CN115438310A

CN115438310A - Power determination method and device for new energy station

Info

Publication number: CN115438310A
Application number: CN202211096380.4A
Authority: CN
Inventors: 吴林林; 王冠楠; 徐曼; 王靖然; 刘海涛; 李蕴红; 王潇; 邓晓洋
Original assignee: State Grid Corp of China SGCC; North China Electric Power Research Institute Co Ltd; State Grid Jibei Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; North China Electric Power Research Institute Co Ltd; State Grid Jibei Electric Power Co Ltd
Priority date: 2022-09-08
Filing date: 2022-09-08
Publication date: 2022-12-06

Abstract

The invention provides a power determination method and a power determination device for a new energy station, wherein the method comprises the following steps: calculating an average value of real-time target parameters related to each new energy device in the target new energy station, and taking the average value as an average target parameter of the target new energy station; determining real-time fitting curve parameters corresponding to the average target parameters according to the relationship between the predetermined fitting curve parameters and the average target parameters; determining a real-time fitting curve of the target new energy station according to the real-time fitting curve parameters; determining the number of new energy equipment corresponding to a plurality of target parameter values of the target new energy station according to the real-time fitting curve; determining output power corresponding to each target parameter value according to a predetermined corresponding relation between the output power of the new energy equipment and the target parameter; and determining the equivalent power of the target new energy station according to the number of the new energy devices corresponding to each target parameter value and the output power. The equivalent power calculated by the scheme is very close to the actual power, and the accuracy is high.

Description

Power determination method and device for new energy station

Technical Field

The application relates to the technical field of new energy, in particular to a power determination method and device for a new energy station.

Background

New energy sources, such as wind energy, electric heat energy, solar energy, etc., can be converted into other forms of energy, such as into electrical energy. New energy devices in the new energy station, such as fans, solar panels, etc., usually have different state characteristics, and therefore, the output characteristics of the new energy station cannot be directly replaced by a single new energy device. In addition, the capacity of a single new energy device is usually small, a new energy station is usually composed of hundreds of new energy devices or even more, and with the continuous development of new energy, the number of new energy devices in one station is increased, the power calculation amount for calculating the new energy devices one by one is large, and the real-time power of the station cannot be obtained in real time. Based on the two reasons, the real-time power is usually calculated in a multi-machine aggregation equivalent mode of the new energy field station.

However, the error between the equivalent real-time power obtained by the existing multi-machine aggregation equivalent method and the actual real-time power is large.

Disclosure of Invention

The application aims to provide a power determination method and device for a new energy station, and the method and device are used for solving the problem that the error between the equivalent real-time power obtained by the existing multi-machine aggregation equivalent method and the actual real-time power is large.

In order to solve the technical problem, a first aspect of the present specification provides a method for determining power of a new energy station, including: calculating the average value of real-time target parameters related to each new energy device in the target new energy station, and taking the average value as the average target parameter of the target new energy station; determining real-time fitted curve parameters corresponding to the average target parameters according to the relationship between the predetermined fitted curve parameters and the average target parameters; the real-time fitting parameter curve is a parameter of a fitting curve of the number of the new energy equipment and the real-time target parameter; determining a real-time fitting curve of the target new energy station according to the real-time fitting curve parameters; determining the number of new energy equipment corresponding to a plurality of target parameter values of the target new energy station according to the real-time fitting curve; determining output power corresponding to each target parameter value according to a predetermined corresponding relation between the output power of the new energy equipment and the target parameter; and determining the equivalent power of the target new energy station according to the number of the new energy devices corresponding to each target parameter value and the output power.

In some embodiments, before calculating an average value of real-time target parameters associated with each new energy device in the target new energy station as an average target parameter of the target new energy station, the method further includes: acquiring real-time target parameters associated with each acquisition time of each new energy device of the target new energy field within a first preset time; and sliding a time window with a second preset time length in the first preset time length according to a preset time length, and calculating the average value of real-time target parameters associated with each new energy equipment at each acquisition time in the time window once each sliding is performed, wherein the average value is used as the real-time target parameter of the new energy equipment.

In some embodiments, the relationship of the fitted curve parameter to the average target parameter, the fitted curve is obtained by: dividing sample real-time target parameters related to each new energy device in a target new energy field into predetermined target parameter intervals, and counting the number of the new energy devices divided into the target parameter intervals; for each target parameter interval, the following operations are performed: determining the number of new energy equipment corresponding to each target parameter value in the current target parameter interval; performing first fitting on the corresponding relation between the number of the new energy devices and the target parameter values to obtain a first fitting curve; calculating the average value of each sample real-time target parameter divided into the current target parameter interval as a sample average target parameter; performing second fitting on the corresponding relation between the parameters of the first fitting curve corresponding to each target parameter interval and the average target parameters of the samples to obtain a second fitting curve; and taking the second fitted curve as a relation curve of the fitted parameters and the average target parameters, and taking the first fitted curve as the fitted curve.

