Battery energy storage power station electromagnetic transient modeling method and system based on grid-connected and grid-related characteristic test

Abstract

The invention provides a battery energy storage power station electromagnetic transient modeling method and system based on a grid-connected and grid-related characteristic test, and belongs to the technical field of electric power system electrochemical energy storage modeling, and the method comprises the steps: firstly, completing the identification modeling of a single energy storage system based on a differential evolution intelligent algorithm according to the response characteristics of an actual energy storage power station in combination with an energy storage unit control model; then, according to an actual energy storage power station topological structure and an energy management system, energy storage power station modeling is completed; finally, the effectiveness of the method is verified by calculating the error between the simulation model and the actual response characteristic.

Description

Technical Field

[0001] The present invention relates to the technical field of electrochemical energy storage modeling of power systems, and in particular to a method and system for electromagnetic transient modeling of a battery energy storage power station based on grid-connected / grid-related characteristic testing. Background Art

[0002] With the continuous increase in the installed capacity of battery energy storage system (BESS), its impact on the dynamic response of the power grid can no longer be ignored. Establishing an accurate battery energy storage system model is of great significance for studying the impact of battery energy storage system grid connection on the power system and better tapping the potential of battery energy storage system in large power grids. At the same time, it can provide a simulation calculation basis for the coordinated operation and control of energy storage and new energy, and improve the utilization rate of new energy.

[0003] At present, there are many modeling methods and engineering simulation examples for electromagnetic transient modeling of electrochemical energy storage power stations.

[0004] However, the existing electromagnetic transient modeling methods of electrochemical energy storage power stations are mostly based on typical response characteristics. Although the actual energy storage power station topology, system parameters and control strategies are referenced, there are still some differences between the model output results and the actual battery energy storage power station response characteristics, and the actual operation controller parameters need to be further clarified. At the same time, the existing energy storage power station modeling methods mostly assume that the consistency of energy storage power stations is the same, ignoring the communication time of EMS issuing instructions and the differences in response characteristics between each energy storage unit, resulting in differences in the simulation and the actual response characteristics of the energy storage power station.

[0005] Therefore, there is an urgent need for a method to perform electromagnetic transient modeling based on the actual response characteristics of the energy storage power station to reflect the real response of the energy storage power station. Summary of the invention

[0006] The purpose of the present invention is to address the problem that the actual response characteristics are not considered when performing electromagnetic transient modeling in electrochemical energy storage power stations, and to propose a battery energy storage power station electromagnetic transient modeling method and system based on grid-connected and grid-related characteristic testing.

[0007] The technical solution of the present invention is:

[0008] The present invention provides a method for electromagnetic transient modeling of a battery energy storage power station based on grid-connected and grid-related characteristic testing, comprising:

[0009] S1. Obtaining grid-connected and grid-related response characteristic test data of the battery energy storage power station;

[0010] S2. Based on the test data, a differential evolution algorithm is used to perform parameter identification on the dq axis decoupling model of the energy storage converter to obtain control circuit parameters of a single energy storage system;

[0011] S3. Based on the identification parameters, a single battery energy storage system model is built in the simulation software, and multiple single models are connected according to the actual spatial topology to construct a battery energy storage power station circuit model;

[0012] S4. Based on the circuit model, establish an energy storage power station control system model with automatic power generation control, automatic voltage control and frequency control functions;

[0013] S5. Run the battery energy storage power station simulation model, compare the simulation data with the test data, calculate the errors of voltage, current, and power electrical quantities, and evaluate the accuracy of the model.

[0014] Furthermore, in S1, the obtaining of grid-connected response characteristic test data of the battery energy storage power station includes:

[0015] Obtain the results of grid-connected point power quality testing, power characteristics testing, primary frequency modulation testing, and fault ride-through capability testing;

[0016] The output voltage and output current of the energy storage converter grid connection point are collected, the grid voltage phase is calculated through the phase-locked loop, and the output active and reactive power of the converter are calculated.

