A control method for suppressing power fluctuation of a new energy grid-connected power plant

Abstract

This invention relates to a control method for smoothing power fluctuations in new energy grid-connected power in smart power plants. The method includes: collecting power fluctuation data from the new energy grid-connected end of the smart power plant and multi-dimensional state data of battery cell groups within an energy storage cluster; classifying smoothing task types based on the fluctuation characteristics of the power fluctuation data; calculating a suitability index for each battery cell group to each smoothing task type based on a suitability algorithm and multi-dimensional state data to quantify the matching degree of each battery cell group to different smoothing task types; constructing a dual-objective control function that integrates power smoothing and battery cell group consistency based on the suitability index; solving the dual-objective control function to obtain a composite smoothing control command; and driving the corresponding battery cell group to perform charging and discharging actions based on the composite smoothing control command to complete the coordinated smoothing of the energy storage cluster. This invention achieves refined management and control of new energy grid-connected power fluctuations by ensuring grid-connected stability and battery cell consistency.

Description

Technical Field

[0001] This invention relates to the field of new energy grid connection control technology, and mainly to a control method for smoothing power fluctuations in new energy grid connection in smart power plants. Background Technology

[0002] With the rapid development of new energy power generation technology, the proportion of new energy in the power system continues to increase. However, during the grid connection of new energy, power fluctuations can cause the grid frequency and voltage to deviate from the rated range, affecting power quality and even threatening the safety of grid equipment. Therefore, effectively mitigating grid-connected power fluctuations has become a key requirement for the operation and control of smart power plants.

[0003] Chinese invention patent application CN117175614A discloses a method and device for grid-connected power fluctuation smoothing control. This technical solution includes determining the current grid-connected reference power based on the real-time power of distributed power sources; determining the total power task of a hybrid energy storage system using a model-free adaptive algorithm based on the current grid-connected reference power; performing frequency domain analysis on the total power task of the hybrid energy storage system to obtain the frequency boundary point between the low-frequency high-energy component and the high-frequency low-energy component corresponding to the total power task; and performing grid-connected power fluctuation smoothing control based on the frequency boundary point. However, the above technical solution only performs task allocation and smoothing control for the total power of the hybrid energy storage system, making it difficult to adapt to different types of power fluctuation characteristics, resulting in a lack of specificity in the smoothing response; and it does not consider the individual characteristics of the internal battery cells. The aforementioned technical solutions suffer from inconsistent state characteristics, leading to issues such as overcharging and over-discharging of some cells, uneven lifespan degradation, and rapid deterioration of cell cluster consistency, resulting in poor long-term operational reliability. Furthermore, these solutions only address fluctuation mitigation through power and frequency boundary points, failing to collect state data such as cell voltage, temperature, health status, and charge / discharge capacity. Moreover, these solutions focus solely on mitigating grid-connected power fluctuations, neglecting to construct a dual objective function that coordinates power mitigation and cell consistency. This prevents the achievement of the dual benefits of optimal mitigation and balanced cell state, and the inability to dynamically adjust parameters and task allocation based on changing operating conditions. Consequently, the mitigation effect exhibits poor stability in complex and variable power fluctuation scenarios, making it difficult to meet the stringent grid requirements for renewable energy grid connection.

[0004] Therefore, there is an urgent need for a highly efficient control method that can balance the effect of smoothing out battery fluctuations with the protection of battery cells. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a control method for smoothing power fluctuations in new energy grid-connected power in smart power plants.

[0006] The technical solution of the present invention is as follows: On the one hand, this invention proposes a control method for smoothing power fluctuations in new energy grid-connected power in smart power plants, the method comprising: Collect power fluctuation data at the grid-connected new energy terminals of smart power plants and multi-dimensional status data of battery cell groups within energy storage clusters; Based on the fluctuation characteristics of power fluctuation data, the smoothing task types are divided; based on the suitability algorithm and multi-dimensional state data, the suitability index of each cell group for each smoothing task type is calculated to quantify the degree of matching of each cell group to different smoothing task types. Based on the suitability index, a dual-objective control function is constructed that integrates power smoothing and cell group consistency. The dual-objective control function is solved to obtain the smoothing power of each cell group corresponding to the minimum dual-objective control function value. The smoothing power is converted into the charge and discharge regulation parameters of each cell group. The charge and discharge regulation parameters of all cell groups are integrated to obtain the composite smoothing control command. Based on the composite smoothing control command, the corresponding cell group is driven to perform charging and discharging actions to complete the coordinated smoothing of the energy storage cluster.

