Power distribution method and device and electronic equipment

Abstract

The invention provides a power distribution method and device and electronic equipment, and relates to the technical field of energy storage. The method comprises the following steps: acquiring a load power signal and a system state parameter of the energy storage system in real time; the system state parameters are related to a flywheel and a lithium battery; performing wavelet decomposition on the load power signal according to load fluctuation intensity to generate corresponding initial distribution power; the initial distribution power comprises a high-frequency component distributed to the flywheel and a low-frequency component distributed to the lithium battery; the load fluctuation intensity is used for representing the change rate of the load power along with time; adjusting the initial distribution power based on the system state parameters by adopting a predetermined multi-objective optimization algorithm, and generating first distribution power corresponding to the flywheel and second distribution power corresponding to the lithium battery; the first distribution power is related to the high-frequency component; the second distribution power is related to the low frequency component. According to the method, the time resolution and the timeliness of high-frequency component distribution can be improved.

Description

Technical Field

[0001] This application belongs to the field of energy storage technology, specifically relating to a power distribution method, device, and electronic equipment. Background Technology

[0002] Lithium-ion batteries are widely used in energy storage due to their high energy density and high charge / discharge efficiency. However, their cycle life is significantly affected by the charge / discharge rate and depth. When faced with high-frequency power fluctuations above 1 Hz, lithium-ion batteries need to be charged and discharged frequently in a short period of time, causing their cycle life to plummet from the usual 2000+ cycles to below 500 cycles. They are also prone to thermal runaway due to sudden temperature rises. Flywheel energy storage, on the other hand, excels in instantaneous power compensation scenarios due to its millisecond-level response speed and cycle life of over one million cycles. However, its low energy density and high self-discharge rate make it difficult to meet continuous power supply requirements.

[0003] To combine the advantages of both, most hybrid energy storage systems currently adopt an initial allocation strategy of "flywheel bearing high-frequency power + lithium battery bearing low-frequency power". This strategy often uses Fourier transform or fixed threshold to divide high and low frequency power, which has a large time resolution, resulting in a lag in the allocation of high-frequency components. As a result, lithium batteries are forced to participate in the high-frequency response, leading to a decline in their lifespan.

[0004] Therefore, the power distribution method of hybrid energy storage systems needs further optimization in related technologies. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a power allocation method, apparatus and electronic device to address the above-mentioned shortcomings of the prior art. Using the power allocation method, the time resolution can be reduced and the timeliness of high frequency component allocation can be improved.

[0006] In a first aspect, embodiments of this application provide a power allocation method, including:

[0007] Real-time acquisition of load power signals and system status parameters of the energy storage system; system status parameters are related to the flywheel and lithium battery;

[0008] The load power signal is decomposed into wavelet based on the load fluctuation intensity to generate the corresponding initial power allocation. The initial power allocation includes the high-frequency component allocated to the flywheel and the low-frequency component allocated to the lithium battery. The load fluctuation intensity is used to characterize the rate of change of load power over time.

[0009] A pre-determined multi-objective optimization algorithm is used to adjust the initial power allocation based on the system state parameters, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery; the first power allocation is related to the high-frequency component; the second power allocation is related to the low-frequency component.

[0010] In some embodiments of the first aspect, wavelet decomposition is performed on the load power signal based on the load fluctuation intensity to generate a corresponding initial power allocation, including:

[0011] If the load fluctuation intensity is greater than the preset intensity threshold, the load power signal is decomposed into wavelet decomposition according to the first preset layer to generate the corresponding initial power allocation; the high frequency component is composed of the superposition of detail coefficients corresponding to the first to the second preset layer of wavelet decomposition; the low frequency component is the approximation coefficients corresponding to the first preset layer of wavelet decomposition; the second preset layer is less than the first preset layer.

[0012] If the load fluctuation intensity is less than or equal to the preset intensity threshold, the load power signal is decomposed into wavelet decomposition according to the third preset layer to generate the corresponding initial power allocation; the high frequency component is composed of the superposition of detail coefficients corresponding to the first to fourth preset layers of wavelet decomposition; the low frequency component is the approximation coefficients corresponding to the third preset layer of wavelet decomposition; the fourth preset layer is less than the third preset layer.

[0013] In some embodiments of the first aspect, if the load fluctuation intensity of a first preset number of consecutive sliding windows is greater than a preset intensity threshold, the method further includes:

[0014] Increase the number of layers in the first preset layer;

[0015] If the load fluctuation intensity of the second consecutive preset number of sliding windows is less than a preset intensity threshold, the method further includes:

[0016] Reduce the number of layers in the second preset layer.

[0017] In some embodiments of the first aspect, the system state parameters include flywheel speed;

[0018] After performing wavelet decomposition on the load power signal based on the load fluctuation intensity to generate the corresponding initial power allocation, the following steps are also included:

[0019] If the power limit value corresponding to the flywheel speed is less than the high-frequency component, then the power limit value is taken as the first initial power corresponding to the flywheel.

[0020] The difference between the high-frequency component and the power limit, along with the low-frequency component, are used as the second initial power for the lithium battery.

[0021] A new initial allocation power is generated based on the first initial power and the second initial power;

[0022] Correspondingly, a pre-determined multi-objective optimization algorithm is used to adjust the initial power allocation based on system state parameters, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery, including:

[0023] A multi-objective optimization algorithm is used to adjust the new initial power allocation based on the system state parameters, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery.

[0024] In some embodiments of the first aspect, system state parameters include: flywheel speed, lithium battery state of charge, and lithium battery temperature;

[0025] A pre-determined multi-objective optimization algorithm is used to adjust the initial power allocation based on system state parameters, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery, including:

[0026] A multi-objective optimization algorithm is adopted to adjust the initial power allocation based on flywheel speed, state of charge, temperature, objective function and constraints, and to generate a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery. The objective function aims to minimize the lithium battery life loss and the system energy loss rate. The constraints include power balance constraints, flywheel safety constraints and lithium battery safety constraints.

[0027] In some embodiments of the first aspect, the method further includes:

[0028] If the system status parameters meet the preset protection trigger conditions, the use of the multi-objective optimization algorithm to adjust the initial power allocation will be suspended, and the preset protection strategy will be executed until the system status parameters meet the preset safety conditions.

[0029] The initial power allocation is adjusted using a multi-objective optimization algorithm to generate a new first power allocation corresponding to the flywheel and a new second power allocation corresponding to the lithium battery.

[0030] In some embodiments of the first aspect, the system state parameters include at least one of the following: flywheel speed, state of charge of the lithium battery, and temperature of the lithium battery;

[0031] If the system status parameters meet the preset protection triggering conditions, the adjustment of the initial power allocation using the multi-objective optimization algorithm will be paused, and the preset protection strategy will be executed until the system status parameters meet the preset safety conditions, including:

[0032] If the flywheel speed is greater than the first preset speed threshold, the multi-objective optimization algorithm for adjusting the initial power allocation will be paused, and the lithium battery forced absorption mode will be triggered until the flywheel speed is less than or equal to the second preset speed threshold; wherein, the absorption power of the lithium battery forced absorption mode is less than or equal to the current maximum charging power of the lithium battery.

