Photovoltaic energy storage system VSG cooperative control method and system

Abstract

The invention relates to the technical field of new energy grid-connected control, and discloses a VSG cooperative control method and system for a photovoltaic energy storage system, and the method comprises the steps: collecting the frequency deviation and frequency change rate of the system in real time, combining with a multi-threshold scene recognition mechanism, and dividing the operation state of the system into a rapid deviation stage, a slow recovery stage and a conventional stage; and based on a scene identification result, dynamically adjusting virtual inertia and a damping coefficient, and introducing a hybrid energy storage charge state and a power fluctuation variable to realize collaborative optimization of VSG parameters and an energy storage state. The system comprises a scene identification module, a coefficient adjustment module, a cooperative control module and a loop optimization module. According to the method, the frequency stability, the dynamic response speed and the safety of energy storage equipment of the optical storage system under complex disturbance are effectively improved, and the method has remarkable practical application value.

Description

Technical Field

[0001] This invention relates to the field of new energy grid connection control technology, and in particular to a photovoltaic energy storage system VSG collaborative control method and system. Background Technology

[0002] Photovoltaic power generation, as one of the most promising clean energy sources, has been widely deployed in distributed energy systems. However, photovoltaic power generation is significantly affected by weather conditions, exhibiting inherent defects such as intermittency and volatility, posing challenges to the stable operation of the power grid. To address this issue, the integration of photovoltaics with energy storage systems has gradually become a key technological approach, forming a "photovoltaic-storage microgrid" system, which is widely used in the following scenarios:

[0003] In off-grid microgrids, especially in remote areas, islands, or mountainous regions, photovoltaic (PV) and energy storage (ESS) systems can provide a stable power supply, replacing traditional diesel generator sets and reducing carbon emissions and operating costs. In grid-connected operation, PV-ESS systems can not only generate and consume their own power but also participate in grid peak shaving and frequency regulation services, improving the distribution network's capacity and power supply reliability. Furthermore, in distributed energy applications such as industrial parks and commercial buildings, PV-ESS systems can optimize electricity consumption patterns, achieving peak shaving and valley filling, reducing electricity costs, and improving energy efficiency.

[0004] Virtual synchronous generator (VSG) technology is one of the core means to improve the grid connection performance of photovoltaic and energy storage systems. VSG uses control algorithms to enable power electronic inverters to simulate the inertia and damping characteristics of traditional synchronous generators, giving photovoltaic and other new energy power generation equipment grid support capabilities similar to traditional generators, thus improving system frequency and voltage stability. When photovoltaic output changes abruptly or load fluctuates, VSG can provide rapid power response and frequency regulation, effectively suppressing system oscillations and maintaining stable grid operation.

[0005] Therefore, photovoltaic energy storage systems combined with VSG control technology have become an important technical means to improve the absorption capacity of new energy sources and enhance the resilience of the power grid in actual production, with significant economic and social benefits.

[0006] Although photovoltaic energy storage systems and VSG technology have been applied to some extent, they still face many technical shortcomings in actual operation, mainly in the following aspects:

[0007] 1. The contradiction between the volatility of photovoltaic power output and the low inertia characteristics of microgrids: Photovoltaic power generation is affected by factors such as irradiance and cloud cover, resulting in strong randomness and intermittency in output power. Especially when irradiance drops suddenly or load increases suddenly, the system frequency may deviate rapidly, exceeding the ±0.5Hz limit specified in the national standard. In traditional VSG control, the virtual inertia (J) and damping coefficient (D) are mostly fixed values ​​or adjusted based on simple rules, which are difficult to adapt to the dynamic response requirements of photovoltaic-storage systems under different disturbance scenarios, resulting in large frequency overshoot, long recovery time, and even system instability.

[0008] 2. Insufficient coordination between VSG parameters and energy storage output: Most existing studies treat energy storage systems as simple power buffer units, failing to achieve deep coordination between VSG control parameters and energy storage output. During load surges or photovoltaic fluctuations, the inertia support capacity of the VSG often mismatches with the power output of the energy storage system, leading to increased frequency oscillations during transient processes. This can cause overcharging and discharging of energy storage devices, affecting their lifespan and system safety.

[0009] 3. The power frequency division strategy lacks scenario adaptability. In hybrid energy storage systems, a frequency division coordination strategy of "high frequency - supercapacitor, low frequency - battery" is usually adopted. However, the existing frequency division thresholds are mostly fixed values, which cannot be dynamically adjusted according to the actual operating status of the system. This leads to unreasonable power distribution under complex disturbance scenarios, untimely response of energy storage devices, and affects the overall dynamic performance of the system.

[0010] 4. Parameter adjustment conflicts and system response lag: In traditional VSG control, the adjustment of virtual inertia and damping coefficient is often carried out independently, lacking a coordination mechanism. While a large inertia can suppress frequency fluctuations, it will slow down the system response speed; while excessively increasing damping may lead to steady-state deviation. This conflict in parameter adjustment makes it difficult to achieve optimal control during the rapid deviation and slow recovery phases, affecting the system's dynamic quality and steady-state accuracy.

[0011] To address the aforementioned technical issues, those skilled in the art urgently need a collaborative control method and system that can adapt to the complex operating scenarios of photovoltaic-storage systems, achieve deep integration of VSG parameters and energy storage output, and possess scenario-adaptive capabilities. Summary of the Invention

[0012] The purpose of this invention is to solve the above problems by designing a VSG collaborative control method and system for photovoltaic energy storage systems. By introducing a scene dynamic recognition mechanism, the system frequency deviation and rate of change are captured in real time, and three types of scenes, namely "rapid deviation", "slow recovery" and "normal operation", are accurately divided. Based on the scene recognition results, the virtual inertia and damping coefficient are dynamically adjusted to achieve deep matching between parameters and system state.

