Control method and device for high-voltage cascade energy storage system

Abstract

The invention discloses a control method and device for a high-voltage cascade energy storage system, and relates to the technical field of electric energy storage, and the method comprises the steps: receiving a charging and discharging instruction of a power grid dispatching center, and carrying out the decomposition to generate a multi-stage power instruction set; monitoring the high-voltage cascade energy storage system in real time to obtain a multi-unit state parameter set, and performing dynamic equilibrium analysis according to the multi-stage power instruction set and the multi-unit state parameter set to generate a multi-stage cooperative scheduling instruction; and mapping the multi-stage cooperative scheduling instruction to the high-voltage cascade energy storage system for predictive control, converting the multi-stage cooperative scheduling instruction into a charge-discharge control instruction set and executing the charge-discharge control instruction set, and meanwhile, performing monitoring recording and generating system response data to perform closed-loop feedback correction on the multi-stage cooperative scheduling instruction to obtain a system optimization control parameter set. The technical problem that in the prior art, a charging and discharging control instruction cannot accurately adapt to power grid load fluctuation is solved, the effect of optimizing the dispatching instruction is achieved, and therefore the technical effect of improving the system response speed and stability is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage, and in particular to a control method and device for a high-voltage cascade energy storage system. Background Art

[0002] In the control process of high-voltage cascade energy storage systems, charge and discharge control instructions are typically adjusted based on grid load fluctuations. However, traditional charge and discharge control methods cannot accurately and promptly respond to actual load fluctuations. Grid load fluctuations have complex frequency characteristics, typically including high-frequency and low-frequency fluctuation components, which have different impacts on energy storage system scheduling at different time scales. Traditional methods fail to effectively identify and allocate these fluctuation components, resulting in insufficient responsiveness of charge and discharge instructions, which in turn affects the overall efficiency and stability of the system. Summary of the Invention

[0003] The present application provides a control method and device for a high-voltage cascade energy storage system, which is used to solve the technical problem in the prior art that charge and discharge control instructions cannot accurately adapt to grid load fluctuations.

[0004] In view of the above problems, the present application provides a control method and device for a high-voltage cascade energy storage system.

[0005] A first aspect of the present application provides a control method for a high-voltage cascade energy storage system, the method comprising: Receive charging and discharging instructions from the power grid dispatching center, retrieve load fluctuation characteristics to decompose the charging and discharging instructions, and generate a multi-level power instruction set; perform real-time monitoring on the high-voltage cascade energy storage system to obtain a multi-unit state parameter set, perform dynamic balance analysis based on the multi-level power instruction set and the multi-unit state parameter set, and generate a multi-level collaborative scheduling instruction; map the multi-level collaborative scheduling instruction to the high-voltage cascade energy storage system for predictive control, convert it into a charging and discharging control instruction set, execute the charging and discharging control instruction set for monitoring and recording, generate system response data, perform closed-loop feedback correction on the multi-level collaborative scheduling instruction, and obtain a system optimization control parameter set.

[0006] A second aspect of the present application provides a control device for a high-voltage cascade energy storage system, the device comprising: An instruction decomposition module is used to receive charging and discharging instructions from the power grid dispatching center, retrieve load fluctuation characteristics to decompose the charging and discharging instructions, and generate a multi-level power instruction set; a real-time monitoring module is used to monitor the high-voltage cascade energy storage system in real time, obtain a multi-unit state parameter set, perform dynamic balance analysis based on the multi-level power instruction set and the multi-unit state parameter set, and generate a multi-level collaborative scheduling instruction; a feedback correction module is used to map the multi-level collaborative scheduling instruction to the high-voltage cascade energy storage system for predictive control, convert it into a charging and discharging control instruction set, execute the charging and discharging control instruction set for monitoring and recording, generate system response data, perform closed-loop feedback correction on the multi-level collaborative scheduling instruction, and obtain a system optimization control parameter set.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application receives the charge and discharge instructions from the power grid dispatching center, retrieves the load fluctuation characteristics to decompose the charge and discharge instructions, and generates a multi-level power instruction set; performs real-time monitoring on the high-voltage cascade energy storage system to obtain a multi-unit state parameter set, performs dynamic balancing analysis based on the multi-level power instruction set and the multi-unit state parameter set, and generates a multi-level collaborative scheduling instruction; maps the multi-level collaborative scheduling instruction to the high-voltage cascade energy storage system for predictive control, converts it into a charge and discharge control instruction set, executes the charge and discharge control instruction set for monitoring and recording, generates system response data, performs closed-loop feedback correction on the multi-level collaborative scheduling instruction, and obtains a system optimization control parameter set. The present invention solves the technical problem in the prior art that the charge and discharge control instructions cannot accurately adapt to the power grid load fluctuations. By decomposing the charge and discharge instructions and combining them with the multi-unit state parameters for dynamic balancing analysis, the effect of optimizing the scheduling instructions is achieved, thereby improving the system response speed and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A flow chart of a control method for a high-voltage cascade energy storage system provided in an embodiment of the present application; Figure 2 A schematic structural diagram of a control device for a high-voltage cascade energy storage system provided in an embodiment of the present application.

[0010] Description of the accompanying drawings: instruction decomposition module 11, real-time monitoring module 12, feedback correction module 13. DETAILED DESCRIPTION

[0011] The present application provides a control method and device for a high-voltage cascade energy storage system, aiming to solve the technical problem in the prior art that charging and discharging control instructions cannot accurately adapt to grid load fluctuations. By decomposing the charging and discharging instructions and performing dynamic balancing analysis in combination with multi-unit state parameters, the effect of optimizing the scheduling instructions is achieved, thereby improving the system response speed and stability.

[0012] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0013] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0014] Example 1, as Figure 1 As shown, the present application provides a control method for a high-voltage cascade energy storage system, the method comprising: Step S100: receiving charging and discharging instructions from a power grid dispatching center, decomposing the charging and discharging instructions by extracting load fluctuation characteristics, and generating a multi-level power instruction set.

[0015] In an embodiment of the present application, a charge and discharge instruction is first received from a power grid dispatching center. The instruction includes a charge or discharge operation that the energy storage system should perform within a specific time period, and is used to guide the operation of the energy storage system.

[0016] Next, the load fluctuation characteristics are retrieved to decompose the charge and discharge instructions. This process first performs spectrum recognition on the grid load fluctuation characteristics to identify high-frequency and low-frequency fluctuation components. The charge and discharge instructions are then traversed, and the response of the high-voltage cascade energy storage system is analyzed to obtain the first and second response speed parameters. Based on these response speed parameters, the first and second energy storage unit clusters are determined, respectively. The high-frequency fluctuation components are assigned to the first-level energy storage unit cluster, and the low-frequency fluctuation components are assigned to the second-level energy storage unit cluster. Finally, a multi-level power instruction set is generated.

[0017] Furthermore, in the method provided in the embodiment of the application, the load fluctuation characteristics are retrieved to decompose the charge and discharge instructions to generate a multi-level power instruction set, and the method further includes: Perform spectrum identification on the load fluctuation characteristics to determine high-frequency fluctuation components and low-frequency fluctuation components; traverse the charge and discharge instructions to perform response analysis on the high-voltage cascade energy storage system to obtain a first response speed parameter and a second response speed parameter; determine a first-level energy storage unit cluster according to the first response speed parameter matching, and determine a second-level energy storage unit cluster according to the second response speed parameter matching; allocate the high-frequency fluctuation component to the first-level energy storage unit cluster, and allocate the low-frequency fluctuation component to the second-level energy storage unit cluster to generate the multi-level power instruction set.

