CN115954951A - Method and device for calculating reliable output level of new energy - Google Patents
Method and device for calculating reliable output level of new energy Download PDFInfo
- Publication number
- CN115954951A CN115954951A CN202210271732.9A CN202210271732A CN115954951A CN 115954951 A CN115954951 A CN 115954951A CN 202210271732 A CN202210271732 A CN 202210271732A CN 115954951 A CN115954951 A CN 115954951A
- Authority
- CN
- China
- Prior art keywords
- new energy
- output
- moment
- prediction error
- reliable
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a device for calculating the reliable output level of new energy, wherein the method comprises the following steps: acquiring annual new energy output prediction data and actual output data of a target area; constructing a new energy output prediction error data set at each moment every day according to the new energy output prediction data and the actual output data; based on the new energy output prediction error data set at each moment, fitting new energy output prediction error probability distribution at each moment through a kernel density estimation method; determining a corresponding unilateral confidence interval and a quantile of the unilateral confidence interval according to a preset confidence level based on the new energy output prediction error probability distribution at each moment; and acquiring a corresponding reliable output level of the new energy according to the new energy output prediction error data set and the quantile at each moment. The problem of the degree that the new forms of energy participated in electric power balance during load low ebb period reduces is solved.
Description
Technical Field
The application relates to the technical field of planning and operation of electric power systems, in particular to a method and a device for calculating a reliable output level of new energy.
Background
Due to the randomness and the fluctuation of the output of the new energy, the predicted output value of the new energy often has larger deviation with the actual output value, so that a scheduling strategy for carrying out the day-ahead unit arrangement by directly depending on the predicted value is often distorted and invalid, huge regulation and control pressure is brought to the day-ahead and real-time scheduling, more auxiliary service expenditure is brought, even electric power balance is difficult to realize, and the safe, stable and reliable operation of an electric power system cannot be guaranteed. Therefore, the determination of the reliable output level of the new energy is important for the day-ahead unit arrangement and is also an important precondition for the new energy to participate in the power balance.
At present, the predicted output curve of the new energy is usually converted according to a fixed coefficient in the actual operation arrangement of the power system to obtain the reliable output of the new energy. In order to ensure the power balance of the power system at each moment, the conversion coefficient can be determined according to the power supply and demand situation at the most severe moment, and the moment with high load and small new energy output is generally selected. And simultaneously, the reliable output of the new energy at other moments is calculated according to the conversion coefficient. Obviously, the current new energy reliable output calculation method ignores the new energy output characteristics at other moments for ensuring the power balance at the most severe moment, and particularly limits the new energy reliable output level at the load valley period, so that the new energy consumption space is compressed to a certain extent. The current method for calculating the reliable output of the new energy is difficult to adapt to the actual requirement that the new energy participates in power balance under the background of a novel power system. Therefore, a flexible and effective new energy reliable output level calculation method is urgently needed to be researched to support the operation arrangement of the power system, reduce the real-time regulation and control pressure and the auxiliary service cost and improve the overall safe and stable operation level of the power grid.
Disclosure of Invention
In order to solve the above problem, the present application provides a method for calculating a reliable output level of new energy, including:
acquiring annual new energy output prediction data and actual output data of a target area;
constructing a new energy output prediction error data set at each moment every day according to the new energy output prediction data and the actual output data;
based on the new energy output prediction error data set at each moment, fitting new energy output prediction error probability distribution at each moment through a kernel density estimation method;
based on the new energy output prediction error probability distribution at each moment, determining a corresponding unilateral confidence interval according to a preset confidence level and the quantile of the unilateral confidence interval;
and acquiring a corresponding new energy reliable output level according to the new energy output prediction error data set and the quantiles at each moment.
Further, after the step of obtaining the corresponding reliable output of the new energy, the method further includes:
forming different new energy reliable output combination schemes according to different quantiles;
and forming a new energy reliable output curve all the year round according to the different new energy reliable output combination schemes.
