Power grid system operation backup space-time quantization evaluation method and device

Abstract

The application provides a power grid system operation backup space-time quantification evaluation method and device, in the technical scheme of the application, the objective function of the operation backup space-time quantification evaluation algorithm model is the minimum difference between the scale information of the maximum accident and the available backup unit capacity information, the model does not define the regional backup demand in advance, but takes it as a model variable, that is, the minimum number of backup capacity required in a certain region or the maximum backup capacity limited, ensuring that the regional backup can be delivered when scheduling. At the same time, the transmission restriction constraint after the backup unit deployment is included in the model, solving the problem of the maximum limited value of backup.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a method and apparatus for quantitative evaluation of the standby time of a power grid system. Background Technology

[0002] The current mainstream method for calculating reserve requirements is to predefine regional reserve requirements and use these regional reserve requirements as reserve requirement constraints. This approach only considers the impact of power balance and energy dispatch on transmission constraints before a fault, and does not explicitly model the impact of power balance and reserve dispatch on transmission constraints after a fault.

[0003] Specifically, the aforementioned approach uses predefined market scope and regional reserve requirements as inputs to the market clearing process. By setting a minimum regional reserve requirement, it aims to address the deliverability issue of reserve capacity. However, practice has shown that this approach cannot completely resolve the issue of reserve capacity deliverability. Although offline reserve regional delivery capacity studies were used to determine the minimum regional operational reserve requirement, the scenarios analyzed in the offline study 48 hours before the operational date may differ significantly from the actual system operation. Therefore, when real-time conditions differ from study predictions, the operational reserves obtained based on the offline study results may not be available. Whenever such delivery issues occur, the real-time scheduler must manually identify and designate undeliverable resources as "unavailable" to mitigate the resulting system reliability risks. Furthermore, the process of designating these resources is not included in the day-ahead market clearing process, causing inconsistencies between the day-ahead market and the real-time market. Summary of the Invention

[0004] This application provides a method and apparatus for the spatiotemporal quantitative assessment of standby power grid system operation, to ensure the successful delivery and dispatch of standby unit capacity in the event of an accident.

[0005] In a first aspect, embodiments of this application provide a spatiotemporal quantitative assessment method for power grid system operational reserve, including:

[0006] Obtain power generation plan information and operating data of each unit within the preset area;

[0007] The power generation plan information and the operation data are processed using a pre-trained spatiotemporal quantitative evaluation algorithm model for operational standby to determine whether there is line congestion in the preset area.

[0008] In the event of line congestion, the available standby unit capacity information and the limited standby unit capacity information within the preset area are calculated using the standby spatiotemporal quantification evaluation algorithm model.

[0009] Using the aforementioned standby spatiotemporal quantification assessment algorithm model, based on the available standby unit capacity information and the limited standby unit capacity information, the scale information of the maximum accident that the preset area can respond to and the limited standby unit capacity information at the time of the maximum accident are calculated.

[0010] Using the aforementioned standby spatiotemporal quantification evaluation algorithm model, based on the characteristics of each unit within the preset area and the limited standby unit capacity information at the time of the maximum accident, the standby capacity allocation result information of at least one unit within the preset area is determined;

[0011] Among them, the standby time-space quantitative evaluation algorithm model includes the objective function, regional standby demand constraints, and transmission limitation constraints after the deployment of standby units;

[0012] The objective function is to minimize the difference between the maximum accident scale information and the available standby unit capacity information; the transmission restriction constraint after the standby unit deployment is used to ensure that the standby unit capacity scheduling execution will not cause line congestion; the regional standby demand constraint is used to limit the resource clearing standby to meet the corresponding regional standby demand variables, and limits the upper limit of the regional standby demand variables.

[0013] Secondly, embodiments of this application provide a spatiotemporal quantitative assessment device for power grid system operation standby, comprising:

[0014] The data acquisition module is used to acquire power generation plan information and operating data of each unit within a preset area;

[0015] The model processing module is used for:

[0016] The power generation plan information and the operation data are processed using a pre-trained spatiotemporal quantitative evaluation algorithm model for operational standby to determine whether there is line congestion in the preset area.

[0017] In the event of line congestion, the available standby unit capacity information and the limited standby unit capacity information within the preset area are calculated using the standby spatiotemporal quantification evaluation algorithm model.

[0018] Using the aforementioned standby spatiotemporal quantification assessment algorithm model, based on the available standby unit capacity information and the limited standby unit capacity information, the scale information of the maximum accident that the preset area can respond to and the limited standby unit capacity information at the time of the maximum accident are calculated.

[0019] Using the aforementioned standby spatiotemporal quantification evaluation algorithm model, based on the characteristics of each unit within the preset area and the limited standby unit capacity information at the time of the maximum accident, the standby capacity allocation result information of at least one unit within the preset area is determined;

[0020] Among them, the standby time-space quantitative evaluation algorithm model includes the objective function, regional standby demand constraints, and transmission limitation constraints after the deployment of standby units;

[0021] The objective function is to minimize the difference between the maximum accident scale information and the available standby unit capacity information; the transmission restriction constraint after the standby unit deployment is used to ensure that the standby unit capacity scheduling execution will not cause line congestion; the regional standby demand constraint is used to limit the resource clearing standby to meet the corresponding regional standby demand variables, and limits the upper limit of the regional standby demand variables.

[0022] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor, when executing the computer program, implements the method described in any of the above-mentioned embodiments.

[0023] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in any of the above-mentioned embodiments.

[0024] Compared with the prior art, this application has the following advantages:

[0025] According to the spatiotemporal quantitative assessment method for power grid system operational reserve in this application, the objective function of the operational reserve spatiotemporal quantitative assessment algorithm model is to minimize the difference between the maximum accident scale information and the available reserve unit capacity information. The model does not predefine regional reserve requirements but treats them as model variables, i.e., the minimum number of reserve unit capacities or the maximum reserve quantity of limited reserve unit capacity that may be required in a certain region, ensuring that regional reserves can be delivered during scheduling. Simultaneously, transmission constraint limitations after reserve unit deployment are incorporated into the model to address the problem of maximum reserve limitations.

[0026] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0027] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments according to this application and should not be construed as limiting the scope of this application.

[0028] Figure 1 This is a flowchart of a spatiotemporal quantitative assessment method for power grid system operation standby according to an embodiment of this application;

[0029] Figure 2 This is a flowchart of a spatiotemporal quantitative assessment method for power grid system standby operation according to another embodiment of this application;

[0030] Figure 3 This is a flowchart of determining a partition according to another embodiment of this application;

[0031] Figure 4A This is a basic information diagram of a three-node basic model, which is another embodiment of this application.

