Method for setting control parameters of virtual synchronous machine control model and related device

Abstract

The embodiment of the invention discloses a method and a related device for setting control parameters of a virtual synchronous machine control model, and the method comprises the steps: carrying out the simulation of a network-building type energy storage system under the condition that the disturbance type is the removal of a power generation unit or a three-phase symmetric short-circuit fault; determining a target value of a control parameter which minimizes the control parameter setting optimization model, the control parameter being a control parameter in the virtual synchronous machine control model; an optimization target of the control parameter setting optimization model is to minimize energy provided by the battery energy storage station in a first preset duration after disturbance of the network-forming type energy storage system occurs, and a numerical value of a control parameter in the virtual synchronous machine control model is set as a target numerical value. The optimization target is decoupled from the system state before disturbance, and the dynamic response characteristics of the system under uncertain disturbance are directly optimized, so that the set control parameters are no longer limited to a certain fixed working condition, and the effectiveness of control parameter setting can be improved.

Description

Technical Field

[0001] This invention relates to the field of power grid control technology, and in particular to a method and related apparatus for tuning control parameters of a virtual synchronous machine control model. Background Technology

[0002] Grid-based energy storage systems refer to energy storage systems that can autonomously establish, regulate, and maintain the voltage and frequency of an AC power grid, thereby providing synchronization stability for the power system.

[0003] For grid-connected energy storage systems, especially those employing virtual synchronous machine (VSM) technology, they play a crucial role in improving the stability of power systems, particularly those operating in isolated grids. VSM technology provides necessary inertia and damping support to grid-connected energy storage systems by simulating the operating characteristics of synchronous generators. Its control performance largely depends on the proper tuning of control parameters (such as virtual inertia and damping coefficients).

[0004] Currently, most existing parameter tuning methods are based on deterministic operating conditions. For example, they rely on known and fixed operating state information of the system before the disturbance occurs to design optimization objectives and perform parameter optimization.

[0005] However, the limitations of such methods become particularly prominent when applied to isolated systems. Due to their limited scale and large fluctuations in source load, isolated systems have highly uncertain operating conditions. These dynamically changing uncertainties mean that the optimal control parameters tuned under a specific, fixed operating condition will significantly degrade in control performance under another operating condition or when a disturbance occurs, and may even lead to system instability.

[0006] Clearly, existing technologies rely heavily on known operating conditions before disturbances, and their tuning results lack adaptability to the dynamic uncertainties of isolated network systems, resulting in severely insufficient generalization ability.

[0007] Therefore, the effectiveness of current control parameter tuning methods is relatively low. Summary of the Invention

[0008] The main objective of this invention is to provide a method and related apparatus for tuning control parameters of a virtual synchronous machine control model, which can solve the problem of low effectiveness of current control parameter tuning methods in the prior art.

[0009] To achieve the above objectives, the first aspect of the present invention provides a method for tuning the control parameters of a virtual synchronous machine control model, the method comprising: Construct a virtual synchronous machine control model for a grid-type energy storage system; When a disturbance occurs in the grid-type energy storage system, the type of disturbance experienced by the grid-type energy storage system is identified; When the disturbance type is a power generation unit disconnection or a three-phase symmetrical short-circuit fault, the target value of the control parameter that minimizes the control parameter tuning optimization model is determined by simulating the grid-type energy storage system; wherein, the control parameter is the control parameter in the virtual synchronous machine control model; the optimization objective of the control parameter tuning optimization model is to minimize the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after the disturbance occurs; The values ​​of the control parameters in the virtual synchronous machine control model are tuned to the target values.

[0010] In some embodiments, the above-mentioned method of determining the target value of the control parameter that minimizes the control parameter tuning optimization model by simulating the grid-type energy storage system includes: generating a first value of the control parameter using a preset optimization algorithm; performing simulations on the grid-type energy storage system sequentially under predefined operating scenarios using the first value to obtain the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time period after being subjected to a disturbance of the same type; determining the sum of the absolute values ​​of the energy provided by the battery energy storage station corresponding to each operating scenario through the control parameter tuning optimization model; and determining the sum of the absolute values ​​of the energy provided by the battery energy storage station to the grid-type energy storage system under each operating scenario. The energy provided by the battery energy storage station within a first preset time after the disturbance occurs is the sum of the absolute values. If the iteration does not meet the convergence condition, a second value of the control parameter is generated using a preset optimization algorithm. The second value is used as the new first value, and the process of performing simulation on the grid-type energy storage system in at least one predefined operating scenario using the first value is repeated to obtain the energy provided by the battery energy storage station within a first preset time after the grid-type energy storage system is subjected to a disturbance of the same type as the disturbance. This process continues until the iteration meets the convergence condition, and the target value that minimizes the control parameter tuning optimization model is output.

[0011] In some embodiments, the above simulation of the grid-type energy storage system satisfies the following constraints: the system frequency of the grid-type energy storage system remains within a first preset range after a disturbance occurs, and the system frequency converges to a given second preset range after a second preset duration following the disturbance; wherein the first preset range is greater than the second preset range.

[0012] In some embodiments, the above-mentioned method of determining the target value of the control parameter that minimizes the control parameter tuning optimization model by simulating the grid-type energy storage system includes: generating a first value of the control parameter using a preset optimization algorithm; for each of the predefined normal and fault modes, for the grid-type energy storage system, under each predefined operating scenario, sequentially using the first value and performing simulation under the mode to obtain the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type; for each mode, determining the sum of the absolute values ​​of the energy provided by the battery energy storage station corresponding to each operating scenario under the mode through the control parameter tuning optimization model; wherein, the grid-type energy storage system... The energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after a disturbance occurs is the sum of the absolute values. If the iteration does not meet the convergence condition, a second value of the control parameter is generated using a preset optimization algorithm. The second value is used as the new first value, and the process is repeated for each of the predefined normal and fault modes. For the grid-type energy storage system, under each predefined operating scenario, the first value is used sequentially and simulation is performed in the mode to obtain the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after a disturbance of the same type as the disturbance. This process continues until the iteration meets the convergence condition, and the target value that minimizes the control parameter tuning optimization model in each mode is output.

[0013] In some embodiments, tuning the control parameters in the virtual synchronous machine control model to the target values ​​includes: tuning the control parameters in the virtual synchronous machine control model to the target values ​​in the normal mode when the measured value of the converter port voltage of the grid-connected energy storage system is greater than or equal to a preset voltage threshold and the system frequency of the grid-connected energy storage system is less than a preset frequency lower limit; and tuning the control parameters in the virtual synchronous machine control model to the target values ​​in the fault mode when the measured value of the converter port voltage is less than the preset voltage threshold and the voltage recovery time of the grid-connected energy storage system is less than a preset voltage recovery time limit.

[0014] In some embodiments, the expression for the virtual synchronous machine control model described above is as follows: ; Where t represents time. This represents the angular frequency deviation of the virtual rotor. This indicates the rated angular frequency of the virtual rotor. H represents the actual angular frequency of the virtual rotor, H represents the virtual inertia, and D represents the virtual damping. Both H and D are control parameters. and These represent the virtual mechanical input power and electrical output power, respectively.

