A method for determining a standard of multi-day low output of wind power in a high-ratio wind power system

Abstract

The application discloses a kind of high proportion wind power system in wind power multi-day low power output scene standard determination method, it is related to power system planning and operation analysis technical field, including: the feature of system multi-day power imbalance is identified, and wind power daily output state transition probability model is constructed;Coupling continuous multi-day low wind speed scene set of " meteorological characteristics-wind power output" is established in combination with historical meteorological data and space synchronous low output index;Scene evaluation index system including the size of output and the dimension of duration is constructed;With the same occurrence frequency of meteorological light wind scene and power low output scene as the core constraint, the optimal measurement threshold is calculated back through iteration, and the final evaluation standard of wind power multi-day low output scene is adaptively determined;Based on the standard, a typical scene set is extracted and input into the power balance adequacy model for safety check.The application provides a calculation basis for the capacity optimization configuration of system long, short-time energy storage and conventional power supply.

Description

Technical Field

[0001] This invention relates to the field of power system planning and operation analysis technology, and more specifically, to a method for determining the standard for multi-day low-output wind power scenarios in a high-proportion wind power system. Background Technology

[0002] Currently, the installed capacity of new energy sources, such as wind power, is rapidly increasing within the new power system. Wind power output is significantly sensitive to weather conditions, and during extreme weather events such as winter cold waves (clear, cold, and windless) or summer heat waves (extreme heat and windless), large-scale, prolonged periods of extremely low output are highly likely to occur. This weather-driven phenomenon of prolonged low wind power output, coupled with sustained peak loads under extreme weather conditions, may pose a severe challenge to the system, potentially leading to prolonged power supply shortages.

[0003] Currently, the methods for assessing the adequacy of new power systems and for power source planning have the following limitations: (1) Lack of scientific measurement standards for wind power low output scenarios for multiple days. Existing production simulation or planning studies usually focus on single short-term load peak-valley balance and long-term power balance throughout the year. When defining extreme low wind scenarios, they often rely on expert experience or artificially set a single fixed threshold (such as simply setting the wind speed to be less than a certain value or the output to be less than a certain fixed percentage), which lacks objective physical basis, resulting in insufficient typicality and coverage of the extracted scenarios.

[0004] (2) The inherent connection between meteorological statistical patterns and power system risks has been ignored. Traditional methods do not link the frequency of extreme meteorological events with the actual supply risks faced by the power system. If the threshold is set too high, the planning scheme will be too conservative and the investment will be wasted; if the threshold is set too low, it will not be able to cover the recurrence period of historical special meteorological events, resulting in a great risk of system load loss.

[0005] (3) Lack of global consideration of multi-spatial scale and source-load complementarity. Existing studies mostly use the average output of the entire grid to mask the extreme scarcity in local areas, and rarely quantitatively assess the complementary effect of photovoltaic output during multiple days of windless periods and the coupling risk of synchronous load increase.

[0006] Therefore, it is urgent to research and propose an adaptive determination method for wind power multi-day low output scenarios that can objectively reflect meteorological statistical patterns and accurately quantify the real power supply risks of the power system, so as to guide the medium and long-term power planning of high-proportion wind power systems. Summary of the Invention

[0007] To address the aforementioned issues, the present invention aims to provide a standard determination technology for scenarios with low wind power output for multiple days in a high-proportion wind power system. By establishing an objective evaluation system that deeply couples meteorological characteristics with wind power output, it aims to break the randomness of traditionally set thresholds.

[0008] To achieve the above technical objectives, this application provides a method for determining the criteria for multi-day low-output wind power scenarios in a high-proportion wind power system, comprising the following steps: Identify typical features based on the dual characteristics of source and load, establish a set of continuous multi-day low wind speed scenarios coupled with meteorological characteristics and wind power output based on the constructed wind power daily power output state transition probability model, and then construct an evaluation index system for multi-day low wind power output scenarios. Based on the evaluation index system for wind power low-output scenarios over multiple days, by determining scenarios based on meteorological and power frequency balance constraints, and after adaptive measurement standards, the output of typical scenario sets and system supply guarantee planning are determined and implemented.