[0032] Figure 4B This is a schematic diagram of a three-node basic model of another embodiment of this application;

[0033] Figure 5A This is a schematic diagram of a three-node optimized model according to another embodiment of this application;

[0034] Figure 5B This is a schematic diagram of the three-node information of the optimized model in another embodiment of this application;

[0035] Figure 5C This is a post-accident power flow diagram according to another embodiment of this application;

[0036] Figure 5D This is a schematic diagram illustrating the calculation of available standby unit capacity according to another embodiment of this application;

[0037] Figure 6A This is one of the schematic diagrams showing the output of unit C in another embodiment of this application;

[0038] Figure 6B This is a second schematic diagram of the output of unit C according to another embodiment of this application;

[0039] Figure 7 This is a structural block diagram of a power grid system operation standby time-space quantitative assessment device according to an embodiment of this application;

[0040] Figure 8 This is a block diagram of an electronic device used to implement embodiments of this application. Detailed Implementation

[0041] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the concept or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0042] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and all of them fall within the protection scope of the embodiments of this application.

[0043] Before describing the spatiotemporal quantitative assessment method for power grid system operation reserve in this application, the basic model that may be used will first be explained. The basic model here can be used as the base model for the spatiotemporal quantitative assessment algorithm model for operation reserve.

[0044] The basic model can be used to determine the reserve capacity reduction in the power grid system network constraints. The limited capacity of a network section is determined by comparing the power flow values ​​of generating units with a network section threshold. The working principle of the basic model is as follows: all generating units are arranged in ascending order of sensitivity, and the power flow value of the network section for each unit at its maximum adjustable output is calculated. The power flow value of the network section is compared with the threshold. If it is less than the threshold, the power flow values ​​are accumulated until the accumulated power flow value is greater than the threshold. All generating units after the unit corresponding to the last power flow value in the accumulation are considered limited units. The difference between the maximum adjustable output and the original output of the limited units is calculated and accumulated to obtain the limited capacity of the network section, i.e., the reserve capacity reduction. Details are as follows:

[0045] Sort all units in ascending order of sensitivity, ensuring that the unit selected and calculated first is the one with the lowest sensitivity. Calculate the power flow value of the network section for each unit at its maximum adjustable output. Compare the power flow values ​​calculated in the previous step with the network section threshold. If the power flow value is less than the threshold, accumulate the power flow values ​​sequentially. When the accumulated total power flow value is greater than the threshold, stop accumulating the power flow values ​​and record the position of the unit where the last power flow value was accumulated. This gives the restricted units, which are all units after the position of the unit where the last power flow value was accumulated in the ascending sensitivity sort. Subtract the maximum adjustable output of each restricted unit from its original output and sum them up to obtain the restricted capacity of the network section.

[0046] The limitations of the basic model are as follows: While its assessment of reserve-constrained capacity is simple and fast, it has certain limitations, mainly in two aspects: First, it ignores units with negative sensitivity during calculation. Second, heavy-load capacity regulation can reduce positive-sensitivity units or increase negative-sensitivity units; neglecting the contribution of negative-sensitivity units in the basic model's calculation easily leads to overly conservative results for capacity constraints. Third, it cannot effectively handle the influence of network cross-sectional coupling. (The last sentence appears to be incomplete and possibly refers to a different model or standard.) Shadong Jia Yi + Jiangxi Yi", 0.51 Taking the two cross-sections "Die Cang Jia Yi + Jiangxi Yi Line <2680MW" as an example, both require reduction of the Jiangmen, Shunde, Yangmao Zhan, and Zhongzhu units (positive sensitivity), but there are the following differences: Because the basic model prioritizes limiting units with higher sensitivity, when assessing the limitation of the Shadong Jiangxi cross-section, it will prioritize limiting the Zhongzhu units, possibly concluding that the Zhongzhu units are limited by 1000MW, while other units are unrestricted. However, when assessing the Die Cang Jiangxi cross-section, the program will prioritize limiting the Jiangmen and Yangmao Zhan units, possibly determining that the Jiangmen and Yangmao Zhan units are limited by 600MW, while other units are unrestricted. Taking the larger of the two results, the conclusion is that a total limitation of 1600MW is considered for both cross-sections. A simple calculation shows that after considering the 1000MW limitation of the Zhongzhu units, the Die Cang Jiangxi cross-section can actually meet the requirements, without needing to further limit the Jiangmen and Yangmao Zhan units. Because the basic model can only assess each cross-section individually, the cross-effect of cross-section limitations cannot be assessed, leading to a conservative result.

[0047] The two issues mentioned above lead to the fact that the cross-sections of the basic model assessment are usually conservative due to limitations. To address the limitations of the basic model, an optimized model is proposed based on the basic model, namely, the running standby spatiotemporal quantization assessment algorithm model. This model comprehensively considers the negative sensitivity units and the cross-influence between limited cross-sections, thereby reducing the conservatism of the network-limited assessment.

[0048] Therefore, the model in this application consists of two parts: a basic model and an optimized model. The basic model is used to determine the standby deduction capacity and its distribution, while the optimized model is used to solve problems that are not addressed in the basic model, such as ignoring units with negative sensitivity and not handling the coupling effects between network sections.

[0049] In a spot market environment, adjusting reserve capacity is a non-relaxed constraint in system clearing calculations. To minimize overall power generation costs, key network sections often operate under extreme or even over-limit conditions for extended periods. Releasing reserved reserve capacity can exacerbate or directly cause these sections to exceed limits, resulting in some reserves being unavailable under real-time monitoring. To address this issue, a spatiotemporal quantitative assessment algorithm model for operational reserve capacity considering system network section constraints is constructed. A refined calculation algorithm covering 96 operational modes within a day is established to achieve integrated spatiotemporal assessment and display of reserve capacity. Based on power flow calculations, the spatiotemporal quantitative assessment algorithm model for operational reserve capacity determines available and restricted reserve capacity under unforeseen circumstances. It utilizes available and restricted reserve unit capacity information to achieve real-time operational status and deduction and allocation of system operational reserve capacity under a defined set of anticipated accidents, as well as dynamic calculation of margins for pre-selected key channels. The results serve as auxiliary decision-making support for dispatchers in real-time congestion management during dispatch operations.

[0050] The objective function of the standby spatiotemporal quantification assessment algorithm model is to minimize the difference between the maximum accident scale information and the available standby unit capacity information. The model does not predefine regional standby requirements but treats them as model variables, i.e., the minimum number of standby unit capacities that may be needed in a certain region or the maximum number of constrained standby unit capacities, ensuring that standby unit capacity within the region is available for scheduling. Simultaneously, transmission constraints after standby unit deployment are incorporated into the model to address the issue of maximum standby capacity limitations.

[0051] The constraints of the standby spatiotemporal quantitative evaluation algorithm model can be divided into the following categories: available standby constraints, transmission limitation constraints before standby unit deployment, market standby demand constraints, regional standby demand constraints, transmission limitation constraints after standby unit deployment, unit constraints, and other constraints.

[0052] The following provides a detailed description of the spatiotemporal quantitative assessment method and apparatus for power grid system operation standby in this application.

