A method, system, medium and product for controlling network-constructed energy storage inertia damping

Abstract

The application discloses a kind of network type energy storage inertia damping control method, system, medium and product, belong to grid network type energy storage control field, the method is: real-time acquisition network type energy storage system operating state and grid point electrical data, calculate the set of grid risk indexes indicating the degree of grid disturbance and the set of equipment capacity indexes indicating the bearing capacity of equipment;Determine adaptive virtual inertia and damping based on index set, and then solve active and reactive current reference value;Detect whether the reference value synthesis amplitude exceeds the dynamic current limit of converter, not over limit then directly generate current instruction, over limit then according to the relative weight of frequency and voltage risk, limit value is adjusted to obtain current limiting current and generate instruction;Finally, based on the current instruction driving converter completes control.Through the application, it can solve the problem of frequency support failure or equipment damage caused by the problem of fixed inertia support under the condition of limited converter current and active and reactive power conflict.

Description

Technical Field

[0001] This invention belongs to the field of grid-based energy storage control, and relates to a control method, system, medium and product for grid-based energy storage inertia damping. Background Technology

[0002] With the continuous increase in installed capacity of new energy sources such as wind power and photovoltaics, the equivalent rotational inertia and short-circuit capacity of the power system have decreased significantly. When the system faces large power shortages or fault disturbances, the frequency stability deteriorates. Grid-based energy storage technology has become an important technical means to ensure the frequency stability of high-proportion new energy power systems because it can actively establish voltage amplitude and phase angle and provide synchronous machine-like inertia and damping support.

[0003] Existing grid-based energy storage frequency stabilization control typically employs strategies such as setting fixed virtual inertia coefficients, damping coefficients, and active-frequency droop coefficients. This allows the energy storage to rapidly generate active power to support the system when the frequency drops. However, in actual engineering operations, the AC output current, DC side voltage, and thermal capacity of the energy storage converter all have physical limits. When a large disturbance occurs in the system, causing a surge in support demand, fixed parameter control cannot sense the real-time capacity of the equipment. This can easily lead to current commands exceeding limits, triggering hardware protection or hard clipping of the current limiting circuit. Furthermore, after entering the current limiting state, the lack of a mechanism to dynamically adjust the priority of active and reactive power support based on real-time grid risks often results in critical frequency or voltage support requirements not being met. In some cases, improper current distribution can even cause system oscillations, making it difficult to balance the effectiveness of frequency support with safe equipment operation. Summary of the Invention

[0004] This application provides a control method, system, medium, and product for grid-type energy storage inertia damping, which can solve the problem of frequency support failure or equipment damage caused by overcurrent and active / reactive power conflict due to fixed inertia support under converter current limitation.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a control method for grid-type energy storage inertia damping, comprising: Real-time acquisition of operational data of grid-connected energy storage systems and electrical data of grid connection points; calculation of a set of grid risk indices characterizing the degree of grid disturbance and a set of equipment capacity indices characterizing the equipment's withstand capability. Based on the set of power grid risk indices and the set of equipment capability indices, the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system are calculated, and the active current reference value and reactive current reference value are calculated based on the adaptive virtual inertia and adaptive virtual damping. The system detects whether the combined amplitude of the active current reference value and the reactive current reference value exceeds the dynamic current limit of the converter. If it does not exceed the limit, the current command is set based on the active current reference value and the reactive current reference value. If it exceeds the limit, the active current reference value and the reactive current reference value are adjusted according to the relative weights of frequency risk and voltage risk in the power grid risk index set, constrained by the dynamic current limit, to obtain the current-limited active current and the current-limited reactive current and set the current command. The converter is driven to output the corresponding three-phase AC current based on the current command, thus completing the control.

[0006] Compared to existing technologies, the embodiments of this application have the following beneficial effects: Calculating a set of grid risk indices characterizing the degree of grid disturbance and a set of equipment capability indices characterizing equipment withstand capability transforms the abstract grid fluctuation intensity and specific equipment hardware limitations into quantifiable control indicators, enabling the controller to have real-time assessment capabilities of the system safety boundary; based on this, calculating the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system according to the set of grid risk indices and equipment capability indices, and using the equipment capability indices as constraint factors to dynamically adjust the inertia and damping response intensity, thereby avoiding overcurrent risks caused by fixed high inertia parameters under extreme operating conditions from the source; detecting whether the combined amplitude of the active current reference value and the reactive current reference value exceeds the dynamic current limit of the converter; if it does not exceed, directly setting the current command ensures a fast and distortion-free response within the safe range; if it exceeds, adjusting the active and reactive current reference values ​​according to the relative weights of frequency risk and voltage risk in the set of grid risk indices, with the dynamic current limit as a constraint, to obtain the current-limited active current and current-limited reactive current and set the current command. This mechanism dynamically allocates limited current capacity according to the real-time urgency level, solving the support misalignment problem that may occur in traditional fixed priority strategies under complex faults. It ensures that the most critical system stability indicators can still receive priority support under the worst operating conditions with current constraints. Finally, based on the current command, the converter outputs the corresponding three-phase AC current, completing the closed-loop control from state perception to execution output. Through the synergistic combination of the above features, this method constructs an integrated control architecture of "risk perception-parameter adaptation-dynamic current limiting allocation". The equipment capacity index provides a safety boundary constraint for the generation of adaptive parameters, preventing overcurrent caused by blind support. The dual risk weight allocation mechanism provides the optimal support strategy when physical current limiting is unavoidable. The two work together to solve the overcurrent hard clipping problem caused by the inability to perceive the real-time capacity of the equipment due to fixed inertia parameters, as well as the frequency or voltage support failure caused by the rigidity of active and reactive power allocation during current limiting. This significantly improves the frequency stability and equipment operation safety of high-proportion new energy power systems under extreme disturbances.

[0007] In some embodiments of the first aspect of this application, the real-time acquisition of operational data of the grid-connected energy storage system and electrical data of the grid connection point, and the calculation of a set of grid risk indices characterizing the degree of grid disturbance and a set of equipment capability indices characterizing the equipment's withstand capability, includes: The system obtains the three-phase voltage, three-phase current, system frequency and frequency change rate at the grid connection point; the DC bus voltage, energy storage state of charge, battery temperature and power device thermal margin; and the rated current and short-time overload capacity preset values ​​of the converter hardware. The voltage deviation is calculated based on the difference between the three-phase voltage and the preset rated voltage reference value. The voltage change rate is calculated based on the slope of the three-phase voltage change over time. The duration of the low voltage is obtained by monitoring the time length during which the amplitude of the three-phase voltage is lower than the preset low voltage threshold. The converter dynamic current limit is calculated based on the hardware rated current, short-time overload capacity preset value, DC bus voltage, battery temperature and power device thermal margin. The equivalent impedance of the power grid is estimated by using the disturbances of the three-phase voltage and three-phase current, and then the short-circuit ratio at the grid connection point is calculated. The short-circuit ratio is then normalized to obtain the power grid strength index. The voltage risk index is obtained by weighting and summing the voltage deviation, voltage change rate, low voltage duration, and grid strength index, and then processing the result with a limiting function. The frequency risk index is obtained by weighting and summing the system frequency, frequency change rate, and predicted frequency minimum point, and then processing the result with a limiting function. The frequency risk index and the voltage risk index are then combined to form the grid risk index set. The current margin index is obtained based on the ratio of the difference between the amplitude of the three-phase current and the dynamic current limit of the converter. The energy margin index is calculated based on the ratio of the disturbance direction of the system frequency and the difference between the energy storage state of charge and the preset safety boundary. The current margin index and the energy margin index are combined to form the set of equipment capability indices.

[0008] Compared to existing technologies, the above embodiments have the following advantages: Voltage deviation is calculated based on the difference between the three-phase voltage and the preset rated voltage reference value; voltage change rate is calculated based on the slope of the three-phase voltage change over time; and the duration of low voltage is obtained by monitoring the length of time the three-phase voltage amplitude is below the preset low voltage threshold, thus comprehensively characterizing the severity of voltage disturbance from three dimensions: amplitude, rate, and duration. Simultaneously, the current dynamic current limit of the converter at the current moment is calculated based on the hardware rated current, the preset short-time overload capacity, the DC bus voltage, the battery temperature, and the thermal margin of the power devices. This makes the current limit no longer a fixed nameplate value, but a real-time safety boundary that dynamically changes with the equipment's thermal state and DC voltage level, improving equipment utilization and preventing overheating damage. The equivalent impedance of the power grid is estimated using the disturbances in three-phase voltage and three-phase current, and then the short-circuit ratio at the grid connection point is calculated and normalized to obtain the grid strength index, quantifying the strength of the grid connection point. The impact on control stability enables the controller to identify oscillation risks under weak power grid conditions. A voltage risk index is obtained by weighting and summing voltage deviation, voltage change rate, low voltage duration, and power grid strength indicators, followed by a limiting function. Similarly, a frequency risk index is obtained by weighting and summing system frequency, frequency change rate, and predicted frequency minimum point, followed by a limiting function. These two indices form a power grid risk index set, integrating multi-dimensional electrical disturbance characteristics into a single risk scalar, simplifying the complexity of subsequent control logic. Furthermore, a current margin index is obtained based on the ratio of the difference between the three-phase current amplitude and the converter dynamic current limit. An energy margin index is calculated based on the disturbance direction of system frequency and the ratio of the difference between the energy storage state of charge and the preset safety boundary. These two indices form a device capacity index set, reflecting the remaining potential of the energy storage system in terms of current output capacity and energy storage in real time, providing a clear capacity constraint reference for adaptive control.

[0009] In some embodiments of the first aspect of this application, the step of estimating the equivalent impedance of the power grid using the disturbances of the three-phase voltage and three-phase current, then calculating the short-circuit ratio at the grid connection point, and normalizing the short-circuit ratio to obtain the power grid strength index includes: Calculate the ratio of the absolute value of the disturbance of the three-phase voltage to the absolute value of the disturbance of the three-phase current to obtain the equivalent impedance of the power grid. Calculate the ratio of the square of the voltage amplitude at the grid connection point to the magnitude of the equivalent impedance of the power grid to obtain the equivalent short-circuit capacity at the grid connection point. The short-circuit ratio is obtained by calculating the ratio of the equivalent short-circuit capacity at the grid connection point to the rated capacity of the converter. The short-circuit ratio is then subtracted from the preset weak grid judgment threshold, divided by the difference between the preset strong grid reference short-circuit ratio and the weak grid judgment threshold, and processed by a limiting function to obtain the grid strength index.

