A battery charging and discharging power regulation method, system, device and storage medium

Abstract

The application discloses a kind of battery charging and discharging power regulation method, system, equipment and storage medium, applied to electric power system and energy storage field, including obtaining the real-time operation data of target energy storage battery, and extracting the battery state evolution characteristic of target energy storage battery from real-time operation data;Based on battery state evolution characteristic, determine the battery health state of target energy storage battery;Get the operating state data of grid side, and determine the grid stability demand based on operating state data;Based on grid stability demand and battery health state, generate power regulation strategy with grid stability demand as constraint condition, and dynamically adjust the charging and discharging power according to battery health state;Based on power regulation strategy, control the charging and discharging power of target energy storage battery.The battery charging and discharging power regulation method provided in the application effectively slows down the health state degradation of energy storage battery under the premise of guaranteeing the stability of power grid, improves the sustainability and safety of electric vehicles participating in grid interaction.

Description

Technical Field

[0001] This invention relates to the field of power systems and energy storage technology, and in particular to a method, system, device and storage medium for regulating battery charging and discharging power. Background Technology

[0002] In the current operation of the power system, the large-scale grid connection of renewable energy sources such as wind power and photovoltaics, as well as the continuous widening of the peak-valley difference in electricity load, have placed higher demands on the real-time balancing capability and the flexibility of regulation resources of the power grid. Among these, electrochemical energy storage systems, due to their rapid response characteristics, are widely used in frequency regulation, peak shaving and valley filling, and backup support scenarios. As distributed mobile energy storage units, electric vehicles, when participating in grid interaction, not only affect the system dispatching effect but also directly relate to the operational safety of the battery itself.

[0003] In existing technologies, the charging and discharging power regulation methods for electric vehicle energy storage batteries typically use grid-side dispatch commands as the sole input, directly generating uniform charging and discharging power commands. This singular control logic makes it difficult to balance system dispatch efficiency and equipment safety in actual operation, resulting in insufficient utilization of grid regulation resources or unexpected performance degradation of energy storage batteries, thereby reducing the long-term availability of energy storage resources and the reliability of coordinated regulation. Summary of the Invention

[0004] This invention provides a battery charging and discharging power regulation method, system, device, and storage medium to solve the technical problem of balancing system stability requirements and energy storage battery health status during grid regulation. It aims to dynamically adapt to individual battery status while ensuring stable grid operation, thereby improving the reliability and long-term availability of energy storage resources participating in grid interaction.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a battery charging and discharging power adjustment method, comprising: Acquire real-time operating data of the target energy storage battery, and extract the battery state evolution characteristics of the target energy storage battery from the real-time operating data; Based on the battery state evolution characteristics, the battery health state of the target energy storage battery is determined; Obtain operational status data from the power grid side, and determine power grid stability requirements based on the operational status data; Based on the grid stability requirements and the battery health status, a power regulation strategy is generated that takes the grid stability requirements as a constraint and dynamically adjusts the charging and discharging power according to the battery health status. Based on the power regulation strategy, the charging and discharging power of the target energy storage battery is controlled.

[0006] As one preferred embodiment, determining the battery health state of the target energy storage battery based on the battery state evolution characteristics includes: The battery state evolution characteristics are input into the constructed battery health prediction model; The battery health prediction model is used to map and calculate the battery state evolution characteristics, and output the battery health state of the target energy storage battery.

[0007] As one preferred embodiment, the process of constructing the battery health prediction model includes: Acquire historical operating data and corresponding actual values ​​of battery health status of sample energy storage batteries, and extract historical health features from the historical operating data; Based on the historical health characteristics and the actual value of the battery health status, the optimized parameter set of the battery health prediction model is calculated through an optimization algorithm; Configure the battery health prediction model using the optimized parameter set.

