A method for quantifying wind energy fluctuation based on relative cumulative arc length

Abstract

The present invention discloses a method for quantifying wind energy fluctuation based on relative cumulative arc length. S1. An index for quantifying wind energy fluctuation is defined as relative cumulative arc length, i.e. RCAL. The index characterization formula is: #imgabs0# S2. Based on the time scale, relative cumulative arc length is used to quantify wind energy fluctuation calculation. The present invention 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 assessments. It can also be used for financial market analysis, meteorological data analysis and monitoring of economic indicators.

Description

Technical Field

[0001] The present 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 poses 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 a symmetrical distribution of the data, 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 temporal dynamics and sensitivity to outliers. The range (R) only considers the maximum and minimum values, ignoring 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 at different time scales, making them difficult to compare uniformly. Summary of the Invention

[0005] In order to address the deficiencies of the above technologies, the present invention provides a method for quantifying wind energy fluctuations 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. Define an index for quantifying wind energy volatility as relative cumulative arc length, that is, , the index characterization formula is:

[0007] , that is, formula 1,

[0008] in, is the cumulative arc length, which represents the cumulative path length of actual wind energy output changing with time; is the rated power cumulative arc length, which 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, including the following steps:

[0011] A1. Obtain wind speed data and calculate power output;

[0012] A2. Calculate the relative cumulative arc length;

[0013] A3. Wind energy volatility assessment.

[0014] Furthermore, in S1, for discrete time series data, the cumulative arc length The approximate calculation is performed by accumulating the Euclidean distance between adjacent points. The formula is as follows:

[0015] , that is, Formula 2,

[0016] in, is the time difference between adjacent time points, in hours; is the power output difference between adjacent time points is the power output difference between adjacent time points is the rated power.

[0017] Furthermore, at rated power, the power output is constant, then , so the rated power cumulative arc length The calculation formula is as follows:

[0018] , that is, Formula 3,

[0019] in, is the total time span.

[0020] Furthermore, in S1, for the power input in the form of a continuous function, the accumulated arc length The calculation formula is:

[0021] , that is, formula 4,

[0022] in, and are the start and end times of the time interval respectively; is the derivative of power output with time.

[0023] Furthermore, at rated power, the power output is constant, then , cumulative arc length The calculation formula is as follows:

[0024] , which is Formula 5.

[0025] Furthermore, in the calculation of S1 under discrete time series data and continuous function, it is derived that Always equal to .

[0026] Furthermore, in A1,

[0027] First, obtain the hourly 100-meter wind speed time series data from the reanalysis dataset ;

[0028] Secondly, for each grid cell, based on the wind speed data Calculate hourly wind turbine power output ; The power output calculation formula is as follows:

[0029] , that is, Formula 6,

[0030] in, The wind speed time data at a height of 100 meters; is the cut-in wind speed of the wind turbine; is the cut-out wind speed; is the rated wind speed; is the rated power of the wind turbine.

[0031] Furthermore, in A2,

[0032] First, according to the wind energy fluctuation assessment requirements, select the required assessment time period [ ] and the temporal resolution of the data;

[0033] Secondly, based on the selected time resolution data, the power output is calculated Cumulative arc length over time Relative cumulative arc length at rated power ;

[0034] Finally, the relative cumulative arc length is calculated according to the definition .

[0035] Furthermore, in A3, It means that the wind energy output is completely stable and there are no fluctuations; indicates increased volatility, and The larger the value, the greater the volatility.

[0036] A method for quantifying wind energy volatility based on relative cumulative arc length quantifies the fluctuation by calculating the relative cumulative arc length of wind energy changing over time within 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 focuses more on the accurate characterization of data volatility. It 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

[0037] Figure 1 This is a flow chart of the relative cumulative arc length calculation method.

[0038] Figure 2 This is the time variation characteristic of wind power output of the wind turbines in the wind farm in Example 2 during 2023.

[0039] 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.

[0040] Figure 4 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

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Example 1

[0043] This embodiment is about a method for quantifying wind energy fluctuations based on relative cumulative arc length. 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 forecasting, and multi-energy coordination. It characterizes the fluctuation status 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.

[0044] S1. Define an index to quantify wind energy volatility as Relative Cumulative Arc Length, i.e. , the index characterization formula is:

[0045] , that is, formula 1,

[0046] in, is the cumulative arc length, which represents the cumulative path length of actual wind energy output changing with time; is the rated power cumulative arc length, which represents the cumulative path length that changes with time when the wind turbine operates continuously at rated power;

[0047] The cumulative arc length includes the calculation cases under discrete time series data and the calculation cases under continuous functions.