[0033] If the state of charge is less than the first preset state of charge threshold, the multi-objective optimization algorithm for adjusting the initial power allocation will be paused, and the system will switch to flywheel independent power supply mode until the state of charge is greater than or equal to the second preset state of charge threshold.

[0034] If the state of charge is greater than the third preset state of charge threshold or the temperature is greater than the first preset temperature threshold, the multi-objective optimization algorithm is paused to adjust the initial power allocation, the flywheel is forced to bear all the power input, and when the temperature is greater than the first preset temperature threshold, the liquid cooling system is started until the state of charge is less than or equal to the fourth preset state of charge threshold and the temperature is less than or equal to the second preset temperature threshold.

[0035] In some embodiments of the first aspect, after resuming the adjustment of the initial power allocation using a multi-objective optimization algorithm to generate a new first power allocation corresponding to the flywheel and a new second power allocation corresponding to the lithium battery, the method further includes:

[0036] Smoothly adjust the flywheel's output power to the new first power distribution;

[0037] The output power of the lithium battery is smoothly adjusted to the new second power allocation.

[0038] Based on the same inventive concept, in a second aspect, embodiments of this application also provide a power distribution device, comprising:

[0039] The acquisition module is used to acquire the load power signal and system status parameters of the energy storage system in real time; the system status parameters are related to the flywheel and lithium battery.

[0040] The decomposition module is used to perform wavelet decomposition on the load power signal based on the load fluctuation intensity to generate the corresponding initial power allocation; the initial power allocation includes the high-frequency component allocated to the flywheel and the low-frequency component allocated to the lithium battery; the load fluctuation intensity is used to characterize the rate of change of load power over time.

[0041] The allocation module is used to adjust the initial allocation power based on the system state parameters using a pre-determined multi-objective optimization algorithm, and generate a first allocation power corresponding to the flywheel and a second allocation power corresponding to the lithium battery; the first allocation power is related to the high-frequency component; the second allocation power is related to the low-frequency component.

[0042] In some embodiments of the second aspect, the decomposition module is specifically used for:

[0043] If the load fluctuation intensity is greater than a preset intensity threshold, the load power signal is decomposed into wavelet decomposition according to the first preset layer to generate the corresponding initial power allocation. The high-frequency component is composed of the superposition of detail coefficients corresponding to the first to the second preset layer of wavelet decomposition. The low-frequency component is the approximation coefficients corresponding to the first preset layer of wavelet decomposition. The second preset layer is less than the first preset layer. If the load fluctuation intensity is less than or equal to the preset intensity threshold, the load power signal is decomposed into wavelet decomposition according to the third preset layer to generate the corresponding initial power allocation. The high-frequency component is composed of the superposition of detail coefficients corresponding to the first to the fourth preset layer of wavelet decomposition. The low-frequency component is the approximation coefficients corresponding to the third preset layer of wavelet decomposition. The fourth preset layer is less than the third preset layer.

[0044] In some embodiments of the second aspect, if the load fluctuation intensity of a first preset number of consecutive sliding windows is greater than a preset intensity threshold, the decomposition module is further configured to:

[0045] Increase the number of layers in the first preset layer;

[0046] If the load fluctuation intensity of the second consecutive preset number of sliding windows is less than the preset intensity threshold, the decomposition module is further used for:

[0047] Reduce the number of layers in the second preset layer.

[0048] In some embodiments of the second aspect, the system state parameters include flywheel speed;

[0049] The decomposition module is also used for:

[0050] If the power limit value corresponding to the flywheel speed is less than the high-frequency component, then the power limit value is taken as the first initial power corresponding to the flywheel; the difference between the high-frequency component and the power limit value, as well as the low-frequency component, are taken as the second initial power corresponding to the lithium battery; a new initial power allocation is generated based on the first initial power and the second initial power.

[0051] Correspondingly, the allocation module is specifically used for:

[0052] A multi-objective optimization algorithm is used to adjust the new initial power allocation based on the system state parameters, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery.

[0053] In some embodiments of the second aspect, the system state parameters include: flywheel speed, lithium battery state of charge, and lithium battery temperature;

[0054] The decomposition module is specifically used for:

[0055] A multi-objective optimization algorithm is adopted to adjust the initial power allocation based on flywheel speed, state of charge, temperature, objective function and constraints, and to generate a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery. The objective function aims to minimize the lithium battery life loss and the system energy loss rate. The constraints include power balance constraints, flywheel safety constraints and lithium battery safety constraints.

[0056] In some embodiments of the second aspect, the apparatus further includes:

[0057] The protection module is used to pause the adjustment of the initial power allocation using the multi-objective optimization algorithm and execute the preset protection strategy until the system state parameters meet the preset safety conditions if the system state parameters meet the preset protection trigger conditions; and resume the adjustment of the initial power allocation using the multi-objective optimization algorithm to generate a new first power allocation corresponding to the flywheel and a new second power allocation corresponding to the lithium battery.

[0058] In some embodiments of the second aspect, the system state parameters include at least one of the following: flywheel speed, lithium battery state of charge, and lithium battery temperature;

[0059] If the system status parameters meet the preset protection triggering conditions, the protection module will suspend the adjustment of the initial power allocation using the multi-objective optimization algorithm and execute the preset protection strategy until the system status parameters meet the preset safety conditions. Specifically, it is used for:

[0060] If the flywheel speed is greater than the first preset speed threshold, the multi-objective optimization algorithm for adjusting the initial power allocation will be paused, and the lithium battery forced absorption mode will be triggered until the flywheel speed is less than or equal to the second preset speed threshold; wherein, the absorption power of the lithium battery forced absorption mode is less than or equal to the current maximum charging power of the lithium battery.

[0061] If the state of charge is less than the first preset state of charge threshold, the multi-objective optimization algorithm for adjusting the initial power allocation will be paused, and the system will switch to flywheel independent power supply mode until the state of charge is greater than or equal to the second preset state of charge threshold.

[0062] If the state of charge is greater than the third preset state of charge threshold or the temperature is greater than the first preset temperature threshold, the multi-objective optimization algorithm is paused to adjust the initial power allocation, the flywheel is forced to bear all the power input, and when the temperature is greater than the first preset temperature threshold, the liquid cooling system is started until the state of charge is less than or equal to the fourth preset state of charge threshold and the temperature is less than or equal to the second preset temperature threshold.

[0063] In some embodiments of the second aspect, the protection module is also used for:

[0064] The flywheel's output power is smoothly adjusted to the new first power allocation; the lithium battery's output power is smoothly adjusted to the new second power allocation.

[0065] Based on the same inventive concept, in a third aspect, embodiments of this application also provide an electronic device, including: a memory and a processor;

[0066] The memory stores instructions that the computer executes;

[0067] The processor executes computer-executable instructions stored in memory to implement a power allocation method such as any of the first aspects.

[0068] Based on the same inventive concept, in a fourth aspect, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the power allocation method as described in any of the first aspects.

