CN111030106A - Wind power fluctuation quality assessment method based on waveform similarity theory - Google Patents

Wind power fluctuation quality assessment method based on waveform similarity theory Download PDF

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CN111030106A
CN111030106A CN201911363377.2A CN201911363377A CN111030106A CN 111030106 A CN111030106 A CN 111030106A CN 201911363377 A CN201911363377 A CN 201911363377A CN 111030106 A CN111030106 A CN 111030106A
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wind power
fluctuation
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CN111030106B (en
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江岳文
陈晓榕
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Fuzhou University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

Abstract

The invention relates to a wind power fluctuation quality evaluation method based on a waveform similarity theory, wherein a wind power fluctuation quality factor reflects whether each wind power plant can effectively track load fluctuation or not, can reflect the value of wind power, and can be used as a reference for apportioning the wind power admission cost of a system. According to the method, a waveform similarity theory is introduced, firstly, equal-electric-quantity load-following equivalent transformation is carried out on the output of each wind power plant, and an equivalent transformation curve is used as a reference for calculating a wind power fluctuation quality factor; secondly, measuring the fluctuation integrity difference of the actual output curve and the equivalent output curve of the wind power plant by using a waveform similarity method, considering the influence of the installed capacity of the wind power plant, and determining the wind power fluctuation quality factor of each wind power plant. The invention can reasonably evaluate the output fluctuation quality of the wind power plant.

Description

Wind power fluctuation quality assessment method based on waveform similarity theory
Technical Field
The invention relates to the technical field of power systems, in particular to a wind power fluctuation quality assessment method based on a waveform similarity theory.
Background
The wind power generation does not consume fuel in the production process, and is beneficial to realizing energy conservation and emission reduction. However, the wind power output has obvious volatility and uncertainty, and the influence of wind power integration on the operation of a power system is more and more obvious along with the continuous expansion of the scale of a wind power installation machine. After wind power is connected to the grid in a large scale, due to the existence of wind power fluctuation, other adjustable conventional units in the system need to ensure the balance of power supply and demand in the system through frequent starting and stopping and output adjustment. The system will need to pay extra cost for providing support for the wind power integration. At present, a great deal of research on wind power admission cost is carried out, but few researches on wind power fluctuation quality evaluation are carried out. In fact, the fluctuating quality of wind power directly affects the acceptance cost of wind power. Through the evaluation of the wind power fluctuation quality, the value of the wind power can be effectively evaluated, a basis can be provided for wind power allocation auxiliary service cost, and the operation of a power grid is facilitated.
In order to compare the wind power fluctuation quality of each wind power plant, a certain reference object is required to be used as a standard. According to the operation characteristics of the power system, all power sources participating in grid-connected power generation need to track load fluctuation, so that real-time power balance is kept. In view of this, the invention provides a reference object which takes the system load curve as wind power fluctuation quality measurement, and if the fluctuation of the wind power and the fluctuation of the load curve have the same tendency, the wind power has better fluctuation quality. Otherwise, the fluctuation quality is poor, and more wind power admission cost needs to be borne.
Disclosure of Invention
In view of the above, the invention aims to provide a wind power fluctuation quality assessment method based on a waveform similarity theory, and the wind power fluctuation quality can be reasonably assessed by applying an equal-electric-quantity following load conversion method to obtain an equal-electric-quantity following load equivalent output curve of each wind power plant as a reference for wind power fluctuation quality measurement.
The invention is realized by adopting the following scheme: a wind power fluctuation quality assessment method based on a waveform similarity theory comprises the following steps: the method comprises the following steps:
step S1: acquiring an output curve and a fluctuation curve of load demand of each wind power plant in a power system; taking a fluctuation curve of the load demand as a tracking target, and respectively carrying out equal-electric quantity load following equivalence on output curves of all wind power plants;
Figure BDA0002337414660000021
Figure BDA0002337414660000022
t＝2,3,…,T
wherein, L (t) is a power system load demand fluctuation curve;
Figure BDA0002337414660000023
actual output curve of the wind power plant; p'w(T) is the equivalent output curve of the isoelectric load, and T is 24 hours;
step S2: based on the waveform similarity theory, measuring the fluctuation integral difference measure S 'between the actual output of the wind power plant and the equivalent curve of the equivalent electric quantity and the equivalent load of the wind power plant'j；
Step S3: measure S 'by fluctuation overall difference'jOn the basis, considering the influence of the installed capacity of the wind power plant, and obtaining the wind power fluctuation quality evaluation factor delta of the wind power plant j through weighted combinationjFor evaluating the quality of the wind farm output fluctuation, deltajThe value range of (a) fluctuates between 0 and 1, and if the wind power field j wind power fluctuation quality evaluation factor deltajThe smaller the wind power of the wind farm j is, the better the fluctuation quality is, when delta isjWhen the fluctuation characteristic is equal to 0, the fluctuation characteristic is consistent with the fluctuation curve of the load demand, namely the load fluctuation is completely tracked, and the fluctuation quality is the best; on the contrary, the less the fluctuation quality is, when deltajThe fluctuation characteristic is exactly opposite to the load demand fluctuation curve, with the worst fluctuation quality.