[0008] As one preferred embodiment, the optimization algorithm is an improved sparrow search algorithm, and the calculation of the optimization parameter set of the battery health prediction model using the optimization algorithm includes: The historical health characteristics are input into the battery health prediction model to obtain the predicted health status value of the sample energy storage battery; A fitness function is constructed based on the deviation between the actual value of the battery health status and the predicted value of the health status. Based on the fitness function, the parameters of the battery health prediction model are optimized using the improved sparrow search algorithm. When the result of the optimization calculation meets the convergence condition, the parameters of the optimized battery health prediction model are output as the optimized parameter set.

[0009] As one preferred embodiment, the step of acquiring the operating status data of the power grid side and determining the power grid stability requirements based on the operating status data includes: Real-time monitoring of electrical operating parameters of power grid nodes, and calculation of the stability margin index of the power grid side based on the electrical operating parameters; The power grid stability requirement is determined based on the deviation between the stability margin index and the preset stability threshold.

[0010] As a preferred embodiment, the step of generating a power regulation strategy based on the grid stability requirements and the battery health status, which uses the grid stability requirements as a constraint and dynamically adjusts the charging and discharging power according to the battery health status, includes: The power grid stability requirements and the battery health status are input into the constructed charge-discharge control model; The power regulation strategy is obtained by processing the grid stability requirements and the battery health status based on the charge-discharge regulation model.

[0011] As one preferred embodiment, the charge / discharge control model is a differential game model. The power control strategy is obtained by processing the grid stability requirements and the battery health status based on the charge / discharge control model, including: The power grid stability requirement is used as the uncertainty input variable of the differential game model, and the battery health status is used as the regulation cost variable of the differential game model. Solve the differential game model to obtain the power regulation solution of the differential game model; The power regulation solution is output as the power regulation strategy.

[0012] Another embodiment of the present invention provides a battery charging and discharging power regulation system, comprising: The feature extraction module is used to acquire real-time operating data of the target energy storage battery and extract the battery state evolution features of the target energy storage battery from the real-time operating data. A health status assessment module is used to determine the battery health status of the target energy storage battery based on the battery state evolution characteristics. The power grid status sensing module is used to acquire the operating status data of the power grid side and determine the power grid stability requirements based on the operating status data. The strategy generation module is used to generate a power regulation strategy based on the grid stability requirements and the battery health status, with the grid stability requirements as a constraint and the charging and discharging power dynamically adjusted according to the battery health status. A power control execution module is used to control the charging and discharging power of the target energy storage battery based on the power regulation strategy.

[0013] Another embodiment of the present invention provides a battery charging and discharging power regulation device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the battery charging and discharging power regulation method as described above.

[0014] In another embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the battery charging and discharging power regulation method as described above.

[0015] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) This invention obtains real-time operating data of the target energy storage battery to extract its state evolution characteristics and determines the battery health status accordingly. At the same time, it obtains grid-side operating status data to determine grid stability requirements. Based on this, a power regulation strategy is generated that uses grid stability requirements as constraints and dynamically adjusts the charging and discharging power according to the battery health status. This regulation strategy not only meets the grid's requirements for regulation response but also adaptively limits the charging and discharging power of the individual battery based on its state, thereby achieving an organic unity between system-level stability and device-level health status at the control level.

[0016] (2) The charging and discharging power regulation method and system of the present invention can effectively improve the sustainability and safety of electric vehicle energy storage resources participating in grid ancillary services: on the one hand, it ensures the stable operation capability of the grid under disturbances, and on the other hand, it significantly slows down the degradation rate of battery health and extends the service life of energy storage units. In addition, since the strategy generation fully considers the individual differences of batteries, it improves the executability of regulation commands and resource utilization efficiency, providing reliable technical support for large-scale electric vehicle clusters to participate in the coordinated regulation of smart grids. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a battery charging and discharging power adjustment method in one embodiment of the present invention; Figure 2 This is a schematic diagram of a battery charging and discharging power regulation system in one embodiment of the present invention; Figure 3 This is a structural block diagram of a battery charging and discharging power regulation device in one embodiment of the present invention.