[0007] Preferably, the method further includes preprocessing, including: use The criteria determine and remove abnormal data that exceed the preset reasonable threshold in power fluctuation data and multi-dimensional state data; Linear interpolation is used to complete the missing power fluctuation data and multi-dimensional state data in a single acquisition cycle; Verify the timestamp discrepancy between power fluctuation data and multi-dimensional status data; The extreme value processing method is used to normalize the fluctuating data and multi-dimensional state data.

[0008] Preferably, the multi-dimensional status data includes cell health, current actual available power, instantaneous heat dissipation capability, aging rate sensitivity, historical power response performance in a specific frequency band, cell terminal voltage, charging and discharging current, operating temperature, state of charge, and number of charge and discharge cycles.

[0009] Preferably, the fluctuation characteristics include fluctuation frequency and fluctuation amplitude; The types of stabilization tasks include high-frequency small-amplitude fluctuation stabilization tasks, low-frequency large-amplitude fluctuation stabilization tasks, and emergency backup stabilization tasks. The high-frequency small-amplitude fluctuation suppression task is defined as the grid-connected power fluctuation frequency being greater than a preset first frequency threshold and the fluctuation amplitude being less than a preset first amplitude threshold; the low-frequency large-amplitude fluctuation suppression task is defined as the grid-connected power fluctuation frequency being less than or equal to a preset first frequency threshold and the fluctuation amplitude being greater than a preset first amplitude threshold and less than a preset second amplitude threshold; the emergency backup suppression task is defined as the grid-connected power fluctuation amplitude being greater than a preset second frequency threshold or the fluctuation change rate being greater than a preset rate threshold.

[0010] Preferably, the suitability index of each cell group for each mitigation task type is calculated based on the suitability algorithm and multi-dimensional state data. The calculation method is as follows: ; In the formula, express Time of the first Group of cells to the first Suitability index for similar tasks; Indicates the first Class-A pacing task for the first Differential weights for item status data; express Time of the first The group of battery cells in the first Standardized values ​​of item status data; express Time of the first The group of battery cells in the first The time-series decay coefficient of the item state data; Indicates the first The adaptation penalty coefficient for tasks similar to sedation; express Time of the first Group of cells to the first Historical deviation values ​​for similar tasks; express Number of cell group status data at any given time.

[0011] Preferably, it also includes assigning smoothing tasks to each cell group based on a suitability index, specifically: The number of battery cells to be executed is determined based on the power regulation requirements of each type of smoothing task, and a preset redundancy is reserved for the battery cells to be executed for each type of smoothing task. If the suitability index of the current cell group is lower than the preset suitability index, the preset redundancy of the current cell group will be used to replace the current cell group to smooth it out; otherwise, the smoothing process will continue.

[0012] Preferably, the dual-objective control function is calculated as follows:

[0013] ; In the formula, express The overall optimization objective value of the bi-objective control function at any given time; express The weighting coefficients of the bi-objective control function at each time step; express Constant grid-connected power output error mitigation; express Constant-time cell pack consistency deviation; This represents the preset adaptability correlation coefficient; express Time of the first Group of cells to the first The suppression power of similar suppression tasks; This indicates the number of battery cells within the energy storage cluster.

[0014] Preferably, the method further includes real-time monitoring of the power smoothing effect and cell group status during the execution process, and updating the control parameter group of the smoothing control process based on the monitoring results, wherein the control parameter group includes differential weight, timing decay coefficient, adaptation penalty coefficient and adaptability correlation coefficient.

[0015] In another aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the present invention.

[0016] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the present invention.

