Network construction energy storage adaptive control method and device based on electrical distance perception and improved virtual impedance

Abstract

The invention provides a network construction energy storage adaptive control method and device based on electrical distance perception and improved virtual impedance, and the method comprises the steps: enabling the virtual inertia and damping coefficient of the energy storage of a network construction VSG to be adaptively adjusted in real time along with the electrical distance of a key node of a micro-grid through an electrical distance dynamic perception mechanism; the response capability of the system to the network topology and the operation state change is enhanced; a frequency differential feedforward link is added to a VSG active control loop, the frequency change rate is compensated in advance, the dynamic response speed to load disturbance or new energy output fluctuation is increased, and frequency deviation is suppressed; a dynamic self-adaptive virtual impedance adjusting strategy is designed in combination with real-time power deviation, the active and reactive power distribution precision among multi-VSG energy storage is optimized, and the circulating current influence is reduced. According to the method, the frequency stability of the micro-grid, the power distribution rationality between VSG energy storage and the operation robustness can be remarkably improved, and the method is suitable for a multi-VSG energy storage access micro-grid scene with high-proportion new energy access and topology dynamic change.

Description

Technical Field

[0001] This invention relates to the field of new energy power plant technology, specifically a grid-based energy storage adaptive control method and device based on electrical distance sensing and improved virtual impedance. Background Technology

[0002] With the increasing penetration of distributed renewable energy sources such as photovoltaics and wind power in the power system, the energy structure transformation has entered a critical stage. These renewable energy sources are intermittent and fluctuating, and their grid connection relies on power electronic converters to complete the conversion and control of electrical energy. This has led to a large number of power electronic converters gradually replacing traditional synchronous generators, becoming the core interface for energy grid connection. From distributed photovoltaic inverters to wind power converters, and then to energy storage converters in microgrids, the proportion of power electronic devices in the system is constantly rising, ultimately resulting in a significantly high proportion of power electronic components in the entire power system. Against this backdrop, the installed capacity of traditional synchronous generators continues to decline, and one of their core advantages, mechanical inertia, has also decreased significantly. Traditional synchronous generators rely on the mechanical inertia of their rotating rotors to release or absorb energy when the system encounters disturbances, buffering frequency fluctuations and providing a buffer for system stability. However, power electronic converters, while employing electronic control and having a fast response speed, lack mechanical inertia support, resulting in a significant weakening of the overall system's ability to withstand disturbances. When a system encounters sudden events such as a sharp drop in renewable energy output or a sudden increase in load, the system's energy balance is quickly disrupted, and frequency fluctuations become significantly larger. Therefore, grid-connected virtual synchronous generator (VSG) technology has been widely adopted. However, in microgrids with multiple VSGs connected in parallel, uneven power distribution due to differences in electrical distance and line impedance can negatively impact system stability.

[0003] Traditional multi-VSG microgrid control schemes have significant limitations: virtual inertia and damping coefficients are often designed with fixed parameters, making them unsuitable for dynamic microgrid conditions. When the network topology is adjusted or the real-time operating status fluctuates, the system's equivalent impedance, power flow direction, and energy balance change accordingly. Fixed parameters are difficult to dynamically match, resulting in delayed response to disturbances and low frequency stability margin. The active power control loop only performs proportional adjustment based on frequency deviation, lacking the ability to predict frequency change trends. Faced with sudden disturbances, this mode cannot detect the direction and speed of frequency fluctuations in advance, making it difficult to intervene and buffer in the early stages of fluctuations. This not only leads to increased frequency fluctuation amplitude but also prolongs the system's recovery time. When multiple VSGs are connected in parallel, power distribution is easily affected by differences in line impedance. Due to the different line lengths and cross-sectional areas from each VSG node to the load center, there are significant differences in line impedance. This can cause uneven distribution of loads, with some VSGs overloaded and others underloaded. Furthermore, small voltage differences between nodes can create circulating currents, which increase converter losses and exacerbate equipment heating, reducing system operating efficiency and potentially leading to converter failures in the long term, thus weakening operational reliability. Therefore, this invention proposes an adaptive virtual inertia and adaptive virtual impedance adjustment strategy for multi-VSG energy storage systems in microgrids with different electrical distances and different line impedances. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance, so as to solve the problem of limited control performance of distributed microgrids when the grid strength changes.

