Electrochemical energy storage system large disturbance transient simulation modeling method and system

Abstract

The application belongs to the technical field of energy storage modeling, and discloses a large disturbance transient simulation modeling method and system for an electrochemical energy storage system, which comprises the following steps: establishing a slow dynamic model, an alternating current side electromagnetic transient model and a direct current side energy storage battery equivalent circuit model, wherein the alternating current side electromagnetic transient model outputs a fast state variable; decomposing the fast state variable into a quasi-steady component and a deviation component, and establishing a linearized dynamic equation of the deviation component; resetting the deviation component when a grid voltage suddenly changes, superimposing the reset deviation component and the quasi-steady component to obtain an initial value of the fast state variable; and solving the slow dynamic model, the linearized dynamic equation of the deviation component and the direct current side energy storage battery equivalent circuit model simultaneously with the initial value of the fast state variable to obtain a transient dynamic response of the energy storage system in the whole process. The application completely retains the fast dynamic details and state continuity in the initial stage of the fault, and the simulation result is closer to the real physical response.

Description

Technical Field

[0001] This invention relates to the field of energy storage modeling technology, and in particular to a method and system for large-disturbance transient simulation modeling of electrochemical energy storage systems. Background Technology

[0002] With the large-scale integration of new energy power generation and the increasing proportion of power electronic equipment, the inertia of the power system is continuously decreasing, and some regional power grids exhibit typical characteristics of weak power grids, such as low short-circuit ratios and low inertia. In such power grids, when large disturbances such as voltage drops, fault clearing, and system recovery occur, the grid voltage and frequency are prone to significant fluctuations, placing higher demands on the stable operation of the system. Energy storage power stations, due to their rapid active and reactive power regulation capabilities, are gradually becoming an important regulatory resource supporting the voltage and frequency stability of weak regional power grids, providing dynamic support during fault impact, fault ride-through, and recovery phases.

[0003] However, under weak grid conditions, the dynamic interaction between energy storage converters and the grid is complex and can easily lead to system oscillations. In recent years, some energy storage power stations in China have experienced low-frequency power oscillations during operation. For example, energy storage power stations in Shandong and Hunan provinces have experienced prolonged periods of low-frequency oscillations, which have had a certain impact on the safe and stable operation of the power grid. Therefore, accurately characterizing the dynamic characteristics of energy storage power stations participating in the voltage and frequency support process of weak grids is of great significance for analyzing the system stability mechanism and dynamic response characteristics.

[0004] Currently, the analysis of grid-connected operation characteristics of energy storage systems mainly relies on average value models or small-signal models. Average value models are typically used for system-level dynamic simulation analysis, reducing model complexity by averaging the switching processes of power electronic devices. However, these models struggle to accurately reflect the electromagnetic transient characteristics of the system under large disturbances. Small-signal models typically analyze system stability through linearization methods, but are only applicable to small disturbance conditions and cannot accurately describe the dynamic characteristics of the entire transient process under large disturbances such as grid voltage dips, fault ride-through, and system recovery. Furthermore, existing models often simplify DC-side battery energy storage as an ideal voltage source, neglecting the dynamic characteristics of the equivalent circuit of the energy storage battery and its coupling relationship with the converter control system. This leads to biases in the model's depiction of the system's dynamic behavior under weak grid conditions. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for large-disturbance transient simulation modeling of electrochemical energy storage systems, so as to solve the problem that the existing technology causes the model to deviate in its characterization of the dynamic behavior of the system under weak grid conditions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for large-disturbance transient simulation modeling of an electrochemical energy storage system, comprising: A slow dynamic model, an AC-side electromagnetic transient model, and a DC-side energy storage battery equivalent circuit model are established. The AC-side electromagnetic transient model outputs fast state variables. The fast state variable is decomposed into a quasi-steady-state component and a deviation component, and a linearized dynamic equation for the deviation component is established at the operating point of the quasi-steady-state component. When the grid voltage changes abruptly, the deviation component is reset, and the reset deviation component is superimposed with the quasi-steady-state component to obtain the initial value of the fast state variable. Using the moment of sudden change in grid voltage as the segment boundary and the initial value of fast state variables, the slow dynamic model, the linearized dynamic equation of the deviation component, and the equivalent circuit model of the DC-side energy storage battery are solved simultaneously to obtain the transient dynamic response of the energy storage system before, during, and throughout the entire fault recovery process.

[0007] Furthermore, the slow dynamic model includes: Dynamic equations for active frequency regulation loop and reactive voltage regulation loop:

[0008] in, and These are the reference values ​​for active power and reactive power, respectively. and Measure active and reactive power at the common coupling node PCC. and These are the voltage reference value and the effective value of the PCC voltage at the common coupling node, respectively. Here, D is the virtual inertia coefficient, and D is the damping coefficient. and To control the adjustment coefficient, Indicates the frequency deviation status. This represents the amplitude deviation of the internal potential of the inverter. As the reference angular frequency, For time differentials, It is a differential operator; The inverter output voltage phase angle is obtained by integrating the angular frequency:

[0009] in, This is the differential increment of the phase angle; The internal voltage amplitude of the inverter is obtained by superimposing the integral output and the rated voltage bias:

[0010] From voltage amplitude With phase angle The inverter's three-phase output voltage reference signal is generated, and the converter is controlled by sinusoidal pulse width modulation (SPWM).

[0011] Furthermore, the AC-side electromagnetic transient model includes: The admittances of the filter inductor, capacitor, mains circuit, and load branch are respectively denoted as... Y f , Y c , Y g and Y LOAD In a synchronous rotating coordinate system, the three-phase voltages of the common coupling node PCC are expressed as equivalent voltage phasors. V The current relationship of the filter network is as follows:

[0012] This refers to the internal potential of the inverter. The equivalent voltage phasor of the common coupling node PCC; The common coupling node PCC satisfies the current balance relationship:

[0013] in I f , I c , I g and I LOAD These are the filter inductor branch current, capacitor branch current, grid-side current, and load current, respectively. This refers to the current flowing from the inverter to the filter network; Power measurements were performed at the common coupling node PCC, and the three-phase complex power was:

[0014] This is the conjugate value of the current phasor.

[0015] Furthermore, the equivalent circuit model of the DC-side energy storage battery includes: Using a first-order resistor-capacitor RC equivalent model, its dynamic relationship is as follows:

[0016] in V p Polarization voltage, I b This refers to the battery current. This is the battery open-circuit voltage. This is the DC bus voltage. This is the equivalent internal resistance of the battery. For time differentials, For differential operators, Polarization resistor, Polarizing capacitor; The DC bus capacitor current balance relationship is as follows:

[0017] in V dc This is the DC bus voltage. I dc This refers to the DC input current of the inverter. For DC bus capacitors; The DC-side current of the inverter is determined by the AC-side power, and its dynamic establishment process is described by a first-order inertial element:

[0018] in Inverter DC input current reference value

[0019] In the formula P e The active power output from the AC side of the inverter. For inverter efficiency; in A time constant is established for the DC-side input current of the inverter, which is used to characterize the dynamic response speed of the inverter's power conversion stage from AC-side output power to DC-side input current:

[0020] in, For the carrier period, This refers to the inverter switching frequency; Introducing an equivalent dynamic element for active power feedback:

[0021] in The actual active power of the common coupling node PCC. P f This is the equivalent active power feedback quantity used by the slow control system. Here is the equivalent time constant, where State matrix of the AC-side fast subsystem A f The dominant modal eigenvalues.