In some embodiments, before dividing the sample real-time target parameters associated with each new energy device in the target new energy field into predetermined target parameter intervals and counting the number of new energy devices corresponding to each target parameter interval, the method further includes: obtaining values of a plurality of parameters influencing the output power of the new energy equipment at a plurality of moments and corresponding output power, and taking the values of the plurality of parameters at the same moment and the output power as a group of values to obtain a plurality of groups of values; calculating a correlation parameter between each parameter and the output power according to the plurality of groups of values; and determining a parameter of which the correlation parameter with the output power reaches a preset threshold value according to the value of the correlation parameter corresponding to each parameter, and taking the parameter as a target parameter.

In some embodiments, before performing the first fitting on the number of new energy devices and the current target parameter value to obtain the first fitting curve, the method further includes: and dividing the number of the new energy devices in the current target parameter interval by the maximum value of the number of the new energy devices corresponding to the current target parameter interval so as to normalize the number of the new energy devices.

In some embodiments, after determining, according to the real-time fitted curve, the number of new energy devices corresponding to a plurality of target parameter values of the target new energy station, the method further includes: acquiring a corresponding relation between the maximum number of new energy equipment corresponding to each target parameter interval and a target parameter interval value corresponding to the maximum number when the new energy equipment is divided into a plurality of predetermined target parameter intervals according to the target parameters; the target parameter interval value is a target parameter value representing a target parameter interval; the real-time target parameters of the new energy equipment are used as target parameter interval values, and the maximum number of the new energy equipment is determined; multiplying the number of the new energy equipment by the maximum number to obtain an actual value of the number of the new energy equipment; the actual values refer to integer values.

In some embodiments, the relationship between the average target parameter, the predetermined fitted curve parameter and the average target parameter is a quadratic function relationship; and/or the parameters of the fitted curve are the parameters of a normally distributed probability density function.

A second aspect of the present specification provides a power determination apparatus for a new energy station, including: the calculating unit is used for calculating the average value of real-time target parameters related to each new energy device in the target new energy station, and the average value is used as the average target parameter of the target new energy station; the device comprises a first determining unit, a second determining unit and a judging unit, wherein the first determining unit is used for determining real-time fitting curve parameters corresponding to average target parameters according to the relation between the predetermined fitting curve parameters and the average target parameters; the real-time fitting parameter curve is a parameter of a fitting curve of the number of the new energy devices and the real-time target parameter; the second determining unit is used for determining a real-time fitting curve of the target new energy station according to the real-time fitting curve parameters; the third determining unit is used for determining the number of the new energy equipment corresponding to the plurality of target parameter values of the target new energy station according to the real-time fitting curve; the fourth determining unit is used for determining output power corresponding to each target parameter value according to the corresponding relation between the output power of the new energy equipment and the target parameter which is determined in advance; and the fifth determining unit is used for determining the equivalent power of the target new energy station according to the number of the new energy devices corresponding to each target parameter value and the output power.

In some embodiments, the apparatus further comprises: the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring real-time target parameters related to each acquisition time of each new energy device of a target new energy field within a first preset time length; and the sliding unit is used for sliding the time window with the second preset time length in the first preset time length according to the preset time length, and calculating the average value of the real-time target parameters associated with each new energy equipment at each acquisition time in the time window every time the sliding unit slides once, and using the average value as the real-time target parameter of the new energy equipment.

In some embodiments, before performing the first fitting on the number of new energy devices and the current target parameter value to obtain a first fitted curve, the method further includes: and dividing the number of the new energy devices in the current target parameter interval by the maximum value of the number of the new energy devices corresponding to the current target parameter interval so as to normalize the number of the new energy devices.

In some embodiments, the apparatus further comprises: the second acquisition unit is used for acquiring the corresponding relation between the maximum number of the new energy devices corresponding to each target parameter interval and the target parameter interval value corresponding to the maximum number when the new energy devices are divided into a plurality of predetermined target parameter intervals according to the target parameters; the target parameter interval value is a target parameter value representing a target parameter interval; a sixth determining unit, configured to determine the maximum number of new energy devices by using the real-time target parameter of the new energy device as a target parameter interval value; the seventh determining unit is used for multiplying the number of the new energy devices by the maximum number of the new energy devices to obtain an actual value of the number of the new energy devices; the actual values refer to integer values.

A third aspect of the present specification provides an electronic apparatus comprising: a memory and a processor, the processor and the memory being communicatively connected to each other, the memory having stored therein computer instructions, the processor implementing the steps of the method of any one of the first aspect or the second aspect by executing the computer instructions.

A fourth aspect of the present description provides a computer storage medium storing computer program instructions which, when executed by a processor, implement the steps of the method of any one of the first or second aspects.