[0053] like Figure 1 The diagram shows a flowchart of a spatiotemporal quantitative assessment method for power grid system operating reserve according to an embodiment of this application. This method is performed by a device with computational and analytical capabilities. Specifically, the spatiotemporal quantitative assessment method for power grid system operating reserve according to this embodiment may include the following steps:

[0054] S110. Obtain power generation plan information and operating data of each unit within the preset area.

[0055] S120. Using a pre-trained operational standby spatiotemporal quantification evaluation algorithm model, the power generation plan information and the operational data are processed to determine whether there is line congestion within a preset area.

[0056] S130. In the event of line congestion, the available standby unit capacity information and the limited standby unit capacity information within the preset area are calculated using the standby spatiotemporal quantification evaluation algorithm model.

[0057] S140. Using the aforementioned reserve spatiotemporal quantification assessment algorithm model, based on the available reserve unit capacity information and the limited reserve unit capacity information, calculate the scale information of the maximum accident that the preset area can respond to, and the limited reserve unit capacity information at the time of the maximum accident. Using the aforementioned reserve spatiotemporal quantification assessment algorithm model, based on the characteristics of each unit in the preset area and the limited reserve unit capacity information at the time of the maximum accident, determine the reserve capacity allocation result information of at least one unit in the preset area.

[0058] The standby time-space quantitative evaluation algorithm model includes an objective function, regional standby demand constraints, and transmission limitation constraints after standby unit deployment. The objective function is to minimize the difference between the maximum accident scale information and the available standby unit capacity information. The transmission limitation constraints after standby unit deployment are used to ensure that standby unit capacity scheduling will not cause line congestion. The regional standby demand constraints are used to limit the regional standby demand variables that must be met when clearing resources for standby, and limit the upper limit of the regional standby demand variables.

[0059] In the absence of line congestion, this application embodiment uses the aforementioned standby spatiotemporal quantification evaluation algorithm model to calculate the scale information of the maximum accident that the preset area can respond to and the limited standby unit capacity information when the maximum accident occurs; using the aforementioned standby spatiotemporal quantification evaluation algorithm model, based on the characteristics of each unit in the preset area and the limited standby unit capacity information when the maximum accident occurs, the standby capacity allocation result information of at least one unit in the preset area is determined.

[0060] The model operation process is as follows: Figure 2 As shown, the power generation plan information and operating data or the set of anticipated accidents are input into the spatiotemporal quantitative evaluation algorithm model for operational standby. The model operation determines whether there is line congestion in the system based on the required standby conditions. If there is congestion, it calculates the available standby unit capacity information and the limited standby unit capacity information. When there is no line congestion, it calculates the scale information of the maximum accident that can be responded to in the preset area and the limited standby unit capacity information when the maximum accident occurs. Then, it determines the standby capacity allocation result information based on the unit characteristics.

[0061] In some embodiments, the above objective function is:

[0062] min (1)

[0063] In the formula, This indicates the scale information of the largest accident within zone K of the preset area. This indicates the available standby unit capacity within the K zone of the preset area.

[0064] In some embodiments, the operational standby spatiotemporal quantitative evaluation algorithm model includes available standby constraints, wherein the available standby constraints are used to constrain the available standby unit capacity and the limited standby unit capacity within the market scope; and / or, to constrain the available standby unit capacity and the limited standby unit capacity within each partition of a preset area. Specifically,

[0065] Available alternative constraints include the following two formulas:

[0066] = (2)

[0067] = (3)

[0068] In the formula, Indicates the available standby unit capacity within the market; This represents the sum of the capacities of all types of standby units within the market scope; This indicates that the market scope is limited by the capacity of standby generator units; This indicates the available standby unit capacity within area K; This represents the sum of the capacities of all types of standby units within Zone K; This indicates the limited standby unit capacity within Zone K.

[0069] Equation (2) defines the available standby unit capacity for the market scope as the difference between the total capacity of all types of standby units in the market scope and the limited standby unit capacity in the market scope. Equation (3) defines the available standby unit capacity for the region as the difference between the total capacity of all types of standby units in the region and the limited standby unit capacity in the region.

[0070] In some embodiments, the operational standby spatiotemporal quantification evaluation algorithm model includes transmission constraint requirements prior to standby unit deployment. These transmission constraint requirements are used to limit the transmission power flow under normal grid system operation scenarios to within preset normal limits. Specifically, the transmission constraint requirements prior to standby unit deployment include the following two formulas:

[0071] (4)

[0072] (5)

[0073] In the formula, Indicates the power flow at the point of transmission limitation; Represents the clearing energy vector; Represents the net fixed injection vector; This indicates the normal limit value for transmission flow; This represents the sensitivity of the power flow of transmission constraint i to node n injection and reference bus extraction. J is the set of all resources, and j is a specific resource in set J. The node where resource j is located.

[0074] Equations (4) and (5) represent the transmission power flow constraints when no backup unit capacity is deployed. The transmission power flow constraints under normal conditions (no accident) are as follows: The power flow limit at the time of the accident is Equation (4) constrains the transmission power flow to operate within its normal limits under normal conditions and prohibits it from exceeding the limits. Equation (5) constitutes the constraints for the transmission power flow. The term represents the power flow distribution of the system's energy over transmission constraints. The term represents the power flow distribution of the node's net fixed injection on the transmission limit. Equation (5) constrains the power flow on the transmission limit, which is the system energy and the power flow of each node's net fixed injection at the limit.

[0075] In some embodiments, the operational standby spatiotemporal quantitative evaluation algorithm model includes market standby demand constraints; wherein, the market standby demand constraints are used to limit the regional standby demand variables to meet the corresponding market standby demands, thus limiting the lower limit of the regional standby demand variables. Regional standby demand is defined as a variable and solved through optimization. This set of constraints determines the lower limit of the regional standby demand variables by requiring regional standby demand to meet the corresponding market standby demands. Specifically, the market standby demand constraints include the following two formulas:

[0076] (6)

[0077] (7)

[0078] (8)

[0079] In the formula, This represents the required K-zone capacity, i.e., the capacity of frequency regulation standby units within the K-zone. This indicates the market demand for frequency regulation standby unit capacity; This represents the required emergency standby unit capacity in area K. This indicates the market's demand for backup power capacity in case of accidents.

[0080] Equations (6)-(8) constrain the regional reserve demand variables. Equation (6) constrains the total demand for frequency regulation reserve units in each region to meet the market demand for frequency regulation reserve units. Equations (7) and (8) respectively constrain the capacity of frequency regulation reserve units, the total capacity of emergency reserve units, and the total capacity of each type of reserve unit in each region. The capacity of reserve units in each region must meet the corresponding market reserve demand. This set of constraints ensures the sufficiency of market reserves.

[0081] In some embodiments, the regional reserve demand constraint determines the upper limit of the regional reserve demand variable by requiring that resource clearing reserves must meet the corresponding regional reserve demand. Specifically, it can include the following three formulas:

[0082] (9)

[0083] (10)

[0084] (11)

[0085] In the formula, This indicates the capacity of the frequency regulation standby unit after resource j has been cleared; This indicates the frequency modulation reserve capacity requirement for area K; This indicates the emergency standby unit capacity after resource j has been cleared; This indicates the emergency backup capacity requirement for Zone K.

