A new energy capacity credibility calculation method and device

Abstract

The application discloses a new energy capacity credibility calculation method and device, and relates to the technical field of electric power. The method comprises the following steps: receiving basic data of a power grid, wherein the basic data of the power grid comprises new energy historical output data and load historical data; obtaining a probability density function of a system load peak time according to the load historical data; setting a cumulative probability value of a target covering a load peak value period according to the probability density function of the system load peak time, and acquiring a duration and a period of the load peak value; and obtaining new energy capacity credibility of new energy in a multi-peak period according to the new energy historical output data and the duration and the period of the load peak value.

Description

Technical Field

[0001] This invention relates to the field of power technology, specifically a method and apparatus for calculating the reliability of new energy capacity. Background Technology

[0002] With a high proportion of renewable energy being integrated into the grid, renewable energy is gradually taking a dominant position in the new power system. Renewable energy also needs to play a role in power balancing. However, how to assess the contribution of renewable energy to the system's power adequacy remains a challenge. Currently, some scholars and dispatching agencies have used fixed-period methods based on historical data to calculate the reliability of renewable energy capacity. However, this method does not take into account the multi-peak load characteristics of my country, making the selection of peak load periods in the Chinese context for accurately assessing the reliability of renewable energy capacity a pressing issue. Against this backdrop, how to effectively assess the supporting capacity of renewable energy for system adequacy has received widespread attention. Therefore, this paper proposes a method and apparatus for calculating the reliability of renewable energy capacity. Summary of the Invention

[0003] To address the shortcomings mentioned in the background section, the present invention aims to provide a method and apparatus for calculating the reliability of new energy capacity.

[0004] The objective of this invention can be achieved through the following technical solution: a method for calculating the reliability of new energy capacity, comprising:

[0005] Receive basic data from the power grid, wherein the basic data from the power grid includes historical power output data from new energy sources and historical load data;

[0006] Based on historical load data, the probability density function of the system load peak moment is obtained;

[0007] Based on the probability density function of the system load peak moment, set the cumulative probability value of the target coverage load peak period, and obtain the duration and period of the load peak;

[0008] Based on historical power output data of new energy sources and the duration and time period of peak load, the reliability of new energy capacity during multi-peak periods is obtained.

[0009] Preferably, the historical output data and load data of the new energy source require one year of data, with a data accuracy of one hour.

[0010] Preferably, the process of obtaining the probability density function of the system load peak moment based on historical load data is as follows:

[0011] By processing historical load data, the maximum load value within a day and the time when the maximum load value within a day occurs are found, thus obtaining the peak load time dataset.

[0012] Based on the peak load time dataset, the ratio of each time point to the total study period is calculated to obtain the probability of a load peak occurring at that time point. Based on the probability of a load peak occurring at that time point, the probability density function of the system load peak time is obtained.

[0013] The probability density function at the peak load time of the system is normalized so that the sum of the probability density function values ​​at all peak load times of the system is 1.

[0014] Preferably, the process of setting the cumulative probability value of the target load peak period based on the probability density function of the system load peak moment, and obtaining the duration and period of the load peak is as follows:

[0015] Set the cumulative probability value for the target coverage period during peak load periods;

[0016] Based on the probability density function of the peak system load, all times are sorted in descending order of probability;

[0017] Starting from the moment with the highest probability, add each moment sequentially to the peak load period and add the probability of that moment to the probability of the corresponding moment in the peak load period. Repeat this step until the sum of the probabilities of the corresponding moments in the peak load period equals the cumulative probability value of the target coverage peak load period. Finally, the duration and period of the peak load are obtained.

[0018] Preferably, the process of obtaining the reliability of new energy capacity during multi-peak periods based on historical new energy output data and the duration and time period of peak load is as follows:

[0019] Determine the duration of the system load peak, denoted as the load peak period, and find the corresponding historical output data of new energy sources within the load peak period;

[0020] Statistical analysis is performed on the renewable energy output data in the historical renewable energy output dataset during peak load periods to calculate the average renewable energy output rate.

