Energy storage system modeling method, system and equipment

Abstract

The embodiment of the invention discloses an energy storage system modeling method, system and equipment, an electrochemical sub-model, a thermal sub-model and an aging sub-model of a battery can track the change of the internal state of the battery in real time, and an obtained first modeling parameter can accurately reflect the working condition of the battery; the droop control model of the converter and the rotor motion model of the virtual synchronous machine can accurately reflect the sequence impedance characteristics of the converter, the obtained second modeling parameter can more accurately simulate the real state of the converter, and the energy storage system is modeled based on the obtained first modeling parameter and the second modeling parameter. And the real-time working conditions and characteristics of the battery and the converter are comprehensively considered, so that the modeling accuracy of the energy storage system is improved, and the real state of the energy storage system is simulated.

Description

Technical Field

[0001] This invention relates to the field of power system modeling and simulation technology, and in particular to a modeling method, system and equipment for energy storage systems. Background Technology

[0002] With a high proportion of renewable energy being integrated into the grid, the inertia and stability support capabilities of power systems are continuously declining. Energy storage technology, by simulating the operating mechanism of synchronous generators, can autonomously construct grid voltage and frequency, providing critical inertia, damping, and short-circuit capacity support for the system, and has become one of the core technologies for improving the stability of new power systems.

[0003] However, accurate modeling of energy storage systems faces significant challenges. First, the core performance of the system depends on the internal state of the battery itself, which is a complex electrochemical-thermal-aging multi-physics coupled system. Its characteristics change with operating conditions and lifespan degradation. Second, the control strategy of grid-connected converters is complex, and its dynamic response is closely related to the interaction with the grid. Traditional average models or ideal power source models cannot accurately reflect its sequence impedance characteristics and internal changes of the battery, and cannot accurately simulate the state of a real energy storage system. Summary of the Invention

[0004] In view of this, the present invention provides a method, system and device for modeling energy storage systems.

[0005] The specific technical solution of the first embodiment of the present invention is as follows: a modeling method for an energy storage system, the method comprising: constructing a battery model of a battery and a sequence impedance model of a converter in the energy storage system, wherein the battery model includes an electrochemical sub-model, a thermal sub-model, and an aging sub-model, and the sequence impedance model includes a droop control model and a virtual synchronous machine rotor motion model; acquiring real-time operating data of the energy storage system; wherein the real-time operating data includes terminal voltage, current, polarization voltage, and ambient temperature, as well as output angular frequency and a first active power; simulating the battery model and the sequence impedance model based on the real-time operating data to obtain the DC voltage and maximum allowable power output by the battery model, and to obtain the second active power output by the sequence impedance model. The active power and reactive power are used as the power commands for the battery. Based on the terminal voltage, current, and ambient temperature in the real-time operating data, the battery model is identified to obtain the first modeling parameters of the battery. The first modeling parameters include the state of charge and core temperature. The DC voltage and the maximum allowable power are used as the boundary conditions for the converter operation. Based on the output angular frequency and the first active power in the real-time operating data, the sequence impedance model is identified to obtain the second modeling parameters of the converter. The second modeling parameters include the damping coefficient and the active droop coefficient. The energy storage system is modeled using the first modeling parameters and the second modeling parameters.

[0006] Preferably, the step of identifying the battery model based on the terminal voltage, current, and ambient temperature in the real-time operating data to obtain the first modeling parameters of the battery includes: obtaining the ohmic internal resistance of the battery based on the terminal voltage, current, polarization voltage, and the electrochemical sub-model; obtaining the core temperature of the battery based on the ohmic internal resistance, ambient temperature, current, polarization voltage, and the thermal sub-model; obtaining the capacity decay and cumulative throughput ampere-hours of the battery; and obtaining the state of charge of the battery based on the core temperature, capacity decay, cumulative throughput ampere-hours, and the aging sub-model.

[0007] Preferably, the electrochemical sub-model is obtained using the following formula:

[0008] The terminal voltage is... The preset open-circuit voltage of the battery. For the current, The ohmic internal resistance is given. and These are the two polarization voltages of the battery. This is the default SOC function.

