Wind energy volatility quantification method based on relative accumulated arc length

Abstract

The invention discloses a wind energy fluctuation quantification method based on relative cumulative arc length, which comprises the following steps of: S1, defining an index for quantifying wind energy fluctuation as the relative cumulative arc length, namely RCAL, and representing the index according to a formula: # imgabs0 # S2, carrying out relative cumulative arc length quantification wind energy fluctuation calculation on the basis of a time scale; the method has a wide application prospect in the field of wind energy, including evaluation in the aspects of wind energy resource evaluation, wind energy field site selection, wind energy field operation, energy planning, risk management and the like, and can also be used for financial market analysis, meteorological data analysis and economic index monitoring.

Description

Technical Field

[0001] The invention relates to a method for quantifying wind energy fluctuation, and in particular to a method for quantifying wind energy fluctuation based on relative cumulative arc length. Background Art

[0002] Wind energy has significant randomness and volatility, which brings challenges to the security and stable operation of power systems. Existing methods for quantifying and analyzing wind energy volatility include standard deviation, coefficient of variation, and range.

[0003] The standard deviation (SD) only reflects the degree of dispersion of the data relative to the mean, and the standard deviation assumes that the data is symmetrically distributed, which makes it difficult to accurately reflect the asymmetry and extreme value characteristics of wind energy; the coefficient of variation (CV) standardizes the standard deviation as a proportion of the mean, but still has the same shortcomings as the standard deviation in terms of time dynamics and sensitivity to outliers; the range (R) only considers the maximum and minimum values, ignores the fluctuation information in the middle, and cannot provide any information on the frequency or rate of fluctuation.

[0004] It should be pointed out that the existing methods of characterizing fluctuations lack intuitive physical meaning, making it difficult to vividly understand the impact of wind energy fluctuations on the power system. The effectiveness and sensitivity of these indicators are also different on different time scales, making them difficult to compare uniformly. Summary of the invention

[0005] In order to solve the shortcomings of the above technologies, the present invention provides a method for quantifying wind energy volatility based on relative cumulative arc length.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for quantifying wind energy volatility based on relative cumulative arc length, S1. An index for quantifying wind energy volatility is defined as relative cumulative arc length, i.e. RCAL, and the index characterization formula is:

[0007]

[0008] Where S is the cumulative arc length, which represents the cumulative path length of actual wind energy output changing with time; S rated The rated power cumulative arc length represents the cumulative path length that changes with time when the wind turbine operates continuously at rated power;

[0009] The cumulative arc length includes the calculation cases under discrete time series data and the calculation cases under continuous functions;

[0010] S2. Based on the time scale, the relative cumulative arc length is used to quantify the wind energy fluctuation calculation.

[0011] Furthermore, in S1, for discrete time series data, the cumulative arc length S is approximately calculated by accumulating the Euclidean distance between adjacent points, and the formula is as follows:

[0012]

[0013] Among them, Δt i is the time difference between adjacent time points, in hours; T scale is the time scale; ΔP i is the power output difference between adjacent time points; P rated is the rated power.

[0014] Furthermore, at rated power, the power output is constant, then ΔP i =0, so the rated power cumulative arc length S rated The calculation formula is as follows:

[0015]

[0016] Among them, T total is the total time span.

[0017] Furthermore, in S1, for the power input in the form of a continuous function, the calculation formula for the accumulated arc length S is:

[0018]

[0019] Among them, t a and t b are the start and end time of the time interval respectively; is the derivative of power output with time.

[0020] Furthermore, at rated power, the power output is constant, then Cumulative arc length S rated The calculation formula is as follows:

[0021] That is Formula 5.

[0022] Furthermore, in the calculation of S1 under discrete time series data and under continuous function, it is deduced that RCAL is always equal to S.

[0023] Further, S2 comprises the following steps:

[0024] A1. Obtain wind speed data and calculate power output;

[0025] A2. Calculate the relative cumulative arc length;

[0026] A3. Wind energy volatility assessment.

