CN106786791B - A kind of generation method of wind power output scene - Google Patents
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Abstract
The invention discloses a kind of generation methods of wind power output scene, comprising: determines peak period, waist lotus period and low-valley interval according to each season typical day load curve；Count wind-force day power generation curve the above three period characteristic index；Power generation of each season wind-force day curve is divided into n subinterval in the average output section of above three period in proportion, section situation is adhered to separately according to three characteristic indexs of curve that generate electricity wind-force day and is classified as n3Class；The curve that generates electricity all kinds of typical wind days is determined in the case where guaranteeing benefit of peak regulation and Utility of Energy, generates wind power output scene and its probability distribution.The present invention considers China's power supply architecture feature, meter and electric system daily load characteristic, per diem generate wind power output scene, randomness, peak-shaving capability, the day generated output correlation of wind power output can preferably be reacted, there are preferable load matching properties again, it is practical to adapt to China's electric system, can be used for the Power System Planning containing wind power plant and the various occasions of operation.
Description
Technical Field
The invention relates to a wind power generation and access technology construction method, in particular to a wind power output scene generation method.
Background
Wind power generation is a clean renewable energy form, and has considerable economic benefits, social benefits and environmental benefits. However, as the installed proportion of the wind power generation in the system is gradually increased, the wind power generation has the characteristics of intermittency, randomness and inverse peak regulation, so that great influence is caused on the conventional power supply planning and the power system operation scheduling, and how to establish an accurate wind power output model is very important. At present, there are 4 main ways for modeling wind power output in China. Firstly, a multi-state unit model is adopted, and wind power output is regarded as a multi-state unit which is possibly valued at certain discrete points; secondly, obtaining a probability distribution model of wind power output by combining a wind power curve based on Weibull distribution of wind speed; thirdly, generating a wind power output scene through a historical wind power output curve, and then deducting wind power serving as a load on an original load curve; and fourthly, a load correction model based on wind power time sequence output simulation. The 4 models are applied more in the wind power output scene generated through the historical wind power output curve, but the existing wind power output scene generation method mainly has the following three problems:
firstly, the generated wind power scene can not reflect the full-time space-time random characteristic of wind power output, most of the generated wind power scene is selected from a plurality of typical scenes, and the scene selection is too extreme and can not be used for guiding the operation optimization of a long-time scale.
And secondly, a daily load curve is not combined when a scene is generated, the wind power output scene is formed only according to the self characteristic clustering of the wind power output data, the mutual influence of the randomness of the wind power and the load change cannot be reflected, and the whole process analysis of the daily cycle of the system is difficult to accurately realize.
Thirdly, the characteristics that China is mainly based on coal power, a peak regulation power supply is insufficient, and a scheduling period is longer than 24 hours are not considered when a scene is generated, and the constructed scene cannot well reflect the correlation and the peak regulation characteristic of daily generated output, so that the fine influence of an original wind power output curve on the peak regulation balance of the system cannot be well reflected.
Disclosure of Invention
Aiming at the defects, the invention provides a method for generating a wind power output scene, and aims to solve the technical problems that the randomness of the wind power output cannot be reflected, the influence of load change on the wind power output cannot be considered, and the refined influence of a wind power output curve on the peak load balancing of a system cannot be considered in the existing scene selection.
In order to achieve the purpose, the invention provides a method for generating a wind power output scene, which comprises the following steps:
(1) determining a peak time period of the season a, a waist load time period of the season a and a valley time period of the season a according to a typical daily load curve of the season a;
(2) determining a peak time period daily generated output characteristic index, a waist load time period daily generated output characteristic index, a valley time period daily generated output characteristic index and a peak regulation capacity index of a d-th wind daily generated power curve in the season a;
(3) counting the average output of all wind power generation curves in the season a in the peak period, the average output of all wind power generation curves in the valley period and the average output of all wind power generation curves in the waist load period to obtain an average output interval in the peak period, an average output interval in the waist load period and an average output interval in the valley period, proportionally dividing the average output interval in the peak period, the average output interval in the waist load period and the average output interval in the valley period into n subintervals,
determining n of season a according to n peak period average output subintervals, n waist period average output subintervals and n valley period average output subintervals3A scene class;
determining the scene class of the d-th wind power generation curve of the season a according to the conditions of the mean output at the peak time period, the mean output at the valley time period and the mean output at the waist load time period of the d-th wind power generation curve of the season a;
determining the probability of each scene class of the season a according to the number of the wind power daily generation curves contained in each scene class of the season a and the total number of the wind power daily generation curves contained in the season a;
(4) determining typical wind daily power generation curves of the scene classes in the season a according to peak regulation capacity indexes of the wind daily power generation curves contained in the scene classes in the season a;
(5) correcting the output of each hour of the typical wind power generation curve of each scene class of the season a according to the total power generation amount of all wind power generation curves of each scene class of the season a and the expected power generation amount of the typical wind power generation curve of each scene class of the season a, and obtaining a wind power output scene and probability distribution of the season a;
the typical daily load curve represents the variation trend of the daily load curve of 80-90% of the season, the daily generated output characteristic index in a peak period comprises the average output in a peak period, the daily generated output characteristic index in a waist load period comprises the average output in a waist load period, the daily generated output characteristic index in a valley period comprises the average output in a valley period, and a is 1,2,3 and 4; respectively represent four seasons of spring, summer, autumn and winter, and d is more than or equal to 1 and less than or equal to Na,NaThe number of wind power generation curves for season a.
