Equivalent method and device for power system containing high-proportion new energy

Abstract

The invention discloses an equivalence method and an equivalence device for a power system containing high-proportion new energy, which are used for solving the problem that the transient characteristics of the power system after equivalence cannot meet the requirements due to the fact that the new energy is simplified in the conventional equivalence method for the power system under the condition of access of the high-proportion new energy. The method comprises the following steps: determining a plurality of new energy stations needing equivalence in a power system, and carrying out shortest electrical distance aggregation on the plurality of new energy stations to obtain a plurality of station classes; on the basis of coherent equivalence simplification, conventional equivalence units corresponding to conventional units in the power system and station equivalence units corresponding to each station class are obtained; based on a conventional equivalent unit, respectively constructing a key transient control model of each station equivalent unit; in combination with curve fitting simulation optimization based on fault observation, respectively solving control parameters of each key transient control model; and substituting the control parameters obtained by optimization solution back to the key transient control model so as to control and reduce transient characteristic errors occurring during equivalence of the power system.

Description

Technical Field

[0001] The present invention relates to the field of power system equivalent technology, and in particular to a power system equivalent method containing a high proportion of new energy, a power system equivalent device containing a high proportion of new energy, an electronic device and a storage medium. Background Art

[0002] Dynamic Equivalence in Power Systems is a method used to simplify power system models. The purpose of dynamic equivalence is to retain the key dynamic characteristics of the research system while simplifying the external system to reduce the complexity and calculation time of simulation calculations. The basic requirement is that the steady-state and transient characteristics of the power system before and after equivalence are consistent, and the error is within the allowable range. Based on this characteristic, dynamic equivalence is particularly suitable for dynamic analysis of large-scale power systems, effective analysis of power system planning and design, and operation modes.

[0003] The commonly used dynamic equivalent methods include the coherent equivalent method, Ward equivalent method, Deventer equivalent method and multi-port equivalent method. At present, these methods can be used to process conventional units, but cannot process new energy units such as wind power and photovoltaics. If the new energy units are simplified as loads, the transient characteristics of new energy will be lost.

[0004] With the construction and development of new power systems based on new energy, the proportion of new energy such as wind power and photovoltaic power is increasing, and its transient characteristics can no longer be ignored. If the simplified method mentioned above is continued, it will often lead to large errors in the transient characteristics of the power system before and after the equivalence, which does not meet the simulation accuracy requirements. Summary of the invention

[0005] The present invention provides an electric power system equivalence method containing a high proportion of renewable energy, an electric power system equivalence device containing a high proportion of renewable energy, an electronic device and a storage medium, which are used to solve or partially solve the problem that when a high proportion of renewable energy is connected, the transient characteristics of the electric power system after equivalence do not meet the requirements due to the simplified processing of renewable energy in the current electric power system equivalence method.

[0006] The present invention provides a method for equivalent power system containing a high proportion of new energy, the method comprising:

[0007] Determine a plurality of new energy sites that need to be equal in the power system, and aggregate the plurality of new energy sites by the shortest electrical distance to obtain a plurality of site classes;

[0008] Based on the coherent equivalent simplification, obtain the conventional equivalent units corresponding to the conventional units in the power system and the station equivalent units corresponding to each of the station types;

[0009] Based on the conventional equivalent units, constructing key transient control models for each of the station equivalent units respectively;

[0010] In combination with curve fitting simulation optimization based on fault observation, the control parameters of each of the key transient control models are solved respectively;

[0011] The control parameters obtained by the optimization solution are substituted back into the key transient control model to control the transient characteristic errors that occur when the power system is equivalent.

[0012] Optionally, the aggregating the multiple new energy stations by the shortest electrical distance to obtain multiple station categories includes:

[0013] Divide the multiple new energy sites into grid-following wind farms, photovoltaic stations, energy storage stations, and grid-building wind farms, photovoltaic stations, and energy storage stations;

[0014] For each type of new energy station collection:

[0015] Using the shortest path method, searching for the shortest path from each new energy station in the new energy station set to each boundary node;

[0016] Summing the reactance values ​​of each line in the shortest path to obtain a reactance value matrix;

[0017] Solve the average reactance value corresponding to the shortest path based on the reactance value matrix as the shortest electrical distance from the new energy station to the boundary node corresponding to the shortest path;

[0018] Combined with the shortest electrical distance, k-means clustering is performed on all the new energy stations in the new energy station set to obtain several station classes;

[0019] All station classes corresponding to each of the new energy station sets are integrated as multiple station classes of the multiple new energy stations that need to be equal.

[0020] Optionally, the obtaining of conventional equivalent units corresponding to conventional units in the power system based on homology equivalent simplification, and the station equivalent units corresponding to each station type, include:

[0021] Classify the conventional units in the power system according to hydropower, thermal power and nuclear power to obtain multiple conventional unit categories;

[0022] The constant power conversion method is used to simplify the export buses of conventional units belonging to the same cluster to obtain the corresponding conventional equivalent buses. At the same time, the export buses of new energy stations belonging to the same cluster are simplified to obtain the corresponding station equivalent buses.

[0023] Conventional units belonging to the same cluster are represented by a conventional equivalent unit connected to the corresponding conventional equivalent busbar, and new energy stations belonging to the same cluster are represented by a station equivalent unit connected to the corresponding station equivalent busbar;

[0024] The current channel simplification method is used to simplify the external system network and simplify the load to the reserved bus.

[0025] Optionally, the types of the station equivalent value units include grid-following type equivalent value units and grid-building type equivalent value units; and based on the conventional equivalent value units, the key transient control model of each station equivalent value unit is constructed respectively, including:

[0026] The model and parameters of the conventional equivalent unit are used to construct the low-throughput power control model and SVG reactive power control model of the grid-following equivalent unit, and the virtual inertia control model of the grid-building equivalent unit is constructed at the same time;

[0027] The low-break power control model includes a low-break active power control model and a low-break reactive power control model, corresponding to the first control parameter to be solved; the first control parameter includes active power during the fault, active power recovery rate after the fault, reactive power during the fault and reactive power recovery rate after the fault;

[0028] The control mode of the SVG reactive power control model is PID control, corresponding to the second control parameter to be solved; the second control parameter includes the proportional link magnification, the integral link magnification and the differential link magnification in the PID control;

[0029] The control mode of the virtual inertia control model is PD control, corresponding to a third control parameter to be solved; the third control parameter includes a proportional link magnification factor and a differential link magnification factor in PD control.