[0010] Compared to existing technologies, the above embodiments have the following advantages: The equivalent impedance of the power grid is obtained by calculating the ratio of the absolute value of the disturbance in the three-phase voltage to the absolute value of the disturbance in the three-phase current. The impedance characteristics of the power grid can be identified in real time using small disturbance data measured online, without the need for additional test signals, thus avoiding interference with the normal operation of the power grid. Furthermore, the equivalent short-circuit capacity at the grid connection point is obtained by calculating the ratio of the square of the voltage amplitude at the grid connection point to the magnitude of the equivalent impedance of the power grid, accurately reflecting the theoretical short-circuit level of the current power grid node. Then, the short-circuit ratio is obtained by calculating the ratio of the equivalent short-circuit capacity at the grid connection point to the rated capacity of the converter, establishing the power grid... The relative quantitative relationship between strength and energy storage capacity is established. Finally, the short-circuit ratio is subtracted from the preset weak grid judgment threshold, divided by the difference between the preset strong grid reference short-circuit ratio and the weak grid judgment threshold, and processed by the amplitude limiting function to obtain the grid strength index. This normalization process eliminates the difference in short-circuit ratio values ​​between systems of different capacity levels, maps the nonlinear short-circuit ratio to a unified relative strength scalar, and enables the controller to intuitively identify the degree of deviation of the current grid connection point from the boundary between strong and weak grids. When the index approaches the lower limit, it indicates the risk of weak grid, and the controller can take measures to suppress oscillations in advance, thereby improving the control robustness under weak grid conditions.

[0011] In some embodiments of the first aspect of this application, the prediction of the lowest frequency point includes: Obtain the preset system equivalent inertia time constant, system reference capacity and load frequency damping coefficient, as well as the frequency support power that has been put into the system at the current moment; Based on the system's equivalent inertia time constant and the system's reference capacity, the system's equivalent inertia coefficient is calculated. Based on the current frequency change rate, frequency deviation, frequency support power, and system's equivalent inertia coefficient, the estimated equivalent power deficit under low-frequency disturbances is calculated. Based on the current available discharge power of the energy storage, the current margin of the converter, and the active power ramping capability, the available supporting power of the energy storage within the prediction time window is constructed. Within the prediction time window, a discrete frequency recursive equation is used to derive the frequency trajectory based on the estimated equivalent power deficit, the available supporting power of the energy storage, and the load frequency damping coefficient. The minimum value among all frequency points within the prediction time window is taken as the predicted minimum frequency point.

[0012] Compared with existing technologies, the above embodiments have the following beneficial effects: By obtaining the preset system equivalent inertia time constant, system reference capacity, load frequency damping coefficient, and the frequency support power currently in the system, a model foundation reflecting the overall inertial characteristics of the system is constructed; further, the system equivalent inertia coefficient is calculated based on the system equivalent inertia time constant and system reference capacity, and the estimated equivalent power deficit under low-frequency disturbances is calculated based on the current frequency change rate, frequency deviation, frequency support power, and system equivalent inertia coefficient. The power gap required to maintain the current frequency trajectory is derived in reverse using the swing equation principle, realizing real-time quantification of the degree of system power imbalance; simultaneously, based on the current available discharge power of energy storage and the converter power... The current margin and active power ramping capability are used to construct the available support power of energy storage within the prediction time window, taking into account the physical response limit of the energy storage itself and avoiding control errors caused by idealized assumptions. Within the prediction time window, a discrete frequency recursive equation is used to derive the frequency trajectory based on the estimated equivalent power deficit, the available support power of energy storage, and the load frequency damping coefficient. The minimum value among all frequency points within the prediction time window is taken as the predicted minimum frequency point. This frequency prediction method based on the rolling time domain can predict the depth of frequency drop in advance, allowing the controller to increase support or adjust the control strategy in advance before the frequency actually reaches the minimum point. This realizes the control transformation from "post-event response" to "pre-event prevention", significantly improving the initiative of frequency defense.

[0013] In some embodiments of the first aspect of this application, calculating the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system based on the set of grid risk indices and the set of equipment capability indices includes: Based on the current margin index, a virtual inertia correction coefficient is determined; wherein, the virtual inertia correction coefficient is positively correlated with the current margin index; Based on the virtual inertia correction coefficient, the frequency risk index, and the energy margin index, the adaptive virtual inertia is calculated. The dynamic oscillation characteristics of the grid-type energy storage system are extracted, and the oscillation risk index is calculated based on the dynamic oscillation characteristics; wherein, the dynamic oscillation characteristics include at least one of active power fluctuation component, frequency fluctuation component, or voltage phase angle change rate; An adaptive virtual damping is calculated based on the frequency risk index, voltage risk index, oscillation risk index, and current margin index; wherein the adaptive virtual damping is positively correlated with the oscillation risk index and negatively correlated with the current margin index.

[0014] Compared to existing technologies, the above embodiments have the following advantages: By determining the virtual inertia correction coefficient based on the current margin index, and since this coefficient is positively correlated with the current margin index, a larger inertia response is allowed to provide strong support when the current is sufficient, while the inertia demand is automatically reduced to prevent overcurrent when the current is tight, thus achieving real-time coupling between inertia response and equipment safety margin; furthermore, an adaptive virtual inertia is calculated based on the virtual inertia correction coefficient, frequency risk index, and energy margin index, ensuring that the inertia coefficient is not only limited by the current but also controlled by the frequency urgency and remaining power, avoiding ineffective inertia consumption under low SOC or low risk conditions; simultaneously, dynamic oscillation characteristics of the grid-type energy storage system are extracted, including active power... The system uses at least one of the following components: power fluctuation component, frequency fluctuation component, or voltage phase angle change rate. Based on this, an oscillation risk index is calculated to keenly capture the trend of stability deterioration within the system. Finally, an adaptive virtual damping is calculated based on the frequency risk index, voltage risk index, oscillation risk index, and current margin index. The adaptive virtual damping is positively correlated with the oscillation risk index and negatively correlated with the current margin index. This design allows the damping to be rapidly increased to suppress fluctuations when the system shows signs of oscillation. At the same time, the damping is also increased to reduce the cumulative drift of the phase angle when the current is limited. Thus, the dynamic stability of the system is improved without increasing the active current output, effectively solving the power oscillation problem under weak grid or current-limited conditions.

[0015] In some embodiments of the first aspect of this application, the calculation of active current reference values ​​and reactive current reference values ​​based on adaptive virtual inertia and adaptive virtual damping includes: Substitute the adaptive virtual inertia and adaptive virtual damping into the preset grid-type virtual swing equation, and combine the virtual angular frequency integral state quantity saved during the integral calculation of the previous control cycle, to calculate the virtual angular frequency and virtual voltage phase angle of the grid-type energy storage system in this control cycle through numerical integration. Based on the deviation between the virtual voltage phase angle and the current grid connection point voltage amplitude, the virtual voltage amplitude is calculated, and combined with the virtual angular frequency, active power reference value and reactive power reference value are generated. The active power reference value and reactive power reference value are mapped to the dq rotating coordinate system to obtain the active current reference value and reactive current reference value for this control cycle.

[0016] Compared to existing technologies, the above embodiments have the following advantages: By substituting adaptive virtual inertia and adaptive virtual damping into the preset grid-type virtual swing equation, and combining the virtual angular frequency integral state quantity saved during the integral calculation of the previous control cycle, the virtual angular frequency and virtual voltage phase angle of the grid-type energy storage system in this control cycle are calculated through numerical integration. This preserves the synchronous machine simulation characteristics of the grid-type control core, ensuring that the energy storage exhibits voltage source behavior rather than simple current source following. Furthermore, the virtual voltage phase angle is calculated based on the deviation between the virtual voltage phase angle and the current grid connection point voltage amplitude. The amplitude value is combined with the virtual angular frequency to generate active power reference values ​​and reactive power reference values, realizing a natural mapping from mechanical quantities (angle / frequency) to electrical quantities (power), ensuring the smoothness and physical consistency of power output; finally, the active power reference values ​​and reactive power reference values ​​are mapped to the dq rotating coordinate system to obtain the active current reference values ​​and reactive current reference values ​​for this control cycle, completing the decoupling conversion from the power control layer to the current control layer, providing standard control variables for subsequent current limiting and allocation, and ensuring the compatibility and execution efficiency of the underlying converter control.

[0017] In some embodiments of the first aspect of this application, after obtaining the active current reference value and reactive current reference value for the current control cycle, the method further includes: Calculate the difference between the current active power reference value and the actual output active power of the converter to obtain the active power deviation; obtain the continuous time length during which the three-phase current at the grid connection point exceeds the dynamic current limit to obtain the current limiting duration. Based on the current set of equipment capacity indices, current limiting duration, and active power deviation, a correction strategy for the virtual angular frequency integral state quantity of the current control cycle is determined. The virtual angular frequency integral state quantity is then corrected based on the active power deviation, and the corrected virtual angular frequency integral state quantity is used as the state quantity for numerical integration calculation in the next control cycle.

[0018] Compared to existing technologies, the above embodiments have the following advantages: The active power deviation is obtained by calculating the difference between the current active power reference value and the actual output active power of the converter, and the current limiting duration is obtained by acquiring the continuous time length during which the three-phase current at the grid connection point exceeds the dynamic current limit. This quantifies in real time the power tracking error caused by physical current limiting and its continuous cumulative effect. Furthermore, a correction strategy for the virtual angular frequency integral state quantity of this control cycle is determined based on the current equipment capacity index set, the current limiting duration, and the active power deviation. The virtual angular frequency integral state quantity is then corrected based on the active power deviation. This step addresses the issue that the actual output power cannot follow the reference power during the current limiting period in the grid-type virtual swing equation. To address the issue of unidirectional accumulation of the integral term caused by the current limiting rate, the active power deviation is introduced as a feedback variable that directly affects the integral state quantity. This proactively offsets or freezes the phase angle drift accumulation caused by the power deficit during the current limiting period, preventing the virtual phase angle from deviating too far from the actual grid phase. Finally, the corrected virtual angular frequency integral state quantity is used as the state quantity for numerical integration calculation in the next control cycle. This ensures that when the current limiting is lifted and the system returns to normal operation, the initial state of the virtual swing equation remains synchronized with the actual grid phase, avoiding sudden changes in the virtual phase angle and severe backlash of active power caused by integral saturation release. This ensures a smooth transition and frequency stability of the energy storage system during the transition from the current limiting state to the normal grid state.