[0018] In the embodiments of the present application, when performing spectral identification of load fluctuation characteristics, historical load data of the high-voltage cascade energy storage system is first collected and the load change rate is calculated. A fast Fourier transform (FFT) is then performed based on the load change rate to extract the spectral energy distribution percentage. Wavelet analysis is then performed based on the fluctuation period and spectral energy distribution percentage, and an appropriate number of decomposition levels is set based on the analysis results. Finally, the load fluctuation characteristics are spectrally decomposed and reconstructed according to the set number of decomposition levels to identify high-frequency and low-frequency fluctuation components.

[0019] Then, based on the identified high-frequency and low-frequency fluctuation components, the received charge and discharge instructions are traversed, and the response analysis of the high-voltage cascade energy storage system is performed. In this step, the first response speed parameter and the second response speed parameter are obtained by real-time acquisition and analysis of the dynamic response of the energy storage system. The first response speed parameter refers to the reaction speed of the energy storage system to high-frequency fluctuations. Its response time is usually required to be no more than 100ms. It is suitable for energy storage units that require fast response (such as supercapacitors). The second response speed parameter is the reaction speed of the energy storage system to low-frequency fluctuations. Its response time is required to be greater than or equal to 1 second. It is suitable for energy storage units with slower response (such as lithium batteries and lead-acid batteries).

[0020] Energy storage unit clusters are matched based on the first and second response speed parameters. The first-level energy storage unit clusters are matched based on the first response speed parameter. These energy storage units are primarily designed to handle high-frequency fluctuations, so faster-response energy storage devices (such as supercapacitors) are selected. The second-level energy storage unit clusters are matched based on the second response speed parameter to handle low-frequency fluctuations, so slower-response energy storage devices (such as lithium batteries and lead-acid batteries) are selected.

[0021] Finally, the high-frequency fluctuation components are allocated to the first-level energy storage unit cluster, and the low-frequency fluctuation components are allocated to the second-level energy storage unit cluster. In this way, a multi-level power command set is generated.

[0022] Furthermore, in the method provided in the embodiment of the application, the spectrum of the load fluctuation characteristics is identified to determine the high-frequency fluctuation component and the low-frequency fluctuation component, and the method further includes: Collect historical load data of the high-voltage cascade energy storage system to perform change calculations and obtain the load change rate; perform fast Fourier transform according to the load change rate to extract the spectrum energy distribution ratio; perform wavelet analysis according to the fluctuation period combined with the spectrum energy distribution ratio, and set the number of decomposition layers; perform spectral decomposition and reconstruction on the load fluctuation characteristics according to the number of decomposition layers to identify the high-frequency fluctuation component and the low-frequency fluctuation component.

[0023] In an embodiment of the present application, historical load data is first obtained from the high-voltage cascade energy storage system. This data is provided by the power grid dispatching center or the real-time monitoring equipment of the energy storage system and contains the load demand record of the power grid within a certain time period. To ensure the accuracy of the data, data is regularly collected from multiple monitoring points (such as substations, energy storage units, load equipment, etc.), and outliers are removed through data cleaning and preprocessing. After the historical load data is collected, the load change calculation is started. In this process, the speed of load change, that is, the load change rate, is obtained by calculating the load difference between adjacent time points.

[0024] After obtaining the load change rate, a fast Fourier transform (FFT) is performed to convert the time-domain load change data into a frequency-domain signal. The FFT is then used to extract the spectral energy distribution percentage, which represents the energy contribution of each frequency component to the total load fluctuation. The FFT then determines the frequency distribution of the load fluctuation. For example, suppose low-frequency fluctuations (0-1 Hz) account for 70% of the total energy, while high-frequency fluctuations (1-5 Hz) account for 30%.

[0025] After spectral analysis, wavelet analysis is performed based on the periodic characteristics of load fluctuations (such as daily fluctuations and seasonal variations in grid load) combined with the spectral energy distribution percentage. Wavelet analysis is a multi-scale signal processing method that can extract signal features at multiple time scales. In this step, the primary period of load fluctuations is first identified. For example, grid load typically exhibits significant fluctuations within a 24-hour period. Based on these periodic characteristics, an appropriate number of decomposition levels is determined. For load fluctuations with shorter periods (such as daily 24-hour fluctuations), a fewer number of decomposition levels (such as three) is selected to capture higher-frequency fluctuations. For fluctuations with longer periods (such as seasonal fluctuations), a higher number of decomposition levels (such as six or more) is selected to capture lower-frequency fluctuations in detail.

[0026] The load fluctuation signal is decomposed into multiple layers using wavelet transforms, depending on the number of decomposition layers. Each wavelet transform layer extracts different frequency components of the signal, analyzing load fluctuations at multiple scales. For example, a three-layer wavelet decomposition can identify both short-term fluctuations (high-frequency components) and long-term trends (low-frequency components) of daily fluctuations.

[0027] Finally, through spectral decomposition and reconstruction, the various frequency components obtained from wavelet analysis are synthesized to reconstruct the complete load fluctuation signal. In this way, high-frequency and low-frequency fluctuation components are identified and extracted. High-frequency fluctuation components reflect short-term fluctuations in the power grid, such as instantaneous changes in load or equipment startup and shutdown; while low-frequency fluctuation components represent long-term changes in power grid load, such as seasonal fluctuations or fluctuations during peak power consumption periods.

[0028] Step S200: Real-time monitoring of the high-voltage cascade energy storage system is performed to obtain a multi-unit state parameter set, dynamic balance analysis is performed based on the multi-level power instruction set and the multi-unit state parameter set, and a multi-level coordinated scheduling instruction is generated.

[0029] In this embodiment of the present application, a high-voltage cascade energy storage system is first monitored in real time, collecting operational data from each energy storage unit. This data includes a set of state parameters for each energy storage unit, with key parameters including the battery's state of charge (SOC), temperature gradient, and voltage deviation rate. This process yields a multi-unit state parameter set. SOC represents the battery's current charge level; temperature gradient represents the temperature difference between different parts of the energy storage unit; and voltage deviation rate represents the deviation between the actual voltage of the energy storage unit and its designed voltage.

[0030] Next, a dynamic balancing analysis is performed based on the multi-level power instruction set and the multi-unit state parameter set. This process first loads and reads the multi-level power instruction set in real time to determine the real-time spectral components of the grid load. The multi-level power instruction set and the multi-unit state parameter set (such as SOC, temperature gradient, and voltage deviation rate) are then synchronized to the health analysis unit to generate multiple health parameters. Based on these parameters, a state transition assessment is performed to generate a state assessment matrix, which is then used for dynamic balancing analysis to obtain the state balancing coefficient. The energy storage system is then traversed, multi-level dispatch identification is performed, and multi-level dispatch data tags are generated. Finally, a dispatch conflict analysis is performed, the dispatch data tags are updated, and finally, multi-level coordinated dispatch instructions are generated.

[0031] Furthermore, in the method provided in the embodiment of the application, dynamic balancing analysis is performed based on the multi-level power instruction set and the multi-unit state parameter set to generate a multi-level coordinated scheduling instruction, further comprising: The multi-level power instruction set is loaded for real-time reading to determine the real-time spectrum components; a health analysis unit is constructed, and the multi-level power instruction set and the multi-unit state parameter set are synchronized to the health analysis unit to generate multiple health parameters; a state transfer evaluation is performed on the multi-level power instruction set and the multi-unit state parameter set according to the multiple health parameters to generate a state evaluation matrix; a dynamic balance analysis is performed according to the state evaluation matrix to generate a state balance coefficient; a multi-level scheduling identification is performed on the high-voltage cascade energy storage system based on the state balance coefficient to generate a multi-level scheduling data tag; a scheduling conflict analysis is performed based on the multi-level scheduling data tag, and the multi-level scheduling data tag is updated according to the analysis result to generate the multi-level collaborative scheduling instruction.