[0030] Optionally, the curve fitting simulation optimization based on fault observation is combined to solve the control parameters of each key transient control model separately, including:

[0031] A first fault set, a second fault set, a first observation set, a second observation set and a third observation set are set; the fault types of the first fault set include DC blocking fault, machine tripping fault and load shedding fault; the fault types of the second fault set include line three-phase short circuit and bus three-phase short circuit; the first observation set and the second observation set are both composed of bus; the third observation set is composed of line;

[0032] Based on the first fault set and the first observation set, a fault simulation observation is performed on the power system before and after the equivalent value, and based on the simulation observation result and combined with curve fitting, a first total average deviation of the power system before and after the equivalent value is constructed;

[0033] Taking the minimum of the first total average deviation as the objective function, a particle swarm optimization algorithm is used to perform parameter optimization to solve the third control parameter;

[0034] Based on the second fault set, the second observation set and the third observation set, a fault simulation observation is performed on the power system before and after the equivalent value, and based on the simulation observation result and combined with curve fitting, a second total average deviation of the power system before and after the equivalent value is constructed;

[0035] The third control parameter is taken as a known quantity, the first control parameter and the second control parameter are taken as unknown quantities, the second total average deviation is minimized as the objective function, and the particle swarm optimization algorithm is used to perform parameter optimization to solve the first control parameter and the second control parameter.

[0036] Optionally, the performing fault simulation observation on the power system before and after the equivalent value based on the first fault set and the first observation set, and constructing a first total average deviation of the power system before and after the equivalent value based on the simulation observation result and in combination with curve fitting, includes:

[0037] Taking out a first fault to be tested from the first fault set, performing fault simulation on the power system before and after equivalence based on the first fault to be tested, and recording first voltage amplitudes, voltage phase angles, and frequencies of all buses in the first observation set;

[0038] By curve fitting, first voltage amplitude curves, voltage phase angle curves and frequency curves of all buses of the power system in the first observation set before and after the equivalent value are obtained;

[0039] The average deviations of the first voltage amplitude curve, the voltage phase angle curve and the frequency curve are calculated respectively, and a first total average deviation of the power system before and after the equivalence is constructed according to the average deviation of the first voltage amplitude curve, the average deviation of the voltage phase angle curve and the average deviation of the frequency curve.

[0040] Optionally, the performing fault simulation observation on the power system before and after the equivalent value based on the second fault set, the second observation set and the third observation set, and constructing a second total average deviation of the power system before and after the equivalent value based on the simulation observation result and in combination with curve fitting, comprises:

[0041] Taking out a second fault to be measured from the second fault set, performing fault simulation on the power system before and after the equalization based on the second fault to be measured, recording the second voltage amplitude of all buses in the second observation set, and the active power and reactive power of all lines in the third observation set;

[0042] By curve fitting, a second voltage amplitude curve of all buses of the power system in the second observation set before and after the equalization, and an active power curve and a reactive power curve of all lines in the third observation set are obtained;

[0043] The average deviations of the second voltage amplitude curve, the active power curve and the reactive power curve are calculated respectively, and a second total average deviation of the power system before and after the equalization is constructed according to the average deviation of the second voltage amplitude curve, the average deviation of the active power curve and the average deviation of the reactive power curve.

[0044] The present invention also provides an equivalent device for an electric power system containing a high proportion of new energy, comprising:

[0045] A station aggregation module is used to determine multiple new energy stations that need to be equal in the power system, and aggregate the multiple new energy stations by the shortest electrical distance to obtain multiple station classes;

[0046] A coherent equivalent simplification module, used for obtaining conventional equivalent units corresponding to conventional units in the power system and station equivalent units corresponding to each station type based on coherent equivalent simplification;

[0047] A model building module, used for building a key transient control model of each of the station equivalent units based on the conventional equivalent units;

[0048] A parameter optimization solution module, used to solve the control parameters of each of the key transient control models respectively in combination with the curve fitting simulation optimization based on fault observation;

[0049] The parameter back-substitution module is used to substitute the control parameters obtained by the optimization solution back into the key transient control model to control and reduce the transient characteristic errors that occur when the power system is equivalent.

[0050] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0051] The memory is used to store program code and transmit the program code to the processor;

[0052] The processor is used to execute the method for equivalent an electric power system containing a high proportion of new energy as described in any of the above items according to the instructions in the program code.

[0053] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the method for equivalent an electric power system containing a high proportion of new energy as described in any of the above items.

[0054] It can be seen from the above technical solutions that the present invention has the following advantages:

[0055] A method for equivalent power systems with a high proportion of renewable energy is proposed. First, multiple renewable energy stations that need to be equivalent in the power system are determined, and the shortest electrical distance of multiple renewable energy stations is aggregated to obtain multiple station classes; then, based on the coherent equivalence simplification, conventional equivalent units corresponding to conventional units in the power system and station equivalent units corresponding to each station class are obtained; then, based on conventional equivalent units, key transient control models of each station equivalent unit are constructed respectively; combined with curve fitting simulation optimization based on fault observation, the control parameters of each key transient control model are solved respectively; finally, the control parameters obtained by optimization are substituted back into the key transient control model to control and reduce the transient characteristic errors that occur when the power system is equivalent. Therefore, when a high proportion of new energy is connected, the technical means provided by the present invention, such as aggregation of the shortest electrical distance between stations, simplification of synchronous equivalence, construction of key transient control models, and curve fitting simulation optimization based on fault observation, can not only handle the dynamic equivalence problem of the power system when there are a large number of wind power and photovoltaic power in the power system, but also overcome the problem that the transient characteristics of the power system after equivalence do not meet the requirements due to the simplified processing of new energy in the conventional power system dynamic equivalence method, and effectively improve the simulation accuracy of the power system after equivalence. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A flowchart of the steps of a method for equivalent power system containing a high proportion of new energy;

[0058] Figure 2 It is a schematic diagram of the overall process of an equivalent method for a power system containing a high proportion of new energy;

[0059] Figure 3 The figure is a structural block diagram of an equivalent device for an electric power system containing a high proportion of new energy. DETAILED DESCRIPTION

[0060] Embodiments of the present invention provide a method for equivalent an electric power system containing a high proportion of renewable energy, a device for equivalent an electric power system containing a high proportion of renewable energy, an electronic device and a storage medium, which are used to solve or partially solve the problem that when a high proportion of renewable energy is connected, the transient characteristics of the electric power system after equivalent do not meet the requirements due to the simplified processing of renewable energy in the current electric power system equivalent method.

