Method for generating extreme fluctuation scenarios of wind power output considering fluctuation speed and fluctuation amplitude of time

Abstract

The application discloses a method for generating wind power output extreme fluctuation scene considering time fluctuation speed and fluctuation amplitude, belongs to the technical field of wind power generation, and is based on the maximum value and minimum value of the predicted wind power output at each dispatching moment, the maximum upward fluctuation speed and maximum downward fluctuation speed of the wind power output between moments, and the maximum value and minimum value of the wind power output possibly reached under super-resolution between moments, finely depicts the upward fluctuation scene and downward fluctuation scene of the extreme fluctuation amplitude of the wind power and the upward climbing scene and downward climbing scene of the extreme fluctuation speed of the wind power according to different cases, can more completely and accurately depict the uncertainty of the wind power under super-resolution between moments, and thus enables the power system to formulate more accurate and effective dispatching instructions when coping with the uncertainty of the wind power, and further improves the wind power consumption capacity of the power system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wind power generation, in particular to a wind power extreme fluctuation scenario generation method for improving the response to wind power output uncertainty between time points. BACKGROUND

[0002] With the promulgation of the Energy Law of the People's Republic of China and the accelerated construction of new power systems, the installed capacity of wind power generation has grown rapidly. As of the end of 2024, the cumulative installed capacity of wind power in China has exceeded 520 million kilowatts, effectively promoting the clean and low-carbon transformation of the power system. However, wind power output has the disadvantages of strong random fluctuation and low prediction accuracy. With large-scale wind power grid-connected generation, the pressure on power system power balance and safe and reliable operation continues to increase. Accurately depicting the uncertainty characteristics of wind power output at each dispatch time and between time points is of great significance to promoting wind power consumption and safe operation of the power grid.

[0003] Traditional methods usually based on the probability or interval distribution model of wind power output at each dispatch time, use random scenario sets, robust uncertainty sets, etc. to depict the uncertainty of wind power output. The above methods focus on the fine modeling of the uncertainty of wind power output at each dispatch time, but do not consider the uncertainty of the transition process of wind power output between time points, which may lead to wind curtailment, over-limit power flow, etc. between time points.

[0004] With the continuous increase of wind power penetration, in order to improve the ability of power system dispatching strategy to respond to the uncertainty of wind power output transition process between different time points, the academic and engineering communities depict the uncertainty of wind power fluctuation range at each time point based on interval model, and construct extreme state transition scenarios to describe the uncertainty of wind power fluctuation speed between adjacent time points by connecting the endpoints of wind power fluctuation interval, but since the constructed extreme scenarios of state transition between time points are not related to the actual fluctuation speed of wind power, it may be difficult to economically and efficiently meet the ramping reserve demand of wind power.

[0005] To address the above problems, some scholars have constructed the most stringent fluctuation trajectory of system ramping resource demand based on the upper and lower bounds of wind power prediction at each time point and the maximum up-ramping and down-ramping speed between time points, trying to depict the severe nonlinear fluctuation characteristics of wind power between time points. Although this research has made breakthroughs in the depiction of extreme fluctuation speed, it does not consider the actual upper and lower limits of wind power output between dispatch time points, so the constructed extreme fluctuation speed ramping scenarios are often too harsh and deviate from the actual fluctuation range, making it difficult to economically and efficiently configure the reserve.

[0006] Therefore, there is an urgent need for a new technical solution to solve the problem of insufficient utilization of dispatching resources due to the lack of precision in depicting the uncertainty of wind power output between time points. SUMMARY

[0007] The technical problem to be solved by the present application is to provide a wind power output extreme fluctuation scenario generation method considering time fluctuation speed and fluctuation amplitude, which can more completely and accurately depict the uncertainty of wind power in time super-resolution, so as to enable the power system to formulate more accurate and effective dispatching instructions when coping with wind power uncertainty, and further improve the wind power accommodation capacity of the power system.