[0005] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0006] An adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance includes the following steps:

[0007] Step S1. Dynamic electrical distance calculation: Obtain real-time electrical parameters of the microgrid, introduce a frequency coupling factor to calculate the dynamic electrical distance, and obtain the electrical distance value updated with the system state;

[0008] Step S2. Virtual Inertia and Damping Coefficient Adjustment: Based on the dynamic electrical distance output in Step S1, the virtual inertia and damping coefficient of the VSG are dynamically adjusted. The closer the electrical distance, the larger the virtual inertia and damping coefficient.

[0009] Step S3. Active power control loop feedforward compensation: Collect the real-time frequency and frequency change rate of the system, add a speed feedforward term to the VSG active power control loop to sense the frequency change in the microgrid, and generate a dynamic reference active power value to compensate for frequency fluctuations in advance.

[0010] Step S4. Virtual Impedance Adjustment: Obtain the instantaneous output power of each VSG and calculate the average power. After calculating the power deviation, introduce a proportional-integral-derivative (PI-DE) circuit to dynamically adjust the virtual impedance coefficient to optimize the virtual impedance design. The virtual impedance coefficient includes an adaptive power deviation proportional coefficient, a dynamic adaptive power deviation integral coefficient, and a dynamic adaptive power deviation derivative coefficient.

[0011] Furthermore, in step S1, the electrical distance The regulation formula is as follows:

[0012] ;

[0013] In the formula, For nodes and Real-time admittance between; and These are voltage and frequency deviations, respectively. These are the weighting coefficients for the voltage coupling factor; These are the weighting coefficients of the frequency coupling factor;

[0014] electrical distance It updates dynamically based on the system voltage and frequency status.

[0015] Furthermore, in step S2, based on the dynamic electrical distance output in step S1, the virtual inertia and damping coefficient of the VSG are dynamically adjusted, as shown below:

[0016] ;

[0017] ;

[0018] In the formula, and The virtual inertia and damping coefficient are the reference values. and For adjustment coefficients, For nodes The neighborhood group, For electrical distance.

[0019] Furthermore, in step S3, the real-time frequency and frequency change rate of the system are collected, and a speed feedforward term for sensing frequency changes in the microgrid is added to the VSG active power control loop to generate a dynamic reference active power value. It is expressed as follows:

[0020] ;

[0021] In the formula, P0 represents the reference active power, f0 represents the system rated frequency, and f represents the system actual frequency. Represents the rate of change of angular frequency. This is the feedforward gain.

[0022] Furthermore, the dynamic adjustment of the virtual impedance coefficient to optimize the design of the virtual impedance, wherein the virtual impedance... It is expressed as follows:

[0023] ;

[0024] In the formula, This is the adaptive power deviation proportional coefficient. For dynamic adaptive power deviation integral coefficient, For the dynamic adaptive power deviation differential coefficient, The average power of all VSGs, For the first Real-time active power of VSG, For power deviation, This represents the rate of change of power deviation.

[0025] Furthermore, the adaptive power deviation proportional coefficient is expressed as follows:

[0026] ;

[0027] In the formula, This is the initial adaptive deviation proportional coefficient. For adaptive strength with proportional coefficient, The absolute value of the power deviation. The average power of all VSGs, This is a coefficient greater than zero, indicating the degree of importance attached to the proportional term, when the power deviation... When the coefficient is large, the proportional term coefficient increases adaptively, making the system adjust faster; when When the value decreases, the effect of the proportional term adjustment is reduced, thus weakening the occurrence of system oscillations.

[0028] Furthermore, the integral coefficient of the dynamic adaptive power deviation is expressed as follows:

[0029] ;

[0030] In the formula, The initial adaptive power deviation integral coefficients, This is the integral smoothing coefficient; when the deviation is large, The reduction limits the integrator's overshoot during severe transient processes; when the deviation decreases, the integral gain automatically recovers to its maximum value. This allows for the rapid elimination of steady-state errors.

[0031] Furthermore, the differential coefficients of the dynamic adaptive power deviation are expressed as follows:

[0032] ;

[0033] In the formula, The initial adaptive power deviation integral coefficients, This is the differential sensitivity coefficient, used to adjust the sensitivity of the differential action. It is a hyperbolic tangent smoothing function that smooths and limits the differential signal, restricting its output to the range [-1, 1].

[0034] An adaptive control device for grid-connected energy storage based on electrical distance sensing and improved virtual impedance includes the following steps:

[0035] The dynamic electrical distance calculation module is used to obtain real-time electrical parameters of the microgrid, introduce a frequency coupling factor to calculate the dynamic electrical distance, and obtain the electrical distance value updated with the system state.