The method and the device for determining the power of the new energy station provided by the specification are based on a statistical rule obtained by pre-research, firstly, an average target parameter corresponding to a real-time target parameter is determined, then, a fitting curve parameter corresponding to the average target parameter is determined, then, the number of new energy devices corresponding to each target parameter value is determined according to the fitting curve, the equivalent power of the new energy station is calculated by combining the power of the new energy devices corresponding to each target parameter value, and the obtained equivalent power is very close to the actual power; according to the method, the influence of the average target parameter on the equivalent power is considered, the average target parameter is used as a grouping basis, and a quantitative curve expression of a statistical rule is given, so that the calculation result of the equivalent power is more accurate; according to the scheme, the corresponding equivalent power can be calculated for the real-time target parameters at each acquisition moment, and real-time dynamic equivalent calculation is realized.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 to 23 show schematic diagrams of wind speed distribution data for 23 time periods;

FIG. 24 shows a schematic of a first scatter plot and a first fitted curve;

FIG. 25 shows a schematic of a second scatter plot and a second fitted curve;

FIG. 26 is a schematic diagram showing a third scatter plot and a curve fitted thereto;

fig. 27 is a schematic diagram illustrating a power determination method of a new energy station provided in the present specification;

FIG. 28 shows a schematic diagram of a fitted curve of average power to average wind speed;

FIG. 29 is a graph showing the comparison of the equivalent power and the actual power obtained by the power determination method for a new energy station provided in the present specification;

fig. 30 is a schematic diagram illustrating another new energy station power determination method provided herein;

fig. 31 is a schematic block diagram of a power determination apparatus of a new energy station provided herein;

FIG. 32 illustrates a functional block diagram of an electronic device provided by the present specification.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application shall fall within the scope of protection of the present application.

The core problem of the new energy field station multi-machine aggregation equivalent lies in the effectiveness of a clustering algorithm. The inventor finds that the research on the new energy equipment clustering equivalence in the new energy field station by the existing multi-machine aggregation equivalence method mainly clusters the new energy equipment based on target parameters influencing power, the target parameter distribution condition of the new energy field station is not quantitatively described, and the influence of the average target parameters of the new energy field station on the equivalent power of the field station is not considered, so that the error of the real-time power obtained by the existing multi-machine aggregation equivalence method is large.

For example, for a wind power plant, the existing research mostly utilizes grouping indexes such as wind speed, wind energy utilization rate, wake flow influence factors, fan rotating speed and fan state variables, and adopts a clustering algorithm to establish a multi-machine equivalent scheme.

The inventor researches the wind speed distribution rule of the wind power plant. The study procedure is described in detail below.

S110: the method comprises the steps of obtaining wind speeds (namely real-time wind speeds) collected by all fans in a wind power plant at all wind speed collection moments in a first time period, and calculating an average value of the wind speeds collected by the fans at all wind speed collection moments for each fan to obtain the average wind speed of the fan. Assuming that the wind farm has 50 wind turbines, 50 average wind speed values can be obtained.

It should be noted that, the purpose of averaging a plurality of real-time wind speeds of a wind turbine herein is to actually use an average wind speed calculated by a plurality of real-time wind speeds as a real-time wind speed, so as to avoid the problem that the stability of the research result is affected by the abnormality of individual real-time wind speed values of the wind turbine due to interference factors.

S120: and dividing the average wind speed of each fan into predetermined wind speed intervals, and counting the number of the fans in each wind speed interval.

Here, the wind speed interval may be divided into sections by a predetermined interval length, and the value of the midpoint of the section may be used as the interval wind speed. For example, the interval length may be 1, and then, the two intervals obtained by dividing this interval division manner may be 1, [ ], 0.5m/s,1.5m/s, 2, [ ], 1.5m/s,2.5m/s ], wherein the value outside the "[ ]" represents the interval wind speed, the value on the left side in the "[ ]" represents one endpoint value of the interval of values, and the value on the right side in the "[ ]" represents the other endpoint value of the interval.

S130: and normalizing the number of the fans corresponding to each wind speed interval in the time period to obtain wind speed distribution data corresponding to the first time period.

The specific method of normalization is as follows: and acquiring the maximum value, namely the maximum number of fans, in the number of fans corresponding to each wind speed interval in the time period, and then dividing the number of fans corresponding to each wind speed interval by the maximum number of fans to obtain the number of fans corresponding to each wind speed interval after normalization. That is, for the number of fans corresponding to each wind speed interval, the following calculation is performed:

wherein, N' | V _aver At an average wind speed of V _aver The number of the fans after normalization corresponding to the time-wind speed interval, N | V _aver At an average wind speed of V _aver Actual number of fans, M | V, corresponding to time-wind speed interval _aver At an average wind speed of V _aver The maximum number of the fans corresponding to each wind speed interval.