[0009] Preferably, the thermal model is obtained using the following formula:

[0010] in, For battery thermal melting, The core temperature, For the current, The ohmic internal resistance is given. and These are the two polarization voltages of the battery. The ambient temperature is... This refers to the thermal resistance from the battery to the environment.

[0011] Preferably, the aging sub-model is obtained using the following formula:

[0012] in, For the capacity decay, , and For preset coefficients, As a preset gas constant, The core temperature, The state of charge, This is a preset state of charge reference value. The cumulative throughput in ampere-hours.

[0013] Preferably, the step of identifying the sequence impedance model based on the output angular frequency and the first active power in the real-time operating data to obtain the second modeling parameters of the converter includes: obtaining the active droop coefficient based on the output angular frequency, the first active power and the droop control model; and obtaining the damping coefficient based on the output angular frequency and the virtual synchronous machine rotor motion model.

[0014] Preferably, the droop control model is obtained using the following formula:

[0015] in, The output angular frequency, The rated angular frequency of the converter. The active power droop coefficient is mentioned above. This is the first active power. This is the preset active power reference value.

[0016] Preferably, the virtual synchronous machine rotor motion model is obtained using the following formula:

[0017] in, To preset the virtual inertial time constant, The output angular frequency, To preset the virtual mechanical torque, To preset the electromagnetic torque, The damping coefficient is... This is the rated angular frequency of the converter.

[0018] The specific technical solution of the second embodiment of the present invention is as follows: an energy storage system modeling system, the system comprising: a model building module, a data acquisition module, a simulation module, a first modeling parameter output module, a second modeling parameter output module, and a modeling module; the model building module is used to build a battery model of the battery and a sequence impedance model of the converter in the energy storage system, the battery model including an electrochemical sub-model, a thermal sub-model, and an aging sub-model, the sequence impedance model including a droop control model and a virtual synchronous machine rotor motion model; the data acquisition module is used to acquire real-time operating data of the energy storage system; the real-time operating data includes terminal voltage, current, polarization voltage, and ambient temperature, as well as output angular frequency and first active power; the simulation module is used to simulate the battery model and the sequence impedance model based on the real-time operating data to obtain the DC voltage and maximum allowable power output by the battery model. The module obtains the second active power and reactive power output from the sequence impedance model; the first modeling parameter output module uses the second active power and the reactive power as power commands for the battery, identifies the battery model based on the terminal voltage, current, and ambient temperature in the real-time operating data, and obtains the first modeling parameters of the battery; the first modeling parameters include the state of charge and core temperature; the second modeling parameter output module uses the DC voltage and the maximum allowable power as boundary conditions for converter operation, identifies the sequence impedance model based on the output angular frequency and the first active power in the real-time operating data, and obtains the second modeling parameters of the converter; the second modeling parameters include the damping coefficient and the active droop coefficient; the modeling module uses the first modeling parameters and the second modeling parameters to model the energy storage system.

[0019] The specific technical solution of the third embodiment of the present invention is as follows: an energy storage system modeling device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0020] Implementing the embodiments of the present invention will have the following beneficial effects: In this invention, the electrochemical sub-model, thermal sub-model, and aging sub-model of the battery can track the changes in the internal state of the battery in real time, and the obtained first modeling parameters can accurately reflect the battery's operating conditions. The droop control model and virtual synchronous machine rotor motion model of the converter can accurately reflect the sequence impedance characteristics of the converter, and the obtained second modeling parameters can more accurately simulate the real state of the converter. Based on the obtained first and second modeling parameters, the energy storage system is modeled, taking into account the real-time operating conditions and characteristics of the battery and the converter, which is conducive to improving the accuracy of energy storage system modeling and simulating the state of the real energy storage system. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart outlining the steps involved in modeling an energy storage system. Figure 2 This is a block diagram of the overall architecture of a grid-type energy storage system model; Figure 3 A schematic diagram of a refined multiphysics model of the battery body; Figure 4 A block diagram of a grid-type converter and its control system; Figure 5 A schematic diagram of the structure of the energy storage system is provided for modeling. Among them, 201 is the model building module; 202 is the data acquisition module; 203 is the simulation module; 204 is the first modeling parameter output module; 205 is the second modeling parameter output module; and 206 is the modeling module. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0024] The terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such processes, methods, products, or apparatus.