[0019] Secondly, the present invention also provides a control system for grid-type energy storage inertia damping, comprising: a data acquisition module, a first calculation module, a second calculation module, and a control module; The data acquisition module is used to collect the operation data of the grid-type energy storage system and the electrical data of the grid connection point in real time, and to calculate the set of grid risk indices characterizing the degree of grid disturbance and the set of equipment capability indices characterizing the equipment's ability to withstand disturbance. The first calculation module is used to calculate the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system according to the set of power grid risk indices and the set of equipment capacity indices, and to calculate the active current reference value and reactive current reference value based on the adaptive virtual inertia and adaptive virtual damping. The second calculation module is used to detect whether the combined amplitude of the active current reference value and the reactive current reference value exceeds the dynamic current limit of the converter. If it does not exceed the limit, a current command is set based on the active current reference value and the reactive current reference value. If it exceeds the limit, the active current reference value and the reactive current reference value are adjusted according to the relative weights of frequency risk and voltage risk in the power grid risk index set, constrained by the dynamic current limit, to obtain the current-limited active current and the current-limited reactive current and set the current command. The control module is used to drive the converter to output the corresponding three-phase AC current based on the current command, thereby completing the control.

[0020] Compared with existing technologies, the above embodiments of this application have the following beneficial effects: Calculating the set of grid risk indices characterizing the degree of grid disturbance and the set of equipment capability indices characterizing equipment withstand capability transforms the abstract grid fluctuation intensity and specific equipment hardware limitations into quantifiable control indicators, enabling the controller to have real-time assessment capabilities of the system safety boundary; based on this, calculating the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system according to the set of grid risk indices and the set of equipment capability indices, and using the equipment capability indices as constraint factors to dynamically adjust the inertia and damping response intensity, thereby avoiding overcurrent risks caused by fixed high inertia parameters under extreme operating conditions from the source; detecting whether the combined amplitude of the active current reference value and the reactive current reference value exceeds the dynamic current limit of the converter; if it does not exceed, directly setting the current command, ensuring a fast and distortion-free response within the safe range; if it exceeds, adjusting the active and reactive current reference values ​​according to the relative weights of frequency risk and voltage risk in the set of grid risk indices, with the dynamic current limit as a constraint, to obtain the current-limited active current and current-limited reactive current and set the current. This mechanism, by dynamically allocating limited current capacity according to the real-time urgency level, solves the support misalignment problem that may occur in traditional fixed priority strategies under complex faults, ensuring that the most critical system stability indicators still receive priority support even under the worst current-limited operating conditions. Finally, based on the current command, the converter outputs the corresponding three-phase AC current, completing the closed-loop control from state perception to execution output. Through the synergistic combination of the above features, this method constructs an integrated control architecture of "risk perception-parameter adaptation-dynamic current limiting allocation". The equipment capacity index provides a safety boundary constraint for the generation of adaptive parameters, preventing overcurrent caused by blind support, while the dual risk weight allocation mechanism provides the optimal support strategy when physical current limiting is unavoidable. The two work together to solve the overcurrent hard clipping problem caused by the inability to perceive the real-time capacity of equipment due to fixed inertia parameters, as well as the frequency or voltage support failure caused by the rigidity of active and reactive power allocation during current limiting, which significantly improves the frequency stability and equipment operation safety of high-proportion new energy power systems under extreme disturbances.

[0021] Thirdly, the present invention also provides a computer program product, including a computer program or instructions, which, when executed, implement any one of the control methods for grid-type energy storage inertia damping of the present invention.

[0022] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any one of the control methods for grid-type energy storage inertia damping of the present invention. Attached Figure Description

[0023] Figure 1This is a flowchart illustrating a control method for grid-type energy storage inertia damping provided in some embodiments of the present invention.

[0024] Figure 2 This is a schematic diagram of a grid-type energy storage inertia damping control system provided in some embodiments of the present invention. Detailed Implementation

[0025] 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.

[0026] Example 1: Please refer to Figure 1 To address the issue of frequency support failure or equipment damage caused by overcurrent and active / reactive power conflict due to fixed inertia support under converter current constraints, an embodiment of the present invention provides a control method for grid-type energy storage inertia damping, comprising steps S1 to S4: Step S1: Real-time acquisition of operational data of grid-connected energy storage systems and electrical data of grid connection points, and calculation of a set of grid risk indices characterizing the degree of grid disturbance and a set of equipment capacity indices characterizing the equipment's withstand capability; Furthermore, step S1 can be implemented through the following preferred embodiments, including steps S11-S16: S11: Obtain the three-phase voltage, three-phase current, system frequency and frequency change rate at the grid connection point; obtain the DC bus voltage, energy storage state of charge, battery temperature and power device thermal margin; obtain the rated current of the converter hardware and the preset value of short-time overload capacity. S12: Calculate the voltage deviation based on the difference between the three-phase voltage and the preset rated voltage reference value; calculate the voltage change rate based on the slope of the three-phase voltage change over time; monitor the duration of time during which the amplitude of the three-phase voltage is lower than the preset low voltage threshold to obtain the low voltage duration. S13: Calculate the current dynamic current limit of the converter at the current moment based on the hardware rated current, short-time overload capacity preset value, DC side bus voltage, battery temperature and power device thermal margin. S14: The equivalent impedance of the power grid is estimated by using the disturbances of the three-phase voltage and three-phase current, and then the short-circuit ratio at the grid connection point is calculated. The short-circuit ratio is normalized to obtain the power grid strength index. S15: Based on the voltage deviation, voltage change rate, low voltage duration, and power grid strength index, a voltage risk index is obtained by weighted summation and then processed by a limiting function. Based on the system frequency, frequency change rate, and predicted frequency minimum point, a frequency risk index is obtained by weighted summation and then processed by a limiting function. The frequency risk index and the voltage risk index are then combined to form the power grid risk index set. S16: Based on the ratio of the difference between the amplitude of the three-phase current and the dynamic current limit of the converter, obtain the current margin index; based on the disturbance direction of the system frequency and the ratio of the difference between the energy storage state of charge and the preset safety boundary, calculate the energy margin index; combine the current margin index and the energy margin index to form the equipment capability index set.

[0027] In this preferred embodiment, existing technologies typically only collect external electrical quantities such as grid connection point voltage, current, and frequency as control inputs, neglecting the physical boundary constraints of the energy storage converter itself. This leads to the controller issuing commands exceeding the equipment's capacity under extreme operating conditions. This step introduces multi-dimensional internal state parameters such as DC-side bus voltage, battery temperature, power device thermal margin, and short-term overload capacity preset values ​​to construct a more complete state perception system. Among these, calculating dynamic current limits using battery temperature and thermal margin can reflect the thermal accumulation effect of power devices in real time, preventing sudden grid disconnection due to overheating protection after continuous high-load operation. Compared with fixed rated current limits, this significantly improves the equipment's usable capacity and safety. Simultaneously, combining the system frequency disturbance direction with the energy storage state of charge to calculate the energy margin index can effectively identify contradictory operating conditions such as "low-frequency discharge but low SOC" or "high-frequency charging but full SOC," preventing support interruption or overcharging damage due to energy depletion. This provides a quantitative basis for subsequent adaptive control that balances grid demand and equipment safety.

[0028] Furthermore, step S14 can be implemented through the following preferred embodiments, including steps S141-S142: S141: Calculate the ratio of the absolute value of the disturbance of the three-phase voltage to the absolute value of the disturbance of the three-phase current to obtain the equivalent impedance of the power grid; calculate the ratio of the square of the voltage amplitude at the grid connection point to the magnitude of the equivalent impedance of the power grid to obtain the equivalent short-circuit capacity at the grid connection point. S142: Calculate the ratio of the equivalent short-circuit capacity at the grid connection point to the rated capacity of the converter to obtain the short-circuit ratio. Subtract the preset weak grid judgment threshold from the short-circuit ratio, divide it by the difference between the preset strong grid reference short-circuit ratio and the weak grid judgment threshold, and process it through a limiting function to obtain the grid strength index.

[0029] In this preferred embodiment, traditional methods often use a fixed short-circuit ratio threshold to determine the strength of the power grid. This absolute numerical determination method lacks universality across power stations of different capacity levels and cannot reflect the relative strength of the power grid relative to the current energy storage capacity. This step normalizes the short-circuit ratio by subtracting the weak power grid determination threshold from the short-circuit ratio and then dividing it by the difference between the strong power grid reference short-circuit ratio and the weak power grid determination threshold. Essentially, it maps the nonlinear short-circuit ratio to a linear relative strength index between 0 and 1. This index eliminates the influence of differences in system baseline capacity, allowing the controller to intuitively quantify the "safe distance" between the current grid connection point and the weak power grid boundary. When this index approaches 0, it indicates that the power grid strength is approaching the critical point. The controller can then reduce the virtual inertia response gain and increase damping in advance, thereby effectively suppressing the risk of broadband oscillations under weak power grids without relying on an accurate power grid model, and improving the adaptability of the control strategy to different grid connection scenarios.

[0030] Furthermore, in step S15, the prediction of the lowest frequency point can be achieved through the following preferred implementation method, including steps S151-S153: S151: Obtain the preset system equivalent inertia time constant, system reference capacity and load frequency damping coefficient, as well as the frequency support power that has been put into the system at the current moment; S152: Calculate the system equivalent inertia coefficient based on the system equivalent inertia time constant and the system reference capacity, and calculate the estimated equivalent power deficit under low-frequency disturbance based on the current frequency change rate, frequency deviation, frequency support power and system equivalent inertia coefficient. S153: Based on the current available discharge power of the energy storage, the current margin of the converter, and the active power ramping capability, construct the available supporting power of the energy storage within the prediction time window. Within the prediction time window, use the discrete frequency recursive equation to derive the frequency trajectory based on the estimated equivalent power deficit, the available supporting power of the energy storage, and the load frequency damping coefficient. Take the minimum value among all frequency points within the prediction time window as the predicted minimum frequency point.

[0031] In this preferred embodiment, existing frequency support strategies are mostly passive response-based, adjusting output only after a frequency drop occurs based on real-time deviations. This inherently introduces control delays and makes it difficult to cope with the extremely rapid frequency change rates under high-proportion renewable energy systems. This step constructs a predictive model based on discrete frequency recursive equations, incorporating system equivalent inertia, load damping, and the energy storage's own ramp-up capability into a unified calculation framework. This enables forward-looking prediction of the frequency trajectory over a future period. In particular, taking the minimum value among all frequency points within the prediction time window as the predicted minimum frequency point is equivalent to providing the controller with a "worst-case" warning signal. This allows the system to prioritize active power support or adjust inertia parameters before the frequency actually reaches the dangerous threshold, transforming the traditional "post-event remedy" into "pre-event defense." This significantly reduces the probability of frequency exceeding limits and avoids wasting energy storage regulation resources due to overly conservative control.