[0069] According to the power allocation method, apparatus, and electronic device provided in the embodiments of this application, the load power signal is decomposed using wavelet decomposition to generate the corresponding initial allocation power, which can improve the time resolution and the timeliness of high-frequency component allocation. Simultaneously, by combining a pre-determined multi-objective optimization algorithm with dynamic adjustment of system state parameters to the initial allocation power, a first allocation power corresponding to the flywheel and a second allocation power corresponding to the lithium battery are generated, achieving dynamic balance in power allocation and improving power allocation accuracy. Attached Figure Description

[0070] Figure 1 This illustration shows a flowchart of a power allocation method provided in an embodiment of this application;

[0071] Figure 2 This illustration shows another flowchart of the power allocation method provided in an embodiment of this application;

[0072] Figure 3 This illustration shows an overall flowchart of a power allocation method provided in an embodiment of this application.

[0073] Figure 4 This diagram illustrates the power decomposition results provided in an embodiment of this application.

[0074] Figure 5 This illustration shows a schematic diagram of the state of charge change of a lithium battery provided in an embodiment of this application;

[0075] Figure 6 This is a schematic diagram of a power distribution device provided in an embodiment of this application. Detailed Implementation

[0076] To enable those skilled in the art to better understand the technical solutions of this application, the application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0077] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0079] Example 1

[0080] The power allocation method provided in this application is applicable to the power allocation process of an energy storage system. This power allocation method is applied to an electronic device. The electronic device can be a computer, a control device, or a device within a computer or control device used to implement the power allocation method. The following description uses the execution of this power allocation method by an electronic device as an example.

[0081] like Figure 1 As shown, the power allocation method provided in this application embodiment may include steps S101 to S103.

[0082] S101. Real-time acquisition of load power signals and system status parameters of the energy storage system. System status parameters are related to the flywheel and lithium battery.

[0083] For example, system state parameters include flywheel speed, lithium battery state of charge (SOC), and lithium battery temperature. Flywheel speed reflects the flywheel's operating state, while the lithium battery SOC and temperature reflect the lithium battery's state.

[0084] For example, the energy storage system includes a lithium battery energy storage unit, a flywheel energy storage unit, a data acquisition module, a central control module, and a power conversion module. The method in this embodiment can be executed by the electronic device corresponding to the central control module. Meanwhile, the load power signal and system status parameters of the energy storage system can be obtained through the data acquisition module.

[0085] The data acquisition module includes acquisition units such as power sensors, temperature sensors, and speed sensors.

[0086] For example, the load power signal and system status parameters are acquired in real time, and there is a corresponding relationship between the load power signal and system status parameters used in subsequent S102-S103.

[0087] S102. Perform wavelet decomposition on the load power signal based on the load fluctuation intensity to generate the corresponding initial power allocation. The initial power allocation includes the high-frequency component allocated to the flywheel and the low-frequency component allocated to the lithium battery. The load fluctuation intensity is used to characterize the rate of change of load power over time.

[0088] For example, the intensity of load fluctuation can be calculated as the ratio of the change in load power to the change in time.

[0089] For example, wavelet decomposition can employ various wavelet basis functions, such as Daubechies wavelet (db4) and Symlet wavelet (sym4). Wavelet decomposition can improve time resolution, which in practical applications can be improved to within 10ms, thus making it applicable to instantaneous fluctuations at the 10ms level.

[0090] After performing wavelet decomposition on the load power signal, the high-frequency components (e.g., 1-100Hz) obtained from the decomposition can be preferentially allocated to the flywheel, and the low-frequency components (e.g., 0-1Hz) can be preferentially allocated to the lithium battery, thereby extending the service life of the lithium battery.

[0091] For example, wavelet decomposition of the load power signal based on the load fluctuation intensity can be performed at a higher level (e.g., 5 or 6 levels) when the load fluctuation intensity is high, and at a lower level (e.g., 3 or 4 levels) when the load fluctuation intensity is low.

[0092] S103. Using a pre-determined multi-objective optimization algorithm, the initial power allocation is adjusted based on the system state parameters to generate a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery. The first power allocation is related to the high-frequency component. The second power allocation is related to the low-frequency component.

[0093] For example, multi-objective optimization algorithms can employ Model Predictive Control (MPC), reinforcement learning, and other algorithms. If an MPC algorithm is used, the state variables can be system state parameters. The control variables include the first and second allocated power mentioned above. If a reinforcement learning algorithm is used, the reward function can be designed according to power allocation requirements, for example, allocating high-frequency components to the lithium battery as a negative reward.

[0094] For example, the multi-objective optimization algorithm can dynamically adjust the first power allocation corresponding to the flywheel and the second power allocation corresponding to the lithium battery, for example, allocating power once every 5ms to 20ms. Its allocation objective can be to ensure that the flywheel and lithium battery are in good operating condition while allocating the highest possible high-frequency components to the flywheel and the low-frequency components to the lithium battery, so as to improve the life and stability of the energy storage unit. For example, the flywheel speed is in a better speed range (e.g., 30% to 90% of the rated speed), and the lithium battery is in a better state of charge (SOC) range (e.g., 20% to 80%, charge / discharge rate ≤ 2C).

[0095] At the same time, the goal can also be to minimize lithium battery life loss and the lowest system energy loss rate.

[0096] According to the power allocation method provided in this application, the load power signal is decomposed using wavelet decomposition to generate the corresponding initial allocation power, which can improve the time resolution and the timeliness of high-frequency component allocation. Simultaneously, by combining a pre-determined multi-objective optimization algorithm with dynamic adjustment of system state parameters to the initial allocation power, a first allocation power corresponding to the flywheel and a second allocation power corresponding to the lithium battery are generated, achieving dynamic balance in power allocation and improving power allocation accuracy.

[0097] Example 2

[0098] like Figure 2 As shown, the power allocation method provided in this application embodiment is based on the power allocation method provided in embodiment 1 of this application and is further described, and may include steps S201 to S204.

[0099] S201. Real-time acquisition of load power signals and system status parameters of the energy storage system.

[0100] S202. If the load fluctuation intensity is greater than a preset intensity threshold, then the load power signal is decomposed into wavelet components according to the first preset layer to generate the corresponding initial power allocation. The high-frequency components are composed of the superposition of detail coefficients corresponding to the first to the second preset layer of the wavelet decomposition. The low-frequency components are the approximation coefficients corresponding to the first preset layer of the wavelet decomposition. The second preset layer is less than the first preset layer.

[0101] For example, the above preset parameters can be set according to actual applications, such as setting the preset intensity threshold to 50kW / s (kilowatts per second), setting the first preset number of layers to 5 or 6 layers, setting the second preset number of layers to 3 or 4 layers, etc.

[0102] S203. If the load fluctuation intensity is less than or equal to a preset intensity threshold, then the load power signal is decomposed into wavelet components according to the third preset layer to generate the corresponding initial power allocation. The high-frequency components are composed of the superposition of detail coefficients corresponding to the first to fourth preset layers of the wavelet decomposition. The low-frequency components are the approximation coefficients corresponding to the third preset layer of the wavelet decomposition. The fourth preset layer is less than the third preset layer.

[0103] For example, the above preset parameters can be set according to the actual application. For instance, the third preset number of layers can be set to 3 or 4 layers, and the fourth preset number of layers can be set to 2 or 3 layers. In this case, the high-frequency components are composed of the superposition of detail coefficients corresponding to 1 to 2 layers, or 1 to 3 layers.