[0015] In some embodiments, the formula for calculating the virtual mechanical input power is as follows: ; ; Where t represents time. Indicates virtual mechanical input power. Indicates the active power reference value. This represents the active power adjustment term caused by the virtual rotor angular velocity error. It represents the active power-frequency droop factor.

[0016] To achieve the above objectives, a second aspect of the present invention provides a device for tuning control parameters of a virtual synchronous machine control model, the device comprising: The building module is used to construct a virtual synchronous machine control model for a grid-type energy storage system; The identification module is used to identify the type of disturbance to the grid-type energy storage system when a disturbance occurs. An optimization module is used to determine the target value of the control parameters that minimize the control parameter tuning optimization model by simulating the grid-type energy storage system under the condition that the disturbance type is power generation unit disconnection or three-phase symmetrical short-circuit fault; wherein, the control parameters are the control parameters in the virtual synchronous machine control model; the optimization objective of the control parameter tuning optimization model is to minimize the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after the disturbance occurs; The tuning module is used to tune the values ​​of the control parameters in the virtual synchronous machine control model to the target values.

[0017] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps described in the first aspect.

[0018] To achieve the above objectives, a fourth aspect of the present invention provides a computer device including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps described in the first aspect.

[0019] The embodiments of the present invention have the following beneficial effects: This invention provides a method and related apparatus for tuning control parameters of a virtual synchronous machine control model. By simulating a grid-connected energy storage system under disturbance conditions such as generator unit disconnection or three-phase symmetrical short-circuit fault, the method determines the target values ​​of the control parameters that minimize the control parameter tuning optimization model. The control parameters are those in the virtual synchronous machine control model, and the optimization objective of the model is to minimize the energy provided by the battery storage station to the grid-connected energy storage system within a first preset time period after the disturbance occurs. The values ​​of the control parameters in the virtual synchronous machine control model are then tuned to the target values.

[0020] Thus, this application sets the optimization objective of the control parameter tuning optimization model as "minimizing the energy provided by the battery storage station within the first preset time after the disturbance occurs," effectively solving the problem of insufficient generalization ability caused by the reliance on specific operating conditions before the disturbance in traditional methods. This optimization objective is decoupled from the system state before the disturbance and directly optimizes the dynamic response characteristics of the system under uncertain disturbances. This makes the tuned control parameters no longer limited to a certain fixed operating condition, but has a wide adaptability to cope with the dynamic changes in the availability of generator units, voltage levels, and synchronous inertia in isolated grids, thereby improving the effectiveness of control parameter tuning. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] in: Figure 1 This is a flowchart illustrating the method for tuning the control parameters of the virtual synchronous machine control model in an embodiment of the present invention. Figure 2 This is a block diagram of the power converter control of a grid-type energy storage system in an embodiment of the present invention; Figure 3 This is a block diagram of the virtual synchronizer control in an embodiment of the present invention; Figure 4 This is a schematic diagram of the reactive power-voltage control principle in an embodiment of the present invention; Figure 5 This is a schematic diagram of the transient virtual impedance control principle in an embodiment of the present invention; Figure 6 This is a structural block diagram of the control parameter tuning device for the virtual synchronous machine control model in this embodiment of the invention; Figure 7 This is a structural block diagram of a computer device in an embodiment of the present invention. Detailed Implementation

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

[0024] like Figure 1 As shown, in one embodiment, a method for tuning the control parameters of a virtual synchronous machine control model is provided. Figure 1 This is a flowchart illustrating the method for tuning the control parameters of a virtual synchronous machine control model provided in this application embodiment. This method can be applied to computer equipment, which can be either a terminal or a server; this embodiment uses a terminal as an example. The specific steps of the method for tuning the control parameters of the virtual synchronous machine control model are as follows: Step 102: Construct a virtual synchronous machine control model for the grid-type energy storage system.

[0025] The virtual synchronous machine control model can refer to a mathematical model that uses advanced control algorithms to enable a power electronic converter to simulate the electromechanical transient characteristics of a traditional synchronous generator. In this invention, the power electronic converter can specifically refer to the battery energy storage converter in a grid-type energy storage system.

[0026] In one embodiment, the expression for the virtual synchronizer control model can be as follows: ; Where t represents time. This represents the angular frequency deviation of the virtual rotor. This indicates the rated angular frequency of the virtual rotor. H represents the actual angular frequency of the virtual rotor, H represents the virtual inertia, and D represents the virtual damping. Both H and D are control parameters. and These represent the virtual mechanical input power and electrical output power, respectively. This represents the angular frequency deviation of the virtual rotor at time t. This represents the actual angular frequency of the virtual rotor at time t. (t) represents the virtual mechanical input power at time t. (t) represents the electrical output power at time t.

[0027] In the case of real-time monitoring and calculation, t can represent the current time.

[0028] Among them, virtual mechanical input power The formula for calculating can be shown below: ; ; Where t represents time. Indicates virtual mechanical input power. Indicates the active power reference value. This represents the active power adjustment term caused by the virtual rotor angular velocity error. This represents the active power-frequency droop factor. This represents the active power reference value at time t. This represents the active power adjustment term generated by the virtual rotor angular velocity error at time t.

[0029] In yet another embodiment, the expression for the virtual synchronous machine control model can also be as follows: ; Where t represents time. H represents the angular frequency deviation of the virtual rotor, H represents the virtual inertia, and D represents the virtual damping. Both H and D are control parameters. and These represent the virtual mechanical input power and electrical output power, respectively. This represents the real-time voltage and frequency measurement (system frequency) at the grid connection point of the grid-connected energy storage system. Indicates the rate of change of frequency. The gain coefficient represents the preset frequency change rate.

[0030] Step S102: When a disturbance occurs in the grid-type energy storage system, identify the type of disturbance that the grid-type energy storage system is subjected to.

[0031] In one embodiment, before identifying the type of disturbance to the grid-type energy storage system when a disturbance occurs, the system frequency f, the rate of change of frequency RoCoF, and the bus voltage U can be obtained. The bus voltage U is the grid-connected bus voltage at the AC side outlet of the energy storage converter. A reasonable threshold is set to determine whether a significant disturbance sufficient to trigger parameter tuning requirements has occurred. Specifically, the grid-type energy storage system is determined to have experienced a disturbance when at least one of the following conditions is met: the system frequency exceeds a first preset threshold, the rate of change of frequency exceeds a second preset threshold, and the bus voltage U exceeds a third preset threshold.

[0032] Step S103: Under the condition of disturbance type of power generation unit disconnection or three-phase symmetrical short circuit fault, the target value of the control parameter that minimizes the control parameter tuning optimization model is determined by simulating the grid-type energy storage system.

[0033] The control parameters in step S103 above are the control parameters in the virtual synchronizer control model. These control parameters can include all control parameters in the virtual synchronizer control model, forming a set of control parameters, such as the virtual inertia H and virtual damping D of the virtual synchronizer. The target values ​​of the control parameters in step S103 above can include the target values ​​corresponding to each control parameter in the set of control parameters.