[0032] Step S2: Based on the set of power grid risk indices and the set of equipment capability indices, calculate the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system, and calculate the active current reference value and reactive current reference value based on the adaptive virtual inertia and adaptive virtual damping. Furthermore, step S2 can be implemented through the following preferred embodiments, including steps S21-S27: S21: Determine the virtual inertia correction coefficient based on the current margin index; wherein the virtual inertia correction coefficient is positively correlated with the current margin index; S22: Based on the virtual inertia correction coefficient, the frequency risk index, and the energy margin index, the adaptive virtual inertia is calculated; S23: Extract the dynamic oscillation characteristic quantity of the grid-type energy storage system, and calculate the oscillation risk index based on the dynamic oscillation characteristic quantity; wherein, the dynamic oscillation characteristic quantity includes at least one of active power fluctuation component, frequency fluctuation component or voltage phase angle change rate; S24: Based on the frequency risk index, voltage risk index, oscillation risk index, and current margin index, an adaptive virtual damping is calculated; wherein the adaptive virtual damping is positively correlated with the oscillation risk index and negatively correlated with the current margin index.

[0033] In this preferred embodiment, existing grid-type control generally uses fixed virtual inertia and damping coefficients. This static parameter setting cannot adapt to the wide range of operating conditions from normal steady state to fault transient state and then to current limiting protection, often leading to the contradiction of insufficient response under small disturbances and overcurrent tripping under large disturbances. This step achieves "elastic expansion and contraction" of inertia support strength by establishing a positive correlation between virtual inertia and current margin index: when current is sufficient, the large inertia advantage of energy storage is fully utilized to smooth frequency fluctuations, while when current is tight, the inertia demand is automatically reduced to prevent triggering hardware current limiting; at the same time, the oscillation risk index and current margin index are used together as adjustment factors for the damping coefficient, so that the damping not only increases to restore stability when the system experiences power oscillations, but also actively increases to suppress the cumulative drift of virtual phase angle when approaching the current limiting boundary. This dual coupling mechanism ensures that the energy storage system can maintain optimal dynamic stability under any operating condition, fundamentally solving the inherent conflict between fixed parameter control and safety constraints.

[0034] S25: Substitute the adaptive virtual inertia and adaptive virtual damping into the preset grid-type virtual swing equation, and combine the virtual angular frequency integral state quantity saved during the integral calculation of the previous control cycle to calculate the virtual angular frequency and virtual voltage phase angle of the grid-type energy storage system in this control cycle through numerical integration. S26: Calculate the virtual voltage amplitude based on the deviation between the virtual voltage phase angle and the current grid connection point voltage amplitude, and generate active power reference value and reactive power reference value in combination with the virtual angular frequency; S27: Map the active power reference value and reactive power reference value to the dq rotating coordinate system to obtain the active current reference value and reactive current reference value for this control cycle.

[0035] In this preferred embodiment, the core of the grid-type control lies in simulating the rotor motion characteristics of a synchronous generator. The integral element in its virtual swing equation is crucial for maintaining the phase continuity of the voltage source, but this element is extremely sensitive to parameter abrupt changes. This step directly substitutes the adaptively generated time-varying inertia and damping coefficient into the virtual swing equation for numerical integration, rather than using table lookup or piecewise linear approximation, ensuring the mathematical continuity and physical consistency of the control law throughout the dynamic process. Simultaneously, by combining the integral state variables saved from the previous cycle for recursive calculation, phase angle jumps caused by real-time parameter updates are avoided, ensuring a smooth transition of the voltage source characteristics presented by the energy storage. Based on this, the active and reactive power reference values ​​are mapped to the dq coordinate system to generate current commands, which not only retains the autonomous voltage and frequency regulation capabilities of the grid-type control but also provides standardized decoupling variables for subsequent current limiting and allocation, achieving seamless integration between high-order electromechanical transient simulation and underlying power electronic control.

[0036] Furthermore, after obtaining the active current reference value and reactive current reference value for this control cycle, steps S28-S29 are also included: S28: Calculate the difference between the current active power reference value and the actual output active power of the converter to obtain the active power deviation; obtain the continuous time length during which the three-phase current at the grid connection point exceeds the dynamic current limit to obtain the current limiting duration. S29: Based on the current set of equipment capacity indices, current limiting duration, and active power deviation, determine the correction strategy for the virtual angular frequency integral state quantity of this control cycle, correct the virtual angular frequency integral state quantity based on the active power deviation, and use the corrected virtual angular frequency integral state quantity as the state quantity for numerical integration calculation in the next control cycle.

[0037] In this preferred embodiment, when the converter is unable to fully execute the active power reference command due to current limitation, the integral element in the virtual swing equation will continuously accumulate the deviation between the theoretical demand and the actual output, causing the virtual phase angle to gradually deviate from the real phase of the power grid. If no intervention is taken, a severe power backlash and secondary frequency drop will be triggered at the moment the current limitation is lifted. This step provides a quantitative basis for correcting the integral state quantity by calculating the active power deviation in real time and obtaining the current limiting duration. The active power deviation directly reflects the degree of mismatch between the output of the virtual swing equation and the actual physical constraints in the current control cycle, and is the core criterion for judging whether the integral term has unidirectional accumulation. The current limiting duration characterizes the persistence of this mismatch state and is used to distinguish between transient disturbances and steady-state current limiting conditions, avoiding overcorrection of transient overcurrent. Furthermore, by combining the set of equipment capacity indices (such as current margin index and energy margin index), the remaining regulation potential and safety boundary of the energy storage system at the current moment can be comprehensively evaluated, thereby dynamically determining the correction direction and magnitude of the integral state quantity. When the equipment capacity is sufficient and the power deviation is small, normal integration can be maintained to preserve the support response capability. When the equipment capacity is tight and the power deviation persists, a suppressive correction needs to be applied to the integral state quantity to prevent excessive drift of the virtual phase angle. By using the modified virtual angular frequency integral state quantity as the state quantity for numerical integration calculation in the next control cycle, the initial conditions of the virtual swing equation are always coordinated with the actual operating state of the power grid and the physical constraints of the equipment, avoiding abrupt phase angle changes caused by integral saturation, and realizing the stability and smoothness of the recovery process of the grid-type control under current-limited conditions.

[0038] Step S3: Detect whether the combined amplitude of the active current reference value and the reactive current reference value exceeds the dynamic current limit of the converter. If it does not exceed the limit, set the current command based on the active current reference value and the reactive current reference value; if it exceeds the limit, use the dynamic current limit as a constraint, adjust the active current reference value and the reactive current reference value according to the relative weights of frequency risk and voltage risk in the power grid risk index set, obtain the current-limited active current and the current-limited reactive current, and set the current command. Step S4: Drive the converter to output the corresponding three-phase AC current based on the current command to complete the control.

[0039] In practical implementation, the following will provide a detailed explanation of the scheme in conjunction with the formula: The first step is to collect the operating status and model the current limiting margin of grid-type energy storage. The purpose of this step is to establish an operating status model that can simultaneously characterize the frequency disturbance intensity, voltage support pressure, converter current capability, and energy storage energy status, and use it as the basis for subsequent adaptive adjustment of inertia-damping parameters and reactive current distribution.

[0040] A grid-based energy storage system is defined as a frequency support unit consisting of energy storage batteries or hybrid energy storage units, DC-side converters, grid-connected inverters, AC filters, grid-connected switches, and a site coordination controller. The virtual voltage phase angle generated by the grid-based controller... and virtual angular frequency For internal reference, three-phase voltage is collected at the grid connection point. Current Active power reactive power System frequency and frequency change rate (The above refers to the electrical data of the grid connection point in the summary); DC bus voltage is collected on the DC side. Energy storage Battery temperature Available charging and discharging power , And the thermal margin of power devices (the above refers to the operating status data in the abstract). In addition, the converter's dynamic current limit is calculated at the current moment by collecting hardware rated current, short-time overload capacity, thermal status, DC-side power capacity, and protection settings. .

[0041] Next, calculate the indicators: Power grid strength index The equivalent impedance can be calculated from the short-circuit ratio, equivalent impedance, or grid connection point voltage sensitivity. This embodiment preferably uses a normalized calculation method based on the equivalent short-circuit ratio. First, at the grid connection point, the equivalent impedance of the power grid is estimated using voltage and current small-disturbance measurement data, natural disturbance data, or fault recording data. In the formula, The voltage amplitude at the grid connection point. This refers to the voltage disturbance at the grid connection point. This refers to the current disturbance at the grid connection point. To prevent the calculation of tiny positive numbers with a denominator of zero, the equivalent short-circuit capacity and short-circuit ratio at the grid connection point are further calculated: ; In the formula, For the equivalent short-circuit capacity at the grid connection point, For short-circuit ratio, This refers to the rated capacity of the grid-connected energy storage converter or the equivalent rated capacity of the power station. Then, the short-circuit ratio is normalized to obtain the grid strength index: In the formula, The threshold for determining a weak power grid. For strong power grid reference short-circuit ratio, This is a limiting function, and its output range is 0-1. When When the value is close to 1, it indicates a high grid strength at the grid connection point, allowing for the use of strong inertia and damping support in grid-connected energy storage systems; when... When the value is close to 0, it indicates that the grid connection point is in a weak grid state. The controller needs to appropriately reduce the virtual inertia response strength and increase the virtual impedance and damping constraints to avoid power oscillation and current overshoot under weak grid conditions.

[0042] Calculation of frequency risk index and voltage risk index: A frequency risk index is constructed based on frequency deviation, frequency change rate, and predicted minimum frequency. : ; , For the rated frequency, ; In the formula: Represents the amplitude limiting function; Frequency safety threshold; Based on the current frequency, The predicted minimum frequency point obtained from the estimation of available energy storage power and system equivalent inertia; , , These are the weighting coefficients. The larger the value, the higher the risk of system frequency instability, and the stronger the demand for active power rapid support and virtual inertia support.

[0043] in, A discrete rolling prediction method based on the frequency response model of a single-machine equivalent system is adopted. First, the equivalent inertia coefficient is calculated based on the system's equivalent inertia time constant and the system's reference capacity: In the formula, The equivalent inertia coefficient of the system. The system's equivalent inertia time constant, As the system's baseline capacity, The rated frequency is used. Then, based on the current frequency change rate, frequency deviation, already deployed frequency support power, and system equivalent inertia, the equivalent power deficit under low-frequency disturbances is estimated: ; In the formula, This is an estimate of the equivalent power deficit. The load frequency damping coefficient is... This refers to the current active power support from fast frequency response, energy storage, or primary frequency regulation. For low-frequency disturbances, It is usually a negative value, therefore It can reflect the power deficit component corresponding to the rate of frequency decrease.

