Energy storage synchronization coordination management method and system based on virtual synchronization technology

Abstract

The invention discloses an energy storage synchronization coordination management method and system based on a virtual synchronization technology, and belongs to the technical field of power system automation control, and the method comprises the steps: obtaining the power grid operation state data of an energy storage inverter grid-connected point; inputting the power grid operation state data into the power grid strength identification model, and outputting a power grid strength grade identifier; selecting a target parameter mapper from a preset parameter mapper set according to the power grid strength grade identifier; inputting the power grid operation state data into the target parameter mapper, and outputting a virtual inertia coefficient, a virtual damping coefficient and a droop control coefficient which are matched with the current power grid strength; and loading the virtual inertia coefficient, the virtual damping coefficient and the droop control coefficient to a virtual synchronous machine control model, generating an active power reference value and a reactive power reference value, and driving an energy storage inverter to execute power output. The frequency stability is remarkably improved, power overshoot and oscillation are restrained, the voltage supporting precision is enhanced, and the safe operation life of equipment is prolonged.

Description

Technical Field

[0001] This invention relates to the field of power system automation control technology, specifically to a method and system for energy storage synchronization coordination management based on virtual synchronization technology. Background Technology

[0002] With the high penetration of new energy sources and the widespread application of power electronic equipment, the grid's inertia support capacity continues to decline, posing a severe challenge to frequency and voltage stability. Virtual Synchronous Machine (VSG) technology, by simulating the external characteristics of a synchronous generator in an energy storage inverter, endows it with inertia response, damping regulation, and autonomous frequency and voltage regulation capabilities, and has become a key technological path to improve the grid-connected stability of new energy sources. Currently, domestic and international research has achieved the engineering deployment of the VSG basic control architecture and has made initial applications in scenarios such as microgrids, photovoltaic-storage power stations, and grid-connected inverters. Some products support adjustable parameters and initially possess "synchronous machine-like" behavior.

[0003] However, existing technologies still have significant drawbacks: virtual inertia, damping, and droop coefficients largely rely on offline tuning or expert experience presets, and cannot be dynamically adjusted according to grid strength (such as short-circuit capacity fluctuations, islanding / grid-connected switching), leading to frequency oscillations, voltage instability, and power response overshoot under weak grid or disturbance conditions; the system cannot identify the current grid support strength online, resulting in blind parameter matching; conservative parameters under strong grid conditions cause sluggish response, while aggressive parameters under weak grid conditions lead to system instability; facing complex and ever-changing actual operating environments, existing solutions lack adaptive mechanisms, requiring frequent manual adjustments or reliance on central controller coordination, making it difficult to meet the requirements of new power systems that are "plug-and-play," "unattended," and "highly resilient." These problems directly restrict the stable support capability of energy storage systems under weak grid and high-fluctuation scenarios, urgently requiring an intelligent coordination and management method that can achieve "grid strength self-sensing, control parameter self-matching, and power output self-adaptation." Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: how to achieve dynamic matching of the virtual inertia, damping, and droop coefficient of the energy storage inverter under different grid strength environments through online grid strength identification and adaptive mapping mechanism of virtual synchronous machine control parameters, thereby improving frequency stability under weak grid conditions, suppressing power response overshoot, avoiding system oscillations caused by control parameter mismatch, and ensuring power output accuracy and inverter operational robustness under multiple operating conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for energy storage synchronization coordination management based on virtual synchronization technology, which includes: acquiring grid operation status data of the grid connection point of the energy storage inverter; The power grid operating status data is input into the power grid strength identification model, and the power grid strength level identifier is output. The power grid strength level identifier represents the range of the ratio between the short-circuit capacity at the grid connection point and the system reference capacity. Based on the power grid strength level identifier, a target parameter mapper is selected from a preset set of parameter mappers. The set of parameter mappers contains multiple parameter mappers, each of which corresponds to a power grid strength level identifier and is associated with a set of virtual synchronous machine control parameters. The virtual synchronous machine control parameters include virtual inertia coefficient, virtual damping coefficient, and droop control coefficient. The power grid operating status data is input into the target parameter mapper, which outputs a virtual inertia coefficient, a virtual damping coefficient, and a droop control coefficient that match the current power grid strength. The virtual inertia coefficient, virtual damping coefficient, and droop control coefficient are loaded into the virtual synchronous machine control model to generate active power reference values ​​and reactive power reference values, and drive the energy storage inverter to perform power output.