[0022] Furthermore, the output of the AC-side electromagnetic transient model consists of fast state variables, including: Define the AC side fast state variable as:

[0023] in, I f For the filter inductor branch current, I g For the branch current on the grid side, V cd This refers to the voltage state of the capacitor in the damping branch; Define the slow state variable as:

[0024] in, This is a frequency deviation state. This represents the offset of the inverter's internal voltage phase angle relative to the grid voltage phase angle. This represents the amplitude deviation of the internal potential of the inverter. Define the DC-side dynamic state variables as follows:

[0025] in, V p This is the voltage of the battery polarization branch. V dc This is the DC bus voltage. I dc This is the DC input current of the inverter. P meas The power measurement is fed into the swing equation; the DC-side state variables are used to describe the polarization dynamics of the energy storage battery, the energy balance of the DC bus, and the DC power transfer dynamics of the inverter, and together with the slow state and the fast dynamics of the AC side, they constitute a unified state-space model of the system.

[0026] in, These are the fast state deviation components, which together form the fast deviation state vector. .

[0027] Furthermore, the step of decomposing the fast-state variable into quasi-steady-state components and deviation components, and establishing a linearized dynamic equation for the deviation component at the operating point of the quasi-steady-state component, includes: The quasi-steady-state component is obtained by solving the AC-side quasi-steady-state network equation using the internal potential determined by the slow state variables, and a linearized dynamic equation for the deviation component is established at the operating point of the quasi-steady-state component. The fast state is decomposed into the sum of quasi-steady-state components and deviation components:

[0028] in, The quasi-steady-state fast state is obtained from the AC side quasi-steady-state network equations; Differentiating with respect to the fast deviation state, we get:

[0029] At the quasi-steady-state operating point, a first-order Taylor expansion of the nonlinear dynamic equations of the AC-side fast subsystem, neglecting higher-order terms, yields the linearized form of the AC-side fast dynamics:

[0030] in, A f The state matrix of the AC-side fast subsystem; AC-side fast subsystem state matrix A f The dynamic equations are established based on the filter inductor branch, the grid-side inductor branch, and the damping capacitor branch. The filter inductor current is selected... I f Grid-side current I g and the voltage of the damping branch capacitor V cd It is a state variable, obtained after linearization near the quasi-steady-state operating point.

[0031] Furthermore, when a sudden change occurs in the grid voltage, the deviation component is reset, and the reset deviation component is superimposed with the quasi-steady-state component after the sudden change to obtain the initial value of the fast state variable, including: When the grid voltage changes abruptly, the quasi-steady-state fast state changes: The fast state satisfies: ; Based on the fast state decomposition relationship, the fast deviation state reset formula is obtained:

[0032] By resetting the deviation, the inductor current and capacitor voltage are ensured to meet the physical continuity condition at the moment of fault switching, while maintaining a unified dynamic model structure. Then, taking the sudden change in grid voltage and the voltage recovery time as the switching points, the operation is divided into three stages: before the fault, during the fault, and after the fault recovery. In each stage, the grid voltage amplitude is regarded as a constant, the system equilibrium point is solved, and the dynamic response of the entire transient process is obtained through segmented simulation.

[0033] Secondly, the present invention provides a large-disturbance transient simulation modeling system for electrochemical energy storage systems, comprising: The model building module is used to build a slow dynamic model, an AC-side electromagnetic transient model, and a DC-side energy storage battery equivalent circuit model. The AC-side electromagnetic transient model outputs fast state variables. The decomposition module is used to decompose the fast state variable into quasi-steady-state components and deviation components, and to establish a linearized dynamic equation for the deviation components at the operating point of the quasi-steady-state components. The reset module is used to reset the deviation component when the grid voltage changes abruptly. The reset deviation component is then superimposed with the quasi-steady-state component to obtain the initial value of the fast state variable. The solution output module is used to solve the slow dynamic model, the linearized dynamic equation of the deviation component, and the equivalent circuit model of the DC-side energy storage battery simultaneously, with the moment of sudden change in grid voltage as the segment boundary and the initial value of fast state variables, to obtain the transient dynamic response of the energy storage system before, during and after the fault.

[0034] Furthermore, the slow dynamic model includes: Dynamic equations for active frequency regulation loop and reactive voltage regulation loop:

[0035] in, and These are the reference values ​​for active power and reactive power, respectively. and Measure active and reactive power at the common coupling node PCC. and These are the voltage reference value and the effective value of the PCC voltage at the common coupling node, respectively. Here, D is the virtual inertia coefficient, and D is the damping coefficient. and To control the adjustment coefficient, Indicates the frequency deviation status. This represents the amplitude deviation of the internal potential of the inverter. As the reference angular frequency, For time differentials, It is a differential operator; The inverter output voltage phase angle is obtained by integrating the angular frequency:

[0036] in, This is the differential increment of the phase angle; The internal voltage amplitude of the inverter is obtained by superimposing the integral output and the rated voltage bias:

[0037] From voltage amplitude With phase angle The inverter's three-phase output voltage reference signal is generated, and the converter is controlled by sinusoidal pulse width modulation (SPWM).

[0038] Furthermore, the AC-side electromagnetic transient model includes: The admittances of the filter inductor, capacitor, mains circuit, and load branch are respectively denoted as... Y f , Y c , Y g and Y LOAD In a synchronous rotating coordinate system, the three-phase voltages of the common coupling node PCC are expressed as equivalent voltage phasors. V The current relationship of the filter network is as follows:

[0039] This refers to the internal potential of the inverter. The equivalent voltage phasor of the common coupling node PCC; The common coupling node PCC satisfies the current balance relationship:

[0040] in I f , I c , I g and I LOAD These are the filter inductor branch current, capacitor branch current, grid-side current, and load current, respectively. This refers to the current flowing from the inverter to the filter network; Power measurements were performed at the common coupling node PCC, and the three-phase complex power was:

[0041] This is the conjugate value of the current phasor.

[0042] Furthermore, the equivalent circuit model of the DC-side energy storage battery includes: Using a first-order resistor-capacitor RC equivalent model, its dynamic relationship is as follows:

[0043] in V p Polarization voltage, I b This refers to the battery current. This is the battery open-circuit voltage. This is the DC bus voltage. This is the equivalent internal resistance of the battery. For time differentials, For differential operators, Polarization resistor, Polarizing capacitor; The DC bus capacitor current balance relationship is as follows:

[0044] in V dc This is the DC bus voltage. I dc This refers to the DC input current of the inverter. For DC bus capacitors; The DC-side current of the inverter is determined by the AC-side power, and its dynamic establishment process is described by a first-order inertial element:

[0045] in Inverter DC input current reference value

[0046] In the formula P e The active power output from the AC side of the inverter. For inverter efficiency; in A time constant is established for the DC-side input current of the inverter, which is used to characterize the dynamic response speed of the inverter's power conversion stage from AC-side output power to DC-side input current:

[0047] in, For the carrier period, This refers to the inverter switching frequency; Introducing an equivalent dynamic element for active power feedback:

[0048] in The actual active power of the common coupling node PCC. P f This is the equivalent active power feedback quantity used by the slow control system. Here is the equivalent time constant, where State matrix of the AC-side fast subsystem A f The dominant modal eigenvalues.

