Virtual synchronous machine adaptive power control method for heterogeneous energy storage
Patent Information
- Application Number
- CN202611216438.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-12
- Publication Date
- 2026-10-27
AI Technical Summary
[0006]为了解决上述技术问题，本发明提供面向异构储能的虚拟同步机自适应功率控制方法，以解决现有技术中无法根据各单元运行状态自适应分配功率，难以发挥异构储能的互补优势等问题
1、本发明通过自适应分配不同类型储能的功率分量，充分发挥电池能量密度高、适合长时间低频功率支撑的优势，同时利用超级电容功率密度高、响应速度快的特点承担高频波动功率，既缓解了电池的功率波动冲击，延长电池使用寿命，又能充分发挥异构储能单元的互补特性，提升系统整体响应能力。能够根据频率变化状态自适应调整虚拟惯量与阻尼系数，在大频率扰动时提供更强的惯性和阻尼支撑，有效改善系统频率动态响应，提升新型电力系统的频率稳定性；结合各储能单元的SOC、健康状态、运行温度、动态能力等多维度信息调整功率分配权重，可有效避免储能单元过充过放，均衡各单元老化程度，降低系统运行损耗，提升异构储能系统整体运行经济性与使用寿命。
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of information technology, specifically a virtual synchronous machine adaptive power control method for heterogeneous energy storage. Background Technology
[0002] With the large-scale grid connection of new energy power generation, the overall rotational inertia and damping level of the power system have decreased significantly, and frequency stability issues have become increasingly prominent. Virtual synchronous machine technology, by introducing virtual inertia and virtual damping into the control of energy storage converters, can simulate the operating characteristics of synchronous generators and provide necessary frequency support for the power grid. It has become one of the core technologies for energy storage to participate in grid frequency regulation.
[0003] With energy transition and technological advancements, power systems with a high proportion of renewable energy and power electronic devices are gradually becoming an important trend in the development of new power systems. However, as the proportion of new energy power generation systems relying on power electronic devices to connect to the grid gradually increases, these systems exhibit characteristics of low inertia and low damping. When power disturbances occur in the system, frequency instability may occur, threatening the safe and stable operation of the system and the reliability of power supply.
[0004] Currently, single-type energy storage systems struggle to simultaneously meet the requirements for both energy density and power density, failing to balance frequency regulation performance with operational economics. Battery energy storage boasts high energy density but short cycle life, and frequent power fluctuations accelerate capacity decay, significantly increasing operating costs. Supercapacitors offer high power density and fast response, but low energy density, making them unsuitable for long-term frequency regulation when used alone. Combining different types of energy storage to form heterogeneous energy storage systems, leveraging their complementary advantages, is an effective way to improve frequency regulation performance and extend the overall system lifespan. However, existing virtual synchronous machine control methods are mostly designed for single energy storage units, failing to fully consider the differences in characteristics between heterogeneous energy storage units. They cannot adaptively allocate power based on the operating status of each unit, thus hindering the realization of the complementary advantages of heterogeneous energy storage.
[0005] In summary, this invention provides a virtual synchronous machine adaptive power control method for heterogeneous energy storage to solve the above problems. Summary of the Invention
[0006] To address the aforementioned technical problems, this invention provides a virtual synchronous machine adaptive power control method for heterogeneous energy storage, which solves the problems in the prior art where power cannot be adaptively allocated according to the operating status of each unit, making it difficult to leverage the complementary advantages of heterogeneous energy storage.