[0021] Based on the average power output rate of new energy sources, the reliability of new energy capacity during peak periods is calculated using the following formula:

[0022] η = P / C g

[0023] In the formula, η represents the reliability of new energy capacity; P represents the average output rate of new energy; C g This refers to the installed capacity of new energy sources.

[0024] In another aspect, the present invention also provides a new energy capacity reliability calculation device, comprising:

[0025] Data acquisition module: used to receive basic data of the power grid, including historical power output data of new energy sources and historical load data;

[0026] First data processing module: used to obtain the probability density function of the system load peak time based on historical load data;

[0027] The second data processing module is used to set the cumulative probability value of the target load peak period based on the probability density function of the system load peak time, and to obtain the duration and period of the load peak.

[0028] Capacity reliability calculation module: used to obtain the reliability of new energy capacity during multi-peak periods based on the historical output data of new energy sources and the duration and time period of load peaks.

[0029] Preferably, the historical output data and load data of the new energy source require one year of data, with a data accuracy of one hour.

[0030] Preferably, the process of obtaining the probability density function of the system load peak moment based on historical load data is as follows:

[0031] By processing historical load data, the maximum load value within a day and the time when the maximum load value within a day occurs are found, thus obtaining the peak load time dataset.

[0032] Based on the peak load time dataset, the ratio of each time point to the total study period is calculated to obtain the probability of a load peak occurring at that time point. Based on the probability of a load peak occurring at that time point, the probability density function of the system load peak time is obtained.

[0033] The probability density function at the peak load time of the system is normalized so that the sum of the probability density function values ​​at all peak load times of the system is 1.

[0034] Preferably, the process of setting the cumulative probability value of the target load peak period based on the probability density function of the system load peak moment, and obtaining the duration and period of the load peak is as follows:

[0035] Set the cumulative probability value for the target coverage period during peak load periods;

[0036] Based on the probability density function of the peak system load, all times are sorted in descending order of probability;

[0037] Starting from the moment with the highest probability, add each moment sequentially to the peak load period and add the probability of that moment to the probability of the corresponding moment in the peak load period. Repeat this step until the sum of the probabilities of the corresponding moments in the peak load period equals the cumulative probability value of the target coverage peak load period. Finally, the duration and period of the peak load are obtained.

[0038] Preferably, the process of obtaining the reliability of new energy capacity during multi-peak periods based on historical new energy output data and the duration and time period of peak load is as follows:

[0039] Determine the duration of the system load peak, denoted as the load peak period, and find the corresponding historical output data of new energy sources within the load peak period;

[0040] Statistical analysis is performed on the renewable energy output data in the historical renewable energy output dataset during peak load periods to calculate the average renewable energy output rate.

[0041] Based on the average power output rate of new energy sources, the reliability of new energy capacity during peak periods is calculated using the following formula:

[0042] η = P / C g

[0043] In the formula, η represents the reliability of new energy capacity; P represents the average output rate of new energy; C g This refers to the installed capacity of new energy sources.

[0044] The beneficial effects of this invention are:

[0045] In use, this invention first acquires power grid data, including renewable energy output data and load data, and sends the data to a first data processing module. The first data processing module statistically analyzes the probability density function of the system load peak time based on the received power grid data and sends it to a second data processing module. The second data processing module sets the cumulative probability value of the target load peak period and obtains the duration and time period of the load peak based on the probability density function in the first data processing module. The corresponding processing results are then passed to a capacity reliability calculation module. The system dispatching agency can select the cumulative probability value of the target load peak period for renewable energy data according to its preferences. The capacity reliability calculation module quantitatively evaluates the contribution of renewable energy to system adequacy, effectively guiding dispatching and planning departments in power balance and power grid planning. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0048] Figure 2 This is a schematic diagram of the result of the device of the present invention;

[0049] Figure 3 This is a schematic diagram of the device provided in Embodiment 3 of the present invention.

[0050] Figure 4 This is the probability density plot for the peak load time of the system. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] like Figure 1 As shown, a method for calculating the reliability of new energy capacity is characterized by comprising:

[0053] Receive basic data from the power grid, wherein the basic data from the power grid includes historical power output data from new energy sources and historical load data;

[0054] The historical output and load data of the new energy sources require one year of data, with a data accuracy of one hour.

