CN113361129A - New energy output simulation method and system considering time and space scales - Google Patents

New energy output simulation method and system considering time and space scales Download PDF

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CN113361129A
CN113361129A CN202110709291.1A CN202110709291A CN113361129A CN 113361129 A CN113361129 A CN 113361129A CN 202110709291 A CN202110709291 A CN 202110709291A CN 113361129 A CN113361129 A CN 113361129A
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王智冬
郑华
彭冬
薛雅玮
张天琪
谢莉
赵朗
王雪莹
刘宏杨
李一铮
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State Grid Economic and Technological Research Institute
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Abstract

The invention relates to a new energy output simulation method and system considering time and space scales, which are characterized by comprising the following steps of: 1) performing data division on 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 new energy generated 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. Compared with the traditional method, the method can reflect the correlation probability distribution characteristics of the new energy power generation output under different time scales and space scales, is used for the time sequence simulation of the new energy power generation output based on the conditional probability and the multivariate kernel density estimation, and adopts a selective sampling method to sample samples, so that the practicability and the applicability of the new energy power generation output probability modeling algorithm are improved. Therefore, the method can be widely applied to the field of power grid stability analysis.

Description

New energy output simulation method and system considering time and space scales
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 the grid-connected capacity of new energy power generation, the planning and operation of a power system face more and more uncertainties, the application of probability trend is more and more extensive, and higher requirements are provided for a probability model of new energy output.
In power grid planning, a large number of refined operation modes are generally generated based on an accurate new energy output probability model, and the traditional new energy output probability model based on wind speed/illumination intensity cannot reflect objective problems of time and space characteristics and cannot provide basic data for refined planning calculation and analysis of a power system, so that the reasonability of power grid safety and stability analysis cannot be guaranteed.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a new energy output simulation method and system in consideration of different time scales such as time, day, month, season, year, etc., and different spatial scales such as region level, etc., so as to realize more accurate and reliable simulation of new energy output.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect of the present invention, a new energy contribution simulation method considering time and space dimensions is provided, which includes the following steps:
1) performing data division on 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) time sequence simulation is carried out on each sample data set corresponding to different time scales and space scales, random samples of new energy power generation output are generated based on time sequence simulation results, and a new energy output probability model capable of reflecting time and space characteristics is established based on the random samples.
Further, in the step 1), the method for performing data partitioning on 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 includes 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 a time scale and a space scale to obtain a corresponding initial sample actual measurement data set;
1.3) screening the actually measured data sets of the samples, and replacing the obtained error data by using an interpolation method to obtain the sample data sets corresponding to the time scales and the space scales.
Further, in the step 1.1), the acquired new energy power generation historical data set 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 the random sample of the new energy generated output based on the time sequence simulation result includes the following steps:
2.1) carrying out time sequence simulation on the sample data set based on the conditional probability and multivariate nuclear density estimation to obtain a new energy power generation output probability density function;
2.2) calculating to obtain joint probability distribution of starting and stopping moments based on the obtained new energy power generation output probability density function;
and 2.3) sampling to generate a random sample of the new energy generated output by adopting a truncation method according to the joint probability distribution at the starting and stopping moments.
Further, in the step 2.1), the new energy power generation output probability density function is as follows:
Figure BDA0003132620530000021
wherein f ish(Pi-1)、fH(Pi-1,Pi) Are respectively f (P)i-1) And f (P)i-1,Pi) A kernel estimation of (c); pi-1、PiGenerating output power for the new energy at the adjacent time; h is a univariate kernel estimate fh(Pi-1) N represents the sample capacity; k is a Gaussian kernel function; pj,iThe output at the moment j is shown under the condition that the output at the moment i is known; pj,i-1The j moment output is shown under the condition that the i-1 moment output is known; h isi、hi-1Are respectively a single variable fhi(Pi) And estimate fhi-1(Pi-1) The bandwidth of (c).
Further, in the step 2.3), the method for sampling the random sample for generating the new energy generated output by using the truncation method comprises the following steps:
probability density function f (P) of new energy obtained by time sequence simulationi|Pi-1) Let f (P)i|Pi-1) Has a value range of [ a, b]，f(Pi|Pi-1) The maximum value of the new energy resource is M, the corresponding sampling sample is c, the generated random sample is e, if the sampling value satisfies that c is not more than e/M, the e is received, and the e is used as the random sample of the new energy resource power generation output.