[0036] The virtual inertia and damping coefficient adjustment module is used to dynamically adjust the virtual inertia and damping coefficient of the VSG based on the dynamic electrical distance. The closer the electrical distance, the greater the virtual inertia and damping coefficient.

[0037] The active power control loop feedforward compensation module is used to collect the real-time frequency and frequency change rate of the system. A speed feedforward term that senses the frequency change in the microgrid is added to the VSG active power control loop to generate a dynamic reference active power value in advance to compensate for frequency fluctuations.

[0038] The virtual impedance adjustment module is used to obtain the instantaneous output power of each VSG and calculate the average power. After calculating the power deviation, a proportional-integral-derivative (PI-DE) circuit is introduced to dynamically adjust the virtual impedance coefficient to optimize the virtual impedance design. The virtual impedance coefficient includes an adaptive power deviation proportional coefficient, a dynamic adaptive power deviation integral coefficient, and a dynamic adaptive power deviation derivative coefficient.

[0039] An adaptive control system for grid-connected energy storage based on electrical distance sensing and improved virtual impedance includes: a computer-readable storage medium and a processor;

[0040] The computer-readable storage medium is used to store executable instructions;

[0041] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the adaptive control method for grid-based energy storage based on electrical distance sensing and improved virtual impedance.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] This invention achieves this through electrical distance sensing. and The adaptive adjustment enables each VSG to dynamically match its inertia and damping according to the degree of electrical connection with key nodes, significantly improving the system's adaptability to topology changes. The frequency differential feedforward stage accelerates the disturbance response speed by proactively compensating for the frequency change rate, effectively suppressing frequency transient deviations. Dynamic virtual impedance adjustment based on power deviation precisely optimizes power distribution among multiple VSGs, significantly reducing circulating current. This invention significantly improves the power adaptive capability of new energy power plants and the overall stability of the power grid system, providing strong support for the safe and efficient operation of the power system. Attached Figure Description

[0044] Figure 1 This is a flowchart of an adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance proposed in an embodiment of the present invention.

[0045] Figure 2 This is a diagram of the microgrid structure in an embodiment of the present invention.

[0046] Figure 3 This is a simulation frequency curve diagram in an embodiment of the present invention.

[0047] Figure 4 This is a simulated voltage curve diagram in an embodiment of the present invention.

[0048] Figure 5 This is a simulated active power deviation diagram in an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 and Figure 2 This invention proposes an adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance, comprising the following steps:

[0051] S1: Obtain real-time electrical distance data of the microgrid, introduce a frequency coupling factor, and dynamically update the calculated electrical distance according to the microgrid status;

[0052] This embodiment introduces electrical distance into microgrid optimization control. By using methods such as dynamic electrical distance calculation and adaptive parameter allocation, it proposes dynamic electrical distance calculation based on real-time measurement data and introduces a frequency coupling factor to enable the electrical distance to be dynamically updated with the system state.

[0053] The electrical distance The regulation formula is as follows:

[0054]

[0055] In the formula, For nodes and Real-time admittance between; and These are voltage and frequency deviations, respectively. For electrical distance weighting coefficients, The frequency change rate weighting coefficient; The VSG with a tight electrical connection takes on more regulation responsibility (such as the VSG near the load center needs stronger support); the sum of the two is 1, avoiding excessive weighting that could lead to parameter saturation.

[0056] S2: Based on the dynamically updated electrical distance, the virtual inertia and damping coefficient of the VSG in the microgrid are dynamically adjusted.

[0057] Among them, the VSG dynamic virtual inertia The expression is:

[0058]

[0059] In the formula, This is the virtual inertia reference value. For adjustment coefficients, For nodes The neighborhood group, Electrical distance;

[0060] When the system experiences frequency disturbances (such as sudden load changes or fluctuations in renewable energy power), the frequency deviation will be affected by electrical distance, thereby triggering virtual inertia. Increase to slow down the rate of change of frequency

[0061] Furthermore, the adaptive damping coefficient is obtained by dynamically adjusting according to changes in electrical distance. , means as follows:

[0062]

[0063] In the formula, This is the reference value for the damping coefficient. For adjustment coefficients, For nodes The neighborhood group, This refers to the electrical distance; if the power station is closely electrically connected to the power grid, it indicates that the power station is greatly affected by the dynamics of the power grid, and the distance needs to be increased. and To enhance its own inertia and damping, it supports the stability of the power grid; if the electrical connection is weak, it needs to be appropriately reduced. and This avoids inertial redundancy and improves control efficiency.

