A method and device for describing the probability distribution of power duration

Abstract

The present invention relates to a method and device for describing the probability distribution of power duration. The method comprises: determining the power duration of each fluctuation in each type of fluctuating power based on the fluctuating power type of historical output data of a power station after normalization within a preset time period; fitting the probability distribution function of each fluctuation in each type of fluctuating power using a three-parameter Burr distribution according to the power duration of each fluctuation in the fluctuating power; and describing the probability distribution of the power duration of each fluctuation using the probability distribution function of each fluctuation in the fluctuating power of each type. The technical solution provided by the present invention can quantitatively describe the time-domain probability characteristics of power, further understand and master the random characteristics of power, and improve the accuracy of output time series modeling.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation, and in particular to a method and device for describing the probability distribution of power duration. Background Art

[0002] my country's renewable energy power generation technology continues to develop rapidly, and the installed capacity of grid-connected wind power is growing rapidly. Due to the uncertainty of wind resources and the operating characteristics of wind turbines, the output power of wind farms is subject to random fluctuations. When large-scale wind power is connected to the grid, this random fluctuation in output power will have a significant impact on the planning and operation of the power system.

[0003] Currently, there are few results on the probability distribution of wind power duration. The inverse Gaussian distribution is mainly used to describe the probability distribution of the duration of wind power state, which describes the length of time that wind power maintains a certain output level. Therefore, it is necessary to provide a probability distribution description method for power duration. Summary of the Invention

[0004] The present invention provides a method and device for describing the probability distribution of power duration, the purpose of which is to quantitatively describe the time-domain probability characteristics of power, so that the random characteristics of power can be further understood and mastered, and the accuracy of output time series modeling can be improved.

[0005] The purpose of the present invention is achieved by adopting the following technical solutions:

[0006] A method for describing the probability distribution of power duration, wherein the improvement is that the method comprises:

[0007] Determine the power duration of each type of fluctuating power according to the fluctuating power type of the historical output data of the power station after normalization within a preset time period;

[0008] According to the power duration of each fluctuation in each type of fluctuating power, a three-parameter Burr distribution is used to fit the probability distribution function of each fluctuation in each type of fluctuating power;

[0009] The probability distribution of the power duration of each fluctuation is described by the probability distribution function of each fluctuation in the various types of fluctuation power.

[0010] Preferably, the fluctuating power type of the plant's historical output includes:

[0011] When P s When >a, the fluctuation power type of the historical output data of the power station after normalization is large output fluctuation;

[0012] When b<P s When <a, the fluctuation power type of the historical output data of the power station after normalization is medium output fluctuation;

[0013] When c<P s When b<b, the fluctuation power type of the historical output data of the power station after normalization is small output fluctuation;

[0014] When P s When <c, the fluctuation power type of the historical output data of the power station after normalization is low output fluctuation;

[0015] Among them, P s is the historical output data of the power station after normalization, a is the first threshold, b is the second threshold, c is the third threshold, a>b>c.

[0016] Furthermore, the historical output data P of the power station after normalization is determined as follows: s :

[0017]

[0018] Among them, P t is the historical output data of the plant, P install For installed capacity.

[0019] Preferably, determining the power duration of each fluctuation in each type of fluctuating power includes:

[0020] Determine the power duration T of the jth fluctuation in the i-th type of fluctuation power by the following formula: ij :

[0021]

[0022] in, is the end time of the jth fluctuation in the i-th type of fluctuation power, is the starting time of the jth fluctuation in the i-th type of fluctuating power, i∈(1,p), p is the total number of fluctuating power types in the historical output data of the power station after normalization within the preset time period, j∈(1,q), q is the total number of fluctuations in the i-th type of fluctuating power.

[0023] Preferably, the method of fitting the probability distribution function of each fluctuation in each type of fluctuating power using a three-parameter Burr distribution according to the power duration of each fluctuation in each type of fluctuating power comprises:

[0024] The probability distribution function f(T ij ):

[0025]

[0026] Among them, T ij is the duration of the jth fluctuation in the i-th type of fluctuation power, αi is the shape fitting parameter of the i-th type of fluctuation power, k i is the first scale fitting parameter of the i-th type of fluctuation power, β i is the second scale fitting parameter of the i-th type of fluctuation power.