In a second aspect of the present invention, a new energy contribution simulation system considering time and space dimensions 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 a time scale and a 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 new energy generated 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.
Further, the sample data set obtaining module includes: the data acquisition module is used for acquiring a new energy power generation historical data set, including geographic information, theoretical output, actual output, electric power and electric quantity balance and other information; the data dividing module is used for dividing the acquired new energy power generation historical data set according to a time scale and a space scale to obtain a corresponding initial sample actual measurement data set; and the data preprocessing module is used for screening the actually measured data sets of the samples and replacing the screened error data by utilizing an interpolation method to obtain the sample data sets corresponding to the time scales and the space scales.
Further, the simulation computation module includes: the time sequence simulation module is used for carrying out time sequence simulation on the sample data set based on the conditional probability and 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 and obtaining joint probability distribution after time sequence simulation based on the obtained new energy power generation output probability density function; and the selection-cut sampling module is used for sampling a random sample of the generated output of the new energy by adopting a selection-cut method according to the joint probability distribution of the starting time and the stopping time.
Further, the new energy power generation output probability density function obtained by the time sequence simulation module is as follows:
Figure BDA0003132620530000031
wherein f ish(Pi-1)、fH(Pi-1,Pi) Are respectively f (P)i-1) And f (P)i-1,Pi) A kernel estimation of (c); pi-1、PiGenerating output power for the new energy at the adjacent time; h is a univariate kernel estimate fh(Pi-1) N represents the sample capacity; k is a Gaussian kernel function; pj,iThe output at the moment j is shown under the condition that the output at the moment i is known; pj,i-1The j moment output is shown under the condition that the i-1 moment output is known; h isi、hi-1Are respectively a single variable fhi(Pi) And estimate fhi-1(Pi-1) The bandwidth of (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, and 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 method is used for time sequence simulation of the new energy power generation output based on the conditional probability and the multivariate kernel density estimation, and a gating sampling method is adopted for sampling samples, so that the practicability and the applicability of a new energy power generation output probability modeling algorithm are improved.
Therefore, the method can be widely applied to the field of power grid stability analysis.
Drawings
FIG. 1 is a flow chart of the new energy contribution probability modeling of the present invention that considers both temporal and spatial dimensions.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
According to the new energy output simulation method and system considering time and space scales, provided by the invention, firstly, based on historical data of new energy power generation, samples are divided 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 actually measured data of the sample based on a conditional probability and multivariate kernel density estimation method; and finally, acquiring a new energy power generation output probability sample by adopting a gating sampling method according to the starting and stopping time joint probability distribution generated by simulation.
As shown in fig. 1, the new energy contribution simulation method considering time and space dimensions provided by the present invention includes the following steps:
1) and performing 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) acquiring data. And acquiring a new energy power generation historical data set, including information such as geographic information, theoretical output, actual output, power and electric quantity balance and the like. The new energy power generation historical data set can adopt power generation historical data derived by a scheduling system.
1.2) data partitioning. And 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 measured data set.
When the new energy power generation historical data set is divided, time scales such as the year, the quarter, the month and the day of new energy output and spatial scales such as the geographical position of the new energy power station are considered, a sampling condition correlation method is adopted, and the actual historical data of the new energy power station are divided to obtain a sample set sigma S (condition i | condition j).
1.3) preprocessing of data. And screening the actually measured data sets of the initial samples, finding out unreasonable data (such as null data, acquired error data, out-of-limit data, format error data and the like) and replacing the unreasonable data by utilizing an interpolation method to obtain sample data sets corresponding to the time scales and the space scales.
2) Time sequence simulation is carried out on each sample data set corresponding to different time scales and space scales, random samples of new energy power generation output are generated based on time sequence simulation results, and a new energy output probability model capable of reflecting time and space characteristics is established based on the random samples.
Specifically, the method comprises the following steps:
2.1) time sequence simulation. And performing time sequence simulation on the divided sample data set based on the conditional probability and multivariate kernel density estimation to respectively obtain new energy power generation output probability density functions (such as new energy power generation output probability density functions in different regions of hours, days, months and quarters) under different space-time scales.