[0104] In some implementations, if the load fluctuation intensity of a first preset number of sliding windows is greater than a preset intensity threshold, the number of layers of the first preset number of layers is increased.

[0105] If the load fluctuation intensity of the second preset number of sliding windows is less than the preset intensity threshold, then the number of layers of the second preset number of layers will be reduced.

[0106] For example, the first and second preset quantities can be set according to the actual application; for example, the first preset quantity can be set to 3 or 4, and the second preset quantity can be set to 5 or 6. The window size of the sliding window can be set to 100ms.

[0107] When the load fluctuation intensity of the first preset number of sliding windows is greater than the preset intensity threshold, it indicates that the power change is large and the high-frequency components contained in the signal are relatively rich and complex. The above-mentioned complex situation can be dealt with by more refined high-level decomposition.

[0108] For example, the number of layers to increase the first preset number can be increased one layer at a time, and similarly, the number of layers to decrease the second preset number can be decreased one layer at a time.

[0109] In some implementations, system state parameters include flywheel speed.

[0110] Following S203, it also includes:

[0111] If the power limit value corresponding to the flywheel speed is less than the high-frequency component, then the power limit value is taken as the first initial power corresponding to the flywheel.

[0112] The difference between the high-frequency component and the power limit, along with the low-frequency component, is used as the second initial power for the lithium battery.

[0113] A new initial allocation power is generated based on the first initial power and the second initial power.

[0114] For example, since the system status parameters are acquired in real time, the flywheel speed is either the real-time flywheel speed or the flywheel speed corresponding to the acquired load power signal. In this case, the power limit value can be calculated using an algorithm, as follows:

[0115] P_f≤(0.8~0.9)×n / n_rated×P_f_rated

[0116] Where P_f is the flywheel output power, n is the real-time flywheel speed, n_rated is the rated speed, P_f_rated is the flywheel rated power, and the part less than or equal to is the power limit value.

[0117] Meanwhile, the power limit value can also be obtained by looking up a table or other means, and this embodiment does not limit it.

[0118] Using the difference between the high-frequency component and the power limit, along with the low-frequency component, as the second initial power for the lithium battery means allocating the remaining high-frequency component and all low-frequency components to the lithium battery to improve the safety of flywheel operation. In this case, the new initial power allocation consists of the first initial power and the second initial power.

[0119] At this point, S204 can be replaced with:

[0120] A multi-objective optimization algorithm is used to adjust the new initial power allocation based on the system state parameters, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery.

[0121] S204. Using a pre-determined multi-objective optimization algorithm, the initial power allocation is adjusted based on the system state parameters to generate the first power allocation corresponding to the flywheel and the second power allocation corresponding to the lithium battery.

[0122] In some implementations, system state parameters include: flywheel speed, lithium battery state of charge, and lithium battery temperature.

[0123] S204 can be specifically described as follows:

[0124] A multi-objective optimization algorithm is employed to adjust the initial power allocation based on flywheel speed, state of charge, temperature, objective function, and constraints, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery. The objective function aims to minimize lithium battery lifespan degradation and the system's energy loss rate. Constraints include power balance constraints, flywheel safety constraints, and lithium battery safety constraints.

[0125] For example, flywheel speed, state of charge, and temperature are used as state variables in the multi-objective optimization algorithm. The first and second allocated power are used as control variables. The objective function and constraints of the multi-objective optimization algorithm can be set according to the specific application requirements.

[0126] For example, the objective function can be specifically defined as follows:

[0127] min ( ×∑(k×DOD²×C_rate^0.8) + × Energy loss rate)

[0128] in, Weighting coefficients ( k is the lifespan degradation coefficient (valued at 1.2~1.5, with the upper limit taken at low temperatures), DOD is the depth of discharge of the lithium battery, C_rate is the charge / discharge rate, C_rate = |lithium battery output power| / lithium battery rated energy (unit: kWh / kilowatt-hour), and the energy loss rate is the ratio of the total system loss to the system input energy.

[0129] The power balance constraint is that the sum of the lithium battery output power and the flywheel output power equals the load power: flywheel output power P_f + lithium battery output power P_b = load power P_load (P_load is positive if the system needs to output power, and negative if the system needs to absorb power).

[0130] Flywheel safety constraints constrain the flywheel to operate in a relatively safe state. For example: 30% n_rated ≤ n ≤ 90% n_rated, and | P_f| ≤ 0.9 × n / n_rated × P_f_rated, where the parameters have the same meaning as described above.

[0131] Lithium-ion battery safety constraints refer to restricting the operation of lithium-ion batteries to a relatively safe state. For example: 20% ≤ s ≤ 80%, T ≤ 55℃ (degrees Celsius), and |P_b| ≤ the maximum charge / discharge current corresponding to the current SOC × nominal voltage. Here, s is the lithium-ion battery's state of charge (SOC), and T is the lithium-ion battery temperature.

[0132] The objective function described above allows for dynamic power allocation aimed at minimizing lithium battery lifespan degradation and system energy loss rate, thereby reducing both lithium battery lifespan degradation and system energy loss rate. Simultaneously, the aforementioned constraints ensure that both the lithium battery and flywheel operate in a safe state, improving the safety of the energy storage system.

[0133] In some implementations, a safety fault-tolerance mechanism can also be added, as follows:

[0134] If the system status parameters meet the preset protection trigger conditions, the use of the multi-objective optimization algorithm to adjust the initial power allocation will be paused, and the preset protection strategy will be executed until the system status parameters meet the preset safety conditions.

[0135] The initial power allocation is adjusted using a multi-objective optimization algorithm to generate a new first power allocation corresponding to the flywheel and a new second power allocation corresponding to the lithium battery.

[0136] For example, when the system state parameters meet the preset protection trigger conditions, the multi-objective optimization algorithm for adjusting the initial power allocation is temporarily stopped. Simultaneously, the corresponding protection strategy is executed until the system state recovers. This ensures the priority of system safety and improves system security.

[0137] For example, preset protection trigger conditions are related to the lithium battery and flywheel, such as excessively high flywheel speed, excessively high lithium battery temperature, or excessively low or high lithium battery SOC. Preset protection strategies correspond to these preset protection trigger conditions; for example, if the flywheel speed is too high, the lithium battery can be forced to absorb the excess energy, thereby preventing the flywheel speed from continuing to increase.

[0138] In some implementations, the system state parameters include at least one of the following: flywheel speed, lithium battery state of charge, and lithium battery temperature.

[0139] If the system status parameters meet the preset protection triggering conditions, the process of pausing the adjustment of the initial power allocation using the multi-objective optimization algorithm and executing the preset protection strategy until the system status parameters meet the preset safety conditions can be specifically described as follows:

[0140] If the flywheel speed exceeds a first preset speed threshold, the multi-objective optimization algorithm for adjusting the initial power allocation is paused, and the lithium battery forced absorption mode is triggered until the flywheel speed is less than or equal to a second preset speed threshold. The absorption power of the lithium battery forced absorption mode is less than or equal to the current maximum charging power of the lithium battery.

[0141] If the state of charge is less than the first preset state of charge threshold, the multi-objective optimization algorithm for adjusting the initial power allocation will be paused, and the system will switch to flywheel independent power supply mode until the state of charge is greater than or equal to the second preset state of charge threshold.