Further, the step (1) is according to the formula TaH＝{t|La(t)≥ρp·Lmax(a) Determining the peak time T of season aaH(ii) a According to the formula TaM＝{t|ρb·Lmin(a)<La(t)<ρp·Lmax(a) Determining waist load time interval T of season aaM(ii) a According to the formula TaL＝{t|La(t)≤ρb·Lmin(a) Determining the valley period T of season aaL；
In the formula, La(t) is a typical daily load curve for season a, Lmax(a) Maximum load, L, representing a typical daily load curve for season amin(a) Minimum load, p, representing a typical daily load curve for season apTaking 0.9-0.98, rhobTaking 1.02-1.2.
Further, the daily generated output characteristic indexes in the peak period in the step (2) further comprise the maximum output in the peak period and the minimum output in the peak period;
the indexes of the daily generated output characteristics of the low-valley period also comprise the maximum output of the low-valley period and the minimum output of the low-valley period;
the peak regulation capacity index comprises maximum reverse peak regulation capacity and minimum reverse peak regulation capacity;
the maximum reverse peak-shaving capacity is the difference value between the maximum output in the low-peak period and the minimum output in the high-peak period, and the minimum reverse peak-shaving capacity is the difference value between the minimum output in the low-peak period and the maximum output in the high-peak period.
Determining a peak time period, a waist load time period and a valley time period of each season according to a typical daily load curve of each season, obtaining daily generated output characteristic indexes of all wind power daily generation curves of each season in the peak time period, the valley time period and the waist load time period, wherein the daily generated output characteristic indexes are used for determining the probabilities of all scenes and the scenes of the season, and obtaining the probabilities of a wind power output scene and the wind power output scene according to the daily maximum counter peak modulation capacity and the daily minimum counter peak modulation capacity indexes, so that the wind power output scene in the season can reflect the full-time wind power randomness and the mutual influence of the wind power output scene and the load change, and can better reflect the fine influence of the original wind power output curve on the peak modulation balance of the system.
Further, the step (3) comprises the following steps:
(31) obtaining the peak time period average output interval U of the season a according to the maximum value of the peak time period average output of the season a and the minimum value of the peak time period average output of the season aH(a) (ii) a Obtaining the average output interval U of the waist load period of the season a according to the maximum value of the average output of the waist load period of the season a and the minimum value of the average output of the waist load period of the season aM(a) (ii) a Obtaining the season a valley period according to the maximum value of the average output of the season a valley period and the minimum value of the average output of the season a valley periodAverage output interval UL(a)；
(32) Averaging the output interval U of the peak time period of the season aH(a) Dividing the average output interval into n peak time intervals, and dividing the average output interval U of the waist load time interval of the season a into n peak time intervalsM(a) Dividing the interval into n waist load time interval average output subintervals, and dividing the interval U of the low valley time interval of the season a intoL(a) Dividing the output voltage into n valley time section average output sub-intervals;
(33) determining n according to the n peak period average output subintervals, the n waist load period average output subintervals and the n valley period average output subintervals3A scene class;
(33) according to the formula
Pxyz(a)＝{PW(da)|PHave(da)∈UHx(a),PMave(da)∈UMy(a),PLave(da)∈ULz(a)}
Determining seasonal a scene class Pxyz(a) The daily power generation curve of wind power contained in the formulaDetermining seasonal a scene class Pxyz(a) Probability p ofxyz(a)；
In the formula, Pxyz(a) X is more than or equal to 1, y is less than or equal to z, n is less than or equal to PW(da) For the d-th wind-power daily generation curve of season a, PHave(da) Mean output at peak time of day power generation curve of No. d wind in season a, PMave(da) Average output of the waist load time interval of the day power generation curve of the d wind in the season a, PLave(da) Mean output of the valley period of the d-th wind power generation curve of season a, Nxyz(a) For seasonal a scene class Pxyz(a) Number of wind-power-day curves involved, NaThe total number of the wind power generation curves in season a, UHx(a) The average output subinterval for the xth peak period of season a,UMy(a) is the average output subinterval of the ith waist load time of the season a, ULz(a) And (4) averaging the output subintervals in the z-th valley period of the season a.
Obtaining an average output interval at a peak time period by counting the maximum value and the minimum value of the average output at the peak time period of the power generation curve of all wind days in the season a, and obtaining an average output interval at a waist load time period and an average output interval at a valley time period in the same way; the method comprises the steps of proportionally dividing the average output interval in the peak period, the average output interval in the waist load period and the average output interval in the valley period into n subintervals, and determining n of seasons a according to the n peak period average output subintervals, the n waist load period average output subintervals and the n valley period average output subintervals3A scene class; and determining the scene class to which the wind power generation curve belongs according to the subinterval conditions of three characteristic indexes of average output at the peak time, average output at the waist load time and average output at the valley time of the power generation curve of each wind day in the season a. The randomness of the wind power output and the mutual influence of the wind power output and the load change can be fully calculated, and the wind power daily generation curves with similar output characteristics are classified into the same scene.
Further, the step (4) of determining the typical wind daily power generation curve of each scene class of the season a according to the peak shaving capacity indexes of all wind daily power generation curves contained in each scene class of the season a comprises the following steps:
(41) according to the formula
Calculating seasonal a scene class Pxyz(a) Middle mth wind power daily generation curve PW(ma) Average weighted Euler distance S from all wind daily power generation curves in the classxyz(ma)；
(42) Weighted average minimum Euler distance Sxyz(ma) Corresponding wind power daily generation curve PW(ma) Scene class P as season axyz(a) Typical wind power daily generation curve
In the formula, CPVmax(ma) For seasonal a scene class Pxyz(a) Maximum inverse peak regulation capacity, C, of the mth wind power generation curvePVmin(ma) For seasonal a scene class Pxyz(a) Minimum inverse peak regulation capacity, C, of the mth wind power generation curvePVmax(la) For seasonal a scene class Pxyz(a) Maximum back peak regulation capacity, C, of the first wind power daily generation curvePVmin(la) For seasonal a scene class Pxyz(a) The minimum back peak regulation capacity of the first wind power daily generation curve is more than or equal to 1 and less than or equal to m, and l and less than or equal to Nxyz(a)，Nxyz(a) For seasonal a scene class Pxyz(a) Number of included wind-power-day curves, kαIs the maximum back peak shaving capacity CPVmaxWeight of (m), kα＞0.7，1≤x,y,z≤n。
The typical wind power generation curve of each scene class of the season a is determined according to the maximum back peak regulation capacity and the minimum back peak regulation capacity of the wind power daily power generation curve in each scene class of the season a, the typical wind power daily power generation curve is used as a wind power output scene after electric quantity correction is carried out on the typical wind power daily power generation curve, the peak regulation benefit of the original wind power daily power generation curve in each scene class can be better reflected, and therefore the fine influence of the original wind power output curve on system peak regulation balance is better reflected.