[0049] Furthermore, the output of the AC-side electromagnetic transient model consists of fast state variables, including: Define the AC side fast state variable as:

[0050] in, I f For the filter inductor branch current, I g For the branch current on the grid side, V cd This refers to the voltage state of the capacitor in the damping branch; Define the slow state variable as:

[0051] in, This is a frequency deviation state. This represents the offset of the inverter's internal voltage phase angle relative to the grid voltage phase angle. This represents the amplitude deviation of the internal potential of the inverter. Define the DC-side dynamic state variables as follows:

[0052] in, V p This is the voltage of the battery polarization branch. V dc This is the DC bus voltage. I dc This is the DC input current of the inverter. P meas The power measurement is fed into the swing equation; the DC-side state variables are used to describe the polarization dynamics of the energy storage battery, the energy balance of the DC bus, and the DC power transfer dynamics of the inverter, and together with the slow state and the fast dynamics of the AC side, they constitute a unified state-space model of the system.

[0053] in, These are the fast state deviation components, which together form the fast deviation state vector. .

[0054] Furthermore, the step of decomposing the fast-state variable into quasi-steady-state components and deviation components, and establishing a linearized dynamic equation for the deviation component at the operating point of the quasi-steady-state component, includes: The quasi-steady-state component is obtained by solving the AC-side quasi-steady-state network equation using the internal potential determined by the slow state variables, and a linearized dynamic equation for the deviation component is established at the operating point of the quasi-steady-state component. The fast state is decomposed into the sum of quasi-steady-state components and deviation components:

[0055] in, The quasi-steady-state fast state is obtained from the AC side quasi-steady-state network equations; Differentiating with respect to the fast deviation state, we get:

[0056] At the quasi-steady-state operating point, a first-order Taylor expansion of the nonlinear dynamic equations of the AC-side fast subsystem, neglecting higher-order terms, yields the linearized form of the AC-side fast dynamics:

[0057] in, A f The state matrix of the AC-side fast subsystem; AC-side fast subsystem state matrix A f The dynamic equations are established based on the filter inductor branch, the grid-side inductor branch, and the damping capacitor branch. The filter inductor current is selected... I f Grid-side current I g and the voltage of the damping branch capacitor V cd It is a state variable, obtained after linearization near the quasi-steady-state operating point.

[0058] Furthermore, when a sudden change occurs in the grid voltage, the deviation component is reset, and the reset deviation component is superimposed with the quasi-steady-state component after the sudden change to obtain the initial value of the fast state variable, including: When the grid voltage changes abruptly, the quasi-steady-state fast state changes: The fast state satisfies: ; Based on the fast state decomposition relationship, the fast deviation state reset formula is obtained:

[0059] By resetting the deviation, the inductor current and capacitor voltage are ensured to meet the physical continuity condition at the moment of fault switching, while maintaining a unified dynamic model structure. Then, taking the sudden change in grid voltage and the voltage recovery time as the switching points, the operation is divided into three stages: before the fault, during the fault, and after the fault recovery. In each stage, the grid voltage amplitude is regarded as a constant, the system equilibrium point is solved, and the dynamic response of the entire transient process is obtained through segmented simulation.

[0060] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for large-disturbance transient simulation modeling of an electrochemical energy storage system.

[0061] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for large-disturbance transient simulation modeling of an electrochemical energy storage system.

[0062] Compared with the prior art, the present invention has the following technical effects: This invention utilizes fast state variable decomposition and a deviation reset mechanism to avoid the high computational cost of directly performing precise integration on nonlinear differential equations while preserving the high-frequency electromagnetic dynamics of the AC side. When establishing the model, fast state variables such as filter inductor current, grid-side current, and damping capacitor voltage are decomposed into quasi-steady-state components determined by slow dynamics and deviation components describing local dynamics. Then, the fast subsystem is linearized around the quasi-steady-state operating point to obtain the constant-coefficient linear dynamic equations for the deviation components. This allows systems with inherently strong rigidity and significant time-scale differences to solve for slow dynamics with large integration steps, while only analyzing or discretizing the fast deviation dynamics with large steps. This significantly reduces the computational load required for the entire time-domain simulation, while still characterizing the high-frequency oscillation process of the AC-side LC filter network.

[0063] This invention resets the deviation components at the time of fault occurrence and recovery, ensuring that the sum of the reset deviation and the quasi-steady-state component after the abrupt change equals the actual fast-state value before the abrupt change. This guarantees that the inductor current and capacitor voltage do not undergo non-physical jumps at the switching points. This allows the model to truly inherit the final state of the previous stage in each initial condition, overcoming the inherent defect of inconsistent states in quasi-steady-state simulations under large disturbances.

[0064] This invention divides the entire process into three stages: pre-fault, during-fault, and post-fault recovery, using the grid voltage surge and recovery time as boundaries. Within each stage, the grid voltage amplitude is considered constant, and the system equilibrium point is solved separately. The reset fast state is then used as the initial value for simultaneous solution. A nonlinear model is used for slow dynamics, a linearized model for fast deviation dynamics, and the DC-side battery dynamics are described by a first-order RC equivalent circuit and a power transfer link. These three are coupled through interface variables and calculated alternately or simultaneously within the same time step. This naturally distinguishes the time scales while maintaining the integrity of dynamic coupling, accurately reproducing the frequency, voltage, power, and DC-side state evolution of a grid-type energy storage system from fault occurrence, duration, to recovery. It is particularly suitable for various large disturbance scenarios such as asymmetrical faults and voltage sags. Compared to a pure electromagnetic transient model, the calculation speed is significantly improved, and there is no need for unreasonable rigid decomposition of the slow control link. Compared to the traditional quasi-steady-state model, it fully preserves the fast dynamic details and state continuity of the initial fault stage, and the simulation results are closer to the real physical response. Attached Figure Description

[0065] Figure 1 This is the topology for the energy storage system of the present invention to be connected to the power grid.

[0066] Figure 2 This is a schematic diagram of the control structure of the grid-type energy storage system of the present invention.

[0067] Figure 3 This is a comparison of simulation and modeling data for the present invention.

[0068] Figure 4 This is a flowchart of the present invention. Detailed Implementation

[0069] The present invention will be further described below with reference to the accompanying drawings: Example 1, please refer to Figure 4 This invention provides a method for large-disturbance transient simulation modeling of an electrochemical energy storage system, comprising: S1. Establish a slow dynamic model, an AC-side electromagnetic transient model, and a DC-side energy storage battery equivalent circuit model. The AC-side electromagnetic transient model outputs fast state variables. The slow dynamic model corresponds to the active frequency loop and reactive voltage loop of the virtual synchronous machine control. Its outputs are the amplitude and phase angle θ of the inverter's internal electromotive force, which are generated by integrating and superimposing the frequency deviation state and amplitude deviation state. The AC-side electromagnetic transient model uses the filter inductor current, grid-side inductor current, and damping branch capacitor voltage as state variables. Dynamic equations for the filter network, grid branch, and load branch are established in a synchronous rotating coordinate system, directly outputting the system's fast state variables. The DC-side energy storage battery equivalent circuit model adopts a first-order RC structure, using polarization voltage, DC bus voltage, and inverter DC input current as state variables. It also introduces an active power feedback equivalent dynamic element, using the AC-side active power P after first-order inertial filtering as the input to the slow control loop.