[0142] If the state of charge is greater than the third preset state of charge threshold or the temperature is greater than the first preset temperature threshold, the multi-objective optimization algorithm is paused to adjust the initial power allocation, the flywheel is forced to bear all the power input, and when the temperature is greater than the first preset temperature threshold, the liquid cooling system is started until the state of charge is less than or equal to the fourth preset state of charge threshold and the temperature is less than or equal to the second preset temperature threshold.

[0143] For example, the above preset thresholds can be set according to actual applications. For instance, the first preset speed threshold can be set to 90% or 85% of the rated speed, and the second preset speed threshold can be set to 85% or 80% of the rated speed, wherein the second preset speed threshold is less than the first preset speed threshold.

[0144] For example, the first preset state of charge threshold can be set to 20%, 15%, etc., and the second preset state of charge threshold can be set to 25%, 20%, etc., wherein the second preset state of charge threshold is greater than the first preset state of charge threshold.

[0145] For example, the third preset state of charge threshold can be set to 80%, 90%, etc., and the fourth preset state of charge threshold can be set to 75%, 85%, etc., where the fourth preset state of charge threshold is less than the third preset state of charge threshold. The first preset temperature threshold can be set to 55℃, 53℃, etc., and the second preset temperature threshold can be set to 50℃, 48℃, etc., where the second preset temperature threshold is less than the first preset temperature threshold.

[0146] For example, the lithium battery forced absorption mode means that the lithium battery is forced to absorb power, and the flywheel no longer absorbs power. The absorption power P_absorb = k1×(n - 90% n_rated) (k1 is a proportionality coefficient, with a value of 0.05~0.1kW / rpm, and P_absorb≤the current maximum charging power of the lithium battery).

[0147] For example, the flywheel independent power supply mode means that only the flywheel is powered, and the lithium battery no longer provides power. The flywheel output power is less than or equal to the power limit. If there is external energy recovery (such as braking energy), it is preferentially input to the flywheel (e.g., charging power ≤ 0.5 × P_f_rated).

[0148] For example, when the state of charge (SBC) is greater than a third preset SBC threshold or the temperature is greater than a first preset temperature threshold, the flywheel is forced to bear all the power input. If the SBC is greater than the third preset SBC threshold and the temperature is greater than the first preset temperature threshold, the liquid cooling system must also be activated to cool it down.

[0149] When the flywheel is forced to bear all the power input, the lithium battery stops charging, and the discharge power P_b≤0.8×current maximum allowable output power (determined according to the lithium battery discharge curve).

[0150] In some implementations, after restoring the initial power allocation by employing a multi-objective optimization algorithm to generate a new first power allocation corresponding to the flywheel and a new second power allocation corresponding to the lithium battery, the following process is also included:

[0151] Smoothly adjust the flywheel's output power to the new first power distribution.

[0152] The output power of the lithium battery is smoothly adjusted to the new second power allocation.

[0153] For example, smoothly adjusting the output power of the flywheel and the lithium battery can prevent sudden power surges from impacting the load. This smooth adjustment can be done at a fixed rate, such as adjusting the flywheel output power P_f at a rate ≤5kW / ms and the lithium battery output power P_b at a rate ≤2kW / ms.

[0154] The method in this embodiment employs wavelet decomposition technology combined with a multi-layer (e.g., 3-6 layers) adaptive decomposition strategy, which can accurately capture high-frequency power fluctuations of 1-100Hz and low-frequency power demands of 0-1Hz, improving the time resolution to within 10ms. This avoids lithium batteries participating in high-frequency responses and extends their cycle life by more than 30%. A multi-objective optimization algorithm comprehensively considers factors such as flywheel speed safety, lithium battery SOC stability, and lifespan loss to achieve dynamic balance in power distribution, with a power distribution error ≤5% under complex load scenarios. Simultaneously, by designing a layered fault-tolerant protection mode and a smooth transition strategy, load shocks are avoided under abnormal conditions, reducing the probability of secondary failures and improving system operational stability by 40%.

[0155] Secondly, the method in this embodiment can be applied to various scenarios requiring high-frequency power regulation, such as grid frequency regulation, electric vehicle energy management, and renewable energy grid-connected power smoothing, demonstrating strong versatility and practicality. Simultaneously, it achieves efficient, stable, and safe operation of the hybrid energy storage system, effectively extending the lifespan of the energy storage unit while improving energy utilization efficiency.

[0156] To better understand the power allocation method provided in the embodiments of this application, a specific application implementation method is described below.

[0157] The method in this embodiment is applied to a hybrid energy storage system, the hardware composition and parameters of which are as follows:

[0158] Lithium-ion battery energy storage unit:

[0159] Lithium battery pack: Uses lithium iron phosphate batteries with a rated voltage of 512V, a rated capacity of 100Ah, a rated energy of 51.2kWh, a charge / discharge rate of 0.5C-2C, and an operating temperature range of -20℃ to 60℃.

[0160] Battery Management System (BMS): Sampling frequency is 1kHz (kilohertz), SOC measurement accuracy is ±1%, temperature measurement range is -40℃~125℃, temperature measurement accuracy is ±0.5℃, and it can output information such as SOC, operating temperature, and charging / discharging current of lithium battery in real time.

[0161] Liquid cooling module: It adopts a plate heat exchanger with an initial flow rate of 5L / min and a maximum flow rate of 10L / min. The flow rate can be dynamically adjusted according to the lithium battery temperature signal, and the temperature control accuracy is ±2℃.

[0162] Flywheel energy storage unit:

[0163] Flywheel body: Made of carbon fiber composite material, rated speed is 30,000 rpm, rated power is 200 kW, rated energy is 5 kWh, and vacuum chamber vacuum degree is ≤1 Pa (Pascal).

[0164] Magnetic levitation bearing system: It adopts active magnetic levitation bearings with radial stiffness ≥50N / μm (Newtons / micrometer) and axial stiffness ≥30N / μm, supporting the flywheel to operate stably within the range of 3000-27000rpm (10%-90% of rated speed).

[0165] Speed ​​sensor: A magnetoelectric speed sensor is used, with a measurement range of 0-36000 rpm, a resolution of 1 rpm, and a sampling frequency of 1 kHz.

[0166] Data acquisition module:

[0167] Three-phase power sensor: The Hall effect power sensor is used, with a measurement range of 0-500kW, a sampling frequency of 1kHz, and a measurement error of ≤0.2%.

[0168] Temperature sensor: PT100 platinum resistance temperature sensor is used, with a measurement range of -40℃ to 125℃, a measurement error of ≤±0.5℃, and a sampling frequency of 100Hz.

[0169] Central control module:

[0170] It adopts an FPGA (Field-Programmable Gate Array) + microprocessor architecture. The FPGA is responsible for high-speed operations such as wavelet decomposition, while the microprocessor is responsible for multi-objective optimization algorithms and fault-tolerant control logic processing. The two communicate with each other through a high-speed bus (with a speed of 1Gbps).

[0171] Wavelet decomposition unit: Built-in wavelet basis functions such as Daubechies wavelet (db4) and Symlet wavelet (sym4), supports 3-6 level adaptive decomposition, and decomposition time ≤8ms.