[0017] The present invention has the following beneficial effects: 1. This invention collects power fluctuation data from the grid-connected new energy terminals of smart power plants. By analyzing the fluctuation characteristics of the power fluctuation data, it classifies different types of smoothing tasks, enabling smoothing control to accurately match the power fluctuation requirements under different scenarios. This avoids the problems of insufficient smoothing accuracy and poor adaptability caused by the "one-size-fits-all" frequency domain decomposition method in the prior art. At the same time, based on the suitability algorithm, it calculates the suitability index of each cell group for each smoothing task type, realizing the accurate matching between smoothing tasks and cell group states. This ensures that each smoothing task is executed by the cell group with the optimal state and the strongest adaptability, effectively avoiding the problems of cell group overcharging and over-discharging and uneven load distribution. 2. This invention constructs a dual-objective control function that integrates power smoothing and cell pack consistency. While achieving the power smoothing objective, it also considers cell pack consistency optimization, effectively balancing the operating status of each cell pack, slowing down cell pack aging, extending the overall lifespan of the energy storage cluster, reducing the operation and maintenance costs of the energy storage system, and improving the safety and reliability of the energy storage system. At the same time, by solving the dual-objective control function, a composite smoothing control command is obtained to drive the corresponding cell pack to perform charging and discharging actions, realizing precise and differentiated control of power smoothing. This effectively smooths the power fluctuations brought about by the grid connection of new energy sources, reduces the impact of fluctuations on the safe and stable operation of the power grid, and improves the reliability of grid-connected operation. Attached Figure Description

[0018] Figure 1 This is a detailed flowchart of an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0021] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0023] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0024] Example 1: See Figure 1 This invention provides a control method for smoothing power fluctuations in new energy grid-connected power in smart power plants, the method comprising: S1. Collect power fluctuation data at the grid-connected new energy terminal of the smart power plant and multi-dimensional status data of the battery cell group in the energy storage cluster; The power fluctuation data at the grid connection end and the multi-dimensional status data of the battery cells in the energy storage cluster are collected at the same time, with the collection period set to 0.5s-2s. The grid-connected terminal collects data from all photovoltaic and wind power generation units of the smart power plant, including their grid-connected junction points and grid-side metering nodes. The data acquisition of the battery cell group covers the individual and cluster-level monitoring points of each battery cell group. The multi-dimensional status data includes the health status of the battery cell group, the current actual available power, the instantaneous heat dissipation capacity, the aging rate sensitivity, the historical performance of power response in a specific frequency band, the battery cell terminal voltage, the charging and discharging current, the operating temperature, the state of charge, and the number of charge and discharge cycles. The method further includes preprocessing, including: use The criteria determine and remove abnormal data that exceed the preset reasonable threshold in power fluctuation data and multi-dimensional state data; Linear interpolation is used to complete the missing power fluctuation data and multi-dimensional state data in a single acquisition cycle; Verify the timestamp deviation of power fluctuation data and multi-dimensional status data, and perform synchronous calibration on datasets with timestamp deviations exceeding 50ms; The extreme value processing method is used to normalize the fluctuating data and multi-dimensional state data; S2. Classify the smoothing task type based on the fluctuation characteristics of power fluctuation data; The fluctuation characteristics include fluctuation frequency and fluctuation amplitude; The types of stabilization tasks include high-frequency small-amplitude fluctuation stabilization tasks, low-frequency large-amplitude fluctuation stabilization tasks, and emergency backup stabilization tasks. The high-frequency small-amplitude fluctuation suppression task is defined as the grid-connected power fluctuation frequency being greater than a preset first frequency threshold and the fluctuation amplitude being less than a preset first amplitude threshold; the low-frequency large-amplitude fluctuation suppression task is defined as the grid-connected power fluctuation frequency being less than or equal to a preset first frequency threshold and the fluctuation amplitude being greater than a preset first amplitude threshold and less than a preset second amplitude threshold; the emergency backup suppression task is defined as the grid-connected power fluctuation amplitude being greater than a preset second frequency threshold or the fluctuation change rate being greater than a preset rate threshold. Specifically, for example, high-frequency small-amplitude fluctuation stabilization tasks correspond to grid-connected power fluctuation frequency > 10Hz and fluctuation amplitude < 50kW; low-frequency large-amplitude fluctuation stabilization tasks correspond to grid-connected power fluctuation frequency ≤ 10Hz and fluctuation amplitude 50kW-200kW; emergency backup stabilization tasks correspond to grid-connected power fluctuation amplitude > 200kW or fluctuation change rate > 100kW / s. S3. Calculate the suitability index of each cell group for each smoothing task type based on the suitability algorithm and multi-dimensional state data to quantify the degree of matching of each cell group to different smoothing task types. S31. The suitability index is calculated as follows: ; In the formula, express Time of the first Group of cells to the first Suitability index for similar tasks; Indicates the first Class-A pacing task for the first Differential weights for item status data; express Time of the first The group of battery cells in the first Standardized values ​​of item status data; express Time of the first The group of battery cells in the first The time-series decay coefficient of the item state data; Indicates the first The adaptation penalty coefficient for tasks similar to sedation; express Time of the first Group of cells to the first Historical deviation values ​​for similar tasks; express The number of cell group status data at any given time; in, The configuration satisfies: when the first When the smoothing task is a high-frequency, small-amplitude fluctuation smoothing task, the sum of the state data weights corresponding to the instantaneous heat dissipation capacity and the state data weights corresponding to the historical power response performance in a specific frequency band shall account for no less than 50% of the total sum of all state data weights for this type of smoothing task; when the first When the smoothing task is a low-frequency, large-fluctuation smoothing task, the sum of the state data weight corresponding to the current actual available power and the state data weight corresponding to the cell health status accounts for no less than 60% of the total sum of all state data weights for this type of smoothing task; When the type of smoothing task is an emergency backup smoothing task, the sum of the weights of the cell health status and the current actual available power shall account for no less than 30% of the total weight of all status data for that type of smoothing task; S32 also includes assigning smoothing tasks to each cell group based on a suitability index, specifically: The number of battery cells to be executed is determined based on the power regulation requirements of each type of smoothing task, and a preset redundancy is reserved for the battery cells to be executed for each type of smoothing task, such as a redundancy of 10%-20%. If the suitability index of the current cell group is lower than the preset suitability index, then the preset redundancy of the current cell group will be used to replace the current cell group to smooth it out; otherwise, the smoothing process will continue. For example, the preset suitability index is 0.6. When the suitability index of the existing battery cell group is lower than 0.6, the redundant battery cell group is activated to complete the replacement. The replacement process is executed synchronously with the power smoothing action without interrupting the power smoothing process. S4. Construct a dual-objective control function for fusion power smoothing and cell pack consistency based on the suitability index; The dual-objective control function includes an external power output prediction part and an internal state consistency prediction part. The external power output prediction part is based on the power fluctuation data at the grid connection end and combined with the power change law of new energy power generation to complete the prediction of the grid connection power fluctuation trend within the next 5s-15s, and to clarify the charging and discharging power and adjustment duration required for various smoothing tasks at different times. The internal state consistency prediction part is based on multi-dimensional state data and combined with the charging and discharging characteristics of the battery pack to predict the changing trends of the state of charge, terminal voltage, and operating temperature of each battery pack under different charging and discharging powers, as well as the deviation evolution law of the above indicators between battery packs, so as to realize the integration of the dual objectives of external power smoothing and internal battery pack consistency management. The bi-objective control function is calculated as follows:

[0025] ; In the formula, express The overall optimization objective value of the bi-objective control function at any given time; express The weighting coefficients of the bi-objective control function at each time step; express Constant grid-connected power output error mitigation; express Constant-time cell pack consistency deviation; This represents the preset adaptability correlation coefficient; express Time of the first Group of cells to the first The suppression power of similar suppression tasks; Indicates the number of battery cells within the energy storage cluster; S5. Solve the dual-objective control function to obtain the power suppression of each cell group corresponding to the minimum dual-objective control function value; S6. Convert the power smoothing into the charging and discharging control parameters of each cell group, integrate the charging and discharging control parameters of all cell groups, and obtain the composite smoothing control command. S61. The composite smoothing control command is a dedicated and precise control command for each cell group in the energy storage cluster. The specific contents include: a unique identification code for the cell group, a matching smoothing task type identifier, a charging and discharging action direction, a real-time charging and discharging power setting value, a charging and discharging action duration, an action synchronization triggering sequence, a power adjustment step size, and a status feedback threshold during the charging and discharging process. S62. Convert the power smoothing into charge and discharge control parameters for each cell group. The specific steps are as follows: The direction of charging and discharging is determined based on the excess / deficient attribute of the grid-connected power fluctuation. When there is excess power, it is charging; when there is a power deficiency, it is discharging. When the power is smoothed, a positive value is discharging and a negative value is charging. The real-time charging and discharging power setting value is obtained by combining the absolute value of the power smoothing with the real-time charging and discharging efficiency of the battery pack. Real-time charge and discharge efficiency is determined by the health of the battery cell assembly. The duration of charging and discharging actions and the power adjustment step size are set differently according to the type of smoothing task. For high-frequency small fluctuation smoothing tasks, the duration is half of the power fluctuation cycle and the adjustment step size is 1% of the smoothing power setting value. For low-frequency large fluctuation smoothing tasks, the duration is determined based on the power fluctuation trend prediction results of 5s-15s and the adjustment step size is 2% of the smoothing power setting value. The timing of action synchronization is based on the reference timestamp of the command issued by the control center. Cell groups of the same type of smoothing task are sorted from high to low according to the suitability index. The cell group with the highest priority is triggered without delay. The delay value of subsequent cell groups increases sequentially and the total deviation is ≤10ms. High-frequency small fluctuation smoothing tasks are triggered first, while low-frequency large fluctuation smoothing tasks are delayed by ≤5ms. The state feedback thresholds include the state-of-charge limit, the upper limit of the operating temperature, and the lower limit of the cell terminal voltage, all of which are set in conjunction with the power smoothing setting. The higher the discharge power, the higher the state-of-charge limit and the lower limit of the terminal voltage, and the lower the upper limit of the operating temperature. S7. In accordance with the unified control timing and communication protocol, the composite smoothing control command is synchronously sent to all energy storage converters. Based on the composite smoothing control command, the corresponding cell group is driven to perform charging and discharging actions to complete the coordinated smoothing of the energy storage cluster. S71. The unified control timing system is set such that the instruction receiving time deviation of all energy storage converters is ≤10ms, the action triggering time deviation is ≤10ms, and the adjustment time deviation of converters for the same type of smoothing task is ≤50ms. The communication protocol is ModbusTCP or Profinet industrial Ethernet protocol, with a communication transmission rate of no less than 100Mbps. CRC verification is used during the command issuance process. The control center splits the composite smoothing control command according to the matching relationship between the energy storage converter and the battery cell group. Each converter only receives the dedicated control command for the corresponding battery cell group. When the converter executes the command, it synchronously feeds back the real-time operating status of the battery cell group. S72. The specific process for completing the coordinated smoothing of energy storage clusters is as follows: After parsing the composite smoothing control command, the energy storage converter completes parameter calibration before the action is triggered; then, based on the power fluctuation type at the new energy grid connection end, it drives the battery cell group matching the smoothing task to perform the corresponding charging and discharging actions. When power excess fluctuations occur at the grid connection point, the control unit will perform charging actions with the power set by the command, storing the excess electrical energy in the grid into the battery pack, thereby reducing and