[0007] A virtual synchronous machine adaptive power control method for heterogeneous energy storage includes the following steps: S1: Modeling of the heterogeneous energy storage system, establishing models for each energy storage unit, and simulating the rotor motion equations of the virtual synchronous machine as follows: ; In the formula, For the virtual inertia of the virtual synchronizer, Here is the damping coefficient of the virtual synchronizer. The rotor angular frequency of the virtual synchronous machine. The rated angular frequency of the virtual synchronizer. For virtual synchronous machine virtual mechanical torque, The electromagnetic power output by the inverter; S2: The total power command is decomposed into low-frequency and high-frequency components through a filter, and the cutoff frequency is adaptively adjusted. Real-time optimization is performed based on the SOC and dynamic capabilities of the energy storage units. The power allocation weights are dynamically adjusted according to the SOC status of each energy storage unit. The equation for dynamically adjusting the power allocation weights is: ; ; In the formula, Low-frequency power components, For high-frequency power components, Assign weights to battery power. Assign weights to the supercapacitor power; S3: Based on the rate of change of frequency and power deviation, adjust the virtual inertia and damping coefficient in real time, and optimize online using fuzzy logic, model predictive control or adaptive algorithms; S4: In response to changes in adaptive parameters, analyze the system root trajectory to ensure that stability conditions are met under all operating conditions. Set hardware and software limits for power, current, and SOC to prevent energy storage overload. During fault ride-through, switch to grid support mode.
[0008] Furthermore, virtual moment of inertia and virtual damping coefficient are set according to the rotor angular frequency change and rated angular frequency of the virtual synchronizer. The virtual damping coefficient is positively correlated with the change in the virtual rotor angular frequency; The virtual moment of inertia is negatively correlated with the rate of change of the virtual rotor angular frequency and positively correlated with the amount of change of the virtual rotor angular frequency.
[0009] Furthermore, the SOC of the supercapacitor is usually expressed by voltage normalization. The weighting design is similar, but a wider SOC operating range can be set. Since the supercapacitor has a fast response, its weight can also be associated with the power change rate, and the high-frequency power sharing ratio can be dynamically adjusted within the allowable SOC range.
[0010] Furthermore, in S3, when there is a large frequency deviation, the virtual inertia is increased to enhance inertial support, suppress frequency abrupt changes, the damping coefficient is increased to improve damping, and overshoot is reduced.
[0011] Furthermore, the power sharing ratio of each energy storage unit is dynamically adjusted according to the SOC status to prevent overcharging or over-discharging and to balance aging. Specifically, when approaching the lower limit of SOC, the discharge weight is reduced; when approaching the upper limit of SOC, the charging weight is reduced to prevent exceeding the limit.
[0012] Furthermore, the weighting equation can simultaneously consider multiple constraints such as SOC, health status, temperature, and power change rate. At high temperatures, the battery weight is reduced to decrease thermal stress, and when the power change rate is too large, the supercapacitor weight is temporarily increased to protect the battery.
[0013] Furthermore, the performance indicators of each energy storage unit are extracted, a performance change prediction model is established based on the performance indicator data, the performance change trend is obtained, the capacity decay rate of different energy storage technologies is calculated based on the performance change trend, and a capacity optimization model is constructed in combination with the capacity decay rate to determine the optimal capacity configuration ratio.
[0014] Furthermore, by collecting real-time operating data of each energy storage unit, comparing the actual operating conditions with the optimized scheduling scheme, dynamically adjusting control parameters and optimization algorithms, and executing energy storage unit fault self-healing strategies, the stable operation of the group is ensured.
[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention adaptively allocates power components from different types of energy storage, fully leveraging the advantages of batteries' high energy density and suitability for long-term low-frequency power support, while utilizing the high power density and fast response speed of supercapacitors to handle high-frequency power fluctuations. This not only mitigates the impact of power fluctuations on batteries and extends their lifespan, but also fully utilizes the complementary characteristics of heterogeneous energy storage units, improving the overall system response capability. It can adaptively adjust virtual inertia and damping coefficients according to frequency changes, providing stronger inertial and damping support during high-frequency disturbances, effectively improving the system's dynamic frequency response and enhancing the frequency stability of the new power system. By combining multi-dimensional information such as the SOC, health status, operating temperature, and dynamic capabilities of each energy storage unit to adjust power allocation weights, it can effectively prevent overcharging and over-discharging of energy storage units, balance the aging degree of each unit, reduce system operating losses, and improve the overall operational economy and lifespan of the heterogeneous energy storage system.
[0016] 2. This invention provides sufficient inertial support when the grid frequency fluctuates significantly by dynamically adjusting the virtual inertia and damping coefficient according to the frequency, suppressing frequency abrupt changes, reducing frequency overshoot, and effectively improving grid frequency stability. This solves the problem that fixed-parameter virtual synchronous machines are difficult to adapt to all operating conditions. Simultaneously, by dynamically adjusting the power distribution weight based on the SOC state and multiple constraints, it can avoid overcharging and over-discharging of energy storage units, balance the aging rate of each unit, improve the overall operational safety and service life of the heterogeneous energy storage system, and ensure stable operation of the system under all operating conditions.