[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] Please see Figure 1 The above is a flowchart of the steps of the energy storage system modeling method in the first embodiment of this application, which simulates the state of a real energy storage system. The method includes: Step 101: Construct the battery model of the battery and the sequence impedance model of the converter in the energy storage system. The battery model includes an electrochemical sub-model, a thermal sub-model and an aging sub-model. The sequence impedance model includes a droop control model and a virtual synchronous machine rotor motion model. Step 102: Obtain the real-time operating data of the energy storage system; the real-time operating data includes terminal voltage, current, polarization voltage and ambient temperature, as well as output angular frequency and first active power; Step 103: Simulate the battery model and the sequence impedance model based on the real-time running data to obtain the DC voltage and maximum allowable power output by the battery model, and the second active power and reactive power output by the sequence impedance model. Step 104: Using the second active power and the reactive power as the power command of the battery, the battery model is identified based on the terminal voltage, the current and the ambient temperature in the real-time operating data to obtain the first modeling parameters of the battery; the first modeling parameters include the state of charge and the core temperature; Step 105: Using the DC voltage and the maximum allowable power as boundary conditions for converter operation, identify the sequence impedance model based on the output angular frequency and the first active power in the real-time operating data to obtain the second modeling parameters of the converter; the second modeling parameters include the damping coefficient and the active power droop coefficient. Step 106: Model the energy storage system using the first modeling parameters and the second modeling parameters.

[0027] Specifically, battery models and converter sequence impedance models are constructed for the energy storage system. The battery model encompasses an electrochemical sub-model, a thermal sub-model, and an aging sub-model. The electrochemical sub-model describes the internal electrochemical reaction process of the battery, the thermal sub-model considers heat generation and transfer during battery operation, and the aging sub-model reflects the battery's aging characteristics over time. The sequence impedance model includes a droop control model and a virtual synchronous machine rotor motion model. The droop control model enables automatic power distribution, and the virtual synchronous machine rotor motion model simulates the rotor characteristics of a synchronous generator. Real-time operating data is acquired through the energy storage power station's monitoring system, including battery terminal voltage, current, polarization voltage, and ambient temperature, as well as the converter's output angular frequency and first active power. This data reflects the real-time operating status of the energy storage system. Based on the acquired real-time operating data, simulations are performed on the battery model and the sequence impedance model. The battery model outputs DC voltage and maximum allowable power, while the sequence impedance model outputs second active power and reactive power. The second active power and reactive power output from the sequence impedance model are used as the battery's power commands. Combined with real-time operating data such as terminal voltage, current, and ambient temperature, the battery model is identified to obtain the battery's state of charge and core temperature, among other first-level modeling parameters. The DC voltage and maximum allowable power output from the battery model are used as boundary conditions for converter operation. Based on the output angular frequency and first-level active power from real-time operating data, the sequence impedance model is identified to obtain the converter's damping coefficient and active power droop coefficient, among other second-level modeling parameters. Using these first and second-level modeling parameters, a precise model of the energy storage system is completed, providing a reliable basis for the optimized operation and control of the energy storage system.

[0028] In a specific embodiment, the step of identifying the battery model based on the terminal voltage, current, and ambient temperature in the real-time operating data to obtain the first modeling parameters of the battery includes: obtaining the ohmic internal resistance of the battery based on the terminal voltage, current, polarization voltage, and the electrochemical sub-model; obtaining the core temperature of the battery based on the ohmic internal resistance, ambient temperature, current, polarization voltage, and the thermal sub-model; obtaining the capacity decay and cumulative throughput ampere-hours of the battery; and obtaining the state of charge of the battery based on the core temperature, capacity decay, cumulative throughput ampere-hours, and the aging sub-model.

[0029] Specifically, calculations are performed based on the obtained terminal voltage, current, and polarization voltage, combined with an electrochemical sub-model. The electrochemical sub-model describes the relationship between the internal electrochemical reactions of the battery and voltage and current. Processing this data using this model allows for the accurate determination of the battery's ohmic internal resistance. The obtained ohmic internal resistance, ambient temperature, current, and polarization voltage are then substituted into a thermal sub-model. This thermal model considers the generation and transfer mechanisms of heat within the battery, and its calculations accurately determine the battery's core temperature. First, the battery's capacity decay and cumulative throughput ampere-hours are obtained. These data can be acquired through long-term monitoring of battery usage. Then, the core temperature, capacity decay, and cumulative throughput ampere-hours are substituted into an aging sub-model. This aging sub-model reflects the battery's aging characteristics over time and with usage. Calculations using this model ultimately yield the battery's state of charge.

