CN116992209A - New energy capacity credibility calculation method and device - Google Patents
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Abstract
The invention discloses a new energy capacity credibility calculation method and a device, and relates to the technical field of electric power, wherein the method comprises the following steps: receiving basic data of a power grid, wherein the basic data of the power grid comprise new energy historical output data and load historical data; obtaining a probability density function of the peak moment of the system load according to the load history data; setting an accumulated probability value of a target coverage load peak time period according to a probability density function of the system load peak time, and acquiring the duration time and the time period of the load peak; and obtaining the new energy capacity credibility of the new energy under the multimodal period according to the historical power data of the new energy and the duration and the period of the load peak.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a new energy capacity credibility calculation method and device.
Background
With the high-proportion new energy being connected into the power grid, the new energy is dominant in the novel power system gradually, and the new energy also needs to bear the function of partial power balance, but how to evaluate the contribution of the new energy to the power adequacy of the system. At present, a scholars and a dispatching mechanism calculate the reliability of the new energy capacity by adopting a method based on historical data in a fixed period, but the method does not consider the load multimodal characteristic of China, so that the selection of the load peak period in the background of China is a problem to be solved in order to evaluate the reliability of the new energy capacity more accurately. Under the background, how to effectively evaluate the supporting capability of the new energy to the system adequacy is widely focused, and for this reason, a new energy capacity credibility calculating method and device are now proposed.
Disclosure of Invention
In order to solve the above-mentioned shortcomings in the background art, the present invention aims to provide a new energy capacity reliability calculation method and device.
The aim of the invention can be achieved by the following technical scheme: a new energy capacity credibility calculation method comprises the following steps:
receiving basic data of a power grid, wherein the basic data of the power grid comprise new energy historical output data and load historical data;
obtaining a probability density function of the peak moment of the system load according to the load history data;
setting an accumulated probability value of a target coverage load peak time period according to a probability density function of the system load peak time, and acquiring the duration time and the time period of the load peak;
and obtaining the new energy capacity credibility of the new energy under the multimodal period according to the historical power data of the new energy and the duration and the period of the load peak.
Preferably, the new energy historical output data and the load historical data need 1 year of data, and the data precision is 1 hour.
Preferably, the process of obtaining the probability density function of the peak moment of the system load according to the load history data is as follows:
processing the load history data, finding the maximum value of the load in one day and the moment when the maximum value of the load in one day appears, and obtaining a peak load moment data set;
calculating the ratio of each time point to the total research period duration based on the peak load moment data set to obtain the probability of the occurrence of a load peak at the time point, and obtaining a probability density function of the system load peak moment according to the probability of the occurrence of the load peak at the time point;
and carrying out normalization processing on the probability density functions at the peak time of the system load, so that the sum of the function values of the probability density functions at the peak time of all the system load is 1.
Preferably, the process of setting the cumulative probability value of the target coverage load peak period according to the probability density function of the system load peak moment, and obtaining the duration time and period of the load peak is as follows:
setting an accumulated probability value of a peak value period of the target coverage load;
sequencing all the moments according to probability from big to small according to a probability density function of the system load peak moment;
and sequentially adding the time to the load peak period from the time with the maximum probability, adding the probability of the time to the probability corresponding to the time in the load peak period, repeating the steps until the probability corresponding to the time in the load peak period is equal to the cumulative probability value of the target coverage load peak period, and finally obtaining the duration and period of the load peak.
Preferably, the process of obtaining the reliability of the new energy capacity of the new energy under the multimodal period according to the historical output data of the new energy and the duration and the period of the load peak is as follows:
determining a system load peak duration time period, recording the system load peak duration time period as a load peak time period, and finding a new energy historical output data set in a corresponding load peak time period in the load peak time period;
counting new energy output data in a new energy historical output data set in a load peak period, and calculating the average output rate of new energy;
according to the average output rate of the new energy, the new energy capacity credibility of the new energy in the multi-peak period is calculated, and the formula is as follows:
η＝P/C g
wherein, eta is the reliability of the new energy capacity; p is the average output rate of new energy; c (C) g And the system is a new energy installation capacity.