[0034] The optimization objective of the above control parameter tuning optimization model is to minimize the energy E provided by the battery energy storage station to the grid-type energy storage system within a first preset time period after a disturbance occurs. For example, the first preset time period can be 20s or 30s, that is, E is the total energy output by the battery energy storage station within 30 seconds after the disturbance.

[0035] Step S104: Tune the values ​​of the control parameters in the virtual synchronous machine control model to the target values.

[0036] For example, if the target values ​​for virtual inertia H and virtual damping D are 0.1 and 0.2 respectively, then the values ​​of virtual inertia H and virtual damping D in the virtual synchronous machine control model will be adjusted to 0.1 and 0.2 respectively.

[0037] In this embodiment, the optimization objective of the control parameter tuning optimization model is set as "minimizing the energy provided by the battery storage station within the first preset time after the disturbance occurs," effectively solving the problem of insufficient generalization ability caused by the reliance on specific operating conditions before the disturbance in traditional methods. This optimization objective is decoupled from the system state before the disturbance and directly optimizes the dynamic response characteristics of the system under uncertain disturbances. This makes the tuned control parameters no longer limited to a certain fixed operating condition, but has a wide adaptability to cope with the dynamic changes in the availability of power generation units, voltage levels, and synchronous inertia in isolated grids, thereby improving the effectiveness of control parameter tuning.

[0038] In some embodiments, step S102 above, which involves simulating the grid-type energy storage system to determine the target value of the control parameter that minimizes the control parameter tuning optimization model, may further include the following steps: The first value of the control parameter is generated using a preset optimization algorithm.

[0039] For a grid-type energy storage system, simulations are performed sequentially under predefined operating scenarios using the first numerical values ​​to obtain the energy provided by the battery energy storage station for a first preset time after the grid-type energy storage system is subjected to a disturbance of the same type as the disturbance.

[0040] By optimizing the control parameter tuning model, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined.

[0041] If the iteration does not meet the convergence condition, a second value of the control parameter is generated using a preset optimization algorithm. The second value is used as the new first value, and the process is repeated to simulate the grid-type energy storage system in at least one predefined operating scenario using the first value. This process is repeated to obtain the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type as the disturbance. The process continues until the iteration meets the convergence condition, and then the target value that minimizes the control parameter tuning optimization model is output.

[0042] The aforementioned preset optimization algorithm can be a particle swarm optimization algorithm or a genetic algorithm, etc.

[0043] In step S102 above, the energy E provided by the battery energy storage station to the grid-type energy storage system within the first preset time after the disturbance occurs is the sum of absolute values. That is, the energy E provided by the battery energy storage station to the grid-type energy storage system within the first preset time after the disturbance occurs is the sum of absolute values ​​of the energy provided by the battery energy storage station for each operating scenario.

[0044] The above-mentioned grid-type energy storage system is simulated using the first numerical value under each predefined operating scenario to obtain the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type. This can be achieved by simulating the grid-type energy storage system under each predefined operating scenario using the first numerical value to obtain the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type.

[0045] For example, the first values ​​include H1 and D1, the first preset duration is 8 seconds, and the operating scenarios include operating scenario A, operating scenario B, and operating scenario C. For a grid-type energy storage system under operating scenario A, simulations are performed using H1 and D1 to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system is subjected to a disturbance of the same type. For a grid-type energy storage system operating in scenario B, simulations were performed using H1 and D1 to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system is subjected to a disturbance of the same type. For a grid-type energy storage system operating in scenario C, simulations were performed using H1 and D1 to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system is subjected to a disturbance of the same type. By optimizing the control parameter tuning model, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined. .

[0046] The above convergence condition can be that the number of iterations is a preset number. That is, if the iteration does not meet the convergence condition, it means that the number of times the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined by tuning the optimization model with control parameters has not reached the preset number. If the iteration meets the convergence condition, it means that the number of times the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined by tuning the optimization model with control parameters has reached the preset number.

[0047] The sum of the absolute values ​​corresponding to the above target values ​​is the minimum of the sum of the absolute values ​​corresponding to each value.

[0048] The following is a complete example of this embodiment: For instance, the preset number of times is 3, the first preset duration is 8 seconds, and the operating scenarios include operating scenario A, operating scenario B, and operating scenario C. First, a genetic algorithm is used to generate first values ​​H1 and D1. The grid-type energy storage system is simulated using H1 and D1 under operating scenario A to obtain the energy provided by the battery energy storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type. For a grid-type energy storage system operating in scenario B, simulations were performed using H1 and D1 to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system is subjected to a disturbance of the same type. For a grid-type energy storage system operating in scenario C, simulations were performed using H1 and D1 to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system is subjected to a disturbance of the same type. By optimizing the control parameter tuning model, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined. At this point, the iteration does not meet the convergence condition. Therefore, a genetic algorithm is used to generate second values ​​H2 and D2. Using the second values ​​H2 and D2 as the new first values, the grid-type energy storage system is simulated under operating scenario A using H2 and D2. This simulation yields the energy provided by the battery storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type. The grid-type energy storage system was simulated using H2 and D2 under operating scenario B. The results showed that the energy provided by the battery energy storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type was obtained. For a grid-type energy storage system operating in scenario C, simulations were performed using H2 and D2 to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system is subjected to a disturbance of the same type. By optimizing the control parameter tuning model, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined. At this point, the iteration has not yet met the convergence condition. Therefore, a genetic algorithm is used to generate second values ​​H3 and D3. Using the second values ​​H3 and D3 as the new first values, the grid-type energy storage system is simulated under operating scenario A using H3 and D3. This yields the energy provided by the battery energy storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type. The grid-type energy storage system was simulated using H3 and D3 under operating scenario B. The results showed that the energy provided by the battery energy storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type was obtained. The grid-type energy storage system was simulated using H3 and D3 under operating scenario C. The results showed that the energy provided by the battery storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type was obtained. By optimizing the control parameter tuning model, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined. At this point, the iteration satisfies the convergence condition, and the judgment is... , b and The relationship between the size of c, if b c, then the output will be the target values ​​H3 and D3 that minimize the control parameter tuning optimization model.

[0049] If at least one of the above operating scenarios can include off-peak hours, midday hours, and peak hours, then the expression for the control parameter tuning optimization model can be as follows: ; Where the subscripts 1, 2, and 3 of E represent the running scenario numbers, This represents the absolute value of energy provided by the battery storage station during off-peak periods. This represents the absolute value of energy provided by the battery storage station during the midday period. This represents the absolute value of energy provided by the battery storage station during peak hours.

[0050] In some embodiments, the simulation of a grid-type energy storage system satisfies the following constraints: The system frequency of the grid-type energy storage system remains within a first preset range after a disturbance occurs, and converges to a given second preset range after a second preset duration following the disturbance.

[0051] The first preset range is larger than the second preset range.

