CN112633630A - Multi-energy power fluctuation interval identification method - Google Patents

Multi-energy power fluctuation interval identification method Download PDF

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CN112633630A
CN112633630A CN202011324285.6A CN202011324285A CN112633630A CN 112633630 A CN112633630 A CN 112633630A CN 202011324285 A CN202011324285 A CN 202011324285A CN 112633630 A CN112633630 A CN 112633630A
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范强
文贤馗
陈园园
李博文
古庭赟
祝健杨
苏立
林呈辉
徐梅梅
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Guizhou Power Grid Co Ltd
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Abstract

The invention discloses a multi-energy power fluctuation interval identification method, which comprises the steps of collecting historical output data of an intermittent power supply in an analysis area; judging and processing the collected historical output data set according to the data; defining power fluctuation indexes of wind power, small hydropower stations, photovoltaic power and other power supplies; obtaining a data sample histogram according to the power fluctuation index dataset, and fitting a probability density function of the data sample through parameter estimation and non-parameter estimation and calculating a corresponding cumulative probability distribution function; utilizing the cumulative probability distribution function to obtain a corresponding inverse cumulative probability distribution function; obtaining a sample interval of a fluctuation data sample appearing in probability through automatic identification, and calculating a multi-energy power fluctuation data sample existing interval according to any probability value; the technical problems that an intermittent power supply in the prior art is high in random fluctuation and poor in self controllability are solved.