smoothing the grid connection power. When a power deficit fluctuation occurs at the grid connection end, the control unit controls the appropriate cell group to discharge at the set power, releasing the stored energy to the grid connection end and thus smoothing out the grid connection power. Simultaneously, each cell group in the energy storage cluster executes actions synchronously according to the set charging and discharging duration and power adjustment step size. Redundant cell groups corresponding to various smoothing tasks are in standby mode. The control unit collects the actual charging and discharging status of each cell group and the real-time power fluctuation data at the grid connection end in real time, compares the actual smoothing effect with the preset smoothing target, and if the actual grid connection power still exceeds the rated fluctuation range, it immediately fine-tunes the charging and discharging power parameters of the corresponding cell group and sends them to the energy storage converter until the grid connection power fluctuation is smoothed to the grid's rated allowable range, ultimately completing the coordinated smoothing of the new energy grid connection power fluctuation by the energy storage cluster. S8 also includes real-time monitoring of the power smoothing effect and cell pack status during the execution process, and updating the control parameter group of the smoothing control process based on the monitoring results. The control parameter group includes differential weights, timing decay coefficients, adaptation penalty coefficients, and adaptability correlation coefficients, and the specific calculation method is as follows: ; In the formula, express The control parameter group for each moment; express The control parameter group for each moment; This represents the preset update sensitivity coefficient; This indicates the maximum value of the preset grid-connected power smoothing error; This indicates the maximum preset value of the cell consistency deviation; express The average suitability index of all cell groups at any given time; In a specific embodiment, it is applied to the new energy grid connection scenario of a smart photovoltaic power plant. The photovoltaic power plant is equipped with a large-scale energy storage cluster, which contains 15 independent battery cell groups. Due to the combined fluctuations of sunlight and wind speed at the grid connection end, there is a superposition of high-frequency small power fluctuations (frequency 15Hz, amplitude 30kW) and low-frequency large power fluctuations (frequency 5Hz, amplitude 120kW). Collect power fluctuation data at the grid connection end and multi-dimensional status data of 15 groups of battery cells in the energy storage cluster. The collection period is set to 1 second. The collection covers all photovoltaic and wind power junction points and individual / cluster level monitoring points of battery cells in the power plant. The two core features of the preprocessed power fluctuation data—fluctuation frequency and fluctuation amplitude—are extracted. Based on preset thresholds, the smoothing task types are categorized. In this scenario, two types of tasks are combined, with no emergency backup task. Based on the characteristics of the task type, differentiated weights are configured for the 10 status data items, ensuring that the total weight is 1 and the weight ratio of the core indicators meets the requirements: when At that time, the task was to smooth out high-frequency, small-amplitude fluctuations. , , ; when At that time, the task was to smooth out low-frequency, small fluctuations. , , ; Based on the suitability algorithm and preprocessed multi-dimensional state data, the suitability index of each cell group in the energy storage cluster for the task of smoothing out high-frequency small fluctuations is calculated by the suitability index calculation formula. With the third group of battery cells hour Taking the suitability index calculation as an example, take , , ; ; Calculate the suitability index of all cell packs for high-frequency and low-frequency small-amplitude fluctuation mitigation tasks, and then select the best suitability indexes. Effective cell groups with a suitability threshold of ≥0.6 were selected (with a preset suitability threshold of 0.6), resulting in 12 effective cell groups (3 of which were discarded). <0.6 cell packs); Based on the power regulation requirements of the two types of tasks, determine the number of battery cells to be executed, and reserve 15% redundancy (within the preset range of 10%~20%). (Total regulating power is 80kW): The maximum regulating power of a single cell group is 20kW, requiring 4 cell groups to execute, with 1 redundant group reserved (5 groups in total). The top 5 cell groups are selected from the effective cell groups. (Total regulating power is 300kW): The maximum regulating power of a single cell group is 50kW, requiring 6 cell groups to execute, with 1 redundant group reserved (7 groups in total). The first 7 cell groups are selected from the effective cell groups. If the battery pack is in operation If the value is less than 0.6, the redundant battery cell pack replacement will be initiated immediately. The replacement and stabilization actions will be performed simultaneously without interrupting the process. A dual-objective control function for integrating power smoothing and cell pack consistency is constructed based on the suitability index of each cell pack. , , , ;for ; The precise suppression power of 12 cell groups for high-frequency and low-frequency minor fluctuation suppression tasks was obtained (e.g.) , Furthermore, all power leveling meets the cell pack capacity constraints.