[0044] Then, based on the current available discharge power of the energy storage, the converter current margin, and the active power ramping capability, the available supporting power of the energy storage within the predicted time window is constructed: ; In the formula, For the first The supporting power that energy storage can provide within a prediction step. To store the current available discharge power, This represents the current actual active power output of energy storage. This represents the upper limit of the active power ramp-up rate for energy storage. To predict the step size, This is the prediction step number.

[0045] In the prediction time window Within this range, the following discrete frequency recursive equation is used to predict the subsequent frequency trajectory: ; in, , Therefore, the point of lowest predicted frequency is obtained: ; when Below the frequency safety threshold When this occurs, it indicates that relying solely on the current support capacity is insufficient to prevent the frequency minimum point from exceeding the limit, and the controller should prioritize active power rapid support; when Higher than and As the frequency gradually decreases, it indicates that the frequency risk is reduced, and the controller can gradually enter the support / hold or exit the judgment phase.

[0046] A voltage risk index is constructed based on grid connection point voltage deviation, voltage change rate, low voltage duration, and grid strength. : ; In the formula: This is the reference value for the grid connection point voltage; This refers to the duration of low voltage. This is a correction factor for weak power grids (essentially a variable representing the degree of weakness of a power grid, derived from or mapped from the power grid strength index). Its value increases when the short-circuit ratio is low or the equivalent impedance is large. , , , These are the weighting coefficients. The larger the value, the stronger the demand for reactive current retention under reactive voltage support and current limiting conditions.

[0047] Current margin index and current limiting operation mode classification: Based on the current output current amplitude (in and (representing the active current component and reactive current component respectively) and dynamic current limit. Calculate the current margin index : ; Meanwhile, the current demand is calculated based on the unconstrained controller. Constructing an overload trend index : ; when Greater than the first threshold and Not greater than When the energy storage is in normal grid support mode, it is determined that the energy storage is in normal grid support mode; when Located at the first threshold Second threshold When the time interval is between, it is determined that the energy storage is in a near-current limiting mode; when Not greater than or Greater than When the frequency and voltage recover, the energy storage is determined to be in current-limiting maintenance mode; when the frequency and voltage recover and Persistently greater than After the preset time is reached, it is determined whether the energy storage enters support mode or exits mode. Recovery mode.

[0048] Energy storage margin and charge / discharge direction constraints, based on frequency perturbation direction and energy storage Calculate the energy margin factor: For low-frequency disturbances, energy storage and discharge support are required, and the discharge energy margin needs to be defined. : ; For high-frequency disturbances, energy storage is needed to absorb power during charging, and a charging energy margin needs to be defined. : ; when or At lower speeds, the controller reduces the sustainable active power support slope and the upper limit of virtual inertia, prioritizing ensuring that the converter does not experience overcurrent and the DC side does not experience overvoltage / undervoltage. In this way, frequency support requirements, current capability, and energy storage status are unified into a single control framework.

[0049] Next, frequency support demand calculation and adaptive generation of inertia-damping parameters are performed. The purpose of this step is to generate online virtual inertia coefficients, damping coefficients and active frequency support commands suitable for the current operating state based on frequency risk, current margin and energy storage margin, so that energy storage can fully support the frequency when the current capacity is sufficient and actively reduce the risk of overcurrent when approaching the current limit.

[0050] (1) Calculation of active frequency support demand: First, calculate the active frequency support requirement based on the frequency deviation and the rate of frequency change. : In the formula: The active power-frequency droop factor; for Response coefficient; This is the correction factor for the lowest frequency point; This indicates that only positive values ​​are retained. When the system experiences low-frequency disturbances, A positive value indicates that the energy storage has generated active power; when the system experiences high-frequency disturbances, A negative value indicates that the energy storage has absorbed active power. This demand is further affected by the available power of the energy storage. Common limiting of DC voltage and converter current margin.

[0051] (2) Virtual swing equation of network type: Constructing a virtual voltage source model for the internal structure of a grid-type energy storage system: ; ; ; In the formula: This is the virtual voltage phase angle; This refers to the virtual angular frequency. For adaptive virtual inertia coefficient; The adaptive damping coefficient; Active power as the benchmark for energy storage; This is the power for current limiting error feedback; This represents the virtual voltage amplitude. This is the voltage regulation coefficient; For virtual impedance; This represents the output current vector. Through the above equations, energy storage can exhibit voltage source characteristics and provide active power support during frequency disturbances.

[0052] (3) Adaptive generation of virtual inertia coefficient: A virtual inertia coefficient (i.e., adaptive virtual inertia) is generated based on frequency risk, current margin, and energy storage margin. ; In the formula: This indicates the amplitude limit between the upper and lower limits; Based on virtual inertia; The energy margin coefficient corresponding to the charging and discharging direction can be taken as... or ; This is the current margin correction factor (corresponding to the virtual inertia correction factor in the weights). When Greater than hour, near This allows for an increase in virtual inertia to suppress ;when Below And when approaching the flow restriction boundary, Follow The virtual inertia is reduced and decreased, thus actively reducing the virtual inertia and avoiding overshoot of instantaneous active current due to excessive inertia response.

[0053] (4) Adaptive generation of damping coefficient: The damping coefficient (i.e., adaptive virtual damping) is generated based on frequency risk, power oscillation risk, and current limiting status. In the formula: Basic damping; The oscillation risk index is derived from active power oscillation, frequency oscillation, or phase angle change rate. , , This is the weighting coefficient. When the system frequency changes rapidly or power oscillations occur, the controller increases the damping coefficient to suppress virtual power angle fluctuations; when energy storage approaches the current limit, the controller reduces the internal angular frequency accumulation deviation by increasing damping and reducing inertia, thereby reducing the risk of power backlash after the current limit is lifted.

[0054] (5) Parameter smoothing and amplitude limiting constraints: To avoid a step jump in power command caused by abrupt changes in inertia and damping parameters, the following measures are taken: , , , Apply slope constraints and first-order smoothing: ; ; In the formula: and For the smoothed inertia and damping of the actual input control; , This is a smoothing factor. The smoothing factor takes a larger value during normal operation to suppress noise, and automatically decreases during severe frequency disturbances to improve control response speed.

[0055] Next, in order to dynamically allocate the limited current capacity between frequency-supported active current and voltage-supported reactive current when the converter output current is limited, and to maintain the stability of the grid-type control by utilizing current-limiting error feedback and virtual impedance correction, this application implements dynamic allocation and current-limiting maintenance control of active / reactive current: (1) Unconstrained active / reactive current reference generation: Based on the virtual voltage source output and frequency support requirements obtained from the preceding steps, calculate the unconstrained active and reactive power reference values. , .exist In the coordinate system, convert it to an unconstrained current reference: ; ; in, To align with the virtual voltage phase angle The axis voltage component. This leads to the unconstrained current requirement. .when No more than When the controller directly outputs the current reference, it supports the frequency according to the enhanced inertia-damping strategy; when Exceed At that time, the dynamic rate limiting allocation process will begin.

[0056] (2) Calculation of frequency-voltage dual-risk current allocation coefficient: According to the frequency risk index Voltage risk index Energy margin and weak grid correction factor Calculate the active power priority coefficient and reactive power priority coefficient : ; ; In the formula: To prevent tiny positive numbers with a denominator of zero. When the frequency drops significantly and the grid connection voltage is within the allowable range, Increase the active current, and the controller will prioritize reserving active current to support the frequency; when the grid connection point voltage drops significantly, the grid is weak, or the demand for low-voltage ride-through is strong. When the frequency and voltage risks are both high, the controller prioritizes reserving reactive current to support the voltage; when both frequency and voltage risks are high, the controller allocates current capacity according to the risk ratio of the two.

[0057] (3) Optimization of active / reactive current allocation under current limiting constraints: In the current limiting maintenance mode, the following current allocation optimization objective is constructed: ; ; ; when When the voltage is below the preset low voltage threshold, .

[0058] In the formula: For smoothing weights; , The limit for the rate of change of current; This represents the minimum reactive current reserve value under low voltage conditions. By solving this constrained optimization problem, the current reference value after current limiting is obtained. , This allocation method avoids the insufficient adaptability of traditional fixed active power priority or fixed reactive power priority strategies under different disturbance scenarios.

[0059] (4) Maintaining the characteristics of a grid-type voltage source and correcting virtual impedance: To avoid the loss of internal voltage source characteristics caused by hard current limiting, when the output current approaches the limit, the controller does not directly cut off the virtual swing equation, but instead introduces an adaptive virtual impedance related to the overload level: ; Will This virtual impedance correction, applied to the virtual voltage amplitude and the inner current loop reference, smoothly transitions the converter's external characteristics from a low-impedance voltage source to a controlled current-limited voltage source. By correcting the virtual impedance, short-time overcurrent peaks can be reduced, current distribution stability under weak grid conditions can be improved, and voltage command distortion caused by current limiting can be avoided.

[0060] (5) Current limiting error feedback and anti-integral saturation processing: Define the active and reactive power deviations before and after current limiting: ; ; Will As current limiting error feedback power Input the virtual oscillation equation: ; Meanwhile, to avoid the continuous accumulation of errors in the internal virtual phase angle, virtual frequency, or active power support integral term during the current limiting period, the controller adjusts the integral term... Three anti-integral saturation handling modes are set: freeze, reverse calculation, and discharge. The current requirement before current limiting is set as follows: ; Assume the current margin is: ; In the formula, For converter dynamic current limits, Let this be the actual output current amplitude of the converter. This refers to the duration of the rate limiting state. This is the ordinary integral input for the frequency-supporting integral stage. When When this occurs, it means that ordinary integration will cause the active power demand to continue to increase beyond the current-limiting boundary, that is, to continue to aggravate saturation.

[0061] First, reverse calculation mode: When the current demand before current limiting exceeds the converter's dynamic current limit, and the current limiting error is large or the current limiting duration is long, reverse calculation anti-saturation processing is triggered. The triggering conditions are as follows: ; At this point, the anti-saturation integral state variable is updated according to the following formula: ; In the formula, This is for calculating the feedback coefficient. The inverse calculation mode uses the deviation between the actual executable power after current limiting and the required power before current limiting to correct the integral state in reverse, so that the internal virtual power command gradually approaches the executable boundary after current limiting, avoiding the continuous deviation of the internal virtual angular frequency and virtual phase angle due to unexecutable commands.

[0062] Second, freeze mode: Freeze mode is triggered when the current demand before current limiting exceeds the converter's dynamic current limit, the current margin is lower than the current limiting margin threshold, the current limiting error has not yet reached the back-calculation trigger threshold, the current limiting duration has not exceeded the back-calculation waiting time, and ordinary integration will further aggravate saturation. The triggering conditions are as follows: At this point, the anti-saturation integral state variables are frozen: ; By freezing the integral state, the frequency support integral term can be prevented from accumulating continuously when the actual active power can no longer increase, thereby avoiding the backflow of active power after the current limit is lifted.