[0017] Further, in S2, the use of a differential evolution algorithm to perform parameter identification on a dq axis decoupling model of an energy storage converter includes:

[0018] Taking active current and reactive current as objective functions, an evolutionary population is set, and the population is iteratively optimized through mutation, crossover and selection operations to obtain the control circuit parameters of a single energy storage system.

[0019] Further, in S2, the energy storage converter dq axis decoupling model takes active current and reactive current as objective functions, and the establishment steps include:

[0020] The energy storage system adopts a dual closed-loop control system, including a power outer loop unit and a current inner loop unit, and adopts a vector control strategy based on grid voltage orientation. When the grid voltage vector coincides with the d-axis of the dq coordinate system, the q-axis component u of the grid-side voltage vector q =0, the power of the energy storage system is expressed in the dq coordinate system as:

[0021]

[0022] Where: P and Q are the active and reactive power of the energy storage system respectively; d and i q are active current and reactive current respectively;

[0023] The PI controller of the power outer loop unit uses the following formula to calculate the active and reactive current reference values ​​i of the current inner loop unit:d_ref and i q_ref :

[0024]

[0025] Where: P ref , Q ref are active and reactive power reference values ​​respectively; k P_P and k I_P are the proportional and integral coefficients of the active PI controller respectively; k P_Q and k I_Q are the proportional and integral coefficients of the reactive power PI controller respectively;

[0026] The PI controller of the current inner loop unit calculates the voltage reference signal v of the energy storage converter based on the active and reactive current reference values ​​using the following formula: dref and v qref , through PWM modulation, the on and off of the converter bridge arm is controlled;

[0027]

[0028] In the formula, u d 、u q are the d-axis and q-axis components of the grid voltage, L f is the filter inductance; k P_c and k I_c are the proportional and integral coefficients of the PI controller of the current inner loop unit respectively;

[0029] By combining formulas (1) to (3) and performing Laplace transform, the expressions of active and reactive currents output by the energy storage converter in the frequency domain are as follows:

[0030]

[0031] Where s is the differential operator.

[0032] Further, in S3, building a single battery energy storage system model in the simulation software includes:

[0033] Build the main circuit model of the energy storage system, including the battery energy storage unit, DC side filter capacitor, AC side filter inductor and parasitic resistor, AC side filter capacitor and parasitic resistor and step-up transformer components;

[0034] A vector control strategy based on grid voltage orientation is adopted to construct a control circuit model of the energy storage system, including a double closed-loop control structure of a power outer loop unit and a current inner loop unit.

[0035] Furthermore, the connecting of multiple single-unit models according to the actual spatial topological structure includes:

[0036] Obtain the location coordinate information of each component of the energy storage power station;

[0037] Arrange the corresponding single battery energy storage system model in the simulation software and arrange it according to the actual spatial topology;

[0038] The three-phase PI segment line model is used to connect adjacent single-unit models to construct the collection line inside the energy storage power station;

[0039] The collector line and the grid-connected point voltage source model are connected through a three-phase double-winding transformer model to construct a step-up grid-connected transformer for the energy storage power station.

[0040] Furthermore, the establishment of the energy storage power station control system model includes:

[0041] The automatic generation control module AGC distributes the power instructions of the entire station to each energy storage unit model according to the preset calculation rules;

[0042] The voltage outer loop and reactive power inner loop control structure are adopted to realize reactive power-voltage control of the automatic voltage control module AVC and adjust the voltage at the grid connection point of the energy storage power station;

[0043] The frequency control module collects the grid frequency signal, calculates the primary frequency regulation power instruction, and distributes it to each energy storage unit after superimposing it with the automatic power generation control instruction, thus realizing the primary frequency regulation function of the energy storage power station.

[0044] Furthermore, the automatic power generation control module distributes the whole station power command to each energy storage unit model according to a preset calculation rule, including:

[0045] According to the power command of the entire station issued by the energy management system of the energy storage power station, combined with the current state and rated capacity of each energy storage unit, power allocation is carried out using a calculation rule based on capacity ratio allocation and taking into account the charge state balance factor of each energy storage unit;

[0046] The calculated power command values ​​of each energy storage unit are transmitted to the corresponding energy storage converter controller, and the active current and reactive current reference values ​​of the current inner loop are generated through the power outer loop control model.