[0027] Furthermore, in A1,

[0028] First, obtain the hourly 100-meter wind speed time series data v in the reanalysis dataset t ;

[0029] Secondly, for each grid cell, based on the wind speed data v t Calculate the hourly wind turbine power output P(t); the power output calculation formula is as follows:

[0030]

[0031] Among them, v t is the wind speed time data at a height of 100 meters; v ci is the cut-in wind speed of the wind turbine; v co is the cut-out wind speed; v r is the rated wind speed; P rated is the rated power of the wind turbine.

[0032] Furthermore, in A2,

[0033] First, according to the wind energy fluctuation assessment requirements, select the required assessment time period [t a ,t b ] and data temporal resolution;

[0034] Secondly, based on the selected time resolution data, the cumulative arc length S of the power output P(t) over time and the relative cumulative arc length S at rated power are calculated. rated ;

[0035] Finally, the relative cumulative arc length RCAL is calculated according to the definition.

[0036] Furthermore, in A3, RCAL=1 indicates that the wind energy output is completely stable without fluctuations; RCAL>1 indicates that the volatility increases, and the larger the RCAL value, the stronger the volatility.

[0037] A method for quantifying wind energy volatility based on relative cumulative arc length, quantifying the volatility by calculating the relative cumulative arc length of wind energy changing over time in a certain period of time. The relative cumulative arc length in this application is similar to indicators such as standard deviation (SD), coefficient of variation (CV) and range (R) in statistics, but it pays more attention to the accurate characterization of data volatility, and is particularly suitable for analyzing the complexity of wind energy resources changing over time. It can capture continuous fluctuation information and quantify the intensity of fluctuations. It not only has important application value in wind energy fluctuation assessment fields such as wind farm planning, energy storage system configuration and wind power forecasting, but can also be used in related fields such as financial fluctuation analysis and climate change research. In addition, the method disclosed in this application has broad application prospects in the wind energy field, including wind energy resource assessment, wind farm site selection, wind farm operation, energy planning and risk management, and can also be used for financial market analysis, meteorological data analysis and monitoring of economic indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of the relative cumulative arc length calculation method.

[0039] Figure 2 2 is the time variation characteristic of wind power output of wind turbines in the wind farm in Example 2 during 2023.

[0040] Figure 3 is the monthly relative cumulative arc length of wind power output of the wind turbines in the wind farm in Example 2 during 2023.

[0041] Figure 4 It is the annual relative cumulative arc length of wind power output of the wind turbines in the wind farm in Example 2 during 2014-2023. DETAILED DESCRIPTION

[0042] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] Example 1

[0044] This embodiment is about a method for quantifying wind energy volatility based on relative cumulative arc length, and its purpose is to be applied to wind energy fluctuation assessment in aspects including wind farm planning and site selection, energy storage system configuration and optimization, wind power prediction and multi-energy coordination. It characterizes the fluctuation condition based on the relative cumulative amount of wind energy changing over time, and can comprehensively, intuitively and sensitively reflect the fluctuation of wind energy over time.

[0045] S1. Define an index to quantify wind energy volatility as Relative Cumulative Arc Length (RCAL), which is represented by the formula:

[0046]

[0047] Where S is the cumulative arc length, which represents the cumulative path length of actual wind energy output changing with time; S rated The rated power cumulative arc length represents the cumulative path length that changes with time when the wind turbine operates continuously at rated power;

[0048] The cumulative arc length includes the calculation cases under discrete time series data and the calculation cases under continuous functions.

[0049] In S1, for discrete time series data, the cumulative arc length S is approximately calculated by accumulating the Euclidean distance between adjacent points, and the formula is as follows:

[0050]

[0051] Among them, Δt i is the time difference between adjacent time points, in hours; T scale is the time scale, for example, T scale =24 hours; ΔP i is the power output difference between adjacent time points; P rated is the rated power;

[0052] It should be noted that Δt i =t i+1 -t i ; ΔP i =P i+1 -P i ; The turbine model adopts the current commercial mainstream wind turbine, with a rated power of P rated =2.5MW;

[0053] It should be noted that the Euclidean distance, that is, the true distance between two points in m-dimensional space, can be calculated based on the Cartesian coordinates of the points by using the Pythagorean theorem.