Further, acquiring annual new energy output prediction data and actual output data of the target area at least comprises the following steps: wind power and photovoltaic output prediction data and actual output data of a whole year.
Further, constructing a new energy output prediction error data set at each time every day includes:
new energy output prediction error data is obtained through the following formula,
in the formula, e t The relative prediction error of the new energy output at the moment t is obtained; p is t The actual force output value of the new energy at the time t is obtained; p t ' predicting a force value for the new energy at the time t;
e is to be t Sequencing according to a preset sampling period to obtain a sequence e, wherein the sequence e is organized as follows,
e＝[e 1 ,e 2 ,…,e t ,e t+1 ,…,e 35040 ]
reconstruct E into matrix E in a period of days as follows
Wherein, e' d,k Representing the relative prediction error of the new energy output at the kth moment in the d day; tau. k And predicting an error data set for the new energy output at the kth moment in each day of the whole year.
Further, fitting the new energy output prediction error probability distribution at each moment by a kernel density estimation method, including:
and (4) fitting to obtain the new energy output prediction error probability distribution at each moment by adopting a kernel density estimation formula under the condition that the kernel function meets the condition.
Further, determining a corresponding unilateral confidence interval according to a preset confidence level, and the quantiles of the unilateral confidence interval, including:
probability density function f for the k time k (x)，x k ∈[-1,X k,max ]Confidence level 1-alpha and its corresponding quantile X k,α Satisfy the requirements ofThe relationship shown in the following formula:
wherein, X k,α An alpha quantile representing a probability distribution of prediction errors at time k.
Further, acquiring corresponding reliable new energy output according to the prediction error data set of new energy output at each moment and the quantile, including:
according to the new energy output prediction error data set and the quantiles at each moment, the reliable output level of the new energy is calculated by the following formula,
wherein the content of the first and second substances,
the predicted value of the new energy output at the kth moment of the day d is obtained; />
The reliable output level of the new energy at the kth time of the day d; x k,α An alpha quantile representing a prediction error probability distribution at a kth time; 1+ X k,α And converting the reliable output coefficient at the k moment.
This application provides a computing device of new forms of energy reliable output level simultaneously, includes:
the data acquisition unit is used for acquiring annual new energy output prediction data and actual output data of a target area;
the data set construction unit is used for constructing a new energy output prediction error data set at each moment every day according to the new energy output prediction data and the actual output data;
the probability distribution fitting unit is used for fitting the probability distribution of the new energy output prediction error at each moment through a nuclear density estimation method based on the new energy output prediction error data set at each moment;
the confidence interval determining unit is used for determining a corresponding unilateral confidence interval and the quantile of the unilateral confidence interval according to a preset confidence level based on the new energy output prediction error probability distribution at each moment;
and the new energy reliable output obtaining unit is used for obtaining the corresponding new energy reliable output level according to the new energy output prediction error data set and the quantile at each moment.
Further, the method also comprises the following steps:
the combination scheme forming unit is used for forming different new energy reliable output combination schemes according to different quantiles;
and the curve forming unit is used for forming a new energy reliable output curve all the year round according to the different new energy reliable output combination schemes.
The present application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.
The present application also provides a readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of any one of the above.
By the method and the device for calculating the reliable output level of the new energy, the problem that the degree of the new energy participating in power balance in the load trough period is reduced is solved, and the overall safe and stable operation level of a power grid is improved.