[0070] A differential equation for the AC side is established in a three-phase stationary coordinate system. By rotating the coordinate system and then extracting the state variables, the resulting model has a clear physical meaning and is easy to integrate with the SPWM modulation stage. The state equations are written in complex vector form using the coordinate system as a reference, eliminating the coordinate transformation step and making the model more compact. Stages with time scale differences of more than two orders of magnitude within the system are clearly defined, providing a structural basis for subsequent step-by-step processing and avoiding the problem of excessive rigidity leading to the simulation step size being forced to take a minimum value after modeling the entire system uniformly.

[0071] S2, decompose the fast state variable into quasi-steady-state components and deviation components, and establish a linearized dynamic equation for the deviation component at the operating point of the quasi-steady-state component; The fast state variable is expressed as the sum of a quasi-steady-state component and a deviation component. The quasi-steady-state component is obtained by solving the AC-side quasi-steady-state network equations using the internal potentials E and θ determined by the slow state variables, representing the steady-state operating point corresponding to the current slow dynamics. The deviation component describes the difference between the actual fast state and the quasi-steady-state value. Near the quasi-steady-state operating point, a first-order Taylor expansion of the AC-side nonlinear dynamic equations is performed, neglecting higher-order terms, to obtain the linearized equations for the deviation component.

[0072] During simulation, interpolation is performed based on a lookup table at the current operating point, making it suitable for scenarios with a fixed power grid structure and a limited voltage variation range. This transforms the dynamics of the deviation component into a linear system with constant coefficients or slow time-varying characteristics, allowing for large-step discretization or analytical solutions. This eliminates the need for intensive iterative solutions to the original nonlinear equations, improving the solution speed of fast subsystems by one to two orders of magnitude while maintaining simulation accuracy.

[0073] S3, when the grid voltage changes abruptly, the deviation component is reset, and the reset deviation component is superimposed with the quasi-steady-state component to obtain the initial value of the fast state variable; At the instant of a sudden change in grid voltage, the quasi-steady-state fast state determined by the slow state will undergo a step change. However, the actual inductor current and capacitor voltage must follow physical continuity and cannot jump. By utilizing the physical condition that the fast state is equal before and after the sudden change, and combining it with the fast state decomposition relationship, the deviation component after reset can be deduced.

[0074] Two reset operations are performed at the moment of fault occurrence and at the moment of voltage recovery, respectively, corresponding to the two boundaries of the grid voltage jumping from the normal value to the fault value and from the fault value back to the normal value. This ensures that the initial conditions of each simulation segment are physically continuous, and solves the problems of energy non-conservation of energy storage elements and loss of transient process caused by the forced fast state jump at the moment of fault in the traditional quasi-steady-state model.

[0075] S4 uses the moment of sudden change in grid voltage as the segment boundary and the initial value of fast state variables to solve the slow dynamic model, the linearized dynamic equation of the deviation component, and the equivalent circuit model of the DC-side energy storage battery simultaneously, so as to obtain the transient dynamic response of the energy storage system before the fault, during the fault, and throughout the entire fault recovery process.

[0076] The solution proceeds sequentially along the time axis, dividing the entire process into three segments: the pre-fault segment, the fault duration segment, and the fault recovery segment, with the grid voltage abrupt change and voltage recovery moments as the dividing points. Within each segment, the grid voltage amplitude is treated as a known constant. First, the slow dynamic model updates the internal potential amplitude and phase based on the power feedback from the previous time step. Then, the quasi-steady-state fast component is updated by the quasi-steady-state network equation. Next, using the reset deviation obtained in step S3 as the initial value, the linearized dynamic equation of the deviation component is advanced. Finally, the quasi-steady-state component and the deviation component are superimposed to obtain the actual AC-side fast state, based on which the PCC node voltage, current, and power are calculated. The DC-side model is synchronously coupled with the AC-side and slow dynamic links: the AC-side output active power is fed back to the DC bus power balance equation via a first-order inertial link. The battery polarization dynamic equation and the bus capacitor voltage equation are solved simultaneously to obtain the DC bus voltage and battery current, which then affect the inverter DC input current.

[0077] The segmented constant voltage approach ensures that each segment's internal model is free from external abrupt excitations, resulting in good numerical stability. The reset mechanism, on the other hand, guarantees physically tight state transitions between segments. The final dynamic response fully covers the pre-fault steady state, the rapid transition during the fault, the adjustment process during the fault duration, and the recovery process after voltage restoration. The simulation results significantly outperform the all-electromagnetic transient model in computational efficiency and are significantly superior to the pure quasi-steady-state model in dynamic integrity.

[0078] Example 2: This invention provides a method for large-disturbance transient simulation modeling of an electrochemical energy storage system, comprising: Structure and control methods of grid-type energy storage systems Appendix Figure 1 This is a schematic diagram of the topology of the energy storage system of the present invention connected to the power grid. It mainly includes a converter, grid equivalent impedance, filter network, load, and energy storage battery. PCC is the grid connection point. L g and R g Indicates the equivalent inductance and resistance on the grid side; L f and R f These are the filter inductor and filter resistor on the converter side, respectively. C f For filtering capacitors, R d For damping resistors; R LOAD This is the equivalent load at PCC; E a , E b and E c This refers to the three-phase output voltage of the converter. Uoa , U ob and U oc Indicates the voltage at the point of common coupling (PCC); i oa , i ob and i oc This represents the current at the PCC point. The energy storage battery is described using a first-order RC equivalent circuit model, where... U OCV This is the battery open-circuit voltage. R batt The internal resistance of the battery is in ohms. R p and C p These are the polarization resistor and polarization capacitor, respectively. R p and C p This is the DC bus capacitor.

[0079] Appendix Figure 2 This is a schematic diagram of the control structure of a grid-type energy storage system. This invention employs a grid-type control strategy based on a virtual synchronous machine (VSG), whose control structure mainly includes an active power-frequency regulation loop and a voltage-reactive power regulation loop. For example... Figure 2 As shown, the system control dynamic equations are: (1) in, P ref and Q ref These are the reference values ​​for active and reactive power, respectively. P and Q are the active and reactive power measured at the PCC node. U rmsref and U rms These are the voltage reference value and the effective value of the PCC node voltage, respectively. J Here, D is the virtual inertia coefficient, and D is the damping coefficient. K Q and K V To control the adjustment coefficient, Indicates the frequency deviation status. This indicates the amplitude deviation status.

[0080] The inverter output voltage phase angle is obtained by integrating the angular frequency: (2) The internal potential amplitude of the inverter is obtained by superimposing the integral output and the rated voltage bias: (3) Ultimately determined by voltage amplitude E m With phase angle The inverter's three-phase output voltage reference signal is generated, and the converter is controlled by SPWM modulation.

[0081] Unified Coupled Dynamic Model of Grid-Based Energy Storage System To describe the dynamic behavior of grid-based energy storage systems under large disturbance conditions, a unified coupled dynamic model is established, including the AC-side network, DC-side energy storage batteries, and the grid-based control system. Its structure is as follows: Figure 1 As shown.