[0048] In S1, for discrete time series data, the cumulative arc length The approximate calculation is done by accumulating the Euclidean distance between adjacent points. The formula is as follows:

[0049] , that is, Formula 2,

[0050] in, is the time difference between adjacent time points, in hours; For time scale, for example, on a daily scale =24 hours; is the power output difference between adjacent time points; is the rated power;

[0051] It should be noted that ; ; The turbine model adopts the current commercial mainstream wind turbine, with a rated power of =2.5MW;

[0052] 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.

[0053] At rated power, the power output is constant, then , so the rated power cumulative arc length The calculation formula is as follows:

[0054] , that is, Formula 3,

[0055] in, = is the total time span.

[0056] In S1, for the power input in the form of a continuous function, the accumulated arc length The calculation formula is:

[0057] , that is, formula 4,

[0058] in, and are the start and end times of the time interval respectively; is the derivative of power output with time.

[0059] At rated power, the power output is constant, then , cumulative arc length The calculation formula is as follows:

[0060] , which is Formula 5.

[0061] Based on the above, it can be seen that in the calculation of S1 under discrete time series data and continuous function, it can be deduced that Always equal to .

[0062] S2. Calculate wind energy fluctuations based on a relative cumulative arc length based on a time scale, where the time scale is, for example, a day or a year, including the following steps:

[0063] A1. Obtain wind speed data and calculate power output;

[0064] First, obtain the hourly 100-meter wind speed time series data from the reanalysis dataset , among which, reanalysis datasets such as ERA5;

[0065] Secondly, for each grid cell, based on the wind speed data Calculate hourly wind turbine power output ; The power output calculation formula is as follows:

[0066] , that is, Formula 6,

[0067] in, The wind speed time data at a height of 100 meters (unit: ms-1); is the cut-in wind speed of the wind turbine (3 ms-1); is the cut-out wind speed (25 ms-1); is the rated wind speed (12 ms-1); is the rated power of the wind turbine. In this embodiment, =2.5MW.

[0068] A2. Calculate the relative cumulative arc length;

[0069] First, according to the wind energy fluctuation assessment requirements, select the required assessment time period [ ] and the temporal resolution of the data, such as hours or days;

[0070] Secondly, based on the selected time resolution data, the power output is calculated Cumulative arc length over time Relative cumulative arc length at rated power , where the cumulative arc length Select formula 2 or formula 4 according to the actual situation, and the relative cumulative arc length at rated power Select Formula 3 or Formula 5 according to actual conditions.

[0071] Finally, the relative cumulative arc length is calculated according to the definition (Formula 1).

[0072] A3. Wind energy volatility assessment, when , which means that the wind energy output is completely stable; when , which indicates increased volatility, and The larger the value, the greater the volatility.

[0073] It should be noted that volatility refers to the degree of change in data.

[0074] 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).

[0075] Example 2

[0076] This embodiment is applied as follows based on the embodiment 1:

[0077] As shown in Table 1, six wind farms in Northeast my country, namely, Chaoyangbao Wind Farm in Kangping, Liaoning, Antai 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.

[0078] 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.

[0079]

[0080] like Figure 2 As shown in Figure 2, the time variation series of wind power output of the six wind farms in 2023 can be used to calculate the relative cumulative arc length per hour in 2023 based on the wind power output data. , which reflects the dynamic change characteristics of wind energy fluctuations in wind farms throughout the year.

[0081] like Figure 3 As shown, the relative cumulative arc length The wind power generation capacity of Huaneng Tongyu Tuanjie Wind Farm and Liaoning Kangping Chaoyangbao Wind Farm showed significant seasonal changes. The highest volatility was observed in April. The wind speed fluctuations are 2.01 and 1.98 respectively, indicating that the wind speed changes drastically in spring. The lowest fluctuations are seen from July to August. For example, the wind speed fluctuations at Yangmugang Wind Farm in Shuangyashan, Heilongjiang Province in August are The value is 1.15, which indicates that the wind energy fluctuation in summer is relatively stable; in autumn and winter (September to December) The value increased, but the fluctuation was not as obvious as in spring.

[0082] In addition, there are significant differences in the volatility among the six wind farms. Among them, the annual volatility of Liaoning Kangping Chaoyangbao Wind Farm and Huaneng Tongyu Tuanjie Wind Farm is The annual wind power fluctuations of Yangmugang Wind Farm in Shuangyashan, Heilongjiang and Wudingshan Wind Farm in Fujin, Heilongjiang are relatively high, and are 1.575 and 1.577 respectively, which indicates that the wind power fluctuations are relatively strong. are relatively low, at 1.377 and 1.384 respectively, which indicates that its wind energy volatility is relatively stable. Based on the above content, it can be seen that wind speed volatility is affected by both seasonal and regional characteristics.