Further, the step S2 specifically includes the following steps:
step S21: calculating the difference measure of the fluctuation change rate of the wind power output curve and the load demand curve of the power system:
before calculating the difference measure of the fluctuation change rate, the wind power output curve needs to be processed, and the output sequence P of the original wind power plant j is assumedj,1，Pj,2，…，Pj,TAbbreviation { Pj,TTaking the difference between the next item and the previous item to form a new sequence aj,1，aj,2，…，aj,T-1Is denoted as { aj,T-1}:
aj,t＝Pj,t+1-Pj,tt＝1,2,…,T-1
Similarly, for equivalent scene output sequence P 'of wind power plant j'j,1,P'j,2,..,P'j,TI.e., { P'j,TIs similarly processed and is counted as { a'j,T-1}：
a'j,t＝P'j,t+1-P'j,tt＝1,2,…,T-1
Calculating the difference measure S of the fluctuation change rate according to the cosine algorithm of the included anglevd,j：
Figure BDA0002337414660000031
Step S22: calculating the difference measure of the fluctuation amplitude of the wind power output curve and the load demand curve;
the actual output curve and the equivalent output curve sequence of the wind power plant j are still marked as { Pj,TAnd { P'j,TAnd then, there are:
Figure BDA0002337414660000032
Figure BDA0002337414660000033
wherein, Pj,tThe actual output value of the wind power plant j at the moment t is obtained; p'j,tThe output value of the equivalent output curve of the wind power plant j at the moment t is obtained; dj,tThe ratio of the absolute value of the difference between the amplitude values of the actual output curve and the equivalent output curve and the amplitude value of the equivalent output curve of the wind power plant j at the moment t; sad,jThen it is the corresponding sequence { D } for wind farm jj,tThe mean value of i.e. the measure of the fluctuation amplitude difference;
step S23: normalization processing is adopted for the difference measurement of the fluctuation variation rate and the difference measurement of the fluctuation amplitude;
Figure BDA0002337414660000041
Figure BDA0002337414660000042
wherein N iswIs the number of grid-connected wind power plants in the system, max ({ S })ad,j,j＝1,2,…,Nw}) represents a set Sad,1，Sad,2，…，Sad,NwThe maximum value in (c);
step S24: to S'Vd,jAnd S'ad,jPerforming weighted average to obtain difference measurement values of the wind power output curves under two scenes;
S′j＝μvdS′vd,j+μadS′ad,j
wherein, muvdAnd muadRespectively representing output curve fluctuation variation rate difference measure S 'under two scenes of wind power plant j'vd,jAnd measure of fluctuation amplitude difference S'ad,jWind power output curve integral difference degree S'jSpecific gravity taken up in calculation, and muvdAnd muadThe sum is 1.
Further, the step S3 specifically includes the following steps:
step S31: introduction of capacity coefficient KcThe influence of installed capacity on wind power fluctuation quality factors is characterized:
Figure BDA0002337414660000043
wherein, Kc,jCalculating a capacity parameter of a j wind power fluctuation quality factor of a wind power plant; cinst,jIs the installed capacity of wind farm j;
step S32: calculating a wind power fluctuation quality factor of the wind power plant j:
Figure BDA0002337414660000051
comprises the following steps:
Figure BDA0002337414660000052
compared with the prior art, the invention has the following beneficial effects:
according to the wind power fluctuation quality evaluation method, the wind power fluctuation quality evaluation method can effectively reflect the fluctuation characteristics of each wind power plant; and the wind power value of each wind power plant can be measured, a reference is provided for site selection and volume fixing of the wind power plants, and the method has a strong application value.