[0047] Furthermore, the step of adjusting the voltage of the grid connection point of the energy storage power station includes:

[0048] Obtain real-time voltage data of the energy storage power station grid connection point, and calculate the required active and reactive power adjustment according to the preset voltage control target;

[0049] Active and reactive power regulation instructions are distributed to each energy storage unit, and the voltage at the grid connection point of the energy storage power station is regulated by adjusting the power output of each energy storage converter.

[0050] An electromagnetic transient modeling system for a battery energy storage power station based on grid-connected and grid-related characteristics testing, comprising:

[0051] A test data acquisition module, used to acquire grid-connected and grid-related response characteristic test data of the battery energy storage power station;

[0052] A circuit parameter identification module, used to perform parameter identification on the dq axis decoupling model of the energy storage converter using a differential evolution algorithm according to the test data, so as to obtain control circuit parameters of a single energy storage system;

[0053] The circuit model building module is used to build a single battery energy storage system model in the simulation software according to the identification parameters, and connect multiple single models according to the actual spatial topology to build a battery energy storage power station circuit model;

[0054] A control model building module, used to build an energy storage power station control system model with automatic power generation control, automatic voltage control and frequency control functions according to the circuit model;

[0055] The simulation model evaluation module is used to run the battery energy storage power station simulation model, compare the simulation data with the test data, calculate the errors of the voltage, current and power electrical quantities, and evaluate the accuracy of the model.

[0056] Beneficial effects of the present invention:

[0057] The present invention provides a battery energy storage power station modeling and simulation method. Aiming at the problem of insufficient accuracy in modeling of battery energy storage power stations, the present invention first obtains the grid-connected response characteristic test data of the energy storage power station, and uses the differential evolution algorithm to perform parameter identification on the energy storage converter model; then builds a single energy storage system model in Matlab / Simulink, and connects multiple single models according to the actual topological structure to build a complete power station model; on this basis, establishes a control system model including automatic power generation control, voltage control, frequency control and other functions. The simulation data is obtained by running the simulation model, and the model accuracy is evaluated by comparing it with the measured data.

[0058] The present invention improves the accuracy of the battery energy storage power station model by combining parameter identification and multi-level modeling, and can provide a reliable simulation platform for the design optimization and operation control of the energy storage power station. This method overcomes the shortcomings of the traditional modeling method, achieves a high degree of consistency between the energy storage power station modeling and the actual system, and provides strong support for the research and application of energy storage power stations.

[0059] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0061] Figure 1 A schematic diagram of a battery energy storage system model according to an embodiment of the present invention is shown;

[0062] Figure 2 A schematic diagram of a battery energy storage control system model according to an embodiment of the present invention is shown;

[0063] Figure 3 A schematic diagram of measured power response characteristics of an energy storage power station according to an embodiment of the present invention is shown;

[0064] Figure 4 A parameter identification flow chart according to an embodiment of the present invention is shown;

[0065] Figure 5 A topological structure diagram of a battery energy storage power station according to an embodiment of the present invention is shown;

[0066] Figure 6 A schematic diagram of a simulation model of an energy storage power station according to an embodiment of the present invention is shown;

[0067] Figure 7 A schematic diagram showing a comparison between simulation and measured results according to an embodiment of the present invention is shown;

[0068] Figure 8 A schematic diagram of the error between simulation and measured results according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0069] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0070] Figure 1 The schematic diagram of the battery energy storage system model of the present invention is composed of energy storage batteries, converters, filters and step-up transformers. The power storage converter (Power Conversion System, PCS) is an important module for power conversion and quality assurance in the energy storage system, and is a key factor for the reliable operation of the system.

[0071] Figure 2 It is a schematic diagram of the energy storage system controller model. The energy storage controller adopts a typical power outer loop and current inner loop control structure to achieve decoupling control of active power and reactive power and quickly respond to instructions.

[0072] Figure 3 It is the power step response result of the energy storage power station.