Further, the step (5) comprises the following steps:
(51) according to the formulaObtaining seasonal a scene class Pxyz(a) The first corrected typical wind power daily generation curve, and judging the season a scene class Pxyz(a) Whether the output of the typical wind power generation curve after the first correction exceeds a season a scene class P or not in each hourxyz(a) If so, fixing the output value at the overflow time as a seasonal a scene class Pxyz(a) Is/are as followsThe maximum output value and the output value at the non-overflow moment are all raisedObtaining seasonal a scene class Pxyz(a) The second corrected typical wind power generation curve, and step (54) is entered; otherwise, entering step (52);
(52) judging season a scene class Pxyz(a) Whether the output of the typical wind power generation curve after the first correction exceeds the scene class P of the season a or notxyz(a) If so, fixing the output value at the overflowing moment as a seasonal a scene class Pxyz(a) The minimum value of the output force and the output force value at the non-overflow time are all reducedObtaining seasonal a scene class Pxyz(a) The second corrected typical wind power generation curve, and step (54) is entered; otherwise, entering a step (53);
(53) class P of season a scenexyz(a) The first corrected typical wind power daily generation curve is used as a wind power output scene of a season a, and the scene of the season a is classified into a class Pxyz(a) Probability p ofxyz(a) The probability of the wind power output scene is taken;
(54) class P of season a scenexyz(a) The second corrected typical wind power daily generation curve is used as a wind power output scene of the season a, and the scene of the season a is classified into a class Pxyz(a) Probability p ofxyz(a) The probability of the wind power output scene is taken;
in the formula,representing seasonal a scene class Pxyz(a) The output value at the t hour of the typical wind power generation curve after the first correction,for seasonal a scene class Pxyz(a) Is/are as followsOutput value, sigma, at t hour of typical wind power generation curvexyz(a) For seasonal a scene class Pxyz(a) The first applied force correction value of (a),Exyz(a) for seasonal a scene class Pxyz(a) The total power generation amount of the wind power,Nxyz(a) for seasonal a scene class Pxyz(a) Number of included wind-power-day curves, PW(mat) Representing seasonal a scene class Pxyz(a) The output of the mth wind power generation curve at the t hour,for seasonal a scene class Pxyz(a) The expected power production of a typical wind power generation curve,
for seasonal a scene class Pxyz(a) The number of overflow moments of the output of the typical wind power generation curve after the first correction,for seasonal a scene class Pxyz(a) The overflow total electric quantity of the typical wind power daily generation curve after the first correction is the overflow season a scene class Pxyz(a) Maximum value of the output ofIf the season is the overflow season a scene class Pxyz(a) The minimum value of the output of (a),
for seasonal a scene class Pxyz(a) The minimum value of the output of (a),for seasonal a scene class Pxyz(a) Maximum value of the output; by counting season a scene class Pxyz(a) Obtaining seasonal a scene class P by the output value of all wind power generation curves in each hourxyz(a) By counting seasonal a scene class Pxyz(a) Obtaining seasonal a scene class P by the output value of all wind power generation curves in each hourxyz(a) Maximum value of the output.
By modifying the scene class Pxyz(a) Typical wind power daily generation curveExpected power generation amountUnder the condition of ensuring that the output value of the corrected typical wind power generation curve at each moment does not overflow the wind power output range of the scene class, the electric quantity to be corrected is spread to the whole day, namely the whole curve is longitudinally translated, so that the expected generated energy of the corrected typical curve is consistent with the total generated energy of all wind power generation curves in the scene class, the peak regulation requirement of the typical curve is not changed, and the electric quantity benefit and the peak regulation benefit of the wind power output of each scene class are ensured.
The wind power output scene generation method adopting the scheme has the remarkable advantages and beneficial effects that:
1. compared with the existing method, the characteristic index of the wind power output is determined according to the day by fully combining the typical daily load characteristic of the system, and the characteristic index is used for generating the wind power output scene and the probability distribution thereof, representing the full-time-space randomness and the load matching characteristic of the wind power output with smaller workload and higher precision, reflecting the wind power output levels of different load periods, and solving the problems that the existing scene generation method is separated from the load characteristic and has poor adaptability.
2. Based on the structural characteristics of a power supply which is mainly based on coal power and has insufficient flexibility peak shaving power supplies such as gas power, water pumping and energy storage and the like in China, the invention determines the typical daily wind power generation curve of each scene by taking the daily maximum reverse peak shaving capacity and the daily minimum reverse peak shaving capacity as characteristic indexes, and corrects the electric quantity. The method can fully reflect the electric quantity benefit and the peak shaving benefit of the wind power output original data, ensure the accuracy of peak shaving electricity discarding calculation, and better adapt to the actual power system of China.