[0082] In a synchronous rotating coordinate system, with the grid voltage as the reference phasor, the internal electromotive force of the inverter is expressed as: (4) in This represents the offset of the inverter's internal voltage phase angle relative to the grid voltage phase angle.

[0083] The AC side includes a filter inductor, capacitor, mains circuit, and load branch, whose admittances are denoted as follows: Y f , Y c , Y g and Y LOAD In a synchronous rotating coordinate system, the three-phase voltage of the PCC is expressed as the equivalent voltage phasor V, and the current relationship of the filter network is as follows: (5) PCC nodes satisfy current balance: (6) in I f , I c , I g and I LOAD These are the filter inductor branch current, capacitor branch current, grid-side current, and load current, respectively.

[0084] Power measurements were performed at the PCC node, and the three-phase complex power was: (7) From this, we can obtain the system's active power P and reactive power Q.

[0085] The DC-side battery adopts a first-order RC equivalent model, and its dynamic relationship is as follows: (8) in V p Polarization voltage, I b This represents the battery current.

[0086] The DC bus capacitor current balance relationship is as follows: (9) in V dc This is the DC bus voltage. I dc This is the DC input current of the inverter.

[0087] The DC-side current of the inverter is determined by the AC-side power, and its dynamic establishment process is described by a first-order inertial element: (10) in (11) In the formula P e The active power output from the AC side of the inverter. This refers to the inverter efficiency.

[0088] in This establishes a time constant for the DC-side input current of the inverter, characterizing the dynamic response speed of the inverter's power conversion stage from AC-side output power to DC-side input current. In engineering practice, it can be selected based on the response speed of the modulation and switching stages; its magnitude is usually consistent with the carrier period and can be approximated as:

[0089] in, For the carrier period, This refers to the inverter switching frequency.

[0090] Inverter AC side output power P e This is not the active power measured at the point of common coupling (PCC), but rather the port output power corresponding to the inverter's internal electromotive force and the filter inductor branch current. This power is used to construct a reference value for the DC-side input current. This characterizes the energy transfer relationship between the AC port and the DC side of the inverter.

[0091] To effectively characterize the smoothing effect of the AC-side fast subsystem on the active power feedback channel in the slow control model, this invention introduces an equivalent dynamic element for active power feedback: (12) in This represents the actual active power of the PCC node.P f This is the equivalent active power feedback quantity used by the slow control system. Here is the equivalent time constant, where The dominant mode eigenvalue of the state matrix Af of the AC-side fast subsystem is given by equation (22).

[0092] The above equations together constitute a unified coupled dynamic model of a grid-type energy storage system.

[0093] Slow-Fast Subsystem Coupled Modeling Method Grid-based energy storage systems simultaneously encompass slow dynamics of the control system, fast dynamics of AC-side electromagnetic transients, and DC-side dynamics under large disturbance conditions. To reduce model complexity while preserving the characteristics of AC-side electromagnetic transients, this invention employs a slow-fast subsystem decomposition method to establish a unified dynamic model.

[0094] State variable definition Define the slow state variable as: (13) in, This is a frequency deviation state. This represents the offset of the inverter's internal voltage phase angle relative to the grid voltage phase angle. This represents the deviation of the internal potential amplitude of the inverter.

[0095] Define the AC side fast state variable as: (14) in, I f For the filter inductor branch current, I g For the branch current on the grid side, V cd This represents the voltage state of the capacitor in the damping branch.

[0096] Define the DC-side dynamic state variables as follows: (15) in, V p This is the voltage of the battery polarization branch. V dc This is the DC bus voltage. I dc This is the DC input current of the inverter. P meas This is the power measurement quantity fed into the oscillation equation. The DC-side state variables are used to describe the polarization dynamics of the energy storage battery, the energy balance of the DC bus, and the DC power transfer dynamics of the inverter, and together with the slow state and the fast dynamics of the AC side, they constitute a unified state-space model of the system.

[0097] Therefore, the unified state vector of the system can be expressed as: (16) in, These are the fast state deviation components, which together form the fast deviation state vector. .

[0098] (2) Fast state decomposition Since the dynamic changes of the control system are much slower than the electromagnetic transients on the AC side, when the slow state variables change slowly, the electromagnetic transients on the AC side can be considered to be close to a certain quasi-steady-state operating point at any given moment. Therefore, the fast state is decomposed into the sum of a quasi-steady-state component and a deviation component, i.e.: (17) in, The quasi-steady-state fast state is obtained from the AC side quasi-steady-state network equations, that is, by solving the PCC node voltage and the current of each branch through equations (4) to (6), thus obtaining the results. .

[0099] (3) Fast Deviation Dynamic Model Taking the derivative with respect to the fast deviation state, we get: (18) Near the quasi-steady-state operating point, a first-order Taylor expansion of the nonlinear dynamic equations of the AC-side fast subsystem, neglecting higher-order terms, yields the linearized form of the AC-side fast dynamics: (19) in, A f This is the state matrix of the AC-side fast subsystem.

[0100] AC-side fast subsystem state matrix A f The dynamic equations are established based on the filter inductor branch, the grid-side inductor branch, and the damping capacitor branch. The filter inductor current is selected... I f Grid-side current I g and the voltage of the damping branch capacitor V cd It is a state variable, obtained after linearization near the quasi-steady-state operating point.

[0101] remember: (20) And define: (twenty one) The state matrix of the AC-side fast subsystem can then be expressed as: (twenty two) Consider a synchronously rotating coordinate system relative to a stationary coordinate system at a reference angular frequency. Rotation allows the state matrix of the AC-side fast subsystem to be further expressed as: (twenty three) in I It is an identity matrix.

[0102] Substituting equation (19) into equation (18), we obtain the dynamic equation for the fast deviation: (twenty four) Since the grid voltage remains constant during each operating phase, the changes in the quasi-steady-state fast state are caused only by changes in the slow state variables.

[0103] Fault switching and segmented solution method During grid voltage dips and fault recovery, the grid voltage amplitude undergoes abrupt changes at different stages. To accurately describe the dynamic response of the energy storage system under large disturbance conditions, this invention employs a piecewise modeling method, dividing the system operation process into three stages: before the fault, during the fault, and after the fault recovery. Within each stage, the grid voltage amplitude can be considered constant, thus allowing for the separate calculation of the corresponding system equilibrium point.

[0104] When the grid voltage changes abruptly, the quasi-steady-state fast state changes: However, since the inductor current and capacitor voltage cannot physically change abruptly, the actual fast state satisfies: .

[0105] Based on the fast state decomposition relationship, the fast deviation state reset formula can be obtained: (25) The above-mentioned deviation reset method can ensure that the inductor current and capacitor voltage meet the physical continuity conditions at the moment of fault switching, while maintaining a unified dynamic model structure.

[0106] Large Disturbance Modeling Process for Energy Storage Systems Based on the above energy storage system topology and slow-fast subsystem coupling modeling method, a dynamic modeling process for large disturbances in energy storage systems can be formed, specifically including: 1. Determine the main circuit topology and grid-type control architecture of the energy storage system; 2. Establish the AC side network model and solve for the expressions for PCC voltage, current, and power; 3. Establish a slow-dynamic model for the network-type control system; 4. Establish an equivalent circuit model for the DC-side energy storage battery; 5. Classify slow-state variables and fast-state variables based on the dynamic characteristics of the system; 6. Solve for the quasi-steady-state fast state using the AC-side quasi-steady-state network equations; 7. Construct the dynamic equations for the fast deviation state; 8. Reset the fast deviation state when the grid voltage changes abruptly; 9. The transient dynamic response of the system during the voltage dip, fault ride-through and recovery phases is obtained by solving in segments.