[0055] Based on historical load data, the probability density function of the system load peak moment is obtained;

[0056] In this embodiment, the specific details are as follows:

[0057] By processing historical load data, the maximum load value within a day and the time when the maximum load value within a day occurs are found, thus obtaining the peak load time dataset.

[0058] Based on the peak load time dataset, the ratio of each time point to the total study period is calculated to obtain the probability of a load peak occurring at that time point. Based on the probability of a load peak occurring at that time point, the probability density function of the system load peak time is obtained.

[0059] The probability density function at the peak load time of the system is normalized so that the sum of the probability density function values ​​at all peak load times of the system is 1.

[0060] Based on the probability density function of the system load peak moment, set the cumulative probability value of the target coverage load peak period, and obtain the duration and period of the load peak;

[0061] In this embodiment, the specific details are as follows:

[0062] Set the cumulative probability value for the target coverage period during peak load periods;

[0063] Based on the probability density function of the peak system load, all times are sorted in descending order of probability;

[0064] Starting from the moment with the highest probability, add each moment sequentially to the peak load period and add the probability of that moment to the probability corresponding to the moment in the peak load period. Repeat this step until the sum of the probabilities corresponding to the moments in the peak load period equals the cumulative probability value of the target coverage peak load period. Finally, the duration and period of the peak load are obtained.

[0065] Based on historical power output data of new energy sources and the duration and time period of peak load, the reliability of new energy capacity during multi-peak periods is obtained.

[0066] In this embodiment, the specific details are as follows:

[0067] Determine the duration of the system load peak, denoted as the load peak period, and find the corresponding historical output data of new energy sources within the load peak period;

[0068] Statistical analysis is performed on the renewable energy output data in the historical renewable energy output dataset during peak load periods to calculate the average renewable energy output rate.

[0069] Based on the average power output rate of new energy sources, the reliability of new energy capacity during peak periods is calculated using the following formula:

[0070] η = P / C g

[0071] In the formula, η represents the reliability of new energy capacity; P represents the average output rate of new energy; C g This refers to the installed capacity of new energy sources.

[0072] Example 2

[0073] Figure 2This is a schematic diagram of a new energy capacity reliability calculation device provided in Embodiment 1 of the present invention. This embodiment is applicable to calculating the new energy capacity reliability of target resources. The device can be implemented in software and / or hardware and can be configured in a terminal device. The determining device includes: a data acquisition module 210, a first data processing module 220, a second data processing module 230, and a capacity reliability calculation module 240.

[0074] The data acquisition module 210 is used to receive basic data of the power grid, including historical power output data of new energy sources and historical load data.

[0075] The first data processing module 220 is used to obtain the probability density function of the system load peak time based on historical load data;

[0076] The second data processing module 230 is used to set the cumulative probability value of the target load peak period based on the probability density function of the system load peak time, and to obtain the duration and period of the load peak.

[0077] The capacity reliability calculation module 240 is used to obtain the capacity reliability of new energy during multi-peak periods based on the historical output data of new energy and the duration and time period of load peaks.

[0078] The new energy capacity reliability calculation device provided in this embodiment of the invention can be used to execute the new energy capacity reliability calculation method provided in this embodiment of the invention, and has the corresponding functions and beneficial effects of the execution method.

[0079] It should be noted that the historical output data and load data of the new energy sources mentioned above require one year of data, with a data accuracy of one hour.

[0080] The process of obtaining the probability density function of the system load peak moment based on historical load data is as follows:

[0081] By processing historical load data, the maximum load value within a day and the time when the maximum load value within a day occurs are found, thus obtaining the peak load time dataset.

[0082] Based on the peak load time dataset, the ratio of each time point to the total study period is calculated to obtain the probability of a load peak occurring at that time point. Based on the probability of a load peak occurring at that time point, the probability density function of the system load peak time is obtained.

[0083] The probability density function at the peak load time of the system is normalized so that the sum of the probability density function values ​​at all peak load times of the system is 1.