[0030] In a specific embodiment, the electrochemical sub-model is obtained using the following formula:

[0031] The terminal voltage is... The preset open-circuit voltage of the battery. For the current, The ohmic internal resistance is given. and These are the two polarization voltages of the battery. This is a preset SOC function. The battery's terminal voltage is an important parameter reflecting its operating state, allowing for more accurate calculation of how the terminal voltage changes over time and other variables. During charging and discharging, the battery experiences polarization due to factors such as hysteresis in electrode reactions. The presence of polarization voltage affects battery performance. Incorporating polarization voltage into the calculation provides a more realistic reflection of the battery's characteristics during dynamic operation.

[0032] The polarization voltage dynamics of the two RC networks are described by the following differential equations:

[0033]

[0034] The battery's state of charge (SOC) is calculated using the ampere-hour integration method:

[0035] in, Terminal voltage, Open circuit voltage, This is the operating current. , Polarization resistor, , Polarized capacitor, This refers to the battery's rated capacity.

[0036] In a specific embodiment, the thermal model is obtained using the following formula:

[0037] in, For battery thermal melting, The core temperature, For the current, The ohmic internal resistance is given. and These are the two polarization voltages of the battery. The ambient temperature is... The thermal resistance between the battery and the environment is given. Current and ohmic internal resistance determine the Joule heat generated inside the battery, polarization voltage reflects the influence of battery polarization on thermal effects, and ambient temperature and thermal resistance reflect the heat exchange between the battery and the surrounding environment. Integrating these factors into a single model can more realistically reflect the temperature change characteristics of the battery under different operating conditions.

[0038] In a specific embodiment, the aging sub-model is obtained using the following formula:

[0039] in, For the capacity decay, , and For preset coefficients, As a preset gas constant, The core temperature, The state of charge, This is a preset state of charge reference value. The cumulative throughput in ampere-hours. This creates a two-way coupling of electricity, heat, and aging. For the current capacity, This refers to the battery's rated capacity. Core temperature reflects the significant impact of temperature on battery aging; high temperatures typically accelerate internal chemical reactions, leading to faster capacity decay. State of charge (SOC) and its changes reflect the battery's aging characteristics at different charge / discharge levels; different SOC ranges have varying impacts on battery life. Cumulative throughput ampere-hours represent the degree of battery usage; the number of charge / discharge cycles and depth of charge / discharge both affect the battery's aging rate.

[0040] In a specific embodiment, the step of identifying the sequence impedance model based on the output angular frequency and the first active power in the real-time operating data to obtain the second modeling parameters of the converter includes: obtaining the active droop coefficient based on the output angular frequency, the first active power and the droop control model; and obtaining the damping coefficient based on the output angular frequency and the virtual synchronous machine rotor motion model.

[0041] Specifically, the real-time monitored output angular frequency and first active power data are substituted into the droop control model, and the active power droop coefficient is calculated through data fitting and parameter optimization algorithms. The real-time acquired output angular frequency data is substituted into the virtual synchronous machine rotor motion model, and a suitable parameter identification method is used to search for the damping coefficient that best matches the dynamic characteristics of the model with the actual system.

[0042] In a specific embodiment, the droop control model is obtained using the following formula:

[0043] in, The output angular frequency, The rated angular frequency of the converter. The active power droop coefficient is mentioned above. This is the first active power. This is a preset active power reference value. When the energy storage system is disturbed, such as by sudden load changes or variations in active power input, the output angular frequency will adjust accordingly according to the formula. This automatic frequency adjustment mechanism can suppress frequency fluctuations in the system, allowing it to recover to a stable operating state more quickly. Furthermore, by combining it with other models such as the virtual synchronous machine rotor motion model, the system's dynamic response characteristics can be further optimized, oscillations reduced, and system robustness improved.

