical data samples of typical scenarios, by modifying and adjusting one or more constraint data, the calculation arrangement of the power grid operation mode is realized, and then the planned result samples that meet this scenario are output based on the calculation arrangement results, which can support the deduction of the future state scenario of the control operation; wherein, the present invention intelligently optimizes, adjusts and verifies the sample data by setting boundary constraints, thereby improving the accuracy and efficiency of sample editing. Specifically, in view of the technical problems that the international standard example data does not contain the panoramic information of the control operation, cannot accurately reflect the real characteristics of the domestic new power system, and the typical scenario sample examples of the power grid under special events in the current power grid control operation process are insufficient, the present invention specifically proposes a scenario sample editing method that supports the simulation and experimental verification of the power grid control operation, which is based on the actual power grid historical data, and can construct sample examples of typical power grid scenarios through sample editing technology (specifically, such as single scenarios such as normal scenarios, boundary scenarios, fault scenarios, and mixed scenarios such as power generation load balance and insufficient load in the load climbing stage), and the generated scenario samples can provide reliable support for the simulation of the entire process of power grid control operation and the experimental verification of new technologies in the field of control.

[0016] In view of the problem of lack of samples of power grid scarce scenarios in the prior art, in the preferred technical solution of the present invention, based on the basic samples of historical data, sample editing is performed considering multiple constraint boundaries. On the one hand, it can ensure that the new samples constructed after editing can meet the requirements of safe power grid operation and be more in line with the actual power grid conditions. On the other hand, it can improve the speed and flexibility of sample construction to meet the needs of rapid changes in power grid regulation and operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flowchart of a scenario-based sample editing method in an embodiment of the present invention.

[0019] Figure 2It is a technical route diagram of a scenario-based sample editing method in a specific embodiment of the present invention.

[0020] Figure 3 It is a schematic diagram of the power grid operation scenario editing interface in the sample management system of the power grid regulation digital service platform in the application embodiment of the present invention.

[0021] Figure 4 It is a schematic diagram of a scenario-based sample editing system in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments and technical solutions are only part of the embodiments of the present invention, not all of the embodiments.

[0023] All other embodiments obtained by those of ordinary skill in the art without creative work based on the technical solutions disclosed in the embodiments of the present invention belong to the scope of protection of the present invention. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] See also Figure 1 , a scenario-based sample editing method provided by an embodiment of the present invention comprises the following steps: Step 1: Determine boundary constraints according to the actual operation of the power grid and the requirements of the selected scenario, and use an optimization algorithm to optimize and adjust the determined boundary constraints to obtain optimized constraints; Step 2: Based on the optimized constraint conditions obtained in step 1, modify and adjust the pre-acquired original historical data samples to obtain edited samples; Step 3, using the power grid operation simulation software or model, to perform calculation arrangement and simulation operation on the edited samples obtained in step 2, and compare and verify the results obtained by the calculation arrangement and simulation operation with the boundary constraints. If all boundary constraints are met, the edited samples obtained in step 2 are output as the scenario samples of the selected scenario, otherwise jump to step 1.

[0025] In the technical solution disclosed in the embodiment of the present invention, based on the modification and adjustment of historical data samples of typical scenarios and the application of optimization algorithm to optimize constraint conditions, accurate calculation and arrangement of power grid operation modes and efficient sample editing can be achieved; by modifying and adjusting constraint data to achieve calculation and arrangement of power grid operation modes, it is possible to output plan result samples that meet specific scenarios, which can provide strong support for the deduction of future state scenarios of regulation and operation, and help the safe, stable and economical operation of the power grid.

[0026] In an exemplary embodiment of the present invention, the step of acquiring the original historical data sample may include: 1) Data collection: Collect a large amount of data samples from the historical database of power grid operation, which may include power generation plans, load data, network topology, equipment status, etc.; 2) Data cleaning: Clean the collected data, remove outliers, fill in missing values, and perform necessary normalization or standardization to ensure data quality and consistency; 3) Feature extraction: Extract characteristic variables that have an important impact on the operation mode of the power grid from the cleaned data as the basis for subsequent sample editing.

[0027] In one embodiment of the present invention, in step 2, based on the optimized constraint conditions obtained in step 1, the pre-acquired original historical data samples are modified and adjusted. In the process of obtaining the edited samples, the power generation plan, load curve, equipment status, etc. can be adjusted to generate samples that meet the requirements of the new scenario. Subsequently, by simulating the actual operation process of the power grid, new power generation plans, voltage levels, current distribution and other operating results can be obtained.