[0032] In an embodiment of the present application, a multi-level power instruction set is first loaded and read in real time, and the multi-level power instruction set is analyzed by fast Fourier transform (FFT) to convert the load data in the time domain into a frequency domain signal, thereby determining the real-time spectrum component.

[0033] Next, a health analysis unit is constructed, and the multi-level power instruction set and the multi-unit state parameter set are synchronized to the health analysis unit. Specifically, a health analysis thread is created through the main controller of the high-voltage cascade energy storage system, and the multi-level power instruction set and the multi-unit state parameter set are synchronized to the health analysis unit. In this process, a triple data buffering mechanism is first set up to ensure the efficiency and accuracy of data processing. The first buffer layer temporarily stores the multi-level power instruction set and the multi-unit state parameter set to generate a first buffer result; the second buffer layer aligns the timestamps of the first buffer result to ensure the time synchronization of the data and generates a second buffer result; the third buffer layer performs unit matching identification on the second buffer result to generate a third buffer result. Finally, priority parameter update compensation is performed based on the third buffer result to generate multiple health parameters.

[0034] Subsequently, the state transition evaluation of the multi-level power instruction set and the multi-unit state parameter set is performed according to multiple health parameters. In this step, the health parameters are first dynamically normalized to obtain a standard health parameter vector. Then, the state transition probability is initialized by calling the events that have not occurred, the initial probability table is obtained, and the state transition rules are defined to construct a discrete state space. The multi-level power instruction set and the multi-unit state parameter set are mapped to the discrete state space, and the state transition analysis is performed. The node transfer statistics are performed according to the initial probability table to obtain the node transfer frequency information. Combined with the standard health parameter vector and the node transfer frequency information, the transfer path is backtracked and matched to determine the distribution data of the state transition. Finally, these data are structured and stored, and a state evaluation matrix is constructed.

[0035] Next, a dynamic balance analysis is performed based on the state assessment matrix. The state balance coefficient is a quantitative value obtained by performing a dynamic balance analysis on the state assessment matrix of the energy storage unit. Specifically, the balance of the current system's health state distribution is assessed by counting the transition frequencies between each state (e.g., healthy, warning, and fault). When calculating the state balance coefficient, the transition frequencies of each state are first calculated, and the number of transitions between each healthy state is counted. The state balance degree is then calculated, that is, the ratio of the transition frequency of each healthy state to the total transition frequency is calculated to obtain the stability of each state. For example, the stability of the healthy state = the frequency of transitions from healthy to warning + the frequency of transitions from healthy to fault divided by the total transition frequency. The calculation process for the stability of the warning state and the stability of the fault state is similar to that of the healthy state. Finally, the state balance coefficient is generated by taking the mean of the stability of the healthy state, the stability of the warning state, and the stability of the fault state.

[0036] Once the state balance coefficient is obtained, the high-voltage cascade energy storage system is traversed based on the coefficient for multi-level dispatch identification. The purpose of this process is to determine the dispatch priority of each energy storage unit based on the health status of the energy storage unit and the state balance coefficient. An energy storage unit with a higher state balance coefficient usually means that its health status is relatively stable and should be dispatched first. By traversing each energy storage unit in the system, a multi-level dispatch data label is generated, which identifies the dispatch priority, dispatch period, and charging and discharging tasks of each energy storage unit. The label divides the energy storage units into different priorities according to their health status and dispatch requirements to ensure that the healthiest energy storage units can respond to the grid load demand first.

[0037] Next, scheduling conflict analysis is performed based on the generated multi-level scheduling data tags. This analysis detects whether there are resource conflicts or scheduling conflicts between energy storage units. For example, if two energy storage units are scheduled to charge at the same time, and their charging demands exceed the system's maximum power output, the system will detect a scheduling conflict. The key to conflict analysis is determining whether there is overload or competition when multiple energy storage units operate in parallel. Especially when the battery charging and discharging capacity is limited, scheduling conflicts can affect the stability of the entire system.

[0038] During scheduling conflict analysis, the charging and discharging tasks of energy storage units are readjusted based on scheduling priorities. For example, if energy storage units A and B experience a power conflict during the same time period, and energy storage unit A is in better health and has a higher priority, the system will adjust the charging task of energy storage unit B, delaying its charging task or redistributing the power load to resolve the scheduling conflict.

[0039] Finally, based on the results of the scheduling conflict analysis, the multi-level scheduling data tags are updated, and the final multi-level coordinated scheduling instructions are generated. The updated data tags adjust the scheduling tasks and priorities of the energy storage units, ensuring that the system can flexibly schedule according to the current health status of the energy storage units, load demand, and scheduling priorities. Based on these adjustments, multi-level coordinated scheduling instructions are generated, specifying specific charging and discharging tasks, scheduling time periods, and related charging and discharging powers for each energy storage unit.

[0040] Furthermore, in the method provided in the embodiment of the application, a health analysis unit is constructed, the multi-level power instruction set and the multi-unit state parameter set are synchronized to the health analysis unit to generate multiple health parameters, and the method further includes: Based on the main controller of the high-voltage cascade energy storage system, a health analysis thread is created to obtain a health analysis unit, and the multi-level power instruction set and the multi-unit status parameter set are synchronized to the health analysis unit: S1: a triple data buffer mechanism is set, and the triple data buffer mechanism includes a first buffer layer, a second buffer layer, and a third buffer layer; S2: the multi-level power instruction set and the multi-unit status parameter set are temporarily stored through the first buffer layer to generate a first buffer result; S3: the first buffer result is timestamp-aligned through the second buffer layer to generate a second buffer result; S4: the second buffer result is unit-matched identified through the third buffer layer to generate a third buffer result; S5: priority parameter update compensation is performed based on the third buffer result to generate the multiple health parameters.

[0041] In this embodiment of the present application, a health analysis thread is first created based on the main controller of the high-voltage cascade energy storage system. This health analysis thread is responsible for real-time analysis of the health status of each energy storage unit. The health analysis unit receives a multi-level power instruction set and a multi-unit status parameter set obtained from the energy storage system.

[0042] To ensure smooth data processing and synchronization, a triple data buffering mechanism is set up, which includes a first buffer layer, a second buffer layer, and a third buffer layer. In the first buffer layer, the multi-level power instruction set and the multi-unit status parameter set are temporarily stored to generate a first buffer result, ensuring that the system can quickly receive and store real-time data from the power grid dispatching center and the energy storage system. The second buffer layer performs timestamp alignment on the first buffer result. Since the multi-level power instruction set and the multi-unit status parameter set may be received at different time points, the timestamp alignment technology is used to ensure that they can be synchronized, thereby generating a second buffer result and ensuring the temporal consistency of the power instruction and the status parameters. The third buffer layer performs unit matching identification on the second buffer result to generate a third buffer result. The core task of this stage is to match the power instruction of each energy storage unit with its corresponding status parameter, ensuring that the dispatch instruction of each energy storage unit is synchronized with its operating status, and avoiding mis-dispatching or overload.

