CN113361129B - New energy output simulation method and system considering time and space scales - Google Patents
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Abstract
The invention relates to a new energy output simulation method and system considering time and space scales, which is characterized by comprising the following steps: 1) Dividing the acquired new energy power generation historical data set according to a time scale and a space scale to obtain a corresponding sample data set; 2) And performing time sequence simulation on each sample data set, generating a random sample of the power generation output of the new energy based on a time sequence simulation result, and establishing a new energy output probability model capable of reflecting time and space characteristics based on the random sample. Compared with the traditional method, the method can reflect the correlation probability distribution characteristics of the power generated by the new energy under different time scales and space scales, is used for time sequence simulation of the power generated by the new energy based on conditional probability and multivariate kernel density estimation, adopts a selective sampling method to sample, and improves the practicability and applicability of a modeling algorithm of the power generated by the new energy. Therefore, the method can be widely applied to the field of power grid stability analysis.
Description
Technical Field
The invention relates to a new energy output simulation method and system considering time and space scales, and belongs to the field of power grid stability analysis.
Background
With the annual increase of new energy power generation grid-connected capacity, the planning and operation of the power system face more and more uncertainties, the probability trend is applied more and more widely, and therefore higher requirements are put forward on a probability model of new energy output.
In power grid planning, a large number of refined operation modes are usually required to be generated based on an accurate new energy output probability model, but the traditional new energy output probability model based on wind speed/illumination intensity cannot reflect objective problems of time and space characteristics, and basic data cannot be provided for refined planning calculation and analysis of a power system, so that the rationality of power grid safety and stability analysis cannot be guaranteed.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a new energy output simulation method and a system which consider different time scales such as time, day, month, season, year and the like and different space scales such as regional level and the like, and can realize more accurate and reliable simulation of new energy output.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the present invention provides a new energy output simulation method considering time and space scales, comprising the following steps:
1) Dividing the acquired new energy power generation historical data set according to a time scale and a space scale to obtain a corresponding sample data set;
2) And carrying out time sequence simulation on each sample data set corresponding to different time scales and space scales, generating a random sample of the power generation output of the new energy based on the time sequence simulation result, and establishing a new energy output probability model capable of reflecting time and space characteristics based on the random sample.
Further, in the step 1), the method for obtaining the corresponding sample data set by dividing the obtained new energy power generation historical data set according to the time scale and the space scale comprises the following steps:
1.1 Acquiring a new energy power generation historical data set;
1.2 Dividing the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding initial sample actual measurement data set;
1.3 Screening the actual measurement data sets of all the samples, and replacing the obtained error data by using an interpolation method to obtain the sample data sets corresponding to all the time scales and the space scales.
Further, in the step 1.1), the obtained historical data set of new energy power generation includes geographic information, theoretical output, actual output and electric power and electric quantity balance data.
Further, in the step 2), the method for performing time sequence simulation on each sample data set and generating a random sample of the new energy power generation output based on the time sequence simulation result includes the following steps:
2.1 Performing time sequence simulation on the sample data set based on the conditional probability and the multivariable kernel density estimation to obtain a new energy power generation output probability density function;
2.2 Based on the obtained new energy power generation output probability density function, calculating to obtain the joint probability distribution of the start and stop moments;
2.3 Sampling by adopting a rounding method according to the joint probability distribution of the starting and ending moments to generate a random sample of the power generation capacity of the new energy.
Further, in the step 2.1), the probability density function of the new energy power generation output is:
wherein f h (P i-1 )、f H (P i-1 ,P i ) Respectively f (P) i-1 ) And f (P) i-1 ,P i ) Is a kernel estimation of (1); p (P) i-1 、P i Generating power for new energy sources at adjacent moments; h is a univariate kernel estimation f h (P i-1 ) N represents the sample size; k is a Gaussian kernel function; p (P) j,i The output at moment j under the condition that the output at moment i is known; p (P) j,i-1 The output at the moment j under the condition that the output at the moment i-1 is known is shown; h is a i 、h i-1 Respectively single variable f hi (P i ) And estimate f hi-1 (P i-1 ) Is not limited to the bandwidth of the (c).
Further, in the step 2.3), the method for sampling and generating the random sample of the new energy power generation capacity by adopting a sorting method comprises the following steps:
for the new energy probability density function f (P i |P i-1 ) Let f (P i |P i-1 ) The value range of (a) is [ a, b ]]，f(P i |P i-1 ) The maximum value of the new energy power generation system is M, the corresponding sampling sample is c, the generated random sample is e, if the sampling value is less than or equal to c and less than or equal to e/M, e is accepted, and e is used as a random sample of the new energy power generation power.