[0009] Preferably, when identifying typical characteristics, the balance characteristics of the power system in the target area are analyzed, and the risk points of multi-day power imbalance under special weather processes are identified from four dimensions: load uncertainty, balance time scale, energy resource coordination means, and limiting factors.

[0010] Preferably, when constructing the wind power daily power output state transition probability model, based on historical wind power output data over many years, the daily average wind power output state levels are divided, the wind power daily power output state transition probability model is constructed, the cross-day transition characteristics of wind power output between different levels are quantitatively calculated, and the probability distribution and seasonal differences of continuously maintaining low output levels are extracted.

[0011] Preferably, when constructing a set of low wind speed scenarios over multiple consecutive days, historical meteorological data is combined with the judgment criterion of wind speeds below a specific threshold over multiple consecutive days. Typical low wind speed meteorological scenarios over multiple consecutive days are extracted, their corresponding meteorological causes are analyzed, and a spatial synchronous low output evaluation index is introduced to quantify the degree of overlap of low wind speed meteorological conditions between different sub-regions, thus forming a set of low wind speed scenarios over multiple consecutive days that corresponds to meteorological physical processes and takes into account spatial correlation.

[0012] Preferably, when obtaining spatially synchronized low-output evaluation indicators, the target area is divided into... N A wind power characteristic sub-region is defined, and a spatial synchronous low-output index is defined. SSLIt Used for quantification t The calculation model for the overlap of large-scale low-wind-speed weather events within a given time period is as follows: In the formula: N The total number of wind power characteristic sub-regions divided; Si,t For the first i Each sub-region t Low wind speed state variable during a given period, when the wind speed in that area is below the extremely low wind speed threshold. Si,t =1, otherwise Si,t =0;αi For the first i The weighting factor of wind power installed capacity in each sub-region relative to the total installed capacity of the target region.

[0013] Preferably, when constructing the evaluation index system for wind power scenarios with multiple days of low output, the evaluation index system for wind power scenarios with multiple days of low output is expressed as follows: In the formula: for t Wind power output rate at any time; for d Daily average wind power output rate; and These are the hourly output measurement standard and the daily average output measurement standard for wind power at low output levels, respectively. and These are duration measurement standards at two time scales: hourly and daily.

[0014] Preferably, when the measurement standard is adaptive, the core constraint is that the frequency of occurrence of low wind speed meteorological scenarios over many years and days is the same as the frequency of occurrence of low wind power output scenarios over many days. Based on iterative calculation, the preliminary measurement threshold that satisfies the frequency balance constraint is calculated in reverse, thereby extracting and forming a typical set of low wind power output scenarios over many days. Finally, based on the specific indicators of all scenarios in the scenario set, the final measurement standard for defining low wind power output scenarios over many days in winter and summer is extracted.

[0015] Based on the same inventive concept, this invention also discloses a standard determination system for multi-day low-output wind power scenarios in high-proportion wind power systems, comprising: The evaluation system construction module is used to identify typical features based on the characteristics of both the source and load sides. Based on the constructed wind power daily output state transition probability model, a set of continuous multi-day low wind speed scenarios coupled with "meteorological characteristics-wind power output" is established, and then an evaluation index system for multi-day low wind power output scenarios is constructed. The planning and execution module is used to determine and execute the typical scenario set output and system supply guarantee plan based on the evaluation index system for wind power low output scenarios over multiple days. It identifies scenarios based on meteorological and power frequency balance constraints, and after adaptive measurement standards, it determines and executes the system supply guarantee plan based on the evaluation index system for wind power low output scenarios.

[0016] The present invention discloses the following technical effects: This invention uses the fact that the occurrence frequency of low wind speed weather scenarios and low power output scenarios is the same as the constraint condition, and solves the technical problem that the manually set threshold in traditional scenario extraction lacks objective basis by iteratively calculating the four-element measurement threshold that includes the power output and the duration.