3. The method provided by the invention generates the wind power day power generation scene and the probability distribution thereof based on the wind power output original data and the typical day load characteristics, can reflect the randomness and the load matching characteristics of the wind power output, and fully ensures the electric quantity benefit and the peak regulation benefit of the original wind power day power generation curve. Each electric power department can bring the scene into the operation optimization or economic dispatch of the electric power system, and evaluate the influence of the wind power generation on the electric power balance, the electric quantity balance, the peak regulation balance and the economic operation of the system; each electric power enterprise can develop the operation optimization software of the electric power system containing wind power according to the invention.
Drawings
FIG. 1 is a flow chart of a method for generating a wind power output scene according to the present invention;
FIG. 2 is a typical daily load curve in spring and the division of peak time, waist load time, and valley time in the embodiment of the present invention;
FIG. 3 is a diagram illustrating distribution of each scene class in spring according to an embodiment of the present invention;
FIG. 4 is a typical daily wind power curve for each scene class in spring in the embodiment provided by the present invention;
FIG. 5 is a comparison graph of a typical wind power daily generation curve of a spring scene class and an original wind power daily generation curve in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 is a flowchart of a method for generating a wind power output scene provided by the present invention. The method comprises the following steps:
(1) determining a typical daily load curve of the season a according to historical data of the system, wherein the typical daily load curve can reflect the variation trend of the daily load curve of 80-90% of the season a, and determining the peak period T of the season a according to the typical daily load curve of the season aaHTime interval T of waist load in season aaMValley period T of season aaLAnd a is 1,2,3,4, 1 indicates spring, 2 indicates summer, 3 indicates fall, and 4 indicates winter.
According to the formula TaH＝{t|La(t)≥ρp·Lmax(a) Determining the peak time T of season aaH；
According to the formula TaM＝{t|ρb·Lmin(a)<La(t)<ρp·Lmax(a) Determining waist load time interval T of season aaM；
According to the formula TaL＝{t|La(t)≤ρb·Lmin(a) Determining the valley period T of season aaL；
In the formula, La(t) is a typical daily load curve for season a, Lmax(a) Maximum load, L, representing a typical daily load curvemin(a) Minimum load, p, representing a typical daily load curvepTaking 0.9-0.98, rhobTaking 1.02-1.2.
FIG. 2 shows typical daily load curve in spring of a certain wind farm location in northwest region, and the division of peak time, waist load time and valley time, where ρpTake 0.97, ρbTaking 1.04, determining 10-12 and 19-21 points as peak load time, 0-6 points as valley load time, and the rest as waist load time.
(2) Calculating a wind power output characteristic index of a wind power generation curve day by day at a peak time period, a wind power output characteristic index at a valley time period, a wind power output characteristic index at a waist load time period and a peak regulation capacity index according to historical data of the system:
calculating the mean value of the output of the ith wind power generation curve in the season a in the peak period according to the historical data of the system to obtain the average output P in the peak periodHave(da) Wherein d is more than or equal to 1 and less than or equal to Na,NaFor the quantity of the wind power generation curves in the season a, calculating the maximum value of the output of the No. d wind power generation curve in the season a in the peak period according to the historical data of the system to obtain the maximum output P in the peak periodHmax(da) According to the historical data of the system, the minimum value of the power generation curve of the ith wind power day in the season a in the peak period is calculated to obtain the minimum power P in the peak periodHmin(da)。
Calculating the average value of the output of the ith wind power generation curve in the valley period in the season a according to the historical data of the system to obtain the average output P in the valley periodLave(da) (ii) a Calculating the maximum output value of the ith wind power generation curve of the season a in the valley period according to the historical data of the system to obtain the maximum output P in the valley periodHmax(da) Calculating the minimum value of the output of the ith wind power generation curve of the season a in the valley period according to the historical data of the system to obtain the minimum output P in the valley periodHmin(da)。
Calculating the average value of the output of the d-th wind power generation curve in the season a in the waist load period according to the historical data of the system to obtain the average output P in the waist load periodMave(da)。
Season aMaximum back peak regulation capacity C of the d-th wind power generation curvePVmax(da) The calculation formula of (2) is as follows:
CPVmax(da)＝PLmax(da)-PHmin(da)
in the formula, PLmax(da) The maximum output of the power generation curve of the No. d wind day in the off-peak period of the season a, PHmin(da) The minimum output is generated during the peak time of the power generation curve of the ith wind day in the season a.
Minimum back peak regulation capacity C of the d wind power generation curve of season aPVmin(da) The calculation formula of (2) is as follows:
CPVmin(da)＝PLmin(da)-PHmax(da)
in the formula, PLmin(da) Minimum output of power P in the off-peak period of the ith wind power generation curve of the season aHmax(da) The maximum output is generated in the peak time of the power generation curve of the ith wind day in the season a.