[0064] In some implementations, the nominal virtual inertia , make The maximum increase is 50% (to avoid excessive inertia leading to sluggish frequency response), balancing inertial support and response speed; , Damping is more sensitive to the rate of frequency change and is directly coupled to it, ensuring that damping is immediately enhanced upon disturbance, quickly suppressing the frequency drop trend; for example... Figure 3 As shown, when the system is disturbed, the frequency will fluctuate. Under the control method of the present invention, the frequency can be quickly restored to near the rated value, the fluctuation range is small, and the recovery speed is fast. This shows that the control method proposed in the present invention can effectively improve the frequency stability of the power system and suppress frequency fluctuations.

[0065] S3: Add a speed feedforward term that senses the frequency change in the microgrid to the active power control loop of the VSG, and integrate the speed feedforward term that senses the frequency change into the active power control loop to generate a dynamic reference active power value.

[0066] The dynamic reference active power It is expressed as follows:

[0067]

[0068] In the formula, Indicates the reference active power value. Indicates the system's rated frequency. Indicates the actual frequency of the system. Represents the rate of change of angular frequency. This is the feedforward gain.

[0069] When the frequency rises rapidly, a negative active power increment is immediately generated, reducing VSG output in advance to avoid frequency overshoot; conversely, when the frequency falls rapidly, a positive active power increment is immediately generated, increasing VSG output in advance to prevent the frequency from becoming too low. This effectively reduces the amplitude and decay time of frequency oscillations, improving system dynamic stability. It can also reduce the maximum transient frequency deviation and recovery time, meeting the grid's requirements for the rapid primary frequency regulation of new energy power plants.

[0070] In some implementations, when At that time, the system poles are calculated using eigenvalues. (The real part is negative), which satisfies the stability requirements, such as Figure 4 As shown, when load switching or grid faults occur, voltage fluctuations will occur. Under the control method of this invention, the voltage can quickly recover to a stable state with a small fluctuation range. This indicates that the control method proposed in this invention can effectively suppress voltage fluctuations and improve the voltage stability of the power system.

[0071] S4: Obtain the instantaneous output power of each VSG and calculate the average power, then determine the [missing value]. VSG power deviation An adaptive virtual impedance value based on power deviation is introduced. .

[0072] In this embodiment, a relatively small line impedance is simulated. and a large line impedance The two VSGs are connected to the point of common coupling (PCC), each with a rated power of 10kW. The system acquires the real-time active power output of each distributed VSG. And calculate the average power of all VSGs. , thereby finding the first VSG power deviation From this, the real-time power values ​​of each component in the adaptive virtual impedance can be obtained, and the VSG virtual impedance can be derived accordingly. The formula is:

[0073] .

[0074] S5: Introducing the virtual impedance adaptive power deviation proportional coefficient from the adaptive virtual impedance formula based on power deviation. To differentiate the "emphasis" placed on different power differences for different frequency deviation values, an adaptive proportional coefficient was introduced. Improve the formula.

[0075] Specifically, VSG virtual impedance is introduced. Adaptive power deviation proportional coefficient This accelerates the redistribution of power, as expressed in the following expression:

[0076]

[0077] In the formula, The initial adaptive power deviation coefficient is set to 0.0005. For the proportional coefficient adaptive intensity, the initial value is set to 5; in this expression, when the power deviation... When the value increases, the proportional term coefficient increases adaptively, making the system adjust faster and improving the system's dynamic performance more quickly; when When the value decreases, the effect of the proportional term adjustment is reduced, the adjustment speed is slowed down, and the occurrence of system oscillations is suppressed.

[0078] S6: Introducing an adaptive power deviation integral coefficient into the adaptive virtual impedance based on power deviation. Considering that different frequency deviations have different effects on the integral coefficient, an integral smoothing coefficient is introduced to address the occurrence of system instability, especially when the power difference is zero.

[0079] Specifically, VSG virtual impedance is introduced. Adaptive power deviation integral coefficient To enhance system recovery stability, the expression is:

[0080]

[0081] In the formula, The initial adaptive power deviation integral coefficient is set to 0.00005. This is the integral smoothing coefficient, a small coefficient of 0.2, to prevent... There are instances where the denominator exceeds 0.