[0083] 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.

[0084] Compared with traditional solutions, this embodiment provides a scientific basis for formulating wind power dispatching strategies and optimizing operation plans.

[0085] Based on this embodiment, the relative cumulative arc length It is also possible to evaluate the fluctuation of wind energy in different years; Figure 4 In the figure, blue represents the relative cumulative arc length Smaller means smaller wind energy fluctuations; red indicates relative cumulative arc length The larger the value, the more significant the wind energy fluctuation.

[0086] like Figure 4 As shown in the figure, the annual wind power fluctuations of each wind farm are relatively stable. Among them, the relative cumulative arc length of the Huaneng Tongyu Tuanjie Wind Farm is Large, especially in 2019 relative to the cumulative arc length The largest, reflecting that the wind energy resources fluctuated most significantly in that year; among them, the relative cumulative arc length of Heilongjiang Fujin Wudingshan Wind Farm and Heilongjiang Shuangyashan Yangmugang Wind Farm The overall value is small, indicating that its fluctuation is relatively stable; among them, the relative cumulative arc length of the Antai Wind Farm in Wafangdian, Liaoning Province is It shows an increasing trend from 2021 to 2023; among them, the Chaoyangbao Wind Farm in Kangping, Liaoning Province had relatively small fluctuations in wind power except in 2014, while the fluctuations in other years were relatively strong.

[0087] 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 and have little fluctuation.

[0088] This application discloses a method for quantifying wind energy fluctuation based on relative cumulative arc length, which quantifies the fluctuation by calculating the relative cumulative arc length of wind energy changing with time in a certain period of time. It is similar to indicators such as standard deviation (SD), coefficient of variation (CV) and range (R) in statistics, but it focuses more on the accurate characterization of data volatility. It 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. Moreover, 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.

[0089] The above embodiments are not limitations of the present invention, and the present invention is not limited to the above examples. Any changes, modifications, additions or substitutions made by technicians in this technical field within the scope of the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A method for quantifying wind energy fluctuation based on relative cumulative arc length, characterized by: S1. Define an index to quantify wind energy volatility as the relative cumulative arc length, i.e. , the index characterization formula is: , that is, formula 1, in, is the cumulative arc length, which represents the cumulative path length of actual wind energy output changing with time; is the rated power cumulative arc length, which 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, including the following steps: A1. Obtain wind speed data and calculate power output; A2. Calculate the relative cumulative arc length; A3. Wind energy volatility assessment.

2. The method for quantifying wind energy fluctuation based on relative cumulative arc length according to claim 1, characterized in that: In S1, for discrete time series data, the cumulative arc length The approximate calculation is performed by accumulating the Euclidean distance between adjacent points. The formula is as follows: , that is, formula 2, in, is the time difference between adjacent time points, in hours; is the time scale; is the power output difference between adjacent time points; is the rated power.

3. The method for quantifying wind energy fluctuation based on relative cumulative arc length according to claim 2, characterized in that: At rated power, the power output is constant, then , so the rated power cumulative arc length The calculation formula is as follows: , that is, Formula 3, in, is the total time span.

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

5. The method for quantifying wind energy fluctuation 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 The calculation formula is as follows: , which 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 continuous function, it is derived Always equal to .

7. The method for quantifying wind energy fluctuation based on relative cumulative arc length according to claim 1, characterized in that: In the A1, First, obtain the hourly 100-meter wind speed time series data from the reanalysis dataset ; Secondly, for each grid cell, based on the wind speed data Calculate hourly wind turbine power output ; The power output calculation formula is as follows: , that is, Formula 6, in, The wind speed time data at a height of 100 meters; is the cut-in wind speed of the wind turbine; is the cut-out wind speed; is the rated wind speed; is the rated power of the wind turbine.

8. The method for quantifying wind energy fluctuation based on relative cumulative arc length according to claim 1, characterized in that: In said A2, First, according to the wind energy fluctuation assessment requirements, select the required assessment time period [ ] and the temporal resolution of the data; Secondly, based on the selected time resolution data, the power output is calculated Cumulative arc length over time Relative cumulative arc length at rated power ; Finally, the relative cumulative arc length is calculated according to the definition .

9. The method for quantifying wind energy fluctuation based on relative cumulative arc length according to claim 1, characterized in that: In said A3, It means that the wind energy output is completely stable and there are no fluctuations; indicates increased volatility, and The larger the value, the greater the volatility.

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