(3) Counting the average output of all wind power generation curves in the season a at the peak time, obtaining the maximum value of the average output of the season a at the peak time and the minimum value of the average output of the season a at the peak time, and thus obtaining the average output interval U of the season a at the peak timeH(a)；
Counting the average output of all wind power generation curves in the season a in the waist load period, obtaining the maximum value of the average output in the season a in the waist load period and the minimum value of the average output in the season a in the waist load period, and thus obtaining the average output interval U in the season a in the waist load periodM(a)；
Calculating the average output of all wind power generation curves in the season a in the valley period, obtaining the maximum value of the average output of the season a in the valley period and the minimum value of the average output of the season a in the valley period, and thus obtaining the average output interval U of the season a in the valley periodL(a)；
General seasonAverage output interval U in the festival a peak periodH(a) Dividing the average output of the season a into n peak time intervals according to the value from small to large, and recording the x peak time interval of the season a as UHx(a) X is more than or equal to 1 and less than or equal to n; and the average output subinterval U of the xth peak period of the season aHx(a) Is less than the x +1 th peak hour average output subinterval U of season aHx+1(a)；
The average output interval U of the season a waist load time intervalM(a) Dividing the average output of the season a into n waist load time intervals according to the value from small to large, and recording the average output of the season a in the y th waist load time interval as UMy(a)，1≤y≤n；
The average output interval U of the off-peak period of the season aL(a) Dividing the average output of the time interval into n valley time intervals according to the value from small to large, and recording the average output of the time interval of the zth valley time interval in the season a as ULz(a)，1≤z≤n。
Determining n of season a according to n peak period average output subintervals, n waist period average output subintervals and n valley period average output subintervals3A scene class; determining a scene class P of the season a according to the average output subinterval at the xth peak period of the season a, the average output subinterval at the ythh waist load period of the season a and the average output subinterval at the zth valley period of the season axyz(a)。
According to the formula
Pxyz(a)＝{PW(da)|PHave(da)∈UHx(a),PMave(da)∈UMy(a),PLave(da)∈ULz(a)}
Determining scene class P of season axyz(a) The included wind power daily generation curve;
in the formula, Pxyz(a) For the scene class of season a, PW(da) For the d-th wind-power daily generation curve of season a, PHave(da) Season a d wind power dayAverage power output, P, at peak time of the power curveMave(da) Average output of curve waist load time interval for the No. d wind power day of season aLave(da) Average output of power during the off-peak period of the ith wind power generation curve in the season a, UHx(a) The average output subinterval of the xth peak period of the season a, UMy(a) The average output force level subinterval of the ith waist load time interval of the season a, ULz(a) And (4) averaging the output subintervals in the z-th valley period of the season a.
According to the formulaDetermining scene class P of season axyz(a) Probability p ofxyz(a)；
Nxyz(a) For seasonal a scene class Pxyz(a) Number of wind-power-day curves involved, NaThe number of wind generation curves per day included for season a.
In the example provided by the invention, the historical wind power generation curve of 365 days in a certain wind power plant in northwest China is taken as a sample for research.
The average output interval P in the peak period of the spring is obtained by counting the maximum value and the minimum value of the average output in the peak period of the power generation curve of all wind days in springHave(1)＝[0.012，0.590]Dividing the average output interval of the spring peak time period into 3 sub-intervals according to the proportion of 25%, 50% and 25%:
UH1(1)＝[0.012,0.157),UH2(1)＝[0.157,0.446),UH3(1)＝[0.446,0.590]
the average output interval P in the waist load period of spring is obtained by counting the average output maximum value and the average output minimum value in the waist load period of all wind power generation curves in springMave(1)＝[0.028，0.607]Dividing the average output interval of the waist load time period into 3 sub-intervals according to the same proportion:
UM1(1)＝[0.028,0.173),UM2(1)＝[0.173,0.462),UM3(1)＝[0.462,0.607]
the average output interval P of the valley period of the spring is obtained by counting the maximum value and the minimum value of the average output of all wind power generation curves of the valley period of the springLave(1)＝[0.054，0.589]And dividing the valley period average output interval into 3 sub-intervals according to the same proportion:
UL1(1)＝[0.054,0.187),UL2(1)＝[0.187,0.455),UL3(1)＝[0.455,0.589]
determining 27 scene classes according to the average output subintervals of the 3 spring peak periods, the 3 valley periods and the 3 spring waist periods, and marking as a scene class Pxyz(1)
From peak period average output subinterval UH1(1) 0.012,0.157) waist load time period average force output subinterval UM1(1) 0.028,0.173), valley period average power sub-interval UL1(1) Determine scene class P [0.054,0.187 ])111(1) If the average output at the peak time period, the average output at the waist load time period and the average output at the valley time period of the wind power generation curve belong to the intervals, classifying the wind power generation curve into a scene class P111(1) And so on.
According to the conditions that the average output in the peak time period, the average output in the waist load time period and the average output in the valley time period of each wind power generation curve in spring belong to intervals, the wind power generation curves in spring are divided into 27 scene classes at most. As shown in fig. 3, the wind power output scenario distribution in spring in the embodiment is shown. The results show that the spring wind power generation curve is actually divided into 14 types, and 13 types do not appear in the 27 types of scenes because of the small sample size in the example, and the division of each index into 3 intervals has high accuracy.
And the distribution of scene classes in other three seasons can be determined similarly.
(4) Determining a typical wind power daily generation curve of each scene class in the season a according to a peak regulation capacity index of the wind power daily generation curve contained in each scene class in the season a, wherein the method comprises the following steps:
(41) according to the formula
Calculating seasonal a scene class Pxyz(a) Middle mth wind power daily generation curve PW(ma) Average weighted Euler distance S from all wind daily power generation curves in the classxyz(ma)。
In the formula, CPVmax(ma) For seasonal a scene class Pxyz(a) Maximum inverse peak regulation capacity, C, of the mth wind power generation curvePVmin(ma) For seasonal a scene class Pxyz(a) Minimum inverse peak regulation capacity, C, of the mth wind power generation curvePVmax(la) For seasonal a scene class Pxyz(a) Maximum back peak regulation capacity, C, of the first wind power daily generation curvePVmin(la) For seasonal a scene class Pxyz(a) The minimum back peak regulation capacity of the first wind power daily generation curve is more than or equal to 1 and less than or equal to m, and l and less than or equal to Nxyz(a)，Nxyz(a) For seasonal a scene class Pxyz(a) Number of included wind-power-day curves, kαIs the maximum back peak shaving capacity CPVmaxWeight of (m), kα＞0.7。
(42) Weighted average minimum Euler distance Sxyz(ma) Corresponding wind power daily generation curve PW(ma) Scene class P as season axyz(a) Typical wind power daily generation curve
(5) Correcting all scene classes P of season a one by onexyz(a) Typical wind power generation curve ofUnder the condition of ensuring that the output value of the corrected typical wind power generation curve at each moment does not overflow the scene wind power output range, the electric quantity to be corrected is spread to the whole day, namely the whole curve is longitudinally translated, and the correction process is as follows:
calculating seasonal a scene class Pxyz(a) Total wind power generation Exyz(a) The formula is as follows:
in the formula, Nxyz(a) For seasonal a scene class Pxyz(a) Number of included wind-power-day curves, PW(mat) Representing seasonal a scene class Pxyz(a) And (4) the output of the mth wind power generation curve at the t hour.