[0073] The present invention provides a modeling method for grid-connected response characteristics of a battery energy storage power station, which specifically includes:

[0074] S1. Obtain the grid-connected response characteristic test data of the battery energy storage power station, including the test results of power quality, power characteristics, primary frequency regulation, and fault ride-through capability.

[0075] Other grid-connected / grid-related tests of the energy storage power station can be completed by issuing instructions through the energy management system of the energy storage power station. The specific test contents are as follows:

[0076] (1) Grid-connected point power quality detection, including harmonic voltage, harmonic current, interharmonics, flicker, three-phase imbalance, active power, reactive power, voltage deviation, and frequency deviation.

[0077] (2) Power characteristics detection, including active output characteristics, active power change, active power control capability, active set value change free limit power control; reactive output characteristics, voltage control, reactive power control capability, constant power control, etc.

[0078] (3) Primary frequency modulation test, including primary frequency modulation dead zone test, primary frequency modulation dynamic performance test, and primary frequency modulation limiting test.

[0079] (4) Fault ride-through capability test, including the response of the energy storage power station under high and low voltage ride-through, active power recovery rate, and dynamic reactive power support capability.

[0080] S2. Based on the acquired test data, the differential evolution algorithm is used to identify the parameters of the d-axis and q-axis decoupling models of the energy storage converter. The differential evolution algorithm obtains the control circuit parameters of a single energy storage system by iteratively optimizing the objective function.

[0081] The differential evolution algorithm is used to identify the parameters of the dq-axis decoupling model of the energy storage converter, set the mutation factor and crossover probability, and initialize the population. Taking the active current as the objective function, the evolutionary population with a population size of NP=50 is set, in which each individual contains the proportional and integral coefficients of the active controller and the current inner loop controller.

[0082] The population is iteratively optimized through mutation, crossover and selection operations until the maximum evolutionary generation is reached or the termination condition is met, and the optimal individual is obtained, that is, the control circuit parameters of the active control loop of a single energy storage system are identified.

[0083] Accordingly, reactive current is used as the objective function to identify control parameters related to reactive power, and the identified parameters are updated to the main circuit model and control circuit model of a single energy storage system.

[0084] S3. Build the main circuit and control circuit model of a single battery energy storage system in Matlab / Simulink, and bring the identified parameters into the model. According to the actual spatial topology of the energy storage power station, use the line model in the power system component library in Matlab / Simulink to connect multiple single battery energy storage system models to build a complete battery energy storage power station circuit model.

[0085] Specifically, according to the topological structure of the energy storage system, the main circuit model of the energy storage system is built in Matlab / Simulink, including battery energy storage unit, DC side filter capacitor, AC side filter inductor and parasitic resistor, AC side filter capacitor and parasitic resistor, step-up transformer and other components.

[0086] According to the actual spatial topology of the energy storage power station, the location coordinate information of each component of the energy storage power station is obtained, including the spatial location of the energy storage battery, converter, step-up transformer and other equipment. The line model in the power system component library is added to the model. According to the location coordinate information of each component of the energy storage power station, the corresponding single battery energy storage system model is arranged in the Simulink model and arranged according to the actual spatial topology. The three-phase PI segment line model in the power system component library is used to connect the adjacent single battery energy storage system models to construct the collector line inside the energy storage power station. According to the actual grid connection point location of the energy storage power station, a three-phase voltage source model is added to the Simulink model as the equivalent grid model of the grid connection point. Through the three-phase double-winding transformer model in the power system component library, the collector line and the grid connection point voltage source model are connected to construct the step-up grid-connected transformer of the energy storage power station.

[0087] S4. Based on the circuit model of the battery energy storage power station, a control system model of the energy storage power station is established, including functional modules of automatic power generation control, automatic voltage control, and frequency control system.

[0088] S5. The automatic power generation control system distributes the power command of the entire station to each energy storage unit model according to the preset calculation rules. The automatic voltage control system adjusts the voltage of the energy storage power station grid connection point by adjusting the reactive power of the entire station.