[0017] This invention uses the standard for measuring wind power's low output for multiple days as the extreme boundary condition input to the power balance adequacy model. By quantifying the system's load loss risk and the continuous discharge deficit of energy storage, it provides a calculation basis for optimizing the capacity configuration of long-term and short-term energy storage and conventional power sources. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the standard determination process for wind power low output scenarios over multiple days, as described in this invention. Figure 2 This is a flowchart of the high-proportion wind power system adequacy calculation and power planning process that considers multi-day low-output scenarios as described in this invention. Figure 3 This is a schematic diagram illustrating the measurement of hourly output indicators in the scenario of multiple days of low wind power output described in this invention. Figure 4 This is a schematic diagram of measuring the daily power output index in the scenario of low wind power output for multiple days as described in this invention. Figure 5 This is a schematic diagram of the simulation operation of the system for ensuring power supply under the typical scenario of multiple days of low wind power output in the planning year described in this invention; Figure 6 This is a schematic diagram of the simulation operation of the system for ensuring power supply under the typical scenario of multiple days of low wind power output in the planning year of the present invention. Figure 7 This refers to the change in the remaining energy storage capacity within the system described in this invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] like Figures 1-7As shown, this invention provides a method for determining the standard for scenarios with multiple days of low wind power output in a high-proportion wind power system, such as... Figure 1 and Figure 2 As shown, it includes the following steps: Step S1: Identification of typical features based on source-load dual-side characteristics. Analyze the balance characteristics of the power system in the target area, and identify the multi-day power imbalance risk points of the system under special weather processes from four dimensions: load uncertainty, balance time scale, energy resource coordination means, and limiting factors.

[0022] Step S2: Construct a probability model for the daily power output state transition of wind power. Based on historical wind power output data over many years, classify the daily average wind power output state levels, construct a probability model for the daily power output state transition of wind power, quantitatively calculate the cross-day transition characteristics of wind power output between different levels, and focus on extracting the probability distribution and seasonal differences of continuously maintaining a low output level.

[0023] Step S3: Establish a set of continuous multi-day low wind speed scenarios coupled with "meteorological characteristics - wind power output". Combining historical meteorological data, using continuous multi-day wind speeds below a specific threshold as the criterion, extract typical multi-day low wind speed meteorological scenarios, analyze their corresponding meteorological causes, and on this basis, introduce spatially synchronized low power output evaluation indicators to quantify the degree of cross-over of low wind speed meteorological conditions in different sub-regions, forming a set of multi-day low wind speed scenarios that correspond to meteorological physical processes and take into account spatial correlation.

[0024] Step S4: Construct an evaluation index system for wind power scenarios with multiple days of low output. Establish a four-element evaluation index system from two dimensions: output magnitude and duration, including: hourly output measurement standard, hourly duration standard, daily average output measurement standard, and daily duration standard.

[0025] Step S5: Adaptive determination of scenario measurement criteria based on meteorological and power frequency balance constraints. The core constraint is that the frequency of long-term low-wind-speed meteorological scenarios and the frequency of long-term low-output wind power scenarios are the same. Based on iterative calculations, a preliminary measurement threshold satisfying this frequency balance constraint is derived, thereby extracting a typical set of long-term low-output wind power scenarios. Finally, based on the specific indicators of all scenarios within the scenario set, the final measurement criteria defining long-term low-output wind power scenarios in winter and summer are extracted.

[0026] Step S6: Output of Typical Scenario Sets Based on Evaluation Criteria and Application in System Supply Guarantee Planning. Based on the final measurement standard for multi-day low-output wind power scenarios determined in Step S5, a typical scenario set is accurately anchored and extracted from historical operating data. This typical scenario set is used as the boundary condition for the supply guarantee adequacy stress test of high-proportion wind power systems facing extreme weather conditions. It is input into the multi-day power balance adequacy calculation model for safety verification, and outputs imbalance risk indicators such as maximum load loss power and load loss power. This transforms the abstract scenario measurement standard into an engineering quantitative basis to guide the optimal configuration of long-term and short-term energy storage and conventional power sources in the system, realizing a closed loop between theoretical modeling and engineering application of the evaluation standard.