[0013] Meanwhile, this invention incorporates hybrid energy storage state of charge (SOC) and system power fluctuations into the regulation equation, establishes a multivariate coupling mechanism of virtual inertia, damping coefficient and energy storage state, and realizes the coordinated optimization of VSG control and energy storage output; in addition, by designing an adaptive frequency division coordination strategy, it realizes the intelligent allocation of high-frequency, medium-frequency and low-frequency power, and improves the system's anti-disturbance capability and operational reliability.

[0014] Therefore, this invention can not only effectively solve the problems of poor adaptability and insufficient coordination of existing VSG control in photovoltaic energy storage systems, but also significantly improve the system frequency stability, dynamic response speed and energy storage equipment lifespan, and has important engineering application value and promotion prospects.

[0015] The technical solution of the present invention to achieve the above objectives is a photovoltaic energy storage system VSG collaborative control method, comprising:

[0016] Step 1, Scene Recognition: Compare the system frequency deviation and frequency change rate with the corresponding thresholds and output the scene recognition results;

[0017] Step 2, Coefficient Adjustment: Based on the scene recognition results, simultaneously adjust the virtual inertia J and the damping coefficient D;

[0018] Step 3, Cooperative Control: The system is dynamically adjusted using an adaptive VSG cooperative control strategy;

[0019] Step 4, iterative optimization: Repeat steps 1 to 3 above until the system frequency and power remain stable.

[0020] The scene recognition is achieved through a scene judgment function, which automatically performs scene recognition based on the relationship between the frequency deviation Δf and a corresponding set threshold, and the relationship between the frequency change rate df / dt and a corresponding set threshold. The scene recognition results include:

[0021] It can be categorized into a rapid deviation phase, a slow recovery phase, or a normal phase.

[0022] The scene determination function is:

[0023]

[0024] In the formula, Δf is the frequency deviation, dΔf / dt is the frequency change rate, scenario 1 is the rapid deviation stage, scenario 2 is the slow recovery stage, and scenario 0 is the normal stage.

[0025] The adjustment equation for the virtual inertia J during the coefficient adjustment process is as follows:

[0026] J=J0+K1∣Δf∣+K2∣df / dt∣+K3f(SOC)

[0027] Where J0 is the reference virtual inertia, K1 and K2 are both frequency-related adjustment coefficients, K3 is the energy storage state adjustment coefficient, and f(SOC) is the hybrid energy storage state function.

[0028] The adjustment equation for the virtual inertia J can be expanded based on the scene recognition results as follows:

[0029]

[0030] Where S represents the scene recognition result.

[0031] The adjustment equation for the damping coefficient D is:

[0032] D=D0+K4∣Δf∣+K5∣ΔP∣

[0033] Where D0 is the reference damping coefficient, K4 and K5 are both adjustment coefficients, and ΔP is the system power deviation;

[0034] The adjustment equation for the damping coefficient D can be expanded based on the scene recognition results as follows:

[0035]

[0036] It should be noted that the state function of hybrid energy storage is:

[0037]

[0038] In the formula, J0 is the reference virtual inertia, K1 and K2 are frequency-related adjustment coefficients, K3 is the energy storage state adjustment coefficient, f(SOC) is the state of charge equation of the hybrid system; D0 is the reference damping coefficient, K4 is the deviation coefficient, K5 is the power fluctuation coefficient, and ΔP = P load -(P pv +P batt +P sc ) represents the system power deviation; α represents the hybrid energy storage weighting coefficient; SOC battnom and SOC scnom The nominal SOC is set to 50%, and max and min are the upper and lower limits of SOC, respectively, which are 80% and 20%.

[0039] The coefficient adjustment includes the following process:

[0040] If the scene recognition result indicates a rapid deviation phase, then increase the virtual inertia and damping coefficient;

[0041] If the scene recognition result indicates a slow recovery phase, then reduce the virtual inertia and damping coefficient;

[0042] If the scene recognition result is in the normal stage, the coefficient remains unchanged.

[0043] When the scene recognition result is in the rapid deviation phase, the adjusted coefficients (including virtual inertia J and damping coefficient D) are amplified proportionally.

[0044] Magnification ratio is Simultaneously increase the deviation coefficient and power fluctuation coefficient, with an amplification ratio of [value missing].

[0045] When the scene recognition result is in the slow recovery phase, the adjusted coefficients (including virtual inertia J and damping coefficient D) are reduced proportionally.

[0046] The reduction ratio is Simultaneously reduce the deviation coefficient and power fluctuation coefficient, by a reduction ratio of

[0047] The collaborative control process is as follows:

[0048] When the system is in the rapid deviation phase, the adjusted virtual inertia and damping coefficient are obtained based on the cooperative control strategy and the adaptive strategy.

[0049] Based on the collaborative control strategy and the virtual inertia and damping coefficient after the system's state of charge is adjusted, a collaborative control process is carried out on the photovoltaic energy storage system.

[0050] When the system is in the normal phase, the adjusted virtual inertia and damping coefficient are obtained based on the cooperative control strategy and the adaptive strategy.

[0051] Based on the collaborative control strategy and the virtual inertia and damping coefficient after the system's state of charge are adjusted, the photovoltaic energy storage system is subjected to collaborative control.

[0052] When the system is in the slow recovery phase, the adjusted virtual inertia and damping coefficient are obtained based on the cooperative control strategy and the adaptive strategy.

[0053] Based on the collaborative control strategy and the virtual inertia and damping coefficient after the system's state of charge is adjusted, a collaborative control process is carried out on the photovoltaic energy storage system.