[0008] The wind power output extreme fluctuation scenario generation method considering time fluctuation speed and fluctuation amplitude comprises the following steps, and the following steps are sequentially performed:

[0009] Step one: in the time interval [t, t+1] with the dispatching time t and the dispatching time t+1 as the interval endpoints, based on the upper and lower bounds of the wind power fluctuation interval at the time t and the time t+1, and the maximum and minimum values of the wind power fluctuation amplitude and fluctuation speed in the time interval [t, t+1], the wind power extreme fluctuation amplitude upper fluctuation scenario trajectory and the wind power extreme fluctuation amplitude lower fluctuation scenario trajectory are obtained;

[0010] Step two: according to the wind power extreme fluctuation amplitude upper fluctuation scenario trajectory segment point obtained in step one, and the wind power maximum downward fluctuation speed, the wind power extreme fluctuation speed lower ramping scenario trajectory is constructed;

[0011] Step three: according to the wind power extreme fluctuation amplitude lower fluctuation scenario trajectory segment point obtained in step one, and the wind power maximum upward fluctuation speed, the wind power extreme fluctuation speed upper ramping scenario trajectory is constructed.

[0012] The calculation formula of the wind power maximum downward fluctuation speed and the wind power maximum upward fluctuation speed is:

[0013]

[0014] In the formula, P u , P d are the probability distribution functions of the upward fluctuation speed and the downward fluctuation speed of the wind power respectively; α is the confidence level; v is the fluctuation speed of the wind power; v u , v d are the maximum upward fluctuation speed and the maximum downward fluctuation speed of the wind power respectively when the confidence level is α.

[0015] The upper fluctuation scenario track of the extreme fluctuation amplitude of the wind power is composed of the segment point 1, the segment point 5, the segment point 6 and the segment point 3, and the lower fluctuation scenario track of the extreme fluctuation amplitude of the wind power is composed of the segment point 2, the segment point 7, the segment point 8 and the segment point 4, and the time t5, t6, t7 and t8 corresponding to the segment point 5, the segment point 6, the segment point 7 and the segment point 8 are calculated as follows:

[0016]

[0017] In the formula, W t min , W t max are respectively the minimum value and the maximum value of the wind power at the time point t; are respectively the minimum value and the maximum value of the fluctuation amplitude of the wind power in the time interval [t, t+1]; u , v d are respectively the maximum upward fluctuation speed and the maximum downward fluctuation speed of the wind power.

[0018] The coordinates of the segment point 1, the segment point 2, the segment point 3, the segment point 4, the segment point 5, the segment point 6, the segment point 7 and the segment point 8 are as follows:

[0019] Segment point 1: (t, W t max ); segment point 2: (t, W t min ); segment point 3: Segment point 4:

[0020] Segment point 5: Segment point 6: Segment point 7: Segment point 8:

[0021] The lower climbing scenario track of the extreme fluctuation speed of the wind power is constructed according to the size relationship of t8, t9, t 10 , wherein:

[0022]

[0023] In the formula, t9, t 10 are respectively the time corresponding to the segment point 9 and the segment point 10; and the power corresponding to the segment point 9 and the segment point 10 is respectively

[0024] When t8≥t9, the lower climbing scenario track of the extreme fluctuation speed is a track formed by the segment point 1, the segment point 5, the segment point 9, the segment point 8 and the segment point 4 in turn; and when t8≤t 10When t 10 When t 11 and wind power P 11 satisfy the formula:

[0025]

[0026] The upper climbing scenario track of the extreme fluctuation speed of the wind power is constructed according to the size relationship of t 12 , t 13 , wherein:

[0027]

[0028] In the formula, t 12 , t 13 are the time points corresponding to the segment points 12 and 13; the powers corresponding to the segment points 12 and 13 are P