Description

Multi-energy power fluctuation interval identification method
Technical Field
The invention belongs to the technical field of intermittent power supply operation and evaluation, and particularly relates to a multi-energy power fluctuation interval identification method.
Background
The small hydropower stations are mostly radial-flow small hydropower stations, which are influenced by meteorological conditions, hysteresis effect, accumulation effect and the like, and the output has great randomness. This makes the fluctuation characteristics of the output power of the region containing a large number of small hydropower stations complicated. Meanwhile, the radial-flow small hydropower station lacks of storage capacity adjustment and has extremely limited active adjusting capacity. The wind power plant is influenced by wind speed change to output large fluctuation, meanwhile, the photovoltaic power generation is influenced by meteorological conditions to have fluctuation, intermittence and low controllability, the problem of inaccurate power prediction also exists, the photovoltaic power generation increases the difficulty in regional active power regulation, and the intermittent power supply has the defects of strong random fluctuation and poor controllability.
The invention content is as follows:
the technical problem to be solved by the invention is as follows: the method for identifying the multi-energy power fluctuation interval is provided to solve the technical problems of strong random fluctuation, poor self controllability and the like of the intermittent power supply in the prior art.
The technical scheme of the invention is as follows:
a multi-energy power fluctuation interval identification method comprises the following steps:
step 1, collecting historical output data of an intermittent power supply in an analysis area;
step 2, judging and processing the collected historical output data set according to data;
step 3, defining power fluctuation indexes delta P of wind power, small hydropower stations, photovoltaic power and other power supplies;
step 4, obtaining a data sample histogram according to the data set of the power fluctuation index delta P, and fitting a probability density function f (x) of the data sample through parameter estimation and non-parameter estimation;
step 5, calculating a corresponding cumulative probability distribution (CDF) function:
Figure BDA0002793847540000021
step 6, utilizing cumulative probability distribution (CDF) function to obtain corresponding inverse cumulative probability distribution (iCDF) function F (x)-1；
Step 7, automatically identifying and obtaining the fluctuating data sample with the probability P through the step 6MNThe sample interval appearing is (xi)-,ξ+) And meanwhile, calculating the existence interval of the multi-energy power fluctuation data sample according to any probability value.
The historical output data of the intermittent power source comprises: historical output data sets P & ltgtPwp, Psh, Ppv, Poth & ltth & gt & lt/th & gt of wind power, small hydropower, photovoltaic and other power supplies, rated capacity data sets Pn & ltwpn, Pshn, Ppvn, Pothn & lt & gt & lt/th & gt & lt & gt & lt/th & gt & lt/; pwp is historical wind power output, Psh is historical small water power output, Ppv is historical photovoltaic output, and Poth is historical output of other power supplies; pwpn is the rated capacity of wind power, Pshn is the rated capacity of small hydropower, Ppvn is the rated capacity of photovoltaic, and Pothn is the rated capacity of other power supplies; pwpss is the starting and stopping state of the wind turbine generator, Pshs is the starting and stopping state of the small hydroelectric turbine generator, Ppvss is the starting and stopping state of the photovoltaic generator, and Pothss is the starting and stopping state of other power generator sets;
the method for judging and processing the collected historical output data set according to the data comprises the following steps:
step 2.1, when the phi-th data PφIs not 0 and has Pφ+10, and when Pss is in the starting state, P is orderedφ+1＝Pφ；
Step 2.2, when the phi-th data PφIf Pn is greater than Pn, let Pφ＝Pn。
Step 2.3, when the phi data Pφ<0, then P is orderedφ＝0；
Step 2.4, when the phi-th data P is judgedφAnd Pφ+1Satisfy the relationship
Figure BDA0002793847540000031
Time of flightLet Pφ+1＝Pφ。
The calculation method of the power fluctuation amount index delta P in the step 3 comprises the following steps:
Figure BDA0002793847540000032
in the formula: pwpi、Pshi、Ppvi、PothiAnd Pwpi+1、Pshi+1、Ppvi+1、Pothi+1And sampling wind power historical output, small hydropower historical output, photovoltaic historical output and other power supply historical output for the ith and i +1 times respectively.
The method for establishing the probability density function f (x) in the step 4 comprises the following steps:
step 4.1, establishing a normal distribution probability density function of parameter estimation as follows:
Figure BDA0002793847540000033
x is the data sample, μ is the sample mean, σ is the covariance;
step 4.2, establishing a t distribution probability density function of parameter estimation as follows:
Figure BDA0002793847540000034
wherein x is a data sample and n is a degree of freedom;
and 4.3, performing nonparametric estimation on a Gaussian kernel probability density function based on a Parzen window method as follows:
Figure BDA0002793847540000041
Figure BDA0002793847540000042
wherein x is a data sample, h is a window width, and m is a sample observed value;
step 4.4, obtaining a practical data sample histogram, a normal distribution probability density estimation function curve, a t distribution probability density estimation function curve and a Gaussian kernel density estimation function curve respectively through a power fluctuation amount index delta P data set;
step 4.5, constructing a probability density function f (x) of the data sample through a normal distribution probability density estimation function curve f1(x), a t distribution probability density estimation function curve f2(x) and a Gaussian kernel density estimation function curve f3 (x);
f(x)＝υf1(x)+(σ-υ)f2(x)+(1-σ)f3(x)
where the parameter υ belongs to (0,1), σ belongs to (0,1) and has υ + σ equal to 1, the parameter υ, σ is found from the observed values of the given data sample x.
For cumulative probability distribution (CDF) function
Figure BDA0002793847540000043
Let PN＜PM，ξ-＜ξ+Corresponding probability PM、PNIn time, there are:
P(X＜ξ+)＝PM
P(X＜ξ-)＝PN
P(ξ-＜X＜ξ+)＝PM-PN＝PMN。
the inverse cumulative probability distribution function is:
F(x)-1＝ξ＝iF(p)
