Energy storage power station state adjustment method and system, electronic device and storage medium

Abstract

The application discloses a kind of energy storage power station state regulation method, system, electronic equipment and storage medium, belong to energy storage power station control technical field, method includes: real-time acquisition energy storage unit multiple dimensions operating state data, according to the deviation of each dimension data relative to average level, calculate state factor;Adopt entropy weight method according to the discrete degree of state factor dynamically calculates each dimension weight coefficient, and carries out internal consistency evaluation to each energy storage unit;The state factor of each unit is regarded as input, carries out fuzzy inference based on preset fuzzy control strategy, and outputs power distribution coefficient;According to total power instruction and power distribution coefficient, calculate initial distribution power;According to internal consistency evaluation result, dynamically adjust operating constraint condition;The out-of-limit check of constraint condition is carried out to initial distribution power, and after power redistribution according to check result, issue execution is carried out.The application can reasonably distribute power according to the state difference of energy storage unit, improve the overall performance and service life of power station.

Description

Technical Field

[0001] This invention belongs to the field of energy storage power station control technology, specifically relating to an energy storage power station state regulation method, system, electronic equipment, and storage medium. Background Technology

[0002] New energy storage technologies, primarily based on electrochemical energy storage, play a crucial role in ensuring the safe and stable operation of large power grids, promoting the consumption of new energy sources, and supporting the construction of new power systems. Large-scale new energy storage power stations typically consist of multiple energy storage units connected in parallel. Due to factors such as manufacturing processes, operating environments, and charge / discharge histories, the states of each energy storage unit, such as State of Health (SOH), State of Energy (SOE), temperature, and internal resistance, often differ. This inconsistency can lead to a decline in the overall performance of the energy storage power station and even pose safety hazards. Studies have shown that a 10% difference in SOE between energy storage units can result in an 8%-12% loss of usable capacity, while a temperature difference exceeding 10°C will produce significant differences in battery aging, shortening cycle life by more than 30%.

[0003] In actual operation, in order to meet the grid's dispatch instructions, the power plant's energy management system needs to allocate the total power command to each energy storage unit. Currently, the mainstream power allocation strategies mostly adopt the average allocation method or the allocation method based on the rated capacity ratio, without considering the state differences of each energy storage unit. Often, after the power plant has been operating for a period of time, inconsistencies will increase, resulting in a decrease in charging and discharging capacity, making it impossible to achieve full-power charging and discharging, and making it difficult to ensure the safe and stable operation of the energy storage power plant. Summary of the Invention

[0004] The purpose of this invention is to address the problems in the prior art by providing a state regulation method, system, electronic device, and storage medium for an energy storage power station. By combining multi-dimensional situation assessment and entropy weight fuzzy control algorithm, the charging and discharging power boundaries can be reasonably determined according to the state of the energy storage unit, and the power can be reasonably allocated according to the state differences of each energy storage unit, so that the state of each energy storage unit tends to be consistent, thereby improving the overall performance and service life of the energy storage power station.

[0005] To achieve the above objectives, the present invention provides the following technical solution: Firstly, a method for regulating the state of an energy storage power station is provided, including: Real-time acquisition of multi-dimensional operational status data of each energy storage unit in the energy storage power station; Based on the deviation of each energy storage unit's operating status data in different dimensions from the average level of all energy storage units, calculate the state factor of each energy storage unit in each dimension. The entropy weight method is used to dynamically calculate the weight coefficients of each dimension at the current moment based on the dispersion of the state factors of each energy storage unit in different dimensions. The internal consistency evaluation of each energy storage unit is then performed based on the weight coefficients to obtain the internal consistency evaluation results of each energy storage unit. The state factors of each energy storage unit in different dimensions are taken as input, and fuzzy inference is performed based on a preset fuzzy control strategy to output the power allocation coefficient of each energy storage unit. Based on the received total power command of the energy storage power station and the power allocation coefficient of each energy storage unit, calculate the initial power allocation command for each energy storage unit. Based on the internal consistency assessment results of each energy storage unit, the operating constraints of each energy storage unit are dynamically adjusted. The initial power allocation command for each energy storage unit is checked for exceeding the operating constraints, and the power is redistributed according to the check results. The determined power command is then sent to each energy storage unit for execution.

[0006] As a preferred embodiment, the multi-dimensional operating status data includes the DC-side voltage, average temperature, internal resistance, capacity, and power of the energy storage unit.

[0007] As a preferred approach, the state factor is obtained by normalizing the deviation of the operating state data of each energy storage unit in each dimension relative to the average level of all energy storage units. The normalization process maps the deviation values ​​of each dimension to a unified numerical range.

[0008] As a preferred embodiment, the step of dynamically calculating the weight coefficients corresponding to each dimension at the current moment using the entropy weight method based on the degree of dispersion of the state factors of each energy storage unit in different dimensions includes: Standardize the state factor data of each energy storage unit across all dimensions; Based on the standardized state factor data, calculate the information entropy of each dimension of the state factor; The weight coefficients for each dimension are calculated based on the information entropy of the state factors in each dimension. The greater the dispersion of the state factors, the greater the information entropy, and the higher the corresponding weight coefficient.

[0009] As a preferred embodiment, the step of performing an internal consistency assessment on each energy storage unit based on weighting coefficients to obtain the internal consistency assessment results for each energy storage unit includes: The range and standard deviation of each individual unit within each energy storage unit are obtained in different dimensions, which serve as secondary consistency indicators for each dimension. After normalizing the secondary consistency index, a weighted linear combination is used to fuse it into primary consistency indices for each dimension. Based on the weighting coefficients calculated using the entropy weighting method, the first-level consistency indexes are weighted and summed to obtain the internal consistency assessment results for each energy storage unit.

[0010] As a preferred approach, the primary consistency index for each dimension is calculated using the following formula:

[0011] In the formula, This is a primary consistency indicator. The smaller the value, the better the consistency; the value range is [0,1]. This represents the range of the second-order consistency index; The standard deviation of the second-order consistency index; represents the Min-Max normalization function, used to eliminate the influence of dimensions and map the range and standard deviation to the same numerical interval [0,1]. This represents the range weight.

[0012] As a preferred embodiment, in the preset fuzzy control strategy, the input variables are the state factors of each energy storage unit in each dimension, and the fuzzy set of each input variable is divided into three levels: low, medium, and high; the output variables are the power allocation coefficients, and the fuzzy set of each output variable is divided into five levels: extremely low, low, medium, high, and extremely high.