In another aspect, the present invention also provides a new energy capacity reliability calculation device, including:
and a data acquisition module: the method comprises the steps of receiving basic data of a power grid, wherein the basic data of the power grid comprise new energy historical output data and load historical data;
a first data processing module: the probability density function is used for obtaining the system load peak time according to the load history data;
and a second data processing module: the method comprises the steps of setting an accumulated probability value of a target coverage load peak time period according to a probability density function of a system load peak time, and acquiring duration time and time period of a load peak;
the capacity credibility calculation module: and the new energy capacity credibility of the new energy under the multimodal period is obtained according to the historical output data of the new energy and the duration and the period of the load peak.
Preferably, the new energy historical output data and the load historical data need 1 year of data, and the data precision is 1 hour.
Preferably, the process of obtaining the probability density function of the peak moment of the system load according to the load history data is as follows:
processing the load history data, finding the maximum value of the load in one day and the moment when the maximum value of the load in one day appears, and obtaining a peak load moment data set;
calculating the ratio of each time point to the total research period duration based on the peak load moment data set to obtain the probability of the occurrence of a load peak at the time point, and obtaining a probability density function of the system load peak moment according to the probability of the occurrence of the load peak at the time point;
and carrying out normalization processing on the probability density functions at the peak time of the system load, so that the sum of the function values of the probability density functions at the peak time of all the system load is 1.
Preferably, the process of setting the cumulative probability value of the target coverage load peak period according to the probability density function of the system load peak moment, and obtaining the duration time and period of the load peak is as follows:
setting an accumulated probability value of a peak value period of the target coverage load;
sequencing all the moments according to probability from big to small according to a probability density function of the system load peak moment;
and sequentially adding the time to the load peak period from the time with the maximum probability, adding the probability of the time to the probability corresponding to the time in the load peak period, repeating the steps until the probability corresponding to the time in the load peak period is equal to the cumulative probability value of the target coverage load peak period, and finally obtaining the duration and period of the load peak.
Preferably, the process of obtaining the reliability of the new energy capacity of the new energy under the multimodal period according to the historical output data of the new energy and the duration and the period of the load peak is as follows:
determining a system load peak duration time period, recording the system load peak duration time period as a load peak time period, and finding a new energy historical output data set in a corresponding load peak time period in the load peak time period;
counting new energy output data in a new energy historical output data set in a load peak period, and calculating the average output rate of new energy;
according to the average output rate of the new energy, the new energy capacity credibility of the new energy in the multi-peak period is calculated, and the formula is as follows:
η＝P/C g
wherein, eta is the reliability of the new energy capacity; p is the average output rate of new energy; c (C) g And the system is a new energy installation capacity.
The invention has the beneficial effects that:
when the method is used, firstly, the data acquisition module acquires the data of the power grid, including the new energy output data and the load data, and sends the data to the first data processing module, and the first data processing module is used for statistically analyzing probability density functions of the system load peak moment according to the received data of the power grid and sending the probability density functions to the second data processing module; the second data processing module sets a cumulative probability value of a target coverage load peak period, obtains duration time and period of the acquired load peak according to a probability density function in the first data processing module, and transmits a corresponding processing result to the capacity reliability calculation module, and the system scheduling mechanism can select the cumulative probability value of the target coverage load peak period of the new energy data according to own preference; and the contribution of the new energy source to the system adequacy is quantitatively evaluated in the capacity credibility calculation module, so that the dispatching and planning departments are effectively guided to carry out power balance and power grid planning.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort;
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a schematic representation of the results of the apparatus of the present invention;
fig. 3 is a schematic structural diagram of an apparatus according to a third embodiment of the present invention.
Fig. 4 is a probability density map of the system load peak time.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a new energy capacity reliability calculation method is characterized by comprising the following steps:
receiving basic data of a power grid, wherein the basic data of the power grid comprise new energy historical output data and load historical data;
the new energy historical output data and the load historical data need 1 year of data, and the data precision is 1 hour;
obtaining a probability density function of the peak moment of the system load according to the load history data;
in this embodiment, the following is specific:
processing the load history data, finding the maximum value of the load in one day and the moment when the maximum value of the load in one day appears, and obtaining a peak load moment data set;
calculating the ratio of each time point to the total research period duration based on the peak load moment data set to obtain the probability of the occurrence of a load peak at the time point, and obtaining a probability density function of the system load peak moment according to the probability of the occurrence of the load peak at the time point;
and carrying out normalization processing on the probability density functions at the peak time of the system load, so that the sum of the function values of the probability density functions at the peak time of all the system load is 1.