[0018] Figure label: Among them, 11 is the feature extraction module, 12 is the health status assessment module, 13 is the power grid status perception module, 14 is the strategy generation module, 15 is the power control execution module, 21 is the processor, and 22 is the memory. 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. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0023] One embodiment of the present invention provides a method for regulating the charging and discharging power of a battery. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The diagram shown is a schematic flowchart of a battery charging and discharging power adjustment method according to one embodiment of the present invention, which includes steps S1 to S5: S1: Obtain the real-time operating data of the target energy storage battery and extract the battery state evolution characteristics of the target energy storage battery from the real-time operating data; Step S1 is the foundation for subsequent health status sensing and power regulation. Without sensing the real-time operating status of the battery, it is impossible to accurately determine the degree of internal degradation, resulting in a loss of targetedness and safety assurance in subsequent regulation decisions. Specifically, in the embodiments of the present invention, sensors and a battery management system deployed on the target energy storage battery continuously collect time-series data during its operation, including but not limited to instantaneous current, terminal voltage, and temperature.

[0024] The aforementioned time-series data is analyzed and processed to extract quantitative indicators that characterize the battery performance degradation process, termed battery state evolution characteristics. This analysis includes calculating the battery capacity increment, DC internal resistance change, current decay time constant during constant-voltage charging, and average temperature rise rate within a fixed time window. After normalizing the calculated indicator values, they collectively form a multi-dimensional feature vector, which is the battery state evolution characteristic extracted from real-time operational data and used to assess battery health.

[0025] S2: Determine the battery health status of the target energy storage battery based on the battery state evolution characteristics; Determining battery health status is a crucial transformation step connecting state perception with subsequent intelligent regulation. The battery state evolution characteristics extracted from operational data are a multi-dimensional and abstract set of physical quantities that cannot be directly understood and used for decision-making by subsequent regulation models. Converting them into a scalarized and normalized index can efficiently and accurately integrate multi-dimensional feature information into the decision-making process, and can establish a fair and comparable health benchmark among different batteries, thereby achieving differentiated and fine-grained regulation.

[0026] Preferably, in one embodiment of the present invention, determining the battery health state of the target energy storage battery based on battery state evolution characteristics includes: The battery state evolution characteristics are input into the constructed battery health prediction model; The battery health prediction model maps and calculates the battery state evolution characteristics to output the battery health status of the target energy storage battery.

[0027] Specifically, the battery state evolution feature vector extracted from the real-time operating data is first input into the pre-built battery health prediction model. In this embodiment, the battery health prediction model is a nonlinear mapping function with trainable parameters. Its input is the battery state evolution feature vector obtained in step S1, and its output is a quantified battery health state value. This output value is the core input variable for the subsequent charge and discharge regulation game.

[0028] In practical applications, the implementation form of the mapping function can be selected according to specific needs. For example, LSTM (Long Short-Term Memory Network), GRU (Gated Recurrent Unit), LSTM+Attention (Long Short-Term Memory Network with Attention Mechanism), or multi-channel LSTM (Multi-channel Long Short-Term Memory Network) can be used. It should be noted that the specific structure, parameters, and metrics of the above models can be selected and configured according to the actual application scenario. This application does not impose specific limitations on them, as long as they can achieve a non-linear mapping from feature vectors to health states.

[0029] In this embodiment, taking the support vector regression model as an example, the input feature vector is mapped to a high-dimensional feature space through a kernel function. In this space, a linear regression relationship between the features and the battery health status is established. The basic form of the model is as follows: Where F is the input battery state evolution feature vector, This is the estimated value of the battery health champion output by the model. For the i-th support vector, The number of support vectors, and For Lagrange multipliers, For bias terms, For kernel functions. In this embodiment, a radial basis function kernel function is used: .