[0013] As a preferred embodiment, the preset fuzzy control strategy includes fuzzy rule bases set separately for charging and discharging scenarios, with each fuzzy rule base containing several core rules. Specifically, in the charging scenario, when both the DC-side voltage state factor and the charge state factor are low, the output power allocation coefficient is extremely high; in the discharging scenario, when both the DC-side voltage state factor and the charge state factor are high, the output power allocation coefficient is extremely high; and in either the charging or discharging scenario, when the average temperature state factor or the internal resistance state factor is high, the output power allocation coefficient is limited to medium or below.

[0014] As a preferred embodiment, the step of performing fuzzy inference based on a preset fuzzy control strategy and outputting the power allocation coefficient of each energy storage unit includes: Calculate the membership degree of each input variable on each fuzzy set; Calculate the activation strength of each rule based on the membership degree of the current input variable and the matching degree of each fuzzy rule; The power allocation coefficient of each energy storage unit is obtained by weighting the output values ​​of each rule by using the activation intensity of each rule as the weight.

[0015] As a preferred embodiment, the step of dynamically adjusting the operating constraints of each energy storage unit based on the internal consistency assessment results of each energy storage unit includes: The consistency score of each energy storage unit is calculated based on the internal consistency assessment results. When the consistency score is lower than a preset threshold, the maximum charge and discharge power limit of the corresponding energy storage unit is reduced and the upper and lower limits of the operable power are narrowed. When the consistency score is higher than a preset threshold, the maximum charge and discharge power limit of the corresponding energy storage unit is increased and the upper and lower limits of the operable power are relaxed.

[0016] As a preferred embodiment, the maximum charge / discharge power limit is dynamically adjusted using the following formula:

[0017]

[0018] In the formula, Indicates the first i Maximum charging power limit for each energy storage unit; Indicates the first i Maximum discharge power limit for each energy storage unit; Indicates the first i The rated power of each energy storage unit; Indicates the maximum charging power limit; Indicates the maximum discharge power limit; Indicates the rate of charge decay; Indicates the discharge decay rate; express t Time of the first i Consistency score of each energy storage unit; The upper and lower limits of the operable power level are dynamically adjusted using the following formula:

[0019]

[0020] In the formula, For the first i Each energy storage unit in t The maximum number of executable SOEs at any given time. For the first i Each energy storage unit in t The lower limit of the operable SOE at any given time; Let t be the system-level upper limit reference value for the SOE that the energy storage unit can operate at time t. Let t be the system-level lower limit baseline value for the SOE that the energy storage unit can operate at time t; Here are the SOE window coefficients, which are based on... t Time of the firsti Consistency score of individual energy storage units as independent variable Sigmoid Function value:

[0021] In the formula, The steepness of the curve indicates the sensitivity to changes in control. The center point parameter determines the score position when the target threshold is reached; 0.15 indicates that the maximum SOE is 95%, preserving a safety margin.

[0022] As a preferred embodiment, the step of performing the over-limit verification of the operating constraints on the initial power allocation command of each energy storage unit and redistributing the power according to the verification result includes: determining whether the initial power allocation command of each energy storage unit exceeds the current maximum charging and discharging power limit of the corresponding energy storage unit; if it exceeds, limiting the initial power allocation command to the maximum charging and discharging power limit and recording the excess power. Calculate the change in power of each energy storage unit within a preset time step based on the initial power allocation command of each energy storage unit, determine whether the operating power will exceed the upper and lower limits of the current operating power of the corresponding energy storage unit, and if it will exceed, convert the excess amount into excess power and record it. All recorded excess power is summed, and the excess power is redistributed to energy storage units that have not exceeded their limits, based on the power allocation coefficient of each energy storage unit.

[0023] As a preferred embodiment, after the step of acquiring multi-dimensional operational status data of each energy storage unit in the energy storage power station in real time, a data preprocessing step is also included: The 3σ criterion is used to remove outliers from the acquired operational status data; Linear interpolation was used to fill in the missing data after outliers were removed; The imputed data is smoothed using a moving average filtering method.

[0024] Secondly, a state regulation system for an energy storage power station is provided, comprising: The multi-dimensional data acquisition module is used to acquire real-time operational status data of each energy storage unit in the energy storage power station from multiple dimensions. The state factor calculation module is used to calculate the state factor of each energy storage unit in each dimension based on the deviation of the operating state data of each energy storage unit in different dimensions from the average level of all energy storage units. The internal consistency assessment module is used to dynamically calculate the weight coefficients corresponding to each dimension at the current moment based on the degree of dispersion of the state factors of each energy storage unit in different dimensions using the entropy weight method, and to perform internal consistency assessment on each energy storage unit based on the weight coefficients, so as to obtain the internal consistency assessment results of each energy storage unit. The power allocation coefficient fuzzy inference module is used to take the state factors of each energy storage unit in different dimensions as input, perform fuzzy inference based on the preset fuzzy control strategy, and output the power allocation coefficient of each energy storage unit. The initial power allocation command calculation module is used to calculate the initial power allocation command for each energy storage unit based on the received total power command of the energy storage power station and the power allocation coefficient of each energy storage unit. The operation constraint adjustment module is used to dynamically adjust the operation constraints of each energy storage unit based on the internal consistency assessment results of each energy storage unit. The power redistribution module is used to perform limit verification on the initial power allocation command of each energy storage unit to check the operating constraints, and redistribute the power according to the verification result, and send the determined power command to each energy storage unit for execution.

[0025] Thirdly, an electronic device is provided, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the energy storage power station state regulation method.

[0026] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the energy storage power station state regulation method.

[0027] Compared with the prior art, the first aspect of the present invention has at least the following beneficial effects: By acquiring real-time operational status data from multiple dimensions of energy storage units and calculating state factors for each dimension, a comprehensive state awareness foundation is constructed, solving the problem of inaccurate control decisions caused by the single dimension of state assessment in existing technologies. The entropy weight method is used to dynamically calculate the weights of each dimension based on the dispersion of state factors, and internal consistency assessment is performed based on these weights. This allows the control strategy to adaptively focus on the indicator with the highest dispersion, achieving a "prioritize the worst performer" adjustment orientation. Fuzzy inference based on state factors and a preset fuzzy control strategy yields power allocation coefficients, effectively handling nonlinearity and uncertainty issues in energy storage system operation, achieving reasonable power allocation decisions without relying on precise mathematical models. The operating constraints of each energy storage unit are dynamically adjusted based on the internal consistency assessment results, subjecting units with poorer conditions to stricter operating restrictions and providing better-performing units with more optimized operating space. Closed-loop control is formed through limit-crossing checks and power reallocation, ensuring that all allocation commands meet the real-time constraints of each unit. The above-mentioned technical means work together and progress step by step to solve the technical problems of inconsistent states between energy storage units and degraded overall power plant performance caused by incomplete state assessment, fixed weights and static constraints in existing technologies. They effectively improve the convergence speed of state consistency between energy storage units and the utilization rate of the power plant throughout its entire life cycle.