[0017] 3. This invention monitors the operating status of different heterogeneous energy storage units in real time, compares the actual operating status with the optimized scheduling scheme based on the collected operating data, and dynamically adjusts the control parameters and optimization strategies. When a single energy storage unit fails, a fault self-healing strategy can be quickly executed to redistribute power tasks, avoiding system-wide operational disorder caused by a single unit failure and ensuring the continuous and stable operation of the heterogeneous energy storage group. At the same time, the capacity configuration can be optimized based on the performance change trend of each energy storage unit, and the capacity allocation ratio can be adjusted based on the capacity decay rate of different energy storage technologies, further improving the system resource utilization rate and reducing the total life cycle operating cost. Attached Figure Description
[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation
[0019] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.
[0020] like Figure 1 As shown, this invention provides a virtual synchronous machine adaptive power control method for heterogeneous energy storage, comprising the following steps: S1: Modeling of the heterogeneous energy storage system, establishing models for each energy storage unit, and simulating the rotor motion equations of the virtual synchronous machine as follows: ; In the formula, For the virtual inertia of the virtual synchronizer, Here is the damping coefficient of the virtual synchronizer. The rotor angular frequency of the virtual synchronous machine. The rated angular frequency of the virtual synchronizer. For virtual synchronous machine virtual mechanical torque, The electromagnetic power output by the inverter; S2: The total power command is decomposed into low-frequency and high-frequency components through a filter, and the cutoff frequency is adaptively adjusted. Real-time optimization is performed based on the SOC and dynamic capabilities of the energy storage units. The power allocation weights are dynamically adjusted according to the SOC status of each energy storage unit. The equation for dynamically adjusting the power allocation weights is: ; ; In the formula, Low-frequency power components, For high-frequency power components, Assign weights to battery power. Assign weights to the supercapacitor power; S3: Based on the rate of change of frequency and power deviation, adjust the virtual inertia and damping coefficient in real time, and optimize online using fuzzy logic, model predictive control or adaptive algorithms; S4: In response to changes in adaptive parameters, analyze the system root trajectory to ensure that stability conditions are met under all operating conditions. Set hardware and software limits for power, current, and SOC to prevent energy storage overload. During fault ride-through, switch to grid support mode.
[0021] As one embodiment of the present invention, virtual moment of inertia and virtual damping coefficient are set according to the rotor angular frequency change and rated angular frequency of the virtual synchronous machine. The virtual damping coefficient is positively correlated with the change in virtual rotor angular frequency; The virtual moment of inertia is negatively correlated with the rate of change of the virtual rotor angular frequency and positively correlated with the amount of change of the virtual rotor angular frequency.
[0022] As one embodiment of the present invention, the SOC of a supercapacitor is usually expressed by voltage normalization. The weight design is similar, but a wider SOC operating range can be set. Since the supercapacitor has a fast response, its weight can also be associated with the power change rate, and the high-frequency power sharing ratio can be dynamically adjusted within the allowable SOC range.
[0023] In one embodiment of the present invention, in S3, when there is a large frequency deviation, the virtual inertia is increased to enhance inertial support, suppress frequency abrupt changes, the damping coefficient is increased to improve damping, and overshoot is reduced.
[0024] As one embodiment of the present invention, the power sharing ratio of each energy storage unit is dynamically adjusted according to the SOC state to prevent overcharging or over-discharging and to balance aging; Specifically, when approaching the lower limit of SOC, the discharge weight is reduced; when approaching the upper limit of SOC, the charging weight is reduced to prevent exceeding the limit.
[0025] As one embodiment of the present invention, the weighting equation can simultaneously consider multiple constraints such as SOC, health status, temperature, and power change rate. When the temperature is high, the battery weight is reduced to reduce thermal stress. When the power change rate is too large, the supercapacitor weight is temporarily increased to protect the battery.