Setting an accumulated probability value of a target coverage load peak time period according to a probability density function of the system load peak time, and acquiring the duration time and the time period of the load peak;
in this embodiment, the following is specific:
setting an accumulated probability value of a peak value period of the target coverage load;
sequencing all the moments according to probability from big to small according to a probability density function of the system load peak moment;
and sequentially adding the time to the load peak period from the time with the maximum probability, adding the probability of the time to the probability corresponding to the time in the load peak period, repeating the steps until the probability corresponding to the time in the load peak period is equal to the cumulative probability value of the target coverage load peak period, and finally obtaining the duration and period of the load peak.
And obtaining the new energy capacity credibility of the new energy under the multimodal period according to the historical power data of the new energy and the duration and the period of the load peak.
In this embodiment, the following is specific:
determining a system load peak duration time period, recording the system load peak duration time period as a load peak time period, and finding a new energy historical output data set in a corresponding load peak time period in the load peak time period;
counting new energy output data in a new energy historical output data set in a load peak period, and calculating the average output rate of new energy;
according to the average output rate of the new energy, the new energy capacity credibility of the new energy in the multi-peak period is calculated, and the formula is as follows:
η＝P/C g
wherein, eta is the reliability of the new energy capacity; p is the average output rate of new energy; c (C) g And the system is a new energy installation capacity.
Example two
Fig. 2 is a schematic diagram of a new energy capacity reliability calculation device according to an embodiment of the present invention. The embodiment can be suitable for calculating the reliability of the new energy capacity of the target resource, the device can be realized in a software and/or hardware mode, and the device can be configured in the terminal equipment. The determining device includes: a data acquisition module 210, a first data processing module 220, a second data processing module 230, and a capacity confidence calculation module 240.
The data acquisition module 210 is configured to receive basic data of a power grid, where the basic data of the power grid includes new energy historical output data and load historical data;
the first data processing module 220 is configured to obtain a probability density function of a peak moment of a system load according to load history data;
the second data processing module 230 is configured to set an accumulated probability value of the target coverage load peak period according to the probability density function of the system load peak moment, and acquire the duration and period of the load peak.
The capacity reliability calculation module 240 is configured to obtain the new energy capacity reliability of the new energy under the multimodal period according to the new energy historical output data and the duration and the period of the load peak.
The new energy capacity credibility calculating device provided by the embodiment of the invention can be used for executing the new energy capacity credibility calculating method provided by the embodiment of the invention, and has the corresponding functions and beneficial effects of the executing method.
The new energy history output data and the load history data need 1 year of data, and the data precision is 1 hour.
The process of obtaining the probability density function of the system load peak moment according to the load history data is as follows:
processing the load history data, finding the maximum value of the load in one day and the moment when the maximum value of the load in one day appears, and obtaining a peak load moment data set;
calculating the ratio of each time point to the total research period duration based on the peak load moment data set to obtain the probability of the occurrence of a load peak at the time point, and obtaining a probability density function of the system load peak moment according to the probability of the occurrence of the load peak at the time point;
and carrying out normalization processing on the probability density functions at the peak time of the system load, so that the sum of the function values of the probability density functions at the peak time of all the system load is 1.
The process of setting the cumulative probability value of the target coverage load peak time period according to the probability density function of the system load peak time moment and acquiring the duration time and the time period of the load peak is as follows:
setting an accumulated probability value of a peak value period of the target coverage load;
sequencing all the moments according to probability from big to small according to a probability density function of the system load peak moment;
and sequentially adding the time to the load peak period from the time with the maximum probability, adding the probability of the time to the probability corresponding to the time in the load peak period, repeating the steps until the probability corresponding to the time in the load peak period is equal to the cumulative probability value of the target coverage load peak period, and finally obtaining the duration and period of the load peak.