[0043] Based on the third buffered result, priority parameter update compensation is then performed. When the power command update frequency exceeds the state parameter update frequency, historical data interpolation compensation is used to fill in the gaps in state parameter updates. This interpolation compensation method uses linear or polynomial interpolation techniques to calculate the state value at the intermediate moment based on the state data at the previous and current moments. This compensation step ensures that even if the state parameters are not updated in real time, charge and discharge command scheduling can be based on the latest health assessment.

[0044] The compensated state parameters are then used to calculate the health parameters. The electrical health parameter is calculated first, based on the voltage deviation rate and actual power tracking error. The voltage deviation rate reflects the difference between the energy storage unit's actual voltage and the target voltage, while the power tracking error indicates the degree of deviation in the energy storage unit's tracking of the grid load. During the calculation, the voltage deviation rate and actual power tracking error are first normalized to ensure they are within the same quantitative range. Specifically, the voltage deviation rate and power tracking error are divided by their maximum values (for example, the maximum value of the voltage deviation rate is 10% and the maximum value of the power tracking error is 5%). Next, the normalized data is weighted to determine the energy storage unit's electrical health score. The weights for the voltage deviation rate and actual power tracking error are pre-set at 0.7 and 0.3, respectively. The electrical health parameter is calculated.

[0045] Next, the thermal health parameter is calculated, assigning a multi-level score to the energy storage unit based on the temperature gradient and local temperature rise rate. The temperature gradient represents the temperature difference between different parts of the energy storage unit. Excessive temperature differences can cause local overheating or reduced efficiency. The local temperature rise rate reflects the rate of temperature rise in a local area of the energy storage unit during charging and discharging. Rapid temperature rise can damage the battery. A thermal health score is generated based on these parameters. Assuming a temperature gradient of 3°C and a temperature rise rate of 0.5°C / s for the energy storage unit, a multi-level scoring mechanism is used, with thresholds for the temperature gradient and temperature rise rate corresponding to different health scores. For example, a temperature gradient of 3°C might result in an 80-point score, while a temperature rise rate of 0.5°C / s might result in a 70-point score. These two scores are weighted according to a pre-set weighting factor to generate the thermal health parameter. This parameter reflects the thermal health of the energy storage unit.

[0046] Finally, the aging health parameter is calculated. This parameter is generated by combining the cycle increment and internal resistance trend of the energy storage unit to generate a life decay index. The increase in internal resistance is a key indicator of battery degradation, and the cycle increment indicates the frequency of use of the energy storage unit. Assuming the cycle increment of the energy storage unit is 500 times and the internal resistance trend is 15%, the cycle increment and internal resistance trend are first normalized, and then the weights of these two factors are set to 0.5 and 0.5 respectively. Through weighted calculation, the life decay index is obtained, which is the aging health parameter.

[0047] Through the above calculations, multiple health parameters are obtained, including electrical health parameters, thermal health parameters, and aging health parameters.

[0048] Furthermore, in the method provided in the embodiment of the application, the state transition evaluation of the multi-level power instruction set and the multi-unit state parameter set is performed according to the multiple health parameters to generate a state evaluation matrix, and the method further includes: The multiple health parameters are dynamically normalized to determine a standard health parameter vector; the state transition probability is initialized by retrieving non-occurring events to obtain an initial probability table, and the state transition rules are defined and combined with the initial probability table to construct a discrete state space; the multi-level power instruction set and the multi-unit state parameter set are mapped to the discrete state space for state transfer, and node transfer statistics are performed according to the initial probability table to obtain node transfer frequency information; transfer path backtracking matching is performed based on the standard health parameter vector combined with the node transfer frequency information to determine state transfer distribution data; the state transfer distribution data is structured and stored to construct the state assessment matrix.

[0049] In an embodiment of the present application, multiple health parameters are first dynamically normalized to unify the electrical health parameters, thermal health parameters, and aging health parameters into the same quantitative range. Specifically, by normalizing each health parameter, their values are mapped to a standard range (usually between 0 and 1). For example, if the maximum value of the thermal health parameter is 100 points, and the thermal health parameter of the current energy storage unit is 80 points, then through normalization, the normalized value of the thermal health parameter is 0.8. The same method applies to electrical health parameters and aging health parameters. After normalization, these values are summarized into a standard health parameter vector, such as [electrical score, thermal score, aging score].

[0050] Next, we retrieve unoccurred events to initialize the state transition probabilities and build an initial probability table for subsequent state transition analysis. This initial probability table is initialized based on the frequency of energy storage unit state changes in historical operating data. We analyze the historical data for energy storage unit transitions from one healthy state to another. For example, we find that the probability of an energy storage unit transitioning from a healthy state to a warning state is 30%, while the probability of a unit transitioning from a warning state to a fault state is 10%. Based on this data, we initialize the probability table to provide probabilistic support for subsequent state transitions. This way, we obtain the initial probabilities of the energy storage unit transitioning between different states.

[0051] Then, the state transition rules are defined, which determine the state transition of the energy storage unit based on the health parameters. Specifically, clear transition conditions are set for each health state. For example, the condition for the energy storage unit to transition from the "healthy" state to the "warning" state is that any health score is lower than the first-level threshold for three consecutive times; the condition for transitioning from the "warning" state to the "fault" state is that key parameters such as internal resistance or temperature rise exceed the second-level threshold and continue to time out; and the transition from the "fault" state to the "healthy" state requires manual reset and benchmark test verification. Based on these transition rules, combined with the initial probability table, a discrete state space is further constructed. The discrete state space includes three types of nodes: healthy state, warning state, and fault state. Each energy storage unit in the system may be in any of these three states.

[0052] The multi-level power instruction set and multi-unit state parameter set are then mapped to a discrete state space for state transition. This process involves mapping the actual state of each energy storage unit to the corresponding node in the discrete state space based on the health parameters (electrical health parameters, thermal health parameters, and aging health parameters) of the energy storage unit and the state transition rules. For example, when the electrical health parameters of the energy storage unit are low and the temperature gradient is high, it will be transferred from the "healthy" state to the "warning" state; when the internal resistance increases significantly and exceeds the secondary threshold, the state of the energy storage unit will be transferred from the "warning" state to the "fault" state. Based on the initial probability table, the probability of state transition is calculated based on the health parameters of the current energy storage unit, and the state transition is simulated.

[0053] After the state transition is complete, node transition statistics are calculated according to the initial probability table, and node transition frequency information is obtained. This process involves tracking and recording the state transitions of the energy storage unit, counting the transition frequencies between each node (healthy state, warning state, and fault state). For example, if the energy storage unit transitions from "healthy" to "warning state" 20 times and from "warning state" to "fault state" 5 times, these transition frequencies are recorded to obtain node transition frequency information.

[0054] After obtaining node transition frequency information, a transition path backtracking match is performed based on the standard health parameter vector combined with the node transition frequency information to determine the state transition distribution data. Backtracking transition path matching involves tracing the transition paths between various states based on historical state transition data and health parameters, and analyzing the frequency of occurrence of different paths. For example, if the energy storage unit frequently transitions from the "healthy" state to the "warning" state, this path is analyzed in detail, and future transition patterns are predicted based on changes in health parameters. Through backtracking analysis, patterns in the changes in the health status of the energy storage unit are identified, and possible state transition paths are determined.