[0007] As a preferred embodiment of the energy storage synchronization and coordination management method based on virtual synchronization technology described in this invention, the power grid operating status data includes: effective values ​​of three-phase line voltages, power grid fundamental frequency, inverter output active power, and inverter output reactive power. The effective values ​​of three-phase line voltages and the power grid fundamental frequency are extracted through digital phase-locked loop tracking, and the inverter output active power and inverter output reactive power are calculated by instantaneous power theory combined with coordinate transformation.

[0008] As a preferred embodiment of the energy storage synchronization and coordination management method based on virtual synchronization technology described in this invention, the output grid strength level identifier includes: Based on the voltage amplitude change and the corresponding active power change in the power grid operation status data, the voltage response sensitivity to power disturbances is calculated as a raw indicator reflecting the power grid's support capacity. Based on the aforementioned response sensitivity, system rated voltage, and power reference values, the equivalent short-circuit capacity at the grid connection point is calculated. The equivalent short-circuit capacity is compared with the preset system reference capacity to obtain the short-circuit ratio value. According to the preset short-circuit ratio interval division rules, the short-circuit ratio values ​​are classified into discrete power grid strength level identifiers.

[0009] As a preferred embodiment of the energy storage synchronization coordination management method based on virtual synchronization technology described in this invention, the target parameter mapper includes: Read the integer encoded value of the power grid strength level identifier as the index basis for the parameter mapper; Access a preset set of parameter mappers, which is stored in key-value pairs, where the key is the grid strength level identifier and the value is the corresponding virtual synchronous machine control parameter group; Based on the integer encoded value of the power grid strength level identifier, match the corresponding key in the parameter mapper set and extract the associated virtual synchronous machine control parameter group; The virtual synchronizer control parameter set is marked as the output of the target parameter mapper. The virtual synchronizer control parameter set includes the virtual inertia coefficient, the virtual damping coefficient, and the droop control coefficient.

[0010] As a preferred embodiment of the energy storage synchronization coordination management method based on virtual synchronization technology described in this invention, the virtual inertia coefficient, virtual damping coefficient, and droop control coefficient that output to match the current grid strength include: The power grid operation status data is used as an input feature vector and fed into the target parameter mapper. Inside the target parameter mapper, the input feature vector is matched with the pre-stored operating condition features to determine the most suitable parameter subgroup. The virtual inertia coefficient, virtual damping coefficient, and droop control coefficient are extracted from the parameter subgroup and used as the optimized control parameters under the current power grid strength.

[0011] As a preferred embodiment of the energy storage synchronization and coordination management method based on virtual synchronization technology described in this invention, the generation of active power reference values ​​and reactive power reference values ​​includes: The virtual inertia coefficient, virtual damping coefficient, and droop control coefficient are written into the parameter register of the virtual synchronous machine control model to complete the parameter loading. Based on the virtual synchronous machine control model, the active power regulation and reactive power regulation are calculated according to the current grid frequency and voltage amplitude, combined with the virtual inertia coefficient, virtual damping coefficient and droop control coefficient. The active power adjustment is superimposed on the basic active power command, and the reactive power adjustment is superimposed on the basic reactive power command to generate the final active power reference value and reactive power reference value. The active power reference value and reactive power reference value are input into the inner current loop controller to generate a modulation wave signal, which drives the energy storage inverter to perform power output.

[0012] As a preferred embodiment of the energy storage synchronization and coordination management method based on virtual synchronization technology described in this invention, wherein: the driving of the energy storage inverter to perform power output includes: The active power reference value and reactive power reference value are input into the current inner loop controller to generate a modulation wave signal; The modulated wave signal is output to the power switching device drive circuit after pulse width limiting and dead zone compensation, driving the energy storage inverter to perform power output.

[0013] This invention provides an energy storage synchronization and coordination management system based on virtual synchronization technology.

[0014] To solve the above-mentioned technical problems, the present invention further provides the following technical solution: an energy storage synchronization and coordination management system based on virtual synchronization technology, comprising: a data acquisition module, used to acquire grid operation status data of the energy storage inverter connection point; The identification module is used to input the power grid operating status data into the power grid strength identification model and output the power grid strength level identifier, which represents the range of the ratio between the short-circuit capacity at the grid connection point and the system reference capacity. The parameter selection module is used to select a target parameter mapper from a preset parameter mapper set based on the power grid intensity level identifier. The parameter mapper set contains multiple parameter mappers, each parameter mapper corresponds to a power grid intensity level identifier, and is associated with a set of virtual synchronous machine control parameters, including virtual inertia coefficient, virtual damping coefficient, and droop control coefficient. The mapping module is used to input the power grid operating status data into the target parameter mapper and output the virtual inertia coefficient, virtual damping coefficient and droop control coefficient that match the current power grid strength. The execution module is used to load the virtual inertia coefficient, virtual damping coefficient and droop control coefficient into the virtual synchronous machine control model, generate active power reference value and reactive power reference value, and drive the energy storage inverter to perform power output.