[0028] See also Figure 2 In a specific embodiment of the present invention, a scenario-based sample editing method is provided, comprising the following steps: (1) Scenario initialization: Before each simulation, a separate scenario is first created, and the scenario is named and basic parameters are set (for example, including but not limited to the scenario description, simulation control area information, simulation basic sample file description and path configuration information, etc.).

[0029] (2) Basic data initialization: According to the selected typical scenarios, historical data is obtained as basic data samples to complete the initialization of simulation data; this mainly includes the display and management functions of the above data interaction data, and supports the query and modification of related data; The specific exemplary technical solution supports the acquisition of model data and model analysis; supports automatic processing of basic data, including but not limited to basic information of the unit, basic information of the power plant, basic information of the interconnection line, section information, etc., and provides relevant management functions to support operations such as query, modification, and deletion of the above data; supports the acquisition of constraint data, including the initialization function of all constraint data such as system load, interconnection line plan, bus load, new energy plan, and unit adjustability.

[0030] (3) Intelligent editing of boundary data, including: (3.1) Statistical analysis of power generation and consumption, including: (3.1.1) Conduct statistical analysis on the power generation and consumption information of the basic samples, including the total added value of the units (such as total added value of thermal power, total added value of wind power, total added value of photovoltaic power, and total added value of hydropower), total added value of tie lines, total added value of net load, and frequency values, and draw the total added value curves of each variable and the frequency fluctuation curves in a continuous time period; (3.1.2) Obtain system load forecast values ​​and support display of system load curves; support modification of load curves within the limit range, including common modification functions such as value modification, translation, and setting deviations; support statistical display of load forecast curves, including display of statistical data such as maximum and minimum values.

[0031] (3.2) Plan the prediction class constraint boundaries, including: (3.2.1) Bus load forecast management: Bus load forecast data mainly includes data on the high-voltage side of the main transformer and equivalent load. In order to realize the construction of load forecast data under special scenarios, it is required to support the adjustment of forecast data of all load equipment in the area by adjusting system load data based on historical load curves; in further specific exemplary technical solutions, it supports the display function of bus and node load curves, and the display function of system load and bus load comparison; supports the display of bus load forecast data and the modification operation of a single bus load; supports batch modification of bus load forecasts, and implements the following logical allocation of load modification values ​​and deviation values ​​in detail, including: proportional allocation scheme, using historical load curve data to allocate proportionally according to the system load forecast adjustment amount; step-by-step proportional allocation scheme, that is, first the provincial system load adjustment amount is allocated proportionally according to the municipal system load; then the load equipment contained in the prefecture-level city is allocated proportionally according to the historical load.

[0032] (3.2.2) Contact line planning management.

[0033] (3.2.3) New energy forecast plan management, including: support for querying total new energy, total wind power, and total photovoltaic data; support for querying new energy power plant curve data by plant; support for modifying new energy power plant plan curves, support batch modification of new energy power plant curves, and distribute them to plants in the following ways: according to historical power generation curves, by modifying the total wind and solar power, adjust it in proportion to the actual power generation; according to historical typical curves, specify the power generation of wind power and photovoltaic power plants (power generation, utilization hours, etc.) to achieve the stretching generation of power curves.

[0034] (3.2.4) Management of generator unit boundary constraints, including: support for querying and modifying the adjustable upper and lower limit curves of thermal power units; support for querying and setting hydropower vibration zones; support for querying and setting the upper and lower limits and planned curves of pumped storage units; support for querying and setting the upper and lower limits and planned curves of energy storage units; support for querying and setting the unit status, support for modification of fixed output, startup, shutdown and other information; support for setting the upper and lower adjustment rates of the unit, support for single unit settings, and support for batch settings according to different adjustment rates of units of different capacity levels.

[0035] (3.2.5) Management of unit group boundary constraints, including: support for querying and modifying adjustable upper and lower limit curves of unit groups; support for setting power constraints for unit groups, support for verification functions related to power settings; support for unit group inclusion settings.

[0036] (3.2.6) In the basic information management of generator sets, the query and modification functions of generator sets are supported.

[0037] (3.2.7) In the basic information management of power plants, the query and modification functions of power plant information are supported.

[0038] (3.3) Safety check constraint boundary, constraint condition management includes: (3.3.1) Unit constraint parameter management, including: supporting unit constraint parameter management, including but not limited to whether the planning curve is stepped, whether it is rounded, whether the power constraint can be relaxed, etc.

[0039] (3.3.2) Section constraint management, including: support for section data query, including section limits, lines included in the section, etc.; support for modification of section limits.

[0040] (3.3.3) Branch constraint management, including: supporting the management of basic branch information; supporting the query and setting of branch resistance, reactance, thermal stability and other information.