[0027] A device for describing the probability distribution of power duration, wherein the device comprises:

[0028] a determination unit, configured to determine the power duration of each type of fluctuating power according to the fluctuating power type of the normalized historical output data of the power plant within a preset time period;

[0029] a fitting unit, configured to fit the probability distribution function of each type of fluctuation power using a three-parameter Burr distribution according to the power duration of each fluctuation in each type of fluctuation power;

[0030] A description unit is used to describe the probability distribution of the power duration of each fluctuation through the probability distribution function of each fluctuation in the various types of fluctuation power.

[0031] Preferably, the determining unit includes:

[0032] The first partitioning module is used when P s When >a, the fluctuation power type of the historical output data of the power station after normalization is large output fluctuation;

[0033] The second partitioning module is used when b<P s When <a, the fluctuation power type of the historical output data of the power station after normalization is medium output fluctuation;

[0034] The third partitioning module is used when c<P s When b<b, the fluctuation power type of the historical output data of the power station after normalization is small output fluctuation;

[0035] The fourth partition module is used when P s When <c, the fluctuation power type of the historical output data of the power station after normalization is low output fluctuation;

[0036] Among them, P s is the historical output data of the power station after normalization, a is the first threshold, b is the second threshold, c is the third threshold, a>b>c.

[0037] Furthermore, the determining unit further includes:

[0038] The first determination module is used to determine the historical output data P of the plant after normalization according to the following formula: s :

[0039]

[0040] Among them, P t is the historical output data of the plant, P install For installed capacity.

[0041] Preferably, the determining unit includes:

[0042] The second determination module is used to determine the power duration T of the jth fluctuation in the i-th type of fluctuation power according to the following formula ij :

[0043]

[0044] in, is the end time of the jth fluctuation in the i-th type of fluctuation power, is the starting time of the jth fluctuation in the i-th type of fluctuating power, i∈(1,p), p is the total number of fluctuating power types in the historical output data of the power station after normalization within the preset time period, j∈(1,q), q is the total number of fluctuations in the i-th type of fluctuating power.

[0045] Preferably, the fitting unit includes:

[0046] The probability distribution function f(T ij ):

[0047]

[0048] Among them, T ij is the duration of the jth fluctuation in the i-th type of fluctuation power, α i is the shape fitting parameter of the i-th type of fluctuation power, k i is the first scale fitting parameter of the i-th type of fluctuation power, β i is the second scale fitting parameter of the i-th type of fluctuation power.

[0049] Beneficial effects of the present invention:

[0050] The technical solution provided by the present invention is as follows: according to the fluctuating power type of the historical output data of the power station after normalization within a preset time period, the power duration of each fluctuation in each type of fluctuating power is determined; according to the power duration of each fluctuation in each type of fluctuating power, the probability distribution function of each fluctuation in the said each type of fluctuating power is fitted using a three-parameter Burr distribution; the probability distribution of the power duration of each fluctuation is described by the probability distribution function of each fluctuation in the said each type of fluctuating power, and by fitting the probability distribution function of each type of fluctuating power using a three-parameter Burr distribution, the probability distribution of the power duration of each fluctuation in each type of fluctuating power can be described, thereby quantitatively describing the time-domain probability characteristics of power, enabling the random characteristics of power to be further understood and mastered, thereby being better utilized and controlled, and improving the accuracy of output time series modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a method for describing the probability distribution of power duration according to the present invention;

[0052] Figure 2 1 is a diagram showing a probability distribution fitting result of the duration of the kth fluctuation in the large output fluctuation according to the embodiment of the present invention;

[0053] Figure 3 1 is a diagram showing a probability distribution fitting result of the duration of the k-th fluctuation in the output fluctuation in the embodiment provided by the present invention;

[0054] Figure 4 1 is a diagram showing a probability distribution fitting result of the duration of the kth fluctuation in the small output fluctuation according to the embodiment of the present invention;

[0055] Figure 5 1 is a diagram showing a probability distribution fitting result of the duration of the k-th fluctuation in the low output fluctuation according to an embodiment of the present invention;

[0056] Figure 6 It is a structural schematic diagram of a probability distribution description device for power duration of the present invention. DETAILED DESCRIPTION

[0057] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0059] The present invention provides a method for describing the probability distribution of power duration, such as Figure 1 Shown, including:

[0060] 101. Determine the power duration of each type of fluctuating power according to the fluctuating power type of the normalized historical output data of the power plant within a preset time period;

[0061] 102. According to the power duration of each fluctuation in each type of fluctuating power, a probability distribution function of each fluctuation in each type of fluctuating power is fitted using a three-parameter Burr distribution;

[0062] 103. The probability distribution of the power duration of each fluctuation is described by the probability distribution function of each fluctuation in the said each type of fluctuation power.