[0015] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the energy storage synchronization coordination management method based on virtual synchronization technology.

[0016] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the energy storage synchronization coordination management method based on virtual synchronization technology.

[0017] The beneficial effects of this invention are as follows: This invention achieves dynamic adaptive matching of the virtual synchronous control parameters of the energy storage inverter to the grid strength through a five-step closed-loop control architecture of "acquiring grid operation status data → identifying grid strength level → selecting matching parameter mapper → outputting adaptive control parameters → loading and driving power output". High-precision electrical quantity acquisition and phase-locked loop calculation ensure the reliability of input data; voltage-power sensitivity derivation and classification of the short-circuit ratio endows the system with grid environment perception capabilities; a key-value pair indexing mechanism enables millisecond-level parameter group switching, ensuring efficient and unambiguous control decisions; operating condition feature matching and parameter subgroup extraction achieve fine-tuning of the operating point based on level adaptation; and a complete execution chain of parameter loading, power superposition, current loop modulation, and drive compensation ensures that optimized parameters truly affect the physical output. The synergistic effect of each step enables the energy storage system to automatically match the optimal virtual inertia, damping, and droop coefficient under strong grid, weak grid, and disturbance conditions. This significantly improves frequency stability, suppresses power overshoot and oscillation, enhances voltage support accuracy, and extends the safe operating life of the equipment. Ultimately, it achieves highly robust synchronous coordination management that requires no manual intervention, does not rely on communication, and adapts to changing grid environments, providing core technical support for the safe and stable operation of high-proportion new energy power systems. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating an energy storage synchronization and coordination management method based on virtual synchronization technology, provided as an embodiment of the present invention; Figure 2 A computer device diagram of an energy storage synchronization coordination management method based on virtual synchronization technology is provided as an embodiment of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1This is the first embodiment of the present invention, which provides a method for energy storage synchronization coordination management based on virtual synchronization technology, including: S1: Obtain grid operation status data at the grid connection point of the energy storage inverter; S2: Input the power grid operation status data into the power grid strength identification model and output the power grid strength level identifier. The power grid strength level identifier represents the range of the ratio between the short-circuit capacity at the grid connection point and the system reference capacity. S3: Based on the power grid strength level identifier, select the target parameter mapper from the preset parameter mapper set. The parameter mapper set contains multiple parameter mappers. Each parameter mapper corresponds to a power grid strength level identifier and is associated with a set of virtual synchronous machine control parameters. The virtual synchronous machine control parameters include virtual inertia coefficient, virtual damping coefficient and droop control coefficient. S4: Input the power grid operation status data into the target parameter mapper and output the virtual inertia coefficient, virtual damping coefficient and droop control coefficient that match the current power grid strength; S5: Load the virtual inertia coefficient, virtual damping coefficient, and droop control coefficient into the virtual synchronous machine control model to generate active power reference values ​​and reactive power reference values, and drive the energy storage inverter to perform power output.

[0022] It should be noted that when an energy storage system is connected to the grid, its inverter control parameters (such as virtual inertia, damping, and droop coefficient) are usually fixed values ​​or rely on manual tuning. This makes it unable to adapt to dynamic changes in grid strength (such as short-circuit capacity fluctuations, topology switching, and islanding / grid-connected conversion), leading to problems such as frequency oscillations, voltage instability, and power response lag under weak grid or disturbance conditions. At the same time, traditional parameter tuning methods rely on offline simulation or expert experience, lack online adaptive capabilities, and are difficult to cope with complex and ever-changing actual operating environments. This results in insufficient control robustness and increased system stability risks.

[0023] Therefore, to address the aforementioned issues of dynamic changes in grid strength and control parameter mismatch, a grid strength online identification and parameter adaptive mapping mechanism is constructed through steps S1-S5. This mechanism achieves the following: real-time calculation of the grid strength level identifier based on the electrical quantities at the grid connection point; automatic selection of matching virtual synchronous machine control parameters based on the strength identifier; and dynamic loading of the parameters into the control model to generate power commands, which drive the inverter output. This ensures that the energy storage system maintains synchronous stability and power response accuracy under different grid strengths, thereby improving the system's resilience and adaptability.