In a second aspect of the present invention, a new energy output simulation system considering time and space scales is provided, which includes: the sample data set acquisition module is used for carrying out data division on the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding sample data set; and the simulation calculation module is used for carrying out time sequence simulation on each sample data set corresponding to different time scales and space scales, generating a random sample of the power generation output of the new energy based on the time sequence simulation result, and establishing a new energy output probability model capable of reflecting time and space characteristics based on the random sample.
Further, the sample dataset acquisition module comprises: the data acquisition module is used for acquiring a new energy power generation historical data set, and comprises information such as geographic information, theoretical output, actual output, electric power and electric quantity balance and the like; the data dividing module is used for dividing the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding initial sample actual measurement data set; and the data preprocessing module is used for screening the actual measurement data sets of all the samples, and replacing the screened error data by using an interpolation method to obtain the sample data sets corresponding to all the time scales and the space scales.
Further, the simulation calculation module includes: the time sequence simulation module is used for performing time sequence simulation on the sample data set based on the conditional probability and the multivariate kernel density estimation to obtain a new energy power generation output probability density function; the joint probability distribution calculation module is used for calculating the joint probability distribution after time sequence simulation based on the obtained power generation output probability density function of the new energy; and the selecting and sampling module is used for sampling and generating random samples of the power generation capacity of the new energy by adopting a selecting method according to the joint probability distribution of the starting and ending moments.
Further, the probability density function of the new energy power generation output obtained by the time sequence simulation module is as follows:
wherein f h (P i-1 )、f H (P i-1 ,P i ) Respectively f (P) i-1 ) And f (P) i-1 ,P i ) Is a kernel estimation of (1); p (P) i-1 、P i Generating power for new energy sources at adjacent moments; h is a univariate kernel estimation f h (P i-1 ) N represents the sample size; k is a Gaussian kernel function; p (P) j,i The output at moment j under the condition that the output at moment i is known; p (P) j,i-1 The output at the moment j under the condition that the output at the moment i-1 is known is shown; h is a i 、h i-1 Respectively single variable f hi (P i ) And estimate f hi-1 (P i-1 ) Is not limited to the bandwidth of the (c).
Due to the adoption of the technical scheme, the invention has the following advantages:
(1) The invention provides a new energy power generation output probability modeling method considering time and space scales, which can reflect the correlation probability distribution characteristics of the new energy power generation output under different time scales and space scales compared with the traditional method.
(2) The invention is used for time sequence simulation of the new energy power generation output based on conditional probability and multivariable kernel density estimation, adopts a selective sampling method to sample samples, and improves the practicability and applicability of the new energy power generation output probability modeling algorithm.
Therefore, the method can be widely applied to the field of power grid stability analysis.
Drawings
FIG. 1 is a flow chart of modeling the probability of new energy output taking into consideration the time and space scales.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and examples.
According to the new energy output simulation method and system considering time and space scales, firstly, based on historical data of new energy power generation, sample division is carried out according to time scales of different moments, days, months, seasons, years and the like and space scales of different areas and the like; then, performing time sequence simulation on the sample actual measurement data based on the conditional probability and the multivariate kernel density estimation method; and finally, acquiring a new energy power generation output probability sample by adopting a selective sampling method according to the start-stop moment joint probability distribution generated by simulation.
As shown in fig. 1, the new energy output simulation method considering time and space scales provided by the invention comprises the following steps:
1) And carrying out data division on the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding sample data set.
Specifically, the method comprises the following steps:
1.1 A) acquiring data. And acquiring a new energy power generation historical data set which comprises geographic information, theoretical output, actual output, electric power and electric quantity balance and other information. The new energy power generation historical data set can adopt power generation historical data derived by a dispatching system.
1.2 Data partitioning. Dividing the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding initial sample actual measurement data set.
When the new energy power generation historical data set is divided, the time scales of new energy output such as the year, quarter, month and sunrise and the space scales of the geographical position of the new energy power station are considered, the condition correlation method is sampled, and the historical data of the actual new energy power station are subjected to sample division to obtain a sample set sigma S (condition i|condition j).
1.3 Data preprocessing). And screening the actual measurement data sets of all the initial samples, finding out unreasonable data (such as null data, collected error data, out-of-limit data, format error data and the like), and replacing the unreasonable data by using an interpolation method to obtain sample data sets corresponding to all the time scales and the space scales.
2) And carrying out time sequence simulation on each sample data set corresponding to different time scales and space scales, generating a random sample of the power generation output of the new energy based on the time sequence simulation result, and establishing a new energy output probability model capable of reflecting time and space characteristics based on the random sample.
Specifically, the method comprises the following steps:
2.1 A) timing simulation. And performing time sequence simulation on the divided sample data set based on the conditional probability and the multivariable kernel density estimation to respectively obtain new energy power generation output probability density functions (such as new energy power generation output probability density functions of different areas of hours, days, months and quarters) under different time-space scales.