[0061] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] As an example, the commonly used dynamic equivalent methods include the coherent equivalent method, Ward equivalent method, Deventer equivalent method and multi-port equivalent method. At present, these methods can be used to process conventional units, but cannot process new energy units such as wind power and photovoltaics. If the new energy units are simplified as loads, the transient characteristics of new energy will be lost.

[0063] With the construction and development of new power systems based on new energy, the proportion of new energy such as wind power and photovoltaic power is increasing, and its transient characteristics can no longer be ignored. If the simplified method mentioned above is continued, it will often lead to large errors in the transient characteristics of the power system before and after the equivalence, which does not meet the simulation accuracy requirements.

[0064] Therefore, one of the core inventive points of the embodiment of the present invention is: in view of the deficiencies in the current technology, a method for equivalent power systems containing a high proportion of new energy is provided. First, multiple new energy stations that need to be equivalent in the power system are determined, and the multiple new energy stations are aggregated by the shortest electrical distance to obtain multiple station classes; then, based on the coherent equivalence simplification, conventional equivalent units corresponding to conventional units in the power system and station equivalent units corresponding to each station class are obtained; then, based on the conventional equivalent units, key transient control models of each station equivalent unit are constructed respectively; combined with the curve fitting simulation optimization based on fault observation, the control parameters of each key transient control model are solved respectively; finally, the control parameters obtained by the optimization solution are substituted back into the key transient control model to control and reduce the transient characteristic errors that occur when the power system is equivalent. Therefore, when a high proportion of new energy is connected, the technical means provided by the present invention, such as aggregation of the shortest electrical distance between stations, simplification of synchronous equivalence, construction of key transient control models, and curve fitting simulation optimization based on fault observation, can not only handle the dynamic equivalence problem of the power system when there are a large number of wind power and photovoltaic power in the power system, but also overcome the problem that the transient characteristics of the power system after equivalence do not meet the requirements due to the simplified processing of new energy in the conventional power system dynamic equivalence method, and effectively improve the simulation accuracy of the power system after equivalence.

[0065] Reference Figure 1 , shows a flowchart of a method for equalizing a power system containing a high proportion of new energy provided by an embodiment of the present invention, which may include the following steps:

[0066] Step 101, determining a plurality of new energy stations that need to be equal in the power system, and performing shortest electrical distance aggregation on the plurality of new energy stations to obtain a plurality of station classes;

[0067] Specifically, multiple new energy sites are aggregated by the shortest electrical distance to obtain multiple site classes, which can be as follows: first, multiple new energy sites are divided into 6 types, including grid-following wind farm, photovoltaic (PV / small-scale photovoltaic system), energy storage (small-scale energy storage system), grid-connected wind farm, photovoltaic power station (Photovoltaic Power Station, referring to a large-scale photovoltaic system dedicated to power generation) and energy storage station (Energy Storage Station, referring to a large-scale energy storage system); then, aggregation processing is taken for each type of new energy site set; finally, all site classes corresponding to each new energy site set are integrated as multiple site classes of multiple new energy sites that need to be equal.

[0068] In some embodiments, the process of aggregating each type of new energy station set is specifically as follows: using the shortest path method to search for the shortest path from each new energy station in the new energy station set to each boundary node; summing up the reactance values ​​of each line in the shortest path to obtain a reactance value matrix; solving the average reactance value corresponding to the shortest path based on the reactance value matrix as the shortest electrical distance from the new energy station corresponding to the shortest path to the boundary node; combining the shortest electrical distance, performing k-means clustering on all new energy stations in the new energy station set to obtain several station classes.

[0069] More specifically, the following takes a grid-connected wind farm as an example to illustrate the specific steps of grid-connected wind farm aggregation based on the shortest electrical distance.

[0070] 1. For grid-following wind farms that need equal values, the collection Indicates. Among them, It is The grid-type wind farm is used to determine the boundary nodes of the equivalent power system. Indicates. Among them, It is Boundary nodes.

[0071] 2. Use the shortest path method to search for the shortest path from each grid-connected wind farm to each boundary node. Sum the reactance values ​​of each line in the shortest path to obtain the reactance value matrix:

[0072]

[0073] in, It is From the boundary node to the The reactance value of a wind farm.

[0074] The average value of the reactance from the grid-connected wind farm to each boundary node is taken as the shortest electrical distance from the grid-connected wind farm to the boundary node.

[0075] It can be expressed as: .in, .

[0076] 3. Use k-means clustering algorithm to The seat-and-grid type wind farms are aggregated into three types of sites.

[0077] In the embodiment of the present invention, new energy stations of the same type are grouped into three categories. The purpose is to use the shortest electrical distance to classify each type of station into three categories: near, medium, and far. According to practical experience, the influence of a short electrical distance is the greatest, and the influence of a far distance is the smallest. This classification is conducive to modeling and simulation analysis.

[0078] Referring to steps 1-3 above, the six types of new energy stations, including grid-connected wind farms, photovoltaics, energy storage, grid-connected wind farms, photovoltaic stations and energy storage stations, can be aggregated into a total of 18 station types.