[0086] Equations (9)-(11) impose constraints on resource clearing reserves. Equation (9) requires that the total frequency regulation reserve capacity of each resource clearing within the region must meet the regional frequency regulation reserve capacity requirements. Equation (10) requires that the total frequency regulation reserve capacity and emergency reserve capacity of each resource clearing within the region must meet the regional frequency regulation reserve capacity and emergency reserve capacity requirements. Equation (11) requires that the total reserve capacity of each type of resource clearing within the region must meet the regional reserve capacity requirements of each type. This set of constraints ensures the sufficiency of the regional reserve capacity requirements.

[0087] In some embodiments, the transmission constraints after the deployment of the standby unit may include the following three formulas:

[0088] (12)

[0089] + (13)

[0090]

[0091] (14)

[0092] In the formula, This represents the aggregation sensitivity of the power flow of transmission limit i to the backup requirements of area k and the reference bus extraction. This indicates the sensitivity of the power flow of transmission limit i to market load center injection and reference bus extraction; This represents the aggregation sensitivity of the transmission limit i-current to the maximum incident in region K; This indicates the scale of the largest accident in Zone K; This represents the emergency backup deployment coefficient under the maximum accident scenario in Zone K. This represents the backup deployment coefficient on the power generation side under the maximum accident scenario in Zone K. This represents the limit value of the transmission flow under accident conditions.

[0093] The impact of regional standby unit capacity deployment on transmission lines is assessed through regional comprehensive sensitivity, and this constraint ensures that the execution of the standby scheduling plan will not lead to line congestion. Equations (12) and (13) are constraints on transmission power flow limitation during frequency modulation. The term represents the power flow of frequency modulation distribution at the transmission limit. The term represents the power flow at the transmission limit point where the market load center changes. Equation (12) constrains the power flow at the transmission limit point to be within the normal limit of the transmission limit power flow when the frequency is adjusted upwards. Equation (13) constrains the power flow at the transmission limit point to be within the normal limit of the transmission limit power flow when the frequency is adjusted downwards.

[0094] Equation (14) represents the constraint on power flow limitation after deploying emergency standby unit capacity and generator-side standby unit capacity under accident conditions. The term represents the power flow where the largest regional incident is located at the transmission restriction point. The item represents the power flow where the deployed emergency backup unit capacity is distributed at transmission-limited locations. The power flow at the transmission limit is distributed for the capacity of the deployed standby generator units on the generator side. Equation (14) constrains that the power flow at the transmission limit should be within its fault limit after deploying the capacity of the emergency standby generator units and the capacity of the generator side standby generator units under fault conditions.

[0095] The regional sensitivity of each standby unit capacity can be aggregated differently based on the performance of the standby products of the resources: Aggregation is performed based on the maximum limit of online generators in each region; Aggregate resources based on their 5-minute ramp-up capability, which qualifies them to provide frequency modulation backup. Aggregate resources based on their 10-minute uphill climb capability, which qualifies them to provide emergency backup. Aggregate resources based on their 30-minute ramp-up capability, which qualifies them to provide backup power generation.

[0096] In some embodiments, the operational standby spatiotemporal quantification evaluation algorithm model includes unit constraints, which are used to limit at least one of the following:

[0097] The unit's cleared energy and the capacity of each type of standby unit operate within their corresponding maximum power output range;

[0098] When the unit's frequency is adjusted downwards, the preset requirements for the unit's minimum power output must be met.

[0099] Unit capacity scheduling must be carried out within the ramp-up rate range of the unit's equipment.

[0100] The various types of standby capacity provided by the generating unit equipment must be within the corresponding types of standby capacity that it is qualified to provide.

[0101] Specifically, unit constraints can include the following four formulas:

[0102] + (15)

[0103] (16)

[0104] (17)

[0105] (18)

[0106] In the formula, This represents a binary parameter; its value is 1 when the resource is online and schedulable, and 0 otherwise. This represents the actual power output of resource j during scheduling interval t; This represents the maximum power output of resource j during scheduling interval t; Indicates the duration of the scheduling interval t, in minutes; This indicates the rate at which resource j reduces output (MW / min). This indicates the backup scheduling target for resource j from the previous time interval t. For a single time interval SCED, this is a given parameter. This indicates the rate at which resource j increases its output ramp rate (MW / min); This represents the maximum available spare x that can be cleared on resource j. If resource j does not meet the condition of providing spare x, then it is 0.

[0107] Equation (15) stipulates that the clearing energy of the constrained generator units and the capacity of each type of standby generator unit must be within their maximum power output range. Equation (16) stipulates that the constrained generator units must meet their minimum power output requirements when adjusting their frequency downwards. Equation (17) stipulates that the constrained generator unit capacity scheduling must be carried out within its ramp rate range; Equation (18) stipulates that the capacity of each type of standby generator unit provided by the constrained generator unit must be within the corresponding type of standby capacity that it is qualified to provide.

[0108] In some embodiments, the runtime spatiotemporal quantification evaluation algorithm model includes other constraints; these other constraints are used to limit the solution value of the regional reserve demand variable to be a positive number. Specifically, the other constraints may include the following formula:

[0109] (19)

[0110] Equation (19) states that the solution value of the reserve demand variable in the constrained region should be a positive number, which is consistent with its physical meaning.

[0111] The above provides a detailed explanation of the characteristics, objective function, and model constraints of the operational standby spatiotemporal quantitative assessment algorithm model. The model takes the minimum difference between the accident scale and the available standby as the objective function, calculates the reduction and allocation of the system's operational standby capacity, and dynamically calculates the margin of pre-selected critical channels, serving as an auxiliary decision-making reference for dispatchers.

[0112] The constraints of the standby spatiotemporal quantitative assessment algorithm model are divided into seven categories: available standby constraints, transmission limit constraints before standby unit deployment, market standby demand constraints, regional standby demand constraints, transmission limit constraints after standby unit deployment, unit constraints, and other constraints. Among them, the transmission limit constraints before and after standby unit deployment restrict the power flow of transmission, avoiding power flow exceeding limits during standby scheduling; the market standby demand constraints and regional standby demand constraints require that standby meet the needs of the market and region, ensuring the sufficiency of standby within the market and regional scope.

[0113] The execution of the standby spatiotemporal quantization evaluation algorithm model involves regional standby requirements, which necessitates pre-determining a partitioning scheme. During standby research and actual scheduling, the configuration of standby partitions and the definition of regional standby requirements are typically used to address situations where transmission limitations prevent the delivery of standby to designated parts of the system. This involves setting up partitions in areas with severe transmission constraints, and the division of standby zones also impacts scheduling permissions.