And repeatedly executing the steps S110, S120 and S130, and processing the wind speed acquired by each wind speed acquisition moment of each fan in the wind power plant in a plurality of time periods such as a first time period, a second time period, a third time period and the like.

S140: and respectively drawing a bar chart for the wind speed distribution data of each time period by taking the interval wind speed as an abscissa and taking the number of the fans as an ordinate.

The statistical graphs corresponding to the time periods are shown in fig. 1 to 23, wherein each statistical graph corresponds to one time period. As can be seen from fig. 1 to 23, the fan number distribution in each time period is approximately in the characteristic of a normal distribution. Based on this finding, the number of fans can be fitted with a gaussian probability density distribution function, i.e., the following step S150 is performed.

S150: and fitting the wind speed distribution data corresponding to each time period by adopting a Gaussian probability density function by taking the interval wind speed as an independent variable and the number of fans as a dependent variable to obtain fitting parameters mu and sigma.

The expression of the gaussian probability density function is:

wherein, N' | V _aver At an average wind speed of V _aver The number of the fans after normalization corresponding to the time-wind speed interval, and the index of e

In, V _act And (3) representing the interval wind speed, and mu and sigma are parameters of a Gaussian probability density function.

The continuous curves in fig. 1 to 23 are fitting curves obtained by fitting using a gaussian probability density function. From these fitted curves, it can be found that the position of the central axis of the fitted curve, the height of the fitted curve, is different for different time periods, while the average wind speed is different for different time periods. Based on this, the relationship between the position of the central axis of the fitted curve (i.e. the parameter μ in the gaussian probability density function), the height of the fitted curve (i.e. the parameter σ in the gaussian probability density function) and the average wind speed can be further studied. That is, the following step S160 is performed.

The interval wind speed in this specification may refer to the midpoint value of the wind speed interval.

S160: drawing a first scatter diagram by taking the interval wind speed as an abscissa and taking a parameter mu of a Gaussian probability density function as an ordinate, and fitting the first scatter diagram to obtain a first fitting curve; and drawing a second scatter diagram by taking the interval wind speed as an abscissa and the parameter sigma of the Gaussian probability density function as an ordinate, and fitting the second scatter diagram to obtain a second fitting curve.

The first scatter diagram is shown in fig. 24, and the second scatter diagram is shown in fig. 25.

Fitting the first scatter diagram and the second scatter diagram by adopting a quadratic function, wherein the expression of the quadratic function is as follows:

wherein, a ₁ 、b ₁ 、c ₁ Is a parameter of the first fitted curve, a ₂ 、b ₂ 、c ₂ Are parameters of the second fitted curve. By the above two fitting operations of step S160, a can be determined ₁ 、b ₁ 、c ₁ 、a ₂ 、b ₂ 、c ₂ I.e. uniquely determining the first fitted curve, and the second fitted curve. For example, a ₁ ＝1.213e-05、b ₁ ＝1.012、c ₁ ＝-0.07344、a ₂ ＝-0.002592、b ₂ ＝0.08663、c ₂ ＝0.434。

It can also be seen from the wind speed distribution data in fig. 1 to 23 in the time periods of 23, that the maximum number of the wind turbines in each time period is uncertain for different average wind speeds, and for this reason, the relationship between the maximum number of the wind turbines and the average wind speed in each time period can be further studied. That is, the following step S170 is performed.

S170: and drawing a third scatter diagram by taking the interval wind speed as a horizontal coordinate and the maximum number of fans as a vertical coordinate, and fitting the third scatter diagram to obtain a relation curve of the maximum number of fans and the interval wind speed.

For example, according to the distribution trend of the scatter diagram which is firstly reduced and then increased, the hook function can be adopted to fit the third scatter diagram. The third scatter plot and its fitted curve are shown in fig. 26. The expression of the fitted curve may be:

wherein, V _aver Representing mean wind speed, M | V _aver Representing the mean wind speed V _aver The maximum number of the fans is measured, and a, b, c and d are fitting parameters. For example, a =1.282, b =818.7, c = -10.07, d = -55.1.

Through the steps S110 to S170, a quantitative expression of the wind speed distribution rule of the wind power plant is obtained.

Based on the research results, the specification provides a power determination method for a new energy station. The new energy station in this specification may be a wind farm, and accordingly, the new energy device may be a wind turbine, and the target parameter may be a wind speed. Of course, the new energy station in this specification may also be other types of stations, and this specification is not listed.

As shown in fig. 27, the method for determining power of a new energy station provided in this specification includes the following steps:

s210: and calculating the average value of real-time target parameters related to each new energy device in the target new energy station, and taking the average value as the average target parameter of the target new energy station.

The target parameter is a parameter whose correlation with the output power of the new energy device reaches a predetermined threshold.

The real-time target parameter in the embodiment shown in fig. 27 is equivalent to the average wind speed of one of the fans described in steps S110 to S170.