[0055] Finally, the state transition distribution data is structured and stored, and a state assessment matrix is constructed based on this data. The state assessment matrix contains the transition probabilities and frequencies between various health states. This matrix is used to assess the possible future state changes of the energy storage unit. The data in the matrix reflects the frequency and probability of the energy storage unit transitioning from one state to another, providing data support for scheduling decisions. For example, the matrix might indicate a 30% transition probability from the "healthy" state to the "warning" state, and a 10% transition probability from the "warning" state to the "fault" state.

[0056] Step S300: Map the multi-level coordinated scheduling instructions to the high-voltage cascade energy storage system for predictive control, convert them into a charge and discharge control instruction set, execute the charge and discharge control instruction set for monitoring and recording, generate system response data, perform closed-loop feedback correction on the multi-level coordinated scheduling instructions, and obtain a system optimization control parameter set.

[0057] In an embodiment of the present application, the multi-level collaborative scheduling instructions are first mapped to the high-voltage cascade energy storage system for predictive control. In this process, the grid interaction is recorded by the grid connection point detection equipment, the grid interaction parameters are obtained, and these parameters are used to perform state constraint mapping, thereby constructing a grid constraint state space. Then, the interval rolling advancement is performed in the grid constraint state space, a rolling time window is set, and the multi-level collaborative scheduling instructions are mapped to the high-voltage cascade energy storage system for scheduling prediction within this time window, thereby generating a collaborative scheduling prediction result. Subsequently, based on the prediction results, multi-objective optimization is performed to generate an optimized solution set, and the charge and discharge compensation data is injected into the solution set according to the control step size, and finally a charge and discharge control instruction set is generated.

[0058] Next, the charge-discharge control instruction set is executed to monitor and record the system response data, generating closed-loop feedback corrections to the multi-level coordinated scheduling instructions. This process begins by executing the charge-discharge control instruction set, driving the power conversion unit for multi-dimensional monitoring and generating multi-dimensional monitoring data, including key information such as the energy storage unit's charge and discharge power, battery health parameters, and temperature. This monitoring data is then timestamped to generate system response data, reflecting the energy storage unit's actual performance during the charge and discharge process. Based on this system response data, a scheduling deviation analysis is performed to identify discrepancies between the scheduling target and actual execution, generating scheduling deviation parameters. Parameter stability analysis is then performed based on these deviation parameters to generate parameter stability coefficients to assess the stability of the scheduling results. By analyzing these stability parameters, scheduling deviation parameters are prioritized to determine which deviations require priority correction. Finally, a scheduling deviation correction sequence is activated, and closed-loop feedback corrections are performed using parameter correction instructions to optimize the energy storage unit's scheduling strategy. This process continuously optimizes the system's optimal control parameter set, ensuring efficient and stable operation of the energy storage system while promptly responding to changes in grid load and energy storage unit status.

[0059] Furthermore, in the method provided in the embodiment of the application, the multi-level coordinated scheduling instructions are mapped to the high-voltage cascade energy storage system for predictive control and converted into a charge and discharge control instruction set, further comprising: Grid interaction is recorded through grid connection point detection equipment to obtain grid interaction parameters, and state constraint mapping is performed based on the grid interaction parameters to construct a grid constraint state space; interval rolling advancement is performed based on the grid constraint state space, and a rolling time window is set; according to the grid constraint state space, the multi-level collaborative scheduling instructions are mapped to the high-voltage cascade energy storage system, and scheduling prediction is performed according to the rolling time window to generate a collaborative scheduling prediction result; multi-objective optimization is performed based on the collaborative scheduling prediction result to generate an optimized solution set; the charging and discharging compensation data injected into the optimized solution set is converted according to the control step size to generate the charging and discharging control instruction set.

[0060] In the embodiment of the present application, the grid interaction is first recorded by the grid connection point detection device to obtain the real-time interaction parameters of the grid, such as voltage, current, frequency, etc. These grid interaction parameters are used to describe the interaction between the grid and the energy storage system.

[0061] Next, a grid constraint state space is constructed through state constraint mapping. This process transforms the actual grid state (such as voltage range and current limit) into a state space containing various constraints based on the grid's operating standards and load requirements. This state space describes the grid's operating boundaries, ensuring that energy storage system scheduling decisions do not lead to grid overload or unstable operation. The grid constraint state space includes physical constraints such as the grid's maximum load, minimum voltage, and maximum current, and provides clear limits for energy storage system scheduling.

[0062] Next, a rolling interval is implemented based on the grid constraint state space, setting a rolling time window. A rolling time window is a fixed-length period used to dynamically adjust and optimize the energy storage system's scheduling plan. Within each time window, the grid's load demand, the energy storage system's charge and discharge capabilities, and the grid's constraints are reassessed, and updated scheduling instructions are generated based on the new data. At the end of each window, a new scheduling plan is recalculated based on the updated grid interaction parameters and state space. This rolling mechanism continuously adjusts the energy storage system's operating strategy, ensuring it can always respond promptly to grid load fluctuations.

[0063] Based on the aforementioned grid constraint state space, multi-level coordinated dispatch instructions are mapped to the high-voltage cascade energy storage system, and dispatch predictions are performed based on a rolling time window. In this step, a predictive control model is used to perform dispatch predictions. The predictive control model is built based on historical system data, the performance characteristics of the energy storage units, and grid load fluctuations. This model uses an LSTM neural network to model the relationship between grid load fluctuations, energy storage unit status, and dispatch instructions. By inputting historical grid data and energy storage system status, the model predicts the charging and discharging requirements of the energy storage units in future time periods, thereby generating coordinated dispatch predictions.

[0064] Based on the collaborative scheduling prediction results, a multi-objective optimization process is then performed to generate an optimized solution set. This multi-objective optimization process comprehensively considers multiple factors, such as the health of the energy storage unit, grid demand, and charge and discharge efficiency. Techniques such as particle swarm optimization (PSO) are used to determine the optimal scheduling solution. This process results in an optimized solution set.

[0065] Finally, the charge and discharge compensation data injected into the optimized solution set is converted according to the control step size. The control step size is a fixed value set in advance and is used to control the adjustment range of the charge and discharge power of the energy storage unit. Specifically, the control step size determines the power change of the energy storage unit each time the charge and discharge power is adjusted. By applying the control step size to the optimized solution set, the charge and discharge tasks of the energy storage unit are finely adjusted according to the real-time grid load changes and the status of the energy storage unit. This adjustment ensures that the energy storage system can respond smoothly and accurately to grid load fluctuations and ensures that each energy storage unit completes the predetermined charge and discharge tasks without exceeding its capacity. Finally, the adjusted instructions are summarized into a charge and discharge control instruction set.

[0066] Furthermore, in the method provided in the embodiment of the application, the charging and discharging control instruction set is executed to monitor and record, and system response data is generated to perform closed-loop feedback correction on the multi-level coordinated scheduling instructions to obtain the system optimization control parameter set, which also includes: The power conversion unit is driven to execute the charge and discharge control instructions to perform multi-dimensional monitoring and generate multi-dimensional monitoring data; based on the multi-dimensional monitoring data, an associated time stamp is performed to generate system response data; according to the system response data, a scheduling deviation analysis is performed on the multi-level collaborative scheduling instructions to generate a scheduling deviation parameter; according to the scheduling deviation parameter, a parameter stability analysis is performed to generate a parameter stability coefficient, the scheduling deviation parameter is prioritized according to the parameter stability coefficient, and a scheduling deviation correction sequence is determined; the parameter correction instruction is activated, and a closed-loop feedback correction is performed according to the scheduling deviation correction sequence through the parameter correction instruction to generate the system optimization control parameter set.