[0079] Step 102, based on the coherent equivalent simplification, obtain the conventional equivalent units corresponding to the conventional units in the power system and the station equivalent units corresponding to each of the station types;

[0080] In this step, the main goal is to achieve conventional unit aggregation, network and load simplification based on the homology equivalence method.

[0081] The general principle of its implementation is as follows: first, conventional units are divided into three categories according to types such as hydropower, thermal power and nuclear power, and then each type of unit is aggregated into several conventional unit types according to actual research and analysis needs (such as referring to the different distances of near, medium and far in the aforementioned aggregation method as consideration factors). Then, the constant power conversion method is used to simplify the export buses of conventional units belonging to the same cluster and various new energy sites such as wind farms and photovoltaic power stations to obtain the corresponding equivalent buses. Then, conventional units belonging to the same cluster and various new energy sites are represented by an equivalent unit connected to the corresponding equivalent bus. Finally, the current sink reduction (CSR) method is used to simplify the external system network and simplify the load to the reserved bus (equivalent bus).

[0082] In a specific implementation, based on the coherent equivalent simplification, the process of obtaining the conventional equivalent units corresponding to the conventional units in the power system and the station equivalent units corresponding to each station class can be as follows: the conventional units in the power system are divided into three types according to hydropower, thermal power and nuclear power, and all conventional units are aggregated in combination with the unit type to obtain multiple conventional unit classes; the export busbars of the conventional units belonging to the same cluster are simplified by the constant power conversion method to obtain the corresponding conventional equivalent busbars, and the export busbars of the new energy stations belonging to the same cluster are simplified to obtain the corresponding station equivalent busbars; the conventional units belonging to the same cluster are represented by a conventional equivalent unit connected to the corresponding conventional equivalent busbar, and the new energy stations belonging to the same cluster are represented by a station equivalent unit connected to the corresponding station equivalent busbar; the current channel simplification method is used to simplify the external system network and simplify the load to the reserved busbar.

[0083] Step 103, based on the conventional equivalent units, construct a key transient control model for each of the station equivalent units;

[0084] In this step, the models and parameters of conventional equivalent units obtained in the above-mentioned synchronous equivalent method are mainly used to obtain the key transient models and parameters of site equivalent units (mainly including wind power, photovoltaic and energy storage equivalent units).

[0085] In actual situations, according to the network topology, the types of station equivalent units can mainly include grid-following equivalent units and grid-building equivalent units. In specific implementation, based on conventional equivalent units, the key transient control model of each station equivalent unit is constructed separately, which can be: using the model and parameters of conventional equivalent units, constructing the low-voltage ride-through (low voltage ride-through) power control model and SVG (Static Var Generator) reactive power control model of the grid-following equivalent unit, and at the same time constructing the virtual inertia control model of the grid-building equivalent unit.

[0086] The low-break power control model can mainly include a low-break active power control model and a low-break reactive power control model, corresponding to the first control parameter to be solved. The first control parameter includes active power during the fault, active power recovery rate after the fault, reactive power during the fault, and reactive power recovery rate after the fault.

[0087] Specifically, the active power control model and reactive power control model of the low voltage ride-through of the grid-type equivalent unit can be set as:

[0088]

[0089]

[0090] in, , Represent active power and reactive power respectively; Indicates the time when the fault disappears; Indicates the time it takes for the active power to recover to the active power before the fault. The control parameter to be determined is the active power during the fault. , Reactive power during fault , Active power recovery rate after fault and reactive power recovery rate after fault .

[0091] The control mode of the SVG reactive power control model is PID (Proportional-Integral-Derivative) control, corresponding to the second control parameter to be solved. The second control parameter includes the proportional link magnification, integral link magnification and differential link magnification in PID control.

[0092] Specifically, the reactive power control model of the SVG configured with the grid-type equivalent unit is set to a PID control model. The control parameter to be determined is the proportional link magnification factor , Integration link magnification and differential link magnification .

[0093] The control mode of the virtual inertia control model is PD (Proportional-Derivative) control, corresponding to the third control parameter to be solved. The third control parameter includes the proportional link magnification and the differential link magnification in PD control.

[0094] Specifically, the virtual inertia control model of the networked equivalent unit is set as the PD control model. The control parameter to be determined is the proportional link magnification factor and differential link magnification .

[0095] Step 104, combining the curve fitting simulation optimization based on fault observation, respectively solving the control parameters of each of the key transient control models;

[0096] Specifically, in combination with the curve fitting simulation optimization based on fault observation, the process of solving the control parameters of each key transient control model can be achieved by executing the following sub-steps S01 to S05:

[0097] Step S01: setting a first fault set, a second fault set, a first observation set, a second observation set and a third observation set; the fault types of the first fault set include DC blocking fault, machine tripping fault and load shedding fault; the fault types of the second fault set include line three-phase short circuit and bus three-phase short circuit; the first observation set and the second observation set are both composed of bus; the third observation set is composed of line;

[0098] For the grid-type equivalent units, a fault set consisting of DC blocking, machine cutting and load shedding fault types can be set according to the specific research and analysis requirements, which is recorded as , and an observation set consisting of the busbar, denoted as .

[0099] For the grid-following equivalent units, a fault set consisting of line three-phase short circuit and bus three-phase short circuit fault types can be set according to the specific research and analysis requirements, recorded as , an observation set consisting of the busbar, denoted by , and an observation set consisting of lines, denoted as .

[0100] Among them, the above-mentioned relevant simulation data sets are set according to the actual parameter identification needs. For the grid-type equivalent units, the PD parameters that need to be identified can be stimulated by faults such as DC blocking, machine cutting and load shedding to stimulate the control characteristics of the parameters, and then identified by observing the bus voltage.

[0101] For grid-following equivalent units, it is necessary to identify the control parameters of the low-throughput power control model and the PID control parameters of the SVG reactive power control model. It is necessary to use the control characteristics of the short-circuit fault excitation parameters. The SVG parameters are identified by observing the bus voltage. The control parameters of the low-throughput power control model also need to observe the active power and reactive power on the line to be identified.