[0024] Example 2, refer to Figure 1 This is the second embodiment of the present invention, which provides a method for energy storage synchronization and coordination management based on virtual synchronization technology.

[0025] S1: Obtain grid operation status data at the grid connection point of the energy storage inverter; Specifically, in this embodiment, grid operation status data at the grid connection point is acquired from the local measurement unit of the energy storage inverter. The local measurement unit comprises a high-precision voltage transformer, a current Hall sensor, and a real-time power calculation unit integrated into the inverter's power conversion circuit. It is deployed between the inverter's AC output and the grid's point of common coupling (PCC) to continuously collect key electrical quantities reflecting the dynamic characteristics of the grid. Grid operation status data includes: the effective value of the three-phase line voltage (U... a U b The voltage and frequency are extracted in real time through a digital phase-locked loop (PLL), while the active and reactive power are calculated by combining instantaneous power theory with αβ coordinate transformation to ensure that the physical meaning of the data is clear and the timing is aligned.

[0026] In practical engineering deployments, data is synchronously acquired by the inverter's main control DSP or FPGA chip via a 16-bit or higher ADC channel at a sampling rate of no less than 10kHz. After anti-aliasing filtering and moving average preprocessing, the data is packaged and output to the control decision layer with a period of 10ms. The acquisition process is strictly synchronized with the inverter carrier cycle to avoid power calculation errors caused by sampling phase offset. To adapt to different grid environments, the voltage sampling range supports an adaptive range of 220V~400V line voltage, the frequency tracking range is 45Hz~55Hz, and the power calculation accuracy is better than ±0.5%. Before being input into the grid strength identification model, the data undergoes normalization processing to be based on the system rated voltage U. n Rated frequency f n Rated power S n Based on this, a dimensionless input vector [U / U] is generated. n ,f / f n P / S n Q / S n This approach enhances the generalization ability and parameter mapping robustness of subsequent models. It ensures that the acquired data possesses high real-time performance, high accuracy, and strong anti-interference capabilities, providing a reliable data foundation for subsequent grid intensity level identification and adaptive matching of virtual synchronous machine parameters, and supporting the stable operation of the entire coordination and management method under complex grid conditions.

[0027] S2: Input the power grid operation status data into the power grid strength identification model and output the power grid strength level identifier. The power grid strength level identifier represents the range of the ratio between the short-circuit capacity at the grid connection point and the system reference capacity. S21: Calculate the voltage response sensitivity to power disturbances based on the voltage amplitude change and the corresponding active power change in the power grid operation status data, as a raw indicator reflecting the power grid's support capacity. In an optional embodiment, the voltage amplitude change and the active power change are obtained by the data difference over three consecutive control cycles, with a control cycle of 10ms, to ensure that the change reflects the short-time dynamic response characteristics and avoid steady-state drift interference with the accuracy of sensitivity calculation. In an optional embodiment, the response sensitivity is calculated using a sliding window mechanism with a window length of 5 control cycles. Within the window, the voltage and power changes are fitted using linear regression, and the absolute value of the slope is taken as the final response sensitivity value to suppress transient noise and improve identification stability. In an optional embodiment, before linear regression fitting, median filtering is applied to the data within the window to remove abnormal jump points caused by switching actions or load changes, and only the continuously monotonic segments are retained for calculation to ensure that the obtained response sensitivity truly reflects the inherent support characteristics of the power grid. In an optional embodiment, if the magnitude of the voltage or power data change within the window is lower than the preset minimum disturbance threshold, the sensitivity calculation is paused and the previous valid value is maintained to avoid introducing computational noise when there is no substantial disturbance and to ensure output continuity.

[0028] S22: Based on response sensitivity, system rated voltage and power reference values, calculate the equivalent short-circuit capacity at the grid connection point. The larger the value, the stronger the power grid. In an optional embodiment, the system rated voltage is taken from the inverter grid connection protocol setting value, and the power reference value is taken from the rated output capacity of the energy storage system. Both are fixed parameters and are written into the controller storage area when the equipment is put into operation to ensure the consistency of the calculation reference. In an optional embodiment, the calculation process of the equivalent short-circuit capacity is triggered after each response sensitivity update, and the calculation result is cached in a register for subsequent steps to avoid repeated calculations that cause control delays.