[0172] Multi-objective optimization algorithm unit: It incorporates either a Model Predictive Control (MPC) algorithm or a reinforcement learning algorithm. The MPC algorithm has a 10-step prediction time domain and a 5-step control time domain, outputting a power allocation command every 10ms. The reinforcement learning algorithm uses a Deep Q-Network (DQN), with a training period of 1 hour, and can adaptively adjust the control strategy online.

[0173] Fault-tolerant control unit: Preset protection modes such as flywheel overspeed protection, lithium battery low SOC protection, and lithium battery overcharge / overtemperature protection, with a response time of ≤1ms.

[0174] Power conversion module:

[0175] Flywheel-side bidirectional DC / DC converter (DC-DC converter): adopts a full-bridge topology, with an input voltage range of 450-650V, an output power range of -200kW-200kW (negative sign indicates charging, positive sign indicates discharging), a conversion efficiency of ≥96%, and a response delay of ≤5ms.

[0176] Lithium battery-side bidirectional inverter: adopts a three-phase full-bridge topology, with an input voltage range of 450-550V, an output voltage of 380VAC (AC) / 50Hz, an output power range of -100kW-100kW, a conversion efficiency of ≥95%, and a response delay of ≤10ms.

[0177] The overall process is as follows Figure 3 As shown, it specifically includes the following steps:

[0178] Step 1, Data Collection:

[0179] The data acquisition module collects load power, flywheel speed, and lithium battery SOC and operating temperature in real time. Specifically: the three-phase power sensor collects load power data every 1ms and transmits it to the FPGA of the central control module. The flywheel speed sensor collects flywheel speed data every 1ms and transmits it to the microprocessor of the central control module. The BMS collects lithium battery SOC and operating temperature data every 10ms and transmits it to the microprocessor of the central control module.

[0180] Step 2: The load power signal is separated into time and frequency components using wavelet decomposition. The specific process is as follows:

[0181] The load power fluctuation intensity is calculated using a sliding window (window size of 100ms), and the calculation formula is |ΔP / Δt|, where ΔP is the power change within the sliding window and Δt is the window time (100ms).

[0182] When the fluctuation intensity of three consecutive windows is greater than 50 kW / s, a 6-level db4 wavelet decomposition is used. When the fluctuation intensity of five consecutive windows is ≤50 kW / s, a 3-level db4 wavelet decomposition is used. In other cases, a 4-5 level db4 wavelet decomposition is used.

[0183] After decomposition, the approximation coefficients of the 5th or 6th layer are taken as the low-frequency components (0-1Hz), and the detail coefficients of the 1st to 4th layers are superimposed as the high-frequency components (1-100Hz). The decomposition results are then transmitted to the multi-objective optimization algorithm unit.

[0184] like Figure 4 As shown in the figure, the total load power and the high-frequency component (flywheel) and low-frequency component (battery) of the power obtained by decomposing it using the Daubechies wavelet (db4) method are displayed in power units. It can be seen that this method can effectively separate the low-frequency component.

[0185] Step 3: The multi-objective optimization algorithm unit performs initial power allocation based on the wavelet decomposition results.

[0186] High-frequency components are preferentially allocated to the flywheel energy storage unit, while low-frequency components are allocated to the lithium battery energy storage unit.

[0187] The mechanical limit for the initial output power of the flywheel is calculated as: P_f ≤ 0.85 × n / n_rated × P_f_rated, where n is the real-time rotational speed, n_rated is the rated rotational speed (30000 rpm), and P_f_rated is the rated power of the flywheel (200 kW). For example, when the real-time rotational speed of the flywheel is 24000 rpm (80% of the rated speed), the mechanical limit for the initial output power of the flywheel is 0.85 × 24000 / 30000 × 200 = 136 kW.

[0188] Step 4: Dynamic power adjustment and fault-tolerant control:

[0189] The multi-objective optimization algorithm unit dynamically adjusts the power allocation ratio every 10ms. The specific process is as follows:

[0190] An optimization model is established with the objective function min (0.7×∑(k×DOD²×C_rate^0.8) + 0.3×energy loss rate), where k is the lifetime degradation coefficient (1.3 at room temperature and 1.5 at low temperature (<0℃)). DOD is the depth of discharge of the lithium battery (DOD=|Δs| during discharge and DOD=0 during charging), and s is the state of charge (SOC). C_rate is the charge / discharge rate (C_rate=|P_b| / 51.2kWh). The energy loss rate is the ratio of total system loss to input energy.

[0191] The constraints include: P_f + P_b = load power P_load, 3000rpm≤n≤27000rpm. 20%≤SOC≤80%. Lithium battery temperature T≤55℃. |P_f|≤0.9×n / n_rated×200kW. |P_b|≤the maximum charge / discharge current corresponding to the current SOC×512V (when SOC is 20%, the maximum discharge current is 200A, and the maximum charging current is 100A. When SOC is 80%, the maximum discharge current is 200A, and the maximum charging current is 50A).

[0192] Constraint checks are performed. If the constraints are met, the lithium battery is directly assigned to handle the low-frequency components, and the flywheel to handle the high-frequency components. If the constraints are not met, fault-tolerant protection is triggered, and the algorithm allocation is paused.

[0193] When the fault-tolerant condition is triggered, the fault-tolerant control unit activates the preset protection mode. The over-limit types are as follows:

[0194] Flywheel overspeed protection: When n > 27000 rpm, the multi-objective optimization algorithm is paused, triggering the lithium battery forced absorption mode. The absorbed power P_absorb = 0.08kW / rpm × (n - 27000rpm), and P_absorb ≤ the current maximum charging power of the lithium battery. At this time, the flywheel can handle the full power output.

[0195] For example, when n = 28000 rpm, P_absorb = 0.08 × (28000 - 27000) = 80 kW. If the maximum charging power of the lithium battery is 100 kW at this time, the absorbed power is 80 kW. If the maximum charging power of the lithium battery is 60 kW, the absorbed power is 60 kW. This mode continues until n drops to 25500 rpm, at which point the multi-objective optimization algorithm is resumed.

[0196] Lithium battery low SOC protection: When SOC < 20%, the multi-objective optimization algorithm is paused, and a forced switch to flywheel independent power supply mode is initiated, with P_b = 0 and P_f = min (P_load, 0.8 × n / n_rated × 200kW). If external energy recovery (such as braking energy) is available, it is preferentially input to the flywheel, with a charging power ≤ 100kW (0.5 × 200kW). The multi-objective optimization algorithm resumes when the SOC recovers to 25%.

[0197] Lithium battery overcharge / overtemperature protection: When SOC > 80% or T > 55℃, the multi-objective optimization algorithm is paused, the flywheel is forced to bear all power input, the lithium battery stops charging (P_b ≥ 0), and the discharge power P_b ≤ 0.8 × the current maximum allowable output power (the current maximum allowable output power is determined according to the lithium battery discharge curve; when SOC is 80%, the maximum allowable output power is 160kW). If T > 55℃, the liquid cooling module is started simultaneously with an initial flow rate of 5L / min, increasing by 0.5L / min for every 1℃ above the temperature, until SOC drops to 75% and T ≤ 50℃, at which point the multi-objective optimization algorithm resumes.