[0027] The specific process for identifying multi-day power imbalance risk points of high-proportion wind power systems under special weather events from four dimensions in step S1 is as follows: (1) Identification of the core challenges of balancing: Identify the dual random fluctuation characteristics brought about by the transformation of system operation risks from single load uncertainty to random uncertainty on both the source and load sides; (2) Identification of the time scale and scope of balance: extract the cross-day coupling characteristics of system balance demand from short time scale to multi-day scale, and from simple power balance to dual balance of power and electricity. (3) Identification of synergistic means of balance: Analyze the output characteristics of the system regulation support means under the condition that they are dominated by single fossil energy and water characteristics to the synergistic effect of multiple types of energy resources; (4) Identification of the constraints on balance: The main constraints on system operation have shifted from fuel supply constraints to weather-sensitive constraints, and special weather processes are extracted as the core driving factors that induce multi-day power imbalance in high-proportion wind power systems.

[0028] The specific logic for classifying the state levels in step S2 is as follows: The average daily output rate of wind power... P W is divided into four intervals, corresponding to different state levels as shown in the table.

[0029] Table 1 Classification of Wind Power Daily Average Output Status Level The specific construction process of establishing the continuous multi-day low wind speed scenario set coupled with "meteorological characteristics-wind power output" in step S3 is as follows: Meteorological wind event extraction: Based on historical meteorological data of the target area, the time period of typical multi-day low wind speed meteorological process is extracted, with the condition that the wind speed is lower than the set low wind speed threshold for several consecutive days (regular observation). The criteria for determining the low wind speed threshold are as follows: the Beaufort scale definition of level 2 (light wind) wind speed is used as the standard, that is, the wind speed at a height of 10 meters is less than 3.3 m / s. (3) Construction of evaluation index for spatial synchronous low output: The target area is divided into N A wind power characteristic sub-region is defined, and a spatial synchronous low-output index is defined. SSLIt Used for quantification t The calculation model for the overlap of large-scale low-wind-speed weather events within a given time period is as follows: (1) In the formula: N The total number of wind power characteristic sub-regions divided; Si,t For the first i Each sub-region t Low wind speed state variable during a given period, when the wind speed in that area is below the extremely low wind speed threshold. Si,t =1, otherwise Si,t =0; αi For the first i The weighting factor of wind power installed capacity in each sub-region relative to the total installed capacity of the target region; (4) Generation of multi-day low wind speed scene sets: setting spatial synchronization critical threshold SSLIth Filter out those that continuously meet SSLIt ≦ SSLIth The data is collected over multiple days of meteorological periods, and combined with typical meteorological causes within those periods, to extract a set of low-wind-speed scenarios that are correlated across a wide area.

[0030] The evaluation index system for wind power low-output scenarios constructed in step S4 is mainly established from two dimensions: the value of low wind power output and the duration of low output, as shown in equation (2) and appendix. Figure 1 and attached Figure 2 As shown, (2) In the formula: for t Wind power output rate at any time; for d Daily average wind power output rate; and These are the hourly output measurement standard and the daily average output measurement standard for wind power at low output levels, respectively. and These are duration measurement standards at two time scales: hourly and daily.