Drawings
FIG. 1 is a diagram of a multi-wind farm output scenario according to an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiment provides a wind power fluctuation quality evaluation method based on a waveform similarity theory, which comprises the following steps of:
step S1: acquiring an output curve and a fluctuation curve of load demand of each wind power plant in a power system; taking a fluctuation curve of the load demand as a tracking target, and respectively carrying out equal-electric quantity load following equivalence on output curves of all wind power plants;
Figure BDA0002337414660000061
Figure BDA0002337414660000062
t＝2,3,…,T
wherein, L (t) is a power system load demand fluctuation curve;
Figure BDA0002337414660000063
actual output curve of the wind power plant; p'w(T) is the equivalent output curve of the isoelectric load, and T is 24 hours;
step S2: based on the waveform similarity theory, measuring the fluctuation integral difference measure S 'between the actual output of the wind power plant and the equivalent curve of the equivalent electric quantity and the equivalent load of the wind power plant'j；
Step S3: measure S 'by fluctuation overall difference'jOn the basis, considering the influence of the installed capacity of the wind power plant, and obtaining the wind power fluctuation quality evaluation factor delta of the wind power plant j through weighted combinationjFor evaluating the quality of the wind farm output fluctuation, deltajThe value range of (a) fluctuates between 0 and 1, and if the wind power field j wind power fluctuation quality evaluation factor deltajThe smaller the wind power of the wind farm j is, the better the fluctuation quality is, when delta isjWhen the fluctuation characteristic is equal to 0, the fluctuation characteristic is consistent with the fluctuation curve of the load demand, namely the load fluctuation is completely tracked, and the fluctuation quality is the best; on the contrary, the less the fluctuation quality is, when deltajThe fluctuation characteristic is exactly opposite to the load demand fluctuation curve, with the worst fluctuation quality.
Further, the step S2 specifically includes the following steps:
step S21: calculating the difference measure of the fluctuation change rate of the wind power output curve and the load demand curve of the power system:
before calculating the difference measure of the fluctuation change rate, the wind power output curve needs to be processed, and the output sequence P of the original wind power plant j is assumedj,1，Pj,2，…，Pj,TAbbreviation { Pj,TTaking the difference between the next item and the previous item to form a new sequence aj,1，aj,2，…，aj,T-1Is denoted as { aj,T-1}:
aj,t＝Pj,t+1-Pj,tt＝1,2,…,T-1
Similarly, for equivalent scene output sequence P 'of wind power plant j'j,1,P'j,2,..,P'j,TI.e. { P'j,TIs similarly processed and is counted as { a'j,T-1}：
a'j,t＝P'j,t+1-P'j,tt＝1,2,…,T-1
Calculating the difference measure S of the fluctuation change rate according to the cosine algorithm of the included anglevd,j：
Figure BDA0002337414660000071
Step S22: calculating the difference measure of the fluctuation amplitude of the wind power output curve and the load demand curve;
the actual output curve and the equivalent output curve sequence of the wind power plant j are still marked as { Pj,TAnd { P'j,TAnd then, there are:
Figure BDA0002337414660000072
Figure BDA0002337414660000073
wherein, Pj,tThe actual output value of the wind power plant j at the moment t is obtained; p'j,tThe output value of the equivalent output curve of the wind power plant j at the moment t is obtained; dj,tThe ratio of the absolute value of the difference between the amplitude values of the actual output curve and the equivalent output curve and the amplitude value of the equivalent output curve of the wind power plant j at the moment t; sad,jThen it is the corresponding sequence { D } for wind farm jj,tThe mean value of i.e. the measure of the fluctuation amplitude difference;
step S23: normalization processing is adopted for the difference measurement of the fluctuation variation rate and the difference measurement of the fluctuation amplitude;
Figure BDA0002337414660000081
Figure BDA0002337414660000082
wherein N iswIs the number of grid-connected wind power plants in the system, max ({ S })ad,j,j＝1,2,…,Nw}) represents a set Sad,1，Sad,2，…，Sad,NwThe maximum value in (c);
step S24: to S'Vd,jAnd S'ad,jPerforming weighted average to obtain difference measurement values of the wind power output curves under two scenes;
S′j＝μvdS′vd,j+μadS′ad,j
wherein, muvdAnd muadRespectively representing output curve fluctuation variation rate difference measure S 'under two scenes of wind power plant j'Vd,jAnd measure of fluctuation amplitude difference S'ad,jWind power output curve integral difference degree S'jSpecific gravity taken up in calculation, and muvdAnd muadThe sum is 1.