[0114] Currently, the power grid dispatching system is divided into four levels: Level 1 is the power grid dispatching and communication center, abbreviated as the central dispatch; Level 2 is the provincial (autonomous region) level dispatching agency, abbreviated as the central dispatch; Level 3 is the regional (city, prefecture) level dispatching agency, abbreviated as the regional dispatch; and Level 4 is the county (county-level city) level dispatching agency, abbreviated as the county dispatch. The jurisdiction of each level of power grid dispatching agency is divided according to region and grid voltage level, without considering transmission restrictions. According to the survey, each province has set up power supply zones based on the actual situation within the province.

[0115] To address the shortcomings of existing partitioning schemes, such as Figure 3 As shown, the partitions can be adjusted using the following steps:

[0116] First, an initial partition range is obtained, such as the four-level partition mentioned above. Then, the initial partition range, the power generation plan information, and the operating data are input into the operating standby spatiotemporal quantification evaluation algorithm model. The operating standby spatiotemporal quantification evaluation algorithm model is used to process the input data to perform at least one transmission restriction identification to obtain the network segments with transmission restrictions within the preset area. Finally, based on the network segments with transmission restrictions, the preset area is repartitioned to obtain the partition range.

[0117] Considering the actual situation of the power grid, and since the zoning scheme involves changes in dispatch authority, the existing zoning was initially implemented. During the initial phase and actual operation, the model repeatedly identified transmission limitations through multiple runs. For areas with severe or frequent limitations, backup delivery was ensured by re-establishing or adding new zoning areas. The results of re-establishing and adding zoning areas were included in the original zoning, affecting the dispatch plan and related data. The modified zoning, dispatch plan, and related data were then input again into the operational backup spatiotemporal quantification evaluation algorithm model, and the model was repeatedly run for verification and identification.

[0118] The standby spatiotemporal quantitative assessment algorithm model outputs real-time information on available and restricted standby units, including available and restricted standby unit capacity information, as well as the dynamic status of critical channel margins. This information serves as supplementary decision-making support for dispatchers. In addition to restricted standby and critical channel margins, the supplementary decision-making support also includes standby resources, namely the order of standby unit capacity deployment and standby plans.

[0119] The order of deployment and backup resource allocation, i.e., backup unit capacity, is determined by two factors: economics and reliability. Economics is assessed by the price of the backup resources provided, while reliability is assessed by the sensitivity of the backup resources' output to transmission limitations. Additionally, an "expert database" can be established based on the dispatchers' experience and incorporated into the backup plan. Resources with lower prices, lower sensitivity, and higher priority in the expert database's recommended backup resource ranking will be given priority in the backup plan.

[0120] The advantages of the technical solution in this application are illustrated below through specific calculation examples.

[0121] Basic model example:

[0122] (1) 3-node example.

[0123] Taking a 3-node example for analysis, the case study includes three nodes: 1, 2, and 3. Nodes 1 and 2 have a load of 5MW each, while node 3 is the balancing node with a load of 1. There are three transmission lines: 11, 22, and 33; two generator sets with a total installed capacity of 60MW. Both generator sets A and B have a planned power of 15MW and a maximum power of 30MW. Generator set A has an output of 21.5MW and a restricted capacity of 8.5MW on line 11, while generator set B has an output of 29MW and a restricted capacity of 1MW on line 22. The tie line connected to node 1 has an input power of 1MW. Its basic information diagram is shown below. Figure 4A As shown.

[0124] Applying the basic model to the 3-node system, we first calculate the sum of the power flow values ​​Fi of each unit corresponding to each line under the adjustable output (maximum adjustable output - actual output) for the transmission line, and compare it with a threshold. If it exceeds the threshold, there is a backup constraint. For all lines with backup constraints, they are sorted from largest to smallest according to the absolute value of Fi. Line 11 has F11 of 20, Line 22 has F22 of -10, and Line 33 has F33 of -10. Lines 11 and 22 exceed their thresholds, so the order is F11 > F22.

[0125] Next, following the sequence, starting with line 11, the generating units corresponding to each line are sorted in ascending order of sensitivity. Units A and B both have a sensitivity of 0.6667 for line 11. When generating units have the same sensitivity, they are randomly sorted. Unit B, sorted first, has priority in output and its limitation is 0. Unit A, sorted later, has limitations and is placed later according to the rules. Next, the generating units corresponding to line 22 are sorted in ascending order of sensitivity. Since unit A is placed later, unit B has priority in output, and its limitation is 0. Unit A has limitations, such as... Figure 4B As shown.

[0126] (2) 118-node example

[0127] The table below shows the specific details of the restricted generating units and restricted capacity of line 104 in the 118-node system during the first period. According to the model operation results, a total of 35 generating units are subject to reserve restrictions. The output and maximum adjustable output of each generating unit are detailed in the table below. The total restricted reserve of line 104 is approximately 1673MW.

[0128] Table 104 lists the restricted capacity and restricted units for the first time period.

[0129]

[0130] Optimization model example:

[0131] (1) 3-node example

[0132] The analysis takes a 3-node single-time period as an example. It includes three nodes: 1, 2, and 3. Nodes 1 and 2 have a load of 15MW, while node 3 is the balancing node with a load of 1. There are three transmission lines: 13, 12, and 23, each with a transmission capacity of 20MW and a reactance of 1. There are two generator sets with a total installed capacity of 60MW. Units A and B each have an input power of 15MW, a maximum power of 30MW, and a reserve of 15MW each. The tie line connected to node 1 has an input power of 1MW. The schematic diagram and basic information diagrams are shown in Figures 5A and 5B.

[0133] The current after the accident

[0134] In such Figure 5B Under the conditions shown in the three-node information diagram, a 30MW accident occurs at node 2, meaning the load at node 2 increases by 30MW. To ensure system balance, 30MW of reserve power needs to be allocated. Without considering line power flow constraints, 15MW of reserve power from both unit A and unit B, totaling 30MW, is allocated to meet the reserve requirement. However, this allocation of power flow from line 12 will cause a power outage, such as... Figure 5C As shown, after the accident, if 15MW of reserve capacity from both Unit A and Unit B is deployed, the power flow on line 12 will reach 30.33, exceeding its transmission limit of 20. Therefore, the reserve deployment plan cannot be implemented in practice. Thus, when an accident occurs, the 15MW of reserve capacity reserved by Units A and B cannot be fully deployed due to line transmission limitations, resulting in limited reserve capacity. It is necessary to calculate the available reserve capacity.

[0135] Backup calculations are available

[0136] Based on the above analysis, in the three-node example, after a 30MW accident occurs at node 2, due to the transmission limitations of line 12, the reserves of units A and B cannot be fully mobilized, resulting in limited reserves. This section calculates the available reserves of the units when the transmission limitations are met.

[0137] In such Figure 5B Under the conditions of the three-node information diagram, a 30MW accident occurs at node 2. To ensure system balance, 30MW of reserve power needs to be mobilized. According to the analysis above, if 15MW of reserve power is mobilized from both unit A and unit B, line 12 will exceed its transmission limit. That is, the reserve power of units A and B is limited and cannot be fully mobilized. The available reserve power of units A and B after the accident, while meeting the transmission limits of each line, is calculated, and the results are as follows. Figure 5D Show.