[0031] Combined with appendix Figure 1 The specific process of scene standard adaptive extraction based on meteorological and power frequency balance constraints in step S5 is as follows: (1) Frequency statistics of meteorological scenes: Statistical history Y The total number of typical multi-day low wind speed meteorological scenarios extracted in step S3 within the year. M met, used to calculate the annual average frequency of light wind scenarios.F met= M met / Y ; (2) Iterative optimization of power scenario frequency and preliminary threshold: Let the preliminary quaternary threshold set for the wind power low output over multiple days be denoted as . X =[ , T , , Td Based on the current threshold set X Extract relevant low-output wind power scenarios from historical wind power output data and count the total number of such scenarios. M elec( X ), calculate the average annual frequency of power scenarios under the current threshold conditions. F elec( X )= M elec( X ) / Y Construct a fitness function min| to minimize the frequency deviation between meteorological and power scenarios. F elec(X)- F met|, adjusting the threshold set through iterative calculation. X The parameters within the function are used to obtain the optimal set that minimizes the function. X ; (3) Determination of wind power low-output scenario standards based on internal indicators of the scenario set: using the optimal set X To constrain the process, a typical set of wind power low-output scenarios for multiple days is extracted and formed. The specific operation data of all scenarios in the scenario set are traversed. Combined with seasonal characteristics, the average and minimum output rates of winter and summer scenarios, as well as the average and maximum duration of the scenarios, are calculated. Based on this, the final measurement standards for winter and summer wind power low-output scenarios applicable to the region are back-calibrated and output.

[0032] Combined with appendix Figure 2 The specific process of outputting the typical scenario set based on the evaluation criteria and applying it to the system supply guarantee planning in step S6 is as follows: (1) Test boundary condition output: The optimal wind power low output scenario evaluation standard output in step S5 is used as a filter to accurately extract typical wind power low output scenarios that meet the standard in winter clear and cold windless and summer extremely hot windless from massive historical meteorological and power data, and define them as the boundary conditions of medium and long-term power planning. (2) Verification of power balance adequacy: Using typical scenario sequences as boundary inputs, establish an adequacy calculation model with the goal of minimizing the power loss and the power curtailment of new energy during the duration of the scenario; Under the condition of satisfying the power output constraints of conventional power sources and the charging and discharging constraints of energy storage units, calculate the power surplus and power deficit of the system in each time period under the boundary conditions, and obtain the maximum power loss, the number of hours of power loss and the power loss risk index; (3) Engineering guidance application of evaluation criteria: Based on the adequacy verification results, quantitative analysis is conducted on the diminishing marginal utility of relying solely on pumped hydro storage and new battery energy storage when dealing with low output for multiple days due to insufficient surplus power in the system; then the multi-day imbalance risk index is transformed into the lower limit of capacity requirements for long-term energy storage systems (such as seasonal hydrogen energy storage systems) and conventional stable power sources, ultimately forming an economically optimal medium- and long-term power and energy storage configuration scheme that can withstand such special weather conditions.

[0033] This invention introduces a spatially synchronized low-output index to first quantitatively identify the overlap of a wide-area power grid under extreme weather conditions, accurately extracting multi-day low-wind-speed weather scenarios. Its core lies in proposing an adaptive method for determining scenario standards based on the constraint of meteorological and power frequency balance. This method uses the similarity between the frequency of multi-day low-wind-speed weather scenarios and the frequency of multi-day low-output wind power scenarios over many years as the core constraint. Through iterative calculation, it inversely calculates the optimal threshold for satisfying this frequency balance constraint, and based on this, extracts the final evaluation standards for winter and summer. Finally, the extracted typical scenario set is used as the extreme boundary input for system power supply adequacy stress testing, providing precise engineering quantitative basis for the differentiated configuration of long- and short-term energy storage and conventional power sources in high-proportion wind power systems.

[0034] Example: This invention achieves deep integration of meteorological statistical patterns and power system planning decisions, overcoming the shortcomings of subjective judgment in the traditional extraction of wind power low-output scenarios. Taking the Northeast my country power grid as an example, a case study is conducted by combining the actual operation of the power grid with meteorological data: (1) Overcome the subjectivity of traditional threshold setting and realize the accurate extraction of wind power low output scenarios for multiple days.