[0029] S23: Calculate the ratio between the equivalent short-circuit capacity and the preset system reference capacity to obtain the short-circuit ratio value that characterizes the strength of the power grid. In an optional embodiment, the system baseline capacity is 10MVA, which is preset based on the typical capacity of the regional distribution network and is applicable to most industrial and commercial energy storage application scenarios, ensuring that the short-circuit ratio calculation results are horizontally comparable. In an optional embodiment, the equivalent short-circuit capacity is limited before the ratio calculation, with the upper limit set to 100MVA and the lower limit set to 0.5MVA, to prevent extreme values ​​from causing abnormal subsequent level mapping.

[0030] S24: According to the preset short-circuit ratio interval division rules, the short-circuit ratio values ​​are classified into discrete power grid strength level identifiers; In an optional embodiment, the level identifier is output as an integer value, with levels "1", "2", and "3" corresponding to the weak network, medium network, and strong network parameter groups in the control strategy library, respectively, which facilitates direct indexing and calling by the controller; In an optional embodiment, if the short-circuit ratio value is within ±0.1 of the interval boundary, the level identifier of the previous cycle is kept unchanged, and a hysteresis mechanism is introduced to avoid frequent level jumps that cause control parameter oscillations. In an optional embodiment, a parameter loading command is triggered when the level identifier does not change within 5 consecutive control cycles; if the level changes, the new parameters are loaded after a 2-cycle delay to ensure that the power grid status is stable before the control switch is executed.

[0031] S3: Based on the power grid strength level identifier, select the target parameter mapper from the preset parameter mapper set. The parameter mapper set contains multiple parameter mappers. Each parameter mapper corresponds to a power grid strength level identifier and is associated with a set of virtual synchronous machine control parameters. The virtual synchronous machine control parameters include virtual inertia coefficient, virtual damping coefficient and droop control coefficient. S31: Read the integer encoded value of the power grid strength level identifier as the index basis for the parameter mapper; In an optional embodiment, the integer encoded value is read from a controller-specific register, which is automatically updated after step S2 is completed, to ensure that the read data is the latest identification result; In an optional embodiment, if the read integer encoded value exceeds the preset range [1,3], the default value is "2", and an exception event log is recorded to prevent subsequent mapping failures due to data anomalies. In an optional embodiment, the read operation is performed at the start of each control cycle, synchronized with the inverter control cycle, to ensure that parameter selection and power control are completed within a unified timing framework.

[0032] S32: Access the preset parameter mapper set, which is stored in key-value pairs. The key is the grid strength level identifier, and the value is the corresponding virtual synchronous machine control parameter group. In an optional embodiment, the parameter mapper set is permanently stored in the controller's non-volatile storage area. Three sets of parameters are preset before the device leaves the factory, corresponding to the level identifiers "1", "2", and "3" respectively. On-site upgrades are supported, but modifications during runtime are prohibited to ensure parameter security. In an optional embodiment, key-value pairs are implemented using a structure array, where array indices correspond one-to-one with the level identifier integer values. Access is performed directly by reading the index, improving parameter retrieval efficiency and reducing response latency to less than 100μs.

[0033] S33: Based on the integer encoding value of the power grid strength level identifier, match the corresponding key in the parameter mapper set and extract the associated virtual synchronous machine control parameter group; In an optional embodiment, the matching process uses a direct table lookup method, which does not require loops or conditional judgments. It directly locates the target parameter group based on the integer encoded value as the array index, ensuring real-time performance and determinism. In an optional embodiment, the integrity of the parameter group is verified before extraction. If any parameter (virtual inertia coefficient, virtual damping coefficient, droop control coefficient) is detected to exceed the preset legal range, the default safety parameter group is enabled and an alarm flag is triggered to prevent control abnormalities.

[0034] S34: Mark the virtual synchronizer control parameter set as the target parameter mapper output. The virtual synchronizer control parameter set includes virtual inertia coefficient, virtual damping coefficient and droop control coefficient, which are used to be loaded into the control model later. In an optional embodiment, the target parameter mapper output is written to a dedicated parameter buffer that is bound to the input port of the virtual synchronizer control model to ensure that the parameter transmission path is independent and interference-free. In an optional embodiment, a rate of change limit is applied to the parameter group before output. If the difference between the parameter and the previous cycle exceeds a preset threshold, the parameter is updated in steps to avoid power oscillation caused by sudden parameter changes.