when P is equal to PM，p＝PNIn time, there are:
ξ+＝iF(PM)
ξ-＝iF(PN)。
the invention has the beneficial effects that:
the method combines the actual historical operating data of the multi-energy sources to construct the power fluctuation indexes of the wind power, the small hydropower station, the photovoltaic power and other power sources, analyzes the output characteristics of the wind power, the small hydropower station, the photovoltaic power and other power sources which are dominant in the region, comprehensively constructs the probability density function of a data sample through a normal distribution probability density estimation function curve, a t distribution probability density estimation function curve and a Gaussian kernel density estimation function curve, automatically identifies the sample interval distribution of the fluctuation data sample in each probability, and finally can calculate the existence interval of the multi-energy power fluctuation data sample according to any probability value; the method can describe the power fluctuation characteristics of multiple energy sources and accurately identify the power fluctuation interval, effectively evaluate the power fluctuation characteristics of the multiple energy sources, formulate an effective means for regulation and control, and provide guarantee for the consumption of the energy sources; the method solves the technical problems that the multi-energy power fluctuation characteristic cannot be accurately described and the power fluctuation interval cannot be accurately identified, multi-energy power fluctuation cannot be inhibited by adopting a targeted technical means and the like in the prior art, and the defects that an intermittent power supply is high in random fluctuation and poor in self controllability and the like.
The specific implementation mode is as follows:
a multi-energy power fluctuation interval identification method comprises the following steps:
step 1, collecting historical output data sets P & ltgtPwp, Psh, Ppv and Poth & ltth & gt, rated capacity data sets Ppn & lthn, Ppvn and Pothn & ltth & gt & lt & gt, and unit start-stop state information sets Pss & ltwpss, Pshs, Ppvss and Pothss & lt & gt in an analysis area.
Wherein Pwp is the historical output of wind power, Psh is the historical output of small hydropower, Ppv is the historical output of photovoltaic, and Poth is the historical output of other power supplies; pwpn is the rated capacity of wind power, Pshn is the rated capacity of small hydropower, Ppvn is the rated capacity of photovoltaic, and Pothn is the rated capacity of other power supplies; pwpss is the wind turbine generator system and opens and stop the state, and Pshs is little hydroelectric generator system and opens and stop the state, and Ppvss is photovoltaic unit and opens and stop the state, and Pothss is other power generating set and opens and stop the state. The other power sources comprise intermittent power sources such as coal bed methane power generation and biological power generation in the analysis area.
And 2, judging and processing the collected historical output data set according to the following conditions.
2.1 when the phi-th data PφIs not 0 and has Pφ+10, and when Pss is in the starting state, P is enabledφ+1＝Pφ。
2.2 when the phi-th data PφIf Pn is greater than Pn, let Pφ＝Pn。
2.3 when the phi data Pφ<0, then P is orderedφ＝0。
2.4 when judging the phi-th data PφAnd Pφ+1Satisfy the relationship
Figure BDA0002793847540000061
Pφ+1＝Pφ。
Step 3, defining power fluctuation quantity indexes delta P of wind power, small hydropower, photovoltaic and other power supplies
Figure BDA0002793847540000062
Wherein Pwpi、Pshi、Ppvi、Pothi，Pwpi+1、Pshi+1、Ppvi+1、Pothi+1The historical output of wind power, the historical output of small hydropower station, the historical output of photovoltaic and the historical output of other power supplies are sampled for the ith and i +1 times respectively.
And 4, obtaining a data sample histogram by using the power fluctuation index delta P data set obtained in the step 3, and fitting a probability density function f (x) of the data sample through parameter estimation and non-parameter estimation.
4.1 Normal distribution probability density function with parameter estimation is:
Figure BDA0002793847540000071
x is the data sample, μ is the sample mean, and σ is the covariance.
4.2 the t-distribution probability density function using parameter estimation is:
Figure BDA0002793847540000072
where x is the data sample and n is the degree of freedom.
4.3 the probability density function of the Gaussian kernel based on the Parzen window method by using nonparametric estimation is as follows:
Figure BDA0002793847540000073
Figure BDA0002793847540000074
where x is the data sample, h is the window width, and m is the sample observed value.
4.4, the power fluctuation amount index delta P data set obtained in the step 3 is utilized to respectively obtain an actual data sample histogram, a normal distribution probability density estimation function curve, a t distribution probability density estimation function curve and a Gaussian kernel density estimation function curve through 4.1-4.3.
4.5 probability density function f (x) fitted to the data samples.
And constructing a probability density function f (x) of the data sample by a normal distribution probability density estimation function curve f1(x), a t distribution probability density estimation function curve f2(x) and a Gaussian kernel density estimation function curve f3 (x).
f(x)＝υf1(x)+(σ-υ)f2(x)+(1-σ)f3(x)。
Where the parameter υ e (0,1), σ e (0,1) and has υ + σ ═ 1. The parameter v, σ is found by the observed value of the given data sample x.
Step 5, on the basis of obtaining the probability density estimation mathematical model, calculating a corresponding cumulative probability distribution (CDF) function:
Figure BDA0002793847540000081
for the cumulative probability distribution function F (x), let PN＜PM，ξ-＜ξ+Corresponding probability PM、PNIn time, there are:
P(X＜ξ+)＝PM
P(X＜ξ-)＝PN
P(ξ-＜X＜ξ+)＝PM-PN＝PMN
step 6, utilizing cumulative probability distribution (CDF) function to obtain corresponding inverse cumulative probability distribution (iCDF) function F (x)-1。
Let the inverse cumulative probability distribution function be:
F(x)-1＝ξ＝iF(p)
when P is equal to PM，p＝PNIn time, there are:
ξ+＝iF(PM)
ξ-＝iF(PN)
step 7, automatically identifying and obtaining the fluctuating data sample with the probability P through the step 6MNThe sample interval appearing is (xi)-,ξ+) Meanwhile, the existence interval of the multi-energy power fluctuation data sample can be calculated according to any probability value.