[0052] In some other embodiments, the simulation of the grid-type energy storage system satisfies the following constraint: after a disturbance occurs, the frequency deviation between the system frequency and the nominal value of the grid-type energy storage system is greater than or equal to the minimum allowable frequency deviation of the grid-type energy storage system, and less than or equal to the maximum allowable frequency deviation. Furthermore, for each operating scenario, within the operating scenario and within the second to third preset durations after the disturbance occurs, the difference between the maximum and minimum frequency deviations is less than or equal to the defined frequency fluctuation bandwidth.

[0053] Therefore, the expression for the above control parameter tuning optimization model can also be shown below:

[0054] Where the subscripts 1, 2, and 3 of E represent the running scenario numbers, This represents the absolute value of energy provided by the battery storage station during off-peak periods. This represents the absolute value of energy provided by the battery storage station during the midday period. This represents the absolute value of energy provided by the battery storage station during peak hours, with the subscript k indicating the operating scenario number. This indicates the frequency deviation between the system frequency and its nominal value. and These represent the minimum and maximum allowable frequency deviation values ​​for a grid-type energy storage system, respectively. To limit the frequency fluctuation bandwidth, Indicates the second preset duration. This indicates the third preset time after the disturbance occurs. This indicates that in the k-th running scenario, within the time window Within, the maximum value of frequency deviation, This indicates that in the k-th running scenario, within the time window Within, the minimum value of frequency deviation.

[0055] for example, For 48.5 Hz and The system frequency is 52.5 Hz, the nominal system frequency is 50 Hz, the frequency fluctuation bandwidth is given as 100 mHz, the second preset duration is 8s, and the third preset duration is 28s.

[0056] In some embodiments, step S103 above, which involves simulating the grid-type energy storage system to determine the target value of the control parameter that minimizes the control parameter tuning optimization model, may further include the following steps: The first value of the control parameter is generated using a preset optimization algorithm; For each of the predefined normal and fault modes, for the grid-type energy storage system, under each predefined operating scenario, the first value is used sequentially and simulation is performed in the mode to obtain the energy provided by the battery energy storage station for the grid-type energy storage system within the first preset time after being subjected to a disturbance of the same type as the disturbance.

[0057] For each mode, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario under the mode is determined by optimizing the model through control parameter tuning.

[0058] If the iteration does not meet the convergence condition, a second value of the control parameters is generated using a preset optimization algorithm. The second value is then used as the new first value. The process is repeated for each of the predefined normal and fault modes. For the grid-type energy storage system, the first value is used sequentially in each of the predefined operating scenarios to simulate the steps of the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type as the disturbance. This process continues until the iteration meets the convergence condition, and the target value that minimizes the control parameter tuning optimization model in each mode is output.

[0059] In step S103 above, the energy provided by the battery energy storage station to the grid-type energy storage system within the first preset time after the disturbance occurs is the sum of absolute values. That is, the energy provided by the battery energy storage station to the grid-type energy storage system within the first preset time after the disturbance occurs is the sum of absolute values ​​of the energy provided by the battery energy storage station for each operating scenario in the mode.

[0060] The above-mentioned normal mode can refer to the fact that all primary equipment (such as lines, transformers, circuit breakers, etc.) of the power grid where the grid-type energy storage system is located are in operation under the design conditions, and no component is out of operation due to fault or maintenance.

[0061] The aforementioned failure modes can refer to the non-rated operating state that the grid-type energy storage system enters after one or more key primary devices in the power grid where the grid-type energy storage system is located are taken out of operation due to failure or planned maintenance.

[0062] In this embodiment, for each mode, a first numerical simulation is used to simulate the energy provided by the battery storage station for a first preset time after the grid-type energy storage system is subjected to a disturbance of the same type in each operating scenario of the mode. k represents the number of the running scenario, and the target value corresponding to each mode is finally determined by the optimization model through control parameter tuning.

[0063] The following is an example of this embodiment: For instance, the preset number of times is 3, the first preset duration is 8 seconds, and the operating scenarios include operating scenario A, operating scenario B, and operating scenario C. For the normal mode, firstly, a genetic algorithm is used to generate the first values ​​H1 and D1. The grid-type energy storage system is simulated using H1 and D1 in operating scenario A and the normal mode to obtain the energy provided by the battery energy storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type as the disturbance. Simulations were conducted using H1 and D1 on a grid-type energy storage system under operating scenario B and normal mode to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system was subjected to a disturbance of the same type. Simulations were conducted using H1 and D1 on a grid-type energy storage system under operating scenarios C and normal mode to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system was subjected to a disturbance of the same type. By optimizing the control parameter tuning model, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined. At this point, the iteration does not meet the convergence condition. Therefore, a genetic algorithm is used to generate second values ​​H2 and D2. Using the second values ​​H2 and D2 as the new first values, simulations are performed on the grid-type energy storage system under operating scenario A and normal mode using H2 and D2. This yields the energy provided by the battery energy storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type. Simulations were conducted using H2 and D2 on a grid-type energy storage system under operating scenario B and normal mode to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system was subjected to a disturbance of the same type. Simulations were conducted using H2 and D2 on a grid-type energy storage system under operating scenarios C and normal mode to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system was subjected to a disturbance of the same type. By optimizing the control parameter tuning model, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined. At this point, the iteration has not yet met the convergence condition. Therefore, a genetic algorithm is used to generate second values ​​H3 and D3. Using the second values ​​H3 and D3 as the new first values, simulations are performed on the grid-type energy storage system under operating scenario A and normal mode using H3 and D3. This yields the energy provided by the battery energy storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type. Simulations were conducted using H3 and D3 on a grid-type energy storage system under operating scenario B and normal mode. The results showed the energy provided by the battery storage station to the grid-type energy storage system within 8 seconds after being subjected to a disturbance of the same type. Simulations were conducted using H3 and D3 on a grid-type energy storage system under operating scenarios C and normal mode to obtain the energy provided by the battery storage station within 8 seconds after the grid-type energy storage system was subjected to a disturbance of the same type. By optimizing the control parameter tuning model, the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined. At this point, the iteration satisfies the convergence condition, and the judgment is... , b and The relationship between the size of c, if b c. Then, the target values ​​that minimize the control parameter tuning optimization model in normal mode are H3 and D3. For fault mode, a similar method is used to determine the target values ​​that minimize the control parameter tuning optimization model in fault mode, which will not be elaborated here.

[0064] Based on determining the target values ​​corresponding to each mode, in some embodiments, the above-mentioned tuning of the control parameters in the virtual synchronous machine control model to the target values ​​may further include the following steps: Measurement of converter port voltage in a grid-type energy storage system Greater than or equal to the preset voltage threshold Furthermore, the system frequency f of the grid-type energy storage system is less than the preset lower frequency limit. In this case, the values ​​of the control parameters in the virtual synchronous machine control model are tuned to the target values ​​in normal mode.

[0065] Measurement of converter port voltage Less than the preset voltage threshold Furthermore, the voltage recovery time of grid-type energy storage systems Less than the preset voltage recovery time limit In this case, the values ​​of the control parameters in the virtual synchronous machine control model are tuned to the target values ​​under fault mode.

[0066] In some implementations... It can be set to 48.5 Hz, but is not limited to 48.5 Hz.