Drawings
Fig. 1 is a schematic flowchart of a method for calculating a reliable output level of new energy according to an embodiment of the present application;
FIG. 2 shows confidence intervals and quantiles of wind power output prediction errors at the kth moment under different confidence levels according to the embodiment of the application;
FIG. 3 is a combination of wind power output prediction error quantiles and wind power reliable output conversion coefficients at the same confidence level adopted at each moment according to the embodiment of the application;
FIG. 4 shows a wind power output prediction error quantile and a wind power reliable output conversion coefficient combination at different time points with different confidence levels according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a new energy reliable output level computing device according to an embodiment of the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
When the reliable output level at each moment is determined, the current reliable output calculation method of the new energy only depends on the new energy output conversion coefficient at the moment with the most severe power supply and demand balance, but neglects the new energy output characteristics at other moments. This makes the degree that the load low ebb hour new forms of energy participated in electric power balance reduce, has also restricted new forms of energy to a certain extent and has consumed the space. In order to meet the development requirement of high-proportion new energy participating in power balance under the background of a novel power system and meet the urgent requirements of reducing the wind curtailment rate of abandoned wind and improving the permeability of the new energy, the invention provides a new energy reliable output level calculation method based on the output prediction error probability distribution of the new energy, and the method comprises the following steps:
step S101, acquiring annual new energy output prediction data and actual output data of a target area.
The data at least includes: wind power and photovoltaic output prediction data and actual output data of a whole year. Wherein, the data sampling period is 15 minutes or 1 hour, that is, 96 or 24 time points are included in the collected data every day.
And S102, constructing a new energy output prediction error data set at each moment every day according to the new energy output prediction data and the actual output data.
The new energy output prediction error data can be obtained by calculation of formula (1)
In the formula, e t The relative prediction error of the new energy output at the moment t; p t Actually outputting a force value for the new energy at the moment t; p t ' predicting a force value for the new energy at the time t; e is to be t And sequencing according to a preset sampling period to obtain a sequence e as shown in a formula (2). The organization of the series e is shown here with a 15 minute sampling period as an example.
e＝[e 1 ,e 2 ,…,e t ,e t+1 ,…,e 35040 ] (2)
Further, E is reconstructed into a matrix E with a period of days as shown in formulas (3) and (4).
In formula (4), e' d,k Representing the relative prediction error of the new energy output at the kth moment in the d day; tau is k And predicting an error data set for the new energy output at the kth moment in each day of the whole year.
And S103, fitting the probability distribution of the new energy output prediction error at each moment through a kernel density estimation method based on the new energy output prediction error data set at each moment.
And (4) fitting to obtain the new energy output prediction error probability distribution at each moment by adopting a kernel density estimation formula under the condition that the kernel function meets the condition.
The new energy output prediction error data set at each moment is tau k . And constructing the new energy output prediction error probability distribution at each moment by a kernel density estimation method. Wherein the nuclear density estimation process is as follows:
assume independent identically distributed random variables x 1 ,x 2 ,…,x N The density function obeying the probability distribution is f (x), x ∈ R, then the kernel density of the density function f (x) is estimated
Can be expressed as formula (5).
Wherein K (·) is a kernel function, and h is a preset positive number, representing a bandwidth or smoothing parameter. Let K h (u) = K (u/h)/h, the formula (5) may be simplified to the formula (6).
From equation (6), the nuclear density estimation
Not only with respect to a given set of sample points, but also with respect to kernel functions and bandwidth parameters. In theory any function can be used as the kernel function, but for the convenience and rationality of the density function estimation, the kernel function is usually required to satisfy the following conditions:
K(-u)＝K(u) (7)
wherein K (·) is a kernel function, and u is a random variable; sup (-) denotes taking the supremum of a function.
Commonly used kernel functions include gaussian kernel functions, trigonometric kernel functions, epanechnikov kernel functions, and the like. The invention selects a Gaussian kernel function as shown in a formula (9).
And step S104, determining a corresponding unilateral confidence interval and the quantile of the unilateral confidence interval according to a preset confidence level based on the new energy output prediction error probability distribution at each moment.
For each tau k Different confidence levels are set in the sets to determine single-side confidence intervals of the new energy output at different moments and determine corresponding quantiles. Probability density function f for the k time k (x)，x k ∈[-1,X k,max ]Confidence level 1-alpha and its corresponding quantile X k,α Satisfies the relationship shown in the formula (10).
Wherein, X k,α An alpha quantile representing a probability distribution of prediction errors at time k.