[0030] The core parameters of this model include Lagrange multipliers and bias terms, which are directly determined by the optimization algorithm. In addition, the model's parameters also depend on the kernel function parameters, penalty coefficients, and insensitive loss parameters. In this embodiment, an improved sparrow search algorithm is used to jointly optimize these parameters and hyperparameters to obtain the parameter set that minimizes the prediction error.

[0031] Preferably, in one embodiment of the present invention, the process of constructing the battery health prediction model includes: Obtain historical operating data and corresponding actual values ​​of battery health status of sample energy storage batteries, and extract historical health features from historical operating data; Based on historical health characteristics and the actual value of battery health status, the optimized parameter set of the battery health prediction model is calculated through an optimization algorithm; Configure the battery health prediction model with optimized parameter set.

[0032] Specifically, historical operational data of multiple sets of sample energy storage batteries throughout their complete lifecycle are acquired, and corresponding accurate true values ​​of battery health status are obtained through standard experimental testing. Following the method for acquiring battery state evolution characteristics, historical health feature vectors are extracted from the historical operational data.

[0033] Then, based on historical health characteristics and their corresponding true values, the parameters of the battery health prediction model are calculated and optimized using an improved sparrow search method to obtain a set of optimized parameters that make the model prediction most accurate.

[0034] In this embodiment, taking the set of parameters to be optimized in the support vector regression model as an example, the set of parameters to be optimized includes Lagrange multipliers, bias terms, kernel function parameters, penalty coefficients, and insensitive loss parameters. An improved sparrow search algorithm is used to jointly optimize the above parameters and hyperparameters. The optimized parameter set obtained by optimization is loaded into the model to complete the construction and configuration of the battery health prediction model.

[0035] Preferably, in one embodiment of the present invention, the optimization algorithm is an improved sparrow search algorithm, which calculates the optimization parameter set of the battery health prediction model, including: Historical health characteristics are input into the battery health prediction model to obtain the predicted health status of the sample energy storage battery. A fitness function is constructed based on the deviation between the actual value and the predicted value of the battery health status. Based on the fitness function, the parameters of the battery health prediction model are optimized by an improved sparrow search algorithm. When the result of the optimization calculation meets the convergence condition, the parameters of the optimized battery health prediction model are output as the optimized parameter set.

[0036] In this embodiment, the optimization algorithm employs an improved sparrow search algorithm. The specific process for calculating the optimization parameter set using this algorithm is as follows: First, historical health features are input into the battery health prediction model to be trained (parameters undetermined) to obtain the corresponding health status prediction values; a fitness function is defined to measure the prediction accuracy, such as the root mean square error between the predicted value and the true value; with the goal of minimizing the fitness function, an improved sparrow search algorithm is run to iteratively optimize the model parameters.

[0037] To efficiently and accurately optimize the parameters of the battery health prediction model, this embodiment introduces the following key improvements based on the classic sparrow search algorithm framework to overcome its shortcomings of premature convergence and insufficient global exploration capability: (1) Initialization: To improve the uniformity of the initial population distribution and avoid the algorithm getting trapped in local optima, this embodiment uses an optimized chaotic mapping sequence instead of random initialization, the expression of which is as follows: In the formula, The index is the iteration step number of the chaotic sequence, and its value is a non-negative integer, representing the... Step iteration; Indicates the first The chaotic sequence value of the step, Indicates the first The chaotic sequence value of the step.

[0038] (2) Discoverer Update: In the original algorithm, the discoverer's position update strategy is insufficient in balancing exploration and development. This embodiment proposes an adaptive strategy: when the safety value is high, the discoverer learns from the current best individual; when the safety value is low (warning state), it performs a random walk to explore new areas. The update formula is: In the formula, This represents the index of an individual in the population, with values ​​ranging from (1, N), where N is the population size. The dimension index of the individual position vector is assigned, with a value range of (1, D), where D is the dimension of the parameter to be optimized; t is the current iteration number, which is a non-negative integer, and when t is 0, it indicates the initial population. For the tth generation The individual in the first Positional components in the dimension; For the first The middle generation The individual in the first Positional components in the dimension.