[0063] Specifically, step 101 includes:

[0064] When P s When >a, the fluctuation power type of the historical output data of the power station after normalization is large output fluctuation;

[0065] When b<P s When <a, the fluctuation power type of the historical output data of the power station after normalization is medium output fluctuation;

[0066] When c<P s When b<b, the fluctuation power type of the historical output data of the power station after normalization is small output fluctuation;

[0067] When P s When <c, the fluctuation power type of the historical output data of the power station after normalization is low output fluctuation;

[0068] Among them, P s is the historical output data of the power station after normalization, a is the first threshold, b is the second threshold, c is the third threshold, a>b>c.

[0069] For example, the length of historical output data is 1 year, the sampling frequency is 15 minutes, the first threshold a is 0.5, the second threshold b is 0.3, and the third threshold c is 0.05.

[0070] After determining the fluctuating power type of the normalized historical output of the power station within the time period, it is necessary to determine the normalized historical output data. Therefore:

[0071] Determine the historical output data P of the plant after normalization s :

[0072]

[0073] Among them, Pt is the historical output data of the plant, P install For installed capacity.

[0074] Determine the power duration of each fluctuation in each type of power fluctuation, including:

[0075] Determine the power duration T of the jth fluctuation in the i-th type of fluctuation power by the following formula: ij :

[0076]

[0077] in, is the end time of the jth fluctuation in the i-th type of fluctuation power, is the starting time of the jth fluctuation in the i-th type of fluctuating power, i∈(1,p), p is the total number of fluctuating power types in the historical output data of the power station after normalization within the preset time period, j∈(1,q), q is the total number of fluctuations in the i-th type of fluctuating power.

[0078] The step 102 includes:

[0079] The probability distribution function f(T ij ):

[0080]

[0081] Among them, T ij is the duration of the jth fluctuation in the i-th type of fluctuation power, α i is the shape fitting parameter of the i-th type of fluctuation power, k i is the first scale fitting parameter of the i-th type of fluctuation power, β i is the second scale fitting parameter of the i-th type of fluctuation power.

[0082] For example, Figure 2 As shown in the figure, it is the probability distribution fitting result of the duration of the kth fluctuation in the large output fluctuation. Figure 3 This is the probability distribution fitting result of the duration of the kth fluctuation in the medium output fluctuation. Figure 4 This is the probability distribution fitting result of the duration of the kth fluctuation in small output fluctuation. Figure 5 This is the probability distribution fitting result of the duration of the kth fluctuation in low output fluctuation.

[0083] The validity of the probability distribution function of each type of fluctuation power obtained by the above method of the present invention can be verified by the following steps, specifically including:

[0084] Determine the measured fitting data according to the following formula

[0085]

[0086] Determine the residual sum of squares W as follows: SSE :

[0087]

[0088] Determine the root mean square W as follows RMSE :

[0089]

[0090] The coefficient W is determined as follows: R-square :

[0091]

[0092] Wherein, f() is the probability distribution function of each type of fluctuating power, The duration of the kth fluctuation in the i-th type of fluctuation power in the measured data is divided into n equal parts, where the middle moment of the mth part, m∈(1,n), y ikm The duration of the kth fluctuation in the i-th type of fluctuation power in the measured data is divided into n equal parts, where the probability density corresponding to the m-th part is, is the average of the probability density corresponding to the duration of the kth fluctuation in the i-th type of fluctuation power in the measured data;

[0093] When W SSE <0.5, W RMSE <0.1, W R-square When ≥0.98, the probability distribution functions of the various types of fluctuating powers are valid.

[0094] The present invention provides a method for describing the probability distribution of power duration, which can be applied to wind power stations and solar power stations to describe the probability distribution of power duration of wind power stations and solar power stations.

[0095] Based on the same inventive concept, the present invention also provides a device for describing the probability distribution of power duration, such as Figure 6 As shown, the device includes:

[0096] a determination unit, configured to determine the power duration of each type of fluctuating power according to the fluctuating power type of the normalized historical output data of the power plant within a preset time period;

[0097] a fitting unit, configured to fit the probability distribution function of each type of fluctuation power using a three-parameter Burr distribution according to the power duration of each fluctuation in each type of fluctuation power;

[0098] A description unit is used to describe the probability distribution of the power duration of each fluctuation through the probability distribution function of each fluctuation in the various types of fluctuation power.