[0029] When t 12 ≤ t6, the upper climbing scenario track of the extreme fluctuation speed is the track formed in sequence by the segment points 2, 7, 12, 6 and 3; when t 13 ≥ t6, the upper climbing scenario track of the extreme fluctuation speed is the track formed in sequence by the segment points 2, 7 and 12; when t 13 <t6 < t 12 , the upper climbing scenario track of the extreme fluctuation speed is the track formed in sequence by the segment points 2, 14, 6 and 3; wherein t 14 and wind power P 14 satisfy the formula:

[0030]

[0031] By the above design scheme, the application can bring the following beneficial effects: based on the fluctuation interval of wind power output at each scheduling time, the application considers the fluctuation speed and fluctuation amplitude prediction information between scheduling times to construct the extreme fluctuation scenario of wind power between times. Based on interval mathematics theory, a description method of wind power extreme fluctuation scenario is proposed: the upper limit value and lower limit value of wind power at each scheduling time are used to describe the uncertainty of wind power at the time point, and the fluctuation speed and fluctuation amplitude of wind power in the time interval are used to describe the uncertainty of wind power between time points. Through the method, the scenario set describing the extreme fluctuation of wind power at each scheduling time and between times is constructed, and the four most severe fluctuation scenarios of wind power, i.e., the upper fluctuation scenario and the lower fluctuation scenario of the extreme fluctuation amplitude of wind power between times, and the lower climbing scenario and the upper climbing scenario of the extreme fluctuation speed of wind power, are realized. The four extreme fluctuation scenarios have the robustness feature, and as long as the power system scheduling strategy can cope with the four types of wind power fluctuation scenarios, other arbitrary fluctuation scenarios under the boundary condition limitation of fluctuation speed and fluctuation amplitude between times can be coped with.

[0032] Further beneficial effects of the application are:

[0033] 1. The application models the uncertainty of wind power at each time point and between time points based on historical data, and fully considers the fluctuation amplitude and fluctuation speed of wind power between times, to establish an extreme fluctuation scenario model that can reflect the uncertainty of wind power.

[0034] 2. The application comprehensively utilizes the interval prediction information of wind power at the time point and the fluctuation amplitude and fluctuation speed information between times to deduce the extreme fluctuation amplitude and extreme fluctuation speed scenario model that can more truly and accurately depict the uncertainty of wind power, so that the power system can more accurately and efficiently utilize flexible scheduling resources when formulating scheduling commands, and further improves the safety and economy of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0035] The application is further described below in combination with the drawings and specific embodiments:

[0036] Figure 1 The application is a wind power output extreme fluctuation scenario generation method considering time fluctuation speed and fluctuation amplitude.

[0037] Figure 2 The application is a schematic diagram of the upper (lower) fluctuation scenario of the extreme fluctuation amplitude of wind power in the specific embodiment.

[0038] Figure 3 The application is a schematic diagram of the lower climbing scenario of the extreme fluctuation speed of wind power when t8≥t9 in the specific embodiment.

[0039] Figure 4For the specific embodiments of the present invention t8≤t 10 Schematic diagram of the up-climbing scenario of the wind power extreme fluctuation speed when t < t6.

[0040] Figure 5 For the specific embodiments of the present invention t 10 Schematic diagram of the up-climbing scenario of the wind power extreme fluctuation speed when t < t6.

[0041] Figure 6 For the specific embodiments of the present invention t 12 Schematic diagram of the up-climbing scenario of the wind power extreme fluctuation speed when t < t6.

[0042] Figure 7 For the specific embodiments of the present invention t 13 Schematic diagram of the up-climbing scenario of the wind power extreme fluctuation speed when t < t6.

[0043] Figure 8 For the specific embodiments of the present invention t 13 Schematic diagram of the up-climbing scenario of the wind power extreme fluctuation speed when t < t6. 12 Schematic diagram of the up-climbing scenario of the wind power extreme fluctuation speed when t < t6.