[0089] Specifically, on the basis of the circuit model of the energy storage power station, the control system framework of the energy storage power station is built, which mainly includes three functional modules: automatic generation control (AGC), automatic voltage control (AVC) and frequency control. The active power command issued by the power grid dispatch is obtained, and the active power command of the entire station is distributed according to a certain proportion through the AGC module to calculate the active power reference value of each energy storage unit. The control structure of the voltage outer loop and the reactive power inner loop is adopted to realize the reactive power-voltage control function of the AVC module and adjust the voltage of the energy storage power station grid connection point. The grid frequency signal is collected through the frequency control module, and the primary frequency modulation power command is calculated. After superimposed with the AGC command, it is distributed to each energy storage unit to realize the primary frequency modulation function of the energy storage power station. In the energy storage power station control system model, the control parameters of the AGC, AVC and frequency control modules are set, including the proportional coefficient, integral coefficient, etc. of the controller.

[0090] S6. Run the battery energy storage power station simulation model to obtain simulation data. Compare the simulation data with the test data obtained in the first step, calculate the errors of voltage, current, and power electrical quantities, and evaluate the accuracy of the established model.

[0091] The energy storage power station control system model is simulated and tested to obtain the dynamic response characteristics of the energy storage power station under different working conditions, including the change curves of key quantities such as active power, reactive power, and voltage.

[0092] By comparing the simulation results with the actual energy storage power station operation data, the errors of various key quantities are calculated, and the accuracy and effectiveness of the energy storage power station control system model are evaluated.

[0093] Furthermore, in S2, the dq axis decoupling model of the energy storage converter has the following calculation steps;

[0094] Energy storage systems usually adopt a vector control strategy based on grid voltage orientation. When the grid voltage vector coincides with the d-axis of the dq coordinate system, the q-axis component u of the grid-side voltage vector q =0, the power of the energy storage system can be expressed in the dq coordinate system as:

[0095]

[0096] Where: P F1 and P F2 are respectively the active and reactive power before the fault; i d and i q They are active current and reactive current respectively.

[0097] Reference values ​​of active current and reactive current in the inner loop of the energy storage system current i d,ref and i q,ref Calculated by the power outer loop:

[0098]

[0099] Where: P ref , Q ref are active and reactive power reference values, P and Q are active and reactive power respectively; k P_P and k I_P are the proportional and integral coefficients of the active (proportional integral, PI) controller respectively; k P_Q and k I_Q are the proportional and integral coefficients of the reactive power PI controller respectively.

[0100] The current reference signal generated by the power outer loop is calculated by the inner loop PI controller to generate the voltage reference signal of the converter (VSC), which is modulated by PWM to control the on and off of the VSC bridge arm.

[0101]

[0102] Where, L f is the filter inductance; k P_c and k I_c They are the proportional and integral coefficients of the current loop PI controller respectively.

[0103] The expressions of active and reactive current output by the energy storage converter are as follows:

[0104]

[0105] It can be seen from the above formula that the parameters to be identified are the proportional and integral coefficients of the active and reactive controllers, and the proportional and integral coefficients of the current inner loop controller.

[0106] Furthermore, a differential evolution algorithm is used in S2 to identify key parameters.

[0107] Differential Evolution (DE) is an evolutionary algorithm based on real number coding. Its overall structure is similar to other evolutionary algorithms and consists of three basic operations: mutation, crossover, and selection. The standard differential evolution algorithm mainly includes the following four steps: Figure 4 As shown, the specific steps are as follows:

[0108] (1) Generate the initial population

[0109] Randomly generate M individuals that meet the constraints in n-dimensional space. The implementation measures are as follows:

[0110]

[0111] In the formula, and are the upper and lower bounds of the jth chromosome, rand ij (0,1) is a random decimal between [0,1].

[0112] (2) Mutation Operation

[0113] Randomly select 3 individuals x from the population p1 , x p2 and x p3 , and i≠p 1 ≠p 2 ≠p 3 , then the basic mutation operation is

[0114] h ij (t+1)=x p1j (t)+F(x p2j (t)-x p3j (t))

[0115] In the formula, x p2j (t)-x p3j (t) is the differential vector. This differential operation is the key to the differential evolution algorithm. F is the scaling factor, and p 1 , p 2 , p 3 is a random integer, representing the sequence number of the individual in the population, x bj (t) is the best individual in the population in the current generation.