[0054] During the rapid deviation phase: increase the coefficient to suppress fluctuation amplitude and accelerate response speed;

[0055] During the slow recovery phase: reduce inertial resistance by decreasing the coefficient to avoid steady-state deviation.

[0056] A photovoltaic energy storage VSG collaborative control system includes:

[0057] The scene recognition module is used to perform the scene recognition process. The scene recognition process is to compare the system frequency deviation and frequency change rate with the corresponding thresholds to obtain the scene recognition result.

[0058] The specific steps of the scene recognition process are as follows:

[0059] The system frequency deviation and frequency change rate are compared with their respective thresholds. If both are greater than the given threshold, it is identified as a rapid deviation phase; if both are less than the given threshold, it is identified as a slow recovery phase.

[0060] Otherwise, output the normal case as the scene recognition result;

[0061] The coefficient adjustment module is used to execute the coefficient adjustment process, which involves adjusting the virtual inertia and damping coefficient based on the scene recognition results.

[0062] The collaborative control module is used to execute the collaborative control process, which involves dynamically adjusting the system using an adaptive VSG collaborative control strategy.

[0063] The loop optimization module is used to execute the loop optimization process, which involves repeatedly executing the scenario identification process, coefficient adjustment process, and collaborative control process until the frequency and power of the photovoltaic energy storage system remain stable.

[0064] It also includes a hybrid energy storage module, which enables high-frequency components to be handled by supercapacitors, low-frequency components by batteries, and mid-frequency power to be dynamically allocated through a weighting coefficient α.

[0065] The scene recognition module uses a multi-threshold comparison circuit to determine the scene.

[0066] The coefficient adjustment module adjusts the virtual inertia and damping coefficient in real time based on the scene recognition results, and receives the energy storage SOC and power fluctuation signals for multi-variable coordinated adjustment.

[0067] Compared with the prior art, the present invention has the following non-obvious technical features:

[0068] Non-obvious technical features compared to existing technologies:

[0069] First, this application adopts a dynamic recognition mechanism based on multiple threshold scenarios. By simultaneously judging the absolute value of the system frequency deviation and the absolute value of the frequency change rate, combined with the segmented threshold design, it achieves accurate recognition of three types of scenarios: "rapid deviation", "slow recovery" and "normal operation", breaking through the limitations of traditional single frequency index criteria.

[0070] Secondly, the technical solution of this application adopts a synchronous adaptive adjustment equation for virtual inertia and damping coefficient. This equation is a virtual inertia (J) and damping coefficient (D) adjustment equation that includes multiple variables such as frequency deviation, frequency change rate, energy storage SOC, and power fluctuation, so as to achieve coordinated adjustment of the two and avoid parameter conflicts.

[0071] Third, this application integrates hybrid energy storage SOC into VSG parameter adjustment logic, introduces energy storage state adjustment coefficient and state of charge function into virtual inertia adjustment equation, realizes safe linkage between VSG control and energy storage energy state, and prevents overcharging and over-discharging of energy storage.

[0072] Fourth, this application employs a scene-adaptive coefficient scaling mechanism, which amplifies the adjustment coefficient during the rapid deviation phase to enhance suppression capabilities, and reduces the coefficient during the slow recovery phase to accelerate convergence. This mechanism enables fine-tuning of control intensity under different scenarios;

[0073] Finally, this application achieves the goal of frequency division coordination and dynamic allocation of weighting coefficients for hybrid energy storage. By using the hybrid energy storage weighting coefficient α, the mid-frequency power is dynamically allocated between the battery and the supercapacitor, thereby improving the frequency band adaptability of the power response.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] 1. This invention can effectively improve the frequency stability of the system. Through scene recognition and adaptive parameter adjustment, it can effectively suppress the frequency fluctuation amplitude, shorten the recovery time, and reduce frequency overshoot.

[0076] 2. This invention enhances the dynamic response capability of the system, coordinates the adjustment of virtual inertia and damping, takes into account both inertial support and response speed, and improves the dynamic performance of the system under disturbance.

[0077] 3. This invention optimizes the energy storage operation status, integrates the energy storage SOC into the control logic, avoids overcharging and discharging, extends the life of energy storage equipment, and improves system economy;

[0078] 4. This invention improves the disturbance resistance of the photovoltaic-storage system. Through multi-scenario adaptive control and hybrid energy storage coordination, it enhances the system's operational reliability under complex operating conditions such as sudden changes in illumination and load fluctuations.

[0079] 5. This invention achieves deep matching between control parameters and system state, and the multi-variable coupling adjustment mechanism improves the precision and adaptability of control, making it suitable for diverse operating scenarios. Attached Figure Description

[0080] Figure 1 This is an overall flowchart of a photovoltaic energy storage system VSG collaborative control method according to Embodiment 1 of the present invention;

[0081] Figure 2 This is a schematic diagram of a photovoltaic energy storage system VSG collaborative control method according to Embodiment 1 of the present invention;

[0082] Figure 3 This is a flowchart illustrating the VSG collaborative control method for a photovoltaic energy storage system as described in Embodiment 1 of the present invention.