[0044] Figure 9 Schematic diagram of the device structure adopted by the method for generating wind power output extreme fluctuation scenarios considering time fluctuation speed and fluctuation amplitude. DETAILED DESCRIPTION

[0045] The method for generating wind power output extreme fluctuation scenarios considering time fluctuation speed and fluctuation amplitude, as shown in Figure 1 includes the following steps:

[0046] 101: Obtain wind power historical data of a wind farm, statistically obtain the probability distribution of the wind power fluctuation speed between time points based on the wind power historical data, and calculate the maximum upward fluctuation speed and the maximum downward fluctuation speed of the wind power under a certain confidence level;

[0047] 102: Describe the uncertainty of the wind power fluctuation amplitude between time points based on the interval model;

[0048] 103: Obtain the upward fluctuation scenario and the downward fluctuation scenario trajectory of the corresponding wind power extreme amplitude based on the maximum and minimum fluctuation amplitude prediction information of the wind power at each time point and between time points and the maximum and minimum values of the wind power fluctuation speed between time points;

[0049] 104: Construct the downward fluctuation scenario trajectory of the wind power extreme fluctuation speed based on the segment points of the wind power extreme amplitude downward fluctuation scenario trajectory and the maximum downward fluctuation speed of the wind power;

[0050] 105: constructing an up-ramp scenario trajectory of the extreme fluctuation speed of the wind power based on the segment points of the trajectory of the extreme amplitude up fluctuation scenario of the wind power and the maximum up fluctuation speed of the wind power.

[0051] In summary, the extreme scenario fluctuation trajectory that can more accurately and truly reflect the wind power uncertainty is depicted by the interval model of the wind power fluctuation amplitude and the interval model of the wind power fluctuation speed according to the time point and the time interval by the steps 101-105, so that the power system is more targeted when formulating the dispatching instruction, and the capacity of the power system for absorbing new energy is further improved.

[0052] Next, specific calculation formulas, examples, Figures 2 to 8 The method of the application is described in detail below:

[0053] First, the probability distribution of the wind power fluctuation speed between time points is calculated according to the historical data of the up fluctuation speed and the down fluctuation speed of the wind power between time points, and the maximum up fluctuation speed and the maximum down fluctuation speed of the wind power are calculated according to the given confidence level, as shown in formula (1).

[0054]

[0055] In the formula, P u , P d are the probability distribution functions of the up fluctuation speed and the down fluctuation speed of the wind power respectively; α is the confidence level; v is the wind power fluctuation speed; v u , v d are the maximum up fluctuation speed and the maximum down fluctuation speed of the wind power respectively when the confidence level is α.

[0056] The uncertainty of the wind power fluctuation amplitude at each time point and between time points is represented by an interval model, as shown in formula (2).

[0057]

[0058] In the formula, W t , W t min , W t max are the random value, the minimum value and the maximum value of the wind power at the time point t respectively; W t→t+1 , are the random value, the minimum value and the maximum value of the wind power fluctuation amplitude between the time interval t and t+1 respectively.

[0059] Based on the interval model of wind power fluctuation amplitude between time points and time intervals based on formula (2) and the maximum upward fluctuation speed and the maximum downward fluctuation speed calculated based on formula (1), the extreme fluctuation amplitude upward scenario track S1 and the extreme fluctuation amplitude downward scenario track S2 of the wind power that can be reached in the continuous time [t, t+1] can be depicted as shown in Figure 2

[0060] As shown in Figure 2 , the fluctuation track corresponding to the extreme fluctuation amplitude upward scenario S1 of the wind power is the segmented points 1→5→6→3, and the fluctuation track corresponding to the extreme fluctuation amplitude downward scenario S2 of the wind power is the segmented points 2→7→8→4. In the following, the segmented points are simply referred to as points, wherein the points t5, t6, t7, and t8 corresponding to the points 5, 6, 7, and 8 are calculated as follows:

[0061]

[0062] For the scenario tracks S1 (1→5→6→3) and S2 (2→7→8→4) of the extreme amplitude, the segmented points of the tracks can be represented by the sequence pairs composed of time points and wind power, and the specific representations are as follows:

[0063] Point 1: (t, W t max );Point 2: (t, W t min );Point 3: Point 4:

[0064] Point 5: Point 6: Point 7: Point 8:

[0065] For the downward ramping scenario S3 of the extreme fluctuation speed of the wind power, the following three cases are constructed:

[0066] (1) As shown in Figure 3 , when t8≥t9, the fluctuation track corresponding to the scenario S3 is 1→5→9→8→4, that is, the maximum possible value of the wind power at the time t fluctuates upward to the point 5 at the maximum upward fluctuation speed, and then fluctuates downward to the point 9 at the maximum downward fluctuation speed. At this time, the relative amplitude of the downward fluctuation is the largest, and is equal to the difference between the wind power of the point 5 and the point 9. The time t9 and the wind power P9 corresponding to the point 9 satisfy the following formula:

[0067]

[0068] (2) As shown in Figure 4 , when t8≤t 10 ​When the scene is S3, the corresponding fluctuating trajectory is 1→10→4. That is, the maximum possible value of the wind power at time t fluctuates downward to point 10 at the maximum downward fluctuation speed, and then fluctuates upward to the minimum possible value at time t+1 at the maximum upward fluctuation speed. At this time, the relative amplitude of the downward fluctuation is the largest, and it is equal to the difference in wind power between point 1 and point 10. The time t corresponding to point 10 10 and the wind power P 10 satisfy the following formula:

[0069]

[0070] (3) As Figure 5 shown, when t 10 <t8 < t9, the corresponding fluctuating trajectory of scene S3 is 1→11→8→4. That is, the maximum possible value of the wind power at time t fluctuates upward to point 11 at the maximum upward fluctuation speed, then fluctuates downward to point 8 at the maximum downward fluctuation speed, and finally fluctuates upward to the minimum possible value point 4 at time t+1 at the maximum upward fluctuation speed. At this time, the relative amplitude of the downward fluctuation is the largest, and it is equal to the difference in wind power between point 11 and point 8. The time t corresponding to point 11 11 and the wind power P 11 satisfy the following formula:

[0071]

[0072] For the up-ramp scenario S4 of the extreme fluctuation speed of wind power, it is constructed in the following three cases:

[0073] (1) As Figure 6 shown, when t 12 ≤t6, the corresponding fluctuating trajectory of scene S4 is 2→7→12→6→3. That is, the minimum possible value point 2 of the wind power at time t fluctuates downward to point 7 at the maximum downward fluctuation speed, and then fluctuates upward to point 12 at the maximum upward fluctuation speed. At this time, the relative amplitude of the upward fluctuation is the largest, and it is equal to the difference in wind power between point 7 and point 12. The time t corresponding to point 12 12 and the wind power P 12 satisfy the following formula:

[0074]

[0075] (2) As Figure 7 shown, when t 13 ≥t6, the corresponding fluctuating trajectory of scene S4 is 2→13→​and wind power P 13 satisfies the following formula:

[0076]

[0077] (3) as shown in Figure 8 , when t 13 <t6<t 12 , the fluctuation trajectory corresponding to scenario S4 is 2→14→6→3, that is, the minimum possible value point 2 of wind power at time t fluctuates downward to point 14 at the maximum downward fluctuation speed, then fluctuates upward to point 6 at the maximum upward fluctuation speed, and finally fluctuates to point 3 at the maximum downward fluctuation speed, at this time, the relative amplitude of upward fluctuation is the largest, and is equal to the difference between the wind power of point 14 and point 6. The time t 14 and wind power P 14 satisfies the following formula:

[0078]

[0079] To verify the effectiveness of the wind power extreme fluctuation scenario generation method considering the fluctuation speed and fluctuation amplitude between times, a single wind farm is selected for analysis, and the upper and lower limits of the predicted wind farm output at each time are shown in Table 1, wherein the time interval between adjacent two times is 15 minutes, the maximum upward fluctuation speed of wind power v u = 30 MW / 15 min, the maximum downward fluctuation speed of wind power v d = 30 MW / 15 min, and the following two schemes are designed for analysis:

[0080] Scheme 1: only considering the maximum upward fluctuation speed and the maximum downward fluctuation speed of wind power between times, without considering the interval prediction information of wind power fluctuation amplitude between times.