[0035] By leveraging the core constraint that "the frequency of historical light winds is equal to the frequency of low wind power output," this invention adaptively iterates to develop a measurement standard (taking Northeast China as an example, the measurement standard for multi-day low wind power output scenarios is: more than 2 consecutive days in winter with an average output rate below 17%, and more than 3 consecutive days in summer with an average output rate below 10%), successfully extracting typical multi-day low power output scenarios averaging 2-4 times per year from massive historical data. Example results (Tables 2 and 3 list the details of multi-day low wind power output scenarios closest to winter and summer load peaks each year) show that these scenarios highly overlap with the load peaks caused by winter cold waves (clear, cold, and windless) and summer high temperatures (extremely hot and windless), ensuring that the extracted scenarios accurately cover the real threats of historical special meteorological events and avoiding the risk of missed detection caused by traditional fixed threshold methods.

[0036] Table 2 Typical Scenarios of Low Wind Power Output for Multiple Days in Northeast China During Winter Table 2 Typical Scenarios of Low Wind Power Output for Some Days in Northeast China during Winter Table 3 Typical Scenarios of Low Wind Power Output for Multiple Days in Northeast China During Summer Table 3 Typical Scenarios of Low Wind Power Output for Some Days in Northeast China During Summer (2) Effectively revealed the limitations of large-scale short-term energy storage in multi-day power supply guarantee.

[0037] This invention directly applies the extracted typical scenarios to the verification of multi-day power balance adequacy. Simulation results show that under continuous low-output conditions for several days, the system's surplus power decreases sharply. Simply relying on increasing the scale of short-term energy storage such as pumped hydro storage or new battery energy storage is insufficient to guarantee power supply for peak loads over several consecutive days due to the duration of continuous power generation (see attached diagrams illustrating the power supply operation mechanism and changes in energy storage capacity during multi-day low-output wind power scenarios under different planning levels). Figures 5 to 7 As shown in Table 4), it will exhibit a significant diminishing marginal utility of energy storage (energy storage utilization rate decreases significantly with the increase of newly built energy storage capacity). This reveals the technical bottleneck of a single short-term energy storage path in dealing with the risk of long-term power shortages due to weather sensitivity.

[0038] Table 4 shows the energy storage supply guarantee effect and utilization hours of the newly built energy storage system in the example system. (3) Effectively fills the gap in the evaluation standard for low output over many days, and provides boundary support for the economical allocation of power sources and energy storage.

[0039] By verifying adequacy and iteratively optimizing capacity, the risk of multi-day supply imbalance is transformed into a lower limit for the capacity demand of various power sources. Case studies and comparative verification demonstrate this (Table 5). Table 5 Power Optimization Schemes for Each Planning Horizon of the Case Study System If the refined scenario division of this invention is lacking and short-term energy storage is blindly adopted for extreme power supply, the planning and investment costs will increase exponentially. However, based on the multi-timescale configuration boundary provided by this invention, the introduction of long-term energy storage with cross-seasonal and multi-day adjustment characteristics (such as seasonal hydrogen energy storage) and conventional backup power sources can significantly reduce the investment cost of extreme power supply to about one-sixth of the original technical path, providing solid data support and scientific tools for the overall planning of safe power supply and economic efficiency in the new power system.

[0040] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0041] In the description of this invention, it should be understood that 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 indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0042] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for determining the standard for scenarios with multiple days of low wind power output in a high-proportion wind power system, characterized in that, Includes the following steps: Identify typical features based on the dual characteristics of source and load, establish a set of continuous multi-day low wind speed scenarios coupled with meteorological characteristics and wind power output based on the constructed wind power daily power output state transition probability model, and then construct an evaluation index system for multi-day low wind power output scenarios. Based on the aforementioned evaluation index system for wind power multi-day low output scenarios, and taking the balance of occurrence frequency of low wind speed scenarios and low power output scenarios as a constraint, the final evaluation standard for wind power multi-day low output scenarios is adaptively determined through iterative calculation; and based on this standard, a set of typical scenarios is extracted and used as boundary conditions to be input into the multi-day power balance sufficiency calculation model for determination and execution.

2. The method for determining the standard for multi-day low-output wind power scenarios in a high-proportion wind power system according to claim 1, characterized in that: When identifying typical characteristics, analyze the balance characteristics of the power system in the target area, and identify the risk points of multi-day power imbalance under special weather processes from four dimensions: load uncertainty, balance time scale, energy resource coordination means, and limiting factors.