[0102] Step S02: performing fault simulation observation on the power system before and after the equivalent value based on the first fault set and the first observation set, and constructing a first total average deviation of the power system before and after the equivalent value based on the simulation observation result and combined with curve fitting;

[0103] In a specific implementation, step S02 may include the following sub-steps S021 to S023:

[0104] Step S021: taking out a first fault to be measured from the first fault set, performing fault simulation on the power system before and after equivalence based on the first fault to be measured, and recording the first voltage amplitude, voltage phase angle and frequency of all buses in the first observation set;

[0105] From the fault set Take a fault from the above, perform fault simulation on the power system before and after equalization, and record the observation set The voltage amplitude, voltage phase angle and frequency of all buses in the system.

[0106] Step S022: obtaining, by curve fitting, first voltage amplitude curves, voltage phase angle curves and frequency curves of all buses in the first observation set of the power system before and after the equivalent value;

[0107] Step S023: Calculate the average deviations of the first voltage amplitude curve, the voltage phase angle curve and the frequency curve respectively, and construct a first total average deviation of the power system before and after the equivalence according to the average deviation of the first voltage amplitude curve, the average deviation of the voltage phase angle curve and the average deviation of the frequency curve.

[0108] The power system in the observation set before and after calculating the equivalent value The average deviation of the voltage amplitude curve, the average deviation of the voltage phase angle curve and the average deviation of the frequency curve of all buses in the .

[0109] Taking the voltage amplitude curve as an example, the average deviation calculation formula is:

[0110]

[0111] in, and They are the voltage amplitude curves of the power system before and after equalization. individual data; is the total number of voltage amplitude curve data.

[0112] The total mean deviation is calculated by the following formula :

[0113]

[0114] in, Represents a fault set The number of failures in Represents the observation set The number of buses in ; Indicated in In the fault simulation, the power system before and after equalization is The average deviation value of the voltage amplitude curve on each bus; Indicated in In the fault simulation, the power system before and after equalization is The average deviation value of the voltage phase angle curve on each bus; Indicated in In the fault simulation, the power system before and after equalization is The average deviation value of the frequency curve on each bus.

[0115] Step S03: Taking the minimum first total average deviation as the objective function, a particle swarm optimization algorithm is used to perform parameter optimization to solve the third control parameter;

[0116] The total mean deviation The minimum is the objective function. The particle swarm optimization algorithm can be used to solve the proportional and differential link magnification in the virtual inertia control model of the network-type equivalent unit. and .

[0117] Step S04: performing fault simulation observation on the power system before and after the equivalent value based on the second fault set, the second observation set and the third observation set, and constructing a second total average deviation of the power system before and after the equivalent value based on the simulation observation results and combined with curve fitting;

[0118] In a specific implementation, step S04 may include the following sub-steps S041 to S043:

[0119] Step S041: taking out the second fault to be measured from the second fault set, performing fault simulation on the power system before and after the equalization based on the second fault to be measured, recording the second voltage amplitude of all buses in the second observation set, and the active power and reactive power of all lines in the third observation set;

[0120] From the fault set Take a fault from the above, perform fault simulation on the power system before and after equalization, and record the observation set The voltage amplitude of all buses in the observation set The active power and reactive power of all lines in the system.

[0121] Step S042: obtaining, by curve fitting, the second voltage amplitude curves of all buses in the second observation set of the power system before and after the equalization, and the active power curves and reactive power curves of all lines in the third observation set;

[0122] Step S043: Calculate the average deviations of the second voltage amplitude curve, the active power curve and the reactive power curve respectively, and construct a second total average deviation of the power system before and after the equalization according to the average deviation of the second voltage amplitude curve, the average deviation of the active power curve and the average deviation of the reactive power curve.

[0123] Calculate the average deviation of the voltage amplitude curves of all buses in the observation set of the power system before and after the equalization, the average deviation of the line active power curve, and the average deviation of the line reactive power curve. The calculation of the average deviation in this step is consistent with the average deviation calculation formula given above, and will not be repeated here.

[0124] The total mean deviation is calculated by the following formula :

[0125]

[0126] in, Represents a fault set The number of failures in Represents the observation set The number of buses in ; Represents the observation set The number of lines in Indicated in In the fault simulation, the power system before and after equalization is The average deviation value of the voltage amplitude curve on each bus; Indicated in In the fault simulation, the power system before and after equalization is The average deviation value of the active power curve on each line; Indicated in In the fault simulation, the power system before and after equalization is The average deviation value of the reactive power curve on the line.

[0127] Step S05: taking the third control parameter as a known quantity, taking the first control parameter and the second control parameter as unknown quantities, taking the second total average deviation as the minimum as the objective function, and using the particle swarm optimization algorithm to perform parameter optimization to solve the first control parameter and the second control parameter.

[0128] The proportional and differential links in the virtual inertia control model of the networked equivalent unit obtained by the previous steps are magnified and As a known quantity, the parameters to be determined in the low-throughput power control model of the grid-equivalent unit are , , , , and the parameters to be determined in the configured SVG reactive power control model , , As the unknown quantity. Take the total mean deviation The minimum is the objective function, and the particle swarm optimization algorithm is used to solve all the undetermined parameters in the relevant control model of the equivalent unit with the same grid type.

[0129] Step 105: Substitute the control parameters obtained by the optimization solution back into the key transient control model to control and reduce the transient characteristic error that occurs when the power system is equivalent.

[0130] Finally, the control parameters obtained by optimization can be substituted into the corresponding key transient control models, that is, , , , Substituting back to the low penetration power control model of the grid-equivalent unit, , , Substitute back to the SVG reactive power control model configured with the same grid type and equivalent units, and , Substitute it back into the virtual inertia control model of the grid-type equivalent unit to control the transient characteristic error that occurs when reducing the power system equivalent.

[0131] It should be noted that, in order to enable those skilled in the art to better distinguish data of the same type but with different actual meanings, some technical features are distinguished by first, second, and third in the embodiments of the present invention. The first, second, and third are only used to distinguish data and have no other special meanings. It can be understood that the present invention is not limited to this.