[0039] (3) Joiner Update: To avoid the loss of diversity caused by joiners simply following the discoverer, this embodiment introduces a sine and cosine search strategy to update the joiner's position. This strategy utilizes the oscillating characteristics of sine and cosine functions, enabling joiners to perform a more refined search around the discoverer. The formula is as follows: In the formula, For the tth generation The positional component of the discoverer followed by each participant.

[0040] (4) Mutation Processing: To maintain population diversity and improve local development accuracy in the later stages of iteration, this embodiment designs an adaptive hybrid mutation strategy that combines the advantages of Cauchy mutation and Gaussian mutation. This strategy applies mutation to the current globally optimal individual, and its formula is as follows: In the formula, The new position of the best individual in generation t after mutation; Let be the position of the currently globally optimal individual in generation t.

[0041] Among them, adaptive weight coefficients and Adjust dynamically as the iteration process progresses: , In the formula, The maximum number of iterations preset for the algorithm. In the initial iteration phase... The algorithm is relatively large, favoring Cauchy mutations with long-tail characteristics, which is beneficial for global exploration; in the later stages of iteration, The algorithm is relatively large and focuses on local Gaussian mutations, which is beneficial for fine-grained search.

[0042] After each iteration, it is checked whether the current iteration meets the preset convergence condition. In this embodiment, the convergence condition may be that the number of iterations has reached the maximum number of iterations, or that the change in the global optimal fitness value over several consecutive generations is less than a preset threshold. If the convergence condition is met, the iteration terminates; if the convergence condition is not met, the process returns to the step of evaluating and ranking individual fitness values ​​for the next iteration. The maximum number of iterations can be set according to the dimension of the parameters to be optimized and the computational resources, for example, it can be set to an integer between 100 and 500; the preset threshold is used to determine whether the fitness value has converged, and an appropriate parameter value can be selected according to the actual application scenario.

[0043] When the iteration terminates, the position vector corresponding to the current globally optimal individual is output as the final optimized parameter set. This optimized parameter set is the combination of model parameters that minimizes the prediction error of the battery health prediction model.

[0044] The optimized parameter set obtained through optimization is loaded into the battery health prediction model to complete the model construction.

[0045] Through the above improvements, the improved sparrow search algorithm provided in this embodiment significantly enhances the balance between global exploration capability and local development accuracy, effectively avoids premature convergence, and thus can find a better parameter set for the battery health prediction model, ultimately improving the accuracy of health status prediction.

[0046] S3: Obtain the operating status data of the power grid side and determine the power grid stability requirements based on the operating status data; The charging and discharging behavior of electric vehicles not only affects their own batteries but also serves as a flexible load / power source for the power grid. Therefore, power regulation must serve the ultimate goal of ensuring the safe and stable operation of the power grid. Without real-time sensing of the power grid status and quantification of its demand, power regulation of electric vehicles will be blind and may even exacerbate grid fluctuations, such as by concentrating charging during peak electricity consumption periods, resulting in "peak-on-peak" effects.

[0047] Preferably, in one embodiment of the present invention, acquiring grid-side operational status data and determining grid stability requirements based on the operational status data includes: Real-time monitoring of electrical operating parameters of power grid nodes, and calculation of stability margin indices on the power grid side based on electrical operating parameters; The power grid stability requirements are determined based on the deviation between the stability margin index and the preset stability threshold.

[0048] This involves real-time monitoring of the electrical operating parameters of power grid nodes, including but not limited to node voltage, system frequency, active power, and reactive power. In this embodiment, the following key electrical operating parameters of the target electric vehicle access point are subscribed to and acquired in real time from the power grid dispatch master station system or a wide-area measurement system composed of synchronous phasor measurement devices deployed in substations, using standard communication protocols such as IEC 61850 (international standard for substation communication networks and systems), IEC 104 (telecommunications equipment and systems transmission protocol, hereinafter referred to as IEC 104), or DL / T 634.5104 (electric power industry telecommunications protocol standard). The acquired raw data needs to undergo preprocessing such as filtering and bad data identification to eliminate measurement noise and communication anomalies, ensuring the reliability of subsequent calculations.