[0041] (3.3.4) Branch N-1 verification and correction.

[0042] (3.3.5) Equipment maintenance and shutdown management, including: supporting management and query of branch information; supporting query and setting of branch maintenance and shutdown.

[0043] (4) In the constraint boundary data verification, the verification function of various boundary data is supported, including but not limited to the following verification items: (4.1) Support the verification of grid balance, such as insufficient negative reserve, insufficient upper reserve, etc.; (4.2) Support the verification of power grid model data, such as power grid islands, abnormal equipment parameters, etc.; (4.3) Support verification of unit bus load data, such as large deviation between bus load and system load; (4.4) Supports verification of fixed output units, such as exceeding the upper limit, exceeding the lower limit, etc.; (4.5) Support verification of unit power constraints, such as power not within the adjustable range, etc. (4.6) Supports checking of ramp constraints, such as abnormal regulation rate.

[0044] (5) The optimization calculation includes optimization calculation parameter management and optimization calculation functions. The "optimization calculation parameter management" function supports the management and setting of various calculation parameters, and can achieve different optimization scenarios and optimization goals according to different parameter configurations. The "optimization calculation" function uses SCUC (Security-Constrained Unit Commitment) or SCED (Security-Constrained Economic Dispatch) to perform optimization calculations, and realizes optimization calculations under the optimization calculation parameter targets. It uses the "Three Publics" (i.e., open, fair, and just) or equal load rate method for debugging, and calculates results that meet the grid balance constraints, unit climbing constraints, grid safety constraints, and other safety operation requirements.

[0045] In the embodiment of the present invention, the constraints and formulas considered by the optimization algorithm are as follows: System load balancing constraints: For each time period t , the load balancing constraint can be described as: ; in, represents the output of unit i in time period t, and N is the total number of units; represents the planned power of tie line j in time period t (input is positive, output is negative), NT is the total number of tie lines; is the system load during period t.

[0046] System spinning reserve constraint: The sum of the upward and downward adjustment capabilities of the unit output in each period must meet the upward and downward adjustment spinning reserve requirements of actual operation, expressed as: ; ; in, For the crew i Maximum climbing rate, For the crew i Maximum downhill climbing rate; , The units are i In the period t Maximum and minimum output; , The time periods t Adjust spinning reserve requirements upward or downward.

[0047] The upper and lower limits of the unit output: The unit output should be within its maximum / minimum output range. The constraints can be described as: ; Among them, for the shut-down unit, All are taken as zero.

[0048] Generator climbing constraint: When the generator is climbing up or down, it should meet the climbing rate requirement. The climbing constraint can be described as: ; .

[0049] The branch flow constraint can be described as: ; In the formula, , Branch l The power flow transmission limit of For the crew i The node is on the branch l The generator output power transfer distribution factor; For contact line j The node is on the branch l The generator output power transfer distribution factor; K is the number of nodes in the system; For Node k For branch road l The generator output power transfer distribution factor; For Node k In the period t The bus load value; , Branch l The forward and reverse power flow relaxation variables.

[0050] Section flow constraints: Consider the flow constraints of key sections, which can be described as: ; In the formula, , are the power flow transmission limits of section s respectively; is the generator output power transfer distribution factor of the node where unit i is located on section s; is the generator output power transfer distribution factor of the section s at the node where the tie line j is located; is the generator output power transfer distribution factor for node k to section s. , SL , are the positive and reverse flow relaxation variables of section s respectively.

[0051] The output constraint of new energy units is expressed as: , ; In the formula, E Gather for new energy sites; For new energy stations i In the period t Explanatory speaking, the output constraint of new energy units is that the market output of new energy power stations on the previous day should be less than the output forecast value of new energy power stations.

[0052] Consider various group constraints, mainly including: The power constraint of the generator group constrains the sum of the active output power of the generator group in each period, including the upper and lower limits of the output of the generator group, which is expressed as: ; In the formula, Av Indicates a group of units; Indicates the lower limit of the unit group output; Indicates the upper limit of the unit group output; among them, and Can be expressed as a percentage of the system load forecast.

[0053] The power constraint of the unit group limits the sum of the active power output of the unit group for a period of time. You can set the upper and lower limits of its power, or require the unit group to achieve its target power as much as possible. This function is particularly suitable for power plants considering coal storage or reservoir capacity, expressed as: ; In the formula, Av represents the unit group; Indicates the lower limit of the power of the unit group; Indicates the upper limit of the power of the unit group.