[0084] The process of setting the cumulative probability value of the target load peak period based on the probability density function of the system load peak moment, and obtaining the duration and period of the load peak is as follows:

[0085] Set the cumulative probability value for the target coverage period during peak load periods;

[0086] Based on the probability density function of the peak system load, all times are sorted in descending order of probability;

[0087] Starting from the moment with the highest probability, add each moment sequentially to the peak load period and add the probability of that moment to the probability corresponding to the moment in the peak load period. Repeat this step until the sum of the probabilities corresponding to the moments in the peak load period equals the cumulative probability value of the target coverage peak load period. Finally, the duration and period of the peak load are obtained.

[0088] The process of obtaining the reliability of new energy capacity during multi-peak periods based on historical output data of new energy sources and the duration and time period of peak load is as follows:

[0089] Determine the duration of the system load peak, denoted as the load peak period, and within the load peak period, find the corresponding historical power output dataset of new energy sources:

[0090] Statistical analysis is performed on the renewable energy output data in the historical renewable energy output dataset during peak load periods to calculate the average renewable energy output rate.

[0091] Based on the average power output rate of new energy sources, the reliability of new energy capacity during peak periods is calculated using the following formula:

[0092] η = P / C g

[0093] In the formula, η represents the reliability of new energy capacity; P represents the average output rate of new energy; C g This refers to the installed capacity of new energy sources.

[0094] It is worth noting that in the embodiments of the above-mentioned determining device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0095] Example 3

[0096] Figure 3This is a schematic diagram of the structure of a device provided in Embodiment 3 of the present invention. The present invention provides services for the implementation of the new energy capacity reliability calculation method considering load multi-peak characteristics in the above embodiments of the present invention, and can be configured with the new energy capacity reliability calculation device considering load multi-peak characteristics in the above embodiments. Figure 3 A block diagram of an exemplary device 12 suitable for implementing embodiments of the present invention is shown. Figure 3 The device 12 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0097] like Figure 3 As shown, device 12 is represented as a general-purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components, including system memory 28 and processing unit 16.

[0098] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0099] Device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by device 12, including volatile and non-volatile media, removable and non-removable media.

[0100] 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 / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 3 Not shown; usually referred to as a "hard drive"). Although Figure 3 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0101] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0102] Device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with device 12, and / or with any device that enables device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 3 As shown, network adapter 20 communicates with other modules of device 12 via bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0103] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the new energy capacity reliability calculation method considering load multi-peak characteristics provided in the embodiments of the present invention.

[0104] The aforementioned equipment provides a reference for calculating the reliability of the system's renewable energy capacity, taking into full account the multi-peak characteristics of the system load.

[0105] Example 4

[0106] Embodiment 4 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for calculating the reliability of new energy capacity. The method includes:

[0107] Data from the power grid is acquired, including renewable energy output data and load data; the data is then input into a data processing and calculation model to obtain the corresponding renewable energy capacity reliability calculation results.

[0108] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0109] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0110] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0111] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltank, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0112] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the above-described method operations, but can also perform related operations in the capacity reliability calculation method considering multi-peak load characteristics provided in any embodiment of the present invention.

[0113] The following specific examples will be used to verify this.

[0114] Based on the renewable energy and load data of a provincial power grid in 2022, the probability density of peak load moments was calculated as follows: Figure 4 The cumulative probability value of the target coverage load peak period is set to 60%, and the calculation results of the new energy capacity credibility are shown in Table 1.

[0115] Table 1 Calculation results of the reliability of new energy capacity

[0116]