For example, a parameter strongly correlated with the output power of the new energy device. The predetermined threshold here may be a correlation of 95% or more. The real-time target parameter is a real-time acquisition value of the target parameter.

In some embodiments, the target parameter may be empirically determined. In some embodiments, the target parameter may be determined by the following steps S01, S02 and S03.

S01: the method comprises the steps of obtaining values of a plurality of parameters influencing the output power of the new energy equipment at a plurality of moments and corresponding output power, and taking the values of the plurality of parameters at the same moment and the output power as a group of values to obtain a plurality of groups of values.

S02: and calculating the correlation parameter of each parameter and the output power according to the multiple groups of values.

S03: and determining a parameter of which the correlation parameter with the output power reaches a preset threshold value according to the value of the correlation parameter corresponding to each parameter, and taking the parameter as a target parameter.

For example, an nonparametric kernel density estimation method is adopted to obtain an edge distribution function of each influence factor, three combined distribution functions (frank parameter, clayton parameter and Gumbel parameter) of wind power output and each relevant factor are established based on copula theory, and Kendall rank correlation coefficients and Spearman correlation coefficients of the three copula functions are calculated and are shown in the following table one.

Correlation analysis of influence factors of meter-wind power output

As can be seen from the table one, among the factors, the correlation between the wind power output and the wind speed is extremely strong, and the wind speed can be regarded as a key influence factor of the wind power output; and the degree of correlation with wind direction, temperature, humidity and air pressure is very weak, and the correlation can be regarded as irrelevant. The wind speed can be taken as the target parameter.

The "real-time target parameter associated with each new energy device" described in this specification refers to a real-time target parameter that affects the output power of the new energy device. For example, the real-time wind speed associated with wind turbine A refers to the real-time wind speed that affects the output power of wind turbine A.

S220: and determining real-time fitted curve parameters corresponding to the average target parameters according to the relationship between the predetermined fitted curve parameters and the average target parameters. The real-time fitting parameter curve is a parameter of a fitting curve of the number of the new energy devices and the real-time target parameter.

The fitting curve parameters herein refer to parameters μ and σ of the gaussian probability density function in steps S110 to S170. That is, step S220 is actually: according to the curve shown in fig. 24, the real-time target parameter is used as the average wind speed to obtain a parameter mu; according to the curve shown in fig. 25, the parameter σ is obtained by using the real-time target parameter as the average wind speed.

S230: and determining a real-time fitting curve of the target new energy station according to the real-time fitting curve parameters.

Step S220 is actually: from the parameters μ, σ, a gaussian probability density function is determined, i.e. the continuous curves in fig. 1 to 23 are determined.

S240: and determining the number of the new energy equipment corresponding to the plurality of target parameter values of the target new energy station according to the real-time fitting curve.

The plurality of target parameter values are the interval wind speeds in steps S110 to S170. That is, the target parameter values are distributed at predetermined time intervals.

As can be seen from fig. 1 to 23, after the curves are determined, the wind speed of each section can be determined, that is, the number of new energy devices corresponding to the target parameter values can be determined.

As can be seen from fig. 1 to 23, the number of fans in the figure has been normalized to the interval of [0,1 ]. Accordingly, after the number of new energy devices is obtained according to the real-time fitted curve in step S240, the inverse step of normalization needs to be performed to convert the number into the actual number of positive integer values. In particular, the following steps may be performed:

s310: acquiring a corresponding relation between the maximum number of new energy equipment corresponding to each target parameter interval and a target parameter interval value corresponding to the maximum number when the new energy equipment is divided into a plurality of predetermined target parameter intervals according to the target parameters; the target parameter interval value is a target parameter value representing a target parameter interval.

The maximum number in step S310 corresponds to the target parameter interval value, that is, the relationship curve obtained in step S170.

S320: and determining the maximum number of the new energy devices by taking the real-time target parameters of the new energy devices as target parameter interval values.

S330: multiplying the number of the new energy equipment by the maximum number to obtain an actual value of the number of the new energy equipment; the actual values refer to integer values.

S250: and determining the output power corresponding to each target parameter value according to the predetermined corresponding relation between the output power of the new energy equipment and the target parameter.

In some embodiments, the relationship between the output power of the new energy device shipped from the factory and the target parameter may be set as the predetermined corresponding relationship in S250.

In some embodiments, based on the embodiments shown in steps S110 to S170, the average power of each fan in each wind speed interval is counted, the average wind speed is taken as an abscissa, the average power is taken as an ordinate, a fourth scatter diagram is drawn, and then the fourth scatter diagram is fitted to obtain an expression of the average power and the average wind speed, that is, to obtain a corresponding relationship between the average power and a target parameter.