Calculating seasonal a scene class Pxyz(a) Expected power generation amount of typical wind power daily power generation curve
In the formula,for seasonal a scene class Pxyz(a) The output value at the t hour of the typical wind power generation curve.
Calculating seasonal a scene class Pxyz(a) Corrected electric quantity delta E (a) of typical wind power daily power generation curve
Calculating seasonal a scene class Pxyz(a) First output correction value sigma of each moment of typical wind power daily generation curvexyz(a)
Obtaining seasonal a scene class Pxyz(a) Typical wind power daily generation curve after first correction of (2)
In the formula,representing seasonal a scene class Pxyz(a) Typical wind power daily generation curve after first correction of (2)The output force value at the t hour,for seasonal a scene class Pxyz(a) The output value at the t hour of the typical wind power generation curve.
(51) Obtaining season a scene class P according to the formulaxyz(a) Typical wind power daily generation curve after first correction of (2)Judging season a scene class Pxyz(a) Whether the output of the typical wind power generation curve after the first correction exceeds a season a scene class P or not in each hourxyz(a) Is/are as followsIf the maximum output value is the maximum output value, fixing the output value at the overflowing moment as a seasonal a scene class Pxyz(a) Maximum value of outputThe output values at the non-overflow moment are all raisedAnd entering step (54); otherwise step (52) is entered.
In the formula,for seasonal a scene class Pxyz(a) The number of overflow moments of the output of the typical wind power generation curve after the first correction,for seasonal a scene class Pxyz(a) The first modified typical wind power generation curve of (2) overflows the total amount of power, for seasonal a scene class Pxyz(a) The maximum value of the output is calculated by counting the class P of the scene a in the seasonxyz(a) Obtaining seasonal a scene class P by the output value of all wind power generation curves in each hourxyz(a) Maximum value of the output;
(52) judging season a scene class Pxyz(a) Whether the output of the typical wind power generation curve after the first correction exceeds a season a scene class P or not in each hourxyz(a) If so, fixing the output value at the overflowing moment as a seasonal a scene class Pxyz(a) Minimum value of outputThe output value at the non-overflow moment is reducedOtherwise step (53) is entered. Wherein, for seasonal a scene class Pxyz(a) By counting seasonal a scene class Pxyz(a) Obtaining seasonal a scene class P by the output value of all wind power generation curves in each hourxyz(a) Minimum force of (d).
(53) Class P of season a scenexyz(a) The first corrected typical wind power daily generation curve is used as a wind power output scene of a season a, and the scene of the season a is classified into a class Pxyz(a) Probability p ofxyz(a) The probability of the wind power output scene is taken;
(54) class P of season a scenexyz(a) The second corrected typical wind power daily generation curve is used as a wind power output scene of the season a, and the scene of the season a is classified into a class Pxyz(a) Probability p ofxyz(a) And the probability is used as the probability of the wind power output scene.
Determining a typical daily load curve capable of representing season a according to historical data, determining peak time periods, waist load time periods and valley time periods of the load, determining average output of the wind power daily generation curve in the three time periods day by day, the average output interval of the wind power generation curves in the three periods is determined by counting the average output of all the wind power generation curves per day, the average output interval is divided into a plurality of subintervals for determining various scene classes, determining the scene class to which the wind power daily generation curve belongs according to the condition that the average output of the wind power daily generation curve in the three periods belongs to the subinterval, determining the probability of each scene class according to the quantity of the wind power daily generation curve contained in each scene class, the method can fully take the randomness of the wind power output and the mutual influence between the randomness and the load change of the wind power output into account, and ensures that the daily wind power generation curves with similar output characteristics are classified into the same scene.
The typical wind daily power generation curve of the scene class is determined according to the maximum reverse peak-shaving capacity and the minimum reverse peak-shaving capacity of the wind daily power generation curve contained in the scene class, and the daily maximum reverse peak-shaving capacity and the daily minimum reverse peak-shaving capacity are defined based on wind power reverse peak-shaving characteristics, so that the peak shaving requirement of the system is obviously influenced. Therefore, the typical wind power generation curve can reflect the peak shaving benefits of all wind power generation curves in the scene class.
Correcting the electric quantity of the typical wind power generation curve per day to ensure that the expected electric quantity of the typical wind power generation curve per day is the same as the total electric quantity of all wind power generation curves in corresponding scene classes, spreading the corrected electric quantity to the whole day of the typical wind power generation curve per day, namely, longitudinally translating the typical wind power generation curve per day, and if the scene class P in season a is used, performing electric quantity correction on the typical wind power generation curve per day to ensure that the expected electric quantity of thexyz(a) Corrected typical wind power daily generation curveSome time wind power output overflows a season a scene class Pxyz(a) The total overflowing electric quantity at the overflowing moment is leveled to the non-overflowing moment.
As shown in FIG. 4, a typical wind power generation curve of each scene class in spring in the embodiment determined according to the method is shown in the diagram (a)111Scene P112Scene P121And scene P122A typical wind power generation curve, and a scene p is shown in a diagram (b)211Scene P212Scene P221Scene P232And scene P233A typical wind power generation curve, and the diagram (c) shows a scene p312Scene P321Scene P322And scene P333The result shows that each typical curve reflects different output levels of wind power in each load time period, and the full-time-space random characteristic of the wind power output can be quantized.