Based on multivariate kernel density estimation and conditional probability theory, performing time sequence simulation on the sample data set to obtain joint probability distribution of start and stop moments of new energy power generation, wherein a probability density function is expressed as follows:
wherein P is i-1 、P i Generating power for new energy sources at adjacent moments; f represents a probability density function of new energy power generation output; f (P) i-1 ) Representing new energy at i-1 momentGenerating a power generation probability density function; f (P) i-1 ,P i ) Represents p i-1 、p i Is a joint probability density function of (a).
Estimating f (P) by adopting univariate kernel density estimation theory i-1 ) The f (P) is estimated by adopting a multivariable kernel density estimation theory with the variable number of 2 i-1 ,P i ) The calculation formula is as follows:
wherein f h (P i-1 )、f H (P i-1 ,P i ) Respectively f (P) i-1 ) And f (P) i-1 ,P i ) Is a kernel estimation of (1); h is a univariate kernel estimation f h (P i-1 ) N represents the sample size; k is a Gaussian kernel function; p (P) j,i The output at moment j under the condition that the output at moment i is known; p (P) j,i-1 The output at the moment j under the condition that the output at the moment i-1 is known is shown; h is a i 、h i-1 Respectively single variableAnd estimationIs not limited to the bandwidth of the (c).
Namely, the probability density function of the new energy power generation output is as follows:
2.2 Joint probability calculation). And calculating to obtain the joint probability distribution of the new energy power generation output of a certain time sequence based on the obtained new energy power generation output probability density function.
2.3 A) selecting samples. Sampling by adopting a rounding method according to the joint probability distribution of the starting and ending moments to generate a random sample of the power generation capacity of the new energy.
Compared with the traditional random sampling method, the selective sampling method is adopted, and the method is suitable for new energy output probability density functions with complex expressions. For the new energy probability density function f (P i |P i-1 ) Let f (P i |P i-1 ) The value range of (a) is [ a, b ]]，f(P i |P i-1 ) The maximum value of (2) is M, the corresponding sampling sample is c, the generated random sample is e, and if the sampling value satisfies c less than or equal to e/M, e is accepted.
Based on the new energy output simulation method considering the time and the space scale, the invention also provides a new energy output simulation system considering the time and the space scale, which comprises the following steps: the sample data set acquisition module is used for carrying out data division on the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding sample data set; and the simulation calculation module is used for carrying out time sequence simulation on each sample data set corresponding to different time scales and space scales, generating a random sample of the power generation output of the new energy based on the time sequence simulation result, and establishing a new energy output probability model capable of reflecting time and space characteristics based on the random sample.
Further, the sample dataset acquisition module comprises: the historical data acquisition module is used for acquiring a new energy power generation historical data set, and comprises information such as geographic information, theoretical output, actual output, electric power and electric quantity balance and the like; the data dividing module is used for dividing the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding initial sample actual measurement data set; and the data preprocessing module is used for screening the actual measurement data sets of all the samples, and replacing the screened error data by using an interpolation method to obtain the sample data sets corresponding to all the time scales and the space scales.
Further, the simulation calculation module includes: the time sequence simulation module is used for performing time sequence simulation on the sample data set based on the conditional probability and the multivariate kernel density estimation to obtain a new energy power generation output probability density function; the joint probability distribution calculation module is used for calculating the joint probability distribution after time sequence simulation based on the obtained power generation output probability density function of the new energy; and the selecting and sampling module is used for sampling and generating random samples of the power generation capacity of the new energy by adopting a selecting method according to the joint probability distribution of the starting and ending moments.
The foregoing embodiments are only for illustrating the present invention, wherein the structures, connection modes, manufacturing processes, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solutions of the present invention should not be excluded from the protection scope of the present invention.
Claims (8)
1. A new energy output simulation method considering time and space scales is characterized by comprising the following steps:
1) Dividing the acquired new energy power generation historical data set according to a time scale and a space scale to obtain a corresponding sample data set;
step 1) comprises the steps of:
1.1 Acquiring a new energy power generation historical data set;
1.2 Dividing the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding initial sample actual measurement data set; the time scale division is to divide the new energy power generation historical data set according to different moments, days, months, seasons and years;
1.3 Screening the actual measurement data sets of all the samples, and replacing the obtained error data by using an interpolation method to obtain sample data sets corresponding to all the time scales and the space scales;
2) Performing time sequence simulation on each sample data set corresponding to different time scales and space scales, generating a random sample of new energy power generation output based on a time sequence simulation result, and establishing a new energy output probability model capable of reflecting time and space characteristics based on the random sample;
step 2) comprises the steps of:
2.1 Performing time sequence simulation on the sample data set based on the conditional probability and the multivariable kernel density estimation to obtain a new energy power generation output probability density function;
2.2 Based on the obtained new energy power generation output probability density function, calculating to obtain the joint probability distribution of the start and stop moments;
2.3 Sampling by adopting a rounding method according to the joint probability distribution of the starting and ending moments to generate a random sample of the power generation capacity of the new energy.