[0138] The transmission capacity of each line is 20MW. Under the condition that the transmission limit is met, if an accident occurs at node 2, the available reserve of unit A is 14.5MW, and the restricted reserve due to the line transmission limit is 0.5MW. The available reserve of unit B is 0MW, and the restricted reserve due to the line transmission limit is 15MW. Based on the above calculations, under the condition that the line transmission limit is met, the maximum scale of an accident at node 2 that the generator units can respond to is 14.5MW.

[0139] Based on the 3-node example, after an incident, without considering line transmission limitations, if the backup capacity is allocated according to the required capacity, it will lead to over-limitation of the transmission lines. Therefore, not all backup capacity can be allocated, meaning that limited backup exists due to transmission limitations. Considering line transmission limitations, the available and limited backup capacities after an incident are calculated, yielding the limited backup capacity for each node and the maximum responsive incident size.

[0140] (2) 118-node example

[0141] Through calculation and analysis of a three-node system, the feasibility of the spatiotemporal quantification assessment algorithm model for operational reserve in simple systems was preliminarily verified for dynamic assessment of real adjustable reserve distribution and capacity quantification. The algorithm's calculation and feasibility for complex systems were verified and analyzed using a 118-node example. The 118-node system comprises 54 generating units and 186 lines. Under the existing power generation plan, unit C (corresponding to node 104) is selected as an example to demonstrate the output of a single unit, such as... Figure 6A Show.

[0142] Under the existing power generation plan, it was detected that the power flow of line 41 has reached its transmission limit 140 at certain times, as shown below.

[0143] Line 41 Trend Real-time Trend

[0144]

[0145] The current after the accident

[0146] Assuming a 150MW accident occurs at node 2 every time period, and ignoring line transmission constraints (i.e., transmission limit constraints), the real-time power flow of line 41 exceeds the transmission limit in time periods 38, 42, 43, 44, and 45, as shown below. Therefore, if a 150MW accident occurs at node 2, calling up the 150MW of standby units will cause the line to exceed the limit. Consequently, not all standby units can be used, resulting in limited standby. It is necessary to calculate the available standby capacity.

[0147] Line 41 Trend Real-time Trend

[0148]

[0149] Backup calculations are available

[0150] Based on the above analysis, in the 118-node example, after a 150MW accident occurs at node 2, due to transmission limitations on the line, not all of the unit's reserves can be mobilized, resulting in limited reserves. This section calculates the available reserves of the units when the transmission limitations are met.

[0151] A 150MW accident occurred at Node 2. To ensure system balance, 150MW of reserve power needs to be deployed. However, based on the previous analysis, if 150MW of reserve power is deployed, line 41 will exceed its transmission limit. This means that the reserve capacity of all units in the 118-node system is limited and cannot be fully deployed. The available reserve capacity for units that meet the transmission limits of each line after an accident is calculated. The maximum scale accident at Node 2 that units can respond to when meeting the transmission limits of each line is calculated, and the results are shown below.

[0152] If a standby unit is available, its capacity can be used for calculation.

[0153]

[0154] As shown in the table above, when the transmission limits of each line in the 118-node system are met, the maximum scale of an accident at node 2 that the units can respond to is 150MW during time periods 1-36. That is, during time periods 1-36, the available reserve of the system units due to line transmission limitations is 150MW. During time periods 37-48, the maximum scale of an accident at node 2 that the units can respond to is shown in Table 8. The available reserve of the system units due to line transmission limitations is equal to the maximum response accident scale in each time period. Taking unit C at node 104 as an example, we can illustrate the limited reserve and dispatch reserve of a single unit. There is only one unit, unit C, at node 104.

[0155] When an incident of the corresponding scale for the time period listed in Table 8 occurs at node 2, provided that the system line power flow does not exceed the limit, the limited standby and dispatch standby of unit C are as follows: Figure 6B Output status of Unit C.

[0156] In the 118-node example above, after an accident occurs at node 2, due to transmission limitations on the line, not all standby units can be deployed, resulting in limited standby. The model is used to calculate the size of the limited standby that satisfies the line transmission constraints. First, the maximum available standby size at node 2 in each time period is calculated. Then, the limited standby capacity for each time period and each node in response to an accident is calculated.

[0157] This application proposes a spatiotemporal quantitative evaluation algorithm model for determining reserve capacity, including a basic model and an optimized model. A detailed analysis is provided of the model's principles, process, mathematical model, partitioning schemes involved, and the auxiliary decision-making output, summarized as follows:

[0158] The basic model works by arranging all generating units in ascending order of sensitivity and calculating the power flow value of the network section for each unit at its maximum adjustable output. The power flow value of the network section is compared with a threshold; if it is less than the threshold, the power flow values ​​are accumulated sequentially until the accumulated power flow value exceeds the threshold. All generating units after the unit corresponding to the last power flow value in the accumulation are then considered restricted units. The restricted capacity of the network section is obtained by subtracting the maximum adjustable output from the original output of each restricted unit and summing these subtractions, i.e., the reserve capacity deduction. To address the limitations of the basic model, which ignores units with negative sensitivity and cannot effectively handle the coupling effects of the network section, an optimized model is proposed.

[0159] The optimization model includes an objective function and constraints. The objective function is to minimize the difference between the maximum accident scale information and the available standby unit capacity information. The constraints are divided into seven categories: available standby constraints, transmission limit constraints before standby unit deployment, market standby demand constraints, regional standby demand constraints, transmission limit constraints after standby unit deployment, unit constraints, and other constraints. Among them, the transmission limit constraints before and after standby unit deployment restrict the power flow of transmission limits, avoiding power flow exceeding limits during standby scheduling; the market standby demand constraints and regional standby demand constraints require that standby meets the needs of the market and region, ensuring the sufficiency of standby within the market and regional scope.

[0160] Because the zoning scheme involves transmission restrictions and scheduling rights issues, it is initially implemented according to the existing zoning of the power grid. During the model operation, transmission restrictions are identified and classified in a loop. For areas with severe or frequent transmission restrictions, zoning is reset or new zoning is added to ensure backup delivery.

[0161] The standby time-space quantification assessment algorithm model outputs real-time information on the unit's available and restricted standby, as well as the dynamic status of critical channel margins, serving as auxiliary decision-making support for dispatchers. This auxiliary decision-making includes restricted standby, critical channel margin status, standby resource allocation order, and standby plans.

[0162] The operation of the spot market has posed greater challenges to the safe and stable operation of the power grid, and many new problems have emerged in the operation and dispatch of the power system. Through surveys and comparative analyses of the current status of the PJM, UK, and Nordic electricity spot markets and the current status of the domestic electricity spot market, this study examines the impact of the spot market operation on real-time power grid operation from two aspects: the characteristics of electricity market reform and the impact of electricity market reform on power grid dispatch.