[0049] However, simply obtaining raw parameters such as voltage and frequency is insufficient to directly and scientifically guide power regulation. For example, a voltage of 0.95 pu represents drastically different stability risks on lightly loaded and heavily loaded lines. Therefore, it is necessary to calculate stability margin indices that better characterize the system's "safe distance" based on these fundamental parameters and using well-known theoretical models in the field of power system analysis. In this embodiment, voltage stability margin indices and frequency stability margin indices are calculated separately to comprehensively assess the stability state of the power grid.

[0050] (1) Voltage stability margin index: In this embodiment, the voltage stability margin index refers to the distance between the current operating point and the voltage collapse threshold. Specifically, it is calculated based on real-time monitored node voltage amplitudes, combined with line parameters (including line resistance and reactance) and the upstream bus voltage, to determine the relative distance between the current operating point and the voltage collapse threshold. The calculated voltage stability margin index value ranges from 0 to 1. A value closer to 1 indicates a larger voltage stability margin and a safer power grid; a value closer to 0 indicates proximity to the voltage collapse threshold and a more dangerous power grid.

[0051] The voltage collapse threshold can be set according to the power system stable operation regulations, and in practical applications it can be adjusted within the range of 0.70pu~0.80pu according to the specific power grid structure.

[0052] (2) Frequency stability margin index: In this embodiment, the frequency stability margin index refers to the distance between the current frequency and the allowable frequency deviation boundary. Specifically, it is calculated as follows: The system frequency is monitored in real time, the absolute value of the deviation between the current frequency and the rated frequency is calculated, and this deviation is compared with the maximum allowable frequency deviation of the system to obtain the normalized frequency stability margin index. The smaller the frequency deviation, the closer the frequency stability margin index value is to 1, indicating a larger frequency stability margin. Conversely, when the frequency deviation is closer to or exceeds the maximum allowable deviation range of the system, the index value is closer to 0, indicating a smaller frequency stability margin and that the power grid is in an unstable or alert state.

[0053] The maximum frequency deviation can be set according to the power grid frequency operation regulations: for large interconnected power grids, it is usually 0.2Hz; for small and medium-sized or isolated power grids, it is usually 0.5Hz. In this embodiment, 0.5Hz is used, but in practical applications, it can be selected within the above range according to the scale of the power grid. Then, the calculated voltage stability margin index and frequency stability margin index are compared with their respective corresponding safe operation thresholds set in advance according to safety and stability standards and local grid structure to generate clear control requirements, i.e., power grid stability requirements.

[0054] When the margin index falls below the safety threshold, the power grid is determined to be in an unstable or alert state. Specifically, when the frequency is too low, the energy storage system needs to release active power to support the frequency; when the frequency is too high, the energy storage system needs to absorb active power. When the voltage is too low, it is equivalent to an active power demand: the greater the voltage deviation, the greater the equivalent active power demand, and the energy storage system assists in voltage recovery by reducing charging or increasing discharging. The active power demand corresponding to the voltage stability margin index and the frequency stability margin index is compared, and the larger of the two values ​​is taken as the overall active power demand of the power grid for the energy storage system. The degree to which the margin index falls below the safety threshold determines the urgency of the demand: the greater the deviation, the more urgent the power grid's need for energy storage power support. This quantitative structure is the power grid stability demand, which is also the uncertain margin demand that subsequently forms a game relationship with the health margin dispatch quantity.

[0055] The setting of the safe operation threshold is based on the power grid's safe and stable operation standards, combined with the local power grid's line parameters and historical operation data statistical characteristics, to ensure that the power grid is within a safe range under most operating conditions and to reserve a reasonable margin for regulation.