[0083] Figure 4 This is a schematic diagram of the structure of a photovoltaic energy storage VSG collaborative control system according to Embodiment 2 of the present invention;

[0084] Figure 5 This is a circuit diagram of the photovoltaic energy storage VSG collaborative control system described in Embodiment 2 of the present invention;

[0085] Figure 6 This is a circuit diagram of the scene recognition module in the photovoltaic energy storage VSG collaborative control system described in Embodiment 2 of the present invention;

[0086] Figure 7 This is a graph of the scene judgment function described in Embodiment 2 of the present invention;

[0087] Figure 8 This is a circuit diagram of the coefficient adjustment module of the photovoltaic energy storage VSG collaborative control system described in Embodiment 2 of the present invention;

[0088] Figure 9 This is a circuit diagram of the three-machine system described in Embodiment 2 of the present invention;

[0089] Figure 10 This is a circuit diagram of the VSC converter module of the photovoltaic energy storage VSG collaborative control system described in Embodiment 2 of the present invention;

[0090] Figure 11 This is a diagram illustrating the frequency modulation effect under conditions of sudden load increase and random light fluctuation as described in Embodiment 2 of the present invention. Detailed Implementation

[0091] The present invention will now be described in detail with reference to the accompanying drawings;

[0092] Example 1;

[0093] A method for VSG coordinated control of a photovoltaic energy storage system, such as Figure 1 As shown, the method includes the following steps:

[0094] Step 1, Scene Recognition: The system frequency deviation and frequency change rate are compared with the corresponding thresholds to obtain the scene recognition results;

[0095] The specific process of scene recognition is as follows:

[0096] The system frequency deviation and frequency change rate are compared with the corresponding thresholds. If both are greater than the given threshold, it is identified as a rapid deviation phase; if both are less than the given threshold, it is identified as a slow recovery phase.

[0097] Otherwise, output the normal case as the scene recognition result;

[0098] Coefficient adjustment process: Adjust the virtual inertia and damping coefficient based on the scene recognition results;

[0099] Cooperative control process: The system is dynamically adjusted using an adaptive VSG cooperative control strategy;

[0100] The iterative optimization process involves repeatedly executing the scenario identification process, coefficient adjustment process, and collaborative control process until the frequency and power of the photovoltaic energy storage system remain stable.

[0101] Therefore, this method, based on real-time system frequency, power deviation, and hybrid energy storage state of charge (SOC) data, accurately captures complex operating conditions such as load fluctuations and sudden changes in illumination in the photovoltaic-storage microgrid through a dynamic scenario identification mechanism. It promptly optimizes virtual inertia and damping parameters, effectively avoiding the large frequency overshoot and slow recovery issues that may result from fixed-parameter strategies. Simultaneously, the coordinated control rules for virtual inertia and damping, and the frequency division coordination strategy for hybrid energy storage, further enhance the system's dynamic response speed and stability, ensuring that the frequency deviation and power fluctuations of the photovoltaic-storage microgrid remain within a reasonable range. This improves the microgrid's anti-disturbance capability and operational reliability, and reduces potential safety hazards. This method provides a strong guarantee for the stable operation of photovoltaic-storage microgrids.

[0102] The cooperative control method provided in this embodiment will be further explained below with reference to the accompanying drawings:

[0103] The following terms are explained:

[0104] VSG (Virtual Synchronous Generator) uses control algorithms to enable power electronic devices to simulate the inertia, damping, and frequency and voltage regulation characteristics of traditional synchronous generators, giving new energy power plants the ability to actively support the power grid and improving grid connection stability.

[0105] J (Virtual Inertia) is the core control parameter of VSG, simulating the rotor inertia of a synchronous generator. It determines the system's ability to resist frequency changes. The larger J is, the smoother the frequency change, and the more resistant it is to disturbances such as sudden changes in illumination and load impacts.

[0106] D (Virtual Damping) is the core control parameter of VSG. Its function is to attenuate transient oscillations of the system and accelerate steady-state convergence. The larger D is, the faster the transient oscillations are attenuated, thus avoiding continuous fluctuations after system disturbances.

[0107] SOC (State of Charge), also known as remaining charge, reflects the remaining capacity of a battery. It is numerically defined as the ratio of remaining capacity to the battery's total capacity, and is usually expressed as a percentage.

[0108] VSC converter (Voltage Source Converter): A voltage source converter, a power electronic conversion device that uses semiconductor switching devices to achieve bidirectional conversion between DC and AC power.

[0109] IGBT (Insulated Gate Bipolar Transistor): An insulated gate bipolar transistor is a power semiconductor switching device that combines the high input impedance of a MOSFET with the low on-state voltage drop of a GTR, enabling rapid on / off control of electrical energy.

[0110] LC filter: A passive filter circuit composed of inductors and capacitors, used to filter out high-frequency harmonics from the output of VSC converters, smooth voltage and current waveforms, improve power quality, and ensure that grid-connected power meets grid standards.

[0111] PWM (Pulse Width Modulation): Pulse width modulation, by changing the duty cycle of the pulse signal, can output voltage / current of different amplitudes.

[0112] BOOST boost circuit: A non-isolated DC boost topology that uses the on / off control of power switching devices to boost the low-voltage DC output from the photovoltaic panel to a suitable DC bus voltage, providing stable voltage support for subsequent energy conversion.

[0113] MPPT (Maximum Power Point Tracking): By monitoring the output voltage and current of the photovoltaic panel in real time and dynamically adjusting the circuit operating parameters, it tracks the maximum power point under different light and temperature conditions, thereby maximizing the photovoltaic power conversion efficiency.

[0114] Bidirectional DC-DC converter: A DC converter with bidirectional boost and buck conversion function, adapting to the voltage difference between the energy storage battery (or supercapacitor) and the DC bus, realizing bidirectional energy flow between the energy storage system and the DC bus.

[0115] Combination Figure 2 and Figure 3 As shown, this embodiment provides a VSG collaborative control method for a photovoltaic energy storage system, including:

[0116] Scene recognition process: The absolute value of the system frequency deviation is compared with 0.045 Hz and 0.025 Hz; at the same time, the absolute value of the frequency change rate is compared with 0.015 Hz / s and 0.0075 Hz / s to obtain the scene recognition result;

[0117] If both values ​​are greater than a given threshold, the scene is identified as a rapid deviation phase; if both values ​​are less than a given threshold, the scene is identified as a slow recovery phase; otherwise, the normal case is output as the scene recognition result.