[0067] To better understand the above method, the embodiments of this application provide the following content as examples to illustrate the method: In remote areas, islands, and where power grids are not yet interconnected or isolated, power systems often operate independently as "isolated grids," forming so-called isolated power systems. These systems are typically small in scale, with limited resource allocation, and are more sensitive to disturbances. With the deepening implementation of the "dual-carbon" goal, traditional fossil fuel units in isolated power systems are gradually being replaced by renewable energy sources such as wind and solar power, and the system's operation increasingly relies on power electronic converters. Against this backdrop, the rotational inertia and frequency support capabilities originally provided by synchronous generators are continuously weakening, and frequency stability issues are becoming increasingly prominent. Especially in the absence of large-scale grid interconnection, isolated systems are more prone to severe frequency fluctuations or even instability when encountering disturbances. Constructing control mechanisms with rapid frequency response capabilities to ensure the dynamic stability of isolated grid systems under high-proportion renewable energy penetration has become a hot research topic in the control of grid-based energy storage systems.

[0068] To address the insufficient frequency support capability of isolated power systems, recent research has focused on grid-based control strategies based on battery energy storage systems. As a typical approach, virtual synchronous machine (VSM) control, by introducing inertia and damping response into the power converter, simulates the dynamic characteristics of traditional synchronous machines, providing a new frequency regulation path for high-penetration renewable energy integration. The control performance of VSMs largely depends on their parameter configuration, especially the setting of inertia and damping. To achieve optimal control performance under different disturbance types, several studies have constructed VSM control parameter tuning models based on frequency response indices. Currently, a control parameter tuning method considering energy storage capacity and response speed limitations has been developed, and a two-layer control structure has been proposed, combining fast energy storage to simulate inertia and damping with slow energy storage to provide long-term support. Furthermore, an adaptive control method for VSMs based on frequency response optimization has been proposed to achieve dynamic tuning of control parameters in islanded microgrids. While these studies provide a theoretical basis for VSM parameter optimization, they primarily focus on single disturbance scenarios and lack systematic research on tuning strategies for multiple disturbances and operating states. As energy storage systems undertake more regulation tasks in isolated grids, there is an urgent need to build generalizable parameter tuning models for typical disturbances to improve the adaptability and stability of control strategies under complex operating conditions.

[0069] To address the issues of response lag and insufficient adaptability in fixed-parameter control under varying disturbance scenarios, research on adaptive adjustment strategies for virtual synchronous machine control parameters has gradually emerged in recent years. One type of research introduces intelligent algorithms such as fuzzy logic and neural networks to dynamically adjust virtual inertia and damping parameters based on operating states such as frequency deviation and power changes, thereby improving frequency regulation performance and response speed. Currently, a virtual synchronous machine controller based on deep reinforcement learning has been proposed, which automatically adjusts virtual inertia and damping through data-driven methods to suppress system frequency oscillations. Another approach uses rule-based logic to preset switching conditions, switching control parameters when the system state changes significantly, thus achieving rapid adaptation under different disturbances. A reference-feedforward-based virtual synchronous machine damping tuning method has been proposed, effectively suppressing low-frequency power oscillations without weakening the inertial response. Adaptive control, compared to fixed-parameter schemes, can better balance frequency stability and the regulation burden of the energy storage system. However, most current adaptive strategies lack a unified basis for disturbance identification, making it difficult to take into account the control requirements of different types of disturbances such as sudden power generation losses and voltage disturbances. Especially under fault conditions with drastic fluctuations in grid voltage, problems such as improper inertia setting and delayed control switching often occur, affecting the rapid recovery of frequency and stable operation of the system.

[0070] Despite some progress in virtual synchronous machine control modeling, parameter tuning, and adaptive strategies, current research still faces numerous challenges. On the one hand, most parameter tuning methods rely on pre-disturbance operating conditions, making it difficult to adapt to uncertainties such as generator availability, voltage levels, and dynamic changes in synchronization inertia in islanded grid systems, resulting in insufficient generalization ability of the tuning results. On the other hand, existing adaptive strategies are mostly based on single criteria such as frequency deviation or power error, lacking multi-source information fusion and disturbance identification mechanisms, making it difficult to achieve precise control for complex operating states. Especially under grid fault conditions, residual voltage drops and the strong coupling dynamics between the synchronous machine and the grid-connected converter often significantly alter the system's frequency response path. Continuing to use control parameters from stable operating conditions can easily lead to exacerbated system frequency drops or amplified power surges. Furthermore, in practical engineering applications, grid-connected energy storage systems often need to cover multiple operating scenarios. How to construct a control parameter configuration scheme that is both widely applicable and dynamically responsive to disturbance changes is a critical issue that urgently needs to be addressed.

[0071] To address the need for dynamic frequency stability support in battery energy storage stations within grid-connected power systems, this embodiment proposes a virtual synchronous machine control parameter tuning and adaptive switching strategy. First, a complete control model is constructed, encompassing virtual inertia, virtual damping, active / reactive droop control, and transient virtual impedance, comprehensively characterizing the dynamic response characteristics of the energy storage system under different operating conditions. Second, for two typical disturbances—generator unit disconnection and three-phase symmetrical short-circuit faults—an optimized control parameter tuning model is established to identify the applicable rules for virtual synchronous machine parameter configuration. Finally, an adaptive switching mechanism for control parameters based on converter port voltage and grid frequency criteria is designed to achieve a unified integration of operating mode identification and dynamic control parameter updates. The main features of this embodiment are: 1) proposing a virtual synchronous machine control parameter tuning method applicable to multiple disturbance conditions, revealing the working mechanism of virtual inertia and damping configuration under different disturbance types; 2) constructing a rule-based parameter adaptive switching strategy, which significantly improves the frequency stability of grid-type energy storage systems under fault disturbance conditions; 3) realizing the integrated design of disturbance classification and identification, parameter tuning optimization and adaptive switching mechanism, enhancing the adaptability and engineering feasibility of the control strategy.

[0072] 1. Control Model of Grid-Based Energy Storage System This section focuses on modeling key control components of the power converter in a grid-connected energy storage system, covering controllers such as virtual synchronous machines, virtual speed regulation, reactive power-voltage, and transient virtual impedance. The "virtual synchronous machine" mentioned here specifically refers to the power converter control system of a battery energy storage system operating in a grid-connected mode. By simulating the dynamic characteristics of a synchronous generator, it achieves a rapid response to frequency disturbances and is widely considered an effective means of improving the frequency stability and inertia support capability of low-inertia power systems. The overall structure of the control system built in this section is as follows: Figure 2 As shown, Figure 2 The power converter control block diagram of the grid-type energy storage system provided in the embodiments of this application is as follows: Figure 2 As shown, the power converter in a grid-type energy storage system includes a DC / AC converter, a pulse width modulator, a transient virtual impedance controller, a reactive power-voltage controller, a virtual speed regulator, and a reactive power-voltage controller.