Performing time sequence simulation on the sample data set based on multivariate kernel density estimation and a conditional probability theory to obtain joint probability distribution of the new energy power generation output starting and stopping moments, wherein a probability density function is expressed as:
Figure BDA0003132620530000041
wherein, Pi-1、PiGenerating output power for the new energy at the adjacent time; f represents a new energy power generation output probability density function; f (P)i-1) Representing a new energy power generation output probability density function at the moment i-1; f (P)i-1,Pi) Represents pi-1、piThe joint probability density function of (a).
Estimating f (P) by adopting univariate nuclear density estimation theoryi-1) Estimating f (P) by using multivariate nuclear density estimation theory with the variable number of 2i-1,Pi) The calculation formula is as follows:
Figure BDA0003132620530000051
Figure BDA0003132620530000052
wherein f ish(Pi-1)、fH(Pi-1,Pi) Are respectively f (P)i-1) And f (P)i-1,Pi) A kernel estimation of (c); h is a univariate kernel estimate fh(Pi-1) N represents the sample capacity; k is a Gaussian kernel function; pj,iThe output at the moment j is shown under the condition that the output at the moment i is known; pj,i-1The j moment output is shown under the condition that the i-1 moment output is known; h isi、hi-1Are respectively a single variable
Figure BDA0003132620530000053
And estimating
Figure BDA0003132620530000054
The bandwidth of (c).
That is, the new energy power generation output probability density function is:
Figure BDA0003132620530000055
2.2) joint probability calculation. And calculating to obtain the joint probability distribution of the new energy generated output at a certain time sequence based on the obtained new energy generated output probability density function.
2.3) selecting and sampling. And sampling to generate a random sample of the new energy generated output by adopting a truncation method according to the joint probability distribution of the starting time and the stopping time.
Compared with the traditional random sampling method, the method adopts a gating sampling method and is suitable for the new energy output probability density function with a complex expression. Probability density function f (P) of new energy obtained by time sequence simulationi|Pi-1) Let f (P)i|Pi-1) Has a value range of [ a, b]，f(Pi|Pi-1) The maximum value of (1) is M, the corresponding sampling sample is c, the generated random sample is e, and if the sampling value satisfies that c is not more than e/M, the e is accepted.
Based on the new energy output simulation method considering the time and space scales, the invention also provides a new energy output simulation system considering the time and space scales, 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 a time scale and a 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 new energy generated 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.
Further, the sample data set obtaining module comprises: the historical data acquisition module is used for acquiring a historical data set of new energy power generation, wherein the historical data set comprises geographic information, theoretical output, actual output, electric power and electric quantity balance and other information; the data dividing module is used for dividing the acquired new energy power generation historical data set according to a time scale and a space scale to obtain a corresponding initial sample actual measurement data set; and the data preprocessing module is used for screening the actually measured data sets of the samples and replacing the screened error data by utilizing an interpolation method to obtain the sample data sets corresponding to the time scales and the space scales.
Further, the simulation calculation module comprises: the time sequence simulation module is used for carrying out time sequence simulation on the sample data set based on the conditional probability and 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 and obtaining joint probability distribution after time sequence simulation based on the obtained new energy power generation output probability density function; and the selection-cut sampling module is used for sampling a random sample of the generated output of the new energy by adopting a selection-cut method according to the joint probability distribution of the starting time and the stopping time.
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims (10)

1. A new energy output simulation method considering time and space dimensions is characterized by comprising the following steps:
1) performing data division on 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) time sequence simulation is carried out on each sample data set corresponding to different time scales and space scales, random samples of new energy power generation output are generated based on time sequence simulation results, and a new energy output probability model capable of reflecting time and space characteristics is established based on the random samples.
2. The method according to claim 1, wherein the simulation method for new energy output considering time and space dimensions comprises: in the step 1), the method for dividing the acquired new energy power generation historical data set into data according to the time scale and the space scale to obtain a corresponding sample data set 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 a time scale and a space scale to obtain a corresponding initial sample actual measurement data set;
1.3) screening the actually measured data sets of the samples, and replacing the obtained error data by using an interpolation method to obtain the sample data sets corresponding to the time scales and the space scales.