[0035] S4: Input the power grid operation status data into the target parameter mapper and output the virtual inertia coefficient, virtual damping coefficient and droop control coefficient that match the current power grid strength; S41: Input the power grid operation status data as an input feature vector and send it to the target parameter mapper; In an optional embodiment, the input feature vector consists of four-dimensional data: voltage amplitude, frequency, active power, and reactive power. Before being fed in, the data is uniformly normalized to the [0,1] interval to eliminate dimensional differences and improve the matching accuracy of the mapper. In an optional embodiment, the input feature vector is appended with a timestamp before being fed in, which is used by the mapper to determine the freshness of the data. If the timestamp deviates from the current control cycle by more than 20ms, the data is discarded and the parameters of the previous cycle are used to prevent control lag. In an optional embodiment, the target parameter mapper has an independent data input interface. The input feature vector is directly written to the mapper memory area through a DMA channel, avoiding CPU involvement in data transfer and ensuring real-time performance.

[0036] S42: Inside the target parameter mapper, the input feature vector is matched with the pre-stored operating condition features to determine the most suitable parameter subgroup. In an optional embodiment, the pre-stored operating condition features are discrete operating condition sample points. Each sample point contains a typical combination of voltage, frequency, and power. During matching, the Euclidean distance between the input feature vector and each sample point is calculated, and the parameter subgroup corresponding to the smallest distance is selected. In an optional embodiment, if the input feature vector is located between multiple working condition sample points, then a linear interpolation method is used to generate intermediate parameters between parameter subgroups corresponding to adjacent sample points to achieve continuous working condition coverage. In an optional embodiment, the operating condition characteristics are pre-stored according to the daily load curve in different time periods. Before matching, the time period to which the current time belongs is determined, and matching is only performed in the operating condition sample set corresponding to that time period, thereby narrowing the search range and improving efficiency.

[0037] S43: Extract the virtual inertia coefficient, virtual damping coefficient, and droop control coefficient from the parameter subgroup as the optimized control parameters under the current grid strength; In an optional embodiment, the parameter subgroup is stored in the form of a structure containing three floating-point fields, which correspond to the virtual inertia coefficient, virtual damping coefficient, and droop control coefficient, respectively. During extraction, the data is read directly in the order of the fields to ensure that the data correspondence is correct. In an optional embodiment, the CRC checksum of the pre-verification parameter subgroup is extracted. If the verification fails, the default parameter subgroup under that level is enabled, and the verification error event is recorded to ensure control security.

[0038] S5: Load the virtual inertia coefficient, virtual damping coefficient, and droop control coefficient into the virtual synchronous machine control model to generate active power reference values ​​and reactive power reference values, and drive the energy storage inverter to perform power output.

[0039] S51: Write the virtual inertia coefficient, virtual damping coefficient, and droop control coefficient into the parameter register of the virtual synchronous machine control model to complete parameter loading; In an optional embodiment, the parameter register is a dedicated memory area for the controller, divided into three independent address segments, corresponding to the virtual inertia coefficient, virtual damping coefficient, and droop control coefficient, respectively. When writing, the parameters are loaded sequentially according to the address order to ensure that there is no misalignment in the parameter mapping. In an optional embodiment, the parameter loading process is executed during the idle period of the control cycle, avoiding the PWM interruption and current loop calculation periods, and preventing write operations from interfering with the real-time control process. In an optional embodiment, a readback verification is performed after each write operation. If the readback value is inconsistent with the written value, a rewrite mechanism is triggered, and the operation is retried up to three times. If the operation still fails, the valid parameters from the previous cycle are enabled and a parameter loading error is reported.

[0040] S52: Based on the virtual synchronous machine control model, according to the current grid frequency and voltage amplitude, combined with the virtual inertia coefficient, virtual damping coefficient and droop control coefficient, calculate the active power regulation and reactive power regulation; In an optional embodiment, the active power regulation is generated by the frequency deviation through a virtual inertia and damping element, and the reactive power regulation is generated by the voltage deviation through a droop control element. The two are calculated independently to avoid coupling interference. In an optional embodiment, the frequency deviation is the difference between the current frequency and the 50Hz rated value, and the voltage deviation is the difference between the current voltage amplitude and the rated voltage. The deviation values ​​are processed by dead-time processing before input, and the dead-time width is ±0.1Hz or ±1% to prevent small disturbances from causing frequent adjustments. In an optional embodiment, the adjustment amount is calculated using discrete difference equations, with the time step consistent with the control cycle, to ensure numerical stability and avoid integral saturation or high-frequency oscillations.