Further, the step S3 specifically includes the following steps:
step S31: introduction of capacity coefficient KcThe influence of installed capacity on wind power fluctuation quality factors is characterized:
Figure BDA0002337414660000083
wherein, Kc,jCalculating a capacity parameter of a j wind power fluctuation quality factor of a wind power plant; cinst,jIs the installed capacity of wind farm j;
step S32: calculating a wind power fluctuation quality factor of the wind power plant j:
Figure BDA0002337414660000091
comprises the following steps:
Figure BDA0002337414660000092
preferably, the equivalent output curve of the equivalent electric quantity in the wind power plant is obtained by applying an equivalent electric quantity in-line load conversion method in the embodiment and is used as a reference for wind power fluctuation quality measurement, and the output fluctuation quality of the wind power plant can be reasonably evaluated. Based on a waveform similarity method, the fluctuation characteristic difference between the output curve of each wind power plant and the equivalent output curve of the equal electric quantity along with the load is calculated, and the output fluctuation quality factor of each wind power plant is determined by considering the influence of the installed capacity of the wind power plant on the fluctuation quality factor. The wind power fluctuation quality factor reflects whether each wind power plant can effectively track the load fluctuation or not, can reflect the value of wind power, and can be used as a reference for allocating the wind power admission cost of the system.
Preferably, an embodiment of the present invention is as follows:
assume the output scenario of a multi-wind farm system as shown in FIG. 1 below. The solid line in the figure represents the fluctuation curve of the load demand in the system; the remaining dashed lines with symbols diamond, circle and triangle represent the wind farm output curves for wind farms 1, 2 and 3, respectively.
As can be seen from the curve fluctuation situation in fig. 1, the output fluctuation curve of the wind farm 1 and the system load fluctuation curve show an obvious trend of reverse fluctuation, and have strong reverse peak shaving performance; the output fluctuation curve of the wind power plant 2 is in good positive fit with the system load on fluctuation, and the overall fluctuation trend is approximately the same as the system load fluctuation trend; the fluctuation characteristic of the output fluctuation curve of the wind power plant 3 is between the wind power plant 1 and the wind power plant 2.
Wind power plant output fluctuation quality evaluation method based on fluctuation similarity theory, and mu is assumedvdAnd muadValues of 0.8 and 0.2 are respectively taken, and results of calculating the simulated wind power fluctuation quality factors of the wind power plants are shown in table 1.
TABLE 1 evaluation factor of output fluctuation quality of each wind farm under multi-wind farm output scene
Figure BDA0002337414660000101
As can be seen from the results in Table 1, the fluctuation overall difference coefficient S 'between the output curve of the wind farm 2 and the system load demand fluctuation curve'2A value 0.2113, which is the minimum value in three wind farms; fluctuation overall difference degree coefficient S 'of wind power plant 1'1The value 1, which is the maximum of the three; fluctuation overall difference degree coefficient S 'of wind power plant 3'3Value 0.5411, between wind farm 1 and wind farm 2. The fluctuation integral difference coefficient in the wind power fluctuation cost calculation method provided by the application can correctly reflect the difference degree of the output curve of each wind power plant and the respective equal-power load-following curve thereof. Finally, the wind power fluctuation quality factors of the wind power plants 1, 2 and 3 are 0.5706, 0.1206 and 0.3088 respectively, the wind power plants 2, 3 and 1 respectively reflect the good to poor sequence of the fluctuation quality of the three wind power plants, and the output of the wind power plant 2 can effectively track the change of system load fluctuation.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims (3)

1. A wind power fluctuation quality assessment method based on a waveform similarity theory is characterized by comprising the following steps: the method comprises the following steps:
step S1: acquiring an output curve and a fluctuation curve of load demand of each wind power plant in a power system; taking a fluctuation curve of the load demand as a tracking target, and respectively carrying out equal-electric quantity load following equivalence on output curves of all wind power plants;
Figure FDA0002337414650000011
Figure FDA0002337414650000012
wherein, L (t) is a power system load demand fluctuation curve;
Figure FDA0002337414650000013
actual output curve of the wind power plant; p'w(T) is the equivalent output curve of the isoelectric load, and T is 24 hours;
step S2: based on the waveform similarity theory, measuring the fluctuation integral difference measure S 'between the actual output of the wind power plant and the equivalent curve of the equivalent electric quantity and the equivalent load of the wind power plant'j；
Step S3: measure S 'by fluctuation overall difference'jOn the basis, considering the influence of the installed capacity of the wind power plant, and obtaining the wind power fluctuation quality evaluation factor delta of the wind power plant j through weighted combinationjFor evaluating the quality of the wind farm output fluctuation, deltajThe value range of (a) fluctuates between 0 and 1, and if the wind power field j wind power fluctuation quality evaluation factor deltajThe smaller the wind power of the wind farm j is, the better the fluctuation quality is, when delta isjWhen the fluctuation characteristic is equal to 0, the fluctuation characteristic is consistent with the fluctuation curve of the load demand, namely the load fluctuation is completely tracked, and the fluctuation quality is the best; on the contrary, the less the fluctuation quality is, when deltajThe fluctuation characteristic is exactly opposite to the load demand fluctuation curve, with the worst fluctuation quality.