[0163] In a spot market environment, the stable operation of the power grid requires a more complete backup system. Based on this, the spatiotemporal characteristics of operational backup were first studied. On this basis, relevant suggestions were put forward on the practicality and configuration principles of the backup system in a spot market environment.

[0164] This study investigates the spatiotemporal characteristics of operational reserves from three aspects: temporal characteristics, spatial characteristics, and influencing factors. Based on these, it analyzes the impact of these spatiotemporal characteristics on reserve availability. Temporal characteristics are reflected in both the provision and demand of operational reserves. Regarding provision, temporal characteristics include reserve resource response time and the timing coordination of reserve type settings. Regarding demand, the study considers the seasonal variations in reserve demand and the differences in peak and off-peak periods within a day, reflecting the temporal characteristics of demand. Spatial characteristics are analyzed, examining the principles for dividing reserve areas abroad and domestic dispatch jurisdictions, and the spatial characteristics of reserves resulting from the distribution of power generation resources. In terms of influencing factors, the study examines factors affecting reserve temporal characteristics, including fault identification and virtual power plants, and factors affecting spatial characteristics, including dispatch authority, maintenance, and congestion. Based on this, it proposes classification criteria for weak links in the power system and corresponding solutions. The impact of the spatiotemporal characteristics of operational reserves on availability is analyzed from three aspects: reserve seasonality, reserve recovery, and secondary emergency reserves. Relevant suggestions for improving reserve availability are proposed.

[0165] Based on the study of the spatiotemporal characteristics of standby operation, combined with the survey and summary of the current status of the power grid electricity market, the construction of the standby market, and the assessment principles of standby ancillary services, this paper analyzes the applicability of the current standby system of the power grid under the spot market environment from three aspects: the progress of standby market construction, the content of standby market construction, and the assessment principles of standby ancillary service compensation. Relevant suggestions are put forward, and further, principles for standby configuration, regulation, and congestion intervention in the southern region adapted to the spot market environment are proposed.

[0166] To address the issue of some backup capacity being unavailable in real-time monitoring, a sophisticated calculation algorithm model for backup capacity reduction was established, covering 96 operational modes throughout the day. The model, including a basic and optimized version, was tested and its principles, procedures, mathematical model, test cases, partitioning schemes, and final decision support outputs were explained in detail. The model achieves integrated spatiotemporal assessment and display of backup capacity, providing a visual basis for system backup adequacy assessment and rapid, scientific, and effective backup deployment during emergencies.

[0167] The following explains the calculation methods or concepts that may be involved in this application.

[0168] Rotate for standby

[0169]

[0170] in, For units that operate in real time, For the unit The maximum adjustable output force, For the unit Real-time output.

[0171] Backup power generation

[0172] The standby capacity that a power plant can deploy within 30 minutes is adjusted at a rate of 1.5% capacity / min for thermal power and 20% capacity / min for hydropower.

[0173] Emergency Backup

[0174] The standby capacity that a power plant can deploy within 10 minutes is adjusted at a rate of 1.5% capacity / min for thermal power and 20% capacity / min for hydropower.

[0175] Primary and secondary frequency modulation backup

[0176] ① When primary frequency regulation is activated, the reserve capacity adjusted for primary frequency regulation of the hydro-thermal power unit is as follows:

[0177]

[0178] ② When primary frequency regulation is activated, the reserve capacity of the hydro-thermal power unit under primary frequency regulation is:

[0179]

[0180] in, For the unit Real-time power generation output, For the unit The generator set has adjustable power output. For the unit The minimum technical output.

[0181] The droop coefficient is set at 5% for the primary frequency regulation rating of thermal and nuclear power units, and at 4% for hydropower units.

[0182] ③ When the entire plant is put into single-unit AGC and is in R or A mode, the secondary frequency regulation reserve capacity of hydropower, coal-fired, gas-fired, and nuclear power units = control upper limit - actual output of AGC unit;

[0183] ④ When the entire plant is put into single-unit AGC and is in R or A mode, the reserve capacity under secondary frequency regulation of hydropower and thermal power (coal-fired and gas-fired) units = actual output of AGC unit - control lower limit;

[0184] Control upper and lower limits: AC unit planned base value + regulation bandwidth, AGC upper and lower limits (power plant setting), manual given upper and lower limits (dispatch setting), secondary frequency regulation reserve in the frequency regulation market area is based on the AGC unit's bid-winning capacity.

[0185] Negative Reserve

[0186] ① The negative reserve capacity of hydropower and thermal power units = unit power generation output - unit minimum technical output;

[0187] ② Negative reserve capacity of pumped storage units = Generator output - Maximum pumping load under pump operating conditions. (Considering cross-sectional constraints, pumped storage units shut down in Guangdong are not included in the negative reserve statistics.)

[0188] ① Nuclear power plants only consider primary frequency regulation reserve, with no other reserves;

[0189] ②The reserves of all new energy sources are 0.

[0190] Corresponding to the application scenarios and methods provided in the embodiments of this application, the embodiments of this application also provide a spatiotemporal quantitative assessment device for power grid system operation standby. For example... Figure 7 The diagram shown is a structural block diagram of a power grid system operation reserve spatiotemporal quantitative assessment device according to an embodiment of this application. The power grid system operation reserve spatiotemporal quantitative assessment device may include:

[0191] The data acquisition module 710 is used to acquire power generation plan information within a preset area and operating data of each unit within the preset area;

[0192] Model processing module 720 is used for:

[0193] The power generation plan information and the operation data are processed using a pre-trained spatiotemporal quantitative evaluation algorithm model for operational standby to determine whether there is line congestion in the preset area.

[0194] In the event of line congestion, the available standby unit capacity information and the limited standby unit capacity information within the preset area are calculated using the standby spatiotemporal quantification evaluation algorithm model.

[0195] Using the aforementioned standby spatiotemporal quantification assessment algorithm model, based on the available standby unit capacity information and the limited standby unit capacity information, the scale information of the maximum accident that the preset area can respond to and the limited standby unit capacity information at the time of the maximum accident are calculated.

[0196] Using the aforementioned standby spatiotemporal quantification evaluation algorithm model, based on the characteristics of each unit within the preset area and the limited standby unit capacity information at the time of the maximum accident, the standby capacity allocation result information of at least one unit within the preset area is determined;

[0197] Among them, the standby time-space quantitative evaluation algorithm model includes the objective function, regional standby demand constraints, and transmission limitation constraints after the deployment of standby units;

[0198] The objective function is to minimize the difference between the maximum accident scale information and the available standby unit capacity information; the transmission restriction constraint after the standby unit deployment is used to ensure that the standby unit capacity scheduling execution will not cause line congestion; the regional standby demand constraint is used to limit the resource clearing standby to meet the corresponding regional standby demand variables, and limits the upper limit of the regional standby demand variables.

[0199] The functions of each module in each device in the embodiments of this application can be found in the corresponding description in the above method, and they have corresponding beneficial effects, which will not be repeated here.