[0067] In an embodiment of the present application, the power conversion unit is first driven to execute the charge and discharge control instructions for multi-dimensional monitoring. During this process, the voltage of the high-voltage cascade energy storage system is first monitored to obtain transient voltage fluctuation data. Then, current monitoring is performed, and multi-branch current data is obtained through multi-branch current monitoring. At the same time, the temperature of the energy storage system is monitored, and a two-dimensional thermal map is generated to display the temperature distribution of the energy storage unit during the charge and discharge process. Finally, the transient voltage fluctuation data, multi-branch current data, and the two-dimensional thermal map are correlated and integrated to generate complete multi-dimensional monitoring data.

[0068] Next, the multi-dimensional monitoring data is associated with the timestamp. Through time synchronization technology, the system ensures that each monitoring data is consistent with the corresponding timestamp, generating system response data that reflects the actual operation of the energy storage system.

[0069] The multi-level coordinated scheduling instructions are then analyzed for scheduling deviations based on the system response data. This process uses variance analysis methods (such as error calculation) to compare the deviation between the actual execution results and the planned schedule, generating a scheduling deviation parameter. This deviation parameter quantitatively describes the difference between the energy storage unit's performance during charging and discharging tasks and the planned target. For example, the system might analyze the difference between the energy storage unit's actual charging power and the target charging power, or the time delay during the charging and discharging process.

[0070] Next, parameter stability analysis is performed based on the scheduling deviation parameters. This stability analysis is performed using time series analysis. Specifically, historical scheduling deviations are compared with current deviations and smoothed using a moving average method to reduce noise fluctuations in the data. This method generates a parameter stability coefficient. Assuming that the scheduling deviations over the past five time points were 3%, 5%, 2%, 4%, and 3%, respectively, the moving average of these deviations is first calculated (for example, the average over the past five time points is 3.4%). The standard deviation is then calculated to reflect the stability of the deviation fluctuations. A smaller standard deviation indicates more stable scheduling, while a larger standard deviation indicates greater fluctuations in system scheduling. Assuming a standard deviation of 1.2%, the parameter stability coefficient = 1 - standard deviation / average = 1 - 1.2 / 3.4 = 0.65.

[0071] The scheduling deviation parameters are then prioritized according to the parameter stability coefficient. During this process, a priority sorting algorithm is used to sort the scheduling deviations based on the magnitude of the parameter stability coefficient. A smaller parameter stability coefficient indicates a more unstable scheduling process and larger deviations, so these deviations are corrected first. Priority identification determines which scheduling deviations should be corrected first. After priority identification, a scheduling deviation correction sequence is generated, and parameter correction instructions are generated based on this sequence. Based on the priority identification results, the scheduling deviation correction sequence determines the deviations that need to be corrected and the order in which they should be corrected. By adjusting the scheduling deviations one by one in this order, the deviations that have the greatest impact on system stability are corrected first. Parameter correction instructions are used to make specific adjustments to the charging and discharging tasks of the energy storage units. For example, if there is a large deviation in the battery charging power, this deviation can be eliminated by adjusting the charging power or extending the charging time. At this point, the correction instructions will make detailed adjustments to the charging or discharging operations of the energy storage units based on the aforementioned analysis results.

[0072] Finally, closed-loop feedback correction is performed, and further optimization is performed based on the revised scheduling parameters. Closed-loop feedback correction is implemented through a real-time feedback mechanism. The revised instructions are applied to the energy storage system and re-evaluated based on the new scheduling execution status. If deviations still exist after the correction, the tasks are adjusted again through the feedback mechanism until the energy storage unit's charging and discharging tasks fully match the predetermined targets. After this series of corrections and feedback processes, the system's optimized control parameter set is ultimately generated. This parameter set provides optimized control instructions for the energy storage unit's charging and discharging tasks, ensuring that the energy storage system can stably and efficiently respond to grid demands as grid load changes, and maximizing system operating efficiency.

[0073] Furthermore, in the method provided in the embodiment of the application, driving the power conversion unit to execute the charge and discharge control instruction to perform multi-dimensional monitoring and generate multi-dimensional monitoring data also includes: The power conversion unit is driven to execute the charge and discharge control instruction to monitor the voltage of the high-voltage cascade energy storage system and obtain transient voltage fluctuation data; the power conversion unit is driven to execute the charge and discharge control instruction to monitor the current of the high-voltage cascade energy storage system and obtain multi-branch current data; the power conversion unit is driven to execute the charge and discharge control instruction to monitor the temperature of the high-voltage cascade energy storage system and draw a two-dimensional thermal map; the transient voltage fluctuation data, the multi-branch current data, and the two-dimensional thermal map are correlated and integrated to generate the multi-dimensional monitoring data.

[0074] In this embodiment, the power conversion unit is first driven to execute charge and discharge control instructions, monitoring the voltage of the high-voltage cascade energy storage system. Voltage data from the energy storage unit is collected in real time through a voltage sensor to obtain transient voltage fluctuation data. This process uses the voltage sensor to measure voltage changes in real time during the charge and discharge process, capturing voltage fluctuations caused by changes in grid load.

[0075] The power conversion unit is then driven to execute charge and discharge control commands, monitoring the current of the high-voltage cascade energy storage system. Current sensors collect current data from the energy storage unit and obtain multi-branch current data. The current sensors record the current changes in each branch in real time, reflecting the current load of the energy storage unit under different charge and discharge paths.

[0076] Then, the system continues to execute charge and discharge control instructions, monitors the temperature of the high-voltage cascade energy storage system, obtains temperature data from the energy storage units through temperature sensors, and generates a two-dimensional heat map showing the temperature distribution of each area of the energy storage unit.

[0077] Finally, all collected transient voltage fluctuation data, multi-branch current data, and two-dimensional thermal maps are correlated and integrated. This process uses data fusion technology to synchronize voltage, current, and temperature data with timestamps, ensuring temporal consistency and ultimately generating complete multi-dimensional monitoring data.

[0078] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application receives the charge and discharge instructions from the power grid dispatching center, retrieves the load fluctuation characteristics to decompose the charge and discharge instructions, and generates a multi-level power instruction set; performs real-time monitoring on the high-voltage cascade energy storage system to obtain a multi-unit state parameter set, performs dynamic balancing analysis based on the multi-level power instruction set and the multi-unit state parameter set, and generates a multi-level collaborative scheduling instruction; maps the multi-level collaborative scheduling instruction to the high-voltage cascade energy storage system for predictive control, converts it into a charge and discharge control instruction set, executes the charge and discharge control instruction set for monitoring and recording, generates system response data, performs closed-loop feedback correction on the multi-level collaborative scheduling instruction, and obtains a system optimization control parameter set. The present invention solves the technical problem in the prior art that the charge and discharge control instructions cannot accurately adapt to the power grid load fluctuations. By decomposing the charge and discharge instructions and combining them with the multi-unit state parameters for dynamic balancing analysis, the effect of optimizing the scheduling instructions is achieved, thereby improving the system response speed and stability.

[0079] Embodiment 2 is based on the same inventive concept as the control method of a high-voltage cascade energy storage system in the above embodiment. Figure 2As shown, the present application provides a control device for a high-voltage cascade energy storage system. The device and method embodiments in the present application are based on the same inventive concept. The device includes: The instruction decomposition module 11 is used to receive the charging and discharging instructions from the power grid dispatching center, retrieve the load fluctuation characteristics to decompose the charging and discharging instructions, and generate a multi-level power instruction set; the real-time monitoring module 12 is used to monitor the high-voltage cascade energy storage system in real time, obtain a multi-unit state parameter set, perform dynamic balance analysis based on the multi-level power instruction set and the multi-unit state parameter set, and generate a multi-level collaborative scheduling instruction; the feedback correction module 13 is used to map the multi-level collaborative scheduling instruction to the high-voltage cascade energy storage system for predictive control, convert it into a charging and discharging control instruction set, execute the charging and discharging control instruction set for monitoring and recording, generate system response data, perform closed-loop feedback correction on the multi-level collaborative scheduling instruction, and obtain a system optimization control parameter set.