[0107] To address the problem that existing energy storage system modeling methods struggle to accurately describe the dynamic behavior of systems under large disturbance conditions, this invention provides a dynamic modeling method for grid-connected inverters under large disturbance conditions that considers the dynamic characteristics of the energy storage battery's equivalent circuit and the electromagnetic transient coupling on the AC side. This method establishes a unified modeling framework that takes into account slow control dynamics, AC-side electromagnetic transients, and energy storage battery dynamics, characterizing the dynamic coupling relationships between the energy storage battery, power electronic converter, and the power grid, thereby achieving an accurate description of the dynamic characteristics of the energy storage system throughout the entire transient process.

[0108] This modeling method introduces an equivalent circuit model of the energy storage battery, considering the dynamic changes in key parameters such as battery open-circuit voltage and equivalent impedance. It establishes the coupling relationship between the energy storage battery's dynamics and the converter control system, thereby characterizing the dynamic response behavior of the energy storage system during large disturbances such as grid voltage dips, fault ride-through, and system recovery. Furthermore, the model supports various battery equivalent structures (including but not limited to the Thevenin model, first-order RC networks, and pure internal resistance models), forming a generalized modeling framework with configurable parameters.

[0109] The modeling method proposed in this invention is applicable to mainstream energy storage technologies such as lithium-ion batteries and sodium-ion batteries. It can provide a theoretical tool for the dynamic characteristic analysis of energy storage systems participating in voltage and frequency support under weak grid conditions, and provide a theoretical basis for the stability assessment and control parameter optimization of energy storage systems.

[0110] To demonstrate the effectiveness of the proposed dynamic modeling method for grid-type energy storage systems considering the characteristics of energy storage batteries, a comparative analysis of theoretical calculations and simulation data was conducted. The simulation model is shown in the appendix. Figure 1 The circuit shown is constructed, and parameters are set and calculated according to the unified coupled dynamic modeling method proposed in this invention. The specific steps are as follows: (1) The grid connection point voltage level is 380 V, the rated frequency is 50 Hz, and the rated capacity of the energy storage system is 150 kW. Based on the system capacity and grid parameters, the equivalent impedance on the grid side is calculated to be Z. g = R g + jw0L g , where R g= 9.6e-3 Ω, L g =6.12e-5 H, corresponding to the equivalent impedance of the power grid is Z. g = 0.0096 + j0.0192 Ω, the energy storage converter adopts a two-level voltage source topology and a grid-type control strategy.

[0111] (2) Establish a unified coupled dynamic model for grid-type energy storage system, including a slow dynamic model of grid-type control system, an AC side electromagnetic transient model, and a DC side energy storage battery dynamic model.

[0112] (3) The AC side main circuit topology parameters of the energy storage system are set as follows: filter inductor L f = 250e-6 H, filter capacitor C f =150e-6 F, damping resistance R d = 0.15 Ω, and set the equivalent load at PCC to R LOAD = 4.8 Ω (4) The control parameters of the energy storage system are set as follows: K V = 32100, K Q = 1000, virtual inertia coefficient J = 0.1, damping coefficient D = 50, system active power reference value P ref = 150 kW, voltage reference value U rmsref = 220 V, reactive power reference value Q ref = 0.

[0113] (5) The DC-side energy storage battery is modeled using a first-order RC equivalent circuit model. The battery open-circuit voltage is U OCV =1000 V, ohmic internal resistance is R batt = 0.03 Ω, polarization branch parameters are R p = 0.02 Ω, C p = 5000 F, DC bus capacitance C dc = 5e-3 F.

[0114] (6) To verify the dynamic characteristics of the established model under large disturbance conditions, the grid voltage was set to drop at t = 2 s, the voltage amplitude dropped to 20% of the rated value, and recovered to the rated voltage at t = 3 s.

[0115] (7) Based on the above parameters, the dynamic response of the energy storage system in voltage drop and recovery process, such as voltage, power and frequency, is calculated and compared with the simulation results.

[0116] Simulation results are attached. Figure 3 As shown, the unified coupled dynamic modeling method for energy storage batteries proposed in this invention can accurately reflect the transient dynamic behavior of grid-type energy storage systems during grid voltage dips, fault persistence, and fault recovery phases. The theoretical calculation results and simulation results maintain good consistency in terms of voltage, current, power, and frequency response, verifying the effectiveness and accuracy of the modeling method of this invention.

[0117] The large disturbance modeling method for grid-type energy storage systems proposed in this invention, which considers the dynamic characteristics of energy storage batteries, is applicable to the dynamic characteristic analysis and stability assessment of energy storage systems or energy storage power stations under weak grid conditions. By establishing a unified coupled dynamic model that includes the slow dynamics of the grid-type control system, the AC-side electromagnetic transient dynamics, and the DC-side energy storage battery dynamics, the dynamic response of the energy storage system during large disturbances such as grid voltage drops, fault ride-through, and system recovery can be analyzed.

[0118] The model established based on this invention helps to assess the potential oscillation and instability risks of energy storage systems under weak grid conditions, and can provide a reference for the selection, design, and optimization of control and circuit parameters for energy storage systems. Simultaneously, this method can provide technical support for grid operators and dispatchers to assess the stability risks after energy storage systems are integrated into the grid.

[0119] In another embodiment of the present invention, a large-disturbance transient simulation modeling system for an electrochemical energy storage system is provided, which can be used to implement the above-mentioned large-disturbance transient simulation modeling method for an electrochemical energy storage system. Specifically, the system includes: The model building module is used to build a slow dynamic model, an AC-side electromagnetic transient model, and a DC-side energy storage battery equivalent circuit model. The AC-side electromagnetic transient model outputs fast state variables. The decomposition module is used to decompose the fast state variable into quasi-steady-state components and deviation components, and to establish a linearized dynamic equation for the deviation components at the operating point of the quasi-steady-state components. The reset module is used to reset the deviation component when the grid voltage changes abruptly. The reset deviation component is then superimposed with the quasi-steady-state component to obtain the initial value of the fast state variable. The solution output module is used to solve the slow dynamic model, the linearized dynamic equation of the deviation component, and the equivalent circuit model of the DC-side energy storage battery simultaneously, with the moment of sudden change in grid voltage as the segment boundary and the initial value of fast state variables, to obtain the transient dynamic response of the energy storage system before, during and after the fault.

[0120] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0121] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a large-disturbance transient simulation modeling method for electrochemical energy storage systems.

[0122] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the large-disturbance transient simulation modeling method for an electrochemical energy storage system described in the above embodiments.