[0028] Furthermore, the multi-dimensional operational status data includes the DC-side voltage, average temperature, internal resistance, capacity, and energy of the energy storage unit. These five dimensions comprehensively cover key operational parameters such as the electrical characteristics (voltage), thermal characteristics (temperature), aging characteristics (internal resistance and capacity), and state of energy (energy). Compared with existing technologies that rely on a single SOE index or a few other indicators for evaluation, this approach can more comprehensively and precisely depict the real-world differences between energy storage units, providing a more reliable data foundation for subsequent power allocation and fundamentally improving the overall consistency control level of the system.

[0029] Furthermore, the state factor is obtained by normalizing the deviation values ​​of the operating state data of each dimension relative to the average level of all energy storage units. This processing method eliminates the influence of different physical dimensions (such as voltage in volts, temperature in degrees Celsius, internal resistance in milliohms, etc.) on subsequent calculations, and uniformly maps the deviation values ​​of each dimension to the same numerical range. This allows indicators with different physical meanings and dimensions, such as voltage, temperature, internal resistance, capacity, and energy, to be compared and weighted in the same framework, laying the foundation for accurate calculation of the entropy weight method and effective reasoning of fuzzy control.

[0030] Furthermore, in the step of dynamically calculating the weight coefficients of each dimension using the entropy weight method, the state factor data is standardized, the information entropy of each dimension's state factor is calculated, and the weight coefficients are determined based on the information entropy. The greater the dispersion of the state factors and the greater the information entropy, the higher the weight coefficient. The core advantage of this scheme is that the greater the dispersion of the indicators among energy storage units, the more likely the corresponding indicator is the main contradiction causing state inconsistency, and thus it should be given a higher adjustment weight, enabling the control strategy to prioritize correcting the worst-performing indicator. Compared to existing technologies that use fixed weights or simple empirical weights, the dynamic weight mechanism of this invention can adaptively adjust in real time according to changes in the state of the energy storage unit, greatly accelerating the convergence speed of multi-dimensional states.

[0031] Furthermore, when conducting internal consistency assessments of each energy storage unit based on weighting coefficients, secondary consistency indicators are calculated based on the range and standard deviation of each individual unit within each unit across various dimensions. These normalized secondary consistency indicators are then fused into primary consistency indicators for each dimension through a weighted linear combination. The weighted summation of these primary consistency indicators using weighting coefficients obtained from the entropy weighting method yields the internal consistency assessment result. This scheme characterizes the internal state of the energy storage unit at two levels: the range reflects the maximum dispersion within the unit, and the standard deviation reflects the overall dispersion within the unit. The combination of these two factors comprehensively characterizes the state differences between individual units within the unit. Assigning different weights to the range and standard deviation improves the flexibility of the assessment. The weighted summation using entropy weighting coefficients ensures that the weights calculated by the entropy weighting method are practically applied in internal consistency assessments, forming a complete closed loop from indicator dispersion, entropy weighting, consistency assessment to constraint adjustment.

[0032] Furthermore, the input variables (state factors of each dimension) of the fuzzy control strategy are divided into three fuzzy levels: low, medium, and high, while the output variables (power allocation coefficients) are divided into five levels: extremely low, low, medium, high, and extremely high. For charging and discharging scenarios, separate fuzzy rule libraries containing several core rules are established, and rule orientations under extreme conditions are specified: during charging, if both voltage and capacity are low, charging is prioritized (extremely high coefficient); during discharging, if both voltage and capacity are high, discharging is prioritized (extremely high coefficient); and when temperature or internal resistance is abnormal, power is limited (medium or lower coefficient). A three-step fuzzy inference process is implemented based on the preset fuzzy control strategy: membership degree calculation, rule activation strength calculation, and weighted average defuzzification. This scheme replaces the entire rule library with carefully selected core rules, significantly reducing online computational complexity while maintaining control accuracy and meeting the time response requirements of real-time control in energy storage power stations. The use of differentiated fuzzy rule libraries for charging and discharging allows the fuzzy control strategy to adapt to different needs for charging safety and discharging efficiency. The setting of rules for extreme conditions ensures that the system's safety protection has the highest priority. Through the above design, the fuzzy control strategy proposed in this invention can map a complex multidimensional state space into explicit power allocation instructions without relying on a precise mathematical model, and has strong robustness and engineering applicability.

[0033] Furthermore, when dynamically adjusting the operating constraints of each energy storage unit based on the internal consistency assessment results, the consistency score of each energy storage unit is calculated according to the internal consistency assessment results. If the score is low, the power limit is reduced and the SOE window is narrowed; if the score is high, the power limit is increased and the SOE window is widened. The Sigmoid function is used to specifically realize the nonlinear dynamic adjustment of the power limit and the upper and lower limits of SOE. The S-shaped characteristics of the Sigmoid function can perfectly match the influence of the energy storage unit consistency score on the operating constraints, that is, the low zone changes slowly (ensuring safety), the sensitive zone changes rapidly (improving differentiation), and the saturation zone tends to be stable (avoiding over-adjustment). Compared with the fixed power limit and rigid threshold restriction methods of the existing technology, the nonlinear dynamic constraint mechanism can smoothly and adaptively limit the power output and SOE operating range of poorly performing units, preventing accelerated performance degradation, while providing sufficient output space for units with good performance, effectively extending the cycle life and operational safety of the entire energy storage power station.

[0034] Furthermore, during limit-over verification and power redistribution, power limit over-limit verification and energy upper and lower limit over-limit verification are performed separately, and the excess power is recorded. Then, all excess power is summed and redistributed to the energy storage units that have not exceeded the limits. The closed-loop power adjustment mechanism of "two verifications, unified recovery, and redistribution" adopted in this invention ensures that the final issued power command simultaneously satisfies the power boundary constraints and SOE operating window constraints of all energy storage units, avoiding the problem of the entire allocation scheme failing due to the over-limit of individual units, and ensuring the engineering feasibility and execution security of the control scheme.