[0073] 1.1 Virtual Synchronous Machine Control Modeling A virtual synchronous machine model based on the oscillation equation is introduced to describe the dynamic behavior of synchronous generators caused by inertia and damping characteristics, such as... Figure 3 As shown, Figure 3 This is a block diagram of a virtual synchronous machine control provided in an embodiment of this application. It is assumed that the rotor angular frequency is its nominal value (i.e., =1pu), the virtual synchronous machine control model expression is written as: (1); Where t represents the current time, This represents the angular frequency deviation of the virtual rotor. H is the actual angular frequency, H is the virtual inertia (unit: s), and D is the virtual damping. Both H and D are dynamically adjustable control parameters. and Let represent the virtual mechanical input power and electrical output power, respectively. The units for the angular frequency and power mentioned above are per-unit values ​​(pu). Therefore, the virtual mechanical power in equation (1) can be expressed as: (2); Among them, P ref P represents the active power reference value. F This is the active power adjustment term generated by the virtual rotor angular velocity error. The module's output includes the angular frequency. With phase angle , Depend on The frequency and phase characteristics of the output voltage are determined by integration.

[0074] 1.2 Virtual Speed ​​Regulation Control Modeling The virtual speed regulation controller is used to implement a load-frequency control frequency regulation function similar to that in a traditional synchronous generator. It dynamically adjusts the output active power based on the virtual rotor angular velocity deviation. The control logic of the virtual speed regulation control model is as follows: (3); Among them, c D This is the active power-frequency droop factor. This module is a key component for achieving primary frequency regulation response in grid-type converters.

[0075] 1.3 Reactive Power-Voltage Control Modeling The reactive power-voltage control module adjusts the converter output voltage amplitude to respond to reactive power deviations, thereby achieving stable system voltage control. Its control principle is as follows: Figure 4 As shown, Figure 4 The reactive power-voltage control principle diagram provided in this application embodiment, and the reactive power-voltage control model expression are written as follows: ; Where t represents the current time, Q ref Q and t represent the reference reactive power and the actual reactive power (unit: pu), respectively. D (Unit: s) represents the time constant of the control loop, k D The reactive power-voltage droop factor, V ref V represents the voltage reference value, and V represents the converter output voltage amplitude. V0 represents the rated voltage, and V represents the converter target voltage amplitude output by the controller.

[0076] 1.4 Transient Virtual Impedance Control Modeling To effectively limit the converter output current under fault conditions, a transient virtual impedance control method is introduced. A virtual impedance is dynamically superimposed at the converter output, and its magnitude is dynamically adjusted according to the current magnitude. For example... Figure 5 As shown, Figure 5 The transient virtual impedance control principle diagram provided in this application embodiment shows that when an overcurrent occurs, the transient virtual impedance controller applies a virtual voltage drop based on the voltage reference value generated by the reactive power-voltage controller, as follows: ; Among them, V C The compensated output voltage amplitude, ΔV VI This is the voltage drop caused by the virtual impedance.

[0077] If the current amplitude is below the threshold The voltage drop is: ; Among them, R VI X VI These are the set maximum virtual resistance and reactance values, respectively. When the output current exceeds the threshold... The controller enables current limiting, and the voltage drop is written as: ; Z VI The virtual impedance, which varies linearly with the current amplitude, is written as: ; The diesel generator set adopts the standard synchronous machine model and the IEEE Type I automatic voltage regulator standard model provided in the MATLAB / Simulink® toolbox. The wind farm and photovoltaic power station system modeling refers to the existing simplified grid-connected control model, which describes the dynamic response based on the first-order transfer function and considers the converter current limiting characteristics.

[0078] In summary, this grid-based energy storage system control model integrates key controllers such as virtual synchronous machines, virtual speed regulation, reactive power-voltage, and transient virtual impedance. Compared to conventional models that simplify the power converter to an ideal voltage source or static controller, it can more accurately characterize the control response characteristics of the power converter under dynamic disturbances, and is particularly suitable for describing the dynamic behavior under typical operating conditions such as generator unit disconnection and short-circuit faults.

[0079] 2. Tuning of network control parameters The control parameters of the virtual synchronous machine have a significant impact on the grid frequency, and vary depending on the type of disturbance. This section proposes a method for tuning the control parameters of the virtual synchronous machine, corresponding to two typical disturbances: 1) generator unit disconnection; 2) three-phase symmetrical short-circuit fault.

[0080] 2.1 Control Parameter Tuning Model The operating scenarios are divided into off-peak, midday, and peak periods, covering two typical challenges: 1) Midday and peak periods: Insufficient spinning reserve capacity provided by diesel generator sets; 2) Off-peak periods: System frequency instability under renewable energy access conditions. The system prioritizes scheduling large-capacity diesel generator set 1 (15.4 MVA), whose normal operating power range is 4.2 - 12.3 MW. The battery energy storage station participates in frequency regulation as a spinning reserve resource, with an initial output power of zero. The permissible system frequency range is 48.5–52.5 Hz; when the frequency falls below the lower permissible limit, the load shelving protection mechanism will be triggered.

[0081] To improve the frequency regulation performance of battery energy storage stations under typical disturbance conditions, a virtual synchronous machine control parameter tuning method is proposed. The optimization objective is to minimize the energy E supporting the frequency of the battery energy storage station's support system. The control variables are the inertia H and damping D of the virtual synchronous machine. Three constraints are established: 1) The system frequency remains within the range (48.5 - 52.5 Hz) after the disturbance occurs (first preset range); 2) The frequency converges to a given range (second preset range) 8 seconds after the disturbance occurs (second preset duration); 3) The optimization must simultaneously satisfy the above constraints under operating scenarios 1–3 (off-peak hours, midday hours, and peak hours). The optimization model (control parameter tuning model) is constructed as follows: ; Wherein, the subscript k represents the operating scenario number. The objective function E is the sum of the absolute values ​​of the energy provided by the battery energy storage station under the three operating scenarios within the first 20 seconds (the first preset duration) after the disturbance occurs. This indicates the frequency deviation from its nominal value of 50 Hz; and These are the tolerances, namely 48.5 Hz and 52.5 Hz. B To limit the frequency fluctuation bandwidth, a value of 100 mHz is given. The system frequency f can be defined as the center frequency of inertia, expressed as: ; in, and Let represent the synchronous inertia and actual angular frequency of the i-th diesel generator set, respectively; m is the number of diesel generator sets currently in operation. This optimization problem is solved using a genetic algorithm, which does not rely on gradient information and has good global search capabilities and adaptability to nonlinear and multi-constraint problems.

[0082] 3. Adaptive switching strategy for control parameters To improve the control performance of the virtual synchronous machine (VSM) under typical disturbances, this paper proposes a rule-based adaptive switching strategy for control parameters. This strategy automatically adjusts the inertia and damping parameters of the VSM when the system operating state changes, thereby enhancing its response to frequency disturbances. Research shows that control parameters have a significant impact on the system frequency response under different types of disturbances, and the optimal configuration is greatly affected by fault characteristics, system synchronization inertia, and voltage changes. Using fixed parameter settings may lead to a decrease in frequency regulation performance or even trigger operational risks such as frequency drop exceeding limits. This strategy divides the system operating state into two modes: normal mode and fault mode. Based on the converter port voltage measurement, grid frequency, and voltage recovery time, the current operating state is determined, and switching is performed between the corresponding two sets of control parameters. The control logic is as follows: ; In this context, the subscripts N and F represent normal mode and fault mode, respectively; Here, f is the measured value of the converter port voltage, and f is the system frequency. Voltage recovery time; , , The control rule parameters represent the voltage threshold, frequency lower limit, and voltage recovery time limit, respectively. Based on system operating requirements, Set to 48.5 Hz.