[0082] when deviation When it increases, the integral gain Automatic reduction limits the integrator's overshoot during severe transient processes; when the deviation... When the value decreases, the integral gain automatically returns to its maximum value. This allows for the rapid elimination of steady-state errors, balancing transient stability with steady-state accuracy.

[0083] S7: In order to suppress power overshoot and high-frequency noise, a virtual impedance adaptive power deviation integral coefficient is introduced to provide appropriate advance compensation (increasing or decreasing virtual impedance); when the change is stable, its effect is almost zero.

[0084] Specifically, VSG virtual impedance is introduced. Adaptive power deviation integral coefficient The expression is:

[0085]

[0086] In the formula, The initial adaptive power deviation integral coefficient is set to 0.035. Let be the differential sensitivity coefficient, used to adjust the sensitivity of the differential action, and let . .

[0087] The function is a hyperbolic tangent smoothing function. Traditional differential terms are extremely sensitive to noise. This function is used to smooth and limit the differential signal, so that its output is limited to the range [-1, 1].

[0088] When the power change trend When the change is rapid, it provides appropriate lead compensation (increasing or decreasing the virtual impedance) to suppress power overshoot; when the change is smooth, its effect is almost zero, avoiding the introduction of high-frequency noise. It is similar to a predictor with saturation characteristics. Figure 5 As shown, the dynamic response of the active power deviation in the simulation process can be seen in the embodiment of the present invention. When the system is disturbed, such as by load changes, the active power deviation will change. However, under the control method of the present invention, the active power deviation can quickly recover to a stable state, indicating that the control method proposed in the present invention has good power response characteristics and stability.

[0089] This invention dynamically adjusts the virtual inertia based on electrical distance. and damping coefficient The closer the electrical distance between the VSG parameters, the stronger the coupling, enabling adaptive coordination and significantly improving the microgrid's operational performance. Furthermore, a feedforward term is added to the VSG's active power control loop to anticipate the rate of frequency change, rather than focusing solely on the magnitude of the deviation. This enhances the VSG's dynamic response speed, damping characteristics, and frequency stability. The virtual impedance coefficient dynamically changes according to the power deviation of the power grid system, allowing the virtual impedance to be dynamically adjusted based on real-time power distribution errors, compensating for differences in physical impedance. This is an effective way to achieve precise power sharing and improve dynamic performance.

[0090] Another aspect of the present invention provides an adaptive control system for grid-connected energy storage based on electrical distance sensing and improved virtual impedance, comprising: a computer-readable storage medium and a processor;

[0091] The computer-readable storage medium is used to store executable instructions;

[0092] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the adaptive control method for grid-based energy storage based on electrical distance sensing and improved virtual impedance.

[0093] In another aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned adaptive control method for grid-based energy storage based on electrical distance sensing and improved virtual impedance.

[0094] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] Finally, 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 the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A grid-based adaptive control method for energy storage based on electrical distance sensing and improved virtual impedance, characterized in that, Includes the following steps: Step S1. Dynamic electrical distance calculation: Obtain real-time electrical parameters of the microgrid, introduce a frequency coupling factor to calculate the dynamic electrical distance, and obtain the electrical distance value updated with the system state; Step S2. Virtual Inertia and Damping Coefficient Adjustment: Based on the dynamic electrical distance output in Step S1, the virtual inertia and damping coefficient of the VSG are dynamically adjusted. The closer the electrical distance, the larger the virtual inertia and damping coefficient. Step S3. Active power control loop feedforward compensation: Collect the real-time frequency and frequency change rate of the system, add a speed feedforward term to the VSG active power control loop to sense the frequency change in the microgrid, and generate a dynamic reference active power value to compensate for frequency fluctuations in advance. Step S4. Virtual Impedance Adjustment: Obtain the instantaneous output power of each VSG and calculate the average power. After calculating the power deviation, introduce a proportional-integral-derivative (PI-DE) circuit to dynamically adjust the virtual impedance coefficient to optimize the virtual impedance design. The virtual impedance coefficient includes an adaptive power deviation proportional coefficient, a dynamic adaptive power deviation integral coefficient, and a dynamic adaptive power deviation derivative coefficient.