[0118] Coefficient adjustment process: Adjust the virtual inertia and damping coefficient based on the scene recognition results;

[0119] By synchronously adjusting the coefficients of virtual inertia J and damping coefficient D, the conflict between virtual inertia and damping adjustment is avoided, and an adjustment equation is designed.

[0120] The adjustment equation for virtual inertia J is:

[0121]

[0122] The damping coefficient D adjustment equation is:

[0123]

[0124] The state equation for the charge of the hybrid system is:

[0125]

[0126] In the formula, J0 is the reference virtual inertia, K1 and K2 are frequency-related adjustment coefficients, K3 is the energy storage state adjustment coefficient, f(SOC) is the state of charge equation of the hybrid system; D0 is the reference damping coefficient, K4 is the deviation coefficient, K5 is the power fluctuation coefficient, and ΔP = P load -(P pv +P batt +P sc ) represents the system power deviation; α represents the hybrid energy storage weighting coefficient; SOC battnom and SOC scnom The nominal SOC is set to 50%, and max and min are the upper and lower limits of SOC, respectively, which are 80% and 20%.

[0127] When the scenario is determined to be a rapid deviation phase, increase the frequency-related adjustment coefficient and the energy storage state adjustment coefficient, with an amplification ratio of [value missing]. Simultaneously increase the deviation coefficient and power fluctuation coefficient, with an amplification ratio of [value missing].

[0128] When the scenario is determined to be in a slow recovery phase, reduce the frequency-related adjustment coefficient and the energy storage state adjustment coefficient, and reduce the proportional gain. Simultaneously reduce the deviation coefficient and power fluctuation coefficient, by a reduction ratio of

[0129] Cooperative control process: The system is dynamically adjusted according to the above adaptive VSG cooperative control strategy;

[0130] The iterative optimization process involves repeatedly executing the scenario identification process, coefficient adjustment process, and collaborative control process until the frequency and power of the photovoltaic energy storage system remain stable.

[0131] In this embodiment, when the system is in the rapid deviation phase, the adjusted virtual inertia and damping coefficient are obtained based on the cooperative control strategy and the adaptive strategy.

[0132] Based on the collaborative control strategy and the virtual inertia and damping coefficient after the system's state of charge is adjusted, a collaborative control process is carried out on the photovoltaic energy storage system.

[0133] When the system is in the normal phase, the adjusted virtual inertia and damping coefficient are obtained based on the cooperative control strategy and the adaptive strategy.

[0134] Based on the collaborative control strategy and the virtual inertia and damping coefficient after the system's state of charge is adjusted, a collaborative control process is carried out on the photovoltaic energy storage system.

[0135] When the system is in the slow recovery phase, the adjusted virtual inertia and damping coefficient are obtained based on the cooperative control strategy and the adaptive strategy.

[0136] Based on the collaborative control strategy and the virtual inertia and damping coefficient after the system's state of charge is adjusted, a collaborative control process is carried out on the photovoltaic energy storage system.

[0137] During the rapid deviation phase: increase the coefficient to suppress fluctuation amplitude and accelerate response speed;

[0138] During the slow recovery phase: reduce inertial resistance by decreasing the coefficient to avoid steady-state deviation.

[0139] Example 2;

[0140] A photovoltaic energy storage VSG collaborative control system, such as Figure 4 As shown, this system is used to implement the aforementioned photovoltaic energy storage system VSG collaborative control method, which includes:

[0141] The scene recognition module is used to execute the scene recognition process. The scene recognition process involves comparing the system frequency deviation and frequency change rate with corresponding thresholds to obtain the scene recognition result.

[0142] The specific steps of the scene recognition process are as follows: The system frequency deviation and frequency change rate are compared with their corresponding thresholds. If both are greater than the given threshold, it is identified as a rapid deviation phase; if both are less than the given threshold, it is identified as a slow recovery phase. Otherwise, the normal case is output as the scene recognition result.

[0143] The coefficient adjustment module is used to execute the coefficient adjustment process; the coefficient adjustment process: adjusts the virtual inertia and damping coefficient based on the scene recognition results;

[0144] The collaborative control module is used to execute the collaborative control process; the collaborative control process: adopts an adaptive VSG collaborative control strategy to dynamically adjust the system;

[0145] The loop optimization module is used to execute the loop optimization process; the loop optimization process: repeatedly executes the scene identification process, coefficient adjustment process, and collaborative control process until the frequency and power of the photovoltaic energy storage system remain stable.

[0146] This collaborative control system also includes a hybrid energy storage module for frequency division coordination. High-frequency components are handled by supercapacitors, low-frequency components by batteries, and mid-frequency power is dynamically allocated through a hybrid energy storage weighting coefficient α.

[0147] The specific implementation process of the photovoltaic energy storage VSG collaborative control system provided in this embodiment is as follows:

[0148] First, such as Figure 5 As shown, in the three-machine system, generator 2 is replaced with a grid-connected photovoltaic-storage power station, and the grid-connected VSG control method is introduced into the photovoltaic-storage grid-connected system.