[0026] As one embodiment of the present invention, performance indicators of each energy storage unit are extracted, a performance change prediction model is established based on the performance indicator data to obtain the performance change trend, the capacity decay rate of different energy storage technologies is calculated based on the performance change trend, and a capacity optimization model is constructed in combination with the capacity decay rate to determine the optimal capacity configuration ratio.
[0027] As one embodiment of the present invention, by real-time monitoring and collecting the operating data of each energy storage unit, comparing the deviation between the actual operating conditions and the optimized scheduling scheme, dynamically adjusting the control parameters and optimization algorithms, and executing the energy storage unit fault self-healing strategy, the stable operation of the group is ensured.
[0028] This invention adaptively allocates power components from different types of energy storage, fully leveraging the advantages of batteries' high energy density and suitability for long-term low-frequency power support, while utilizing the high power density and fast response of supercapacitors to handle high-frequency power fluctuations. This mitigates the impact of power fluctuations on batteries, extending their lifespan, and fully utilizes the complementary characteristics of heterogeneous energy storage units, improving the overall system response capability. By dynamically adjusting the virtual inertia and damping coefficient according to frequency, sufficient inertial support is provided when the grid frequency fluctuates significantly, suppressing frequency abrupt changes, reducing frequency overshoot, and effectively improving grid frequency stability. This solves the problem that fixed-parameter virtual synchronous machines are difficult to adapt to all operating conditions. Furthermore, by dynamically adjusting the power allocation weight based on SOC status and multiple constraints, overcharging and over-discharging of energy storage units can be avoided, balancing the aging rate of each unit, improving the overall operational safety and lifespan of the heterogeneous energy storage system, and ensuring stable operation of the system under all operating conditions.
[0029] By monitoring the operational status of different heterogeneous energy storage units in real time and comparing the deviation between the actual operational status and the optimized scheduling scheme with the collected operational data, control parameters and optimization strategies are dynamically adjusted. When a single energy storage unit fails, a fault self-healing strategy can be quickly executed to redistribute power tasks, preventing the overall system from becoming disordered due to a single unit failure and ensuring the continuous and stable operation of the heterogeneous energy storage group. At the same time, capacity configuration can be optimized based on the performance change trends of each energy storage unit, and the capacity allocation ratio can be adjusted in combination with the capacity decay rate of different energy storage technologies, further improving the system resource utilization rate and reducing the total life cycle operating cost.
[0030] This invention first models each unit of a heterogeneous energy storage system and derives the rotor motion equation of a virtual synchronous machine, providing a basic model framework for subsequent control strategies. Then, it uses an adaptive cutoff frequency filter to split the total power command of the system according to frequency characteristics, allowing different energy storage units to bear the power component that matches their own characteristics. Then, it combines various constraints such as the current SOC, health status, and operating temperature of each unit to calculate and adjust the power allocation weight of the two types of energy storage units in real time, ensuring that the power allocation is in line with the current operating capacity of each unit. Subsequently, based on the real-time rate of change of the grid frequency and the power deviation, fuzzy logic, model predictive control, or adaptive algorithms are used to optimize and adjust the virtual inertia and damping coefficient online to match the system stability requirements under different operating conditions. Finally, system stability analysis is carried out on the adjusted control parameters, and multiple hardware and software limiting protection energy storage units are set up. Under fault ride-through conditions, the system automatically switches to grid support mode to ensure stable operation of the system under all operating conditions.
[0031] During the control process, real-time data on changes in grid frequency are collected. Based on the magnitude and rate of frequency deviation, a preset adaptive algorithm is used to adjust the virtual inertia and damping coefficient online, and the optimal support parameters are matched for different frequency fluctuation scenarios. Finally, during the full parameter adaptive adjustment process, the root trajectory characteristics of the system are continuously analyzed to confirm that all operating conditions meet the system stability requirements. At the same time, multi-dimensional operation limit protection is set to avoid overload problems of energy storage units. When the system encounters a grid fault and needs to perform fault ride, the control mode is directly switched to grid support mode to ensure the system's support capability for the grid and realize the stable and efficient operation of the heterogeneous energy storage system in grid frequency and voltage regulation.
[0032] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.