[0044] In a specific embodiment, the virtual synchronous machine rotor motion model is obtained using the following formula:

[0045] in, To preset the virtual inertial time constant, The output angular frequency, To preset the virtual mechanical torque, To preset the electromagnetic torque, The damping coefficient is... This represents the rated angular frequency of the converter. Specifically, the power output of the energy storage system can be controlled by adjusting the virtual mechanical torque, and the inertial response capability of the system can be adjusted by changing the virtual inertial time constant. By solving the small-signal model of the system containing the above control loops, the output impedance matrix of the converter in the dq coordinate system is obtained. Then, the positive sequence impedance is obtained through matrix transformation. and negative sequence impedance .

[0046] The recursive least squares method is used for online parameter identification, and its core iterative formula is as follows:

[0047] in, The vector of parameters to be estimated contains the parameters from the battery model. , Parameters, Let k be the observation value at time k. For the regression vector, Forgetting factor, Here is the gain matrix. Let be the covariance matrix.

[0048] This embodiment uses a 200MW / 400MWh lithium iron phosphate battery energy storage power station as an example to construct its digital twin model. Its overall architecture is as follows: Figure 2 As shown, the unified model consists of four core parts: 1. Refined Multiphysics Model of the Battery: As the energy source of the system, its output (DC voltage) Maximum allowable power () are the boundary conditions for converter operation.

[0049] Sequence impedance model of grid-connected converter: As the interface between the system and the power grid, it incorporates the power demand of the power grid. This is converted into a power command for the battery.

[0050] Multi-timescale interface: coordinates data exchange and simulation step size between battery model (seconds to hours) and converter model (milliseconds).

[0051] Data-driven online calibration module: Utilizing real-time measured voltage Current and temperature Closed-loop correction is performed on the model parameters.

[0052] 2. Refined Multiphysics Model of Battery Body (corresponding to) Figure 3 ) Electrochemical model: A second-order RC equivalent circuit model and its formula are adopted. The model parameters are obtained through hybrid pulse power characteristic (HPPC) experiments at different SOCs and temperatures.

[0053] Thermal model: A lumped-parameter thermal model and its formulas are used. Parameters and Obtained through calorimetric experiments or manufacturer data.

[0054] Aging model: A semi-empirical model is used. Parameters , , This was obtained by fitting data from accelerated aging experiments.

[0055] Coupling relationship: In actual simulation, the electrochemical response and heat generation are first calculated based on the current operating conditions, and then the thermal model is updated. The aging model is based on stress and renew Finally, the parameters of the electrochemical model are based on the latest... and By performing table lookups or function calculations to update, closed-loop coupling can be achieved.

[0056] 3. Sequence impedance model of grid-type converter (corresponding to) Figure 4 ) This model accurately describes how the converter transforms the DC power from the battery into AC power with grid-connecting capability.

[0057] Control strategy: Virtual synchronous machine (VSG) control is adopted, including droop equation and rotor motion equation.

[0058] Sequence impedance modeling: A detailed switching model was built in MATLAB / Simulink. A small signal perturbation was injected near the rated operating point, and a frequency domain scan was performed to obtain impedance curves from -100Hz to 1000Hz. Using vector fitting techniques, the impedance curves were fitted into transfer function form, facilitating system-level stability analysis.

[0059] Applications: This impedance model can be used in the planning and design phase to compare with the grid impedance, perform Nyquist stability criterion analysis, and avoid subsynchronous oscillations.

[0060] 4. Data-driven online calibration module This module ensures that the model can track battery aging and system characteristic drift.

[0061] Data Acquisition: Deploy sensors to collect data from the battery clusters every 5 seconds. , , data.

[0062] Parameter identification: The recursive least squares (RLS) algorithm is used. The terminal voltage equation of the electrochemical model is discretized, and a regression equation is constructed: ; Parameters to be estimated A forgetting factor λ = 0.995 was set to track the slow time-varying characteristics of the parameters.

[0063] Specifically, this embodiment also includes model updating: updating the new parameters identified online by the RLS algorithm. , The parameters are updated in real time to the refined model of the battery body, replacing the original fixed parameters.

[0064] The advantages and positive effects of this embodiment are: 1. High precision and comprehensiveness: Through the electro-thermal-aging coupled model, the intrinsic relationship between the internal state of the battery and the external characteristics of the system is revealed within a unified framework, and the model has high precision.