[0035] Furthermore, after acquiring multi-dimensional operational status data of each energy storage unit in the energy storage power station in real time, a data preprocessing step is also included. This step employs the 3σ criterion to remove outliers, linear interpolation to fill in missing data, and moving average filtering to smooth the data. These three levels of preprocessing ensure the quality of the data entering the main control algorithm. The 3σ criterion effectively identifies and removes abnormal jump values ​​caused by sensor faults or communication interference, linear interpolation ensures the continuity of the data sequence, and moving average filtering suppresses high-frequency noise interference with control decisions, thereby improving the anti-interference capability and output stability of the entire control system.

[0036] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 Flowchart of the energy storage power station state regulation method according to an embodiment of the present invention; Figure 2 A schematic diagram illustrating the design of the fuzzy set membership function for input variables in this embodiment of the invention; Figure 3 A schematic diagram of the design of the fuzzy set membership function for the output variable in an embodiment of the present invention. Detailed Implementation

[0039] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0040] The purpose of this invention is to provide a state regulation method for energy storage power stations based on multi-dimensional situation assessment and entropy weight fuzzy control. This method utilizes the entropy weight method to comprehensively estimate the operating state of energy storage units and employs a fuzzy algorithm to calculate the regulation power of each energy storage unit during each charge and discharge cycle. This method can rationally determine the charge and discharge power boundaries based on the energy storage unit state and rationally allocate power according to the state differences of each energy storage unit, making the states of each energy storage unit more consistent, thereby improving the overall performance and service life of the energy storage power station. However, in related prior art, for example, patent application CN108631348A discloses a "control method for energy storage power stations based on energy storage unit state assessment," which has the following problem: when allocating energy among multiple energy storage units, it is based on the charge and discharge priority of the energy storage units, without considering state inconsistencies, and the charge and discharge priority ranking is not conducive to the consistency of energy storage unit states. The operation of "adjusting the operating state regulation priority of energy storage units with fewer charge and discharge cycles to a higher priority" will accelerate the performance degradation of energy storage units with more serious faults or performance degradation. The technical solution disclosed in this patent application specifies the range of index values ​​as a fuzzy interval based on the proposed indicators, and proposes weighted fuzzy operation rules to evaluate the operating status of the energy storage system. This approach has low flexibility, and its applicability to different brands and types of energy storage batteries in terms of the specific range of index values ​​is unknown. In addition, there are the following defects: (1) Single index system: It only relies on the number of charge and discharge cycles and simple state assessment, and does not establish a multi-dimensional quantitative assessment system of voltage, temperature, internal resistance, capacity, and power; (2) Fixed weights: It uses fixed weights for fuzzy operation, which cannot be dynamically adjusted according to the degree of index dispersion, making it difficult to achieve "worst index convergence first"; (3) Static constraints: It does not dynamically adjust the power boundary and SOE operating window according to the internal consistency of the cell, and cannot adapt to the differentiated operating needs of cells in different health states; (4) Simple rules: It lacks differentiated control strategies for charge and discharge scenarios, making it difficult to balance charging safety and discharge efficiency. With the rapid development of battery energy storage technology and the continuous expansion of cell capacity in recent years, the single state assessment and simple control mode of this solution are no longer suitable for the current development needs of energy storage battery technology. For example, patent application CN116683501A discloses "a power distribution method and system for an electrochemical energy storage power station". The power distribution method includes the following steps: S1: measuring the internal resistance Rn of each battery pack in the electrochemical energy storage power station; S2: measuring the remaining capacity Cn of each battery pack in the electrochemical energy storage power station; S3: calculating the performance coefficient Ln of each battery pack in the electrochemical energy storage power station based on the measured internal resistance Rn and remaining capacity Cn.The above scheme can calculate the performance coefficient (Ln) of each battery pack in an electrochemical energy storage power station and determine the power allocation to each battery pack based on the performance coefficient Ln. This effectively avoids the drawback of reduced performance of battery packs with better lifespans when the power is evenly distributed among the battery packs in an electrochemical energy storage power station, resulting in a more reasonable power allocation and optimizing the performance of each battery pack. It also avoids the drawback of the electrochemical energy storage power station continuing to operate when the battery capacity is too low, ensuring the safe operation of the battery packs. However, there are problems: this scheme mainly calculates the power performance coefficient by measuring internal resistance and remaining capacity. Furthermore, the balancing algorithm is one-sided: there are known defects in the balancing strategy. For example, if the balancing strategy only compares the highest and lowest energy cells, when the system is overcharged or over-discharged, the remaining energy has nowhere to be released or replenished. The SOH measurement assumption is inaccurate: its assumption that "lifespan is positively correlated with actual capacity" ignores the fact that the internal resistance surge of some aged batteries occurs much earlier than capacity decay, which can easily lead to the algorithm misjudging their health performance and causing unfair allocation. The control strategy is overly simplistic: when the remaining capacity falls below a threshold, the instruction is to "directly stop discharging." This rigid and simplistic limitation lacks a gradual and smooth derating mechanism, resulting in a large instantaneous impact on the power grid and easily wasting remaining available capacity. Compared with existing technologies, the energy storage power station state regulation method proposed in this invention uses voltage, temperature, capacity, internal resistance, and power indicators as the basis for operational state evaluation and regulation. It establishes a fuzzy control model for the energy storage power station, realizes operational state regulation, improves operational state consistency, and maintains high consistency even at the end of charging and discharging, thereby ensuring that the energy storage power station maintains its maximum output capacity.

[0041] Please see Figure 1 The energy storage power station state regulation method of this invention includes the following steps: S1. Real-time acquisition of multi-dimensional operational status data of each energy storage unit in the energy storage power station; S2. Based on the deviation of the operating status data of each energy storage unit in different dimensions from the average level of all energy storage units, calculate the state factor of each energy storage unit in each dimension. S3. Using the entropy weight method, the weight coefficients corresponding to each dimension at the current moment are dynamically calculated based on the degree of dispersion of the state factors of each energy storage unit in different dimensions. The internal consistency evaluation of each energy storage unit is then performed based on the weight coefficients to obtain the internal consistency evaluation results of each energy storage unit. S4. Using the state factors of each energy storage unit in different dimensions as input, perform fuzzy inference based on the preset fuzzy control strategy, and output the power allocation coefficient of each energy storage unit. S5. Calculate the initial power allocation command for each energy storage unit based on the received total power command of the energy storage power station and the power allocation coefficient of each energy storage unit. S6. Based on the internal consistency assessment results of each energy storage unit, dynamically adjust the operating constraints of each energy storage unit. S7. Perform limit verification on the initial power allocation command of each energy storage unit for the operation constraints, and redistribute the power according to the verification result, and send the determined power command to each energy storage unit for execution.

[0042] In one possible implementation, the multi-dimensional operating status data described in this embodiment includes the DC-side voltage, average temperature, internal resistance, capacity, and power of the energy storage unit.

[0043] After acquiring the operational status data of each energy storage unit in the energy storage power station in real time according to step S1, a data preprocessing step is also included. The data preprocessing specifically includes: The 3σ criterion is used to remove outliers from the acquired operational status data; Linear interpolation was used to fill in the missing data after outliers were removed; The imputed data is smoothed using a moving average filtering method.

[0044] In one possible implementation, step S2 of this embodiment first calculates the average value based on the voltage, temperature, capacity, internal resistance, and charge of all energy storage units at time t. , , , , Then, the state factor is calculated according to Table 1-1, which is the normalization treatment of the deviation of the state index of all units.

[0045] Table 1-1 Calculation Formula for State Factor of Inter-unit Consistency Index

[0046] In Table 1-1, This represents the voltage state factor of the i-th energy storage unit at time t; This represents the temperature state factor of the i-th energy storage unit at time t; This represents the internal resistance state factor of the i-th energy storage unit at time t; This represents the capacity state factor of the i-th energy storage unit at time t; Let represent the energy state factor of the i-th energy storage unit at time t during charging.

[0047] In one possible implementation, step S3 of this embodiment characterizes the internal consistency of each energy storage unit based on the range and standard deviation of various indicators among the individual energy storage cells at a certain moment, as shown in Table 1-2: Table 1-2 Internal Consistency Characterization Indicators of Energy Storage Units

[0048] In one possible implementation, step S3 performs an internal consistency assessment on each energy storage unit based on weighting coefficients, and the internal consistency assessment results for each energy storage unit specifically include: The range and standard deviation of each individual unit within each energy storage unit are obtained in different dimensions, which serve as secondary consistency indicators for each dimension. After normalizing the secondary consistency index, a weighted linear combination is used to fuse it into primary consistency indices for each dimension. Based on the weighting coefficients calculated using the entropy weighting method, the first-level consistency indexes are weighted and summed to obtain the internal consistency assessment results for each energy storage unit.

[0049] Furthermore, the primary consistency index for each dimension is calculated using the following formula:

[0050] In the formula, This is a primary consistency indicator. The smaller the value, the better the consistency; the value range is [0,1]. This represents the range of the second-order consistency index; The standard deviation of the second-order consistency index; represents the Min-Max normalization function, used to eliminate the influence of dimensions and map the range and standard deviation to the same numerical interval [0,1]. The range weight can be determined using expert experience scoring.

[0051] The expression for the Min-Max normalization function is as follows:

[0052] In the formula, The value that needs to be normalized; This represents the minimum value of the corresponding Level 2 consistency indicator within the current statistical period; This indicates the maximum value of the corresponding Level 2 consistency indicator within the current statistical period.

[0053] In one possible implementation, step S3 involves determining the weights of the internal consistency indicators of the energy storage unit, reasonably reflecting the role of DC voltage, temperature, internal resistance, capacity, and power consumption within the energy storage unit in the consistency evaluation, and calculating the weights of the primary consistency indicators within the energy storage unit.

[0054] First, perform data standardization. By standardizing the range, the data is scaled to the [0,1] interval:

[0055] In the formula, for t Time of the first i The energy storage unit j Individual indicator values, and They are respectively t Time of the first i The energy storage unit j The minimum and maximum values ​​of each indicator.

[0056] For the first j After calculating the weight of each indicator and adjusting the standardized data to avoid zero values, we obtain:

[0057] In the formula, For a very small amount, it can be taken as .

[0058] No. j The information entropy of each indicator is:

[0059] In the formula, n This refers to the number of indicators.

[0060] The weights of each indicator are further calculated using the following formula to reflect the dispersion of each indicator:

[0061] The larger the entropy value of the indicator (the greater the dispersion), the higher the weight, and the more attention is paid to adjusting the indicator during the power allocation process.

[0062] It should be noted that when users prioritize the uniformity of a particular metric, they can artificially increase the weight of that metric to achieve consistency more quickly. In reality, the capacity and internal resistance of energy storage batteries change very little in a short period; therefore, in practice, a relatively small weight can be set for that metric. Calculations... t The weights corresponding to the internal voltage consistency, temperature consistency, internal resistance consistency, capacity consistency, and energy consistency of the energy storage unit at all times are: , , , , .

[0063] The internal consistency assessment results for each energy storage unit are calculated using the following formula based on the weighting coefficients:

[0064] In the formula, express t Time of the first iThe consistency value of each energy storage unit is as follows: the higher the value, the worse the consistency, and the lower the value, the better the consistency. , , , , These represent the voltage consistency value, temperature consistency value, internal resistance consistency value, capacity consistency value, and energy consistency value of the energy storage unit at time t, respectively.

[0065] Scores are calculated based on the consistency calculation results of the energy storage units. To ensure that the internal consistency assessment results of the energy storage units are distributed between 60 and 100 points, where 60 points represents the worst consistency and 100 points represents the best consistency, scores are awarded. for:

[0066] In the formula, Indicates the consistency score of the energy storage unit; This represents the highest weighted consistency score for each energy storage unit at time t, i.e., the worst consistency. This represents the lowest weighted consistency score for each energy storage unit at time t, indicating the best consistency. The score is used for display on the system interface.

[0067] In one possible implementation, step S6, which dynamically adjusts the operating constraints of each energy storage unit based on the internal consistency evaluation results of each unit, specifically includes: The consistency score of each energy storage unit is calculated based on the internal consistency assessment results. When the consistency score is lower than a preset threshold, the maximum charge and discharge power limit of the corresponding energy storage unit is reduced and the upper and lower limits of the operable power are narrowed. When the consistency score is higher than a preset threshold, the maximum charge and discharge power limit of the corresponding energy storage unit is increased and the upper and lower limits of the operable power are relaxed.

[0068] Furthermore, the maximum charging power limit Maximum discharge power limit Dynamically adjust using the following formula:

[0069]

[0070] In the formula, Indicates the first i The rated power of each energy storage unit; Indicates the maximum charging power limit; Indicates the maximum discharge power limit; Indicates the rate of charge decay; Indicates the discharge decay rate; express t Time of the first i Consistency score of each energy storage unit; charge decay rate and discharge decay rate This indicates the sensitivity of control power to changes in score; a smaller value means slower power growth and stricter constraints. Empirically, the battery charging process is riskier than the discharging process, and to avoid irreversible damage from overcharging, it is generally... This results in a slower increase in charging power.