Claims
1. A virtual synchronous machine adaptive power control method for heterogeneous energy storage, characterized in that, Includes the following steps: S1: Modeling of the heterogeneous energy storage system, establishing models for each energy storage unit, and simulating the rotor motion equations of the virtual synchronous machine as follows: ； In the formula, For the virtual inertia of the virtual synchronizer, Here is the damping coefficient of the virtual synchronizer. The rotor angular frequency of the virtual synchronous machine. The rated angular frequency of the virtual synchronizer. For virtual synchronous machine virtual mechanical torque, The electromagnetic power output by the inverter; S2: The total power command is decomposed into low-frequency and high-frequency components through a filter, and the cutoff frequency is adaptively adjusted. Real-time optimization is performed based on the SOC and dynamic capabilities of the energy storage units. The power allocation weights are dynamically adjusted according to the SOC status of each energy storage unit. The equation for dynamically adjusting the power allocation weights is: ； ； In the formula, Low-frequency power components, For high-frequency power components, Assign weights to battery power. Assign weights to the supercapacitor power; S3: Based on the rate of change of frequency and power deviation, adjust the virtual inertia and damping coefficient in real time, and optimize online using fuzzy logic, model predictive control or adaptive algorithms; S4: In response to changes in adaptive parameters, analyze the system root trajectory to ensure that stability conditions are met under all operating conditions. Set hardware and software limits for power, current, and SOC to prevent energy storage overload. During fault ride-through, switch to grid support mode.
2. The virtual synchronous machine adaptive power control method for heterogeneous energy storage as described in claim 1, characterized in that, The virtual moment of inertia and virtual damping coefficient are set according to the rotor angular frequency change and rated angular frequency of the virtual synchronous machine. The virtual damping coefficient is positively correlated with the change in the virtual rotor angular frequency; The virtual moment of inertia is negatively correlated with the rate of change of the virtual rotor angular frequency and positively correlated with the amount of change of the virtual rotor angular frequency.
3. The virtual synchronous machine adaptive power control method for heterogeneous energy storage as described in claim 1, characterized in that, The SOC of the supercapacitor is usually expressed by voltage normalization. The weighting design is similar, but a wider SOC operating range can be set. Since the supercapacitor has a fast response, its weight can also be related to the power change rate, and the high-frequency power sharing ratio can be dynamically adjusted within the allowable SOC range.
4. The virtual synchronous machine adaptive power control method for heterogeneous energy storage as described in claim 1, characterized in that, In S3, when there is a large frequency deviation, the virtual inertia is increased to enhance inertial support, suppress frequency abrupt changes, and the damping coefficient is increased to improve damping and reduce overshoot.
5. The virtual synchronous machine adaptive power control method for heterogeneous energy storage as described in claim 1, characterized in that, The power sharing ratio of each energy storage unit is dynamically adjusted according to the SOC status to prevent overcharging or over-discharging and to balance aging. Specifically, when approaching the lower limit of SOC, the discharge weight is reduced; when approaching the upper limit of SOC, the charging weight is reduced to prevent exceeding the limit.
6. The virtual synchronous machine adaptive power control method for heterogeneous energy storage as described in claim 1, characterized in that, The weighting equation can simultaneously consider multiple constraints such as SOC, health status, temperature, and power change rate. At high temperatures, the battery weight is reduced to reduce thermal stress, and when the power change rate is too large, the supercapacitor weight is temporarily increased to protect the battery.
7. The virtual synchronous machine adaptive power control method for heterogeneous energy storage as described in claim 1, characterized in that, The performance indicators of each energy storage unit are extracted. A performance change prediction model is established based on the performance indicator data to obtain the performance change trend. Based on the performance change trend, the capacity decay rate of different energy storage technologies is calculated. Combined with the capacity decay rate, a capacity optimization model is constructed to determine the optimal capacity configuration ratio.
8. The virtual synchronous machine adaptive power control method for heterogeneous energy storage as described in claim 1, characterized in that, The system collects operational data from each energy storage unit in real time, compares the actual operating conditions with the optimized scheduling scheme, dynamically adjusts control parameters and optimization algorithms, and executes energy storage unit fault self-healing strategies to ensure stable operation of the group.