[0056] S4: Based on grid stability requirements and battery health status, generate a power regulation strategy that uses grid stability requirements as a constraint and dynamically adjusts charging and discharging power according to battery health status. There is often a conflict between grid demand and battery status: the moment when the grid needs the most power support (such as peak load) may be the moment when the pressure on battery health is greatest (high-rate discharge). Simple rule-based control (such as fixed power or priority scheduling) cannot scientifically resolve this conflict. This embodiment constructs a mathematical model of collaborative optimization and game theory, treating the rigid demand of the grid as a constraint that must be met, while transforming battery health status into a cost factor in the optimization objective. Under the premise of meeting the minimum demand of the grid, it intelligently seeks the power allocation scheme that "minimizes damage" to battery health or has the optimal overall cost.

[0057] Preferably, in one embodiment of the present invention, a power regulation strategy is generated based on grid stability requirements and battery health status, which uses grid stability requirements as a constraint and dynamically adjusts charging and discharging power according to battery health status, including: Input the grid stability requirements and battery health status into the constructed charge and discharge control model; Based on the charge-discharge regulation model, the power regulation strategy is obtained by processing the grid stability requirements and battery health status.

[0058] The charge / discharge control model is the core computing unit for intelligent decision-making in this embodiment. This model receives two heterogeneous inputs: grid stability requirements and battery health status. Through internally established mathematical optimization relationships, it outputs the optimal charge / discharge power command. Deployed in the grid cloud or within a charging pile controller with edge computing capabilities, its decision-making frequency is synchronized with the grid scheduling cycle or battery status update cycle.

[0059] Preferably, in one embodiment of the present invention, the charge-discharge regulation model is a differential game model. Based on the charge-discharge regulation model, the power grid stability requirements and battery health status are processed to obtain a power regulation strategy, including: The grid stability requirement is used as the uncertainty input variable of the differential game model, and the battery health status is used as the regulation cost variable of the differential game model. Solve the differential game model to obtain the power regulation solution of the differential game model; The power regulation solution is output as a power regulation strategy.

[0060] To effectively integrate grid stability requirements and battery health status in the mathematical model, the two types of input information are first mathematically abstracted and mapped. Grid stability requirements are mapped as an uncertain input variable in the model. This variable represents the volatility and randomness of the grid's power support demand, reflecting the unpredictable but necessary disturbance components in the grid's state. Secondly, battery health status is directly used as the control cost variable in the model. In the subsequently constructed differential game model, the battery health status value will directly participate in the cost function or cost weight of the control action. Specifically, the lower the battery health status value (indicating more severe aging), the higher the "health cost" corresponding to the same power control action.

[0061] Based on the above mapping, a differential game model is constructed, and its expression is as follows: This formula defines the objective function of the differential game, aiming to find the optimal function for the worst-case uncertainty input. This makes the overall performance index Minimum optimal control strategy . For the output vector The norm of the system measures the magnitude of the overall output. δ The gain constant, also known as the health coefficient gain from uncertainty to margin scheduling, determines the level of suppression of uncertainty w(t) by the system; T is the optimization time interval, representing a scheduling cycle or the length of the optimization time domain.

[0062] Constraints include dynamic system equations and various physical limitations: This differential equation describes the dynamic evolution of the system's state variable x(t) over time and is the core dynamic constraint of the game theory model. Among them, The first derivative of the state variable x(t) with respect to time; x(t) is the system state variable, which in this model is the differential game variable between the margin health dispatch quantity u(t) and the uncertain margin demand quantity w(t), and can be understood as the cumulative amount of grid demand tracking error; β(t) is the model structure parameter, which is the dispatch expectation coefficient of the uncertain margin demand variable w(t), used to adjust the degree of influence of uncertain demand on system dynamics.