[0063] Third, discharge mode: When the current limit is released, the current margin is restored, and the frequency and voltage return to the allowable range, and the above state is maintained for more than the preset recovery time, the discharge mode is triggered. The triggering conditions are as follows: ; At this point, the remaining integral amount is released: ; In the formula, This is the discharge factor. The discharge mode is used to smoothly release the integral residual after the current limiting is lifted or the frequency disturbance ends, preventing a sudden rebound in active power or a sudden withdrawal of frequency support.

[0064] When none of the above three modes are satisfied, the controller enters the normal integration mode: .

[0065] In terms of mode switching priority, the reverse calculation mode is judged first, followed by the freeze mode, then the discharge mode, and finally the normal integration mode. Through the above anti-saturation logic, when the current limiting causes the actual active power to fail to meet the frequency support requirements, the controller can suppress the continuous accumulation of errors in the internal virtual phase angle; when the current limiting is lifted, the controller smoothly releases the error according to the discharge function, avoiding a sudden rebound in active power and a secondary frequency drop.

[0066] Step 4: Frequency Support Exit, SOC Recovery, and Online Closed-Loop Update. The purpose of this step is to smoothly restore the grid-type energy storage from the frequency support state to the normal operating state after the frequency disturbance ends, and to continuously correct the control parameters using the disturbance process data to improve its adaptability to subsequent disturbances. (1) Frequency support exit criterion: The system can enter the support exit phase when the following conditions are met: Less than the frequency recovery threshold , Less than the rate of change recovery threshold Grid connection point voltage Within the allowable range, current margin Persistently greater than And the duration of the above state exceeds If any condition is not met, the frequency support or current limiting mode will continue to be maintained.

[0067] (2) Smooth withdrawal of inertia, damping and active power support: During the support exit phase, the controller will... , , , , Gradually restore to normal operating values: ; ; ; In the formula: , , These are active power support, virtual inertia, and damping recovery slope, respectively. If a frequency deviation is detected again during the exit process, or... If the price increases again, the pattern will immediately pause and re-enter the frequency support mode. This mechanism can prevent a secondary price drop caused by a sudden withdrawal of frequency support.

[0068] (3) SOC recovery and frequency safety interlock: After both frequency and voltage have recovered to the safe range, according to the target With the present Difference generation Power recovery : ; At the same time Set power and ramp rate limits, and add frequency safety lockout conditions. When the system frequency falls below the recovery lockout threshold, Pause when the threshold is exceeded or the current margin is insufficient. Recovery will proceed gradually once the system stabilizes. This avoids the energy storage system absorbing additional active power during the replenishment process, which could exacerbate low-frequency risks to the system.

[0069] (4) Disturbance event recording and online parameter updates: After each frequency disturbance, the controller records the lowest and highest frequency points before and after the disturbance. Maximum output current, current limiting duration, Cumulative value The changes in quantity and voltage support effect constitute an event sample set. Based on event samples, the frequency risk weight, voltage risk weight, virtual inertia upper limit, damping upper limit, current limiting threshold, and current allocation weight are updated exponentially with smoothness. ; In the formula: This represents the set of control parameters to be updated; Forgetting factor. When the current limiting time is too long or the frequency minimum point is lower than expected during multiple consecutive disturbances, the upper limit of inertia should be appropriately reduced and the damping and active power reserve should be increased; when the energy storage current margin is sufficient for a long period of time but the frequency support effect is insufficient, the upper limit of inertia should be appropriately increased. Response coefficient and frequency support slope.

[0070] (5) Results output, including: virtual inertia coefficient of grid-type energy storage Damping coefficient Active frequency support instructions Active current reference Reactive current reference Virtual voltage amplitude Virtual phase angle Current operating mode and frequency risk index Voltage risk index Current margin index , Power recovery And current limiting event recording. The above outputs can be directly applied to the inner and outer loop controllers of the energy storage converter, the station coordination controller, or the fast frequency response interface on the grid dispatch side.

[0071] In summary, compared with the prior art, the above embodiments of this application have the following beneficial effects: Calculating the set of grid risk indices characterizing the degree of grid disturbance and the set of equipment capability indices characterizing the equipment's withstand capability transforms the abstract grid fluctuation intensity and specific equipment hardware limitations into quantifiable control indicators, enabling the controller to have the ability to assess the system's safety boundary in real time; based on this, calculating the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system according to the set of grid risk indices and the set of equipment capability indices, and using the equipment capability indices as constraint factors to dynamically adjust the inertia and damping response intensity, thereby avoiding overcurrent risks caused by fixed high inertia parameters under extreme operating conditions from the source; detecting whether the combined amplitude of the active current reference value and the reactive current reference value exceeds the converter's dynamic current limit; if it does not exceed, directly setting the current command ensures a fast and distortion-free response within the safe range; if it exceeds, adjusting the active and reactive current reference values ​​according to the relative weights of frequency risk and voltage risk in the set of grid risk indices, with the dynamic current limit as a constraint, to obtain the current-limited active current and current-limited reactive current and set... The current command mechanism dynamically allocates limited current capacity according to the real-time urgency level, solving the support misalignment problem that may occur in traditional fixed priority strategies under complex faults. This ensures that the most critical system stability indicators still receive priority support even under the worst current-limited operating conditions. Finally, the converter outputs the corresponding three-phase AC current based on the current command, completing the closed-loop control from state perception to execution output. Through the synergistic combination of the above features, this method constructs an integrated control architecture of "risk perception-parameter adaptation-dynamic current limiting allocation". The equipment capacity index provides a safety boundary constraint for the generation of adaptive parameters, preventing overcurrent caused by blind support. The dual risk weight allocation mechanism provides the optimal support strategy when physical current limiting is unavoidable. The two work together to solve the overcurrent hard clipping problem caused by the inability to perceive the real-time capacity of equipment due to fixed inertia parameters, as well as the frequency or voltage support failure caused by the rigidity of active and reactive power allocation during current limiting. This significantly improves the frequency stability and equipment operation safety of high-proportion new energy power systems under extreme disturbances.

[0072] Example 2: Please refer to Figure 2 Based on the same inventive concept, the present invention discloses a control system for grid-type energy storage inertia damping, comprising: a data acquisition module M1, a first calculation module M2, a second calculation module M3, and a control module M4; The data acquisition module M1 is used to collect the operation data of the grid-type energy storage system and the electrical data of the grid connection point in real time, and to calculate the set of grid risk indices that characterize the degree of grid disturbance and the set of equipment capability indices that characterize the equipment's ability to withstand disturbance. Furthermore, the data acquisition module M1 includes: a data acquisition unit, a first electrical calculation unit, a second electrical calculation unit, a third electrical calculation unit, a first collection unit, and a second collection unit; The acquisition unit is used to acquire the three-phase voltage, three-phase current, system frequency and frequency change rate at the grid connection point, acquire the DC bus voltage, energy storage state of charge, battery temperature and power device thermal margin, and acquire the rated current of the converter hardware and the preset value of short-time overload capacity. The first electrical calculation unit is used to calculate the voltage deviation based on the difference between the three-phase voltage and the preset rated voltage reference value, calculate the voltage change rate based on the slope of the three-phase voltage change over time, and monitor the duration of time during which the amplitude of the three-phase voltage is lower than the preset low voltage threshold to obtain the low voltage duration. The second electrical calculation unit is used to calculate the current dynamic current limit of the converter at the current moment based on the hardware rated current, the short-time overload capacity preset value, the DC side bus voltage, the battery temperature and the thermal margin of the power device. The third electrical calculation unit is used to estimate the equivalent impedance of the power grid using the disturbances of the three-phase voltage and three-phase current, and then calculate the short-circuit ratio at the grid connection point, and normalize the short-circuit ratio to obtain the power grid strength index. The first set unit is used to obtain a voltage risk index by weighted summation of the voltage deviation, voltage change rate, low voltage duration and the power grid strength index and then processing it through a limiting function; and to obtain a frequency risk index by weighted summation of the system frequency, frequency change rate and predicted frequency minimum point and then processing it through a limiting function; and to combine the frequency risk index and the voltage risk index into the power grid risk index set. The second set unit is used to obtain the current margin index based on the ratio of the difference between the amplitude of the three-phase current and the dynamic current limit of the converter, and to calculate the energy margin index based on the ratio of the disturbance direction of the system frequency and the difference between the energy storage state of charge and the preset safety boundary; and to combine the current margin index and the energy margin index into the set of equipment capability indices.

[0073] In this preferred embodiment, the voltage deviation is calculated based on the difference between the three-phase voltage and the preset rated voltage reference value; the voltage change rate is calculated based on the slope of the three-phase voltage change over time; and the duration of the low voltage is obtained by monitoring the length of time the three-phase voltage amplitude is below the preset low voltage threshold. This comprehensively characterizes the severity of voltage disturbances from three dimensions: amplitude, rate, and duration. Simultaneously, the converter dynamic current limit is calculated based on the hardware rated current, the preset short-time overload capacity, the DC bus voltage, the battery temperature, and the thermal margin of the power devices. This ensures that the current limit is no longer a fixed nameplate value but a real-time safety boundary that dynamically changes with the equipment's thermal state and DC voltage level, improving equipment utilization and preventing overheating damage. The equivalent impedance of the power grid is estimated using the disturbances in the three-phase voltage and current, and then the short-circuit ratio at the grid connection point is calculated and normalized to obtain the grid strength index, quantifying the impact of grid connection strength on control stability. The system incorporates various factors to enable the controller to identify oscillation risks under weak grid conditions. A voltage risk index is obtained by weighting and summing voltage deviation, voltage change rate, low voltage duration, and grid strength indicators, followed by a limiting function. Similarly, a frequency risk index is obtained by weighting and summing system frequency, frequency change rate, and predicted frequency minimum point, followed by a limiting function. These two indices form a grid risk index set, integrating multi-dimensional electrical disturbance characteristics into a single risk scalar, simplifying the complexity of subsequent control logic. Furthermore, a current margin index is obtained based on the ratio of the difference between the amplitude of the three-phase current and the converter's dynamic current limit. An energy margin index is calculated based on the disturbance direction of the system frequency and the ratio of the difference between the energy storage state of charge and the preset safety boundary. These two indices form a set of equipment capacity indices, reflecting in real time the remaining potential of the energy storage system in terms of current output capacity and energy storage, providing a clear capacity constraint reference for adaptive control.