[0026] Example 2: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a control method for smoothing power fluctuations in new energy grid connection of a smart power plant as described in any one of Embodiment 1.

[0027] Example 3: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a control method for smoothing power fluctuations in new energy grid connection of a smart power plant as described in any one of Embodiment 1.

[0028] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0029] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0030] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0031] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0032] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A control method for smoothing power fluctuations in new energy grid-connected power in a smart power plant, characterized in that, The method includes: Collect power fluctuation data at the grid-connected new energy terminals of smart power plants and multi-dimensional status data of battery cell groups within energy storage clusters; Based on the fluctuation characteristics of power fluctuation data, the smoothing task types are divided; based on the suitability algorithm and multi-dimensional state data, the suitability index of each cell group for each smoothing task type is calculated to quantify the degree of matching of each cell group to different smoothing task types. Based on the suitability index, a dual-objective control function is constructed that integrates power smoothing and cell group consistency. The dual-objective control function is solved to obtain the smoothing power of each cell group corresponding to the minimum dual-objective control function value. The smoothing power is converted into the charge and discharge regulation parameters of each cell group. The charge and discharge regulation parameters of all cell groups are integrated to obtain the composite smoothing control command. Based on the composite smoothing control command, the corresponding cell group is driven to perform charging and discharging actions to complete the coordinated smoothing of the energy storage cluster.