[0054] At rated power, the power output is constant, then ΔP i =0, so the rated power cumulative arc length S rated The calculation formula is as follows:

[0055]

[0056] Among them, T total =T scale is the total time span.

[0057] In S1, for the power input in the form of a continuous function, the calculation formula for the accumulated arc length S is:

[0058]

[0059] Among them, t a and t b are the start and end time of the time interval respectively; is the derivative of power output with time.

[0060] At rated power, the power output is constant, then Cumulative arc length S rated The calculation formula is as follows:

[0061]

[0062] Based on the above, it can be seen that in the calculation of S1 under discrete time series data and under continuous function, it is deduced that RCAL is always equal to S.

[0063] S2. Based on the time scale, the relative cumulative arc length is used to quantify the wind energy fluctuation calculation, wherein the time scale is, for example, a day or a year, including the following steps:

[0064] A1. Obtain wind speed data and calculate power output;

[0065] First, obtain the hourly 100-meter wind speed time series data v in the reanalysis dataset t , among which, reanalysis datasets such as ERA5;

[0066] Secondly, for each grid cell, based on the wind speed data v t Calculate the hourly wind turbine power output P(t); the power output calculation formula is as follows:

[0067]

[0068] Among them, v t is the wind speed time data at a height of 100 meters (in ms-1); v ci is the cut-in wind speed of the wind turbine (3ms-1); v co is the cut-out wind speed (25ms-1); v r is the rated wind speed (12ms-1); P rated is the rated power of the wind turbine. In this embodiment, P rated =2.5MW.

[0069] A2. Calculate the relative cumulative arc length;

[0070] First, according to the wind energy fluctuation assessment requirements, select the required assessment time period [t a ,t b ] and the temporal resolution of the data, such as hours or days;

[0071] Secondly, based on the selected time resolution data, the cumulative arc length S of the power output P(t) over time and the relative cumulative arc length S at rated power are calculated. rated , where the cumulative arc length S is selected from formula 2 or formula 4 according to the actual situation. The relative cumulative arc length S at rated power is rated Select Formula 3 or Formula 5 according to actual conditions.

[0072] Finally, the relative cumulative arc length RCAL is calculated according to the definition (Formula 1).

[0073] A3. Wind energy volatility assessment: when RCAL=1, it indicates that the wind energy output is completely stable; when RCAL>1, it indicates that the volatility increases, and the larger the RCAL value, the stronger the volatility.

[0074] It should be noted that volatility refers to the degree of change in data.

[0075] In addition, it should be noted that the rated power of a wind turbine refers to the maximum electrical power that the wind turbine can continuously output under rated wind speed conditions, usually in kilowatts (kW) or megawatts (MW).

[0076] Example 2

[0077] This embodiment is applied on the basis of embodiment 1 as follows:

[0078] As shown in Table 1, six wind farms in Northeast my country, namely, Chaoyangbao Wind Farm in Kangping, Liaoning, Andai Wind Farm in Wafangdian, Liaoning, Wudingshan Wind Farm in Fujin, Heilongjiang, Yangmugang Wind Farm in Shuangyashan, Heilongjiang, Huaneng Tongyu Tuanjie Wind Farm and Fuyu Sanjingzi Wind Farm, are used as application examples.

[0079] Table 1 shows the basic information of the wind farm selected in this embodiment and the relative cumulative arc length of wind energy fluctuations in 2023.

[0080]

[0081]

[0082] like Figure 2 As shown in the figure, the time variation series of wind power output of the six wind farms in 2023 is shown. Therefore, based on the wind power output data, the relative cumulative arc length RCAL per hour in 2023 can be calculated to reflect the dynamic change characteristics of wind energy fluctuations in the wind farms throughout the year.