Confidence intervals under different confidence levels are displayed according to the probability distribution of the wind power output prediction error at the kth moment, and are shown in figure 2. When alpha =0.05, the probability of 5% of the true value of the wind power output prediction error falls in the interval [ -1,X ] k,α | α＝0.05 ]I.e. in the interval (X) at 95% probability k,α | α＝0.05 ,X k,max ]. This means that the true value of the wind power output prediction error is greater than X at the probability of 95% k,α | α＝0.05 Prediction is accurate, i.e. falls within (X) k,α | α＝0.05 ,0]Or more toward the forward prediction error, i.e., (0, X) k,max ]. Similarly, when α =0.1 and α =0.2, the true values of the prediction errors corresponding to the wind power output will be greater than X at the probabilities of 90% and 80%, respectively k,α | α＝0.1 And X k,α | α＝0.2 The prediction is accurate or more biased towards the forward prediction error.
And S105, acquiring a corresponding new energy reliable output level according to the new energy output prediction error data set and the quantiles at each moment.
According to the new energy output prediction error data set and the quantile at each moment, the reliable output level of the new energy is calculated and obtained by the formula (11),
wherein the content of the first and second substances,
the predicted value of the new energy output at the kth moment of the day d is obtained; />
The reliable output level of the new energy at the kth time of the day d; x k,α An alpha quantile representing the probability distribution of the prediction error at the kth moment can be obtained by the formula (10); 1+ X k,α And converting the reliable output coefficient at the k moment.
By means of time-of-day data sets τ k Determined new energy contribution prediction error probability distribution, and X at different confidence levels k,α Or reliable output conversion coefficient 1+ X k,α And the reliable output of the new energy at each moment can be calculated. Since the selection of confidence levels may be different at each time, the corresponding quantile X k,α And reliable output conversion coefficient 1+ X k,α And the difference is also formed, so that a flexible quantile combination or a reliable output conversion coefficient combination can be constructed. On the basis, different new energy reliable output combination schemes can be constructed. Fig. 3 and 4 show quantiles combinations and reliable output conversion coefficient combinations at the same confidence level and different confidence levels at different times, respectively, by taking the wind power output prediction error probability distribution as an example.
After the corresponding new energy reliable output is obtained, different new energy reliable output combination schemes are formed according to different quantiles; and forming a new energy reliable output curve all the year around according to the different new energy reliable output combination schemes. And (4) performing subsequent source network load development integrated production simulation calculation according to the annual new energy reliable output curve.
The specific application examples are as follows:
in the section, based on the wind power and photovoltaic power generation prediction data and actual data of 8760 hours in the whole year of Xinjiang in 2019, the following four schemes are subjected to production simulation calculation respectively, and the effectiveness and the rationality of the method provided by the invention are analyzed and verified.
Scheme 1: determining a reliable output curve of the new energy at the whole time period based on the probability distribution and the confidence level of the output prediction error of the new energy at the load peak period;
scheme 2: independently determining the reliable output of the new energy at each moment according to the probability distribution of the prediction error of the output of the new energy at each moment based on the same confidence level;
scheme 3: independently determining the reliable output of the new energy at each moment according to the probability distribution of the prediction error of the output of the new energy at each moment based on different confidence levels;
scheme 4: and using the actual data of the new energy output.
The new energy reliable output curves determined by the four schemes are used for production simulation calculation, and the conditions of wind curtailment and light curtailment of Xinjiang all the year are shown in table 1.