[0080] Furthermore, the device is also used to implement the following functions: Perform spectrum identification on the load fluctuation characteristics to determine high-frequency fluctuation components and low-frequency fluctuation components; traverse the charge and discharge instructions to perform response analysis on the high-voltage cascade energy storage system to obtain a first response speed parameter and a second response speed parameter; determine a first-level energy storage unit cluster according to the first response speed parameter matching, and determine a second-level energy storage unit cluster according to the second response speed parameter matching; allocate the high-frequency fluctuation component to the first-level energy storage unit cluster, and allocate the low-frequency fluctuation component to the second-level energy storage unit cluster to generate the multi-level power instruction set.

[0081] Furthermore, the device is also used to implement the following functions: Collect historical load data of the high-voltage cascade energy storage system to perform change calculations and obtain the load change rate; perform fast Fourier transform according to the load change rate to extract the spectrum energy distribution ratio; perform wavelet analysis according to the fluctuation period combined with the spectrum energy distribution ratio, and set the number of decomposition layers; perform spectral decomposition and reconstruction on the load fluctuation characteristics according to the number of decomposition layers to identify the high-frequency fluctuation component and the low-frequency fluctuation component.

[0082] Furthermore, the device is also used to implement the following functions: The multi-level power instruction set is loaded for real-time reading to determine the real-time spectrum components; a health analysis unit is constructed, and the multi-level power instruction set and the multi-unit state parameter set are synchronized to the health analysis unit to generate multiple health parameters; a state transfer evaluation is performed on the multi-level power instruction set and the multi-unit state parameter set according to the multiple health parameters to generate a state evaluation matrix; a dynamic balance analysis is performed according to the state evaluation matrix to generate a state balance coefficient; a multi-level scheduling identification is performed on the high-voltage cascade energy storage system based on the state balance coefficient to generate a multi-level scheduling data tag; a scheduling conflict analysis is performed based on the multi-level scheduling data tag, and the multi-level scheduling data tag is updated according to the analysis result to generate the multi-level collaborative scheduling instruction.

[0083] Furthermore, the device is also used to implement the following functions: Based on the main controller of the high-voltage cascade energy storage system, a health analysis thread is created to obtain a health analysis unit, and the multi-level power instruction set and the multi-unit status parameter set are synchronized to the health analysis unit: S1: a triple data buffer mechanism is set, and the triple data buffer mechanism includes a first buffer layer, a second buffer layer, and a third buffer layer; S2: the multi-level power instruction set and the multi-unit status parameter set are temporarily stored through the first buffer layer to generate a first buffer result; S3: the first buffer result is timestamp-aligned through the second buffer layer to generate a second buffer result; S4: the second buffer result is unit-matched identified through the third buffer layer to generate a third buffer result; S5: priority parameter update compensation is performed based on the third buffer result to generate the multiple health parameters.

[0084] Furthermore, the device is also used to implement the following functions: The multiple health parameters are dynamically normalized to determine a standard health parameter vector; the state transition probability is initialized by retrieving non-occurring events to obtain an initial probability table, and the state transition rules are defined and combined with the initial probability table to construct a discrete state space; the multi-level power instruction set and the multi-unit state parameter set are mapped to the discrete state space for state transfer, and node transfer statistics are performed according to the initial probability table to obtain node transfer frequency information; transfer path backtracking matching is performed based on the standard health parameter vector combined with the node transfer frequency information to determine state transfer distribution data; the state transfer distribution data is structured and stored to construct the state assessment matrix.

[0085] Furthermore, the device is also used to implement the following functions: Grid interaction is recorded through grid connection point detection equipment to obtain grid interaction parameters, and state constraint mapping is performed based on the grid interaction parameters to construct a grid constraint state space; interval rolling advancement is performed based on the grid constraint state space, and a rolling time window is set; according to the grid constraint state space, the multi-level collaborative scheduling instructions are mapped to the high-voltage cascade energy storage system, and scheduling prediction is performed according to the rolling time window to generate a collaborative scheduling prediction result; multi-objective optimization is performed based on the collaborative scheduling prediction result to generate an optimized solution set; the charging and discharging compensation data injected into the optimized solution set is converted according to the control step size to generate the charging and discharging control instruction set.

[0086] Furthermore, the device is also used to implement the following functions: The power conversion unit is driven to execute the charge and discharge control instructions to perform multi-dimensional monitoring and generate multi-dimensional monitoring data; based on the multi-dimensional monitoring data, an associated time stamp is performed to generate system response data; according to the system response data, a scheduling deviation analysis is performed on the multi-level collaborative scheduling instructions to generate a scheduling deviation parameter; according to the scheduling deviation parameter, a parameter stability analysis is performed to generate a parameter stability coefficient, the scheduling deviation parameter is prioritized according to the parameter stability coefficient, and a scheduling deviation correction sequence is determined; the parameter correction instruction is activated, and a closed-loop feedback correction is performed according to the scheduling deviation correction sequence through the parameter correction instruction to generate the system optimization control parameter set.

[0087] Furthermore, the device is also used to implement the following functions: The power conversion unit is driven to execute the charge and discharge control instruction to monitor the voltage of the high-voltage cascade energy storage system and obtain transient voltage fluctuation data; the power conversion unit is driven to execute the charge and discharge control instruction to monitor the current of the high-voltage cascade energy storage system and obtain multi-branch current data; the power conversion unit is driven to execute the charge and discharge control instruction to monitor the temperature of the high-voltage cascade energy storage system and draw a two-dimensional thermal map; the transient voltage fluctuation data, the multi-branch current data, and the two-dimensional thermal map are correlated and integrated to generate the multi-dimensional monitoring data.

[0088] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0090] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A control method for a high-voltage cascade energy storage system, characterized in that: The method comprises: Receive charging and discharging instructions from the power grid dispatching center, decompose the charging and discharging instructions based on load fluctuation characteristics, and generate a multi-level power instruction set; Performing real-time monitoring on the high-voltage cascade energy storage system to obtain a multi-unit state parameter set, performing dynamic balancing analysis based on the multi-level power instruction set and the multi-unit state parameter set, and generating a multi-level coordinated scheduling instruction; The multi-level coordinated scheduling instructions are mapped to a high-voltage cascade energy storage system for predictive control, converted into a charge and discharge control instruction set, executed to monitor and record, and system response data is generated to perform closed-loop feedback correction on the multi-level coordinated scheduling instructions to obtain a system optimization control parameter set.

2. A control method for a high-voltage cascade energy storage system according to claim 1, characterized in that: The load fluctuation characteristics are retrieved to decompose the charge and discharge instructions to generate a multi-level power instruction set, and the method includes: Performing spectrum recognition on the load fluctuation characteristics to determine high-frequency fluctuation components and low-frequency fluctuation components; Perform response analysis on the high-voltage cascade energy storage system by traversing the charge and discharge instructions to obtain a first response speed parameter and a second response speed parameter; Determine a first-level energy storage unit cluster according to the first response speed parameter matching, and determine a second-level energy storage unit cluster according to the second response speed parameter matching; The high-frequency fluctuation component is allocated to the first-level energy storage unit cluster, and the low-frequency fluctuation component is allocated to the second-level energy storage unit cluster to generate the multi-level power instruction set.