[0083] 3.1 Control rule parameter tuning The fault mode is set as a three-phase short circuit between busbars B1 and B2, lasting for 200 ms before being cleared. Different fault resistance values ​​are considered. This is to reflect different residual voltage levels. The control parameters are based on the results of aggregated scenario optimization: under normal mode... Fault mode By comparing frequency stability with the regulation behavior of the energy storage system, the rule parameters are determined. and The value of .

[0084] Under normal mode control The lowest port voltage is approximately 0.38 pu, while The voltage was consistently above 0.4 pu. If at... Using fault mode parameters at times will lead to increased frequency fluctuations or even drops exceeding limits. Therefore, a voltage threshold is set. This serves as a boundary criterion for mode switching.

[0085] Under the three fault resistors, the voltage recovered to 0.9 pu within 0.2 seconds. However, if it were to immediately return to normal mode parameters, the system frequency would oscillate unnecessarily due to sudden changes in inertia, increasing the regulation burden on the energy storage system. To avoid accidental disconnection and power surges, a minimum voltage recovery hold time was set. This serves as a delay condition for switching back to normal mode from fault mode, ensuring a stable system transition.

[0086] This paper addresses the dynamic frequency stability requirements of battery energy storage stations in grid-connected power systems, proposing a virtual synchronous machine control parameter tuning and adaptive switching strategy to improve the frequency regulation performance of grid-connected energy storage systems under multi-disturbance environments. First, based on the grid-connected control structure, a complete control model encompassing virtual inertia, virtual damping, droop control, and transient virtual impedance is constructed. Second, for two typical disturbances—generator unit disconnection and three-phase symmetrical short circuit—parameter tuning optimization models are established to clarify the optimal configuration of virtual synchronous machine inertia and damping under different operating conditions. Finally, a rule-based adaptive switching strategy for control parameters is proposed. By combining the analysis of the impact of control parameters on frequency response, joint parameter tuning under aggregated scenarios of multiple disturbances, and a real-time state identification mechanism based on voltage / frequency criteria, an adaptive control framework that can dynamically switch control parameters during fault occurrence is constructed. This method is applied to the control parameter tuning and operational state simulation analysis of an isolated grid-mode power system, yielding the following main conclusions: 1) When a grid-type energy storage system undertakes frequency regulation tasks in an isolated grid system, its control performance is highly dependent on the virtual synchronous machine parameter settings. In the event of a sudden disconnection of a large-capacity generating unit, a larger virtual inertia should be selected to enhance frequency support capability; while in the event of a three-phase symmetrical short-circuit fault, low inertia and high damping parameters are required to suppress oscillations and ensure transient stability.

[0087] 2) Joint parameter tuning for multiple operating conditions can effectively improve the applicability of control strategies. Compared with single-scenario parameter optimization, the results of aggregated scenario parameter tuning show good frequency maintenance capability in all scenarios, and the consistency of system operation state control and engineering feasibility are significantly improved.

[0088] 3) The rule-based adaptive switching strategy for control parameters significantly improves the system's dynamic response performance. It determines the system state by measuring port voltage, frequency, and recovery time, and automatically switches the virtual synchronous machine control mode, effectively balancing regulation speed, frequency stability, and energy storage power load, demonstrating excellent dynamic adaptability.

[0089] To better implement the above method, this application provides a device for tuning the control parameters of a virtual synchronous machine control model, referring to... Figure 6 , Figure 6 This is a structural block diagram of a device for tuning control parameters of a virtual synchronous machine control model provided in an embodiment of this application. The device 600 includes: Module 601 is used to build a virtual synchronous machine control model for a grid-type energy storage system.

[0090] The identification module 602 is used to identify the type of disturbance to the grid-type energy storage system when a disturbance occurs.

[0091] The optimization module 603 is used to determine the target value of the control parameters that minimize the control parameter tuning optimization model by simulating the grid-type energy storage system under the condition that the disturbance type is power generation unit disconnection or three-phase symmetrical short circuit fault; wherein, the control parameters are the control parameters in the virtual synchronous machine control model; the optimization objective of the control parameter tuning optimization model is to minimize the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after the disturbance occurs.

[0092] The tuning module 604 is used to tune the values ​​of the control parameters in the virtual synchronous machine control model to the target values.

[0093] In some embodiments, the expression for the virtual synchronous machine control model in the above-described construction module 601 is as follows: ; Where t represents time. This represents the angular frequency deviation of the virtual rotor. This indicates the rated angular frequency of the virtual rotor. H represents the actual angular frequency of the virtual rotor, H represents the virtual inertia, and D represents the virtual damping. Both H and D are control parameters. and These represent the virtual mechanical input power and electrical output power, respectively.

[0094] In some embodiments, the calculation formula for the virtual mechanical input power in the above-described construction module 601 is as follows: ; ; Where t represents time. Indicates virtual mechanical input power. Indicates the active power reference value. This represents the active power adjustment term caused by the virtual rotor angular velocity error. It represents the active power-frequency droop factor.

[0095] In some embodiments, the optimization module 603 is specifically used to: generate a first value of the control parameters using a preset optimization algorithm; perform simulations on the grid-type energy storage system sequentially under predefined operating scenarios using the first value to obtain the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type; determine the sum of the absolute values ​​of the energy provided by the battery energy storage station corresponding to each operating scenario through the control parameter tuning optimization model; and determine the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after the disturbance occurs. The energy provided by the energy station is the sum of the absolute values. If the iteration does not meet the convergence condition, a second value of the control parameter is generated using a preset optimization algorithm. The second value is used as the new first value, and the process of performing simulation on the grid-type energy storage system in at least one predefined operating scenario using the first value is repeated to obtain the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type as the disturbance. This process continues until the iteration meets the convergence condition, and the target value that minimizes the control parameter tuning optimization model is output.

[0096] In some embodiments, the simulation of the grid-type energy storage system by the optimization module 603 satisfies the following constraints: the system frequency of the grid-type energy storage system remains within a first preset range after a disturbance occurs, and the system frequency converges to a given second preset range after a second preset duration of the disturbance occurs; wherein, the first preset range is greater than the second preset range.