TABLE 1 wind and light abandon in Xinjiang under four schemes
The theoretical electricity generation amounts of wind power generation and photovoltaic power generation are 39.56 hundred million kilowatt-hours and 11.76 hundred million kilowatt-hours respectively. The scheme 4 uses the actual output data of the new energy to carry out operation arrangement, can greatly utilize the adjusting capacity of the power system, and enables the wind power and photovoltaic power generation consumptions to reach 35.81 and 11.04 hundred million kilowatts, and the wind and light abandoning rates to be respectively 9.5 percent and 6.1 percent and to be at a relatively low level; in contrast, the wind curtailment and the light curtailment of the scheme 1 are 52.7% and 92.2%, respectively, which are much higher than those of the scheme 4. Therefore, the reliable output of the new energy at the whole time is determined by using the reliable output characteristic of the new energy at a single moment, so that the new energy consumption space is severely limited, and a large amount of resources are wasted; in the scheme 2, the reliable output of the new energy is independently determined at each moment, and the over-conservative confidence space is widened to a certain extent, so that the wind and light abandoning rates are reduced to 46.3% and 25.4%; scheme 3 further relaxes the reliable output level of new energy, so that wind power and photovoltaic power generation can participate in electric power and electric quantity balance more, and the wind abandoning rate and the light abandoning rate are further reduced to 29.8 percent and 22.0 percent respectively.
In conclusion, the operation arrangement is carried out by using the predicted data close to the actual value of the new energy output, the system adjusting capacity can be utilized to a large extent, and the phenomena of wind abandonment and light abandonment are avoided to a large extent; in actual operation, the method shown in scheme 3 (namely the method of the invention) can be used for preprocessing the prediction data, the reliable output of the new energy at each moment can be determined based on different confidence levels, the electric power and electric quantity balance is guaranteed, the new energy consumption space is enlarged as much as possible, and the phenomena of wind abandonment and light abandonment are reduced.
Based on the same inventive concept, the present application also provides a computing apparatus 500 for reliable output level of new energy, as shown in fig. 5, including:
a data obtaining unit 510, configured to obtain annual new energy output prediction data and actual output data of a target area;
a data set construction unit 520, configured to construct a new energy output prediction error data set at each time every day according to the new energy output prediction data and the actual output data;
a probability distribution fitting unit 530, configured to fit, based on the new energy output prediction error data set at each time, a new energy output prediction error probability distribution at each time by a kernel density estimation method;
a confidence interval determining unit 540, configured to determine, based on the predicted error probability distribution of the new energy output at each time, a corresponding unilateral confidence interval and a quantile of the unilateral confidence interval according to a preset confidence level;
and a new energy reliable output obtaining unit 550, configured to obtain a corresponding new energy reliable output level according to the new energy output prediction error data set and the quantile at each time.
Further, the method also comprises the following steps:
the combination scheme forming unit is used for forming different new energy reliable output combination schemes according to different quantiles;
and the curve forming unit is used for forming a new energy reliable output curve all the year round according to the different new energy reliable output combination schemes.
According to the method and the device for calculating the reliable output level of the new energy, provided by the invention, the output characteristics of the new energy and the power balance requirement of a system at each moment can be fully concerned according to the output prediction error data set of the new energy at each moment, different confidence levels are adopted at each moment to determine the reliable output level of the new energy, and a flexibly constructed combination scheme is adopted. Aiming at the probability distribution of new energy output prediction errors at different moments, the method ensures the power balance under the most severe supply and demand situation to a great extent by setting a relatively conservative confidence level; the new energy consumption space during the load trough period is fully released by setting a relatively aggressive confidence level. The flexibility and convenience of the reliable output calculation process and the combination scheme of the new energy at each moment are improved; under the condition of guaranteeing electric power balance at each moment, the degree of new energy participating in electric power balance is effectively promoted, new energy consumption space is further released, wind and light abandoning rate reduction is facilitated, the permeability of new energy is improved, the problem of reduction of the degree of new energy participating in electric power balance in the load valley period is solved, and the whole safe and stable operation level of a power grid is improved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the invention.
Claims (11)
1. A method for calculating a reliable output level of new energy is characterized by comprising the following steps:
acquiring annual new energy output prediction data and actual output data of a target area;
constructing a new energy output prediction error data set at each moment every day according to the new energy output prediction data and the actual output data;
based on the new energy output prediction error data set at each moment, fitting new energy output prediction error probability distribution at each moment through a kernel density estimation method;
based on the new energy output prediction error probability distribution at each moment, determining a corresponding unilateral confidence interval according to a preset confidence level and the quantile of the unilateral confidence interval;
and acquiring a corresponding reliable output level of the new energy according to the new energy output prediction error data set and the quantile at each moment.