[0065] 2. Forward-looking analysis capability: The converter sequence impedance model provides a key tool for analyzing the stability of power grids with a high proportion of new energy sources, and can effectively prevent oscillation risks.

[0066] 3. Full lifecycle applicability: The data-driven correction mechanism enables the model to have "self-learning" capabilities, allowing it to maintain high fidelity as the energy storage system's performance degrades, greatly enhancing its practical value in condition assessment, lifespan prediction, and operation and maintenance decisions.

[0067] 4. High engineering practicality: The model has a clear structure, and each module can be used independently or work together, making it suitable for various scenarios such as scientific research, planning and design, and real-time simulation.

[0068] In a specific embodiment, please refer to Figure 5This is a schematic diagram of the structure of an energy storage system modeling system according to the second embodiment of this application. The system includes: a model building module 201, a data acquisition module 202, a simulation module 203, a first modeling parameter output module 204, a second modeling parameter output module 205, and a modeling module 206. The model building module 201 is used to build a battery model of the battery and a sequence impedance model of the converter in the energy storage system. The battery model includes an electrochemical sub-model, a thermal sub-model, and an aging sub-model. The sequence impedance model includes a droop control model and a virtual synchronous machine rotor motion model. The data acquisition module 202 is used to acquire real-time operating data of the energy storage system. The real-time operating data includes terminal voltage, current, polarization voltage, ambient temperature, output angular frequency, and first active power. The simulation module 203 is used to simulate the battery model and the sequence impedance model based on the real-time operating data to obtain the DC power output by the battery model. The first modeling parameter output module 204 is used to use the second active power and the reactive power as power commands for the battery, and to identify the battery model based on the terminal voltage, the current and the ambient temperature in the real-time operating data to obtain the first modeling parameters of the battery; the first modeling parameters include the state of charge and the core temperature; the second modeling parameter output module 205 is used to use the DC voltage and the maximum allowable power as boundary conditions for the converter operation, and to identify the sequence impedance model based on the output angular frequency and the first active power in the real-time operating data to obtain the second modeling parameters of the converter; the second modeling parameters include the damping coefficient and the active droop coefficient; the modeling module 206 is used to model the energy storage system using the first modeling parameters and the second modeling parameters.

[0069] In this embodiment, the battery's electrochemical, thermal, and aging sub-models can track changes in the battery's internal state in real time, and the obtained first modeling parameters accurately reflect the battery's operating conditions. The converter's droop control model and virtual synchronous machine rotor motion model accurately reflect the converter's sequence impedance characteristics, and the obtained second modeling parameters more accurately simulate the converter's true state. Modeling the energy storage system based on these first and second modeling parameters comprehensively considers the real-time operating conditions and characteristics of the battery and converter, which helps improve the accuracy of energy storage system modeling and simulate the true state of the energy storage system. In a specific embodiment, the third embodiment of this application provides an energy storage system modeling device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0070] In a specific embodiment, the fourth embodiment of this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method as described in any one of the first embodiments of this application.

[0071] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A modeling method for an energy storage system, characterized in that, The method includes: A battery model and a sequence impedance model of the converter in the energy storage system are constructed. The battery model includes an electrochemical sub-model, a thermal sub-model, and an aging sub-model. The sequence impedance model includes a droop control model and a virtual synchronous machine rotor motion model. Acquire real-time operating data of the energy storage system; the real-time operating data includes terminal voltage, current, polarization voltage and ambient temperature, as well as output angular frequency and first active power; Simulations are performed on the battery model and the sequence impedance model based on the real-time operating data to obtain the DC voltage and maximum allowable power output by the battery model, and the second active power and reactive power output by the sequence impedance model. The second active power and the reactive power are used as the power command of the battery. Based on the terminal voltage, the current and the ambient temperature in the real-time operating data, the battery model is identified to obtain the first modeling parameters of the battery. The first modeling parameters include the state of charge and the core temperature. Using the DC voltage and the maximum allowable power as boundary conditions for converter operation, the sequence impedance model is identified based on the output angular frequency and the first active power in the real-time operating data to obtain the second modeling parameters of the converter; the second modeling parameters include the damping coefficient and the active power droop coefficient. The energy storage system is modeled using the first modeling parameters and the second modeling parameters.