[0117] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for calculating the reliability of new energy capacity, characterized in that, include: Receive basic data from the power grid, wherein the basic data from the power grid includes historical power output data from new energy sources and historical load data; Based on historical load data, the probability density function of the system load peak moment is obtained; The process of obtaining the probability density function of the system load peak moment based on historical load data is as follows: By processing historical load data, the maximum load value within a day and the time when the maximum load value within a day occurs are found, thus obtaining the peak load time dataset. Based on the peak load time dataset, the ratio of each time point to the total study period is calculated to obtain the probability of a load peak occurring at that time point. Based on the probability of a load peak occurring at that time point, the probability density function of the system load peak time is obtained. The probability density function at the peak load time of the system is normalized so that the sum of the function values ​​of the probability density function at all peak load times of the system is 1. Based on the probability density function of the system load peak moment, set the cumulative probability value of the target coverage load peak period, and obtain the duration and period of the load peak; Based on the probability density function of the system load peak moment, the cumulative probability value of the target coverage load peak period is set, and the process of obtaining the duration and period of the load peak is as follows: Set the cumulative probability value for the target coverage period during peak load periods; Based on the probability density function of the peak system load, all times are sorted in descending order of probability; Starting from the moment with the highest probability, add each moment to the peak load period sequentially, and add the probability of that moment to the probability corresponding to the moment in the peak load period. Repeat this step until the sum of the probabilities corresponding to the moments in the peak load period equals the cumulative probability value of the target covered peak load period. Finally, the duration and period of the peak load are obtained. Based on the historical output data of new energy sources and the duration and time period of peak load, the reliability of new energy capacity during multi-peak periods is obtained. The process of obtaining the reliability of renewable energy capacity during multi-peak periods based on historical renewable energy output data and the duration and time period of peak load is as follows: Determine the duration of the system load peak, denoted as the load peak period, and find the corresponding historical output data of new energy sources within the load peak period; Statistical analysis is performed on the renewable energy output data in the historical renewable energy output dataset during peak load periods to calculate the average renewable energy output rate. Based on the average power output rate of new energy sources, the reliability of new energy capacity during peak periods is calculated using the following formula: In the formula, To ensure the credibility of new energy capacity; This represents the average power output rate of new energy sources. This refers to the installed capacity of new energy sources.

2. The method for calculating the reliability of new energy capacity according to claim 1, characterized in that, The historical output and load data of the new energy sources require one year of data, with a data accuracy of one hour.

3. A new energy capacity reliability calculation device, characterized in that, include: Data acquisition module: used to receive basic data of the power grid, including historical power output data of new energy sources and historical load data; First data processing module: used to obtain the probability density function of the system load peak time based on historical load data; The process of obtaining the probability density function of the system load peak moment based on historical load data is as follows: By processing historical load data, the maximum load value within a day and the time when the maximum load value within a day occurs are found, thus obtaining the peak load time dataset. Based on the peak load time dataset, the ratio of each time point to the total study period is calculated to obtain the probability of a load peak occurring at that time point. Based on the probability of a load peak occurring at that time point, the probability density function of the system load peak time is obtained. The probability density function at the peak load time of the system is normalized so that the sum of the function values ​​of the probability density function at all peak load times of the system is 1. The second data processing module is used to set the cumulative probability value of the target load peak period based on the probability density function of the system load peak time, and to obtain the duration and period of the load peak. Based on the probability density function of the system load peak moment, the cumulative probability value of the target coverage load peak period is set, and the process of obtaining the duration and period of the load peak is as follows: Set the cumulative probability value for the target coverage period during peak load periods; Based on the probability density function of the peak system load, all times are sorted in descending order of probability; Starting from the moment with the highest probability, add each moment to the peak load period sequentially, and add the probability of that moment to the probability corresponding to the moment in the peak load period. Repeat this step until the sum of the probabilities corresponding to the moments in the peak load period equals the cumulative probability value of the target covered peak load period. Finally, the duration and period of the peak load are obtained. Capacity reliability calculation module: used to obtain the capacity reliability of new energy during multi-peak periods based on the historical output data of new energy and the duration and time period of load peaks; The process of obtaining the reliability of renewable energy capacity during multi-peak periods based on historical renewable energy output data and the duration and time period of peak load is as follows: Determine the duration of the system load peak, denoted as the load peak period, and within the load peak period, find the corresponding historical power output dataset of new energy sources: Statistical analysis is performed on the renewable energy output data in the historical renewable energy output dataset during peak load periods to calculate the average renewable energy output rate. Based on the average power output rate of new energy sources, the reliability of new energy capacity during peak periods is calculated using the following formula: In the formula, To ensure the credibility of new energy capacity; This represents the average power output rate of new energy sources. This refers to the installed capacity of new energy sources.

4. The new energy capacity reliability calculation device according to claim 3, characterized in that, The historical output and load data of the new energy sources require one year of data, with a data accuracy of one hour.

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