For example, based on the technical solutions described in steps S110 to S170, the fitted graph of the average power and the average wind speed obtained by fitting the method is shown in fig. 28, and the fitting formula may be

Wherein the parameter k ₁ ＝2027，k ₂ ＝616.1，k ₃ ＝0.8531。

S260: and calculating the equivalent power of the target new energy field according to the number of the new energy devices corresponding to each target parameter value and the output power.

For example, the equivalent power of the target new energy field can be calculated using the following expression:

wherein P represents the equivalent power of the new energy field of the target, m represents the number of target parameter values, N _i Indicates the number of new energy devices corresponding to the ith target parameter value, P _i And representing the output power of the new energy equipment corresponding to the ith target parameter value.

Fig. 29 shows a comparison graph of the equivalent power and the actual power of the wind farm at each collection time obtained by the above steps S210 to S260, wherein a thin line represents an actual power curve, and a thick line represents an equivalent power curve. As can be seen from fig. 29, the equivalent power is very close to the actual power, which indicates that the accuracy of the power determination method for the new energy station provided in the specification is high.

The method for determining the power of the new energy station provided by the specification is based on a statistical rule obtained by pre-research, firstly determining an average target parameter corresponding to a real-time target parameter, then determining a fitting curve parameter corresponding to the average target parameter, then determining the number of new energy devices corresponding to each target parameter value according to the fitting curve, calculating the equivalent power of the new energy station by combining the power of the new energy devices corresponding to each target parameter value, wherein the obtained equivalent power is very close to the actual power; according to the method, the influence of the average target parameter on the equivalent power is considered, the average target parameter is used as a grouping basis, and a quantitative curve expression of a statistical rule is given, so that the calculation result of the equivalent power is more accurate; according to the scheme, the corresponding equivalent power can be calculated for the real-time target parameters at each acquisition moment, and real-time dynamic equivalent calculation is realized.

As shown in fig. 30, in some embodiments, before S210, the method may further include:

s410: and acquiring real-time target parameters associated with each acquisition time of each new energy device of the target new energy field within a first preset time.

S420: and setting a time window of a second preset time length in the first preset time length.

S430: and calculating the average value of the real-time target parameters associated with each new energy device at each acquisition time in the time window, and taking the average value as the real-time target parameters of the new energy devices.

After step S430, S210 to S260 are performed.

S440: and sliding the time window once in a first preset time length along a preset direction according to a preset step length.

After step S440, S210 to S260 are performed.

S450: and judging whether the sliding can be continued along the preset direction according to the preset step length. If yes, jumping to S430 to continue execution; otherwise, ending.

In some embodiments, the relationship of the fitted curve parameter to the average target parameter, the fitted curve, is obtained by:

s510: dividing the real-time target parameters of the samples associated with each new energy device in the target new energy field into predetermined target parameter intervals, and counting the number of the new energy devices divided into the target parameter intervals.

In some embodiments, in order to avoid the problem that the stability of the research result is affected due to the fact that the individual real-time wind speed value of the wind turbine is abnormal due to interference factors, the average value of the target parameters acquired by each new energy device at each acquisition time within a period of time may be used as the sample real-time target parameter.

S520: for each target parameter interval, the following operations are performed: determining the number of new energy equipment corresponding to each target parameter value in the current target parameter interval; performing first fitting on the corresponding relation between the number of the new energy devices and the target parameter values to obtain a first fitting curve; and calculating the average value of each sample real-time target parameter divided into the current target parameter interval as a sample average target parameter.

S530: and performing second fitting on the corresponding relation between the parameters of the first fitting curve corresponding to each target parameter interval and the average target parameters of the samples to obtain a second fitting curve.

S540: and taking the second fitting curve as a relation curve of the fitting parameters and the average target parameters, and taking the first fitting curve as a fitting curve.

Steps S510 to S540 can be understood with reference to steps S110 to 170, and are not described herein again.

In some embodiments, S520 may fit the actual number of new energy devices. However, when the average wind speeds of all new energy devices are different, the difference between the maximum number of new energy devices is large, so that the height difference of the fitting curve is large, and the rule of the fitting curve is not easy to find, so that the actual number of new energy devices can be normalized first, and then fitting and other operations are performed. The specific method of fitting may be: and dividing the number of each new energy device by the maximum value of the number of the new energy devices corresponding to the current target parameter interval so as to normalize the number of each new energy device.

The present specification provides a power determination device for a new energy station, which can be used to implement the power determination method for a new energy station shown in fig. 27. As shown in fig. 31, the apparatus includes a calculation unit 10, a first determination unit 20, a second determination unit 30, a third determination unit 40, a fourth determination unit 50, and a fifth determination unit 60.

The calculating unit 10 is configured to calculate an average value of real-time target parameters associated with each new energy device in the target new energy station, and use the average value as an average target parameter of the target new energy station.