2. The energy storage system modeling method as described in claim 1, characterized in that, The process of identifying the battery model based on the terminal voltage, current, and ambient temperature from the real-time operating data to obtain the first modeling parameters of the battery includes: The ohmic internal resistance of the battery is obtained based on the terminal voltage, the current, the polarization voltage, and the electrochemical quantum model. The core temperature of the battery is obtained based on the ohmic internal resistance, the ambient temperature, the current, the polarization voltage, and the thermal model. The capacity decay and cumulative throughput ampere-hours of the battery are obtained, and the state of charge of the battery is obtained based on the core temperature, the capacity decay of the battery, the cumulative throughput ampere-hours, and the aging sub-model.

3. The energy storage system modeling method as described in claim 2, characterized in that, The electrochemical sub-model was obtained using the following formula: The terminal voltage is... The preset open-circuit voltage of the battery. For the current, The ohmic internal resistance is given. and These are the two polarization voltages of the battery. This is the default SOC function.

4. The energy storage system modeling method as described in claim 2, characterized in that, The thermal model is obtained using the following formula: in, For battery thermal melting, The core temperature, For the current, The ohmic internal resistance is given. and These are the two polarization voltages of the battery. The ambient temperature is... This refers to the thermal resistance from the battery to the environment.

5. The energy storage system modeling method as described in claim 2, characterized in that, The aging sub-model is obtained using the following formula: in, For the capacity decay, , and For preset coefficients, As a preset gas constant, The core temperature, The state of charge, This is a preset state of charge reference value. The cumulative throughput in ampere-hours.

6. The energy storage system modeling method as described in claim 1, characterized in that, The process of identifying the sequence impedance model based on the output angular frequency and the first active power in the real-time operating data to obtain the second modeling parameters of the converter includes: The active power droop coefficient is obtained based on the output angular frequency, the first active power, and the droop control model. The damping coefficient is obtained based on the output angular frequency and the virtual synchronous machine rotor motion model.

7. The energy storage system modeling method as described in claim 6, characterized in that, The droop control model is obtained using the following formula: in, The output angular frequency, The rated angular frequency of the converter. The active power droop coefficient is mentioned above. This is the first active power. This is the preset active power reference value.

8. The energy storage system modeling method as described in claim 6, characterized in that, The virtual synchronous machine rotor motion model is obtained using the following formula: in, To preset the virtual inertial time constant, The output angular frequency, To preset the virtual mechanical torque, To preset the electromagnetic torque, The damping coefficient is... This is the rated angular frequency of the converter.

9. An energy storage system modeling system, characterized in that, The system includes: a model building module, a data acquisition module, a simulation module, a first modeling parameter output module, a second modeling parameter output module, and a modeling module; The model building module is used to build battery models of batteries and sequence impedance models of converters in the energy storage system. The battery model includes an electrochemical sub-model, a thermal sub-model, and an aging sub-model. The sequence impedance model includes a droop control model and a virtual synchronous machine rotor motion model. The data acquisition module is used to acquire real-time operating data of the energy storage system; the real-time operating data includes terminal voltage, current, polarization voltage and ambient temperature, as well as output angular frequency and first active power; The simulation module is used to simulate the battery model and the sequence impedance model based on the real-time running data, to obtain the DC voltage and maximum allowable power output by the battery model, and to obtain the second active power and reactive power output by the sequence impedance model. The first modeling parameter output module is used to take the second active power and the reactive power as the power command of the battery, and identify the battery model based on the terminal voltage, the current and the ambient temperature in the real-time operating data to obtain the first modeling parameters of the battery; the first modeling parameters include the state of charge and the core temperature; The second modeling parameter output module is used to take the DC voltage and the maximum allowable power as boundary conditions for converter operation, and to identify the sequence impedance model based on the output angular frequency and the first active power in the real-time operating data to obtain the second modeling parameters of the converter; the second modeling parameters include damping coefficient and active power droop coefficient; The modeling module is used to model the energy storage system using the first modeling parameters and the second modeling parameters.

10. An energy storage system modeling device, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

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