[0074] Furthermore, the third electrical calculation unit includes: a capacity calculation subunit and a strength calculation subunit; The capacity calculation subunit is used to calculate the ratio of the absolute value of the disturbance of the three-phase voltage to the absolute value of the disturbance of the three-phase current to obtain the equivalent impedance of the power grid, and to calculate the ratio of the square of the voltage amplitude at the grid connection point to the magnitude of the equivalent impedance of the power grid to obtain the equivalent short-circuit capacity at the grid connection point. The strength calculation subunit is used to calculate the ratio of the equivalent short-circuit capacity at the grid connection point to the rated capacity of the converter to obtain the short-circuit ratio. After subtracting the preset weak grid judgment threshold from the short-circuit ratio, it is divided by the difference between the preset strong grid reference short-circuit ratio and the weak grid judgment threshold, and then processed by the amplitude limiting function to obtain the grid strength index.

[0075] In this preferred embodiment, the equivalent grid impedance is obtained by calculating the ratio of the absolute value of the three-phase voltage disturbance to the absolute value of the three-phase current disturbance. The grid impedance characteristics can be identified in real time using small disturbance data measured online, without the need for additional test signals, thus avoiding interference with the normal operation of the grid. Furthermore, the equivalent short-circuit capacity at the grid connection point is obtained by calculating the ratio of the square of the voltage amplitude at the grid connection point to the magnitude of the equivalent grid impedance, accurately reflecting the theoretical short-circuit level of the current grid node. Then, the short-circuit ratio is obtained by calculating the ratio of the equivalent short-circuit capacity at the grid connection point to the rated capacity of the converter, establishing the relationship between grid strength and energy storage capacity. The relative quantitative relationship is determined; finally, the short-circuit ratio is subtracted from the preset weak grid judgment threshold, divided by the difference between the preset strong grid reference short-circuit ratio and the weak grid judgment threshold, and processed by the amplitude limiting function to obtain the grid strength index. This normalization process eliminates the difference in short-circuit ratio values ​​between systems of different capacity levels, maps the nonlinear short-circuit ratio into a unified relative strength scalar, and enables the controller to intuitively identify the degree of deviation of the current grid connection point from the boundary between strong and weak grids. When the index approaches the lower limit, it indicates the risk of weak grid, and the controller can take measures to suppress oscillations in advance, thereby improving the control robustness under weak grid conditions.

[0076] Furthermore, the first collection unit includes: a collection subunit, a deficit calculation subunit, and a prediction subunit; The acquisition subunit is used to acquire the preset system equivalent inertia time constant, system reference capacity and load frequency damping coefficient, as well as the frequency support power that has been put into the system at the current moment. The deficit calculation subunit is used to calculate the system equivalent inertia coefficient based on the system equivalent inertia time constant and the system reference capacity, and to calculate the estimated value of the equivalent power deficit under low frequency disturbance based on the current frequency change rate, frequency deviation, frequency support power and system equivalent inertia coefficient. The prediction subunit is used to construct the available supporting power of energy storage within the prediction time window based on the current available discharge power of energy storage, the current margin of the converter, and the active power ramping capability. Within the prediction time window, the discrete frequency recursive equation is used to derive the frequency trajectory based on the estimated equivalent power deficit, the available supporting power of energy storage, and the load frequency damping coefficient. The minimum value among all frequency points within the prediction time window is taken as the predicted minimum frequency point.

[0077] In this preferred embodiment, a model foundation reflecting the overall inertial characteristics of the system is constructed by obtaining the preset system equivalent inertia time constant, system reference capacity, load frequency damping coefficient, and the frequency support power currently in the system. Furthermore, the system equivalent inertia coefficient is calculated based on the system equivalent inertia time constant and system reference capacity. The estimated equivalent power deficit under low-frequency disturbances is calculated based on the current frequency change rate, frequency deviation, frequency support power, and system equivalent inertia coefficient. The power gap required to maintain the current frequency trajectory is derived in reverse using the swing equation principle, achieving real-time quantification of the system power imbalance. Simultaneously, based on the current available discharge power of the energy storage, the converter current margin, and the active power... The ramp-up capability construction predicts the available support power of energy storage within the predicted time window, taking into account the physical response limits of the energy storage itself and avoiding control errors caused by idealized assumptions. Within the predicted time window, a discrete frequency recursive equation is used to derive the frequency trajectory based on the estimated equivalent power deficit, the available support power of energy storage, and the load frequency damping coefficient. The minimum value among all frequency points within the predicted time window is taken as the predicted minimum frequency point. This frequency prediction method based on the rolling time domain can predict the depth of frequency drop in advance, allowing the controller to increase support or adjust the control strategy in advance before the frequency actually reaches the minimum point. This realizes the control transformation from "post-event response" to "pre-event prevention," significantly improving the initiative of frequency defense.

[0078] The first calculation module M2 is used to calculate the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system according to the set of power grid risk index and the set of equipment capability index, and to calculate the active current reference value and reactive current reference value based on the adaptive virtual inertia and adaptive virtual damping. Furthermore, the first calculation module M2 includes: a coefficient calculation unit, an inertia calculation unit, an oscillation calculation unit, and a damping calculation unit; The coefficient calculation unit is used to determine the virtual inertia correction coefficient based on the current margin index; wherein the virtual inertia correction coefficient is positively correlated with the current margin index. The inertia calculation unit is used to calculate an adaptive virtual inertia based on the virtual inertia correction coefficient, the frequency risk index, and the energy margin index. The oscillation calculation unit is used to extract the dynamic oscillation characteristic quantities of the grid-type energy storage system and calculate the oscillation risk index based on the dynamic oscillation characteristic quantities; wherein, the dynamic oscillation characteristic quantities include at least one of active power fluctuation component, frequency fluctuation component, or voltage phase angle change rate; The damping calculation unit is used to calculate adaptive virtual damping based on the frequency risk index, voltage risk index, oscillation risk index, and current margin index; wherein the adaptive virtual damping is positively correlated with the oscillation risk index and negatively correlated with the current margin index.

[0079] In this preferred embodiment, a virtual inertia correction coefficient is determined based on the current margin index, and this coefficient is positively correlated with the current margin index. This allows for a larger inertia response to provide strong support when the current is ample, while automatically reducing the inertia requirement to prevent overcurrent when the current is tight, thus achieving real-time coupling between inertia response and equipment safety margin. Furthermore, an adaptive virtual inertia is calculated based on the virtual inertia correction coefficient, frequency risk index, and energy margin index, ensuring that the inertia coefficient is not only limited by the current but also controlled by the frequency urgency and remaining power, avoiding ineffective inertia consumption under low SOC or low risk conditions. Simultaneously, dynamic oscillation characteristics of the grid-type energy storage system, including active power fluctuation components, are extracted. The system uses at least one of the following components: frequency fluctuation component or voltage phase angle change rate, and calculates the oscillation risk index based on this to keenly capture the trend of deterioration in system stability. Finally, an adaptive virtual damping is calculated based on the frequency risk index, voltage risk index, oscillation risk index, and current margin index. The adaptive virtual damping is positively correlated with the oscillation risk index and negatively correlated with the current margin index. This design allows the damping to be rapidly increased to suppress fluctuations when the system shows signs of oscillation. At the same time, the damping is also increased to reduce the cumulative drift of the phase angle when the current is limited. Thus, the dynamic stability of the system is improved without increasing the active current output, effectively solving the power oscillation problem under weak grid or current-limited conditions.

[0080] Furthermore, the first calculation module M2 also includes: an integration unit, a reference calculation unit, and a conversion unit; The integrator unit is used to substitute the adaptive virtual inertia and adaptive virtual damping into the preset grid-type virtual swing equation, and combine the virtual angular frequency integral state quantity saved during the integral calculation of the previous control cycle to calculate the virtual angular frequency and virtual voltage phase angle of the grid-type energy storage system in the current control cycle through numerical integration. The reference calculation unit is used to calculate the virtual voltage amplitude based on the deviation between the virtual voltage phase angle and the current grid connection point voltage amplitude, and to generate active power reference value and reactive power reference value in combination with the virtual angular frequency. The conversion unit is used to map the active power reference value and reactive power reference value to the dq rotating coordinate system to obtain the active current reference value and reactive current reference value for the current control cycle.

[0081] In this preferred embodiment, by substituting adaptive virtual inertia and adaptive virtual damping into the preset grid-type virtual swing equation, and combining the virtual angular frequency integral state quantity saved during the integral calculation of the previous control cycle, the virtual angular frequency and virtual voltage phase angle of the grid-type energy storage system in this control cycle are calculated by numerical integration. This preserves the synchronous machine simulation characteristics of the grid-type control core, ensuring that the energy storage exhibits voltage source behavior rather than simple current source following. Furthermore, the virtual voltage amplitude is calculated based on the deviation between the virtual voltage phase angle and the voltage amplitude at the current grid connection point, and active power reference values ​​and reactive power reference values ​​are generated by combining the virtual angular frequency. This achieves a natural mapping from mechanical quantities (angle / frequency) to electrical quantities (power), ensuring the smoothness and physical consistency of power output. Finally, the active power reference values ​​and reactive power reference values ​​are mapped to the dq rotating coordinate system to obtain the active current reference values ​​and reactive current reference values ​​for this control cycle. This completes the decoupling conversion from the power control layer to the current control layer, providing standard control variables for subsequent current limiting and allocation, and ensuring the compatibility and execution efficiency of the underlying converter control.

[0082] Furthermore, after obtaining the active current reference value and reactive current reference value for this control cycle, the first calculation module M2 further includes: a deviation calculation unit and a correction unit. The deviation calculation unit is used to calculate the difference between the current active power reference value and the actual output active power of the converter to obtain the active power deviation; and to obtain the continuous time length during which the three-phase current at the grid connection point exceeds the dynamic current limit to obtain the current limiting duration. The correction unit is used to determine the correction strategy of the virtual angular frequency integral state quantity of the current control cycle based on the current equipment capacity index set, current limiting duration and active power deviation, and to correct the virtual angular frequency integral state quantity based on the active power deviation, and to use the corrected virtual angular frequency integral state quantity as the state quantity for numerical integration calculation in the next control cycle.

[0083] In this preferred embodiment, the active power deviation is obtained by calculating the difference between the current active power reference value and the actual output active power of the converter, and the current limiting duration is obtained by acquiring the continuous length of time that the three-phase current at the grid connection point exceeds the dynamic current limit. This quantifies the power tracking error caused by physical current limiting and its continuous cumulative effect in real time. Furthermore, based on the current equipment capacity index set, the current limiting duration, and the active power deviation, a correction strategy for the virtual angular frequency integral state quantity of this control cycle is determined, and the virtual angular frequency integral state quantity is corrected based on the active power deviation. This step addresses the issue of the integrator error caused by the actual output power failing to follow the reference power during the current limiting period in the grid-type virtual swing equation. The problem of unidirectional accumulation of components is solved by introducing active power deviation as a feedback variable that directly affects the integral state quantity. This can actively offset or freeze the phase angle drift accumulation caused by power deficit during the current limiting period, preventing the virtual phase angle from deviating too far from the real grid phase. Finally, the corrected virtual angular frequency integral state quantity is used as the state quantity for numerical integration calculation in the next control cycle. This ensures that when the current limiting is lifted and the system resumes normal operation, the initial state of the virtual swing equation remains synchronized with the actual grid phase, avoiding sudden changes in virtual phase angle and violent backlash of active power caused by integral saturation release. This ensures a smooth transition and frequency stability of the energy storage system during the switch from current limiting state to normal grid connection state.