[0198] If the battery temperature is too high and it is in a discharging state, the flywheel will bear the full power output.

[0199] When the flywheel speed is too low, the multi-objective optimization algorithm is paused, and the battery assumes all power output.

[0200] When exiting protection mode, a smooth transition strategy is adopted: the flywheel power is adjusted at a rate of ≤5kW / ms, and the lithium battery power is adjusted at a rate of ≤2kW / ms. For example, if the flywheel needs to be adjusted from 100kW to 150kW, the adjustment time is ≥(150-100) / 5=10ms.

[0201] like Figure 5 As shown, Figure 5 The graph shows the changes in battery SOC when using a hybrid energy storage method and when using lithium batteries alone. As can be seen from the graph, the hybrid energy storage method can effectively smooth out changes in lithium battery SOC, reduce the number of charge-discharge cycles, and thus better extend battery life.

[0202] In this embodiment, the aforementioned hybrid energy storage system and its dynamic energy management method achieve a power response speed of 10ms and a frequency regulation accuracy of ≤±0.02Hz in a grid frequency regulation scenario. In an electric vehicle energy management scenario, it can extend the cycle life of lithium batteries by more than 35%, extend the flywheel lifespan to more than 10 years, and improve the overall system energy efficiency to more than 90%.

[0203] Example 3

[0204] like Figure 6 As shown, the power distribution device 400 provided in this application embodiment is located in an electronic device, and the power distribution device 400 may include:

[0205] The acquisition module 401 is used to acquire the load power signal and system status parameters of the energy storage system in real time. The system status parameters are related to the flywheel and lithium battery.

[0206] The decomposition module 402 is used to perform wavelet decomposition on the load power signal based on the load fluctuation intensity to generate the corresponding initial power allocation. The initial power allocation includes the high-frequency component allocated to the flywheel and the low-frequency component allocated to the lithium battery. The load fluctuation intensity is used to characterize the rate of change of load power over time.

[0207] The allocation module 403 is used to adjust the initial allocation power based on system state parameters using a pre-determined multi-objective optimization algorithm, generating a first allocation power corresponding to the flywheel and a second allocation power corresponding to the lithium battery. The first allocation power is related to high-frequency components. The second allocation power is related to low-frequency components.

[0208] In some implementations, the decomposition module 402 is specifically used for:

[0209] If the load fluctuation intensity is greater than a preset intensity threshold, the load power signal is decomposed into wavelet coefficients according to the first preset layer to generate the corresponding initial power allocation. The high-frequency component is composed of the superposition of detail coefficients corresponding to the first to the second preset layer of wavelet decomposition. The low-frequency component is the approximation coefficients corresponding to the first preset layer of wavelet decomposition. The second preset layer is less than the first preset layer. If the load fluctuation intensity is less than or equal to the preset intensity threshold, the load power signal is decomposed into wavelet coefficients according to the third preset layer to generate the corresponding initial power allocation. The high-frequency component is composed of the superposition of detail coefficients corresponding to the first to the fourth preset layer of wavelet decomposition. The low-frequency component is the approximation coefficients corresponding to the third preset layer of wavelet decomposition. The fourth preset layer is less than the third preset layer.

[0210] In some implementations, if the load fluctuation intensity of a first preset number of consecutive sliding windows is greater than a preset intensity threshold, the decomposition module 402 is further configured to:

[0211] Increase the number of layers in the first preset layer.

[0212] If the load fluctuation intensity of the second consecutive preset number of sliding windows is less than the preset intensity threshold, then the decomposition module 402 is further used to:

[0213] Reduce the number of layers in the second preset layer.

[0214] In some implementations, system state parameters include flywheel speed.

[0215] The decomposition module 402 is also used for:

[0216] If the power limit corresponding to the flywheel speed is less than the high-frequency component, then the power limit is taken as the first initial power corresponding to the flywheel. The difference between the high-frequency component and the power limit, along with the low-frequency component, is taken as the second initial power corresponding to the lithium battery. A new initial power allocation is generated based on the first and second initial powers.

[0217] Correspondingly, the allocation module 403 is specifically used for:

[0218] A multi-objective optimization algorithm is used to adjust the new initial power allocation based on the system state parameters, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery.

[0219] In some implementations, system state parameters include: flywheel speed, lithium battery state of charge, and lithium battery temperature.

[0220] The state variables of the multi-objective optimization algorithm include: flywheel speed, state of charge, and temperature.

[0221] The decomposition module 403 is specifically used for:

[0222] A multi-objective optimization algorithm is employed to adjust the initial power allocation based on flywheel speed, state of charge, temperature, objective function, and constraints, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery. The objective function aims to minimize lithium battery lifespan degradation and the system's energy loss rate. Constraints include power balance constraints, flywheel safety constraints, and lithium battery safety constraints.

[0223] In some embodiments, the power distribution device 400 further includes:

[0224] The protection module is used to pause the adjustment of the initial power allocation using a multi-objective optimization algorithm and execute a preset protection strategy if the system state parameters meet the preset protection trigger conditions, until the system state parameters meet the preset safety conditions. It then resumes the adjustment of the initial power allocation using the multi-objective optimization algorithm, generating a new first power allocation corresponding to the flywheel and a new second power allocation corresponding to the lithium battery.

[0225] In some implementations, the system state parameters include at least one of the following: flywheel speed, lithium battery state of charge, and lithium battery temperature.

[0226] If the system status parameters meet the preset protection triggering conditions, the protection module will suspend the adjustment of the initial power allocation using the multi-objective optimization algorithm and execute the preset protection strategy until the system status parameters meet the preset safety conditions. Specifically, it is used for:

[0227] If the flywheel speed exceeds a first preset speed threshold, the multi-objective optimization algorithm for adjusting the initial power allocation is paused, and the lithium battery forced absorption mode is triggered until the flywheel speed is less than or equal to a second preset speed threshold. The absorption power of the lithium battery forced absorption mode is less than or equal to the current maximum charging power of the lithium battery.

[0228] If the state of charge is less than the first preset state of charge threshold, the multi-objective optimization algorithm for adjusting the initial power allocation will be paused, and the system will switch to flywheel independent power supply mode until the state of charge is greater than or equal to the second preset state of charge threshold.

[0229] If the state of charge is greater than the third preset state of charge threshold or the temperature is greater than the first preset temperature threshold, the multi-objective optimization algorithm is paused to adjust the initial power allocation, the flywheel is forced to bear all the power input, and when the temperature is greater than the first preset temperature threshold, the liquid cooling system is started until the state of charge is less than or equal to the fourth preset state of charge threshold and the temperature is less than or equal to the second preset temperature threshold.

[0230] In some implementations, the protection module is also used for:

[0231] Smoothly adjust the flywheel's output power to the new first power allocation. Smoothly adjust the lithium battery's output power to the new second power allocation.

[0232] The power distribution device provided in this application has the beneficial effects and implementation methods of the power distribution methods provided in Embodiments 1 and 2 of this application. For details, please refer to the specific descriptions of the power distribution methods in Embodiments 1 and 2 above. This embodiment will not repeat them here.

[0233] Example 4

[0234] This application also provides an electronic device, which includes:

[0235] Memory and processor.

[0236] The memory stores the instructions that the computer executes.