FIG. 5 shows a scenario p in the wind power output scenario in spring of the embodiment111Scene p121Scene p212And scene p222The four scene type typical wind power daily generation curves with higher probability are compared with the original wind power daily generation curve, and the result shows that the typical wind power daily generation curve can better reflect the shape characteristics of the original wind power daily generation curve, the electric quantity benefit and the peak regulation benefit of the original wind power daily generation curve can be ensured on the clustering method, and the effectiveness of the method can be verified.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. A method for generating a wind power output scene is characterized by comprising the following steps:
(1) determining a peak time period of the season a, a waist load time period of the season a and a valley time period of the season a according to a typical daily load curve of the season a;
(2) determining a peak time period daily generated output characteristic index, a waist load time period daily generated output characteristic index, a valley time period daily generated output characteristic index and a peak regulation capacity index of a d-th wind daily generated power curve in the season a;
(3) counting the average output at peak time, the average output at waist load time and the average output at valley time of all wind power generation curves in the season a to obtain an average output interval at peak time, an average output interval at waist load time and an average output interval at valley time, dividing the average output interval at peak time, the average output interval at waist load time and the average output interval at valley time into n subintervals according to the proportion,
determining n of seasons a according to the n peak period average output subintervals, the n waist period average output subintervals and the n valley period average output subintervals3A scene class;
determining the scene class of the d-th wind power generation curve of the season a according to the conditions of the mean output at the peak time period, the mean output at the valley time period and the mean output at the waist load time period of the d-th wind power generation curve of the season a;
determining the probability of each scene class of the season a according to the number of the wind power daily generation curves contained in each scene class of the season a and the total number of the wind power daily generation curves contained in the season a;
(4) determining typical wind daily power generation curves of the scene classes in the season a according to peak regulation capacity indexes of the wind daily power generation curves contained in the scene classes in the season a;
(5) correcting the hourly output of the typical wind daily generation curves of the scene classes in the season a according to the total generated energy of all wind daily generation curves contained in the scene classes in the season a and the expected generated energy of the typical wind daily generation curves of the scene classes in the season a, and obtaining wind power output scenes and probability distribution of the season a;
the typical daily load curve represents the variation trend of the daily load curve of 80-90% of the season, the daily generated output characteristic index in the peak period comprises the average output in the peak period, the daily generated output characteristic index in the waist load period comprises the average output in the waist load period, the daily generated output characteristic index in the valley period comprises the average output in the valley period, and a is 1,2,3 and 4; respectively represent four seasons of spring, summer, autumn and winter, and d is more than or equal to 1 and less than or equal to Na，NaThe number of wind power generation curves for season a.
2. The method of generating as claimed in claim 1, wherein said step (1) is according to formula TaH＝{t|La(t)≥ρp·Lmax(a) Determining the peak time T of season aaH(ii) a According to the formula TaM＝{t|ρb·Lmin(a)＜La(t)＜ρp·Lmax(a) Determining waist load time interval T of season aaM(ii) a According to the formula TaL＝{t|La(t)≤ρb·Lmin(a) Determining the valley period T of season aaL；
In the formula, La(t) is a typical daily load curve for season a, Lmax(a) Maximum load, L, representing a typical daily load curve for season amin(a) Minimum load, p, representing a typical daily load curve for season apTaking 0.9-0.98, rhobTaking 1.02-1.2.
3. The method of generating as claimed in claim 1, wherein said peak hour daily generated output characteristic indicators of step (2) further comprise peak hour maximum output, peak hour minimum output;
the characteristic indexes of the daily generated output of the low-valley period also comprise the maximum output of the low-valley period and the minimum output of the low-valley period;
the peak shaving capacity index comprises maximum reverse peak shaving capacity and minimum reverse peak shaving capacity;
the maximum reverse peak-shaving capacity is the difference value between the maximum output in the low-peak period and the minimum output in the high-peak period, and the minimum reverse peak-shaving capacity is the difference value between the minimum output in the low-peak period and the maximum output in the high-peak period.
4. The generation method according to claim 1, wherein the step (3) comprises the steps of:
(31) obtaining the peak time period average output interval U of the season a according to the maximum value of the peak time period average output of the season a and the minimum value of the peak time period average output of the season aH(a) (ii) a Maximum value of average output force according to waist load time period of season aObtaining the average output interval U of the waist load time period of the season a from the minimum value of the average output of the waist load time period of the season aM(a) (ii) a Obtaining the average output interval U of the off-peak period of the season a according to the maximum value of the average output of the off-peak period of the season a and the minimum value of the average output of the off-peak period of the season aL(a)；
(32) Averaging the output interval U of the peak time period of the season aH(a) Dividing the average output interval into n peak time intervals, and dividing the average output interval U of the waist load time interval of the season a into n peak time intervalsM(a) Dividing the interval into n waist load time interval average output subintervals, and dividing the interval U of the low valley time interval of the season a intoL(a) Dividing the output voltage into n valley time section average output sub-intervals;
(33) determining n according to the n peak period average output subintervals, the n waist load period average output subintervals and the n valley period average output subintervals3A scene class;
(34) according to the formula
Pxyz(a)＝{PW(da)|PHave(da)∈UHx(a),PMave(da)∈UMy(a),PLave(da)∈ULz(a)}
Determining seasonal a scene class Pxyz(a) The daily power generation curve of wind power contained in the formulaDetermining seasonal a scene class Pxyz(a) Probability p ofxyz(a)；
In the formula, Pxyz(a) X is more than or equal to 1, y is less than or equal to z, n is less than or equal to PW(da) For the d-th wind-power daily generation curve of season a, PHave(da) Mean output during peak hours of the d-th wind power generation curve in season a, PMave(da) Average output of the waist load time interval of the day power generation curve of the d wind in the season a, PLave(da) Mean output of the valley period of the d-th wind power generation curve of season a, Nxyz(a) For seasonal a scene class Pxyz(a) Number of wind-power-day curves involved, NaIs season of the yeara total number of wind power generation curves per day, UHx(a) The average output subinterval of the xth peak period of the season a, UMy(a) Is the average output subinterval of the ith waist load time of the season a, ULz(a) And (4) averaging the output subintervals in the z-th valley period of the season a.