[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for large-disturbance transient simulation modeling of an electrochemical energy storage system, characterized in that, include: A slow dynamic model, an AC-side electromagnetic transient model, and a DC-side energy storage battery equivalent circuit model are established. The AC-side electromagnetic transient model outputs fast state variables. The fast state variable is decomposed into a quasi-steady-state component and a deviation component, and a linearized dynamic equation for the deviation component is established at the operating point of the quasi-steady-state component. When the grid voltage changes abruptly, the deviation component is reset, and the reset deviation component is superimposed with the quasi-steady-state component to obtain the initial value of the fast state variable. Using the moment of sudden change in grid voltage as the segment boundary and the initial value of fast state variables, the slow dynamic model, the linearized dynamic equation of the deviation component, and the equivalent circuit model of the DC-side energy storage battery are solved simultaneously to obtain the transient dynamic response of the energy storage system before, during, and throughout the entire fault recovery process.

2. The method for large-disturbance transient simulation modeling of an electrochemical energy storage system according to claim 1, characterized in that, The slow dynamic model includes: Dynamic equations for active frequency regulation loop and reactive voltage regulation loop: in, and These are the reference values ​​for active power and reactive power, respectively. and Measure active and reactive power at the common coupling node PCC. and These are the voltage reference value and the effective value of the PCC voltage at the common coupling node, respectively. Here, D is the virtual inertia coefficient, and D is the damping coefficient. and To control the adjustment coefficient, Indicates the frequency deviation status. This represents the amplitude deviation of the internal potential of the inverter. As the reference angular frequency, For time differentials, It is a differential operator; The inverter output voltage phase angle is obtained by integrating the angular frequency: in, This is the differential increment of the phase angle; The internal voltage amplitude of the inverter is obtained by superimposing the integral output and the rated voltage bias: From voltage amplitude With phase angle The inverter's three-phase output voltage reference signal is generated, and the converter is controlled by sinusoidal pulse width modulation (SPWM).

3. The method for large-disturbance transient simulation modeling of an electrochemical energy storage system according to claim 1, characterized in that, The AC-side electromagnetic transient model includes: The admittances of the filter inductor, capacitor, mains circuit, and load branch are respectively denoted as... Y f , Y c , Y g and Y LOAD In a synchronous rotating coordinate system, the three-phase voltages of the common coupling node PCC are expressed as equivalent voltage phasors. V The current relationship of the filter network is as follows: This refers to the internal potential of the inverter. The equivalent voltage phasor of the common coupling node PCC; The common coupling node PCC satisfies the current balance relationship: in I f , I c , I g and I LOAD These are the filter inductor branch current, capacitor branch current, grid-side current, and load current, respectively. This refers to the current flowing from the inverter to the filter network; Power measurements were performed at the common coupling node PCC, and the three-phase complex power was: This is the conjugate value of the current phasor.

4. The method for large-disturbance transient simulation modeling of an electrochemical energy storage system according to claim 1, characterized in that, The equivalent circuit model of the DC-side energy storage battery includes: Using a first-order resistor-capacitor RC equivalent model, its dynamic relationship is as follows: in V p Polarization voltage, I b This refers to the battery current. This is the battery open-circuit voltage. This is the DC bus voltage. This is the equivalent internal resistance of the battery. For time differentials, For differential operators, Polarization resistor, Polarizing capacitor; The DC bus capacitor current balance relationship is as follows: in V dc This is the DC bus voltage. I dc This refers to the DC input current of the inverter. For DC bus capacitors; The DC-side current of the inverter is determined by the AC-side power, and its dynamic establishment process is described by a first-order inertial element: in Inverter DC input current reference value In the formula P e The active power output from the AC side of the inverter. For inverter efficiency; in A time constant is established for the DC-side input current of the inverter, which is used to characterize the dynamic response speed of the inverter's power conversion stage from AC-side output power to DC-side input current: in, For the carrier period, This refers to the inverter switching frequency; Introducing an equivalent dynamic element for active power feedback: in The actual active power of the common coupling node PCC. P f This is the equivalent active power feedback quantity used by the slow control system. Here is the equivalent time constant, where State matrix of the AC-side fast subsystem A f The dominant modal eigenvalues.

5. The method for large-disturbance transient simulation modeling of an electrochemical energy storage system according to claim 1, characterized in that, The output of the AC-side electromagnetic transient model consists of fast state variables, including: Define the AC side fast state variable as: in, I f For the filter inductor branch current, I g For the branch current on the grid side, V cd This refers to the voltage state of the capacitor in the damping branch; Define the slow state variable as: in, This is a frequency deviation state. This represents the offset of the inverter's internal voltage phase angle relative to the grid voltage phase angle. This represents the amplitude deviation of the internal potential of the inverter. Define the DC-side dynamic state variables as follows: in, V p This is the voltage of the battery polarization branch. V dc This is the DC bus voltage. I dc This is the DC input current of the inverter. P meas The power measurement is fed into the swing equation; the DC-side state variables are used to describe the polarization dynamics of the energy storage battery, the energy balance of the DC bus, and the DC power transfer dynamics of the inverter, and together with the slow state and the fast dynamics of the AC side, they constitute a unified state-space model of the system. in, These are the fast state deviation components, which together form the fast deviation state vector. .

6. The method for large-disturbance transient simulation modeling of an electrochemical energy storage system according to claim 5, characterized in that, The step of decomposing the fast state variable into quasi-steady-state components and deviation components, and establishing a linearized dynamic equation for the deviation component at the operating point of the quasi-steady-state component, includes: The quasi-steady-state component is obtained by solving the AC-side quasi-steady-state network equation using the internal potential determined by the slow state variables, and a linearized dynamic equation for the deviation component is established at the operating point of the quasi-steady-state component. The fast state is decomposed into the sum of quasi-steady-state components and deviation components: in, The quasi-steady-state fast state is obtained from the AC side quasi-steady-state network equations; Differentiating with respect to the fast deviation state, we get: At the quasi-steady-state operating point, a first-order Taylor expansion of the nonlinear dynamic equations of the AC-side fast subsystem, neglecting higher-order terms, yields the linearized form of the AC-side fast dynamics: in, A f The state matrix of the AC-side fast subsystem; AC-side fast subsystem state matrix A f The dynamic equations are established based on the filter inductor branch, the grid-side inductor branch, and the damping capacitor branch. The filter inductor current is selected... I f Grid-side current I g and the voltage of the damping branch capacitor V cd It is a state variable, obtained after linearization near the quasi-steady-state operating point.

7. The method for large-disturbance transient simulation modeling of an electrochemical energy storage system according to claim 6, characterized in that, When a sudden change occurs in the grid voltage, the deviation component is reset, and the reset deviation component is superimposed with the quasi-steady-state component after the sudden change to obtain the initial value of the fast state variable, including: When the grid voltage changes abruptly, the quasi-steady-state fast state changes: The fast state satisfies: ; Based on the fast state decomposition relationship, the fast deviation state reset formula is obtained: By resetting the deviation, the inductor current and capacitor voltage are ensured to meet the physical continuity condition at the moment of fault switching, while maintaining a unified dynamic model structure. Then, taking the sudden change in grid voltage and the voltage recovery time as the switching points, the operation is divided into three stages: before the fault, during the fault, and after the fault recovery. In each stage, the grid voltage amplitude is regarded as a constant, the system equilibrium point is solved, and the dynamic response of the entire transient process is obtained through segmented simulation.