[0041] S53: The active power adjustment is superimposed on the basic active power command, and the reactive power adjustment is superimposed on the basic reactive power command to generate the final active power reference value and reactive power reference value. In an optional embodiment, the basic active power command is issued by the upper-level energy management system, and the basic reactive power command is zero by default or converted according to the power factor setting value. After being superimposed, it is limited to the rated capacity range of the inverter. In an optional embodiment, the adjustment amount is limited before the superposition operation, with the active power adjustment amount limited to ±30% of the rated power and the reactive power adjustment amount limited to ±20% of the rated power, to prevent the command from exceeding the limit and causing equipment overload. In an optional embodiment, if the superimposed reference value exceeds the inverter's safe operating boundary, the active and reactive components are compressed proportionally to prioritize reactive power support and maintain voltage stability.

[0042] S54: Input the active power reference value and reactive power reference value into the inner current loop controller to generate a modulation wave signal and drive the energy storage inverter to perform power output.

[0043] In an optional embodiment, the current inner loop controller adopts a dual PI structure in the dq coordinate system, with the active reference value corresponding to the d-axis current command and the reactive reference value corresponding to the q-axis current command, and outputs the voltage modulation amount after current tracking. In an optional embodiment, a harmonic suppression compensation term is added before the modulation wave signal is generated. The compensation term is calculated in real time based on the harmonic components of the grid voltage, which improves the quality of the output current waveform and reduces the THD to less than 3%. In an optional embodiment, the drive signal is output to the IGBT drive circuit after being subject to minimum pulse width limitation and dead time compensation to ensure the safety of the switching device, while maintaining the accuracy of the output power and a response delay of less than 200μs.

[0044] Example 3 is the third embodiment of the present invention. This embodiment provides an energy storage synchronization and coordination management system based on virtual synchronization technology, including: The data acquisition module is used to acquire grid operation status data at the grid connection point of the energy storage inverter; The identification module is used to input the power grid operating status data into the power grid strength identification model and output the power grid strength level identifier, which represents the range of the ratio between the short-circuit capacity at the grid connection point and the system reference capacity. The parameter selection module is used to select a target parameter mapper from a preset parameter mapper set based on the power grid intensity level identifier. The parameter mapper set contains multiple parameter mappers, each parameter mapper corresponds to a power grid intensity level identifier, and is associated with a set of virtual synchronous machine control parameters, including virtual inertia coefficient, virtual damping coefficient, and droop control coefficient. The mapping module is used to input the power grid operating status data into the target parameter mapper and output the virtual inertia coefficient, virtual damping coefficient and droop control coefficient that match the current power grid strength. The execution module is used to load the virtual inertia coefficient, virtual damping coefficient and droop control coefficient into the virtual synchronous machine control model, generate active power reference value and reactive power reference value, and drive the energy storage inverter to perform power output.

[0045] Example 4, refer to Figure 2 This is the fourth embodiment of the present invention, which differs from the previous three embodiments in that: if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0046] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0047] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0048] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for energy storage synchronization and coordination management based on virtual synchronization technology, characterized in that: include, Acquire grid operation status data at the grid connection point of the energy storage inverter; The power grid operating status data is input into the power grid strength identification model, and the power grid strength level identifier is output. The power grid strength level identifier represents the range of the ratio between the short-circuit capacity at the grid connection point and the system reference capacity. Based on the power grid strength level identifier, a target parameter mapper is selected from a preset set of parameter mappers. The set of parameter mappers contains multiple parameter mappers, each of which corresponds to a power grid strength level identifier and is associated with a set of virtual synchronous machine control parameters. The virtual synchronous machine control parameters include virtual inertia coefficient, virtual damping coefficient, and droop control coefficient. The power grid operating status data is input into the target parameter mapper, which outputs a virtual inertia coefficient, a virtual damping coefficient, and a droop control coefficient that match the current power grid strength. The virtual inertia coefficient, virtual damping coefficient, and droop control coefficient are loaded into the virtual synchronous machine control model to generate active power reference values ​​and reactive power reference values, and drive the energy storage inverter to perform power output.

2. The energy storage synchronization and coordination management method based on virtual synchronization technology as described in claim 1, characterized in that: The power grid operating status data includes: effective values ​​of three-phase line voltage, power grid fundamental frequency, inverter output active power, and inverter output reactive power. Among them, the effective values ​​of three-phase line voltage and power grid fundamental frequency are extracted through digital phase-locked loop tracking, and the inverter output active power and inverter output reactive power are calculated by instantaneous power theory combined with coordinate transformation.