3. The method of claim 2, wherein the time and space scale-based new energy contribution simulation method comprises: in the step 1.1), the acquired new energy power generation historical data set comprises geographic information, theoretical output, actual output and electric power and electric quantity balance data.
4. The method according to claim 1, wherein the simulation method for new energy output considering time and space dimensions comprises: in the step 2), the method for performing time sequence simulation on each sample data set and generating the random sample of the new energy generated output based on the time sequence simulation result comprises the following steps:
2.1) carrying out time sequence simulation on the sample data set based on the conditional probability and multivariate nuclear density estimation to obtain a new energy power generation output probability density function;
2.2) calculating to obtain joint probability distribution of starting and stopping moments based on the obtained new energy power generation output probability density function;
and 2.3) sampling to generate a random sample of the new energy generated output by adopting a truncation method according to the joint probability distribution at the starting and stopping moments.
5. The method according to claim 4, wherein the simulation method for new energy output considering time and space dimensions comprises: in the step 2.1), the new energy power generation output probability density function is as follows:
Figure FDA0003132620520000011
wherein f ish(Pi-1)、fH(Pi-1,Pi) Are respectively f (P)i-1) And f (P)i-1,Pi) A kernel estimation of (c); pi-1、PiGenerating output power for the new energy at the adjacent time; h is a univariate kernel estimate fh(Pi-1) N represents the sample capacity; k is a Gaussian kernel function; pj,iThe output at the moment j is shown under the condition that the output at the moment i is known; pj,i-1The j moment output is shown under the condition that the i-1 moment output is known; h isi、hi-1Are respectively a single variable
Figure FDA0003132620520000021
And estimating
Figure FDA0003132620520000022
The bandwidth of (c).
6. The method according to claim 4, wherein the simulation method for new energy output considering time and space dimensions comprises: in the step 2.3), the method for sampling the random sample generating the new energy generated output by adopting the truncation method comprises the following steps:
probability density function f (P) of new energy obtained by time sequence simulationi|Pi-1) Let f (P)i|Pi-1) Has a value range of [ a, b]，f(Pi|Pi-1) The maximum value of the new energy resource is M, the corresponding sampling sample is c, the generated random sample is e, if the sampling value satisfies that c is not more than e/M, the e is received, and the e is used as the random sample of the new energy resource power generation output.
7. A new energy contribution simulation system considering time and space dimensions, suitable for use in the method according to any one of claims 1 to 6, 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 a time scale and a 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 new energy generated 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.
8. The system according to claim 7, wherein the sample data set obtaining module comprises: the data acquisition module is used for acquiring a new energy power generation historical data set, including geographic information, theoretical output, actual output, electric power and electric quantity balance and other information; the data dividing module is used for dividing the acquired new energy power generation historical data set according to a time scale and a space scale to obtain a corresponding initial sample actual measurement data set; and the data preprocessing module is used for screening the actually measured data sets of the samples and replacing the screened error data by utilizing an interpolation method to obtain the sample data sets corresponding to the time scales and the space scales.
9. The new energy contribution simulation system considering temporal and spatial dimensions of claim 7, wherein the simulation computation module comprises: the time sequence simulation module is used for carrying out time sequence simulation on the sample data set based on the conditional probability and 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 and obtaining joint probability distribution after time sequence simulation based on the obtained new energy power generation output probability density function; and the selection-cut sampling module is used for sampling a random sample of the generated output of the new energy by adopting a selection-cut method according to the joint probability distribution of the starting time and the stopping time.
10. The new energy output simulation system considering time and space dimensions as claimed in claim 9, wherein the probability density function of the new energy output generated by the time sequence simulation module is:
Figure FDA0003132620520000023
wherein f ish(Pi-1)、fH(Pi-1,Pi) Are respectively f (P)i-1) And f (P)i-1,Pi) A kernel estimation of (c); pi-1、PiGenerating output power for the new energy at the adjacent time; h is a univariate kernel estimate fh(Pi-1) N represents the sample capacity; k is a Gaussian kernel function; pj,iThe output at the moment j is shown under the condition that the output at the moment i is known; pj,i-1The j moment output is shown under the condition that the i-1 moment output is known; h isi、hi-1Are respectively a single variable
Figure FDA0003132620520000031
And estimating
Figure FDA0003132620520000032
The bandwidth of (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
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