The process of obtaining the new energy capacity credibility of the new energy under the multi-peak period according to the historical output data of the new energy and the duration and the period of the load peak value is as follows:
determining a system load peak duration, recording the system load peak duration as a load peak duration, and finding a new energy historical output data set in a corresponding load peak duration in the load peak duration:
counting new energy output data in a new energy historical output data set in a load peak period, and calculating the average output rate of new energy;
according to the average output rate of the new energy, the new energy capacity credibility of the new energy in the multi-peak period is calculated, and the formula is as follows:
η＝P/C g
wherein, eta is the reliability of the new energy capacity;p is the average output rate of new energy; c (C) g And the system is a new energy installation capacity.
It should be noted that, in the embodiment of the determining apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding function can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example III
Fig. 3 is a schematic structural diagram of an apparatus according to a third embodiment of the present invention, where the third embodiment of the present invention provides services for implementing the new energy capacity reliability calculation method according to the foregoing embodiment of the present invention, and the new energy capacity reliability calculation device according to the foregoing embodiment of the present invention according to the foregoing load multimodal feature may be configured. Fig. 3 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 3 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 3, device 12 is in the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects the various system components including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, commonly referred to as a "hard disk drive"). Although not shown in fig. 3, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with device 12, and/or any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, device 12 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, via network adapter 20. As shown in fig. 3, network adapter 20 communicates with other modules of device 12 over bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the new energy capacity reliability calculation method taking account of the load multimodal feature provided by the embodiment of the invention.
By the device, under the condition of fully considering the multimodal characteristics of the system load, a reference is provided for the calculation of the reliability of the new energy capacity of the system.
Example IV
The fourth embodiment of the present invention also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a new energy capacity reliability calculation method, the method comprising:
acquiring data of a power grid, wherein the data of the power grid comprise new energy output data and load data; inputting the data into a data processing and calculating model to obtain a corresponding new energy capacity credibility calculating result.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, sma l l ta l k, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Of course, the storage medium containing computer executable instructions provided in the embodiments of the present invention is not limited to the above method operations, but may also perform the related operations in the capacity reliability calculation method that accounts for the load multimodal feature provided in any embodiment of the present invention.
The verification is performed using specific examples below.
Based on new energy and load data of a provincial power grid 2022, the probability density of the moment of obtaining the load peak is calculated as shown in fig. 4, the cumulative probability value of the target coverage load peak period is set to be 60%, and the calculation result of the new energy capacity reliability is shown in table 1.
TABLE 1 calculation of New energy Capacity credibility
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (10)
1. The new energy capacity credibility calculating method is characterized by comprising the following steps of:
receiving basic data of a power grid, wherein the basic data of the power grid comprise new energy historical output data and load historical data;
obtaining a probability density function of the peak moment of the system load according to the load history data;
setting an accumulated probability value of a target coverage load peak time period according to a probability density function of the system load peak time, and acquiring the duration time and the time period of the load peak;
and obtaining the new energy capacity credibility of the new energy under the multimodal period according to the historical power data of the new energy and the duration and the period of the load peak.
2. The method for calculating the reliability of new energy capacity according to claim 1, wherein the new energy historical output data and the load historical data are 1 year data, and the data precision is 1 hour.
3. The method for calculating the reliability of new energy capacity according to claim 1, wherein the process of obtaining the probability density function of the peak moment of the system load according to the load history data is as follows:
processing the load history data, finding the maximum value of the load in one day and the moment when the maximum value of the load in one day appears, and obtaining a peak load moment data set;
calculating the ratio of each time point to the total research period duration based on the peak load moment data set to obtain the probability of the occurrence of a load peak at the time point, and obtaining a probability density function of the system load peak moment according to the probability of the occurrence of the load peak at the time point;
and carrying out normalization processing on the probability density functions at the peak time of the system load, so that the sum of the function values of the probability density functions at the peak time of all the system load is 1.