3. The energy storage synchronization and coordination management method based on virtual synchronization technology as described in claim 2, characterized in that: The output power grid strength level identifier includes: Based on the voltage amplitude change and the corresponding active power change in the power grid operation status data, the voltage response sensitivity to power disturbances is calculated as a raw indicator reflecting the power grid's support capacity. Based on the aforementioned response sensitivity, system rated voltage, and power reference values, the equivalent short-circuit capacity at the grid connection point is calculated. The equivalent short-circuit capacity is compared with the preset system reference capacity to obtain the short-circuit ratio value. According to the preset short-circuit ratio interval division rules, the short-circuit ratio values ​​are classified into discrete power grid strength level identifiers.

4. The energy storage synchronization and coordination management method based on virtual synchronization technology as described in claim 3, characterized in that: The target parameter mapper includes: Read the integer encoded value of the power grid strength level identifier as the index basis for the parameter mapper; Access a preset set of parameter mappers, which is stored in key-value pairs, where the key is the grid strength level identifier and the value is the corresponding virtual synchronous machine control parameter group; Based on the integer encoded value of the power grid strength level identifier, match the corresponding key in the parameter mapper set and extract the associated virtual synchronous machine control parameter group; The virtual synchronizer control parameter set is marked as the output of the target parameter mapper. The virtual synchronizer control parameter set includes the virtual inertia coefficient, the virtual damping coefficient, and the droop control coefficient.

5. The energy storage synchronization and coordination management method based on virtual synchronization technology as described in claim 4, characterized in that: The virtual inertia coefficient, virtual damping coefficient, and droop control coefficient that match the current power grid strength include: The power grid operation status data is used as an input feature vector and fed into the target parameter mapper. Inside the target parameter mapper, the input feature vector is matched with the pre-stored operating condition features to determine the most suitable parameter subgroup. The virtual inertia coefficient, virtual damping coefficient, and droop control coefficient are extracted from the parameter subgroup and used as the optimized control parameters under the current power grid strength.

6. The energy storage synchronization and coordination management method based on virtual synchronization technology as described in claim 5, characterized in that: The generated active power reference value and reactive power reference value include: The virtual inertia coefficient, virtual damping coefficient, and droop control coefficient are written into the parameter register of the virtual synchronous machine control model to complete the parameter loading. Based on the virtual synchronous machine control model, the active power regulation and reactive power regulation are calculated according to the current grid frequency and voltage amplitude, combined with the virtual inertia coefficient, virtual damping coefficient and droop control coefficient. The active power adjustment is superimposed on the basic active power command, and the reactive power adjustment is superimposed on the basic reactive power command to generate the final active power reference value and reactive power reference value. The active power reference value and reactive power reference value are input into the inner current loop controller to generate a modulation wave signal, which drives the energy storage inverter to perform power output.

7. The energy storage synchronization and coordination management method based on virtual synchronization technology as described in claim 6, characterized in that: The drive energy storage inverter performs power output, including: The active power reference value and reactive power reference value are input into the current inner loop controller to generate a modulation wave signal; The modulated wave signal is output to the power switching device drive circuit after pulse width limiting and dead zone compensation, driving the energy storage inverter to perform power output.

8. An energy storage synchronization and coordination management system based on virtual synchronization technology, employing the energy storage synchronization and coordination management method based on virtual synchronization technology as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire grid operation status data at the grid connection point of the energy storage inverter; The identification module is used to input the power grid operating status data into the power grid strength identification model and output the power grid strength level identifier, which represents the range of the ratio between the short-circuit capacity at the grid connection point and the system reference capacity. The parameter selection module is used to select a target parameter mapper from a preset parameter mapper set based on the power grid intensity level identifier. The parameter mapper set contains multiple parameter mappers, each parameter mapper corresponds to a power grid intensity level identifier, and is associated with a set of virtual synchronous machine control parameters, including virtual inertia coefficient, virtual damping coefficient, and droop control coefficient. The mapping module is used to input the power grid operating status data into the target parameter mapper and output the virtual inertia coefficient, virtual damping coefficient and droop control coefficient that match the current power grid strength. The execution module is used to load the virtual inertia coefficient, virtual damping coefficient and droop control coefficient into the virtual synchronous machine control model, generate active power reference value and reactive power reference value, and drive the energy storage inverter to perform power output.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the energy storage synchronization coordination management method based on virtual synchronization technology as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy storage synchronization coordination management method based on virtual synchronization technology as described in any one of claims 1 to 7.

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