[0132] In an embodiment of the present invention, a method for equivalent power systems containing a high proportion of new energy is provided. In the case of high proportion of new energy access, through the technical means provided by the present invention such as aggregation of the shortest electrical distance between stations, simplification of coherent equivalent, construction of key transient control models, and curve fitting simulation optimization based on fault observation, it is possible to not only handle the dynamic equivalent problem of the power system when there are a large number of wind power and photovoltaic power in the power system, but also overcome the problem that the transient characteristics of the power system after equivalent do not meet the requirements caused by the simplified processing of new energy in the conventional power system dynamic equivalent method, and effectively improve the simulation accuracy of the power system after equivalent.

[0133] For better explanation, refer to Figure 2 , showing an overall flow diagram of a method for equivalent power system with a high proportion of renewable energy provided by an embodiment of the present invention. It should be pointed out that this embodiment only briefly describes the general process of equivalent power system with a high proportion of renewable energy, and the specific implementation process of each step can be understood by referring to the relevant content in the aforementioned embodiment, which will not be described here. It can be understood that the present invention does not limit this.

[0134] Step 201: determining a plurality of new energy stations that need to be equal in the power system, and performing shortest electrical distance aggregation on the plurality of new energy stations to obtain a plurality of station classes;

[0135] Step 202: Based on the coherent equivalent simplification, obtain the conventional equivalent units corresponding to the conventional units in the power system and the station equivalent units corresponding to each station type; the types of station equivalent units include grid-following type equivalent units and grid-building type equivalent units;

[0136] Step 203: using the model and parameters of the conventional equivalent unit, constructing the low penetration power control model and SVG reactive power control model of the grid-following type equivalent unit, and constructing the virtual inertia control model of the grid-building type equivalent unit; the low penetration power control model corresponds to the first control parameter to be solved; the SVG reactive power control model corresponds to the second control parameter to be solved; and the virtual inertia control model corresponds to the third control parameter to be solved;

[0137] Step 204: setting a first fault set, a second fault set, a first observation set, a second observation set, and a third observation set;

[0138] Step 205: Based on the first fault set and the first observation set, a fault simulation observation is performed on the power system before and after the equivalent value, and based on the simulation observation result, combined with curve fitting, a first total average deviation of the power system before and after the equivalent value is constructed; taking the minimum first total average deviation as the objective function, a particle swarm optimization algorithm is used to perform parameter optimization to solve the third control parameter;

[0139] Step 206: performing fault simulation observation on the power system before and after the equivalent value based on the second fault set, the second observation set and the third observation set, and constructing a second total average deviation of the power system before and after the equivalent value based on the simulation observation result and combined with curve fitting;

[0140] Step 207: combining the third control parameter, taking the second total average deviation minimum as the objective function, using the particle swarm optimization algorithm to perform parameter optimization, and solving the first control parameter and the second control parameter;

[0141] Step 208: Substitute the control parameters obtained by the optimization solution back into the corresponding control model to control and reduce the transient characteristic error that occurs when the power system is equivalent.

[0142] Reference Figure 3 , shows a structural block diagram of an equivalent device of a power system containing a high proportion of new energy provided by an embodiment of the present invention, which may specifically include:

[0143] A station aggregation module 301 is used to determine a plurality of new energy stations that need to be equal in the power system, and aggregate the plurality of new energy stations by the shortest electrical distance to obtain a plurality of station classes;

[0144] A coherent equivalent simplification module 302 is used to obtain conventional equivalent units corresponding to conventional units in the power system and station equivalent units corresponding to each station type based on coherent equivalent simplification;

[0145] A model building module 303 is used to build a key transient control model of each of the station equivalent units based on the conventional equivalent units;

[0146] The parameter optimization solution module 304 is used to solve the control parameters of each of the key transient control models in combination with the curve fitting simulation optimization based on the fault observation;

[0147] The parameter back-substitution module 305 is used to substitute the control parameters obtained by the optimization solution back into the key transient control model to control and reduce the transient characteristic error that occurs when the power system is equivalent.

[0148] In an optional embodiment, the site aggregation module 301 includes:

[0149] A station type classification module is used to classify the multiple new energy stations into six types: grid-following wind farms, photovoltaic stations, energy storage stations, and grid-building wind farms, photovoltaic stations, and energy storage stations;

[0150] A shortest path search module, used to search for the shortest path from each new energy station in the new energy station set to each boundary node by using the shortest path method;

[0151] A reactance value summing module, used for summing the reactance values ​​of each line in the shortest path to obtain a reactance value matrix;

[0152] A shortest electrical distance solving module, used to solve the average reactance value corresponding to the shortest path based on the reactance value matrix as the shortest electrical distance from the new energy station to the boundary node corresponding to the shortest path;

[0153] A station clustering module, used to perform k-means clustering on all new energy stations in the new energy station set in combination with the shortest electrical distance to obtain a plurality of station classes;

[0154] The station class integration module is used to integrate all station classes corresponding to each of the new energy station sets as multiple station classes of the multiple new energy stations that need to be equal.

[0155] In an optional embodiment, the homology equivalence simplification module 302 includes:

[0156] A conventional unit aggregation module is used to classify the conventional units in the power system into three types according to hydropower, thermal power and nuclear power, and aggregate all conventional units based on the unit type to obtain multiple conventional unit classes;

[0157] The equivalent bus simplification module is used to simplify the export bus of conventional units belonging to the same cluster by using the constant power conversion method to obtain the corresponding conventional equivalent bus, and to simplify the export bus of the new energy station belonging to the same cluster to obtain the corresponding station equivalent bus;

[0158] The equivalent unit definition module is used to represent conventional units belonging to the same cluster with a conventional equivalent unit connected to the corresponding conventional equivalent bus, and to represent new energy stations belonging to the same cluster with a station equivalent unit connected to the corresponding station equivalent bus;

[0159] The external network simplification module is used to simplify the external system network by adopting the current channel simplification method and simplify the load to the reserved bus.