4. The method for calculating the reliability of new energy capacity according to claim 1, wherein the process of setting the cumulative probability value of the target coverage load peak period according to the probability density function of the system load peak moment, and obtaining the duration and period of the load peak is as follows:
setting an accumulated probability value of a peak value period of the target coverage load;
sequencing all the moments according to probability from big to small according to a probability density function of the system load peak moment;
and sequentially adding the time to the load peak period from the time with the maximum probability, adding the probability of the time to the probability corresponding to the time in the load peak period, repeating the steps until the probability corresponding to the time in the load peak period is equal to the cumulative probability value of the target coverage load peak period, and finally obtaining the duration and period of the load peak.
5. The method for calculating the reliability of the new energy capacity according to claim 1, wherein the process of obtaining the reliability of the new energy capacity of the new energy under the multimodal period according to the historical output data of the new energy and the duration and the period of the load peak is as follows:
determining a system load peak duration time period, recording the system load peak duration time period as a load peak time period, and finding a new energy historical output data set in a corresponding load peak time period in the load peak time period;
counting new energy output data in a new energy historical output data set in a load peak period, and calculating the average output rate of new energy;
according to the average output rate of the new energy, the new energy capacity credibility of the new energy in the multi-peak period is calculated, and the formula is as follows:
η＝P/C g
wherein, eta is the reliability of the new energy capacity; p is the average output rate of new energy; c (C) g And the system is a new energy installation capacity.
6. A new energy capacity reliability calculation device, characterized by comprising:
and a data acquisition module: the method comprises the steps of receiving basic data of a power grid, wherein the basic data of the power grid comprise new energy historical output data and load historical data;
a first data processing module: the probability density function is used for obtaining the system load peak time according to the load history data;
and a second data processing module: the method comprises the steps of setting an accumulated probability value of a target coverage load peak time period according to a probability density function of a system load peak time, and acquiring duration time and time period of a load peak;
the capacity credibility calculation module: and the new energy capacity credibility of the new energy under the multimodal period is obtained according to the historical output data of the new energy and the duration and the period of the load peak.
7. The device for calculating the reliability of new energy capacity according to claim 6, wherein the historical output data and the load historical data of new energy require 1 year of data, and the data precision is 1 hour.
8. The new energy capacity credibility calculating apparatus as claimed in claim 6, wherein the process of obtaining the probability density function of the peak time of the system load according to the load history data is as follows:
processing the load history data, finding the maximum value of the load in one day and the moment when the maximum value of the load in one day appears, and obtaining a peak load moment data set;
calculating the ratio of each time point to the total research period duration based on the peak load moment data set to obtain the probability of the occurrence of a load peak at the time point, and obtaining a probability density function of the system load peak moment according to the probability of the occurrence of the load peak at the time point;
and carrying out normalization processing on the probability density functions at the peak time of the system load, so that the sum of the function values of the probability density functions at the peak time of all the system load is 1.
9. The apparatus for calculating the reliability of new energy capacity according to claim 6, wherein the process of setting the cumulative probability value of the target coverage load peak period according to the probability density function of the system load peak time, and obtaining the duration and period of the load peak is as follows:
setting an accumulated probability value of a peak value period of the target coverage load;
sequencing all the moments according to probability from big to small according to a probability density function of the system load peak moment;
and sequentially adding the time to the load peak period from the time with the maximum probability, adding the probability of the time to the probability corresponding to the time in the load peak period, repeating the steps until the probability corresponding to the time in the load peak period is equal to the cumulative probability value of the target coverage load peak period, and finally obtaining the duration and period of the load peak.
10. The new energy capacity credibility calculating device according to claim 6, wherein the process of obtaining the new energy capacity credibility of the new energy under the multimodal period according to the new energy history output data and the duration and the period of the load peak is as follows:
determining a system load peak duration, recording the system load peak duration as a load peak duration, and finding a new energy historical output data set in a corresponding load peak duration in the load peak duration:
counting new energy output data in a new energy historical output data set in a load peak period, and calculating the average output rate of new energy;
according to the average output rate of the new energy, the new energy capacity credibility of the new energy in the multi-peak period is calculated, and the formula is as follows:
η＝P/C g
wherein, eta is the reliability of the new energy capacity; p is the average output rate of new energy; c (C) g And the system is a new energy installation capacity.
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