[0099] Preferably, the determining unit includes:

[0100] The first partitioning module is used when P s When >a, the fluctuation power type of the historical output data of the power station after normalization is large output fluctuation;

[0101] The second partitioning module is used when b<P s When <a, the fluctuation power type of the historical output data of the power station after normalization is medium output fluctuation;

[0102] The third partitioning module is used when c<P s When b<b, the fluctuation power type of the historical output data of the power station after normalization is small output fluctuation;

[0103] The fourth partition module is used when P s When <c, the fluctuation power type of the historical output data of the power station after normalization is low output fluctuation;

[0104] Among them, P s is the historical output data of the power station after normalization, a is the first threshold, b is the second threshold, c is the third threshold, a>b>c.

[0105] Furthermore, the determining unit further includes:

[0106] The first determination module is used to determine the historical output data P of the plant after normalization according to the following formula: s :

[0107]

[0108] Among them, P t is the historical output data of the plant, P install For installed capacity.

[0109] Preferably, the determining unit includes:

[0110] The second determination module is used to determine the power duration T of the jth fluctuation in the i-th type of fluctuation power according to the following formula ij :

[0111]

[0112] in, is the end time of the jth fluctuation in the i-th type of fluctuation power, is the starting time of the jth fluctuation in the i-th type of fluctuating power, i∈(1,p), p is the total number of fluctuating power types in the historical output data of the power station after normalization within the preset time period, j∈(1,q), q is the total number of fluctuations in the i-th type of fluctuating power.

[0113] Preferably, the fitting unit includes:

[0114] The probability distribution function f(T ij ):

[0115]

[0116] Among them, T ij is the duration of the jth fluctuation in the i-th type of fluctuation power, α i is the shape fitting parameter of the i-th type of fluctuation power, k i is the first scale fitting parameter of the i-th type of fluctuation power, β i is the second scale fitting parameter of the i-th type of fluctuation power.

[0117] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for describing the probability distribution of power duration, characterized in that: The method comprises: Determine the power duration of each type of fluctuating power according to the fluctuating power type of the historical output data of the power station after normalization within a preset time period; According to the power duration of each fluctuation in each type of fluctuating power, a three-parameter Burr distribution is used to fit the probability distribution function of each fluctuation in each type of fluctuating power; Describing the probability distribution of the power duration of each fluctuation by a probability distribution function of each fluctuation in each type of fluctuation power; The types of fluctuating power of the plant's historical output include: When P s When >a, the fluctuation power type of the historical output data of the power station after normalization is large output fluctuation; When b < P s <When a, the fluctuation power type of the historical output data of the substation after normalization is medium output fluctuation; When c < P s <When b, the fluctuation power type of the historical output data of the substation after normalization is small output fluctuation; When P s <c, the fluctuation power type of the historical output data of the substation after normalization is low output fluctuation; Among them, P s is the historical output data of the power station after normalization, a is the first threshold, b is the second threshold, c is the third threshold, a>b>c; Among them, when the length of historical output data is 1 year, the sampling frequency is 15 minutes, the first threshold a is 0.5, the second threshold b is 0.3, and the third threshold c is 0.05; The probability distribution function of each fluctuation in each type of fluctuation power is used to describe the probability distribution of the power duration of each fluctuation, including: Determine the measured fitting data according to the following formula Determine the residual sum of squares W as follows: SSE : Determine the root mean square W as follows RMSE : The coefficient W is determined as follows: R-square : Wherein, f() is the probability distribution function of each type of fluctuating power, C ikm The duration of the kth fluctuation in the i-th type of fluctuation power in the measured data is divided into n equal parts, where the middle moment of the mth part, m∈(1,n), y ikm The duration of the kth fluctuation in the i-th type of fluctuation power in the measured data is divided into n equal parts, where the probability density corresponding to the mth part is y ik is the average of the probability density corresponding to the duration of the kth fluctuation in the i-th type of fluctuation power in the measured data; When W SSE <0.5, W RMSE <0.1,W R-square When ≥0.98, the probability distribution function of each type of fluctuating power is valid.

2. The method according to claim 1, wherein Determine the historical output data P of the plant after normalization as follows: s : Among them, P t is the historical output data of the plant, P install For installed capacity.