2. The new energy output simulation method considering time and space scales as claimed in claim 1, wherein: in the step 1.1), the obtained new energy power generation historical data set comprises geographic information, theoretical output, actual output and electric power and electric quantity balance data.
3. The new energy output simulation method considering time and space scales as claimed in claim 1, wherein: in the step 2.1), the probability density function of the new energy power generation output is:
wherein f h (P i-1 )、f H (P i-1 ,P i ) Respectively f (P) i-1 ) And f (P) i-1 ,P i ) Is a kernel estimation of (1); p (P) i-1 、P i Generating power for new energy sources at adjacent moments; h is a univariate kernel estimation f h (P i-1 ) N represents the sample size; k is a gaussian kernel function; p (P) j,i The output at moment j under the condition that the output at moment i is known; p (P) j,i-1 The output at the moment j under the condition that the output at the moment i-1 is known is shown; h is a i 、h i-1 Respectively single variable f hi (P i ) And estimate f hi-1 (P i-1 ) Is not limited to the bandwidth of the (c).
4. The new energy output simulation method considering time and space scales as claimed in claim 1, wherein: in the step 2.3), the method for sampling and generating the random sample of the new energy power generation capacity by adopting a rounding method comprises the following steps:
for the new energy probability density function f (P i |P i-1 ) Let f (P i |P i-1 ) The value range of (a) is [ a, b ]]，f(P i |P i-1 ) The maximum value of the new energy power generation system is M, the corresponding sampling sample is c, the generated random sample is e, if the sampling value is less than or equal to c and less than or equal to e/M, e is accepted, and e is used as a random sample of the new energy power generation power.
5. A new energy output simulation system taking into account time and space dimensions, suitable for use in a method according to any of claims 1-4, comprising: the sample data set acquisition module is used for carrying out data division on the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding sample data set; and the simulation calculation module is used for carrying out time sequence simulation on each sample data set corresponding to different time scales and space scales, generating a random sample of the power generation output of the new energy based on the time sequence simulation result, and establishing a new energy output probability model capable of reflecting time and space characteristics based on the random sample.
6. The new energy output simulation system considering both temporal and spatial scales as claimed in claim 5, wherein said sample data set acquisition module comprises: the data acquisition module is used for acquiring a new energy power generation historical data set, and comprises information such as geographic information, theoretical output, actual output, electric power and electric quantity balance and the like; the data dividing module is used for dividing the acquired new energy power generation historical data set according to the time scale and the space scale to obtain a corresponding initial sample actual measurement data set; and the data preprocessing module is used for screening the actual measurement data sets of all the samples, and replacing the screened error data by using an interpolation method to obtain the sample data sets corresponding to all the time scales and the space scales.
7. The new energy output simulation system considering time and space scales as claimed in claim 5, wherein said simulation calculation module comprises: the time sequence simulation module is used for performing time sequence simulation on the sample data set based on the conditional probability and the multivariate kernel density estimation to obtain a new energy power generation output probability density function; the joint probability distribution calculation module is used for calculating the joint probability distribution after time sequence simulation based on the obtained power generation output probability density function of the new energy; and the selecting and sampling module is used for sampling and generating random samples of the power generation capacity of the new energy by adopting a selecting method according to the joint probability distribution of the starting and ending moments.
8. The new energy output simulation system considering time and space scales as claimed in claim 7, wherein the new energy power generation output probability density function obtained by the time sequence simulation module is:
wherein f h (P i-1 )、f H (P i-1 ,P i ) Respectively f (P) i-1 ) And f (P) i-1 ,P i ) Is a kernel estimation of (1); p (P) i-1 、P i Generating power for new energy sources at adjacent moments; h is a univariate kernel estimation f h (P i-1 ) N represents the sample size; k is a gaussian kernel function; p (P) j,i The output at moment j under the condition that the output at moment i is known; p (P) j,i-1 The output at the moment j under the condition that the output at the moment i-1 is known is shown; h is a i 、h i-1 Respectively single variableAnd estimate->Is not limited to the bandwidth of the (c).
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| CN108074038A (en) * | 2017-12-11 | 2018-05-25 | 国网江苏省电力有限公司经济技术研究院 | A kind of power generation analogy method for considering regenerative resource and load multi-space distribution character |
| CN109038675A (en) * | 2018-08-31 | 2018-12-18 | 中国南方电网有限责任公司电网技术研究中心 | Modeling method based on wind power fluctuation multi-scale decomposition |
| CN109492315A (en) * | 2018-11-19 | 2019-03-19 | 西安交通大学 | A kind of temporal and spatial correlations scene series model method based on Copula function |
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