[0237] The processor executes computer execution instructions stored in memory to implement the power allocation methods as described in Examples 1 and 2.

[0238] The electronic device provided in this application has the beneficial effects and implementation methods of the power allocation method provided in Embodiments 1 and 2 of this application. For details, please refer to the specific description of the power allocation method in Embodiments 1 and 2 above. This embodiment will not repeat the description here.

[0239] Example 5

[0240] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the power allocation method in Embodiment 1 or Embodiment 2 above.

[0241] The computer-readable storage medium provided in this application has the beneficial effects and implementation methods of the power allocation method in Embodiments 1 and 2 of this application. For details, please refer to the specific description of the power allocation method in Embodiments 1 and 2 above. This embodiment will not repeat the description here.

[0242] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A power distribution method, characterized in that, include: Real-time acquisition of load power signals and system status parameters of the energy storage system; the system status parameters are related to the flywheel and lithium battery. The load power signal is decomposed into wavelet components based on the load fluctuation intensity to generate a corresponding initial power allocation; the initial power allocation includes a high-frequency component allocated to the flywheel and a low-frequency component allocated to the lithium battery. The load fluctuation intensity is used to characterize the rate of change of load power over time; A pre-determined multi-objective optimization algorithm is used to adjust the initial power allocation based on the system state parameters, generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery; the first power allocation is related to the high-frequency component; the second power allocation is related to the low-frequency component.

2. The method according to claim 1, characterized in that, The step of performing wavelet decomposition on the load power signal based on the load fluctuation intensity to generate the corresponding initial power allocation includes: If the load fluctuation intensity is greater than a preset intensity threshold, the load power signal is decomposed into wavelet decomposition according to a first preset layer to generate a corresponding initial power allocation; the high-frequency component is composed of the superposition of detail coefficients corresponding to the first to the second preset layer of wavelet decomposition; the low-frequency component is the approximation coefficients corresponding to the first preset layer of wavelet decomposition; the second preset layer is less than the first preset layer. If the load fluctuation intensity is less than or equal to a preset intensity threshold, the load power signal is decomposed into wavelet components according to the third preset layer to generate the corresponding initial power allocation; the high-frequency component is composed of the superposition of detail coefficients corresponding to the first to fourth preset layers of wavelet decomposition; the low-frequency component is the approximation coefficients corresponding to the third preset layer of wavelet decomposition; the fourth preset layer is less than the third preset layer.

3. The method according to claim 2, characterized in that, If the load fluctuation intensity of a first preset number of consecutive sliding windows is greater than the preset intensity threshold, then the method further includes: Increase the number of layers by the first preset number; If the load fluctuation intensity of a second consecutive preset number of sliding windows is less than the preset intensity threshold, then the method further includes: Reduce the number of layers in the second preset layer.

4. The method according to claim 1, characterized in that, The system status parameters include the flywheel speed; After performing wavelet decomposition on the load power signal based on the load fluctuation intensity to generate the corresponding initial power allocation, the method further includes: If the power limit value corresponding to the flywheel speed is less than the high-frequency component, then the power limit value is taken as the first initial power corresponding to the flywheel. The difference between the high-frequency component and the power limit value, along with the low-frequency component, are used as the second initial power corresponding to the lithium battery. A new initial allocation power is generated based on the first initial power and the second initial power; Correspondingly, the step of using a pre-determined multi-objective optimization algorithm to adjust the initial power allocation based on the system state parameters, and generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery, includes: The multi-objective optimization algorithm is used to adjust the new initial power allocation based on the system state parameters, thereby generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery.

5. The method according to claim 1, characterized in that, The system status parameters include: flywheel speed, lithium battery state of charge, and lithium battery temperature; The step of using a pre-determined multi-objective optimization algorithm to adjust the initial power allocation based on the system state parameters, and generating a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery, includes: The multi-objective optimization algorithm is used to adjust the initial power allocation based on the flywheel speed, the state of charge, the temperature, the objective function, and the constraints to generate a first power allocation corresponding to the flywheel and a second power allocation corresponding to the lithium battery. The objective function aims to minimize the lithium battery life loss and the system energy loss rate. The constraints include power balance constraints, flywheel safety constraints, and lithium battery safety constraints.

6. The method according to claim 1, characterized in that, The method further includes: If the system status parameters meet the preset protection triggering conditions, the adjustment of the initial power allocation using the multi-objective optimization algorithm is paused, and the preset protection strategy is executed until the system status parameters meet the preset safety conditions. The initial power allocation is adjusted using the multi-objective optimization algorithm to generate a new first power allocation corresponding to the flywheel and a new second power allocation corresponding to the lithium battery.

7. The method according to claim 6, characterized in that, The system state parameters include at least one of the following: flywheel speed, lithium battery state of charge, and lithium battery temperature; If the system state parameters meet the preset protection triggering conditions, then the adjustment of the initial power allocation using the multi-objective optimization algorithm is paused, and a preset protection strategy is executed until the system state parameters meet the preset safety conditions, including: If the flywheel speed is greater than the first preset speed threshold, the multi-objective optimization algorithm for adjusting the initial power allocation is paused, and the lithium battery forced absorption mode is triggered until the flywheel speed is less than or equal to the second preset speed threshold; wherein, the absorption power of the lithium battery forced absorption mode is less than or equal to the current maximum charging power of the lithium battery. If the state of charge is less than the first preset state of charge threshold, the multi-objective optimization algorithm for adjusting the initial power allocation is paused, and the system switches to flywheel independent power supply mode until the state of charge is greater than or equal to the second preset state of charge threshold. If the state of charge is greater than the third preset state of charge threshold or the temperature is greater than the first preset temperature threshold, the multi-objective optimization algorithm is paused to adjust the initial power allocation, the flywheel is forced to bear all power input, and when the temperature is greater than the first preset temperature threshold, the liquid cooling system is started until the state of charge is less than or equal to the fourth preset state of charge threshold and the temperature is less than or equal to the second preset temperature threshold.

8. The method according to claim 6, characterized in that, The recovery process, after adjusting the initial power allocation using the multi-objective optimization algorithm to generate a new first power allocation corresponding to the flywheel and a new second power allocation corresponding to the lithium battery, further includes: The output power of the flywheel is smoothly adjusted to the new first power distribution. The output power of the lithium battery is smoothly adjusted to the new second power allocation.

9. A power distribution device, characterized in that, include: The acquisition module is used to acquire the load power signal and system status parameters of the energy storage system in real time. The system state parameters are related to the flywheel and the lithium battery; The decomposition module is used to perform wavelet decomposition on the load power signal according to the load fluctuation intensity to generate a corresponding initial allocation power; the initial allocation power includes a high-frequency component allocated to the flywheel and a low-frequency component allocated to the lithium battery; The load fluctuation intensity is used to characterize the rate of change of load power over time; The allocation module is used to adjust the initial allocation power based on the system state parameters using a pre-determined multi-objective optimization algorithm, and generate a first allocation power corresponding to the flywheel and a second allocation power corresponding to the lithium battery; the first allocation power is related to the high-frequency component; and the second allocation power is related to the low-frequency component.

10. An electronic device, characterized in that, include: Memory and processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the power allocation method as described in any one of claims 1 to 8.

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