[0149] Traditional VSG control parameter adjustment lacks scenario adaptability, employing fixed inertia and damping coefficients or a single adjustment logic. This makes it difficult to respond to differentiated needs such as rapid frequency deviations and slow frequency recovery. Furthermore, it is insufficiently adaptable to sudden load changes, abrupt changes in illumination, and superimposed operating conditions, resulting in poor system fluctuation suppression, slow steady-state convergence, and susceptibility to deviations. In this embodiment, a scenario judgment function is introduced. By automatically identifying operating scenarios based on frequency deviation and rate of change, the adjustment coefficients and linkage rules of J and D are adjusted accordingly to resolve parameter adjustment conflicts in traditional VSG control. Under complex dynamic conditions, it enhances the ability to suppress fluctuations, accelerates steady-state convergence, reduces deviation risks, and improves the pertinence of parameter estimation and the stability of system operation.

[0150] The scene determination function is:

[0151]

[0152] In the formula, Δf is the frequency deviation. The frequency change rate is represented by the following: Scenario 1 is the rapid deviation phase, Scenario 2 is the slow recovery phase, and Scenario 0 is the normal phase.

[0153] like Figure 6 As shown, when |Δf|≥0.045 and A high-level signal is generated, the selector receives a high-level signal, and outputs S=1, indicating the scene is identified as a rapid deviation phase; if this condition is not met, the next step is determined: when |Δf|≤0.025 and When a high level is generated, the selector receives the high level and outputs S=2, indicating that the scene is identified as the slow deviation stage; if this condition is not met, the selector outputs S=0, indicating that the scene is identified as the normal stage.

[0154] like Figure 7 As shown, under complex working conditions of random changes in illumination and sudden changes in load, the scene recognition function continuously changes among three values: 0, 1, and 2 to determine different suitable scenes.

[0155] This embodiment utilizes frequency deviation and frequency change rate as multiple thresholds, and employs reasonable segmentation and appropriate threshold types for scene identification. Compared to traditional methods, this approach overcomes the limitation of a single frequency index in accurately characterizing the dynamic characteristics of the system. It can finely distinguish between various operating conditions such as rapid deviation, slow recovery, and normal operation, providing accurate criteria for scene-adaptive adjustment of inertial damping parameters. Simultaneously, the multi-threshold design can adapt to the dynamic requirements of different disturbance intensities and system response stages, avoiding misjudgments or response lag problems caused by a single threshold.

[0156] Secondly, based on the scenario judgment function, the virtual inertia and damping coefficient are adaptively adjusted. Under conditions such as sudden load changes and illumination changes, the system selects appropriate adjustment rules by identifying the dynamic characteristics of the system frequency. At the same time, the hybrid energy storage state of charge (SOC) and system power fluctuation (ΔP) are linked, and the adjustment coefficients and linkage logic of the virtual inertia and damping coefficient are adjusted in a targeted manner. The parameter adjustment is deeply coupled with the dynamic characteristics of the system, the energy storage state, and the power fluctuation, avoiding the blind adjustment of traditional fixed parameters.

[0157] like Figure 8 As shown, during the coefficient adjustment process, based on scene recognition and judgment, the coefficients of virtual inertia J and damping coefficient D are adjusted simultaneously to avoid conflicts between virtual inertia and damping adjustment. The adjustment equations for virtual inertia and damping coefficient are established as follows:

[0158]

[0159] The state equation for the charge of the hybrid system is:

[0160]

[0161] In the formula, J0 is the reference virtual inertia, K1 and K2 are frequency-related adjustment coefficients, K3 is the energy storage state adjustment coefficient, f(SOC) is the state of charge equation of the hybrid system; D0 is the reference damping coefficient, K4 is the deviation coefficient, K5 is the power fluctuation coefficient, and ΔP = P load -(P pv +P batt+P sc ) represents the system power deviation; α represents the hybrid energy storage weighting coefficient; SOC battnom and SOC scnom The nominal SOC is set to 50%, and max and min are the upper and lower limits of SOC, respectively, which are 80% and 20%.

[0162] From equations (2) and (3), when S = 1, the values ​​of the virtual inertia J and the damping coefficient D are:

[0163]

[0164] When S = 2, the values ​​of the virtual inertia J and the damping coefficient D are:

[0165]

[0166] When S = 0, the values ​​of the virtual inertia J and the damping coefficient D are:

[0167]

[0168] By implementing the above adaptive adjustment process, the virtual inertia and damping coefficients that adapt to the real-time operating status of the system are finally output, ensuring a balance between system frequency stability and energy storage operation safety under various dynamic operating conditions.

[0169] This embodiment introduces adaptive virtual inertia J and damping coefficient D. By associating the virtual inertia with the system frequency deviation, frequency change rate and hybrid energy storage state of charge, and the damping coefficient with the system power fluctuation, the frequency stability and operational reliability of the system are improved under complex dynamic conditions compared with traditional fixed parameter VSG control.

[0170] Third, the design of the photovoltaic energy storage VSG collaborative control system, such as Figure 9 As shown, in the three-machine system, generator 2 is replaced with a grid-connected photovoltaic-storage power station, which mainly includes a photovoltaic energy storage module, a VSC converter module, a scene recognition module, and a coefficient adjustment module.

[0171] (1) Design the VSC converter module;

[0172] This example employs a two-level three-phase full-bridge voltage source converter. Each phase consists of two IGBTs (upper and lower), with each device connected in anti-parallel to a freewheeling diode. By controlling the on / off state of these devices, each phase outputs a two-level voltage, representing a positive / negative DC bus voltage. An LC filter is configured to filter out switching harmonics generated by PWM modulation, optimizing the AC output power quality. Considering the VSC's switching frequency, rated power, voltage level, and harmonic suppression requirements, the filter inductor is set to 0.5mH, exhibiting extremely low impedance. This allows for unimpeded transmission of fundamental frequency power while displaying significantly high impedance to switching harmonics generated by PWM control, effectively blocking high-frequency harmonics from propagating to the grid. The filter capacitor is 500μF, with extremely low capacitive reactance to switching harmonics, enabling rapid bypassing of high-frequency harmonic currents to prevent them from flowing into the grid and causing electromagnetic interference or equipment damage. Simultaneously, the synergistic effect of both filters smooths the VSC output voltage and reduces the thermal stress and insulation losses of the VSC power devices and grid-connected equipment caused by harmonics, providing support for stable and reliable system operation. Figure 10 As shown.