2. The control method for smoothing power fluctuations in new energy grid-connected power in a smart power plant according to claim 1, characterized in that, The method further includes preprocessing, including: use The criteria determine and remove abnormal data that exceed the preset reasonable threshold in power fluctuation data and multi-dimensional state data; Linear interpolation is used to complete the missing power fluctuation data and multi-dimensional state data in a single acquisition cycle; Verify the timestamp discrepancy between power fluctuation data and multi-dimensional status data; The extreme value processing method is used to normalize the fluctuating data and multi-dimensional state data.

3. The control method for smoothing power fluctuations in new energy grid-connected power in a smart power plant according to claim 1, characterized in that, The multi-dimensional status data includes cell health, current actual available power, instantaneous heat dissipation capacity, aging rate sensitivity, historical power response performance in a specific frequency band, cell terminal voltage, charging and discharging current, operating temperature, state of charge, and number of charge and discharge cycles.

4. The control method for smoothing power fluctuations in new energy grid-connected power in a smart power plant according to claim 1, characterized in that, The fluctuation characteristics include fluctuation frequency and fluctuation amplitude; The types of stabilization tasks include high-frequency small-amplitude fluctuation stabilization tasks, low-frequency large-amplitude fluctuation stabilization tasks, and emergency backup stabilization tasks. The high-frequency small-amplitude fluctuation smoothing task is when the grid-connected power fluctuation frequency is greater than a preset first frequency threshold and the fluctuation amplitude is less than a preset first amplitude threshold. The low-frequency large fluctuation smoothing task is when the frequency of grid-connected power fluctuation is less than or equal to the preset first frequency threshold and the fluctuation amplitude is greater than the preset first amplitude threshold and less than the preset second amplitude threshold; the emergency backup smoothing task is when the amplitude of grid-connected power fluctuation is greater than the preset second frequency threshold or the fluctuation change rate is greater than the preset rate threshold.

5. The control method for smoothing power fluctuations in new energy grid-connected power in a smart power plant according to claim 4, characterized in that, The suitability index of each cell group for each mitigation task type is calculated based on the suitability algorithm and multi-dimensional state data. The calculation method is as follows: ； In the formula, express Time of the first Group of cells to the first Suitability index for similar tasks; Indicates the first Class-A pacing task for the first Differential weights for item status data; express Time of the first The group of battery cells in the first Standardized values ​​of item status data; express Time of the first The group of battery cells in the first The time-series decay coefficient of the item state data; Indicates the first The adaptation penalty coefficient for tasks similar to sedation; express Time of the first Group of cells to the first Historical deviation values ​​for similar tasks; express Number of cell group status data at any given time.

6. The control method for smoothing power fluctuations in new energy grid-connected power in a smart power plant according to claim 5, characterized in that, This also includes assigning smoothing tasks to each cell pack based on a suitability index, specifically: The number of battery cells to be executed is determined based on the power regulation requirements of each type of smoothing task, and a preset redundancy is reserved for the battery cells to be executed for each type of smoothing task. If the suitability index of the current cell group is lower than the preset suitability index, the preset redundancy of the current cell group will be activated to replace the current cell group to smooth it out. Otherwise, the suppression process will continue.

7. The control method for smoothing power fluctuations in new energy grid-connected power in a smart power plant according to claim 6, characterized in that, The bi-objective control function is calculated as follows: ； In the formula, express The overall optimization objective value of the bi-objective control function at any given time; express The weighting coefficients of the bi-objective control function at each time step; express Constant grid-connected power output error mitigation; express Constant-time cell pack consistency deviation; This represents the preset adaptability correlation coefficient; express Time of the first Group of cells to the first The suppression power of similar suppression tasks; This indicates the number of battery cells within the energy storage cluster.

8. The control method for smoothing power fluctuations in new energy grid-connected power in a smart power plant according to claim 1, characterized in that, It also includes real-time monitoring of the power smoothing effect and cell group status during the execution process, and updating the control parameter group of the smoothing control process based on the monitoring results. The control parameter group includes differential weight, timing decay coefficient, adaptation penalty coefficient and adaptability correlation coefficient.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.

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