The first determining unit 20 is configured to determine a real-time fitted curve parameter corresponding to an average target parameter according to a relationship between the predetermined fitted curve parameter and the average target parameter; the real-time fitting parameter curve is a parameter of a fitting curve of the number of the new energy devices and the real-time target parameter.

The second determining unit 30 is configured to determine a real-time fitted curve of the target new energy station according to the real-time fitted curve parameters.

The third determining unit 40 is configured to determine, according to the real-time fitted curve, the number of new energy devices corresponding to the plurality of target parameter values of the target new energy station.

The fourth determining unit 50 is configured to determine output powers corresponding to the target parameter values respectively according to a predetermined correspondence between the output powers of the new energy devices and the target parameters.

The fifth determining unit 60 is configured to determine the equivalent power of the target new energy station according to the number of new energy devices and the output power corresponding to each target parameter value.

In some embodiments, the apparatus further comprises a first acquisition unit and a sliding unit.

The first obtaining unit is used for obtaining real-time target parameters associated with each new energy device of the target new energy field at each collecting time within a first preset time length. The sliding unit is used for sliding a time window with a second preset time length within the first preset time length according to a preset time length, and calculating the average value of real-time target parameters associated with each new energy equipment at each acquisition time within the time window every time the sliding unit slides once, and the average value is used as the real-time target parameters of the new energy equipment.

In some embodiments, before performing the first fitting on the number of new energy devices and the current target parameter value to obtain the first fitting curve, the method further includes: and dividing the number of the new energy equipment in the current target parameter interval by the maximum value of the number of the new energy equipment corresponding to the current target parameter interval so as to normalize the number of the new energy equipment.

In some embodiments, the apparatus further comprises a second obtaining unit, a sixth determining unit, and a seventh determining unit.

The second acquisition unit is used for acquiring the corresponding relation between the maximum number of the new energy devices corresponding to each target parameter interval and the target parameter interval value corresponding to the maximum number when the new energy devices are divided into a plurality of predetermined target parameter intervals according to the target parameters; the target parameter interval value is a target parameter value representing a target parameter interval. And the sixth determining unit is used for determining the maximum number of the new energy devices by taking the real-time target parameters of the new energy devices as target parameter interval values. The seventh determining unit is used for multiplying the number of the new energy devices by the maximum number of the new energy devices to obtain an actual value of the number of the new energy devices; the actual values refer to integer values.

The description and the beneficial effects of the power determining apparatus of the new energy station may refer to the description and the beneficial effects of the method part, and are not described again.

An embodiment of the present invention further provides an electronic device, as shown in fig. 32, the electronic device may include a processor 3201 and a memory 3202, where the processor 3201 and the memory 3202 may be connected by a bus or in another manner, and fig. 32 illustrates an example of connection by a bus.

Processor 3201 may be a Central Processing Unit (CPU). The Processor 3201 may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.

The memory 3202, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the power determination method of the new energy station in the embodiment of the present invention (for example, the calculation unit 10, the first determination unit 31, the second determination unit 30, the third determination unit 40, the fourth determination unit 50, and the fifth determination unit 60 shown in fig. 31). The processor 3201 executes various functional applications and data classification of the processor by running non-transitory software programs, instructions and modules stored in the memory 3202, namely, implements the power determination method of the new energy station in the above method embodiment.

The memory 3202 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 3201, and the like. Further, the memory 3202 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 3202 may optionally include memory remotely located from the processor 3201, which may be connected to the processor 3201 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory 3202 and, when executed by the processor 3201, perform the power determination method of the new energy station of the embodiment shown in fig. 27.

The details of the electronic device can be understood with reference to the description and effects of the embodiment shown in fig. 27, and are not described herein again.

The present description provides a computer storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the method of fig. 27.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments.

The systems, apparatuses, modules or units described in the foregoing embodiments may be implemented by a computer chip or an entity, or by a product with certain functions.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus a necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of software products, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of some parts of the embodiments of the present application.

The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

Although the present application has been described in terms of embodiments, those of ordinary skill in the art will recognize that there are numerous variations and modifications of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and modifications without departing from the spirit of the application.

Claims

1. A power determination method for a new energy station is characterized by comprising the following steps:

calculating the average value of real-time target parameters related to each new energy device in the target new energy station, and taking the average value as the average target parameter of the target new energy station;

determining real-time fitted curve parameters corresponding to the average target parameters according to the relationship between the predetermined fitted curve parameters and the average target parameters; the real-time fitting parameter curve is a parameter of a fitting curve of the number of the new energy equipment and the real-time target parameter;

determining a real-time fitting curve of the target new energy station according to the real-time fitting curve parameters;

determining the number of new energy equipment corresponding to a plurality of target parameter values of the target new energy station according to the real-time fitting curve;

determining output power corresponding to each target parameter value according to a predetermined corresponding relation between the output power of the new energy equipment and the target parameter;

and determining the equivalent power of the target new energy station according to the number of the new energy devices corresponding to each target parameter value and the output power.