[0083] like Figure 3As shown in the figure, the relative cumulative arc length RCAL shows significant seasonal changes, among which the volatility is the highest in April. For example, the RCALs of Huaneng Tongyu Tuanjie Wind Farm and Liaoning Kangping Chaoyangbao Wind Farm are 2.01 and 1.98 respectively, indicating that the wind speed changes dramatically in spring; the volatility is the lowest from July to August. For example, the RCAL of Heilongjiang Shuangyashan Yangmugang Wind Farm in August is 1.15, reflecting that the wind energy fluctuations in summer are relatively stable; the RCAL values ​​increase in autumn and winter (September-December), but the fluctuations are not as obvious as in spring.

[0084] In addition, there are significant differences in volatility among the six wind farms. Among them, the annual RCAL of Liaoning Kangping Chaoyangbao Wind Farm and Huaneng Tongyu Tuanjie Wind Farm are relatively high, and are 1.575 and 1.577 respectively, which indicates that their wind energy fluctuations are relatively strong; among them, Heilongjiang Shuangyashan Yangmugang Wind Farm and Heilongjiang Fujin Wudingshan Wind Farm have relatively low annual RCALs, and are 1.377 and 1.384 respectively, which indicates that their wind energy volatility is relatively stable; Based on the above content, it can be seen that wind speed volatility is jointly affected by seasonal and regional characteristics;

[0085] Furthermore, the high volatility in April and the low volatility in summer are of great reference significance for the operation and management of wind farms.

[0086] Compared with the traditional solution, this embodiment provides a scientific basis for formulating wind power dispatching strategies and optimizing operation plans.

[0087] Based on this embodiment, the relative cumulative arc length RCAL can also evaluate the wind energy fluctuations in different years; Figure 4 In the figure, blue represents a smaller relative cumulative arc length RCAL, which means that the wind energy fluctuation is also smaller; red represents a larger relative cumulative arc length RCAL, indicating that the wind energy fluctuation is more significant.

[0088] like Figure 4 As shown, the annual wind power fluctuations of each wind farm are relatively stable. Among them, the relative cumulative arc length RCAL of Huaneng Tongyu Tuanjie Wind Farm is relatively large, especially in 2019, when the relative cumulative arc length RCAL is the largest, reflecting that the wind energy resources fluctuated most significantly in that year; among them, the relative cumulative arc length RCAL of Heilongjiang Fujin Wudingshan Wind Farm and Heilongjiang Shuangyashan Yangmugang Wind Farm are generally small, indicating that their fluctuations are relatively stable; among them, the relative cumulative arc length RCAL of Liaoning Wafangdian Andai Wind Farm shows an upward trend from 2021 to 2023; among them, except for the small wind power fluctuations in 2014, the fluctuations in other years of Liaoning Kangping Chaoyangbao Wind Farm are relatively strong.

[0089] Based on the above content, it can be reflected that the wind energy in Liaoning wind farms fluctuates greatly but has abundant resources, while the resources in Heilongjiang are relatively stable with small fluctuations.

[0090] The present application discloses a method for quantifying wind energy volatility based on relative cumulative arc length, which quantifies the volatility by calculating the relative cumulative arc length of wind energy changing over time in a certain period of time. The relative cumulative arc length RCAL in the present application is similar to indicators such as standard deviation (SD), coefficient of variation (CV) and range (R) in statistics, but it pays more attention to the accurate characterization of data volatility, and is particularly suitable for analyzing the complexity of wind energy resources changing over time. It can capture continuous fluctuation information and quantify the intensity of fluctuations. It not only has important application value in wind energy fluctuation assessment fields such as wind farm planning, energy storage system configuration and wind power forecasting, but can also be used in related fields such as financial fluctuation analysis and climate change research. In addition, the method disclosed in the present application has broad application prospects in the field of wind energy, including wind energy resource assessment, wind farm site selection, wind farm operation, energy planning and risk management, and can also be used for financial market analysis, meteorological data analysis and monitoring of economic indicators.