[0097] In some embodiments, the optimization module 603 is specifically used to: generate a first value of the control parameters using a preset optimization algorithm; for each mode in the predefined normal mode and fault mode, for the grid-type energy storage system, under each predefined operating scenario, sequentially use the first value and perform simulation under the mode to obtain the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type as the disturbance; for each mode, determine the sum of the absolute values ​​of the energy provided by the battery energy storage station for each operating scenario under the mode through the control parameter tuning optimization model; wherein, the first preset time after the disturbance occurs for the grid-type energy storage system The energy provided by the battery energy storage station is the sum of the absolute values. If the iteration does not meet the convergence condition, a second value of the control parameter is generated using a preset optimization algorithm. The second value is used as the new first value. The process is repeated for each of the predefined normal and fault modes. For the grid-type energy storage system, under each predefined operating scenario, the first value is used sequentially and simulation is performed in the mode to obtain the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type as the disturbance. This process continues until the iteration meets the convergence condition, and the target value that minimizes the control parameter tuning optimization model in each mode is output.

[0098] In some embodiments, the optimization module 603 is specifically used to: when the measured value of the converter port voltage of the grid-type energy storage system is greater than or equal to a preset voltage threshold and the system frequency of the grid-type energy storage system is less than a preset frequency lower limit, adjust the value of the control parameter in the virtual synchronous machine control model to the target value in the normal mode; when the measured value of the converter port voltage is less than the preset voltage threshold and the voltage recovery time of the grid-type energy storage system is less than a preset voltage recovery time limit, adjust the value of the control parameter in the virtual synchronous machine control model to the target value in the fault mode.

[0099] The control parameter tuning device 600 of the virtual synchronous machine control model provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0100] Figure 7 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 7As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program causes the processor to perform the steps in the above-described method embodiments. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform the steps in the above-described method embodiments. Those skilled in the art will understand that... Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0101] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the above-described method for tuning the control parameters of the virtual synchronous machine control model.

[0102] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method for tuning the control parameters of the virtual synchronous machine control model described above.

[0103] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0104] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0105] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, 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.

Claims

1. A method for tuning control parameters of a virtual synchronous machine control model, characterized in that, The method includes: Construct a virtual synchronous machine control model for a grid-type energy storage system; When a disturbance occurs in the grid-type energy storage system, the type of disturbance experienced by the grid-type energy storage system is identified; When the disturbance type is a power generation unit disconnection or a three-phase symmetrical short-circuit fault, the target value of the control parameter that minimizes the control parameter tuning optimization model is determined by simulating the grid-type energy storage system; wherein, the control parameter is the control parameter in the virtual synchronous machine control model; the optimization objective of the control parameter tuning optimization model is to minimize the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after the disturbance occurs; The values ​​of the control parameters in the virtual synchronous machine control model are tuned to the target values.

2. The method according to claim 1, characterized in that, The step of determining the target values ​​of the control parameters that minimize the control parameter tuning optimization model by simulating the grid-type energy storage system includes: The first value of the control parameter is generated using a preset optimization algorithm; For the grid-type energy storage system, simulations are performed sequentially under each predefined operating scenario using the first value to obtain the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type as the disturbance. The sum of absolute values ​​of the energy provided by the battery energy storage station for each operating scenario is determined by the control parameter tuning optimization model; the energy provided by the battery energy storage station for the grid-type energy storage system within the first preset time after the disturbance occurs is the sum of the absolute values. If the iteration does not meet the convergence condition, a second value of the control parameter is generated using a preset optimization algorithm. The second value is then used as the new first value. The process of performing simulation on the grid-type energy storage system in at least one predefined operating scenario using the first value is repeated to obtain the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type. This process continues until the iteration meets the convergence condition, and the target value that minimizes the control parameter tuning optimization model is output.

3. The method according to claim 2, characterized in that, The simulation of the grid-type energy storage system satisfies the following constraints: The system frequency of the grid-type energy storage system remains within a first preset range after a disturbance occurs, and converges to a given second preset range after a second preset duration following the disturbance; wherein the first preset range is greater than the second preset range.

4. The method according to claim 1, characterized in that, The step of determining the target values ​​of the control parameters that minimize the control parameter tuning optimization model by simulating the grid-type energy storage system includes: The first value of the control parameter is generated using a preset optimization algorithm; For each of the predefined normal mode and fault mode, for the grid-type energy storage system, under each predefined operating scenario, the first value is used sequentially and simulation is performed under the mode to obtain the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type as the disturbance. For each mode, the sum of absolute values ​​of the energy provided by the battery energy storage station for each operating scenario under the mode is determined by the control parameter tuning optimization model; wherein, the energy provided by the battery energy storage station for the grid-type energy storage system within the first preset time after the disturbance occurs is the sum of absolute values. If the iteration does not meet the convergence condition, a second value of the control parameter is generated using a preset optimization algorithm. The second value is then used as the new first value. The process is repeated for each of the predefined normal and fault modes. For the grid-type energy storage system, the first value is used sequentially in each of the predefined operating scenarios to simulate the energy provided by the battery energy storage station for the grid-type energy storage system within a first preset time after being subjected to a disturbance of the same type as the disturbance. This process continues until the iteration meets the convergence condition, and the target value that minimizes the control parameter tuning optimization model in each mode is output.

5. The method according to claim 4, characterized in that, The step of tuning the control parameters in the virtual synchronizer control model to the target values ​​includes: When the measured value of the converter port voltage of the grid-type energy storage system is greater than or equal to a preset voltage threshold, and the system frequency of the grid-type energy storage system is less than a preset frequency lower limit, the values ​​of the control parameters in the virtual synchronous machine control model are tuned to the target values ​​in the normal mode. When the measured value of the converter port voltage is less than the preset voltage threshold and the voltage recovery time of the grid-type energy storage system is less than the preset voltage recovery time limit, the values ​​of the control parameters in the virtual synchronous machine control model are tuned to the target values ​​under the fault mode.

6. The method according to claim 1, characterized in that, The expression for the virtual synchronous machine control model is as follows: ； Where t represents time. This represents the angular frequency deviation of the virtual rotor. This indicates the rated angular frequency of the virtual rotor. H represents the actual angular frequency of the virtual rotor, H represents the virtual inertia, and D represents the virtual damping. Both H and D are control parameters. and These represent the virtual mechanical input power and electrical output power, respectively.

7. The method according to claim 6, characterized in that, The formula for calculating the virtual mechanical input power is as follows: ； ； Where t represents time. Indicates virtual mechanical input power. Indicates the active power reference value. This represents the active power adjustment term caused by the virtual rotor angular velocity error. It represents the active power-frequency droop factor.

8. A device for tuning control parameters of a virtual synchronous machine control model, characterized in that, The device includes: The building module is used to construct a virtual synchronous machine control model for a grid-type energy storage system; The identification module is used to identify the type of disturbance to the grid-type energy storage system when a disturbance occurs. An optimization module is used to determine the target value of the control parameters that minimize the control parameter tuning optimization model by simulating the grid-type energy storage system under the condition that the disturbance type is power generation unit disconnection or three-phase symmetrical short-circuit fault; wherein, the control parameters are the control parameters in the virtual synchronous machine control model; the optimization objective of the control parameter tuning optimization model is to minimize the energy provided by the battery energy storage station to the grid-type energy storage system within a first preset time after the disturbance occurs; The tuning module is used to tune the values ​​of the control parameters in the virtual synchronous machine control model to the target values.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer 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 7.

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