[0173] (2) The photovoltaic energy storage VSG collaborative control system provided in this embodiment also includes a photovoltaic energy storage module, as detailed below:

[0174] To provide energy support for VSG network construction, quickly respond to power frequency fluctuations, provide virtual inertia and damping, suppress transient oscillations, reduce steady-state deviations, ensure system stability, and simultaneously smooth out photovoltaic output fluctuations, extend energy storage life, and design photovoltaic energy storage modules. For example... Figure 5 As shown, the DC side uses photovoltaics, energy storage batteries and supercapacitors, and is connected to the grid via a grid-controlled VSC converter. The aggregation multiplier model is adopted, and the 200-state equivalent is used.

[0175] The DC power output from the DC photovoltaic panel is first connected to the BOOST boost circuit. This circuit uses closed-loop control to boost and stabilize the photovoltaic side voltage, providing an appropriate voltage level for the charging and discharging of the energy storage unit and the DC bus. Simultaneously, MPPT control is integrated, which adaptively adjusts the circuit operating parameters in real time based on the power characteristic curve of the photovoltaic unit. This overcomes the power output fluctuation problem caused by changes in sunlight, ensuring that the photovoltaic panel continuously operates at maximum power output, maximizing the capture of light energy and converting it into stable DC power, ensuring the high efficiency of photovoltaic power output. This provides stable and controllable DC energy input for the subsequent photovoltaic-storage modules to participate in the VSG network, for power regulation, and for the AC-DC conversion of the VSC converter.

[0176] The energy storage battery is connected to the DC bus via a bidirectional DC-DC converter. The converter employs a constant power control strategy to achieve precise and constant control of the energy storage unit's charging and discharging power. It collects real-time data on the energy storage battery's charging and discharging current, terminal voltage, and DC bus voltage signals. A closed-loop adjustment algorithm dynamically controls the duty cycle of the converter's power switching devices, precisely regulating the energy storage battery's output / absorption power to stably track the set power value of 0.1MW, avoiding large power fluctuations. When photovoltaic output is abundant, the energy storage is controlled to charge at a constant power for efficient energy storage. Conversely, when photovoltaic output is insufficient or the system experiences power deficits, the energy storage is controlled to discharge at a constant power to quickly fill the power gap, providing stable energy support for VSG grid construction, suppressing system transient power oscillations and frequency fluctuations, and ensuring the stability of the photovoltaic-energy storage system and grid-connected power.

[0177] The supercapacitor is connected to the system via a bidirectional DC-DC converter, which employs constant DC voltage control. Using the VSC DC-side rated voltage of 1.5kV as the control target, the supercapacitor's charging and discharging state is controlled through voltage closed-loop regulation. This rapidly smooths out DC bus voltage disturbances caused by photovoltaic power output and load fluctuations, stabilizing the bus voltage within the rated range. It provides constant DC support for photovoltaic boost, energy storage DC-DC power control, and subsequent VSC conversion. Simultaneously, it works in conjunction with the VSG grid to quickly respond to transient power surges, ensuring stable system operation.

[0178] (3) Design a scene recognition module;

[0179] In this embodiment, the scene recognition module is an important part of the system, consisting of four parts: data acquisition and preprocessing, multi-threshold criterion setting, and scene classification. Using system frequency deviation, frequency change rate, and power as the core criteria, it distinguishes three scenarios in real time: normal stability, rapid deviation, and slow recovery, and outputs accurate scene judgment commands to support VSG inertia damping adjustment and optical-storage collaborative control, ensuring the system's adaptive and stable operation.

[0180] like Figure 6 As shown, the scene recognition process is as follows: If the collected system frequency deviation and frequency change rate satisfy |Δf|≥0.045 and A high-level signal is generated, the selector receives the high-level signal, switches to A, outputs S=1, and the scene is identified as a rapid deviation phase; if this condition is not met, it switches to B for the next step of judgment; if |Δf|≤0.025 and When a high level is generated, the selector receives the high level, switches to A, outputs S=2, and the scene is identified as the slow deviation stage; if this condition is not met, the selector switches to B, outputs S=0, and the scene is identified as the normal stage.

[0181] (4) Design coefficient adjustment module;

[0182] In this example, the coefficient adjustment module is the core part of the system. Based on the scene recognition results, it realizes real-time adaptive adjustment of virtual inertia J and damping coefficient D. At the same time, it integrates multi-dimensional factors such as frequency deviation, frequency change rate, power fluctuation and energy storage SOC into the adjustment logic to achieve deep adaptation of parameters and system state. The two work together to balance the relationship between inertial suppression and response speed, and adapt to various complex operating conditions of photovoltaic energy storage power stations.