[0160] In an optional embodiment, the types of the station equivalent value group include a network-following type equivalent value group and a network-building type equivalent value group, and the model building module 303 is specifically used for:

[0161] The model and parameters of the conventional equivalent unit are used to construct the low-throughput power control model and SVG reactive power control model of the grid-following equivalent unit, and the virtual inertia control model of the grid-building equivalent unit is constructed at the same time;

[0162] The low-break power control model includes a low-break active power control model and a low-break reactive power control model, corresponding to the first control parameter to be solved; the first control parameter includes active power during the fault, active power recovery rate after the fault, reactive power during the fault and reactive power recovery rate after the fault;

[0163] The control mode of the SVG reactive power control model is PID control, corresponding to the second control parameter to be solved; the second control parameter includes the proportional link magnification, the integral link magnification and the differential link magnification in the PID control;

[0164] The control mode of the virtual inertia control model is PD control, corresponding to a third control parameter to be solved; the third control parameter includes a proportional link magnification factor and a differential link magnification factor in PD control.

[0165] In an optional embodiment, the parameter optimization solution module 304 includes:

[0166] The data set setting module is used to set the first fault set, the second fault set, the first observation set, the second observation set and the third observation set; the fault types of the first fault set include DC blocking fault, machine tripping fault and load shedding fault; the fault types of the second fault set include line three-phase short circuit and bus three-phase short circuit; the first observation set and the second observation set are both composed of bus; the third observation set is composed of line;

[0167] A first total average deviation construction module is used to perform fault simulation observation on the power system before and after the equivalent value based on the first fault set and the first observation set, and to construct a first total average deviation of the power system before and after the equivalent value based on the simulation observation result and in combination with curve fitting;

[0168] A first parameter optimization solution module is used to use the particle swarm optimization algorithm to perform parameter optimization and solve the third control parameter by taking the first total average deviation as the objective function;

[0169] A second total average deviation construction module is used to perform fault simulation observation on the power system before and after the equivalent value based on the second fault set, the second observation set and the third observation set, and to construct a second total average deviation of the power system before and after the equivalent value based on the simulation observation result and in combination with curve fitting;

[0170] The second parameter optimization solution module is used to take the third control parameter as a known quantity, the first control parameter and the second control parameter as unknown quantities, minimize the second total average deviation as the objective function, use the particle swarm optimization algorithm to perform parameter optimization, and solve the first control parameter and the second control parameter.

[0171] In an optional embodiment, the first total average deviation building module includes:

[0172] A first fault simulation module is used to extract a first fault to be measured from the first fault set, perform fault simulation on the power system before and after the equivalence based on the first fault to be measured, and record the first voltage amplitude, voltage phase angle and frequency of all buses in the first observation set;

[0173] A first curve fitting module is used to obtain, by curve fitting, first voltage amplitude curves, voltage phase angle curves and frequency curves of all buses of the power system in the first observation set before and after equalization;

[0174] The first average deviation calculation module is used to calculate the average deviations of the first voltage amplitude curve, the voltage phase angle curve and the frequency curve respectively, and further construct a first total average deviation of the power system before and after the equivalent value.

[0175] In an optional embodiment, the second total average deviation building module includes:

[0176] A second fault simulation module is used to extract a second fault to be measured from the second fault set, perform fault simulation on the power system before and after the equivalence based on the second fault to be measured, and record the second voltage amplitude of all buses in the second observation set, and the active power and reactive power of all lines in the third observation set;

[0177] A second curve fitting module is used to obtain, by curve fitting, a second voltage amplitude curve of all buses of the power system in the second observation set before and after the equalization, and an active power curve and a reactive power curve of all lines in the third observation set;

[0178] The second average deviation calculation module is used to calculate the average deviations of the second voltage amplitude curve, the active power curve and the reactive power curve respectively, and further construct a second total average deviation of the power system before and after the equivalent value.

[0179] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the aforementioned method embodiment.

[0180] An embodiment of the present invention further provides an electronic device, the device comprising a processor and a memory:

[0181] The memory is used to store the program code and transmit the program code to the processor;

[0182] The processor is used to execute the method for equivalent power system containing a high proportion of renewable energy according to any embodiment of the present invention according to the instructions in the program code.

[0183] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the equivalent method of an electric power system containing a high proportion of new energy according to any embodiment of the present invention.

[0184] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0185] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0186] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0188] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0189] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for equivalent power system containing a high proportion of new energy, characterized in that: include: Determine a plurality of new energy sites that need to be equal in the power system, and aggregate the plurality of new energy sites by the shortest electrical distance to obtain a plurality of site classes; Based on the coherent equivalent simplification, obtain the conventional equivalent units corresponding to the conventional units in the power system and the station equivalent units corresponding to each of the station types; Based on the conventional equivalent units, constructing key transient control models for each of the station equivalent units respectively; In combination with curve fitting simulation optimization based on fault observation, the control parameters of each of the key transient control models are solved respectively; The control parameters obtained by the optimization solution are substituted back into the key transient control model to control the transient characteristic errors that occur when the power system is equivalent.

2. The method for equivalent power system with high proportion of new energy according to claim 1 is characterized in that: The shortest electrical distance aggregation of the multiple new energy sites is performed to obtain multiple site categories, including: Divide the multiple new energy sites into grid-following wind farms, photovoltaic stations, energy storage stations, and grid-building wind farms, photovoltaic stations, and energy storage stations; For each type of new energy station collection: Using the shortest path method, searching for the shortest path from each new energy station in the new energy station set to each boundary node; Summing the reactance values ​​of each line in the shortest path to obtain a reactance value matrix; Solve the average reactance value corresponding to the shortest path based on the reactance value matrix as the shortest electrical distance from the new energy station to the boundary node corresponding to the shortest path; Combined with the shortest electrical distance, k-means clustering is performed on all the new energy stations in the new energy station set to obtain several station classes; All station classes corresponding to each of the new energy station sets are integrated as multiple station classes of the multiple new energy stations that need to be equal.