[0054] The spinning reserve constraint of the unit group requires that the ramping and output reduction rate capabilities of the unit group in each period meet the corresponding reserve requirements, which can be expressed as: ; ; In the formula, For the crew exist Upward rotation reserve provided when For the group Av Increase spinning reserve demand when necessary; For the crew exist Downward rotation reserve provided when For the group Av The spinning reserve requirement is reduced when necessary.

[0055] In one embodiment of the present invention, in step 3, the edited samples obtained in step 2 are calculated and simulated using power grid operation simulation software or model, and the results of the calculation and simulation are compared and verified with the boundary constraints, including power grid balance analysis, unit calculation result analysis, and safety verification result analysis; wherein, Grid balance analysis: Supports power balance analysis, power generation and consumption balance, supports statistics and display functions of curves, data, and deviations; supports statistical analysis of positive and negative standby; Analysis of unit calculation results: Supports query function for unit plan display; supports statistical analysis function for power generation plan results of various types of units; supports statistical analysis function for unit start and stop status; supports analysis and verification function for optimization target results, such as unit load rate statistics and unit planned power statistics; Safety verification result analysis: supports statistical analysis of section current flow and section over-limit conditions; supports statistical analysis of line current flow, line over-limit and overload conditions; supports query and analysis functions for sensitivity factors.

[0056] In summary, the embodiments of the present invention provide an efficient and accurate scenario-based sample editing solution that can support simulation and experimental verification of power grid control operations. It significantly improves the efficiency of sample construction for digital deduction of power grid control operations through the rapid generation of scenario-based sample examples, intelligent editing of boundary data, and analysis and verification techniques. It can be used to test the functions of key applications in dispatching automation systems, panoramic simulation of power grid control operations, and experimental verification of new technologies in different power grid scenarios. It can effectively promote the rapid evolution of power grid control technology and improve its practical level. By way of further example and explanation, the scenario-based sample editing method proposed in the present invention is crucial in the construction of a digital service platform for power grid control that supports scientific and technological innovation and digital research and development in the field of control, and has broad application prospects, such as Figure 3In the future, with the development of energy Internet and the expansion of power system scale, the present invention is expected to be promoted and applied on a larger scale, especially in the fields of smart grid, microgrid and energy management system. It can effectively deal with complex data, support dynamic regulation and intelligent management, and further promote the intelligent and efficient operation of power grid. In the future energy industry, the present invention will become a key tool for power system data management and intelligent dispatching, and has the potential for large-scale promotion and application.

[0057] The following are device embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0058] See also Figure 4 In an embodiment of the present invention, a scenario-based sample editing system is provided, including: The constraint condition acquisition module is used to determine the boundary constraint conditions according to the actual operation of the power grid and the requirements of the selected scenario, and optimize and adjust the determined boundary constraint conditions using the optimization algorithm to obtain the optimized constraint conditions; A sample editing module is used to modify and adjust the pre-acquired original historical data samples based on the obtained optimized constraint conditions to obtain edited samples; The verification module is used to use the power grid operation simulation software or model to perform calculation arrangement and simulation operation on the edited samples obtained, and compare and verify the results obtained from the calculation arrangement and simulation operation with the boundary constraints. If all boundary constraints are met, the edited samples are output as scenario samples of the selected scenario, otherwise the boundary constraints are re-determined according to the actual power grid operation situation and the requirements of the selected scenario.

[0059] In one embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor described in the embodiment of the present invention can be used to perform the operation of the scenario-based sample editing method.

[0060] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM (Random Access Memory) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the scene-based sample editing method in the above embodiment.

[0061] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program codes.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A scenario-based sample editing method, characterized in that: The following steps are involved: Determine boundary constraints based on actual grid operation conditions and selected scenario requirements, and use optimization algorithms to optimize and adjust the determined boundary constraints to obtain optimized constraints; Based on the obtained optimized constraint conditions, the pre-acquired original historical data samples are modified and adjusted to obtain edited samples; The obtained edited samples are calculated and simulated by using power grid operation simulation software or model, and the results of the calculation and simulation are compared and verified with the boundary constraints. If all boundary constraints are met, the edited samples are output as scenario samples of the selected scenario, otherwise the boundary constraints are re-determined according to the actual power grid operation situation and the requirements of the selected scenario.

