A method and system for power system operating mode extraction

Abstract

The application discloses a kind of methods and systems for power system operating mode extraction, belong to power system planning and operation technical field.The method of the application comprises: obtaining the production data of multi-zone power system, using the production data for production simulation calculation, obtain the operating mode data under the annual scene of the power system;The experience index value corresponding to the operating mode data is calculated, and the experience index value is sorted to obtain a sorting result;Based on the sorting result, the typical operating mode corresponding to each season of the power system is extracted.The method of the application helps to extract the typical operating mode that can reflect the safety and stability level of power system from time series production simulation results, which can be used for subsequent power grid safety and stability calculation and checking, and provides important data support for power system planning and operation.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning and operation, and more particularly, to a method and system for extracting power system operation modes. Background Art

[0002] The selection of typical operating modes is a prerequisite for power system safety and stability calculations, analysis, and verification. The operating modes used for safety and stability calculations should fully reflect the safety and stability issues faced by power grid operations, thereby ensuring the adaptability, safety, and reliability of power grid planning and operation.

[0003] Currently, the operating mode used for safe and stable calculations of power systems relies on manual construction and splicing by computer personnel, which is prone to seasonal and time period misalignment. The resulting operating mode may not be the actual mode, and it is impossible to cover all year-round scenarios. Summary of the Invention

[0004] In response to the above problems, the present invention proposes a method for extracting the operating mode of a power system, comprising:

[0005] Acquire production data of a multi-zone power system, perform production simulation calculations using the production data, and obtain operating mode data of the power system under a full-year scenario;

[0006] Calculating the empirical index values ​​corresponding to the operating mode data, and sorting the empirical index values ​​to obtain a sorting result;

[0007] Based on the ranking results, typical operating modes corresponding to each season of the power system are extracted.

[0008] Optional production data includes: annual load of each sub-area power system, wind power and photovoltaic output curve data, DC operating power curve data, rated capacity and output characteristic data of various types of generator sets, rated power and power limit data of interconnection sections and DC lines between sub-areas.

[0009] Optionally, the sampling period of the load, wind power and photovoltaic output curve data and DC operation power curve data of each sub-area power system throughout the year is 1 hour.

[0010] Optional, production simulation calculation, including: time series production simulation calculation;

[0011] The time series production simulation calculation includes:

[0012] Based on the timing constraints of renewable energy power generation output and unit operation, the power balance of the power system is calculated time period by time, and the corresponding generation or DC power value and load power value of each node in the power system for 8760 hours are obtained.

[0013] Optional, empirical index values, including: heavy load mode index value, stability index value and new energy index value.

[0014] The optional calculation formula for the large load mode index value is as follows:

[0015] EI 1,t =P Load,t +P DCO,t +P ACO,t -P New,t

[0016] Among them, EI 1,t is the heavy load mode index value at time t, t is the time, t is equal to 1 to 8760 hours, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between the partitions at time t, P New,t is the total power output of renewable energy at time t.

[0017] Optionally, the calculation formula for the stability index value is as follows:

[0018]

[0019] Among them, EI S,t is the stability index value at time t, P Sync,t is the total operating capacity of conventional synchronous units in the power system at time t, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between zones at time t.

[0020] Optional, the calculation formula for the new energy vehicle index value is as follows:

[0021]

[0022] Among them, EI 2,t is the new energy vehicle mode index value at time t, P New,t is the total power output of renewable energy at time t, EI S,t is the stability index value at time t.

[0023] Optionally, the empirical index values ​​are sorted, including: sorting according to the magnitude of the heavy load mode index value and the new energy heavy generation index value in each season.

[0024] Optionally, the operating modes corresponding to the top indicator values ​​in the sorting results are extracted as the typical operating modes for each season.

[0025] In another aspect, the present invention further provides a system for extracting an operating mode of a power system, comprising:

[0026] A data acquisition unit is used to obtain production data of a multi-zone power system, perform production simulation calculations using the production data, and obtain operating mode data of the power system under a full-year scenario;

[0027] a sorting unit, configured to calculate the empirical index values ​​corresponding to the operating mode data, and sort the empirical index values ​​to obtain a sorting result;

[0028] An extraction unit is used to extract the typical operation mode corresponding to each season of the power system based on the sorting result.

[0029] Optional production data includes: annual load of each sub-area power system, wind power and photovoltaic output curve data, DC operating power curve data, rated capacity and output characteristic data of various types of generator sets, rated power and power limit data of interconnection sections and DC lines between sub-areas.

[0030] Optionally, the sampling period of the load, wind power and photovoltaic output curve data and DC operation power curve data of each sub-area power system throughout the year is 1 hour.

[0031] Optional, production simulation calculation, including: time series production simulation calculation;

[0032] The time series production simulation calculation includes:

[0033] Based on the timing constraints of renewable energy power generation output and unit operation, the power balance of the power system is calculated time period by time, and the corresponding generation or DC power value and load power value of each node in the power system for 8760 hours are obtained.

[0034] Optional, empirical index values, including: heavy load mode index value, stability index value and new energy index value.

[0035] The optional calculation formula for the large load mode index value is as follows:

[0036] EI 1,t =P Load,t +P DCO,t +P ACO,t -P New,t

[0037] Among them, EI 1,t is the heavy load mode index value at time t, t is the time, t is equal to 1 to 8760 hours, P Load,t is the total load power at time t, P DCO,tis the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between the partitions at time t, P New,t is the total power output of renewable energy at time t.

[0038] Optionally, the calculation formula for the stability index value is as follows:

[0039]

[0040] Among them, EI S,t is the stability index value at time t, P Sync,t is the total operating capacity of conventional synchronous units in the power system at time t, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between zones at time t.

[0041] Optional, the calculation formula for the new energy vehicle index value is as follows:

[0042]

[0043] Among them, EI 2,t is the new energy vehicle mode index value at time t, P New,t is the total power output of renewable energy at time t, EI S,t is the stability index value at time t.

[0044] Optionally, the empirical index values ​​are sorted, including: sorting according to the magnitude of the heavy load mode index value and the new energy heavy generation index value in each season.

[0045] Optionally, the operating modes corresponding to the top indicator values ​​in the sorting results are extracted as the typical operating modes for each season.

[0046] In yet another aspect, the present invention further provides a computing device comprising: one or more processors;

[0047] a processor for executing one or more programs;

[0048] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0049] In another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a method for extracting power system operating modes, comprising: obtaining production data for a multi-zone power system, performing production simulation calculations using the production data to obtain operating mode data for the power system under year-round scenarios; calculating empirical index values ​​corresponding to the operating mode data, and sorting the empirical index values ​​to obtain sorting results; and extracting typical operating modes corresponding to each season of the power system based on the sorting results. The method of the present invention facilitates extracting typical operating modes that reflect the safety and stability level of the power system from time-series production simulation results, which can be used for subsequent grid safety and stability calculation verification, providing important data support for power system planning and operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the method of the present invention;

[0053] Figure 2 is a flow chart of an embodiment of the method of the present invention;

[0054] Figure 3 This is a graph of expert experience indicators corresponding to January 1st of a regional power grid calculation scenario according to an embodiment of the method of the present invention;

[0055] Figure 4 A scatter plot of the 8760-hour operation mode of a regional power grid example scenario according to an embodiment of the method of the present invention;

[0056] Figure 5 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0057] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0058] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0059] Example 1:

[0060] The present invention also proposes a method for extracting the operation mode of the power system, such as Figure 1 Shown, including:

[0061] Step 1: Obtain production data of a multi-zone power system, perform production simulation calculations using the production data, and obtain operating mode data of the power system under a full-year scenario;

[0062] Step 2: Calculate the empirical index values ​​corresponding to the operating mode data, and sort the empirical index values ​​to obtain a sorting result;

[0063] Step 3: Based on the sorting results, extract the typical operating modes corresponding to each season of the power system.

[0064] Among them, production data includes: the load of each sub-region power system throughout the year, wind power and photovoltaic output curve data, DC operating power curve data, rated capacity and output characteristics data of various types of generator sets, and rated power and power limit data of interconnection sections and DC lines between sub-regions.

[0065] Among them, the sampling period for the load, wind power and photovoltaic output curve data and DC operating power curve data of each sub-regional power system throughout the year is 1 hour.

[0066] Among them, production simulation calculation includes: time series production simulation calculation;

[0067] The time series production simulation calculation includes:

[0068] Based on the timing constraints of renewable energy power generation output and unit operation, the power balance of the power system is calculated time period by time, and the corresponding generation or DC power value and load power value of each node in the power system for 8760 hours are obtained.

[0069] Among them, the empirical index values ​​include: heavy load mode index value, stability index value and new energy index value.

[0070] The calculation formula for the large load mode index value is as follows:

[0071] EI 1,t =P Load,t +P DCO,t +P ACO,t -P New,t

[0072] Among them, EI 1,t is the heavy load mode index value at time t, t is the time, t is equal to 1 to 8760 hours, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between the partitions at time t, P New,t is the total power output of renewable energy at time t.

[0073] The calculation formula of the stability index value is as follows:

[0074]

[0075] Among them, EI S,t is the stability index value at time t, P Sync,t is the total operating capacity of conventional synchronous units in the power system at time t, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between zones at time t.

[0076] The calculation formula for the new energy vehicle index value is as follows:

[0077]

[0078] Among them, EI 2,t is the new energy vehicle mode index value at time t, P New,t is the total power output of renewable energy at time t, EI S,t is the stability index value at time t.

[0079] The step of sorting the empirical index values ​​includes sorting the values ​​according to the magnitude of the heavy load mode index value and the new energy heavy generation index value in each season.

[0080] Among them, the operating mode corresponding to the top indicator value in the sorting results is extracted as the typical operating mode of each season.

[0081] The present invention will be further described below with reference to specific implementation cases of the present invention:

[0082] Specific implementation steps are as follows: Figure 2 Shown, including:

[0083] Step 1: Input the data required for multi-zone power system production simulation calculation;

[0084] Step 2: Perform production simulation calculations to obtain the operating mode data of the power system throughout the year;

[0085] Step 3: Calculate the expert experience index values ​​corresponding to the full-year operation mode data;

[0086] Step 4: Sort the expert experience index values ​​corresponding to the annual operation mode data by season;

[0087] Step 5: Based on the ranking results of the expert experience index values, the typical operating mode corresponding to each season is obtained.

[0088] In step 1:

[0089] The data required for the multi-zone power system production simulation calculations includes annual load, wind power, and photovoltaic output curve data for each zone, DC operating power curve data, the rated capacity and output characteristics of various generator types, and the rated power and power limits of inter-zone interconnection sections and DC lines. The curve data is sampled every hour, meaning that the annual curve data includes 8,760 time points.

[0090] In step 2:

[0091] The production simulation calculation is a time-series production simulation. It takes into account the timing constraints of renewable energy power generation output and unit operation, and performs time-series power balance calculations on the power system on a long-term and refined time scale. The generated / DC power value and load power value corresponding to 8760 hours for each node in the system are obtained, that is, the operating mode data corresponding to 8760 hours.

[0092] In step 3:

[0093] The operating mode expert experience index values ​​include: a high-load mode index, a stability index, and a new energy generation index. The high-load mode is used to reflect the power system operation status during the power supply guarantee period. According to expert experience, a typical high-load mode requires the system load and AC / DC external power to be as large as possible, and the new energy output to be as small as possible. In this case, the spinning reserve will be as low as possible. The expression for the high-load mode index is constructed as follows:

[0094] EI 1,t =P Load,t +P DCO,t +P ACO,t -P New,t

[0095] Where, t is the time from 1 to 8760 hours; P Load,t is the total load power at time t; P DCO,t P is the total power of DC transmission between zones at time t; ACO,t P is the total AC power transmitted between zones at time t; New,t is the total power output of renewable energy at time t; EI 1,t is the heavy load mode index value at time t.

[0096] The stability index is used to evaluate the stability of the power system. According to expert experience, the greater the number of conventional synchronous units in the system, the smaller the load and AC / DC power transmission, the better the system stability. The expression for the stability index is constructed as follows:

[0097]

[0098] Where, P Sync,t is the total operating capacity of conventional synchronous units in the system at time t; EI S,t is the system stability index value at time t. The smaller the value, the lower the stability level of the operating mode at the corresponding moment.

[0099] According to expert experience, a typical new energy generation method requires that the new energy output be as large as possible and the system stability level be as low as possible. Therefore, the expression of the new energy generation index is constructed as follows:

[0100]

[0101] Where, EI 2,t is the new energy vehicle mode index value at time t.

[0102] In step 4:

[0103] The seasons include spring, summer, autumn, and winter. The operating modes corresponding to the 8760 hours of the year are divided into spring mode, summer mode, autumn mode, and winter mode. The heavy load mode index values ​​and new energy heavy power mode index values ​​corresponding to the operating modes of each season are sorted in descending order.

[0104] In step 5:

[0105] In the ranking results of the expert experience index values, the operating mode with the highest ranking heavy load mode index value in each season is the typical heavy load mode extracted in each season, and the operating mode with the highest ranking new energy large-scale index value is the typical new energy large-scale mode extracted in each season.

[0106] The method of the present invention combines the power generation output, load and AC / DC transmission characteristics to construct system stability indicators, heavy load mode indicators and new energy heavy load mode indicators. Based on the constructed indicators, typical heavy load modes and new energy heavy load mode indicators for each season are screened from the time series production simulation results.

[0107] This paper verifies the rationality and effectiveness of the power grid planning data scenario of a certain region in a certain year.

[0108] First, a time-series production simulation calculation is performed under the example data scenario to obtain the operating mode data results corresponding to 8760 hours of the example scenario.

[0109] According to the 8760-hour operation mode data results, the corresponding large load mode index EI1 and system stability index EI are calculated. S And the new energy vehicle index EI2, taking the calculation results of 24 hours on January 1 as an example, as shown in Table 1.

[0110] Table 1

[0111]

[0112] According to the calculation results in Table 1, the change curves of the heavy load mode index EI1 and the new energy heavy load mode index EI2 on January 1 of the example scenario are drawn, as shown in the figure below: Figure 3 shown.

[0113] Observe Table 1 and Figure 1 It can be seen that in the calculation scenario, the heavy load mode indicator reaches its maximum at 19:00 on January 1, and the new energy heavy power mode indicator reaches its maximum at 13:00, indicating that the operation mode corresponding to 13:00 is the new energy heavy power mode of the day, and the operation mode corresponding to 19:00 is the heavy load operation mode of the day.

[0114] The 8760-hour operating mode data results are sorted from large to small according to the heavy load mode index EI1. The operating mode ranked highest in each season is the extracted heavy load operating mode, as shown in Table 2.

[0115] Table 2

[0116]

[0117] The 8760-hour operating mode data results are sorted from large to small according to the new energy vehicle emission index EI2. The operating mode with the highest ranking in each season is the extracted new energy vehicle emission mode, as shown in Table 3.

[0118] Table 3

[0119]

[0120] With new energy output as the horizontal axis and stability index EI S As the vertical axis, draw a scatter plot of the 8760-hour operation mode data and mark the extracted heavy load mode and new energy heavy load mode, such as Figure 4 Among them, the heavy load mode is referred to as the heavy mode, and the new energy mode is referred to as the waist mode.

[0121] Depend on Figure 4 It can be seen that the typical operating modes extracted from the case scenarios all fall on the boundaries of the 8760-hour operating mode data distribution, providing good representation of the 8760-hour operating mode data. The high-load modes corresponding to each season fall on the boundaries where both the stability index and renewable energy output are low, while the high-energy renewable energy mode falls on the boundaries where the stability index is low and renewable energy output is high, fully reflecting the safety and stability of the system.

[0122] In summary, the power system operation mode extraction method based on expert experience indicators proposed in the present invention can extract typical operation modes reflecting the stability level of the power system according to the production simulation calculation results. The calculation of the proposed mode indicators is simple and highly interpretable, providing a scientific data basis for the next step of safety and stability calculation verification.

[0123] Example 2:

[0124] The present invention also proposes a system 200 for extracting the operation mode of a power system, such as Figure 5 Shown, including:

[0125] The data acquisition unit 201 is used to obtain production data of the multi-zone power system, perform production simulation calculations using the production data, and obtain operation mode data of the power system under the full-year scenario;

[0126] A sorting unit 202 is configured to calculate the empirical index values ​​corresponding to the operating mode data and sort the empirical index values ​​to obtain a sorting result;

[0127] The extraction unit 203 is configured to extract typical operating modes corresponding to each season of the power system based on the sorting result.

[0128] Among them, production data includes: the load of each sub-region power system throughout the year, wind power and photovoltaic output curve data, DC operating power curve data, rated capacity and output characteristics data of various types of generator sets, and rated power and power limit data of interconnection sections and DC lines between sub-regions.

[0129] Among them, the sampling period for the load, wind power and photovoltaic output curve data and DC operating power curve data of each sub-regional power system throughout the year is 1 hour.

[0130] Among them, production simulation calculation includes: time series production simulation calculation;

[0131] The time series production simulation calculation includes:

[0132] Based on the timing constraints of renewable energy power generation output and unit operation, the power balance of the power system is calculated time period by time, and the corresponding generation or DC power value and load power value of each node in the power system for 8760 hours are obtained.

[0133] Among them, the empirical index values ​​include: heavy load mode index value, stability index value and new energy index value.

[0134] The calculation formula for the large load mode index value is as follows:

[0135] EI 1,t =P Load,t +PDCO,t +P ACO,t -P New,t

[0136] Among them, EI 1,t is the heavy load mode index value at time t, t is the time, t is equal to 1 to 8760 hours, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between the partitions at time t, P New,t is the total power output of renewable energy at time t.

[0137] The calculation formula of the stability index value is as follows:

[0138]

[0139] Among them, EI S,t is the stability index value at time t, P Sync,t is the total operating capacity of conventional synchronous units in the power system at time t, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between zones at time t.

[0140] The calculation formula for the new energy vehicle index value is as follows:

[0141]

[0142] Among them, EI 2,t is the new energy vehicle mode index value at time t, P New,t is the total power output of renewable energy at time t, EI S,t is the stability index value at time t.

[0143] The step of sorting the empirical index values ​​includes sorting the values ​​according to the magnitude of the heavy load mode index value and the new energy heavy generation index value in each season.

[0144] Among them, the operating mode corresponding to the top indicator value in the sorting results is extracted as the typical operating mode of each season.

[0145] The method of the present invention helps to extract typical operating modes that can reflect the safety and stability level of the power system from the time-series production simulation results, which can be used for subsequent grid safety and stability calculation verification, and provides important data support for power system planning and operation.

[0146] Example 3:

[0147] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the method in the above embodiment.

[0148] Example 4:

[0149] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0150] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0155] 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 method for extracting the operating mode of a power system, characterized in that: include: Acquire production data of a multi-zone power system, perform production simulation calculations using the production data, and obtain operating mode data of the power system under a full-year scenario; Calculating the empirical index values ​​corresponding to the operating mode data, and sorting the empirical index values ​​to obtain a sorting result; Based on the ranking results, extracting typical operating modes corresponding to each season of the power system; The production simulation calculation includes: time series production simulation calculation; The time series production simulation calculation includes: Based on the timing constraints of renewable energy power generation output and unit operation, the power balance of the power system is calculated time period by time, and the corresponding generation or DC power value and load power value of each node in the power system for 8760 hours are obtained; The empirical index values ​​include: heavy load mode index value, stability index value and new energy index value; The calculation formula of the heavy load mode index value is as follows: EI 1,t =P Load,t +P DCO,t +P ACO,t -P New,t Among them, EI 1,t is the heavy load mode index value at time t, t is the time, t is equal to 1 to 8760 hours, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between the partitions at time t, P New,t is the total power output of renewable energy at time t; The calculation formula of the stability index value is as follows: Among them, EI S,t is the stability index value at time t, P Sync,t is the total operating capacity of conventional synchronous units in the power system at time t, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between zones at time t; The calculation formula of the new energy vehicle index value is as follows: Among them, EI 2,t is the new energy index value at time t, P New,t is the total power output of renewable energy at time t, EI S,t is the stability index value at time t; The sorting of the empirical index values ​​includes: sorting the values ​​according to the magnitude of the heavy load mode index value and the new energy heavy load index value in each season; Among them, the operating mode corresponding to the top indicator value in the sorting results is extracted as the typical operating mode of each season.

2. The method according to claim 1, characterized in that The production data include: the load of each sub-area power system throughout the year, wind power and photovoltaic output curve data, DC operating power curve data, rated capacity and output characteristics data of various types of generator sets, and rated power and power limit data of interconnection sections and DC lines between sub-areas.

3. The method according to claim 2, characterized in that The sampling period of the load, wind power and photovoltaic output curve data and DC operation power curve data of each sub-area power system throughout the year is 1 hour.

4. A system for extracting the operating mode of a power system, characterized in that: include: A data acquisition unit is used to obtain production data of a multi-zone power system, perform production simulation calculations using the production data, and obtain operating mode data of the power system under a full-year scenario; a sorting unit, configured to calculate the empirical index values ​​corresponding to the operating mode data, and sort the empirical index values ​​to obtain a sorting result; an extraction unit, configured to extract, based on the sorting result, typical operating modes corresponding to each season of the power system; The production simulation calculation includes: time series production simulation calculation; The time series production simulation calculation includes: Based on the timing constraints of renewable energy power generation output and unit operation, the power balance of the power system is calculated time period by time, and the corresponding generation or DC power value and load power value of each node in the power system for 8760 hours are obtained; The empirical index values ​​include: heavy load mode index value, stability index value and new energy index value; The calculation formula of the heavy load mode index value is as follows: EI 1,t =P Load,t +P DCO,t +P ACO,t -P New,t Among them, EI 1,t is the heavy load mode index value at time t, t is the time, t is equal to 1 to 8760 hours, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between the partitions at time t, P New,t is the total power output of renewable energy at time t; The calculation formula of the stability index value is as follows: Among them, EI S,t is the stability index value at time t, P Sync,t is the total operating capacity of conventional synchronous units in the power system at time t, P Load,t is the total load power at time t, P DCO,t is the total DC power transmitted between the zones at time t, P ACO,t is the total AC power transmitted between zones at time t; The calculation formula of the new energy vehicle index value is as follows: Among them, EI 2,t is the new energy index value at time t, P New,t is the total power output of renewable energy at time t, EI S,t is the stability index value at time t; The sorting of the empirical index values ​​includes: sorting the values ​​according to the magnitude of the heavy load mode index value and the new energy heavy load index value in each season; Among them, the operating mode corresponding to the top indicator value in the sorting results is extracted as the typical operating mode of each season.

5. The system according to claim 4, characterized in that The production data include: the load of each sub-area power system throughout the year, wind power and photovoltaic output curve data, DC operating power curve data, rated capacity and output characteristics data of various types of generator sets, and rated power and power limit data of interconnection sections and DC lines between sub-areas.

6. The system according to claim 5, characterized in that The sampling period of the load, wind power and photovoltaic output curve data and DC operation power curve data of each sub-area power system throughout the year is 1 hour.

7. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 3 is implemented.

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