2. The wind power fluctuation quality assessment method based on the waveform similarity theory according to claim 1, characterized in that: the step S2 specifically includes the following steps:
step S21: calculating the difference measure of the fluctuation change rate of the wind power output curve and the load demand curve of the power system:
before calculating the difference measure of the fluctuation change rate, the wind power output curve needs to be processed, so that the output sequence P of the original wind power plant jj,1，Pj,2，…，Pj,TAbbreviation { Pj,TTaking the difference between the next item and the previous item to form a new sequence aj,1，aj,2，…，aj,T-1Is denoted as { aj,T-1}:
aj,t＝Pj,t+1-Pj,tt＝1,2,…,T-1
Similarly, for equivalent scene output sequence P 'of wind power plant j'j,1,P'j,2,..,P'j,TI.e. { P'j,TIs similarly processed and is counted as { a'j,T-1}：
a'j,t＝P'j,t+1-P'j,tt＝1,2,…,T-1
Calculating the difference measure S of the fluctuation change rate according to the cosine algorithm of the included anglevd,j：
Figure FDA0002337414650000021
Step S22: calculating the difference measure of the fluctuation amplitude of the wind power output curve and the load demand curve;
the actual output curve and the equivalent output curve sequence of the wind power plant j are still marked as { Pj,TAnd { P'j,TAnd then, there are:
Figure FDA0002337414650000022
Figure FDA0002337414650000023
wherein, Pj,tThe actual output value of the wind power plant j at the moment t is obtained; p'j,tThe output value of the equivalent output curve of the wind power plant j at the moment t is obtained; dj,tThe ratio of the absolute value of the difference between the amplitude values of the actual output curve and the equivalent output curve and the amplitude value of the equivalent output curve of the wind power plant j at the moment t; sad,jThen it is the corresponding sequence { D } for wind farm jj,tThe mean value of i.e. the measure of the fluctuation amplitude difference;
step S23: normalization processing is adopted for the difference measurement of the fluctuation variation rate and the difference measurement of the fluctuation amplitude;
Figure FDA0002337414650000031
Figure FDA0002337414650000032
wherein the content of the first and second substances,Nwis the number of grid-connected wind power plants in the system, max ({ S })ad,j,j＝1,2,…,Nw}) represents a set Sad,1，Sad,2，…，Sad,NwThe maximum value in (c);
step S24: to S'Vd,jAnd S'ad,jPerforming weighted average to obtain difference measurement values of the wind power output curves under two scenes;
S’j＝μvdS’vd,j+μadS’ad,j
wherein, muvdAnd muadRespectively representing output curve fluctuation variation rate difference measure S 'under two scenes of wind power plant j'Vd,jAnd measure of fluctuation amplitude difference S'ad,jWind power output curve integral difference degree S'jSpecific gravity taken up in calculation, and muvdAnd muadThe sum is 1.
3. The wind power fluctuation quality assessment method based on the waveform similarity theory according to claim 1, characterized in that: the step S3 specifically includes the following steps:
step S31: introduction of capacity coefficient KcThe influence of installed capacity on wind power fluctuation quality factors is characterized:
Figure FDA0002337414650000033
wherein, Kc,jCalculating a capacity parameter of a j wind power fluctuation quality factor of a wind power plant; cinst,jIs the installed capacity of wind farm j;
step S32: calculating a wind power fluctuation quality factor of the wind power plant j:
Figure FDA0002337414650000034
comprises the following steps:
Figure FDA0002337414650000041
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