2. The method of claim 1, further comprising, after the step of obtaining the corresponding new-energy reliable contribution:
forming different new energy reliable output combination schemes according to different quantiles;
and forming a new energy reliable output curve all the year round according to the different new energy reliable output combination schemes.
3. The method of claim 1, wherein obtaining the predicted and actual energy output data for the target area over the year comprises at least: wind power and photovoltaic output prediction data and actual output data of a whole year.
4. The method of claim 1, wherein constructing the new energy contribution prediction error dataset for each time of day comprises:
new energy output prediction error data is obtained through the following formula,
in the formula, e t The relative prediction error of the new energy output at the moment t; p t Is at t timeCarving the actual force value of the new energy; p t ' predicting a force value for the new energy at the time t;
e is to be t Sequencing according to a preset sampling period to obtain a sequence e, wherein the sequence e is organized as follows,
e＝[e 1 ,e 2 ,…,e t ,e t+1 ,…,e 35040 ]
e is reconstructed into a matrix E with a period of days as follows
Wherein, e' d,k Representing the relative prediction error of the new energy output at the kth moment in the d day; tau is k And predicting an error data set for the new energy output at the kth moment in each day of the whole year.
5. The method of claim 1, wherein fitting the new energy contribution prediction error probability distribution at each time by a kernel density estimation method comprises:
and fitting to obtain the new energy output prediction error probability distribution at each moment by adopting a kernel density estimation formula under the condition that a kernel function meets a preset condition, wherein the kernel function is a Gaussian kernel function, and the preset condition comprises the following steps:
k (-u) = K (u) and
wherein K (·) is a kernel function, and u is a random variable; sup (-) denotes taking the supremum of a function.
6. The method of claim 1, wherein determining the corresponding unilateral confidence interval and the quantile thereof according to a predetermined confidence level comprises:
probability density function f for the k time k (x)，x k ∈[-1,X k,max ]Confidence level 1-alpha and its corresponding quantile X k,α Satisfies the relationship shown in the following formula:
wherein, X k,α An alpha quantile representing a probability distribution of prediction errors at time k.
7. The method of claim 1, wherein obtaining a corresponding reliable new energy contribution based on the set of new energy contribution prediction error data and the quantile at each time comprises:
according to the new energy output prediction error data set and the quantiles at each moment, the reliable output level of the new energy is calculated by the following formula,
wherein, the first and the second end of the pipe are connected with each other,
the predicted value of the new energy output at the kth moment of the day d is obtained; />
The reliable output level of the new energy at the kth time of the day d; x k,α An alpha quantile representing a probability distribution of a prediction error at a kth time; 1+ X k,α And converting the reliable output coefficient at the k moment.
8. A device for calculating a reliable output level of a new energy source, comprising:
the data acquisition unit is used for acquiring annual new energy output prediction data and actual output data of a target area;
the data set construction unit is used for constructing a new energy output prediction error data set at each moment every day according to the new energy output prediction data and the actual output data;
the probability distribution fitting unit is used for fitting the probability distribution of the new energy output prediction error at each moment through a nuclear density estimation method based on the new energy output prediction error data set at each moment;
the confidence interval determining unit is used for determining a corresponding unilateral confidence interval and the quantiles of the unilateral confidence interval according to a preset confidence level based on the new energy output prediction error probability distribution at each moment;
and the new energy reliable output obtaining unit is used for obtaining the corresponding new energy reliable output level according to the new energy output prediction error data set and the quantile at each moment.
9. The apparatus of claim 8, further comprising:
the combination scheme forming unit is used for forming different new energy reliable output combination schemes according to different quantiles;
and the curve forming unit is used for forming a new energy reliable output curve all the year round according to the different new energy reliable output combination schemes.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the computer program is executed by the processor.