3. The method for determining the standard for multi-day low-output wind power scenarios in a high-proportion wind power system according to claim 2, characterized in that: When constructing the probability model for the daily power output state transition of wind power, based on historical wind power output data over many years, the daily average power output state levels of wind power are divided, and the probability model for the daily power output state transition of wind power is constructed. The cross-day transition characteristics of wind power output between different levels are quantitatively calculated, and the probability distribution and seasonal differences of continuously maintaining low power output levels are extracted.

4. The method for determining the standard for multi-day low-output wind power scenarios in a high-proportion wind power system according to claim 3, characterized in that: When constructing a set of continuous low wind speed scenarios, historical meteorological data is combined with the judgment criterion of continuous low wind speed below a certain threshold. Typical multi-day low wind speed meteorological scenarios are extracted, their corresponding meteorological causes are analyzed, and spatial synchronous low output evaluation index is introduced to quantify the degree of cross-over of low wind speed meteorological conditions in different sub-regions, forming a set of multi-day low wind speed scenarios that corresponds to meteorological physical processes and takes into account spatial correlation.

5. The method for determining the standard for multi-day low-output wind power scenarios in a high-proportion wind power system according to claim 4, characterized in that: When obtaining evaluation indicators for low-output space synchronization, the target area is divided into: N A wind power characteristic sub-region is defined, and a spatial synchronous low-output index is defined. SSLIt Used for quantification t The calculation model for the overlap of large-scale low-wind-speed weather events within a given time period is as follows: In the formula: N The total number of wind power characteristic sub-regions divided; Si,t For the first i Each sub-region t Low wind speed state variable during a given period, when the wind speed in that area is below the extremely low wind speed threshold. Si,t =1, otherwise Si,t =0; αi For the first i The weighting factor of wind power installed capacity in each sub-region relative to the total installed capacity of the target region.

6. The method for determining the standard for multi-day low-output wind power scenarios in a high-proportion wind power system according to claim 5, characterized in that: When constructing the evaluation index system for wind power low-output scenarios over multiple days, the evaluation index system for wind power low-output scenarios over multiple days is expressed as follows: In the formula: for t Wind power output rate at any time; for d Daily average wind power output rate; and These are the hourly output measurement standard and the daily average output measurement standard for wind power at low output levels, respectively. and These are duration measurement standards at two time scales: hourly and daily.

7. The method for determining the standard for multi-day low-output wind power scenarios in a high-proportion wind power system according to claim 6, characterized in that: When the measurement criteria are adaptive, the core constraint is that the frequency of occurrence of long-term low wind speed meteorological scenarios and the frequency of occurrence of long-term low wind power output scenarios are the same. Based on iterative calculation, the preliminary measurement threshold that satisfies the frequency balance constraint is calculated in reverse, thereby extracting a typical set of long-term low wind power output scenarios. Finally, based on the specific indicators of all scenarios in the scenario set, the final measurement criteria for defining long-term low wind power output scenarios in winter and summer are extracted.

8. A system for determining the standard of long-term low-output wind power scenarios in a high-proportion wind power system, used to implement the method for determining the standard of long-term low-output wind power scenarios in a high-proportion wind power system as described in claim 1, characterized in that, include: The evaluation system construction module is used to identify typical characteristics of the source and load sides. Based on the constructed wind power daily output state transition probability model, a set of continuous multi-day low wind speed scenarios coupled with "meteorological characteristics-wind power output" is established, and then an evaluation index system for multi-day low wind power output scenarios is constructed. The standard determination and planning execution module is used to adaptively determine the final evaluation standard based on the evaluation index system and with the balance of occurrence frequency of low wind speed meteorological scenarios and low power output scenarios as a constraint. Based on this standard, a set of typical scenarios is extracted and used as boundary conditions to be input into the adequacy calculation model for verification, so as to guide the planning of system power supply and energy storage.

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