[0200] Figure 8 This is a block diagram of an electronic device used to implement embodiments of this application. For example... Figure 8 As shown, the electronic device includes a memory 810 and a processor 820. The memory 810 stores a computer program that can run on the processor 820. When the processor 820 executes the computer program, it implements the method described in the above embodiments. The number of memories 810 and processors 820 can be one or more.

[0201] The electronic device also includes:

[0202] The communication interface 830 is used to communicate with external devices and exchange and transmit data.

[0203] If the memory 810, processor 820, and communication interface 830 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0204] Optionally, in a specific implementation, if the memory 810, processor 820, and communication interface 830 are integrated on a single chip, then the memory 810, processor 820, and communication interface 830 can communicate with each other through an internal interface.

[0205] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this application.

[0206] This application also provides a chip including a processor for calling and executing instructions stored in a memory, causing a communication device with the chip installed to perform the method provided in this application.

[0207] This application also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in the application embodiment.

[0208] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0209] Further, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0210] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0211] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0212] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0213] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0214] The logic and / or steps described in the flowchart or otherwise herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0215] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0216] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0217] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope described in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A spatiotemporal quantitative assessment method for the operational reserve of a power grid system, characterized in that, include: Obtain power generation plan information and operating data of each unit within the preset area; The power generation plan information and the operation data are processed using a pre-trained spatiotemporal quantitative evaluation algorithm model for operational standby to determine whether there is line congestion in the preset area. In the event of line congestion, the available standby unit capacity information and the limited standby unit capacity information within the preset area are calculated using the standby spatiotemporal quantification evaluation algorithm model. Using the aforementioned standby spatiotemporal quantification assessment algorithm model, based on the available standby unit capacity information and the limited standby unit capacity information, the scale information of the maximum accident that the preset area can respond to and the limited standby unit capacity information at the time of the maximum accident are calculated. Using the aforementioned standby spatiotemporal quantification evaluation algorithm model, based on the characteristics of each unit within the preset area and the limited standby unit capacity information at the time of the maximum accident, the standby capacity allocation result information of at least one unit within the preset area is determined; Among them, the standby time-space quantitative evaluation algorithm model includes the objective function, regional standby demand constraints, and transmission limitation constraints after the deployment of standby units; The objective function is to minimize the difference between the maximum accident scale information and the available standby unit capacity information; the transmission restriction constraint after the standby unit deployment is used to ensure that the standby unit capacity scheduling execution will not cause line congestion; the regional standby demand constraint is used to limit the resource clearing standby to meet the corresponding regional standby demand variables, and limits the upper limit of the regional standby demand variables; The operational standby time-space quantitative evaluation algorithm model includes market standby demand constraints; Among them, the market reserve demand constraint is used to ensure that the regional reserve demand variable meets the corresponding market reserve demand, and limits the lower limit of the regional reserve demand variable. The method for quantitative assessment of the standby time and space of the power grid system also includes: In the absence of line congestion, the scale information of the maximum accident that the preset area can respond to and the limited standby unit capacity information when the maximum accident occurs are calculated using the backup spatiotemporal quantification evaluation algorithm model. Using the aforementioned standby spatiotemporal quantification evaluation algorithm model, based on the characteristics of each unit within the preset area and the limited standby unit capacity information at the time of the maximum accident, the standby capacity allocation result information of at least one unit within the preset area is determined.

2. The method according to claim 1, characterized in that, The operational standby time-space quantitative evaluation algorithm model includes available standby constraints, wherein the available standby constraints are used to constrain the available standby unit capacity and the limited standby unit capacity within the market scope; and / or, to constrain the available standby unit capacity and the limited standby unit capacity within each partition of a preset area.

3. The method according to claim 1, characterized in that, The operational standby spatiotemporal quantitative evaluation algorithm model includes transmission constraints before the deployment of standby units; The transmission restriction constraints prior to the deployment of the standby unit are used to limit the transmission power flow under normal grid system operation scenarios to within preset normal limits.

4. The method according to claim 1, characterized in that, The operational standby time-space quantitative evaluation algorithm model includes unit constraints, which are used to limit at least one of the following: The unit's cleared energy and the capacity of each type of standby unit operate within their corresponding maximum power output range; When the unit's frequency is adjusted downwards, the preset requirements for the unit's minimum power output must be met. Unit capacity scheduling must be carried out within the ramp-up rate range of the unit's equipment. The various types of standby capacity provided by the generating unit equipment must be within the corresponding types of standby capacity that it is qualified to provide.

5. The method according to claim 1, characterized in that, The operational standby spatiotemporal quantitative evaluation algorithm model includes other constraints; these other constraints are used to limit the solution value of the regional standby demand variable to be a positive number.

6. The method according to claim 2, characterized in that, It also includes the step of determining the range of the partition: Get the initial partition range; The initial partition range, the power generation plan information, and the operation data are input into the operation standby spatiotemporal quantification evaluation algorithm model. The operation standby spatiotemporal quantification evaluation algorithm model is used to process the input data to perform at least one transmission restriction identification to obtain the network segments with transmission restrictions in the preset area. Based on the network segments with transmission restrictions, the preset area is re-partitioned to obtain the partition range.

7. The method according to claim 2, characterized in that, Also includes: The available standby unit capacity information and the limited standby unit capacity information are output to assist relevant personnel in scheduling the available standby unit capacity.

8. A spatiotemporal quantitative assessment device for standby operation of a power grid system, characterized in that, The apparatus, used in any one of claims 1 to 7, comprises: The data acquisition module is used to acquire power generation plan information and operating data of each unit within a preset area; The model processing module is used for: The power generation plan information and the operation data are processed using a pre-trained spatiotemporal quantitative evaluation algorithm model for operational standby to determine whether there is line congestion in the preset area. In the event of line congestion, the available standby unit capacity information and the limited standby unit capacity information within the preset area are calculated using the standby spatiotemporal quantification evaluation algorithm model. Using the aforementioned standby spatiotemporal quantification assessment algorithm model, based on the available standby unit capacity information and the limited standby unit capacity information, the scale information of the maximum accident that the preset area can respond to and the limited standby unit capacity information at the time of the maximum accident are calculated. Using the aforementioned standby spatiotemporal quantification evaluation algorithm model, based on the characteristics of each unit within the preset area and the limited standby unit capacity information at the time of the maximum accident, the standby capacity allocation result information of at least one unit within the preset area is determined; Among them, the standby time-space quantitative evaluation algorithm model includes the objective function, regional standby demand constraints, and transmission limitation constraints after the deployment of standby units; The objective function is to minimize the difference between the maximum accident scale information and the available standby unit capacity information; the transmission restriction constraint after the standby unit deployment is used to ensure that the standby unit capacity scheduling execution will not cause line congestion; the regional standby demand constraint is used to limit the resource clearing standby to meet the corresponding regional standby demand variables, and limits the upper limit of the regional standby demand variables.

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