[0081] Scheme 2: considering the prediction information of wind power fluctuation speed and fluctuation amplitude between times, and the minimum and maximum values of wind power fluctuation amplitude between times are shown in Table 2.

[0082] Table 1 Upper and lower limits of predicted wind farm output at each time

[0083]

[0084] Table 2 Maximum and minimum values of wind farm power fluctuation amplitude between times in scheme 2

[0085]

[0086] For scheme 1, since the prediction information of the wind power fluctuation amplitude between time points is not used, the maximum and minimum values of the wind power fluctuation amplitude between time points can be calculated based on the interval prediction value of the wind power at each time point and the maximum upward and downward fluctuation speed between time points according to formula (10) as shown in Table 3. By comparing Table 2 and Table 3, it can be seen that when the maximum and minimum values of the wind power prediction between time points are not considered, the maximum value of the wind power between time points evaluated based on the wind power prediction information at each time point and the wind power fluctuation speed between time points is often higher than the actual possible maximum value, and the minimum value is often less than the actual minimum value. That is, by comprehensively considering the wind power prediction information at each time point and the wind power fluctuation amplitude and fluctuation speed information between time points, the upward fluctuation scenario S1 of the extreme fluctuation amplitude and the downward fluctuation scenario S2 of the extreme fluctuation amplitude of the wind power can be more accurately depicted.

[0087]

[0088] Table 3 Maximum and minimum values of wind farm power fluctuation amplitude between time points calculated by scheme 1

[0089]

[0090] According to formula (4) to formula (6), the downward climbing scenarios S3 of the extreme fluctuation speed of the wind power under two schemes can be calculated respectively, as shown in Table 4 and Table 5. Analysis shows that the fluctuation trajectories of the extreme fluctuation speed downward climbing scenarios S3 determined by the two schemes are the same in the time interval [2, 3], and different in other time periods. Further analysis of Table 5 shows that t8, t9, t 10 The size relationship changes with the change of the fluctuation amplitude between time points, thereby making S3 present different fluctuation trajectories, and in scheme 1, since the information of the fluctuation amplitude between time points is considered, t8≤t 10 always holds, and the fluctuation trajectory is always 1→10→4, which is difficult to adaptively adjust according to the actual maximum and minimum values between time points.

[0091] Table 4 Fluctuation trajectory of the extreme fluctuation speed downward climbing scenario S3 of the wind power between time points by scheme 1

[0092]

[0093] Table 5 Fluctuation trajectory of the extreme fluctuation speed downward climbing scenario S3 of the wind power between time points by scheme 2

[0094]

[0095] The up ramping scenarios S4 of the wind power extreme fluctuation speed under the two schemes can be calculated according to formula (7)-formula (9), and are shown in Table 6 and Table 7 respectively. It can be known through analysis that the fluctuation trajectories of the extreme fluctuation speed up ramping scenarios determined by the two schemes are the same in the time interval [1, 2] and [2, 3], and are different in other time periods. Further analysis of Table 5 shows that the size relationship between t 12 , t 13 changes with the change of the fluctuation amplitude between time points, thereby making S3 present different fluctuation trajectories, and in scheme 1, because the information of the fluctuation amplitude between time points is considered, t 13 ≥ t6 is always satisfied, the fluctuation trajectory is always 2→13→3, and it is difficult to adaptively adjust according to the actual maximum and minimum values between time points.