3. The method for equivalent power system containing a high proportion of new energy according to claim 1, characterized in that: The conventional equivalent units corresponding to the conventional units in the power system are obtained based on the coherent equivalent simplification, and the station equivalent units corresponding to each of the station types include: Classify the conventional units in the power system according to hydropower, thermal power and nuclear power to obtain multiple conventional unit categories; The constant power conversion method is used to simplify the export buses of conventional units belonging to the same cluster to obtain the corresponding conventional equivalent buses. At the same time, the export buses of new energy stations belonging to the same cluster are simplified to obtain the corresponding station equivalent buses. Conventional units belonging to the same cluster are represented by a conventional equivalent unit connected to the corresponding conventional equivalent busbar, and new energy stations belonging to the same cluster are represented by a station equivalent unit connected to the corresponding station equivalent busbar; The current channel simplification method is used to simplify the external system network and simplify the load to the reserved bus.

4. The method for equivalent power system containing a high proportion of new energy according to claim 1, characterized in that: The types of the station equivalent units include grid-following type equivalent units and grid-building type equivalent units; based on the conventional equivalent units, the key transient control model of each station equivalent unit is constructed respectively, including: The model and parameters of the conventional equivalent unit are used to construct the low-throughput power control model and SVG reactive power control model of the grid-following equivalent unit, and the virtual inertia control model of the grid-building equivalent unit is constructed at the same time; The low-break power control model includes a low-break active power control model and a low-break reactive power control model, corresponding to the first control parameter to be solved; the first control parameter includes active power during the fault, active power recovery rate after the fault, reactive power during the fault and reactive power recovery rate after the fault; The control mode of the SVG reactive power control model is PID control, corresponding to the second control parameter to be solved; the second control parameter includes the proportional link magnification, the integral link magnification and the differential link magnification in the PID control; The control mode of the virtual inertia control model is PD control, corresponding to a third control parameter to be solved; the third control parameter includes a proportional link magnification factor and a differential link magnification factor in PD control.

5. The method for equivalent power system containing a high proportion of new energy according to claim 4 is characterized in that: The curve fitting simulation optimization based on fault observation is combined to solve the control parameters of each key transient control model respectively, including: A first fault set, a second fault set, a first observation set, a second observation set and a third observation set are set; the fault types of the first fault set include DC blocking fault, machine tripping fault and load shedding fault; the fault types of the second fault set include line three-phase short circuit and bus three-phase short circuit; the first observation set and the second observation set are both composed of bus; the third observation set is composed of line; Based on the first fault set and the first observation set, a fault simulation observation is performed on the power system before and after the equivalent value, and based on the simulation observation result and combined with curve fitting, a first total average deviation of the power system before and after the equivalent value is constructed; Taking the minimum of the first total average deviation as the objective function, a particle swarm optimization algorithm is used to perform parameter optimization to solve the third control parameter; Based on the second fault set, the second observation set and the third observation set, a fault simulation observation is performed on the power system before and after the equivalent value, and based on the simulation observation result and combined with curve fitting, a second total average deviation of the power system before and after the equivalent value is constructed; The third control parameter is taken as a known quantity, the first control parameter and the second control parameter are taken as unknown quantities, the second total average deviation is minimized as the objective function, and the particle swarm optimization algorithm is used to perform parameter optimization to solve the first control parameter and the second control parameter.

6. The method for equivalent power system with high proportion of new energy according to claim 5 is characterized in that: The method of performing fault simulation observation on the power system before and after the equivalent value based on the first fault set and the first observation set, and constructing a first total average deviation of the power system before and after the equivalent value based on the simulation observation result and in combination with curve fitting, includes: Taking out a first fault to be tested from the first fault set, performing fault simulation on the power system before and after equivalence based on the first fault to be tested, and recording first voltage amplitudes, voltage phase angles, and frequencies of all buses in the first observation set; By curve fitting, first voltage amplitude curves, voltage phase angle curves and frequency curves of all buses of the power system in the first observation set before and after the equivalent value are obtained; The average deviations of the first voltage amplitude curve, the voltage phase angle curve and the frequency curve are calculated respectively, and a first total average deviation of the power system before and after the equivalence is constructed according to the average deviation of the first voltage amplitude curve, the average deviation of the voltage phase angle curve and the average deviation of the frequency curve.

7. The method for equivalent power system containing a high proportion of new energy according to claim 5, characterized in that: The method of performing fault simulation observation on the power system before and after the equivalent value based on the second fault set, the second observation set and the third observation set, and constructing a second total average deviation of the power system before and after the equivalent value based on the simulation observation result and in combination with curve fitting, includes: Taking out a second fault to be measured from the second fault set, performing fault simulation on the power system before and after the equalization based on the second fault to be measured, recording the second voltage amplitude of all buses in the second observation set, and the active power and reactive power of all lines in the third observation set; By curve fitting, a second voltage amplitude curve of all buses of the power system in the second observation set before and after the equalization, and an active power curve and a reactive power curve of all lines in the third observation set are obtained; The average deviations of the second voltage amplitude curve, the active power curve and the reactive power curve are calculated respectively, and a second total average deviation of the power system before and after the equalization is constructed according to the average deviation of the second voltage amplitude curve, the average deviation of the active power curve and the average deviation of the reactive power curve.

8. An equivalent device for an electric power system containing a high proportion of new energy, characterized in that: include: A station aggregation module is used to determine multiple new energy stations that need to be equal in the power system, and aggregate the multiple new energy stations by the shortest electrical distance to obtain multiple station classes; A coherent equivalent simplification module, used for obtaining conventional equivalent units corresponding to conventional units in the power system and station equivalent units corresponding to each station type based on coherent equivalent simplification; A model building module, used for building a key transient control model of each of the station equivalent units based on the conventional equivalent units; A parameter optimization solution module, used to solve the control parameters of each of the key transient control models respectively in combination with the curve fitting simulation optimization based on fault observation; The parameter back-substitution module is used to substitute the control parameters obtained by the optimization solution back into the key transient control model to control and reduce the transient characteristic errors that occur when the power system is equivalent.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the method for equivalent an electric power system containing a high proportion of new energy as described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the method for equivalent an electric power system containing a high proportion of new energy as described in any one of claims 1-7.

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