A multi-time-scale adaptive power supply reliability control method and system

Abstract

The present invention relates to the technical field of power system power supply reliability, and more specifically, to a multi-time-scale adaptive power supply reliability control method and system. The scheme includes setting a time division scale to determine the current reliability analysis scale; retrieving offline data and online data of wind power, photovoltaics, loads and energy storage through the power supply company database; calculating the data in the reliability data group; when the reliability analysis scale is 1, the data in the reliability data group is not updated; when the reliability analysis scale is 2 or 3, the data in the reliability data group under different time scales is automatically updated according to the distribution and region of different types of energy based on the offline data and the online data. The scheme provides a method for dynamically adjusting the time scale to perform power supply reliability control, and constructs a dynamic quantitative evaluation system that takes into account the intermittent impact of new energy and energy storage equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power system power supply reliability, and more specifically, to a multi-time scale adaptive power supply reliability control method and system. Background Art

[0002] The power supply reliability in the traditional power system mainly relies on power supply reliability assessment data. These data include power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages. These data have clear calculation methods for power systems mainly based on thermal power generation and synchronous motors.

[0003] Prior to the technology of the present invention, the impact of renewable energy volatility on the power supply reliability assessment data of power systems mainly based on thermal power generation and synchronous motors had not been considered, specifically including the calculation method of power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages. This may lead to underestimation or overestimation of power supply reliability assessment data under some power source types, resulting in resource mismatch and thus reducing the power supply reliability of the entire power system. Summary of the Invention

[0004] In view of the above problems, the present invention proposes a multi-time-scale adaptive power supply reliability control method and system, provides a method for dynamically adjusting time-scale wiring to control power supply reliability, and constructs a dynamic quantitative evaluation system that takes into account the intermittent impact of new energy and energy storage equipment.

[0005] According to a first aspect of an embodiment of the present invention, a multi-time-scale adaptive power supply reliability control method is provided.

[0006] In one or more embodiments, preferably, the multi-time-scale adaptive power supply reliability control method includes:

[0007] Set the time division scale to determine the current reliability analysis scale;

[0008] Retrieve offline data on wind power, photovoltaic power, load, and energy storage from the power supply company database;

[0009] Obtain online data on wind power, photovoltaic power, load, and energy storage through sensors in different zones or areas;

[0010] Calculating data in a reliability data group, wherein the reliability data group includes power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages;

[0011] When the reliability analysis scale is 1, no data update is performed in the reliability data group;

[0012] When the reliability analysis scale is 2 or 3, data in the reliability data groups at different time scales are automatically updated according to the offline data and the online data and the distribution and regions of different types of energy.

[0013] In one or more embodiments, preferably, setting the time division scale and determining the current reliability analysis scale specifically includes:

[0014] Set the time division scale and the first time division margin and second time division margin for each area;

[0015] Obtain the current time division scale and use the first calculation formula to determine the current reliability analysis scale;

[0016] The first calculation formula is:

[0017]

[0018] Wherein, C is the reliability analysis scale, Y1 is the first time boundary margin, Y2 is the second time decomposition margin, and T is the current time division scale.

[0019] In one or more embodiments, preferably, retrieving offline data of wind power, photovoltaic power, load, and energy storage from the power supply company database specifically includes:

[0020] Retrieve the wind power and photovoltaic power ratios from the power supply company database;

[0021] Retrieve the total capacity of energy storage from the power supply company database;

[0022] The rated power of wind power, photovoltaic power and load in the corresponding area is retrieved through the power supply company database.

[0023] In one or more embodiments, preferably, obtaining online data of wind power, photovoltaic power, load, and energy storage through sensors in different zones or regions specifically includes:

[0024] The current remaining proportion of energy storage is collected in real time through sensors;

[0025] The real-time power of wind power, photovoltaic power and load is collected in real time through sensors.

[0026] In one or more embodiments, preferably, the data in the reliability data group is calculated, and the reliability data group includes power supply reliability, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages, and specifically includes:

[0027] Obtain historical assessment data for the corresponding area. If these data include power supply reliability, average power outage duration for users, average number of power outages for users, and equivalent hours of system power outages, they are automatically stored in the reliability data group.

[0028] If not included, the reliability data set is supplemented by performing a quick calculation using the historical evaluation data.

[0029] In one or more embodiments, preferably, when the reliability analysis scale is 1, no data in the reliability data group is updated, specifically including:

[0030] Periodically collecting the reliability analysis scale, and when the reliability analysis scale is 1, not updating the data in the reliability data group;

[0031] The power supply reliability rate, the average power outage time of users, the average number of power outages of users, and the equivalent number of system power outage hours are directly used as the analysis results of the current area on a long time scale as the reliability data group, wherein the power supply reliability rate is stored as the long-scale power supply reliability rate.

[0032] In one or more embodiments, preferably, when the reliability analysis scale is 2 or 3, automatically updating the data in the reliability data groups at different time scales according to the offline data and the online data and the distribution and regions of different energy types includes:

[0033] When the reliability analysis scale is 2, the long-scale power supply reliability rate, the wind power proportion, and the photovoltaic power proportion are obtained, and the photovoltaic mesoscale power supply reliability and the wind power mesoscale power supply reliability are updated using the second calculation formula;

[0034] When the reliability analysis scale is 3, the presence or absence of energy storage indicator is calculated using the third calculation formula, and the presence or absence of new energy indicator is calculated using the fourth calculation formula;

[0035] When there is energy storage and no new energy, the fifth calculation formula is used to update the data in the reliability data group;

[0036] When there is energy storage and new energy, the data in the reliability data group is updated using the sixth calculation formula;

[0037] When there is energy storage and new energy, and an additional active power source, the seventh calculation formula is used to update the data in the reliability data group;

[0038] The second calculation formula is:

[0039]

[0040] Among them, KF is the medium-scale power supply reliability of wind power, KG is the medium-scale power supply reliability of photovoltaic power, K0 is the long-scale power supply reliability, EF is the proportion of wind power, and EG is the proportion of photovoltaic power;

[0041] The third calculation formula is:

[0042]

[0043] Among them, N is the mark of whether there is energy storage, SOE is the current remaining proportion of energy storage, P c is the total capacity of energy storage, P e is the rated power, TT is the average annual power outage time for users in the area;

[0044] The fourth calculation formula is:

[0045]

[0046] Among them, X is whether there is a new energy logo, P x It is the rated power of new energy;

[0047] The fifth calculation formula is a revised formula for reliability assessment with energy storage and no new energy:

[0048]

[0049] Among them, TJ is the reduction of power outage time, load is the rated power of load, and KK is the power supply reliability rate;

[0050] The sixth calculation formula is a revised formula for reliability assessment with energy storage and new energy:

[0051]

[0052] The seventh calculation formula is:

[0053]

[0054] Among them, TT1 is the corrected value of the average annual power outage time for users in the region, and TK is the proportion of power supplied by the active power source.

[0055] According to a second aspect of an embodiment of the present invention, a multi-time-scale adaptive power supply reliability control system is provided.

[0056] In one or more embodiments, preferably, the multi-time-scale adaptive power supply reliability control system includes:

[0057] Multi-scale division module, used to set the time division scale and determine the current reliability analysis scale;

[0058] Offline data acquisition module, used to retrieve offline data of wind power, photovoltaic power, load and energy storage through the power supply company database;

[0059] Online data acquisition module, used to obtain online data of wind power, photovoltaic power, load and energy storage through sensors in different zones or areas;

[0060] An online reliability analysis module is used to calculate data in a reliability data set, wherein the reliability data set includes power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages;

[0061] a long-scale correction module, configured to not update the data in the reliability data group when the reliability analysis scale is 1;

[0062] The short-scale correction module is used to automatically update the data in the reliability data group at different time scales according to the offline data and the online data and the distribution and region of different types of energy when the reliability analysis scale is 2 or 3.

[0063] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method according to any one of the first aspect of the embodiment of the present invention is implemented.

[0064] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0065] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0066] In the embodiment of the present invention, dynamic quantitative evaluation in different situations of short cycle and long cycle is achieved through online evaluation at different time scales.

[0067] In the embodiment of the present invention, by quickly performing short-scale corrections, correction value calculations are completed in regions of different types, thereby achieving a dynamic quantitative evaluation of the impact of new energy and energy storage on intermittency.

[0068] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0069] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 This is a flow chart of a multi-time-scale adaptive power supply reliability control method according to an embodiment of the present invention.

[0072] Figure 2 This is a flowchart of setting a time division scale and determining a current reliability analysis scale in a multi-time scale adaptive power supply reliability control method according to an embodiment of the present invention.

[0073] Figure 3 This is a flow chart of retrieving offline data of wind power, photovoltaic power, load and energy storage from a power supply company database in a multi-time-scale adaptive power supply reliability control method according to an embodiment of the present invention.

[0074] Figure 4 This is a flowchart of a multi-time-scale adaptive power supply reliability control method according to an embodiment of the present invention for obtaining online data of wind power, photovoltaic power, load and energy storage through sensors in different partitions or areas.

[0075] Figure 5 It is a flowchart of calculating data in a reliability data group in a multi-time-scale adaptive power supply reliability control method of an embodiment of the present invention. The reliability data group includes power supply reliability rate, average power outage time of users, average number of power outages of users, and equivalent hours of system power outages.

[0076] Figure 6 This is a flowchart of not updating data in a reliability data group when the reliability analysis scale is 1 in a multi-time-scale adaptive power supply reliability control method according to an embodiment of the present invention.

[0077] Figure 7 This is a flowchart of a multi-time-scale adaptive power supply reliability control method in an embodiment of the present invention, in which when the reliability analysis scale is 2 or 3, the data in the reliability data group at different time scales is automatically updated according to the offline data and the online data and the distribution and region of different types of energy.

[0078] Figure 8 This is a structural diagram of a multi-time-scale adaptive power supply reliability control system according to an embodiment of the present invention.

[0079] Figure 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0080] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0081] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0082] The power supply reliability in the traditional power system mainly relies on power supply reliability assessment data. These data include power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages. These data have clear calculation methods for power systems mainly based on thermal power generation and synchronous motors.

[0083] Prior to the technology of the present invention, the impact of renewable energy volatility on the power supply reliability assessment data of power systems mainly based on thermal power generation and synchronous motors had not been considered, specifically including the calculation method of power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages. This may lead to underestimation or overestimation of power supply reliability assessment data under some power source types, resulting in resource mismatch and thus reducing the power supply reliability of the entire power system.

[0084] In an embodiment of the present invention, a multi-timescale adaptive power supply reliability control method and system are provided. This solution provides a method for controlling power supply reliability by dynamically adjusting timescale connections, and constructs a dynamic quantitative evaluation system that considers the intermittent impact of new energy and energy storage equipment.

[0085] According to a first aspect of an embodiment of the present invention, a multi-time-scale adaptive power supply reliability control method is provided.

[0086] Figure 1 This is a flow chart of a multi-time-scale adaptive power supply reliability control method according to an embodiment of the present invention.

[0087] In one or more embodiments, preferably, the multi-time-scale adaptive power supply reliability control method includes:

[0088] S101, setting a time division scale and determining the current reliability analysis scale;

[0089] S102. Retrieving offline data of wind power, photovoltaic power, load, and energy storage from the power supply company database;

[0090] S103. Obtain online data on wind power, photovoltaic power, load, and energy storage through sensors in different zones or areas;

[0091] S104, calculating data in the reliability data group, wherein the reliability data group includes power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent power outage hours for the system;

[0092] S105, when the reliability analysis scale is 1, no data update is performed in the reliability data group;

[0093] S106 : When the reliability analysis scale is 2 or 3, automatically update the data in the reliability data groups at different time scales according to the offline data and the online data and the distribution and regions of different types of energy.

[0094] In an embodiment of the present invention, in order to enable analysis of blocks of different types and time scales, scale division is performed. For long time scales, online calculations are directly performed using traditional power supply reliability, average user power outage time, average number of user power outages, and system power outage equivalent hours. For short-scale data, data updates and calculations are automatically performed when combined with control of different partitions and different time scales.

[0095] Figure 2 This is a flowchart of setting a time division scale and determining a current reliability analysis scale in a multi-time scale adaptive power supply reliability control method according to an embodiment of the present invention.

[0096] like Figure 2 As shown, in one or more embodiments, preferably, the setting of the time division scale and the determination of the current reliability analysis scale specifically include:

[0097] S201, setting a time division scale and a first time boundary margin and a second time decomposition margin for each region;

[0098] S202, obtaining the current time division scale, and determining the current reliability analysis scale using the first calculation formula;

[0099] The first calculation formula is:

[0100]

[0101] Wherein, C is the reliability analysis scale, Y1 is the first time boundary margin, Y2 is the second time decomposition margin, and T is the current time division scale;

[0102] In an embodiment of the present invention, in order to meet the needs of online analysis of different types of areas, a first time boundary margin and a second time decomposition margin are set for each area based on experience to distinguish the specific reliability analysis requirements and response speed. Generally, the reliability basic data during online control is on a short time scale, while the reliability basic data during offline analysis is on a long time scale.

[0103] Figure 3 This is a flow chart of retrieving offline data of wind power, photovoltaic power, load and energy storage from a power supply company database in a multi-time-scale adaptive power supply reliability control method according to an embodiment of the present invention.

[0104] like Figure 3 As shown, in one or more embodiments, preferably, the retrieving offline data of wind power, photovoltaic power, load and energy storage from the power supply company database specifically includes:

[0105] S301. Retrieve the wind power ratio and photovoltaic power ratio from the power supply company database;

[0106] S302, retrieve the total capacity of energy storage from the power supply company database;

[0107] S303. Retrieve the rated power of wind power, photovoltaic power and load in the corresponding area from the power supply company database.

[0108] In an embodiment of the present invention, these data are subjected to offline analysis for further analysis.

[0109] Figure 4 This is a flowchart of a multi-time-scale adaptive power supply reliability control method according to an embodiment of the present invention for obtaining online data of wind power, photovoltaic power, load and energy storage through sensors in different partitions or areas.

[0110] like Figure 4 As shown, in one or more embodiments, preferably, obtaining online data of wind power, photovoltaic power, load and energy storage through sensors in different partitions or areas specifically includes:

[0111] S401, collecting the current remaining proportion of energy storage in real time through sensors;

[0112] S402. Real-time power of wind power, photovoltaic power and load is collected through sensors.

[0113] In an embodiment of the present invention, these numbers are further analyzed as online analytical data.

[0114] Figure 5 It is a flowchart of calculating data in a reliability data group in a multi-time-scale adaptive power supply reliability control method of an embodiment of the present invention. The reliability data group includes power supply reliability rate, average power outage time of users, average number of power outages of users, and equivalent hours of system power outages.

[0115] like Figure 5 As shown, in one or more embodiments, preferably, the data in the reliability data group is calculated, and the reliability data group includes power supply reliability, average power outage time of users, average number of power outages of users, and equivalent hours of system power outages, and specifically includes:

[0116] S501. Obtain historical assessment data for the corresponding area. If these data include power supply reliability, average power outage duration for users, average number of power outages for users, and equivalent hours of system power outages, they are automatically stored in the reliability data group.

[0117] S502: If not included, quickly calculate and complete the reliability data set using the historical evaluation data.

[0118] In the embodiment of the present invention, the corresponding reliability data group in each region or zone is mainly supplemented based on historical evaluation data, so as to provide a data basis for subsequent online long- and short-time scale analysis.

[0119] Figure 6 This is a flowchart of not updating data in a reliability data group when the reliability analysis scale is 1 in a multi-time-scale adaptive power supply reliability control method according to an embodiment of the present invention.

[0120] like Figure 6 As shown, in one or more embodiments, preferably, when the reliability analysis scale is 1, no data in the reliability data group is updated, specifically including:

[0121] S601, periodically collecting the reliability analysis scale, and when the reliability analysis scale is 1, not updating the data in the reliability data group;

[0122] S602. Directly use the power supply reliability rate, the average power outage time of users, the average number of power outages of users, and the equivalent number of system power outage hours as the analysis results of the current area on a long time scale as the reliability data group, wherein the power supply reliability rate is stored as the long-scale power supply reliability rate.

[0123] In an embodiment of the present invention, the reliability data group is a data that requires long-term calculation. After the calculation is completed, the corresponding required reliability data group is obtained in different regions according to the specific reliability analysis scale. If it is a long scale, there is no need to update these data.

[0124] Figure 7 This is a flowchart of a multi-time-scale adaptive power supply reliability control method in an embodiment of the present invention, in which when the reliability analysis scale is 2 or 3, the data in the reliability data group at different time scales is automatically updated according to the offline data and the online data and the distribution and region of different types of energy.

[0125] like Figure 7 As shown, in one or more embodiments, preferably, when the reliability analysis scale is 2 or 3, data in the reliability data group at different time scales is automatically updated according to the offline data and the online data and the distribution and region of different types of energy, specifically including:

[0126] S701: When the reliability analysis scale is 2, obtain the long-scale power supply reliability rate, the wind power proportion, and the photovoltaic power proportion, and update the photovoltaic mesoscale power supply reliability and the wind power mesoscale power supply reliability using a second calculation formula;

[0127] S702: When the reliability analysis scale is 3, use the third calculation formula to calculate whether there is an energy storage indicator, and use the fourth calculation formula to calculate whether there is a new energy indicator;

[0128] S703: When there is energy storage and no new energy, update the data in the reliability data group using the fifth calculation formula;

[0129] S704: When there is energy storage and new energy, update the data in the reliability data group using the sixth calculation formula;

[0130] S705: When there is energy storage and new energy, and an additional active power source, update the data in the reliability data group using the seventh calculation formula;

[0131] The second calculation formula is:

[0132]

[0133] Among them, KF is the medium-scale power supply reliability of wind power, KG is the medium-scale power supply reliability of photovoltaic power, K0 is the long-scale power supply reliability, EF is the proportion of wind power, and EG is the proportion of photovoltaic power;

[0134] The third calculation formula is:

[0135]

[0136] Among them, N is the mark of whether there is energy storage, SOE is the current remaining proportion of energy storage, P c is the total capacity of energy storage, P e is the rated power, TT is the average annual power outage time for users in the area;

[0137] The fourth calculation formula is:

[0138]

[0139] Among them, X is whether there is a new energy logo, P x It is the rated power of new energy;

[0140] The fifth calculation formula is a revised formula for reliability assessment with energy storage and no new energy:

[0141]

[0142] Among them, TJ is the reduction of power outage time, load is the rated power of load, and KK is the power supply reliability rate;

[0143] The sixth calculation formula is a revised formula for reliability assessment with energy storage and new energy:

[0144]

[0145] The seventh calculation formula is:

[0146]

[0147] Among them, TT1 is the corrected value of the average annual power outage time for users in the region, and TK is the proportion of power supplied by the active power source.

[0148] In an embodiment of the present invention, on the one hand, dynamic quantitative evaluation under different short-term and long-term conditions is achieved through online evaluation at different time scales. On the other hand, by quickly performing short-scale corrections, the correction value calculation within different types of regions is completed, thereby achieving a dynamic quantitative evaluation of the impact of new energy and energy storage on intermittency.

[0149] According to a second aspect of an embodiment of the present invention, a multi-time-scale adaptive power supply reliability control system is provided.

[0150] Figure 8 This is a structural diagram of a multi-time-scale adaptive power supply reliability control system according to an embodiment of the present invention.

[0151] In one or more embodiments, preferably, the multi-time-scale adaptive power supply reliability control system includes:

[0152] The multi-scale division module 801 is used to set the time division scale and determine the current reliability analysis scale;

[0153] Offline data acquisition module 802, used to retrieve offline data of wind power, photovoltaic power, load and energy storage from the power supply company database;

[0154] Online data acquisition module 803, used to obtain online data of wind power, photovoltaic power, load and energy storage through sensors in different zones or areas;

[0155] Online reliability analysis module 804, used to calculate data in the reliability data group, the reliability data group including power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages;

[0156] A long-scale correction module 805 is configured to not update the data in the reliability data group when the reliability analysis scale is 1;

[0157] The short-scale correction module 806 is used to automatically update the data in the reliability data group at different time scales according to the offline data and the online data and the distribution and region of different types of energy when the reliability analysis scale is 2 or 3.

[0158] In the embodiment of the present invention, a system applicable to different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0159] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method according to any one of the first aspect of the embodiment of the present invention is implemented.

[0160] According to a fourth aspect of the embodiments of the present invention, an electronic device is provided. Figure 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Figure 9 The electronic device shown is a general multi-time scale adaptive power supply reliability control device. Figure 9 The electronic device may be a smart phone, a tablet computer, or the like. The electronic device 900 includes a processor 901 and a memory 902. The processor 901 is electrically connected to the memory 902.

[0161] The processor 901 is the control center of the electronic device 900. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or calling computer programs stored in the memory 902 and calling data stored in the memory 902, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole.

[0162] In this embodiment, the processor 901 in the electronic device 900 will load instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 will run the computer program stored in the memory 902 to implement various functions, such as: setting a time division scale and determining the current reliability analysis scale; retrieving offline data of wind power, photovoltaics, load and energy storage through the power supply company database; obtaining online data of wind power, photovoltaics, load and energy storage through sensors in different partitions or regions; calculating data in the reliability data group, wherein the reliability data group includes power supply reliability rate, average power outage time of users, average number of power outages of users, and equivalent hours of system power outages; when the reliability analysis scale is 1, the data in the reliability data group is not updated; when the reliability analysis scale is 2 or 3, the data in the reliability data group at different time scales is automatically updated according to the offline data and the online data and the distribution and region of different types of energy.

[0163] In some embodiments, the electronic device 900 may further include: a display 903, a radio frequency circuit 904, an audio circuit 905, a wireless fidelity module 906, and a power supply 907. The display 903, the radio frequency circuit 904, the audio circuit 905, the wireless fidelity module 906, and the power supply 907 are electrically connected to the processor 901, respectively.

[0164] The display 903 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces. These graphical user interfaces can be composed of graphics, text, icons, videos, or any combination thereof. The display 903 may include a display panel. In some embodiments, the display panel can be configured in the form of a liquid crystal display (LCD) or an organic light-emitting diode (OLED).

[0165] The radio frequency circuit 904 can be used to transmit and receive radio frequency signals, so as to establish wireless communication with a network device or other electronic devices through wireless communication, and to transmit and receive signals with the network device or other electronic devices.

[0166] The audio circuit 905 may be configured to provide an audio interface between a user and the electronic device via a speaker and a microphone.

[0167] The Wi-Fi module 906 can be used for short-range wireless transmission, and can help users send and receive emails, browse websites, and access streaming media, etc. It provides users with wireless broadband Internet access.

[0168] The power supply 907 can be used to supply power to various components of the electronic device 900. In some embodiments, the power supply 907 can be logically connected to the processor 901 through a power management system, thereby managing charging, discharging, and power consumption through the power management system.

[0169] although Figure 9 Not shown, the electronic device 900 may also include a camera, a Bluetooth module, etc., which will not be described in detail here.

[0170] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0171] In the embodiment of the present invention, dynamic quantitative evaluation in different situations of short cycle and long cycle is achieved through online evaluation at different time scales.

[0172] In the embodiment of the present invention, by quickly performing short-scale corrections, correction value calculations are completed in regions of different types, thereby achieving a dynamic quantitative evaluation of the impact of new energy and energy storage on intermittency.

[0173] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0174] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0177] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multi-time-scale adaptive power supply reliability control method, characterized in that: The method includes: Set the time division scale to determine the current reliability analysis scale; Retrieve offline data on wind power, photovoltaic power, load, and energy storage from the power supply company database; Obtain online data on wind power, photovoltaic power, load, and energy storage through sensors in different zones or areas; Calculating data in a reliability data group, wherein the reliability data group includes power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages; When the reliability analysis scale is 1, no data update is performed in the reliability data group; When the reliability analysis scale is 2 or 3, data in the reliability data groups at different time scales are automatically updated according to the offline data and the online data and the distribution and regions of different types of energy; The setting of the time division scale and the determination of the current reliability analysis scale specifically include: Set the time division scale and the first time boundary margin and the second time boundary margin for each area; Obtain the current time division scale and use the first calculation formula to determine the current reliability analysis scale; The first calculation formula is: Wherein, C is the reliability analysis scale, Y1 is the first time boundary margin, Y2 is the second time boundary margin, and T is the current time division scale; The offline data of wind power, photovoltaic power, load and energy storage obtained from the power supply company database specifically includes: Retrieve the wind power and photovoltaic power ratios from the power supply company database; Retrieve the total capacity of energy storage from the power supply company database; Retrieve the rated power of wind power, photovoltaic power and load in the corresponding area through the power supply company database; When the reliability analysis scale is 1, no data update is performed on the reliability data group, specifically including: Periodically collecting the reliability analysis scale, and when the reliability analysis scale is 1, not updating the data in the reliability data group; The power supply reliability rate, the average power outage time of users, the average number of power outages of users, and the equivalent number of hours of system power outages are directly used as the analysis results of the current area at a long time scale as the reliability data group, wherein the power supply reliability rate is stored as the long-scale power supply reliability rate; When the reliability analysis scale is 2 or 3, data in the reliability data group at different time scales is automatically updated according to the offline data and the online data and the distribution and region of different types of energy, specifically including: When the reliability analysis scale is 2, the long-scale power supply reliability rate, the wind power proportion, and the photovoltaic power proportion are obtained, and the photovoltaic mesoscale power supply reliability and the wind power mesoscale power supply reliability are updated using the second calculation formula; When the reliability analysis scale is 3, the presence or absence of energy storage indicator is calculated using the third calculation formula, and the presence or absence of new energy indicator is calculated using the fourth calculation formula; When there is energy storage and no new energy, the fifth calculation formula is used to update the data in the reliability data group; When there is energy storage and new energy, the data in the reliability data group is updated using the sixth calculation formula; When there is energy storage and new energy, and an additional active power source, the seventh calculation formula is used to update the data in the reliability data group; The second calculation formula is: Among them, KF is the medium-scale power supply reliability of wind power, KG is the medium-scale power supply reliability of photovoltaic power, K0 is the long-scale power supply reliability, EF is the proportion of wind power, and EG is the proportion of photovoltaic power; The third calculation formula is: Among them, N is the mark of whether there is energy storage, SOE is the current remaining proportion of energy storage, P c is the total capacity of energy storage, P e is the rated power, TT is the average annual power outage time for users in the area; The fourth calculation formula is: Among them, X is whether there is a new energy logo, P x It is the rated power of new energy; The fifth calculation formula is a revised formula for reliability assessment with energy storage and no new energy: Among them, TJ is the reduction of power outage time, load is the rated power of load, and KK is the power supply reliability rate; The sixth calculation formula is a revised formula for reliability assessment with energy storage and new energy: The seventh calculation formula is: Among them, TT1 is the corrected value of the average annual power outage time for users in the region, and TK is the proportion of power supplied by the active power source.

2. The multi-time-scale adaptive power supply reliability control method according to claim 1, characterized in that: The online data of wind power, photovoltaic power, load and energy storage obtained through sensors in different zones or areas specifically includes: The current remaining proportion of energy storage is collected in real time through sensors; The real-time power of wind power, photovoltaic power and load is collected in real time through sensors.

3. The multi-time-scale adaptive power supply reliability control method according to claim 1, characterized in that: The data in the reliability data group are calculated, and the reliability data group includes power supply reliability, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages, specifically including: Obtain historical assessment data for the corresponding area. If these data include power supply reliability, average power outage duration for users, average number of power outages for users, and equivalent hours of system power outages, they are automatically stored in the reliability data group. If not included, the reliability data set is supplemented by performing a quick calculation using the historical evaluation data.

4. A multi-time-scale adaptive power supply reliability control system, characterized in that: The system is used to implement the method according to any one of claims 1 to 3, and the system comprises: Multi-scale division module, used to set the time division scale and determine the current reliability analysis scale; Offline data acquisition module, used to retrieve offline data of wind power, photovoltaic power, load and energy storage through the power supply company database; Online data acquisition module, used to obtain online data of wind power, photovoltaic power, load and energy storage through sensors in different zones or areas; An online reliability analysis module is used to calculate data in a reliability data set, wherein the reliability data set includes power supply reliability rate, average power outage time for users, average number of power outages for users, and equivalent hours of system power outages; a long-scale correction module, configured to not update the data in the reliability data group when the reliability analysis scale is 1; The short-scale correction module is used to automatically update the data in the reliability data group at different time scales according to the offline data and the online data and the distribution and region of different types of energy when the reliability analysis scale is 2 or 3.

5. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 3 when executed by a processor.

6. An electronic device comprising a memory and a processor, characterized in that: The memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 3.

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