3. A control method for a high-voltage cascade energy storage system according to claim 2, characterized in that: Performing spectrum recognition on the load fluctuation characteristics to determine high-frequency fluctuation components and low-frequency fluctuation components, the method includes: Collect historical load data of the high-voltage cascade energy storage system to calculate changes and obtain the load change rate; Performing a fast Fourier transform according to the load change rate to extract a spectrum energy distribution ratio; Perform wavelet analysis according to the fluctuation period and the spectral energy distribution ratio, and set the number of decomposition layers; The load fluctuation characteristics are subjected to spectrum decomposition and reconstruction according to the number of decomposition layers to identify the high-frequency fluctuation component and the low-frequency fluctuation component.

4. A control method for a high-voltage cascade energy storage system according to claim 1, characterized in that: Performing dynamic balancing analysis on the multi-level power instruction set and the multi-unit state parameter set to generate a multi-level coordinated scheduling instruction, the method comprising: Loading the multi-level power instruction set for real-time reading to determine real-time spectrum components; Constructing a health analysis unit, synchronizing the multi-level power instruction set and the multi-unit state parameter set to the health analysis unit, and generating a plurality of health parameters; Performing a state transition evaluation on the multi-level power instruction set and the multi-unit state parameter set according to the multiple health parameters to generate a state evaluation matrix; Performing dynamic equilibrium analysis based on the state evaluation matrix to generate a state equilibrium coefficient; Based on the state balance coefficient, the high-voltage cascade energy storage system is traversed to perform multi-level dispatch identification and generate a multi-level dispatch data tag; A scheduling conflict analysis is performed based on the multi-level scheduling data tags, the multi-level scheduling data tags are updated according to the analysis results, and the multi-level collaborative scheduling instructions are generated.

5. A control method for a high-voltage cascade energy storage system according to claim 4, characterized in that: Constructing a health analysis unit, synchronizing the multi-level power instruction set and the multi-unit state parameter set to the health analysis unit, and generating a plurality of health parameters, the method includes: Based on the main controller of the high-voltage cascade energy storage system, a health analysis thread is created to obtain a health analysis unit, and the multi-level power instruction set and the multi-unit state parameter set are synchronized to the health analysis unit: S1: Setting a triple data buffering mechanism, wherein the triple data buffering mechanism includes a first buffer layer, a second buffer layer, and a third buffer layer; S2: temporarily storing the multi-level power instruction set and the multi-unit state parameter set in the first buffer layer to generate a first buffer result; S3: aligning timestamps of the first buffer result through the second buffer layer to generate a second buffer result; S4: performing unit matching identification on the second buffer result through the third buffer layer to generate a third buffer result; S5: Perform priority parameter update compensation based on the third buffer result to generate the multiple health parameters.

6. A control method for a high-voltage cascade energy storage system according to claim 4, characterized in that: Performing a state transition evaluation on the multi-level power instruction set and the multi-unit state parameter set according to the multiple health parameters to generate a state evaluation matrix, the method comprising: Dynamically normalizing the multiple health parameters to determine a standard health parameter vector; Retrieve the unoccurred events to initialize the state transition probability, obtain the initial probability table, define the state transition rules and construct the discrete state space based on the initial probability table; Mapping the multi-level power instruction set and the multi-unit state parameter set to the discrete state space for state transfer, performing node transfer statistics according to the initial probability table, and obtaining node transfer frequency information; Perform transfer path backtracking matching based on the standard health parameter vector and the node transfer frequency information to determine state transfer distribution data; The state transition distribution data is stored in a structured manner to construct the state evaluation matrix.

7. The control method of a high-voltage cascade energy storage system according to claim 1, characterized in that: The multi-level coordinated scheduling instructions are mapped to a high-voltage cascade energy storage system for predictive control and converted into a charge and discharge control instruction set, the method comprising: Recording grid interaction through a grid connection point detection device to obtain grid interaction parameters, performing state constraint mapping based on the grid interaction parameters, and constructing a grid constraint state space; Perform interval rolling advancement based on the grid constraint state space and set a rolling time window; Mapping the multi-level coordinated dispatch instructions to the high-voltage cascade energy storage system according to the grid constraint state space, performing dispatch prediction according to the rolling time window, and generating a coordinated dispatch prediction result; Perform multi-objective optimization based on the collaborative scheduling prediction results to generate an optimized solution set; The charge-discharge compensation data injected into the optimization solution set is converted according to the control step size to generate the charge-discharge control instruction set.

8. The control method of a high-voltage cascade energy storage system according to claim 1, characterized in that: The charge and discharge control instruction set is executed to monitor and record, and system response data is generated to perform closed-loop feedback correction on the multi-level coordinated scheduling instructions to obtain a system optimization control parameter set. The method includes: driving the power conversion unit to execute the charge and discharge control instructions to perform multi-dimensional monitoring and generate multi-dimensional monitoring data; Associating timestamps based on the multi-dimensional monitoring data to generate system response data; Performing a scheduling deviation analysis on the multi-level collaborative scheduling instructions according to the system response data to generate a scheduling deviation parameter; Perform parameter stability analysis based on the scheduling deviation parameters to generate parameter stability coefficients, prioritize the scheduling deviation parameters according to the parameter stability coefficients, and determine a scheduling deviation correction sequence; Activate the parameter correction instruction, perform closed-loop feedback correction according to the scheduling deviation correction sequence through the parameter correction instruction, and generate the system optimization control parameter set.

9. A control method for a high-voltage cascade energy storage system according to claim 8, characterized in that: The method of driving the power conversion unit to execute the charge and discharge control instruction to perform multi-dimensional monitoring and generate multi-dimensional monitoring data includes: driving the power conversion unit to execute the charge and discharge control instruction to perform voltage monitoring on the high-voltage cascade energy storage system and obtain transient voltage fluctuation data; Driving the power conversion unit to execute the charge and discharge control instruction to perform current monitoring on the high-voltage cascade energy storage system to obtain multi-branch current data; driving the power conversion unit to execute the charge and discharge control instructions to monitor the temperature of the high-voltage cascade energy storage system and draw a two-dimensional thermal map; The transient voltage fluctuation data, the multi-branch current data, and the two-dimensional thermal map are correlated and integrated to generate the multi-dimensional monitoring data.

10. A control device for a high-voltage cascade energy storage system, characterized in that: The device is used to execute a control method for a high-voltage cascade energy storage system according to any one of claims 1 to 9, and the device includes: An instruction decomposition module is used to receive charging and discharging instructions from the power grid dispatching center, decompose the charging and discharging instructions based on load fluctuation characteristics, and generate a multi-level power instruction set; A real-time monitoring module is used to monitor the high-voltage cascade energy storage system in real time, obtain a multi-unit state parameter set, perform dynamic balance analysis based on the multi-level power instruction set and the multi-unit state parameter set, and generate a multi-level coordinated scheduling instruction; A feedback correction module is used to map the multi-level coordinated scheduling instructions to the high-voltage cascade energy storage system for predictive control, convert them into a charge and discharge control instruction set, execute the charge and discharge control instruction set for monitoring and recording, generate system response data, perform closed-loop feedback correction on the multi-level coordinated scheduling instructions, and obtain a system optimization control parameter set.

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