[0084] The second calculation module M3 is used to detect whether the combined amplitude of the active current reference value and the reactive current reference value exceeds the dynamic current limit of the converter. If it does not exceed the limit, a current command is set based on the active current reference value and the reactive current reference value. If it exceeds the limit, the active current reference value and the reactive current reference value are adjusted according to the relative weights of frequency risk and voltage risk in the power grid risk index set, constrained by the dynamic current limit, to obtain the current-limited active current and the current-limited reactive current and set the current command. The control module M4 is used to drive the converter to output the corresponding three-phase AC current based on the current command, thereby completing the control.

[0085] In summary, compared with the prior art, the embodiments of this application have the following beneficial effects: Example 3: This invention also provides a computer program product, including a computer program or instructions, capable of running on a computing device or stored in any available medium. When the computer program product is run on at least one computing device, it causes the at least one computing device to execute any of the grid-type energy storage inertia damping control methods of this invention.

[0086] Example 4: This invention also provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is executed on the control system of the grid-type energy storage inertia damping, the control system of the grid-type energy storage inertia damping performs a control method for grid-type energy storage inertia damping in any of the above method embodiments.

[0087] The specific working processes of each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. The division of modules is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple modules may be combined or integrated into another system.

[0088] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A control method for inertial damping in a grid-type energy storage system, characterized in that, include: Real-time acquisition of operational data of grid-connected energy storage systems and electrical data of grid connection points; calculation of a set of grid risk indices characterizing the degree of grid disturbance and a set of equipment capacity indices characterizing the equipment's withstand capability. Based on the set of power grid risk indices and the set of equipment capability indices, the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system are calculated, and the active current reference value and reactive current reference value are calculated based on the adaptive virtual inertia and adaptive virtual damping. The combined amplitude of the active current reference value and the reactive current reference value is checked to see if it exceeds the dynamic current limit of the converter. If it does not exceed the limit, the current command is set based on the active current reference value and the reactive current reference value. If the limit is exceeded, the dynamic current limit will be used as a constraint. The active current reference value and reactive current reference value will be adjusted according to the relative weights of frequency risk and voltage risk in the power grid risk index set to obtain the current-limited active current and current-limited reactive current and set the current command. The converter is driven to output the corresponding three-phase AC current based on the current command, thus completing the control.

2. The control method for inertia damping of a grid-type energy storage system as described in claim 1, characterized in that, The real-time acquisition of operational data from the grid-connected energy storage system and electrical data from the grid connection point, and the calculation of a set of grid risk indices characterizing the degree of grid disturbance and a set of equipment capability indices characterizing the equipment's withstand capability, include: The system obtains the three-phase voltage, three-phase current, system frequency and frequency change rate at the grid connection point; the DC bus voltage, energy storage state of charge, battery temperature and power device thermal margin; and the rated current and short-time overload capacity preset values ​​of the converter hardware. The voltage deviation is calculated based on the difference between the three-phase voltage and the preset rated voltage reference value. The voltage change rate is calculated based on the slope of the three-phase voltage change over time. The duration of the low voltage is obtained by monitoring the time length during which the amplitude of the three-phase voltage is lower than the preset low voltage threshold. The converter dynamic current limit is calculated based on the hardware rated current, short-time overload capacity preset value, DC bus voltage, battery temperature and power device thermal margin. The equivalent impedance of the power grid is estimated by using the disturbances of the three-phase voltage and three-phase current, and then the short-circuit ratio at the grid connection point is calculated. The short-circuit ratio is then normalized to obtain the power grid strength index. Based on the voltage deviation, voltage change rate, low voltage duration, and power grid strength index, a voltage risk index is obtained by weighted summation and then processed by a limiting function. Based on the system frequency, frequency change rate, and predicted frequency minimum point, a frequency risk index is obtained by weighted summation and then processed by a limiting function. The frequency risk index and the voltage risk index are then combined to form the power grid risk index set. The current margin index is obtained based on the ratio of the difference between the amplitude of the three-phase current and the dynamic current limit of the converter. The energy margin index is calculated based on the ratio of the disturbance direction of the system frequency and the difference between the energy storage state of charge and the preset safety boundary. The current margin index and the energy margin index are combined to form the set of equipment capability indices.

3. The control method for inertia damping of a grid-type energy storage system as described in claim 2, characterized in that, The process of estimating the equivalent impedance of the power grid using the disturbances in the three-phase voltage and three-phase current, then calculating the short-circuit ratio at the grid connection point, and normalizing the short-circuit ratio to obtain the power grid strength index includes: Calculate the ratio of the absolute value of the disturbance of the three-phase voltage to the absolute value of the disturbance of the three-phase current to obtain the equivalent impedance of the power grid. Calculate the ratio of the square of the voltage amplitude at the grid connection point to the magnitude of the equivalent impedance of the power grid to obtain the equivalent short-circuit capacity at the grid connection point. The short-circuit ratio is obtained by calculating the ratio of the equivalent short-circuit capacity at the grid connection point to the rated capacity of the converter. The short-circuit ratio is then subtracted from the preset weak grid judgment threshold, divided by the difference between the preset strong grid reference short-circuit ratio and the weak grid judgment threshold, and processed by a limiting function to obtain the grid strength index.

4. The control method for inertia damping of a grid-type energy storage system as described in claim 2, characterized in that, The prediction of the lowest frequency point includes: Obtain the preset system equivalent inertia time constant, system reference capacity and load frequency damping coefficient, as well as the frequency support power that has been put into the system at the current moment; Based on the system's equivalent inertia time constant and the system's reference capacity, the system's equivalent inertia coefficient is calculated. Based on the current frequency change rate, frequency deviation, frequency support power, and system's equivalent inertia coefficient, the estimated equivalent power deficit under low-frequency disturbances is calculated. Based on the current available discharge power of the energy storage, the current margin of the converter, and the active power ramping capability, the available supporting power of the energy storage within the prediction time window is constructed. Within the prediction time window, a discrete frequency recursive equation is used to derive the frequency trajectory based on the estimated equivalent power deficit, the available supporting power of the energy storage, and the load frequency damping coefficient. The minimum value among all frequency points within the prediction time window is taken as the predicted minimum frequency point.

5. The control method for inertia damping of a grid-type energy storage system as described in claim 2, characterized in that, The calculation of the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system based on the set of power grid risk indices and the set of equipment capability indices includes: Based on the current margin index, a virtual inertia correction coefficient is determined; wherein, the virtual inertia correction coefficient is positively correlated with the current margin index; Based on the virtual inertia correction coefficient, the frequency risk index, and the energy margin index, the adaptive virtual inertia is calculated. The dynamic oscillation characteristics of the grid-type energy storage system are extracted, and the oscillation risk index is calculated based on the dynamic oscillation characteristics; wherein, the dynamic oscillation characteristics include at least one of active power fluctuation component, frequency fluctuation component, or voltage phase angle change rate; An adaptive virtual damping is calculated based on the frequency risk index, voltage risk index, oscillation risk index, and current margin index; wherein the adaptive virtual damping is positively correlated with the oscillation risk index and negatively correlated with the current margin index.

6. The control method for inertia damping of a grid-type energy storage system as described in claim 1, characterized in that, The calculation of active current reference values ​​and reactive current reference values ​​based on adaptive virtual inertia and adaptive virtual damping includes: Substitute the adaptive virtual inertia and adaptive virtual damping into the preset grid-type virtual swing equation, and combine the virtual angular frequency integral state quantity saved during the integral calculation of the previous control cycle, to calculate the virtual angular frequency and virtual voltage phase angle of the grid-type energy storage system in this control cycle through numerical integration. Based on the deviation between the virtual voltage phase angle and the current grid connection point voltage amplitude, the virtual voltage amplitude is calculated, and combined with the virtual angular frequency, active power reference value and reactive power reference value are generated. The active power reference value and reactive power reference value are mapped to the dq rotating coordinate system to obtain the active current reference value and reactive current reference value for this control cycle.

7. The control method for inertia damping of a grid-type energy storage system as described in claim 6, characterized in that, After obtaining the active current reference value and reactive current reference value for this control cycle, the process further includes: Calculate the difference between the current active power reference value and the actual output active power of the converter to obtain the active power deviation; obtain the continuous time length during which the three-phase current at the grid connection point exceeds the dynamic current limit to obtain the current limiting duration. Based on the current set of equipment capacity indices, current limiting duration, and active power deviation, a correction strategy for the virtual angular frequency integral state quantity of the current control cycle is determined. The virtual angular frequency integral state quantity is then corrected based on the active power deviation, and the corrected virtual angular frequency integral state quantity is used as the state quantity for numerical integration calculation in the next control cycle.

8. A control system for grid-type energy storage inertia damping, characterized in that, include: The system comprises a data acquisition module, a first calculation module, a second calculation module, and a control module. The data acquisition module is used to collect the operation data of the grid-type energy storage system and the electrical data of the grid connection point in real time, and to calculate the set of grid risk indices characterizing the degree of grid disturbance and the set of equipment capability indices characterizing the equipment's ability to withstand disturbance. The first calculation module is used to calculate the adaptive virtual inertia and adaptive virtual damping of the grid-type energy storage system according to the set of power grid risk indices and the set of equipment capacity indices, and to calculate the active current reference value and reactive current reference value based on the adaptive virtual inertia and adaptive virtual damping. The second calculation module is used to detect whether the combined amplitude of the active current reference value and the reactive current reference value exceeds the dynamic current limit of the converter. If it does not exceed the limit, a current command is set based on the active current reference value and the reactive current reference value. If it exceeds the limit, the active current reference value and the reactive current reference value are adjusted according to the relative weights of frequency risk and voltage risk in the power grid risk index set, constrained by the dynamic current limit, to obtain the current-limited active current and the current-limited reactive current and set the current command. The control module is used to drive the converter to output the corresponding three-phase AC current based on the current command, thereby completing the control.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the control method for grid-type energy storage inertia damping as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a control method for grid-type energy storage inertia damping as described in any one of claims 1-7.

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