Claims (8)

1. A multi-energy power fluctuation interval identification method comprises the following steps:
step 1, collecting historical output data of an intermittent power supply in an analysis area;
step 2, judging and processing the collected historical output data set according to data;
step 3, defining power fluctuation indexes delta P of wind power, small hydropower stations, photovoltaic power and other power supplies;
step 4, obtaining a data sample histogram according to the data set of the power fluctuation index delta P, and fitting a probability density function f (x) of the data sample through parameter estimation and nonparametric estimation;
step 5, calculating a corresponding cumulative probability distribution (CDF) function:
Figure FDA0002793847530000011
step 6, utilizing cumulative probability distribution (CDF) function to obtain corresponding inverse cumulative probability distribution (iCDF) function F (x)-1；
Step 7, automatically identifying and obtaining the fluctuating data sample with the probability P through the step 6MNThe sample interval of appearance is (xi)-,ξ+) And meanwhile, calculating the existence interval of the multi-energy power fluctuation data sample according to any probability value.
2. The method according to claim 1, wherein the method further comprises: the historical output data of the intermittent power source comprises: historical output data sets P & ltgtPwp, Psh, Ppv, Poth & ltth & gt & lt/th & gt of wind power, small hydropower, photovoltaic and other power supplies, rated capacity data sets Pn & ltwpn, Pshn, Ppvn, Pothn & lt & gt & lt/th & gt & lt & gt & lt/th & gt; pwp is historical wind power output, Psh is historical small hydropower output, Ppv is historical photovoltaic output, and Poth is historical output of other power supplies; pwpn is the rated capacity of wind power, Pshn is the rated capacity of small hydropower, Ppvn is the rated capacity of photovoltaic, and Pothn is the rated capacity of other power supplies; pwpss is the wind turbine generator system and opens and stop the state, and Pshs is little hydroelectric generator system and opens and stop the state, and Ppvss is photovoltaic unit and opens and stop the state, and Pothss is other power generating set and opens and stop the state.
3. The method according to claim 1, wherein the method further comprises: the method for judging and processing the collected historical output data set according to the data comprises the following steps:
step 2.1, when the phi-th data PφIs not 0 and has Pφ+10, and when Pss is in the starting state, P is orderedφ+1＝Pφ；
Step 2.2, when the phi-th data PφIf Pn is greater than Pn, let Pφ＝Pn。
Step 2.3, when the phi data Pφ<0, then P is orderedφ＝0；
Step 2.4, when the phi-th data P is judgedφAnd Pφ+1Satisfy the relationship
Figure FDA0002793847530000021
When it is, let Pφ+1＝Pφ。
4. The method according to claim 1, wherein the method further comprises: the calculation method of the power fluctuation amount index delta P in the step 3 comprises the following steps:
Figure FDA0002793847530000022
in the formula: pwpi、Pshi、Ppvi、PothiAnd Pwpi+1、Pshi+1、Ppvi+1、Pothi+1And respectively sampling wind power historical output, small hydropower historical output, photovoltaic historical output and other power supply historical output for the ith and i +1 th times.
5. The method according to claim 1, wherein the method further comprises: the method for establishing the probability density function f (x) in the step 4 comprises the following steps:
step 4.1, establishing a normal distribution probability density function of parameter estimation as follows:
Figure FDA0002793847530000031
x is the data sample, μ is the sample mean, σ is the covariance;
step 4.2, establishing a t distribution probability density function of parameter estimation as follows:
Figure FDA0002793847530000032
wherein x is a data sample and n is a degree of freedom;
and 4.3, performing nonparametric estimation by using a Gaussian kernel probability density function based on a Parzen window method as follows:
Figure FDA0002793847530000033
Figure FDA0002793847530000034
wherein x is a data sample, h is a window width, and m is a sample observed value;
step 4.4, obtaining a practical data sample histogram, a normal distribution probability density estimation function curve, a t distribution probability density estimation function curve and a Gaussian kernel density estimation function curve respectively through a power fluctuation amount index delta P data set;
and 4.5, constructing a probability density function f (x) of the data sample through a normal distribution probability density estimation function curve f1(x), a t distribution probability density estimation function curve f2(x) and a Gaussian kernel density estimation function curve f3 (x).
6. The method according to claim 5, wherein the method further comprises: the probability density function f (x) of the data samples is expressed as:
f(x)＝υf1(x)+(σ-υ)f2(x)+(1-σ)f3(x)
where the parameter υ belongs to (0,1), σ belongs to (0,1) and has υ + σ equal to 1, the parameter υ, σ is found from the observed values of the given data sample x.
7. The method according to claim 1, wherein the method further comprises: for cumulative probability distribution (CDF) function
Figure FDA0002793847530000041
Let PN＜PM，ξ-＜ξ+Corresponding probability PM、PNIn time, there are:
P(X＜ξ+)＝PM
P(X＜ξ-)＝PN
P(ξ-＜X＜ξ+)＝PM-PN＝PMN。
8. the method according to claim 1, wherein the method further comprises: let the inverse cumulative probability distribution function be:
F(x)-1＝ξ＝iF(p)
when P is equal to PM，p＝PNIn time, there are:
ξ+＝iF(PM)
ξ-＝iF(PN)。
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