[0071] The upper and lower limits of the operable power level are dynamically adjusted using the following formula:

[0072]

[0073] In the formula, For the first i Each energy storage unit in t The maximum number of executable SOEs at any given time. For the first i Each energy storage unit in t The lower limit of the operable SOE at any given time; for t The system-level upper limit reference value for the energy storage unit to operate SOE at any given time. for t The system-level lower limit reference value for when the energy storage unit can operate SOE; Here are the SOE window coefficients, which are based on... t Time of the first i Consistency score of individual energy storage units as independent variable Sigmoid Function value:

[0074] In the formula, The slope of the S-curve, representing the steepness of the curve, determines the sensitivity of the SOE window to changes. A larger value results in a steeper curve in the middle region and a more rapid change. The center point parameter determines the score position at which the target threshold is reached; the base value is set according to the actual situation, and can be set to 0.8, which corresponds to the score threshold of 80% of the SOE window, ensuring that even when the score is the lowest (60 points), the SOE window still maintains an 80% usable range, avoiding excessive restriction of basic operating capabilities; the amplitude of 0.15 indicates that the maximum window is limited to 95%, preventing the battery from working at extreme SOE (such as <5% or >95%), and retaining a 5% safety margin.

[0075] This invention employs the Sigmoid function to more accurately achieve dynamic adjustment of the SOE operating range. The Sigmoid function better matches the influence of consistency scores on the SOE window: low partitions change slowly (ensuring safety), sensitive areas change rapidly (improving discrimination), and saturated areas tend to be stable (avoiding over-adjustment).

[0076] In one possible implementation, a fuzzy algorithm is used to calculate the power allocation among energy storage units based on five consistency state factors. (See also...) Figure 2 and Figure 3 Step S4 involves designing a fuzzy control strategy as follows: The input variables are the state factors of each energy storage unit in each dimension, and the fuzzy set of each input variable. They are all divided into three levels: low, medium, and high, as shown in the following expressions:

[0077] In the formula, This indicates values ​​that are significantly or slightly below the average, and is defined as a trapezoidal function with parameters approximately [-1, -1, -0.3, 0]. This indicates that the value is near the average, and is defined as a trigonometric function with parameters approximately [-0.3, 0, 0.3]. This indicates values ​​that are much higher than or slightly higher than the average, and is defined as a trapezoidal function with parameters approximately [0, 0.3, 1, 1].

[0078] The output variables are power allocation coefficients, and the fuzzy set of each output variable is divided into five levels: extremely low, low, medium, high, and extremely high, as shown in the following expression:

[0079] In the formula, The power command, which represents a power level far below the average distribution, is defined as a trigonometric function with parameters [0, 0, 0.5]. This represents a power command slightly below the average distribution, defined as a trigonometric function with parameters [0.2, 0.5, 0.8]. This indicates that the value of the average power distribution command is near the value of the trigonometric function, with parameters [0.5, 1.0, 1.5]. This represents a power command slightly above the average distribution, defined as a triangular function with parameters [1.0, 1.5, 2.0]. This indicates a value much higher than the average distribution instruction, defined as a trigonometric function with parameters [1.5, 2.0, 2.0].

[0080] Furthermore, setting two different sets of fuzzy rules for charging and discharging would require 3^5 = 243 rules if a full rule library were used, resulting in excessive computational complexity. This invention adopts the principle of "key scenario coverage," selecting 25 core rules for each charging and discharging scenario, covering the following typical operating conditions: extreme operating conditions (rules 1-2) where all indicators are low / high; balanced operating conditions (rules 3) where all indicators are moderate; dual-indicator anomalies (rules 4-5) where voltage and capacity are both low / high; single-indicator protection (rules 6-9) where temperature / internal resistance / capacity anomalies; and multi-indicator coupling (rules 10-25) for common combination scenarios.

[0081] During charging: Table 2 Core Logic During Charging

[0082] During discharge: Table 3 Core Logic Corresponding to Discharge

[0083] In one possible implementation, for 5 input quantities , , , , Calculate the membership degree on the three fuzzy sets:

[0084] Among them, 15 membership values ​​were calculated, representing the degree of agreement between the current state and each fuzzy rule.

[0085] Calculate the matching degree between the current state and each core rule using the following formula: Input 15 membership values ​​and compare them with the 25 core rule bases to calculate the similarity of each rule:

[0086] in, This represents the fuzzy set that rule i requires of variable j. It is the membership degree of variable j to this set. These are the weights of the rules. Output 25 similarity values.

[0087] The final value is calculated using a weighted average method:

[0088] in, This indicates that the centroid values ​​of the fuzzy set output by the i-th rule are VS=0.3, S=0.7, M=1.0, L=1.3, VL=1.7. This indicates the relevance of each rule. This represents the power allocation weight of the i-th energy storage unit.

[0089] Initial power allocation:

[0090] in, This indicates the power command issued to the entire station at a specific moment.

[0091] In one possible implementation, after power allocation is completed in step S7 using the fuzzy control method, a power constraint traversal verification is performed on each energy storage unit. If any power exceeds the limit, the maximum power is taken, and the excess power is calculated. Then, the SOE constraints of each energy storage unit are checked by traversing the system according to the initial power allocation and time integration. If any SOE exceeds the limit, the excess power is calculated by power-time differentiation. The larger of the two is denoted as . Sum the above excess power:

[0092] in, This represents the total over-limit power after power allocation. This represents the over-limit power of the i-th energy storage unit.

[0093] Energy storage units exceeding their power limits are set to their determined maximum power. Units exceeding their SOE limits are adjusted according to the maximum charge / discharge power calculated from the SOE limit. Units not exceeding their limits are dynamically adjusted based on the internal consistency assessment results of each energy storage unit. The power allocation process ends when all units reach their power or SOE limits, or meet the power requirements.

[0094] In one possible implementation, the calculated power allocation of the energy storage units is distributed to the station-level control system for execution. The control system is divided into a data acquisition layer, a calculation layer, and a station control layer. The data acquisition layer refers to the energy storage unit data acquisition system, the calculation layer performs power allocation calculations after receiving the total output command, and the station control layer is mainly the station-level EMS of the energy storage power station.

[0095] Furthermore, the acquisition layer includes a BMS (Battery Management System) and a PCS (Power Conversion System), with a sampling frequency ≥1Hz and communication protocol supporting Modbus TCP / IEC 61850. The BMS is responsible for the implementation steps calculation before power command allocation calculation. The station control layer is the Energy Management System (EMS) of the energy storage power station, which uses an industrial control computer or embedded controller with a calculation cycle ≤100ms. It receives power commands from the grid dispatch and performs allocation calculations for the power commands.