11. A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210271732.9A CN115954951A (en) | 2022-03-18 | 2022-03-18 | Method and device for calculating reliable output level of new energy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210271732.9A CN115954951A (en) | 2022-03-18 | 2022-03-18 | Method and device for calculating reliable output level of new energy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115954951A true CN115954951A (en) | 2023-04-11 |
Family
ID=87284627
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210271732.9A Pending CN115954951A (en) | 2022-03-18 | 2022-03-18 | Method and device for calculating reliable output level of new energy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115954951A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116780643A (en) * | 2023-05-31 | 2023-09-19 | 国网山东省电力公司经济技术研究院 | Confidence output calculation method and system for new energy participation in electric power balance |
| CN117424290A (en) * | 2023-10-07 | 2024-01-19 | 国家电网有限公司华东分部 | New energy source inclusion proportion calculating method, device, equipment and storage medium |
-
2022
- 2022-03-18 CN CN202210271732.9A patent/CN115954951A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116780643A (en) * | 2023-05-31 | 2023-09-19 | 国网山东省电力公司经济技术研究院 | Confidence output calculation method and system for new energy participation in electric power balance |
| CN116780643B (en) * | 2023-05-31 | 2024-04-02 | 国网山东省电力公司经济技术研究院 | Confidence output calculation method and system for new energy participation in electric power balance |
| CN117424290A (en) * | 2023-10-07 | 2024-01-19 | 国家电网有限公司华东分部 | New energy source inclusion proportion calculating method, device, equipment and storage medium |
| CN117424290B (en) * | 2023-10-07 | 2024-04-19 | 国家电网有限公司华东分部 | New energy source inclusion proportion calculating method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115954951A (en) | Method and device for calculating reliable output level of new energy | |
| CN112396220B (en) | Optimized scheduling method for wind power-containing and demand side resources based on scene reduction | |
| CN104915737A (en) | Coordinating economic dispatching method for power system containing large-scale wind power based on risk control | |
| CN112104005A (en) | Power grid dispatching method and system considering new energy prediction uncertainty | |
| CN109978277B (en) | Regional internet load prediction method and device in photovoltaic power generation | |
| CN116823000B (en) | Hydropower compensation peak regulation analysis and evaluation method and device thereof | |
| CN115566680B (en) | New energy power system time sequence production simulation operation optimization method and device | |
| CN112491080A (en) | New energy installation layout planning method and system | |
| CN109787217B (en) | Standby clearing method based on wind power multi-state model and opportunity cost correction | |
| CN114336739B (en) | Cloud-edge cooperation-based method and system for configuring energy storage power of optical storage station | |
| CN115117886A (en) | Energy storage control method and device for wind power plant and storage medium | |
| CN114844119A (en) | Energy storage power generation installation and capacity optimal configuration method and system | |
| CN110717694B (en) | Energy storage configuration random decision method and device based on new energy consumption expected value | |
| CN114819573A (en) | Day-ahead scheduling method and device of new energy system and computer equipment | |
| CN114498767A (en) | Wind power plant energy storage capacity optimization method and device and electronic equipment | |
| CN114142470A (en) | Reasonable scale measuring and calculating method and system for new energy power station matched energy storage of power system | |
| CN109038623B (en) | Valley filling method and device for pumped storage power station and storage medium | |
| CN113435673A (en) | Deep peak regulation cost allocation calculation method and system based on load rate | |
| CN114372650A (en) | Feasibility assessment method for cross-regional consumption of clean energy | |
| CN116683487B (en) | Scheduling method and device for pumped storage power grid, storage medium and computer equipment | |
| CN111030088B (en) | Method and device for predicting capacity of power transmission channel for power transmission | |
| CN111861794B (en) | Solar power generation capacity reliability acquisition method and device considering probability distribution | |
| CN115986824A (en) | Energy storage configuration method and system considering correlation between load and new energy output | |
| CN117937495A (en) | Distributed photovoltaic participation power grid electric quantity balance adjustment demand analysis method and system | |
| CN113937769A (en) | Unit combination determination method and device considering wind energy output uncertainty |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |