Method and system for extracting operation mode of power system

Abstract

The invention discloses a method and a system for extracting an operation mode of a power system, and belongs to the technical field of planning and operation of the power system. The method comprises the following steps: acquiring production data of a multi-partition power system, and performing production simulation calculation by using the production data to obtain operation mode data of the power system in a full-year scene; calculating empirical index values corresponding to the operation mode data, and sorting the empirical index values to obtain a sorting result; and based on the sorting result, extracting a typical operation mode corresponding to each season of the power system. According to the method, a typical operation mode capable of reflecting the safety and stability level of the power system can be extracted from a time sequence production simulation result, the method can be used for subsequent power grid safety and stability calculation and checking, and important data support is provided for planning and operation of the power system.

Description

Technical Field

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

[0002] The selection of typical operation modes is the premise for the safety and stability calculation, analysis and verification of the power system. The operation mode used for safety and stability calculation should be able to fully reflect the safety and stability issues faced by the power grid operation, so as to ensure the adaptability, safety and reliability of power grid planning and operation.

[0003] At present, 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 the whole year's scenarios. Summary of the invention

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

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

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

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

[0008] Optional production data, including: 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 characteristic data of various types of generator sets, rated power and power limit data of inter-subarea interconnection sections and DC lines.

[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 power generation or DC power value and load power value corresponding to 8760 hours of each node in the power system are obtained.

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

[0014] Optional, the 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 index value of the heavy load mode 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the two partitions at time t, P New,t It 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 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the partitions at time t.

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

[0021]

[0022] Among them, EI 2,t is the new energy generation 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 size of the heavy load mode index value and the new energy heavy power 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 operation mode of a power system, comprising:

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

[0027] A sorting unit, used to calculate the experience index value corresponding to the operation mode data, and sort the experience index value 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, including: 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 characteristic data of various types of generator sets, rated power and power limit data of inter-subarea interconnection sections and DC lines.

[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 power generation or DC power value and load power value corresponding to 8760 hours of each node in the power system are obtained.

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

[0035] Optional, the 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 index value of the heavy load mode 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the two partitions at time t, P New,t It 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 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the partitions at time t.

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

[0042]

[0043] Among them, EI 2,t is the new energy generation 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 size of the heavy load mode index value and the new energy heavy power 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 yet 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 the operation mode of an electric power system, comprising: obtaining production data of a multi-partitioned electric power system, using the production data to perform production simulation calculations, and obtaining the operation mode data of the electric power system under the full-year scenario; calculating the empirical index values ​​corresponding to the operation mode data, and sorting the empirical index values ​​to obtain sorting results; based on the sorting results, extracting the typical operation modes corresponding to each season of the electric power system. The method of the present invention helps to extract typical operation modes that can reflect the safety and stability level of the electric power system from the time-series production simulation results, which can be used for subsequent calculation and verification of the safety and stability of the power grid, and provides important data support for the planning and operation of the electric power system. 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 It is an expert experience index curve chart corresponding to January 1 of the regional power grid calculation example scenario of the method embodiment of the present invention;

[0055] Figure 4 It is a scatter diagram of the 8760-hour operation mode of the regional power grid example scenario of the method embodiment of the present invention;

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

[0057] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0058] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the 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] Embodiment 1:

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

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

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

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

[0064] Among them, the production data include: the load of each sub-district 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 inter-district interconnection sections and DC lines.

[0065] Among them, the sampling period for 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.

[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 power generation or DC power value and load power value corresponding to 8760 hours of each node in the power system 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 of 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 index value of the heavy load mode 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the two partitions at time t, P New,t It 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 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the partitions at time t.

[0076] Among them, the calculation formula of the new energy vehicle index value is as follows:

[0077]

[0078] Among them, EI 2,t is the new energy generation 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 large load mode index value and the new energy large generation index value in each season.

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

[0081] The present invention is further described below in conjunction with specific implementation cases of the present invention:

[0082] Specific implementation steps are as follows: Figure 2 As 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 operation mode data of the power system throughout the year;

[0085] Step 3: Calculate the expert experience index values ​​corresponding to the annual operation mode data respectively;

[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 operation mode corresponding to each season is obtained.

[0088] In step 1:

[0089] The data required for the multi-zone power system production simulation calculation includes the load, wind power, photovoltaic output curve data of each zone throughout the year, DC operating power curve data, rated capacity and output characteristic data of various types of generator sets, and rated power and power limit of interconnection sections and DC lines between zones. The sampling period of the curve data is 1 hour, that is, the curve data for the whole year contains 8760 time points.

[0090] In step 2:

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

[0092] In step 3:

[0093] The expert experience index values ​​of the operation mode include: large load mode index, stability index, and new energy generation index. The large load mode is used to reflect the operation of the power system during the power supply guarantee period. According to expert experience, the typical large load mode requires the system load and AC / DC transmission power to be as large as possible and the new energy output to be as small as possible. At this time, the rotating reserve will be as low as possible. The expression of the large 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 time 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 the two partitions at time t; ACO,t P is the total AC power transmitted between the two partitions 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 transmission power, the better the system stability. The expression of the stability index is constructed as follows:

[0097]

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

[0099] According to expert experience, the typical new energy generation mode requires the new energy output to be as large as possible and the system stability level to be as low as possible. 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 operation modes corresponding to 8760 hours throughout the year are divided into spring mode, summer mode, autumn mode and winter mode, and the heavy load mode index values ​​and new energy heavy load mode index values ​​corresponding to the operation 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 of the 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 of the 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 large-scale generation mode indicators. According to the constructed indicators, typical heavy load modes and new energy large-scale generation modes in each season are screened from the time series production simulation results.

[0107] The present invention verifies its rationality and effectiveness based on 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 Figure 3 shown.

[0113] Observe Table 1 and Figure 1 It can be seen that in the example scenario, the heavy load mode index reaches the maximum at 19:00 on January 1, and the new energy heavy generation mode index reaches the maximum at 13:00, indicating that the operation mode corresponding to 13:00 is the new energy heavy generation 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 operation mode data results are sorted from large to small according to the heavy load mode index EI1. The operation mode with the highest ranking in each season is the extracted heavy load operation mode, as shown in Table 2.

[0115] Table 2

[0116]

[0117] The 8760-hour operating mode data results are sorted in descending order according to the new energy mode index EI2. The operating mode with the highest ranking in each season is the extracted new energy 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 high-load mode and new energy high-generation mode, such as Figure 4 Among them, the heavy load mode is referred to as the heavy mode, and the new energy large power mode is referred to as the waist mode.

[0121] Depend on Figure 4 It can be seen that the typical operation modes extracted from the example scenarios are all located on the boundary of the 8760-hour operation mode data distribution, and have a good representative effect on the 8760-hour operation mode data. Among them, the large load mode corresponding to each season is located at the boundary where the stability index and new energy output are low, and the new energy large output mode is located at the boundary where the stability index is low and the new energy output is high, which can fully reflect 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 the typical operation mode 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, which provides a scientific data basis for the next step of safety and stability calculation verification.

[0123] Embodiment 2:

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

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

[0126] A sorting unit 202 is used to calculate the experience index value corresponding to the operation mode data, and sort the experience index value to obtain a sorting result;

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

[0128] Among them, the production data include: the load of each sub-district 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 inter-district interconnection sections and DC lines.

[0129] Among them, the sampling period for 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.

[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 power generation or DC power value and load power value corresponding to 8760 hours of each node in the power system 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 of 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 index value of the heavy load mode 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the two partitions at time t, P New,t It 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 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the partitions at time t.

[0140] Among them, the calculation formula of the new energy vehicle index value is as follows:

[0141]

[0142] Among them, EI 2,t is the new energy generation 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 large load mode index value and the new energy large generation index value in each season.

[0144] Among them, the operating mode corresponding to the top indicator value in the sorting result 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 power grid safety and stability calculation verification, and provides important data support for power system planning and operation.

[0146] Embodiment 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 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, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0148] Embodiment 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 is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and 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 appreciated by those skilled in the art that 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. Moreover, 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 codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0154] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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 can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for extracting an operating mode of a power system, characterized in that: include: Acquire production data of a multi-zone power system, use the production data to perform production simulation calculations, and obtain operation mode data of the power system under the full-year scenario; Calculating the empirical index values ​​corresponding to the operation mode data, and sorting the empirical index values ​​to obtain a sorting result; Based on the ranking results, typical operation modes corresponding to each season of the power system are extracted.

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

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. The method according to claim 1, characterized in that 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 power generation or DC power value and load power value corresponding to 8760 hours of each node in the power system are obtained.

5. The method according to claim 1, characterized in that The empirical index values ​​include: heavy load mode index value, stability index value and new energy index value.

6. The method according to claim 5, characterized in that 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the two partitions at time t, P New,t It is the total power output of renewable energy at time t.

7. The method according to claim 5, characterized in that 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 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the partitions at time t.

8. The method according to claim 5, characterized in that The calculation formula of the new energy vehicle index value is as follows: Among them, EI 2,t is the new energy generation 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.

9. The method according to claim 1, characterized in that: The sorting of the empirical index values ​​includes: sorting according to the magnitude of the heavy load mode index value and the new energy heavy generation index value in each season.

10. The method according to claim 1, characterized in that The operating modes corresponding to the top indicator values ​​in the sorting results are extracted as the typical operating modes of each season.

11. A system for extracting the operation mode of a power system, characterized in that: include: A data acquisition unit, used to obtain production data of a multi-zone power system, use the production data to perform production simulation calculations, and obtain operation mode data of the power system under the full-year scenario; A sorting unit, used to calculate the experience index value corresponding to the operation mode data, and sort the experience index value to obtain a sorting result; An extraction unit is used to extract the typical operation mode corresponding to each season of the power system based on the sorting result.

12. The system according to claim 11, characterized in that The production data include: the load of each sub-district power system throughout the year, wind power and photovoltaic output curve data, DC operating power curve data, rated capacity and output characteristic data of various types of generator sets, and rated power and power limit data of inter-district interconnection sections and DC lines.

13. The system according to claim 12, 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.

14. The system according to claim 11, characterized in that 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 power generation or DC power value and load power value corresponding to 8760 hours of each node in the power system are obtained.

15. The system according to claim 11, characterized in that The empirical index values ​​include: heavy load mode index value, stability index value and new energy index value.

16. The system according to claim 15, characterized in that 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the two partitions at time t, P New,t It is the total power output of renewable energy at time t.

17. The system according to claim 15, characterized in that 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 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 power of DC transmission between the two partitions at time t, P ACO,t is the total AC power transmitted between the partitions at time t.

18. The system according to claim 15, characterized in that The calculation formula of the new energy vehicle index value is as follows: Among them, EI 2,t is the new energy generation 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.

19. The system according to claim 11, characterized in that The sorting of the empirical index values ​​includes: sorting according to the magnitude of the heavy load mode index value and the new energy heavy generation index value in each season.

20. The system according to claim 11, characterized in that The operating modes corresponding to the top indicator values ​​in the sorting results are extracted as the typical operating modes of each season.

21. 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 10 is implemented.

22. 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 10 is implemented.

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