[0096] In this embodiment, the control system is capable of monitoring the status information of each energy storage unit, the total output command, etc., estimating the maximum capacity of the energy storage power station and sending it to the centralized control layer of the energy storage power station, and receiving real-time control commands.

[0097] The energy storage power station state regulation method of this invention solves the problem of a single dimension in the state evaluation of energy storage units, improving the accuracy and balance of regulation. By comprehensively considering five key operating state indicators—voltage, temperature, internal resistance, capacity, and power—a multi-dimensional evaluation system is constructed, which can more comprehensively and precisely characterize the differences between units. This provides a direct and reliable basis for implementing precise differentiated power allocation, thereby fundamentally improving the overall consistency control level of the system.

[0098] The state adjustment method for energy storage power stations proposed in this invention solves the problems of slow convergence and poor adaptability of traditional fixed-weight allocation methods, significantly improving state equalization efficiency. This invention proposes a dynamic weight calculation method, the core of which is to adaptively adjust the weight of each state index in power allocation based on the dispersion of that index across all units at the current moment. The more dispersed the index data, the higher its weight, allowing system resources to more quickly tilt towards correcting this difference, thereby driving the operating states of each unit to converge more quickly and optimizing the convergence speed.

[0099] The energy storage power station state regulation method of this invention solves the problem of accurate modeling and rule formulation in complex nonlinear systems, enhancing the intelligence and robustness of control. By introducing fuzzy control theory, complex multi-dimensional state evaluation results are transformed into explicit power allocation commands. The constructed dedicated fuzzy rule library can effectively handle uncertainties and nonlinear relationships in system operation, achieving stable and reasonable power allocation decisions without relying on precise mathematical models, thus improving the applicability and reliability of the method in practical engineering.

[0100] Another embodiment of the present invention also proposes an energy storage power station state regulation system, comprising: The multi-dimensional data acquisition module is used to acquire real-time operational status data of each energy storage unit in the energy storage power station from multiple dimensions. The state factor calculation module is used to calculate the state factor of each energy storage unit in each dimension based on the deviation of the operating state data of each energy storage unit in different dimensions from the average level of all energy storage units. The internal consistency assessment module is used to dynamically calculate the weight coefficients corresponding to each dimension at the current moment based on the degree of dispersion of the state factors of each energy storage unit in different dimensions using the entropy weight method, and to perform internal consistency assessment on each energy storage unit based on the weight coefficients, so as to obtain the internal consistency assessment results of each energy storage unit. The power allocation coefficient fuzzy inference module is used to take the state factors of each energy storage unit in different dimensions as input, perform fuzzy inference based on the preset fuzzy control strategy, and output the power allocation coefficient of each energy storage unit. The initial power allocation command calculation module is used to calculate the initial power allocation command for each energy storage unit based on the received total power command of the energy storage power station and the power allocation coefficient of each energy storage unit. The operation constraint adjustment module is used to dynamically adjust the operation constraints of each energy storage unit based on the internal consistency assessment results of each energy storage unit. The power redistribution module is used to perform limit verification on the initial power allocation command of each energy storage unit to check the operating constraints, and redistribute the power according to the verification result, and send the determined power command to each energy storage unit for execution.

[0101] Another embodiment of the present invention also proposes an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the energy storage power station state regulation method.

[0102] Another embodiment of the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the energy storage power station state regulation method.

[0103] The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals. For ease of explanation, the above content only shows the parts related to the embodiments of the present invention; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. This computer-readable storage medium is non-transitory and can be stored in storage devices formed by various electronic devices, enabling the execution process described in the method of the embodiments of the present invention.

[0104] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for regulating the state of an energy storage power station, characterized in that, include: Real-time acquisition of multi-dimensional operational status data of each energy storage unit in the energy storage power station; Based on the deviation of each energy storage unit's operating status data in different dimensions from the average level of all energy storage units, calculate the state factor of each energy storage unit in each dimension. The entropy weight method is used to dynamically calculate the weight coefficients of each dimension at the current moment based on the dispersion of the state factors of each energy storage unit in different dimensions. The internal consistency evaluation of each energy storage unit is then performed based on the weight coefficients to obtain the internal consistency evaluation results of each energy storage unit. The state factors of each energy storage unit in different dimensions are taken as input, and fuzzy inference is performed based on a preset fuzzy control strategy to output the power allocation coefficient of each energy storage unit. Based on the received total power command of the energy storage power station and the power allocation coefficient of each energy storage unit, calculate the initial power allocation command for each energy storage unit. Based on the internal consistency assessment results of each energy storage unit, the operating constraints of each energy storage unit are dynamically adjusted. The initial power allocation command for each energy storage unit is checked for exceeding the operating constraints, and the power is redistributed according to the check results. The determined power command is then sent to each energy storage unit for execution. The step of dynamically adjusting the operating constraints of each energy storage unit based on the internal consistency assessment results of each energy storage unit includes: The consistency score of each energy storage unit is calculated based on the internal consistency assessment results. When the consistency score is lower than a preset threshold, the maximum charge and discharge power limit of the corresponding energy storage unit is reduced and the upper and lower limits of the operable power are narrowed. When the consistency score is higher than a preset threshold, the maximum charge and discharge power limit of the corresponding energy storage unit is increased and the upper and lower limits of the operable power are relaxed. The maximum charge / discharge power limit is dynamically adjusted using the following formula: In the formula, Indicates the first i Maximum charging power limit for each energy storage unit; Indicates the first i Maximum discharge power limit for each energy storage unit; Indicates the first i The rated power of each energy storage unit; Indicates the maximum charging power limit; Indicates the maximum discharge power limit; Indicates the rate of charge decay; Indicates the discharge decay rate; express t Time of the first i Consistency score of each energy storage unit; The upper and lower limits of the operable power level are dynamically adjusted using the following formula: In the formula, For the first i Each energy storage unit in t The maximum number of executable SOEs at any given time. For the first i Each energy storage unit in t The lower limit of the operable SOE at any given time; for t The system-level upper limit reference value for the energy storage unit to operate SOE at any given time. for t The system-level lower limit reference value for when the energy storage unit can operate SOE; Here is the SOE window coefficient, which is based on... t Time of the first i Consistency score of individual energy storage units as independent variable Sigmoid Function value: In the formula, The steepness of the curve indicates the sensitivity to changes in control. The center point parameter determines the score position at which the target threshold is reached; 0.15 indicates that the maximum SOE is 95%, preserving a safety margin.