[0091] The above implementation modes are not limitations of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the scope of the technical solution of the present invention also belong to the protection scope of the present invention.

Claims

1. A method for quantifying wind energy volatility based on relative cumulative arc length, characterized in that: S1. Define an index to quantify wind energy volatility as relative cumulative arc length, or RCAL, and the index characterization formula is: That is, formula 1, Where S is the cumulative arc length, which represents the cumulative path length of actual wind energy output changing with time; S rated The rated power cumulative arc length represents the cumulative path length that changes with time when the wind turbine operates continuously at rated power; The cumulative arc length includes calculation situations under discrete time series data and calculation situations under continuous functions; S2. Based on the time scale, the relative cumulative arc length is used to quantify the wind energy fluctuation calculation.

2. The method for quantifying wind energy volatility based on relative cumulative arc length according to claim 1, characterized in that: In S1, for discrete time series data, the cumulative arc length S is approximately calculated by accumulating the Euclidean distances between adjacent points, and the formula is as follows: That is, formula 2, Among them, Δt i is the time difference between adjacent time points, in hours; T scale is the time scale; ΔP i is the power output difference between adjacent time points; P rated is the rated power.

3. The method for quantifying wind energy volatility based on relative cumulative arc length according to claim 2, characterized in that: At rated power, the power output is constant, then ΔP i =0, so the rated power cumulative arc length S rated The calculation formula is as follows: That is, formula 3, Among them, T total is the total time span.

4. The method for quantifying wind energy volatility based on relative cumulative arc length according to claim 3 is characterized in that: In S1, for the power input in the form of a continuous function, the calculation formula for the accumulated arc length S is: That is, formula 4, Among them, t a and t b are the start and end time of the time interval respectively; is the derivative of power output with time.

5. The method for quantifying wind energy volatility based on relative cumulative arc length according to claim 4, characterized in that: At rated power, the power output is constant, then Cumulative arc length S rated The calculation formula is as follows: That is Formula 5.

6. The method for quantifying wind energy fluctuation based on relative cumulative arc length according to claim 5, characterized in that: In the calculation of S1 under discrete time series data and under continuous function, it is deduced that RCAL is always equal to S.

7. The method for quantifying wind energy volatility based on relative cumulative arc length according to claim 1, characterized in that: The S2 comprises the following steps: A1. Obtain wind speed data and calculate power output; A2. Calculate the relative cumulative arc length; A3. Wind energy volatility assessment.

8. The method for quantifying wind energy volatility based on relative cumulative arc length according to claim 7, characterized in that: In the A1, First, obtain the hourly 100-meter wind speed time series data v in the reanalysis dataset t ; Secondly, for each grid cell, based on the wind speed data v t Calculate the hourly wind turbine power output P(t); the power output calculation formula is as follows: That is, formula 6, Among them, v t is the wind speed time data at a height of 100 meters; v ci is the cut-in wind speed of the wind turbine; v co is the cut-out wind speed; v r is the rated wind speed; P rated is the rated power of the wind turbine.

9. The method for quantifying wind energy fluctuation based on relative cumulative arc length according to claim 7, characterized in that: In A2, First, according to the wind energy fluctuation assessment requirements, select the required assessment time period [t a ,t b ] and data temporal resolution; Secondly, based on the selected time resolution data, the cumulative arc length S of the power output P(t) over time and the relative cumulative arc length S at rated power are calculated. rated ; Finally, the relative cumulative arc length RCAL is calculated according to the definition.

10. The method for quantifying wind energy fluctuation based on relative cumulative arc length according to claim 7, characterized in that: In A3, RCAL=1 indicates that the wind energy output is completely stable without fluctuations; RCAL>1 indicates that the volatility increases, and the larger the RCAL value, the stronger the volatility.

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