2. The method according to claim 1, wherein before calculating an average value of real-time target parameters associated with each new energy device in the target new energy station as an average target parameter of the target new energy station, the method further comprises:

acquiring real-time target parameters associated with each acquisition time of each new energy device of the target new energy field within a first preset time;

and sliding a time window with a second preset time length in the first preset time length according to a preset time length, and calculating the average value of real-time target parameters associated with each new energy equipment at each acquisition time in the time window once each sliding is performed, wherein the average value is used as the real-time target parameter of the new energy equipment.

3. The method of claim 1, wherein the fitted curve parameters are related to an average target parameter, and wherein the fitted curve is obtained by:

dividing sample real-time target parameters related to each new energy device in a target new energy field into predetermined target parameter intervals, and counting the number of the new energy devices divided into the target parameter intervals;

for each target parameter interval, the following operations are performed: determining the number of new energy equipment corresponding to each target parameter value in the current target parameter interval; performing first fitting on the corresponding relation between the number of the new energy devices and the target parameter values to obtain a first fitting curve; calculating the average value of each sample real-time target parameter divided into the current target parameter interval as a sample average target parameter;

performing second fitting on the corresponding relation between the parameters of the first fitting curve corresponding to each target parameter interval and the average target parameters of the samples to obtain a second fitting curve;

and taking the second fitted curve as a relation curve of the fitted parameters and the average target parameters, and taking the first fitted curve as the fitted curve.

4. The method according to claim 3, wherein before dividing the sample real-time target parameters associated with each new energy device in the target new energy field into predetermined target parameter intervals and counting the number of new energy devices corresponding to each target parameter interval, the method further comprises:

obtaining values of a plurality of parameters influencing the output power of the new energy equipment at a plurality of moments and corresponding output power, and taking the values of the plurality of parameters at the same moment and the output power as a group of values to obtain a plurality of groups of values;

calculating a correlation parameter between each parameter and the output power according to the plurality of groups of values;

and determining a parameter of which the correlation parameter with the output power reaches a preset threshold value according to the value of the correlation parameter corresponding to each parameter, and taking the parameter as a target parameter.

5. The method according to claim 3, wherein before performing the first fitting on the number of new energy devices and the current target parameter value to obtain the first fitting curve, the method further comprises:

and dividing the number of the new energy equipment in the current target parameter interval by the maximum value of the number of the new energy equipment corresponding to the current target parameter interval so as to normalize the number of the new energy equipment.

6. The method according to claim 1, wherein after determining the number of new energy devices corresponding to the plurality of target parameter values of the target new energy station according to the real-time fitted curve, the method further comprises:

acquiring a corresponding relation between the maximum number of new energy equipment corresponding to each target parameter interval and a target parameter interval value corresponding to the maximum number when the new energy equipment is divided into a plurality of predetermined target parameter intervals according to the target parameters; the target parameter interval value is a target parameter value representing a target parameter interval;

the real-time target parameters of the new energy equipment are used as target parameter interval values, and the maximum number of the new energy equipment is determined;

multiplying the number of the new energy equipment by the maximum number to obtain an actual value of the number of the new energy equipment; the actual values refer to integer values.

7. The method of claim 1, wherein the relationship between the average target parameter, the predetermined fitted curve parameter and the average target parameter is a quadratic function relationship; and/or the parameters of the fitted curve are the parameters of a normally distributed probability density function.

8. A power determination device for a new energy station, comprising:

the calculating unit is used for calculating the average value of real-time target parameters related to each new energy device in the target new energy station, and the average value is used as the average target parameter of the target new energy station;

the device comprises a first determining unit, a second determining unit and a judging unit, wherein the first determining unit is used for determining real-time fitting curve parameters corresponding to average target parameters according to the relation between the predetermined fitting curve parameters and the average target parameters; the real-time fitting parameter curve is a parameter of a fitting curve of the number of the new energy devices and the real-time target parameter;

the second determining unit is used for determining a real-time fitting curve of the target new energy station according to the real-time fitting curve parameters;

the third determining unit is used for determining the number of new energy equipment corresponding to the target parameter values of the target new energy station according to the real-time fitting curve;

the fourth determining unit is used for determining output power corresponding to each target parameter value according to the corresponding relation between the output power of the new energy equipment and the target parameter which is determined in advance;

and the fifth determining unit is used for determining the equivalent power of the target new energy station according to the number of the new energy devices corresponding to each target parameter value and the output power.

9. An electronic device, comprising:

a memory and a processor, the processor and the memory being communicatively connected to each other, the memory having stored therein computer instructions, the processor implementing the steps of the method of any one of claims 1 to 7 by executing the computer instructions.

10. A computer storage medium, characterized in that the computer 7 storage medium stores computer program instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 7.