2. The energy storage power station state regulation method according to claim 1, characterized in that, The multi-dimensional operational status data includes the DC-side voltage, average temperature, internal resistance, capacity, and power of the energy storage unit.

3. The energy storage power station state regulation method according to claim 1, characterized in that, The state factor is obtained by normalizing the deviation of the operating state data of each energy storage unit in each dimension relative to the average level of all energy storage units. The normalization process maps the deviation values ​​of each dimension to a unified numerical range.

4. The energy storage power station state regulation method according to claim 1, characterized in that, The step of dynamically calculating the weight coefficients for each dimension at the current moment using the entropy weight method based on the dispersion of the state factors of each energy storage unit in different dimensions includes: Standardize the state factor data of each energy storage unit across all dimensions; Based on the standardized state factor data, calculate the information entropy of each dimension of the state factor; The weight coefficients for each dimension are calculated based on the information entropy of the state factors in each dimension. The greater the dispersion of the state factors, the greater the information entropy, and the higher the corresponding weight coefficient.

5. The energy storage power station state regulation method according to claim 4, characterized in that, The step of performing an internal consistency assessment of each energy storage unit based on weighting coefficients to obtain the internal consistency assessment results of each energy storage unit includes: The range and standard deviation of each individual unit within each energy storage unit are obtained in different dimensions, which serve as secondary consistency indicators for each dimension. After normalizing the secondary consistency index, a weighted linear combination is used to fuse it into primary consistency indices for each dimension. Based on the weighting coefficients calculated using the entropy weighting method, the first-level consistency indexes are weighted and summed to obtain the internal consistency assessment results for each energy storage unit.

6. The energy storage power station state regulation method according to claim 5, characterized in that, The primary consistency index for each dimension is calculated using the following formula: In the formula, This is a primary consistency indicator. The smaller the value, the better the consistency; the value range is [0,1]. This represents the range of the second-order consistency index; The standard deviation of the second-order consistency index; represents the Min-Max normalization function, used to eliminate the influence of dimensions and map the range and standard deviation to the same numerical interval [0,1]. This represents the range weight.

7. The energy storage power station state regulation method according to claim 1, characterized in that, In the preset fuzzy control strategy, the input variables are the state factors of each energy storage unit in each dimension, and the fuzzy set of each input variable is divided into three levels: low, medium, and high; the output variables are the power allocation coefficients, and the fuzzy set of each output variable is divided into five levels: extremely low, low, medium, high, and extremely high.

8. The energy storage power station state regulation method according to claim 7, characterized in that, The preset fuzzy control strategy includes fuzzy rule bases set for charging and discharging scenarios respectively, with each fuzzy rule base containing several core rules. Specifically, in the charging scenario, when both the DC-side voltage state factor and the charge state factor are low, the output power allocation coefficient is extremely high. In the discharging scenario, when both the DC-side voltage state factor and the charge state factor are high, the output power allocation coefficient is extremely high. In the charging or discharging scenario, when the average temperature state factor or the internal resistance state factor is high, the output power allocation coefficient is limited to medium or below.

9. The energy storage power station state regulation method according to claim 8, characterized in that, The step of performing fuzzy inference based on a preset fuzzy control strategy and outputting the power allocation coefficient for each energy storage unit includes: Calculate the membership degree of each input variable on each fuzzy set; Calculate the activation strength of each rule based on the membership degree of the current input variable and the matching degree of each fuzzy rule; The power allocation coefficient of each energy storage unit is obtained by weighting the output values ​​of each rule by using the activation intensity of each rule as the weight.

10. The energy storage power station state regulation method according to claim 1, characterized in that, The step of performing the over-limit verification of the initial power allocation command of each energy storage unit under the operating constraints, and redistributing power according to the verification result includes: determining whether the initial power allocation command of each energy storage unit exceeds the current maximum charging and discharging power limit of the corresponding energy storage unit; if it exceeds, limiting the initial power allocation command to the maximum charging and discharging power limit and recording the excess power. Calculate the change in power of each energy storage unit within a preset time step based on the initial power allocation command of each energy storage unit, determine whether the operating power will exceed the upper and lower limits of the current operating power of the corresponding energy storage unit, and if it will exceed, convert the excess amount into excess power and record it. The excess power of all recorded data is summed, and the excess power is redistributed to the energy storage units that have not exceeded their limits, based on the power allocation coefficient of each energy storage unit.

11. The energy storage power station state regulation method according to claim 1, characterized in that, Following the step of acquiring real-time operational status data of each energy storage unit in the energy storage power station across multiple dimensions, a data preprocessing step is also included: The 3σ criterion is used to remove outliers from the acquired operational status data; Linear interpolation was used to fill in the missing data after outliers were removed; The imputed data is smoothed using a moving average filtering method.

12. A state regulation system for an energy storage power station, characterized in that, Implementing the energy storage power station state regulation method as described in any one of claims 1 to 11, comprising: The multi-dimensional data acquisition module is used to acquire real-time operational status data of each energy storage unit in the energy storage power station from multiple dimensions. The state factor calculation module is used to calculate the state factor of each energy storage unit in each dimension based on the deviation of the operating state data of each energy storage unit in different dimensions from the average level of all energy storage units. The internal consistency assessment module is used to dynamically calculate the weight coefficients corresponding to each dimension at the current moment based on the degree of dispersion of the state factors of each energy storage unit in different dimensions using the entropy weight method, and to perform internal consistency assessment on each energy storage unit based on the weight coefficients, so as to obtain the internal consistency assessment results of each energy storage unit. The power allocation coefficient fuzzy inference module is used to take the state factors of each energy storage unit in different dimensions as input, perform fuzzy inference based on the preset fuzzy control strategy, and output the power allocation coefficient of each energy storage unit. The initial power allocation command calculation module is used to calculate the initial power allocation command for each energy storage unit based on the received total power command of the energy storage power station and the power allocation coefficient of each energy storage unit. The operation constraint adjustment module is used to dynamically adjust the operation constraints of each energy storage unit based on the internal consistency assessment results of each energy storage unit. The power redistribution module is used to perform limit verification on the initial power allocation command of each energy storage unit to check the operating constraints, and redistribute the power according to the verification result, and send the determined power command to each energy storage unit for execution.

13. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the energy storage power station state regulation method as described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the energy storage power station state regulation method as described in any one of claims 1 to 11.

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