[0116] (3) Crossover operation

[0117] The crossover operation is to increase the diversity of the group. The specific operations are as follows:

[0118]

[0119] Among them, randl ij is a random decimal between [0,1], CR is the crossover probability, CR∈[0,1].

[0120] (4) Select an operation

[0121] In order to determine x i (t) Whether to become a member of the next generation, the test vector v i (t+1) and the target vector x i (t) Compare the evaluation functions:

[0122]

[0123] Repeat steps (2) to (4) until the maximum number of evolution generations is reached.

[0124] Furthermore, taking the active power response characteristics of an actual energy storage power station as an example, the active current I d As the objective function, identify the control parameters of the active power loop. The specific steps are as follows:

[0125] (1) Set the mutation factor F = 0.3-0.6, CR between [0.6, 0.9], and initialize the population;

[0126] (2) Set the evolution population with a population size of NP = 50:

[0127]

[0128] In the formula, Corresponding to t 1 The proportional and integral coefficients k of the active controller in the i-th population at the moment P_P and k I_P ; Corresponding to t 1 The proportional and integral coefficients k of the active current inner loop controller in the i-th population at the moment P_c and k I_c ; Corresponding to t 1 The filter inductance L in the i-th population at the moment f .

[0129] Similarly, the reactive current I q As the objective function, identify the control parameters related to reactive power.

[0130] Furthermore, in S4, the energy management system of the energy storage power station is modeled, specifically including AGC control, AVC control and frequency control.

[0131] Under the premise of ensuring the safe operation of energy storage equipment, energy storage AGC control utilizes its fast power regulation characteristics to achieve active optimization control of energy storage power stations by controlling each PCS controller to meet the requirements of secondary frequency regulation and peak shaving and valley filling of the power grid. Active target value proportional distribution is carried out based on the rated active capacity of PCS as the distribution coefficient. When the rated active capacity of all PCSs is the same, it is distributed in an average distribution manner. The total active target value allocated is the active target instruction or planned curve target value issued by the dispatcher.

[0132]

[0133] Where: P tari K is the active power regulation target value sent to each PCS; i is the rated active capacity of each PCS; P tar It is the target value of grid-connected active power for the whole station.

[0134] The energy storage system voltage reactive control (AVC) ensures the safe operation of energy storage equipment and uses its fast power regulation characteristics to control the reactive power of each PCS controller to achieve reactive control of the energy storage power station, participate in grid voltage regulation, and meet the requirements of safe and stable grid operation. There are grid-connected point constant voltage mode, constant reactive power mode, and constant power factor mode, and the AVC control target is achieved by controlling the reactive power of the PCS. The allocation algorithm adopts equal reactive reserve allocation.

[0135]

[0136] Where: Q dtari The incremental reactive power regulation target value allocated to each PCS; Q id It is the real-time adjustable reactive capacity of each PCS; Q dtar It is the incremental reactive power demand of the whole station.

[0137] The energy storage power station collects and calculates the grid frequency, generates a total active power increment ΔP for primary frequency regulation, and obtains the total active power instruction by superimposing it with the AGC instruction, which is then distributed to each PCS unit in accordance with the capacity ratio and SOC balancing method.

[0138] Furthermore, according to the actual spatial topological structure of the energy storage power station shown in the figure, a detailed transient model of the energy storage power station was built on the Matlab / Simulink electromagnetic simulation platform. The results are as follows: Figure 6 shown.

[0139] Furthermore, in order to verify the effectiveness of the simulation model, the accuracy of the model is assessed by calculating the deviation between the simulation data and the test data according to national standards. The electrical quantities calculated for the deviation between simulation and test include: voltage U s , current I, reactive current I Q , active power P, reactive power Q, the calculation formula is as follows:

[0140]

[0141] Where: F 1 is the average error in the steady-state interval, F 2 is the average error in the transient interval, F 3 is the maximum deviation in the steady-state interval, F G is the interval weighted average total deviation, X S , X M are the per unit values ​​of voltage and current model simulation data and experimental data respectively; K S_Start , K S_End K is the first and last serial number of the model simulation data within the calculation error interval. M_Start , K M_EndIt is the first and last serial number of the test data within the calculation error range.