[0096] Table 6: Fluctuation trajectory of the wind power extreme fluctuation speed up ramping scenario S4 between time points in scheme 1

[0097]

[0098] Table 7: Fluctuation trajectory of the wind power extreme fluctuation speed up ramping scenario S4 between time points in scheme 2

[0099]

[0100] In summary, compared with the wind power fluctuation scenario modeling method considering only the wind power prediction interval at each time point and the fluctuation speed between time points, the model proposed in the application can not only fully utilize the fluctuation amplitude of the wind power between time points predicted by the historical data to accurately depict the up fluctuation scenario and the down fluctuation scenario of the wind power extreme fluctuation amplitude, but also can adaptively depict the up ramping scenario and the down ramping scenario of the wind power extreme fluctuation speed according to the actual maximum and minimum values of the wind power between time points, thereby ensuring that the power system is more targeted when formulating the dispatching instruction, and finally improving the economy and safety of the power system.

[0101] Based on the same inventive concept, the embodiment of the application also provides a wind power output extreme fluctuation scenario generation device considering the uncertainty of the fluctuation speed and the fluctuation amplitude between time points. Referring to Figure 9 The device includes a processor and a memory, and the memory stores program instructions. The processor calls the program instructions stored in the memory to make the device execute the method steps of the application.

[0102] The execution subject of the processor and the memory described above can be a computer, a single-chip microcomputer, a microcontroller or other devices with computing functions. In specific implementation, the execution subject is not limited in the embodiment of the application, and is selected according to the actual application needs.

[0103] The data signal is transmitted between the memory and the processor through a bus, and embodiments of the present application do not repeat the description.

[0104] Based on the same inventive concept, the embodiments of the present application also provide a computer readable storage medium, which comprises a stored program, and the program controls the device where the storage medium is located to execute the method steps in the above embodiments when the program is running.

[0105] The computer readable storage medium includes, but is not limited to, a flash memory, a hard disk, a solid state disk, etc.

[0106] It should be pointed out here that the readable storage medium description in the above embodiments is corresponding to the method description in the embodiments, and the embodiments of the present application do not repeat the description here.

[0107] In the above embodiments, all or part of the embodiments can be realized by software, hardware, firmware or any combination thereof. When realized by software, all or part of the embodiments can be realized in the form of a computer program product. The computer program product comprises 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 the embodiments of the present application are generated.

[0108] The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in or transmitted by a computer readable storage medium. The computer readable storage medium can be any available medium accessible by the computer or a data storage device such as a server, data center, etc. containing one or more available media sets. The available medium can be a magnetic medium or a semiconductor medium, etc.

[0109] The model of each device is not limited unless otherwise specified, and any device that can complete the above functions can be used.

[0110] Those skilled in the art can understand that the drawings are only a schematic diagram of a preferred embodiment, and the above-mentioned serial numbers of the embodiments of the present application only describe the embodiments, and do not represent the advantages and disadvantages of the embodiments.