5. The generation method according to claim 1, wherein the step (4) of determining the typical wind daily power generation curve of each scene class of season a according to the peak shaving capacity index of all wind daily power generation curves included in each scene class of season a comprises the following steps:
(41) according to the formulaCalculating seasonal a scene class Pxyz(a) Middle mth wind power daily generation curve PW(ma) Average weighted Euler distance S from all wind daily power generation curves in the classxyz(ma)；
(42) Weighted average minimum Euler distance Sxyz(ma) Corresponding wind power daily generation curve PW(ma) Scene class P as season axyz(a) Typical wind power daily generation curve
In the formula, CPVmax(ma) For seasonal a scene class Pxyz(a) Maximum inverse peak regulation capacity, C, of the mth wind power generation curvePVmin(ma) For seasonal a scene class Pxyz(a) Minimum inverse peak regulation capacity, C, of the mth wind power generation curvePVmax(la) For seasonal a scene class Pxyz(a) Maximum back peak regulation capacity, C, of the first wind power daily generation curvePVmin(la) For seasonal a scene class Pxyz(a) The minimum back peak regulation capacity of the first wind power daily generation curve is more than or equal to 1 and less than or equal to m, and l and less than or equal to Nxyz(a)，Nxyz(a) For seasonal a scene class Pxyz(a) Number of included wind-power-day curves, kαIs the maximum back peak shaving capacity CPVmaxWeight of (m), kα＞0.7，1≤x,y,z≤n。
6. The generation method according to claim 1, characterized in that said step (5) comprises the steps of:
(51) according to the formulaObtaining seasonal a scene class Pxyz(a) The first corrected typical wind power daily generation curve, and judging the season a scene class Pxyz(a) Whether the output of the typical wind power generation curve after the first correction exceeds a season a scene class P or not in each hourxyz(a) If so, fixing the output value at the overflow time as a seasonal a scene class Pxyz(a) The maximum value of the output force and the output force value at the non-overflow moment are all raisedObtaining seasonal a scene class Pxyz(a) The second corrected typical wind power generation curve, and step (54) is entered; otherwise, entering step (52);
(52) judging season a scene class Pxyz(a) Whether the output of the typical wind power generation curve after the first correction exceeds the scene class P of the season a or notxyz(a) If so, fixing the output value at the overflowing moment as a seasonal a scene class Pxyz(a) The minimum value of the output force and the output force value at the non-overflow time are all reducedObtaining seasonal a scene class Pxyz(a) The second corrected typical wind power generation curve, and step (54) is entered; otherwise, entering a step (53);
(53) class P of season a scenexyz(a) The first corrected typical wind power daily generation curve is used as a wind power output scene of a season a, and the scene of the season a is classified into a class Pxyz(a) Probability p ofxyz(a) The probability of the wind power output scene is taken;
(54) class P of season a scenexyz(a) The second corrected typical wind power daily generation curve is used as a wind power output scene of the season a, and the scene of the season a is classified into a class Pxyz(a) Probability p ofxyz(a) The probability of the wind power output scene is taken;
in the formula,representing seasonal a scene class Pxyz(a) The output value at the t hour of the typical wind power generation curve after the first correction,for seasonal a scene class Pxyz(a) The output value, sigma, at t hour of the typical wind power generation curvexyz(a) For seasonal a scene class Pxyz(a) The first applied force correction value of (a),Exyz(a) for seasonal a scene class Pxyz(a) The total power generation amount of the wind power,Nxyz(a) for seasonal a scene class Pxyz(a) Number of included wind-power-day curves, PW(mat) Representing seasonal a scene class Pxyz(a) The output of the mth wind power generation curve at the t hour,for seasonal a scene class Pxyz(a) The expected power production of a typical wind power generation curve,
for seasonal a scene class Pxyz(a) The number of overflow moments of the output of the typical wind power generation curve after the first correction,for seasonal a scene class Pxyz(a) The overflow total electric quantity of the typical wind power daily generation curve after the first correction is the overflow season a scene class Pxyz(a) Maximum value of the output ofIf the season is the overflow season a scene class Pxyz(a) The minimum value of the output of (a),
for seasonal a scene class Pxyz(a) The minimum value of the output of (a),for seasonal a scene class Pxyz(a) Maximum value of the output; by counting season a scene class Pxyz(a) Obtaining seasonal a scene class P by the output value of all wind power generation curves in each hourxyz(a) By counting seasonal a scene class Pxyz(a) Obtaining seasonal a scene class P by the output value of all wind power generation curves in each hourxyz(a) Maximum value of the output.
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| CN103296679A (en) * | 2013-05-20 | 2013-09-11 | 国家电网公司 | Modeling method for medium and long-term wind power output model of power system capable of optimally running for medium and long terms |
| CN103346563A (en) * | 2013-07-15 | 2013-10-09 | 国家电网公司 | Method for evaluating maximum permeability of distributed generation based on time scene access analysis |
| CN103745023A (en) * | 2013-11-22 | 2014-04-23 | 华中科技大学 | Coupling modeling method for hydropower station power generated output scheme making and optimal load distribution |
| CN104268800A (en) * | 2014-09-30 | 2015-01-07 | 清华大学 | Wind power integration peak-load regulating balance judgment method based on scene library |
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