[0142] In this embodiment, the AC side power output of the energy storage power station model is compared with the actual power station power response. Figure 7 As shown in the figure, the error calculation results of the AC voltage, current, active power and reactive power output by the simulation model are as follows Figure 8 shown.

[0143] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for electromagnetic transient modeling of a battery energy storage power station based on grid-connected and grid-related characteristics testing, characterized in that: include: S1. Obtaining grid-connected and grid-related response characteristic test data of the battery energy storage power station; S2. Based on the test data, a differential evolution algorithm is used to perform parameter identification on the dq axis decoupling model of the energy storage converter to obtain control circuit parameters of a single energy storage system; S3. Based on the identification parameters, a single battery energy storage system model is built in the simulation software, and multiple single models are connected according to the actual spatial topology to construct a battery energy storage power station circuit model; S4. Based on the circuit model, establish an energy storage power station control system model with automatic power generation control, automatic voltage control and frequency control functions; S5. Run the battery energy storage power station simulation model, compare the simulation data with the test data, calculate the errors of voltage, current, and power electrical quantities, and evaluate the accuracy of the model.

2. The electromagnetic transient modeling method of a battery energy storage power station based on grid-connected and grid-related characteristics testing according to claim 1 is characterized in that In S1, the step of obtaining the grid-connected and grid-related response characteristic test data of the battery energy storage power station includes: Obtain the results of grid-connected point power quality testing, power characteristics testing, primary frequency modulation testing, and fault ride-through capability testing; The output voltage and output current of the energy storage converter grid connection point are collected, the grid voltage phase is calculated through the phase-locked loop, and the output active and reactive power of the converter are calculated.

3. The electromagnetic transient modeling method of a battery energy storage power station based on grid-connected and grid-related characteristics testing according to claim 1 is characterized in that In S2, the differential evolution algorithm is used to perform parameter identification on the dq axis decoupling model of the energy storage converter, including: Taking active current and reactive current as objective functions, an evolutionary population is set, and the population is iteratively optimized through mutation, crossover and selection operations to obtain the control circuit parameters of a single energy storage system.

4. The electromagnetic transient modeling method of a battery energy storage power station based on grid-connected and grid-related characteristics testing according to claim 3 is characterized in that In S2, the energy storage converter dq axis decoupling model takes active current and reactive current as objective functions, and the establishment steps include: The energy storage system adopts a dual closed-loop control system, including a power outer loop unit and a current inner loop unit, and adopts a vector control strategy based on grid voltage orientation. When the grid voltage vector coincides with the d-axis of the dq coordinate system, the q-axis component u of the grid-side voltage vector q =0, the power of the energy storage system is expressed in the dq coordinate system as: Where: P and Q are the active and reactive power of the energy storage system respectively; d and i q are active current and reactive current respectively; The PI controller of the power outer loop unit uses the following formula to calculate the active and reactive current reference values ​​i of the current inner loop unit: d_ref and i q_ref : Where: P ref , Q ref are active and reactive power reference values ​​respectively; k P_P and k I_P are the proportional and integral coefficients of the active PI controller respectively; k P_Q and k I_Q are the proportional and integral coefficients of the reactive power PI controller respectively; The PI controller of the current inner loop unit calculates the voltage reference signal v of the energy storage converter based on the active and reactive current reference values ​​using the following formula: dref and v qref , through PWM modulation, the on and off of the converter bridge arm is controlled; In the formula, u d 、u q are the d-axis and q-axis components of the grid voltage, L f is the filter inductance; k P_c and k I_c are the proportional and integral coefficients of the PI controller of the current inner loop unit respectively; By combining formulas (1) to (3) and performing Laplace transform, the expressions of active and reactive currents output by the energy storage converter in the frequency domain are as follows: Where s is the differential operator.