[0111] The above is only a preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for generating extreme fluctuation scenarios in wind power output considering both time fluctuation velocity and fluctuation amplitude, characterized by: The steps are as follows, and they are performed in sequence: Step 1: In the scheduling time t and scheduling time t +1 represents the time interval at the endpoint of the interval. t , t +1] within, based on wind power in t Time and t The upper and lower bounds of the fluctuation range at time +1, and the wind power output within the time interval [ t , t +1] The maximum and minimum values ​​of fluctuation amplitude and fluctuation speed are used to obtain the fluctuation scenario trajectory above the extreme fluctuation amplitude of wind power and the fluctuation scenario trajectory below the extreme fluctuation amplitude of wind power; Step 2: Based on the segment points of the extreme fluctuation amplitude of wind power obtained in Step 1, and the maximum downward fluctuation velocity of wind power, construct the downhill scenario trajectory of the extreme fluctuation velocity of wind power. Step 3: Based on the segment points of the fluctuation scenario trajectory under the extreme fluctuation amplitude of wind power obtained in Step 1, and the maximum upward fluctuation velocity of wind power, construct the upward climbing scenario trajectory of the extreme fluctuation velocity of wind power. The trajectory of the upper fluctuation scenario of extreme wind power fluctuation amplitude is composed of segment points 1, 5, 6, and 3. The trajectory of the lower fluctuation scenario of extreme wind power fluctuation amplitude is composed of segment points 2, 7, 8, and 4. The times corresponding to segment points 5, 6, 7, and 8 are... t 5. t 6. t 7. t 8. The calculation is as follows: ， In the formula: W t min , W t max Wind power at time points t The minimum and maximum values; , The wind power output in the time intervals are respectively [ t , t +1] Minimum and maximum values ​​of fluctuation amplitude; v u , v d These represent the maximum upward and downward fluctuation rates of wind power, respectively. The coordinates of segment point 1, segment point 2, segment point 3, segment point 4, segment point 5, segment point 6, segment point 7, and segment point 8 are as follows: Segmentation point 1: ( t , Segmentation point 2: ( t , Segmentation point 3: ( t +1, Segmentation point 4: ( t +1, ); Segmentation point 5: ( t 5, ); Segmentation point 6: ( t 6, ); Segmentation point 7: ( t 7, ); Segmentation point 8: ( t 8, ).

2. The method for generating extreme fluctuation scenarios of wind power output considering time fluctuation speed and fluctuation amplitude according to claim 1, characterized in that: The formulas for calculating the maximum downward fluctuation rate of wind power and the maximum upward fluctuation rate of wind power are as follows: ， In the formula: P u 、P d , , respectively, are the probability distribution functions for the upward and downward fluctuation velocities of wind power; 'a' is the confidence level; 'v' is the wind power fluctuation velocity; v u 、v d These represent the maximum upward and downward fluctuation rates of wind power at a confidence level of a, respectively.

3. The method for generating extreme fluctuation scenarios of wind power output considering time fluctuation speed and fluctuation amplitude according to claim 1, characterized in that: The downhill trajectory of the extreme fluctuation speed of wind power is based on t 8. t 9. t 10 The size relationship is constructed, where: ， In the formula: t 9. t 10 These are the times corresponding to segmentation points 9 and 10; the powers corresponding to segmentation points 9 and 10 are respectively... , ; when t 8≥ t At 9:00, the trajectory of the downhill scenario with extreme fluctuation speed is the trajectory formed sequentially by segment points 1, 5, 9, 8, and 4; when t 8≤ t 10 At that time, the trajectory of the downhill scenario with extreme fluctuation speed is the trajectory formed by segment points 1, 10, and 4 in sequence; when t 10 < t 8 < t At 9:00, the trajectory of the downhill scenario with extreme fluctuation velocity is the trajectory formed sequentially by segment points 1, 11, 8, and 4; where the time corresponding to segment point 11 is... t 11 and wind power P 11 Satisfying the formula: 。 4. The method for generating extreme wind power output fluctuation scenarios considering time fluctuation speed and fluctuation amplitude according to claim 1, characterized in that: The upward trajectory of the extreme fluctuation speed of wind power is based on t 6. t 12 , t 13 The size relationship is constructed based on different cases, where: ， In the formula: t 12 , t 13 These are the times corresponding to segment points 12 and 13; the powers corresponding to segment points 12 and 13 are respectively , ; when t 12 ≤ t At 6 o'clock, the trajectory of the uphill scenario with extreme fluctuation speed is the trajectory formed sequentially by segment points 2, 7, 12, 6, and 3; when t 13 ≥ t At 6 o'clock, the trajectory of the uphill scenario with extreme fluctuation speed is the trajectory formed sequentially by segment points 2, 7, and 12; when t 13 < t 6< t 12 At that time, the trajectory of the uphill scenario with extreme fluctuation velocity is the trajectory formed sequentially by segment points 2, 14, 6, and 3; where the time corresponding to segment point 14 is... t 14 and wind power P 14 Satisfy the following formula: 。

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