8. A large-disturbance transient simulation modeling system for an electrochemical energy storage system, characterized in that, include: The model building module is used to build a slow dynamic model, an AC-side electromagnetic transient model, and a DC-side energy storage battery equivalent circuit model. The AC-side electromagnetic transient model outputs fast state variables. The decomposition module is used to decompose the fast state variable into quasi-steady-state components and deviation components, and to establish a linearized dynamic equation for the deviation components at the operating point of the quasi-steady-state components. The reset module is used to reset the deviation component when the grid voltage changes abruptly. The reset deviation component is then superimposed with the quasi-steady-state component to obtain the initial value of the fast state variable. The solution output module is used to solve the slow dynamic model, the linearized dynamic equation of the deviation component, and the equivalent circuit model of the DC-side energy storage battery simultaneously, with the moment of sudden change in grid voltage as the segment boundary and the initial value of fast state variables, to obtain the transient dynamic response of the energy storage system before, during and after the fault.

9. The large-disturbance transient simulation modeling system for an electrochemical energy storage system according to claim 8, characterized in that, The model building module is used to build a slow dynamic model, including: Dynamic equations for active frequency regulation loop and reactive voltage regulation loop: in, and These are the reference values ​​for active power and reactive power, respectively. and Measure active and reactive power at the common coupling node PCC. and These are the voltage reference value and the effective value of the PCC voltage at the common coupling node, respectively. Here, D is the virtual inertia coefficient, and D is the damping coefficient. and To control the adjustment coefficient, Indicates the frequency deviation status. This represents the amplitude deviation of the internal potential of the inverter. As the reference angular frequency, For time differentials, It is a differential operator; The inverter output voltage phase angle is obtained by integrating the angular frequency: in, This is the differential increment of the phase angle; The internal voltage amplitude of the inverter is obtained by superimposing the integral output and the rated voltage bias: From voltage amplitude With phase angle The inverter's three-phase output voltage reference signal is generated, and the converter is controlled by sinusoidal pulse width modulation (SPWM).

10. The large-disturbance transient simulation modeling system for an electrochemical energy storage system according to claim 8, characterized in that, The model building module is used to build an AC-side electromagnetic transient model, including: The admittances of the filter inductor, capacitor, mains circuit, and load branch are respectively denoted as... Y f , Y c , Y g and Y LOAD In a synchronous rotating coordinate system, the three-phase voltages of the common coupling node PCC are expressed as equivalent voltage phasors. V The current relationship of the filter network is as follows: This refers to the internal potential of the inverter. The equivalent voltage phasor of the common coupling node PCC; The common coupling node PCC satisfies the current balance relationship: in I f , I c , I g and I LOAD These are the filter inductor branch current, capacitor branch current, grid-side current, and load current, respectively. This refers to the current flowing from the inverter to the filter network; Power measurements were performed at the common coupling node PCC, and the three-phase complex power was: This is the conjugate value of the current phasor.

11. The large-disturbance transient simulation modeling system for an electrochemical energy storage system according to claim 8, characterized in that, The model building module is used to build an equivalent circuit model of the DC-side energy storage battery, including: Using a first-order resistor-capacitor RC equivalent model, its dynamic relationship is as follows: in V p Polarization voltage, I b This refers to the battery current. This is the battery open-circuit voltage. This is the DC bus voltage. This is the equivalent internal resistance of the battery. For time differentials, For differential operators, Polarization resistor, Polarizing capacitor; The DC bus capacitor current balance relationship is as follows: in V dc This is the DC bus voltage. I dc This refers to the DC input current of the inverter. For DC bus capacitors; The DC-side current of the inverter is determined by the AC-side power, and its dynamic establishment process is described by a first-order inertial element: in Inverter DC input current reference value In the formula P e The active power output from the AC side of the inverter. For inverter efficiency; in A time constant is established for the DC-side input current of the inverter, which is used to characterize the dynamic response speed of the inverter's power conversion stage from AC-side output power to DC-side input current: in, For the carrier period, This refers to the inverter switching frequency; Introducing an equivalent dynamic element for active power feedback: in The actual active power of the common coupling node PCC. P f This is the equivalent active power feedback quantity used by the slow control system. Here is the equivalent time constant, where State matrix of the AC-side fast subsystem A f The dominant modal eigenvalues.

12. The large-disturbance transient simulation modeling system for an electrochemical energy storage system according to claim 8, characterized in that, The output of the AC-side electromagnetic transient model consists of fast state variables, including: Define the AC side fast state variable as: in, I f For the filter inductor branch current, I g For the branch current on the grid side, V cd This refers to the voltage state of the capacitor in the damping branch; Define the slow state variable as: in, This is a frequency deviation state. This represents the offset of the inverter's internal voltage phase angle relative to the grid voltage phase angle. This represents the amplitude deviation of the internal potential of the inverter. Define the DC-side dynamic state variables as follows: in, V p This is the voltage of the battery polarization branch. V dc This is the DC bus voltage. I dc This is the DC input current of the inverter. P meas The power measurement is fed into the swing equation; the DC-side state variables are used to describe the polarization dynamics of the energy storage battery, the energy balance of the DC bus, and the DC power transfer dynamics of the inverter, and together with the slow state and the fast dynamics of the AC side, they constitute a unified state-space model of the system. in, These are the fast state deviation components, which together form the fast deviation state vector. .

13. The large-disturbance transient simulation modeling system for an electrochemical energy storage system according to claim 12, characterized in that, The decomposition module is used for: The quasi-steady-state component is obtained by solving the AC-side quasi-steady-state network equation using the internal potential determined by the slow state variables, and a linearized dynamic equation for the deviation component is established at the operating point of the quasi-steady-state component. The fast state is decomposed into the sum of quasi-steady-state components and deviation components: in, The quasi-steady-state fast state is obtained from the AC side quasi-steady-state network equations; Differentiating with respect to the fast deviation state, we get: At the quasi-steady-state operating point, a first-order Taylor expansion of the nonlinear dynamic equations of the AC-side fast subsystem, neglecting higher-order terms, yields the linearized form of the AC-side fast dynamics: in, A f The state matrix of the AC-side fast subsystem; AC-side fast subsystem state matrix A f The dynamic equations are established based on the filter inductor branch, the grid-side inductor branch, and the damping capacitor branch. The filter inductor current is selected... I f Grid-side current I g and the voltage of the damping branch capacitor V cd It is a state variable, obtained after linearization near the quasi-steady-state operating point.

14. The large-disturbance transient simulation modeling system for an electrochemical energy storage system according to claim 13, characterized in that, The reset module is used for: When the grid voltage changes abruptly, the quasi-steady-state fast state changes: The fast state satisfies: ; Based on the fast state decomposition relationship, the fast deviation state reset formula is obtained: By resetting the deviation, the inductor current and capacitor voltage are ensured to meet the physical continuity condition at the moment of fault switching, while maintaining a unified dynamic model structure. Then, taking the moment when the grid voltage suddenly changes and the moment when the voltage recovers as the switching points, the operation is divided into three stages: before the fault, during the fault, and after the fault recovery. Within each stage, the grid voltage amplitude is treated as a constant, the system equilibrium point is solved separately, and the transient dynamic response of the entire process is obtained through segmented simulation.

15. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the large-disturbance transient simulation modeling method for an electrochemical energy storage system as described in any one of claims 1 to 7.

16. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the large-disturbance transient simulation modeling method for an electrochemical energy storage system as described in any one of claims 1 to 7.

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