2. The adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance as described in claim 1, characterized in that: Electrical distance in step S1 The regulation formula is as follows: ； In the formula, For nodes and Real-time admittance between; and These are voltage and frequency deviations, respectively. These are the weighting coefficients for the voltage coupling factor; These are the weighting coefficients of the frequency coupling factor; electrical distance It updates dynamically based on the system voltage and frequency status.

3. The adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance as described in claim 1, characterized in that: In step S2, based on the dynamic electrical distance output in step S1, the virtual inertia and damping coefficient of the VSG are dynamically adjusted, as shown below: ； ； In the formula, and The virtual inertia and damping coefficient are the reference values. and For adjustment coefficients, For nodes The neighborhood group, For electrical distance.

4. The adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance as described in claim 1, characterized in that: In step S3, the real-time frequency and frequency change rate of the system are collected, and a speed feedforward term for sensing frequency changes in the microgrid is added to the VSG active power control loop to generate a dynamic reference active power value. It is expressed as follows: ； In the formula, P0 represents the reference active power, f0 represents the system rated frequency, and f represents the system actual frequency. Represents the rate of change of angular frequency. This is the feedforward gain.

5. The adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance as described in claim 1, characterized in that: The dynamic adjustment of the virtual impedance coefficient is used to optimize the design of the virtual impedance, wherein the virtual impedance... It is expressed as follows: ； In the formula, This is the adaptive power deviation proportional coefficient. For dynamic adaptive power deviation integral coefficient, For the dynamic adaptive power deviation differential coefficient, The average power of all VSGs, For the first Real-time active power of VSG, For power deviation, This represents the rate of change of power deviation.

6. The adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance as described in claim 5, characterized in that: The adaptive power deviation proportional coefficient is expressed as follows: ； In the formula, This is the initial adaptive deviation proportional coefficient. For adaptive strength with proportional coefficient, The absolute value of the power deviation. The average power of all VSGs, This is a coefficient greater than zero, indicating the degree of importance attached to the proportional term, when the power deviation... When the coefficient is large, the proportional term coefficient increases adaptively, making the system adjust faster; when When the value decreases, the effect of the proportional term adjustment is reduced, thus weakening the occurrence of system oscillations.

7. The adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance as described in claim 5, characterized in that: The integral coefficient of the dynamic adaptive power deviation is expressed as follows: ； In the formula, The initial adaptive power deviation integral coefficients, This is the integral smoothing coefficient; when the deviation is large, The reduction limits the integrator's overshoot during severe transient processes; when the deviation decreases, the integral gain automatically recovers to its maximum value. This allows for the rapid elimination of steady-state errors.

8. The adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance as described in claim 5, characterized in that: The differential coefficients of the dynamic adaptive power deviation are expressed as follows: ； In the formula, The initial adaptive power deviation integral coefficients, This is the differential sensitivity coefficient, used to adjust the sensitivity of the differential action. It is a hyperbolic tangent smoothing function that smooths and limits the differential signal, restricting its output to the range [-1, 1].

9. A grid-connected energy storage adaptive control device based on electrical distance sensing and improved virtual impedance, characterized in that, Includes the following steps: The dynamic electrical distance calculation module is used to obtain real-time electrical parameters of the microgrid, introduce a frequency coupling factor to calculate the dynamic electrical distance, and obtain the electrical distance value updated with the system state. The virtual inertia and damping coefficient adjustment module is used to dynamically adjust the virtual inertia and damping coefficient of the VSG based on the dynamic electrical distance. The closer the electrical distance, the greater the virtual inertia and damping coefficient. The active power control loop feedforward compensation module is used to collect the real-time frequency and frequency change rate of the system. A speed feedforward term that senses the frequency change in the microgrid is added to the VSG active power control loop to generate a dynamic reference active power value in advance to compensate for frequency fluctuations. The virtual impedance adjustment module is used to obtain the instantaneous output power of each VSG and calculate the average power. After calculating the power deviation, a proportional-integral-derivative (PI-DE) circuit is introduced to dynamically adjust the virtual impedance coefficient to optimize the virtual impedance design. The virtual impedance coefficient includes an adaptive power deviation proportional coefficient, a dynamic adaptive power deviation integral coefficient, and a dynamic adaptive power deviation derivative coefficient.

10. An adaptive control system for grid-connected energy storage based on electrical distance sensing and improved virtual impedance, comprising: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the adaptive control method for grid-connected energy storage based on electrical distance sensing and improved virtual impedance as described in any one of claims 1-8.

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