[0183] like Figure 8 As shown, the coefficient adjustment process is as follows:

[0184] When S = 1, the values ​​of the virtual inertia J and the damping coefficient D are:

[0185]

[0186] When S = 2, the values ​​of the virtual inertia J and the damping coefficient D are:

[0187]

[0188] When S = 0, the values ​​of the virtual inertia J and the damping coefficient D are:

[0189]

[0190] The reference virtual inertia J0 and reference virtual damping D0 are the core control parameters for the VSG under normal stable operating conditions. Their values ​​must take into account the system's dynamic performance, equipment constraints, and grid standards. The values ​​are constrained by the rate of change of frequency and the support of photovoltaic energy storage. Referring to the analogy of traditional synchronous generators and using formulas, J0 = 0.2 and D0 = 60 are designed. At the same time, K1 to K5 are the core gains for adaptive adjustment of the J / D coefficient under scenario recognition. K1 and K2 adjust the virtual inertia J to adapt to the frequency deviation and rate of change; K4 and K5 adjust the virtual damping D to achieve transient vibration suppression; K3 constrains J to ensure energy storage safety. The values ​​are selected based on the core principles of operating condition adaptation, cooperative matching, and equipment constraints. After simulation tuning and debugging optimization, K1 = 1.5, K2 = 200, K3 = 0.02, K4 = 100, and K5 = 100 are designed to achieve stable operation of the VSG network system.

[0191] The application and implementation of the VSG collaborative control method for photovoltaic energy storage systems provided in this example are as follows:

[0192] like Figure 11 As shown, a 60MW load was applied to the three-machine system after 5 seconds of operation to simulate a sudden load increase. Simultaneously, a random fluctuation function of illumination was added to simulate changes in illumination, thus validating the aforementioned VSG coordinated control method and control system. It is evident that, compared to the traditional VSG control method, the lowest point of frequency drop is significantly higher, the frequency fluctuation amplitude is significantly lower, the frequency recovery time is significantly shortened, and the frequency modulation efficiency is significantly increased.

[0193] In summary, this invention provides a photovoltaic energy storage VSG collaborative control method and system, which has the following advantages compared with existing control strategies:

[0194] This invention proposes an adaptive collaborative control method based on scene recognition, combining multiple parameters such as real-time system frequency, power deviation, and hybrid energy storage state of charge. It designs a collaborative control strategy and system based on real-time scene recognition, dynamic optimization of virtual inertia and damping coefficients, and coordination of hybrid energy storage. This method captures complex operating condition changes such as load fluctuations and illumination fluctuations in the photovoltaic-storage microgrid in real time, dynamically adapting to parameter adjustment needs. It effectively avoids the problems of large frequency overshoot and slow recovery that may occur with fixed parameter strategies, reduces the system stability risks caused by sudden changes in operating conditions, thereby improving the dynamic response speed and anti-disturbance capability of the photovoltaic-storage microgrid, ensuring system operational reliability, reducing potential safety hazards, and providing strong support for the stable operation of the photovoltaic-storage microgrid.

[0195] The above technical solutions only embody the preferred technical solutions of the present invention. Any modifications that may be made by those skilled in the art to certain parts therein embody the principles of the present invention and fall within the protection scope of the present invention.

Claims

1. A VSG collaborative control method for a photovoltaic energy storage system, characterized in that, include: Step 1, Scene Recognition: Compare the system frequency deviation and frequency change rate with the corresponding thresholds and output the scene recognition results; Step 2, Coefficient Adjustment: Based on the scene recognition results, simultaneously adjust the virtual inertia J and the damping coefficient D; Step 3, Cooperative Control: The system is dynamically adjusted using an adaptive VSG cooperative control strategy; Step 4, iterative optimization: Repeat steps 1 to 3 above until the system frequency and power remain stable.

2. The method according to claim 1, characterized in that, The scene recognition is achieved through a scene judgment function, which can automatically perform scene recognition based on the relationship between frequency deviation and a corresponding set threshold and the relationship between frequency change rate and a corresponding set threshold. The scene recognition results include: rapid deviation stage, slow recovery stage, or normal stage.

3. The method according to claim 1, characterized in that, The adjustment equation for the virtual inertia J during the coefficient adjustment process is as follows: J=J0+K1∣Δf∣+K2∣df / dt∣+K3f(SOC) Where J0 is the reference virtual inertia, K1 and K2 are both frequency-related adjustment coefficients, K3 is the energy storage state adjustment coefficient, and f(SOC) is the hybrid energy storage state function.

4. The method according to claim 3, characterized in that, The adjustment equation for the damping coefficient D is: D=D0+K4∣Δf∣+K5∣ΔP∣ Where D0 is the reference damping coefficient, K4 and K5 are both adjustment coefficients, and ΔP is the system power deviation.

5. The method according to claim 4, characterized in that, The coefficient adjustment includes the following process: If the scene recognition result indicates a rapid deviation phase, then increase the virtual inertia and damping coefficient; If the scene recognition result indicates a slow recovery phase, then reduce the virtual inertia and damping coefficient; If the scene recognition result is in the normal stage, the coefficient remains unchanged.

6. The method according to claim 5, characterized in that, When the scene recognition result is in the rapid deviation phase, the adjustment coefficient is increased proportionally; when the scene recognition result is in the slow recovery phase, the adjustment coefficient is decreased proportionally.

7. A photovoltaic energy storage VSG collaborative control system, capable of implementing the method according to any one of claims 1 to 6, characterized in that, include: The scene recognition module is used to perform the scene recognition process; The coefficient adjustment module is used to execute the coefficient adjustment process; The collaborative control module is used to execute collaborative control processes; The loop optimization module is used to perform loop optimization processes.

8. The system according to claim 7, characterized in that, It also includes a hybrid energy storage module, which enables high-frequency components to be handled by supercapacitors, low-frequency components by batteries, and mid-frequency power to be dynamically allocated through a weighting coefficient α.

9. The system according to claim 8, characterized in that, The scene recognition module uses a multi-threshold comparison circuit to determine the scene.

10. The system according to claim 8, characterized in that, The coefficient adjustment module adjusts the virtual inertia and damping coefficient in real time based on the scene recognition results, and receives the energy storage SOC and power fluctuation signals for multi-variable coordinated adjustment.

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