3. The method according to claim 1, wherein Determining the power duration of each fluctuation in each type of fluctuating power includes: Determine the power duration T of the jth fluctuation in the i-th type of fluctuation power by the following formula: ij : in, is the end time of the jth fluctuation in the i-th type of fluctuation power, is the starting time of the jth fluctuation in the i-th type of fluctuating power, i∈(1,p), p is the total number of fluctuating power types in the historical output data of the power station after normalization within the preset time period, j∈(1,q), q is the total number of fluctuations in the i-th type of fluctuating power.

4. The method according to claim 1, wherein The method of fitting the probability distribution function of each fluctuation in each type of fluctuating power using a three-parameter Burr distribution according to the power duration of each fluctuation in each type of fluctuating power includes: The probability distribution function f(T ij ): Among them, T ij is the duration of the jth fluctuation in the i-th type of fluctuation power, α i is the shape fitting parameter of the i-th type of fluctuation power, k i is the first scale fitting parameter of the i-th type of fluctuation power, β i is the second scale fitting parameter of the i-th type of fluctuation power.

5. A device for describing the probability distribution of power duration, characterized in that: The device comprises: a determination unit, configured to determine the power duration of each type of fluctuating power according to the fluctuating power type of the normalized historical output data of the power plant within a preset time period; a fitting unit, configured to fit the probability distribution function of each type of fluctuation power using a three-parameter Burr distribution according to the power duration of each fluctuation in each type of fluctuation power; a description unit, configured to describe the probability distribution of the power duration of each fluctuation by using a probability distribution function of each fluctuation in each type of fluctuation power; The determining unit includes: The first partitioning module is used when P s When >a, the fluctuation power type of the historical output data of the power station after normalization is large output fluctuation; A second partitioning module, which is used to normalize the fluctuating power type of the historical output data of the substation to medium output fluctuation when b < P s <when a; A third partitioning module, configured to, when c < P s <and b, the fluctuation power type of the historical output data of the substation after normalization is small output fluctuation; The fourth partitioning module is used to, when P s <is less than c, the fluctuation power type of the historical output data of the substation after normalization is low output fluctuation; Among them, P s is the historical output data of the power station after normalization, a is the first threshold, b is the second threshold, c is the third threshold, a>b>c; Among them, when the length of historical output data is 1 year, the sampling frequency is 15 minutes, the first threshold a is 0.5, the second threshold b is 0.3, and the third threshold c is 0.05; The description unit specifically includes: Determine the measured fitting data according to the following formula Determine the residual sum of squares W as follows: SSE : Determine the root mean square W as follows RMSE : The coefficient W is determined as follows: R-square : Wherein, f() is the probability distribution function of each type of fluctuating power, The duration of the kth fluctuation in the i-th type of fluctuation power in the measured data is divided into n equal parts, where the middle moment of the mth part, m∈(1,n), y ikm The duration of the kth fluctuation in the i-th type of fluctuation power in the measured data is divided into n equal parts, where the probability density corresponding to the mth part is y ik is the average of the probability density corresponding to the duration of the kth fluctuation in the i-th type of fluctuation power in the measured data; When W SSE <0.5, W RMSE <0.1,W R-square When ≥0.98, the probability distribution function of each type of fluctuating power is valid.

6. The device according to claim 5, characterized in that The determining unit further includes: The determination module is used to determine the historical output data P of the plant after normalization according to the following formula: s : Among them, P t is the historical output data of the plant, P install For installed capacity.

7. The device according to claim 5, characterized in that The determining unit includes: The second determination module is used to determine the power duration T of the jth fluctuation in the i-th type of fluctuation power according to the following formula ij : in, is the end time of the jth fluctuation in the i-th type of fluctuation power, is the starting time of the jth fluctuation in the i-th type of fluctuating power, i∈(1,p), p is the total number of fluctuating power types in the historical output data of the power station after normalization within the preset time period, j∈(1,q), q is the total number of fluctuations in the i-th type of fluctuating power.

8. The device according to claim 5, wherein The fitting unit comprises: The probability distribution function f(T ij ): Among them, T ij is the duration of the jth fluctuation in the i-th type of fluctuation power, α i is the shape fitting parameter of the i-th type of fluctuation power, k i is the first scale fitting parameter of the i-th type of fluctuation power, β i is the second scale fitting parameter of the i-th type of fluctuation power.

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