2. A scenario-based sample editing method according to claim 1, characterized in that: The step of determining the boundary constraint conditions according to the actual operation of the power grid and the requirements of the selected scenario includes: intelligent editing of boundary data and verification of constraint boundary data; wherein, The intelligent editing of boundary data includes statistical analysis of power generation and consumption of original historical data samples, planning and forecasting constraint boundary management and safety verification constraint boundary management; wherein, the statistical analysis of power generation and consumption of original historical data samples includes statistical analysis of power generation and consumption based on original historical data samples, and modifying the system load curve within the limit value to obtain the system load forecast curve; the planning and forecasting constraint boundary management includes one or more of bus load forecast management, tie line plan management, new energy forecast plan management, generator unit boundary constraint management, unit group boundary constraint management, generator unit information management and power plant information management; the safety verification constraint boundary management includes one or more of unit constraint parameter management, section constraint management, branch constraint management, branch N-1 calibration and correction and equipment maintenance and shutdown management; The constraint boundary data verification includes one or more of power grid balance verification, power grid model data verification, unit bus load data verification, fixed output unit verification, unit power constraint verification and climbing constraint verification.

3. A scenario-based sample editing method according to claim 2, characterized in that: The bus load forecasting management includes: adjusting the forecasting data of all load devices in the area by adjusting the system load data based on the historical load curve; in the bus load forecasting management, the scheme adopted when allocating the load modification value and the deviation value is an equal proportion allocation scheme or a step-by-step equal proportion allocation scheme; The new energy forecast plan management includes: querying the total new energy, total wind power, and total photovoltaic data, querying the curve data of the new energy power plant according to the plant station, and modifying the planned curve of the new energy power plant; In the new energy forecast plan management, the way of allocating wind and solar power generation to the plant station includes: according to the historical power generation curve, by modifying the total wind and solar power, adjusting in proportion to the actual power generation; according to the historical typical curve, specifying the power generation of wind power and photovoltaic power plants to achieve the stretching generation of the power curve; The generator group boundary constraint management includes: querying and modifying the adjustable upper and lower limit curves of thermal power units, querying and setting the hydropower vibration zone, querying and setting the upper and lower limits and plan curves of pumped storage units, querying and setting the upper and lower limits and plan curves of energy storage units, querying and setting the unit status, modifying the fixed output, startup and shutdown information, and setting one or more of the upper and lower adjustment rates of the units; The unit group boundary constraint management includes: querying and modifying the adjustable upper and lower limit curves of the unit group, setting the unit group power constraint, and setting one or more of the power related verification functions.

4. The scenario-based sample editing method according to claim 2, characterized in that: The unit constraint parameter management includes: setting of planning curve and power constraint; The section constraint management includes: section data query and section limit modification; The branch constraint management includes: querying and setting the resistance, reactance and thermal stability information of the branch; The equipment maintenance and shutdown management includes: management query of branch information and query and setting of branch maintenance and shutdown.

5. The scenario-based sample editing method according to claim 1, characterized in that: The step of optimizing and adjusting the determined boundary constraint conditions by using an optimization algorithm to obtain the optimized constraint conditions comprises: Use SCUC or SCED for optimization calculation, use three-way or equal load rate method for debugging, obtain results that meet the preset constraint safety operation requirements, and obtain optimized constraint conditions; Among them, the preset constraints include one or more of system load balance constraints, system spinning reserve constraints, unit output upper and lower limit constraints, unit climbing constraints, branch flow constraints, section flow constraints, new energy unit output constraints and unit group constraints.

6. A scenario-based sample editing method according to claim 5, characterized in that: The generator group constraints include one or more of generator group power constraints, generator group electricity constraints and generator group spinning reserve constraints.

7. The scenario-based sample editing method according to claim 1, characterized in that: In the step of using the power grid operation simulation software or model to perform calculation arrangement and simulation operation on the obtained edited samples, and comparing and verifying the results obtained by the calculation arrangement and simulation operation with the boundary constraint conditions, When conducting comparative verification, one or more of the following methods shall be used: power grid balance analysis, unit calculation result analysis and safety verification result analysis.

8. A scenario-based sample editing system, characterized in that: include: The constraint condition acquisition module is used to determine the boundary constraint conditions according to the actual operation of the power grid and the requirements of the selected scenario, and optimize and adjust the determined boundary constraint conditions using the optimization algorithm to obtain the optimized constraint conditions; A sample editing module is used to modify and adjust the pre-acquired original historical data samples based on the obtained optimized constraint conditions to obtain edited samples; The verification module is used to use the power grid operation simulation software or model to perform calculation arrangement and simulation operation on the edited samples obtained, and compare and verify the results obtained from the calculation arrangement and simulation operation with the boundary constraints. If all boundary constraints are met, the edited samples are output as scenario samples of the selected scenario, otherwise the boundary constraints are re-determined according to the actual power grid operation situation and the requirements of the selected scenario.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the scenario-based sample editing method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the scenario-based sample editing method according to any one of claims 1 to 7 is implemented.

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