5. The electromagnetic transient modeling method of a battery energy storage power station based on grid-connected and grid-related characteristics testing according to claim 1 is characterized in that In S3, building a single battery energy storage system model in the simulation software includes: Build the main circuit model of the energy storage system, including the battery energy storage unit, DC side filter capacitor, AC side filter inductor and parasitic resistor, AC side filter capacitor and parasitic resistor and step-up transformer components; A vector control strategy based on grid voltage orientation is adopted to construct a control circuit model of the energy storage system, including a double closed-loop control structure of a power outer loop unit and a current inner loop unit.

6. The electromagnetic transient modeling method of a battery energy storage power station based on grid-connected and grid-related characteristics testing according to claim 1 is characterized in that: The method of connecting multiple single-unit models according to the actual spatial topological structure includes: Obtain the location coordinate information of each component of the energy storage power station; Arrange the corresponding single battery energy storage system model in the simulation software and arrange it according to the actual spatial topology; The three-phase PI segment line model is used to connect adjacent single-unit models to construct the collection line inside the energy storage power station; The collector line and the grid-connected point voltage source model are connected through a three-phase double-winding transformer model to construct a step-up grid-connected transformer for the energy storage power station.

7. The electromagnetic transient modeling method of a battery energy storage power station based on grid-connected and grid-related characteristics testing according to claim 1 is characterized in that: The step of establishing the energy storage power station control system model includes: The automatic generation control module AGC distributes the power instructions of the entire station to each energy storage unit model according to the preset calculation rules; The voltage outer loop and reactive power inner loop control structure are adopted to realize reactive power-voltage control of the automatic voltage control module AVC and adjust the voltage at the grid connection point of the energy storage power station; The frequency control module collects the grid frequency signal, calculates the primary frequency regulation power instruction, and distributes it to each energy storage unit after superimposing it with the automatic power generation control instruction, thus realizing the primary frequency regulation function of the energy storage power station.

8. The electromagnetic transient modeling method of a battery energy storage power station based on grid-connected and grid-related characteristics testing according to claim 7 is characterized in that: The automatic power generation control module distributes the whole station power command to each energy storage unit model according to the preset calculation rules, including: According to the power command of the entire station issued by the energy management system of the energy storage power station, combined with the current state and rated capacity of each energy storage unit, power allocation is carried out using a calculation rule based on capacity ratio allocation and taking into account the charge state balance factor of each energy storage unit; The calculated power command values ​​of each energy storage unit are transmitted to the corresponding energy storage converter controller, and the active current and reactive current reference values ​​of the current inner loop are generated through the power outer loop control model.

9. The electromagnetic transient modeling method of a battery energy storage power station based on grid-connected and grid-related characteristics testing according to claim 7 is characterized in that: The step of adjusting the voltage at the grid connection point of the energy storage power station comprises: Obtain real-time voltage data of the energy storage power station grid connection point, and calculate the required active and reactive power adjustment according to the preset voltage control target; Active and reactive power regulation instructions are distributed to each energy storage unit, and the voltage at the grid connection point of the energy storage power station is regulated by adjusting the power output of each energy storage converter.

10. An electromagnetic transient modeling system for a battery energy storage power station based on grid-connected and grid-related characteristics testing, adopted by the method described in any one of claims 1 to 9, characterized in that: include: A test data acquisition module, used to acquire grid-connected and grid-related response characteristic test data of the battery energy storage power station; A circuit parameter identification module, used to perform parameter identification on the dq axis decoupling model of the energy storage converter using a differential evolution algorithm according to the test data, so as to obtain control circuit parameters of a single energy storage system; The circuit model building module is used to build a single battery energy storage system model in the simulation software according to the identification parameters, and connect multiple single models according to the actual spatial topology to build a battery energy storage power station circuit model; A control model building module, used to build an energy storage power station control system model with automatic power generation control, automatic voltage control and frequency control functions according to the circuit model; The simulation model evaluation module is used to run the battery energy storage power station simulation model, compare the simulation data with the test data, calculate the errors of the voltage, current and power electrical quantities, and evaluate the accuracy of the model.

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