[0063] This formula defines the model's output vector, which consists of two parts, reflecting the system state and the health cost of regulation, respectively. Here, y(t) is the model output vector used to calculate the performance index in the objective function; λ(t)u(t) is the regulation health cost term, where λ(t) is the health coefficient and u(t) is the regulation amount, which directly reflects the impact of regulation actions on battery health.

[0064] These two inequality constraints ensure that the key parameters in the model conform to their physical meaning and take non-negative values.

[0065] These two equations establish the mathematical relationships between the uncertain demand w(t) and the health regulation quantity u(t) and the relevant fundamental quantities of the system, respectively. The uncertainty margin requirement under a zero initial state; This is the system health margin control vector under zero initial state; This serves as the initial value for system margin and as a reference benchmark.

[0066] This formula defines how the total health cost C is calculated, expressed as a weighted sum of the health costs of various regulatory measures. Wherein, Let λ be the inner product of the health coefficient vector λ and the transpose of the control vector u. The total number of margin scheduling measures in the system is a positive integer. The health coefficient for measure type v; This represents the regulatory amount for the v-type measure.

[0067] These two equations explicitly list the constituent elements of the health coefficient and the control vector in vector form. Specifically, vectors λ and u consist of the coefficients and values ​​corresponding to each sub-category of measures, respectively.

[0068] The first equation indicates that the total control amount is the sum of the control amounts of each subcategory; the second equation specifies the proportional coordination relationship that should be satisfied between the control amounts of different categories; the third equation defines the coordination coefficient vector. Among these, The amount of regulation for the i-th type of regulation at time t; This is the coordination coefficient among various margin scheduling measures, used to maintain a balance between different control actions.

[0069] This formula provides a specific method for calculating the comprehensive health coefficient λ(t), through the coordination coefficient. Health coefficients for each subcategory Weighted consolidation is performed. Among them, The summation index variable takes values ​​in the range (1, ...). ), indicating the number of times the calculation is currently in progress. Class measures; i is the multiplication index variable, with a value range of (1, ..., i) ), representing various measures involving the product of parameters; Excluding the current product when multiplying together The coordination coefficient of the measures themselves.

[0070] These two inequality constraints respectively limit the feasible upper and lower bounds of the control quantity u(t) and assume that the uncertain demand quantity w(t) is non-negative.

[0071] These two set constraints generally indicate that decision variables u(t) and w(t) must belong to their physically feasible strategy sets U and W, respectively. U is the set of feasible strategies for the health margin regulation amount u(t), encompassing all physical and electrical constraints; W is the set of feasible strategies for the uncertainty margin demand amount w(t), reflecting the possible fluctuation range of grid demand.

[0072] S5: Based on the power regulation strategy, control the charging and discharging power of the target energy storage battery.

[0073] Specifically, in one embodiment of the present invention, the power regulation strategy is issued to the local controller of the target energy storage battery in the form of a clear power command value. This command includes the power magnitude and direction, with a positive value representing discharge and a negative value representing charging. Upon receiving the power command, the controller immediately performs a safety boundary check to ensure that the command value does not exceed the physical safety limits of the battery and the converter. The check logic is as follows: the command value is compared with the derating power limit calculated based on the battery's current health state, and the smaller of the two is taken as the final execution command. If the command exceeds the limit, the limit is applied and an anomaly is reported.

[0074] Subsequently, high-precision power point tracking is achieved through a dual-loop control structure. The outer loop is the power loop, which uses a proportional-integral controller to process the deviation between the power command and the actual measured power, generating a current reference command. The inner loop is the current loop, which also uses a proportional-integral controller to quickly adjust the current deviation and generate a control signal. The control signal generated by the inner loop is converted into a pulse signal to drive the power switching devices. By precisely controlling the on and off of the switching devices, the output voltage and current of the energy storage converter are adjusted, thereby controlling the charging and discharging power.

[0075] During execution, the controller continuously monitors battery voltage, temperature, current, and key converter parameters. If any parameter exceeds a safety threshold, a protection mechanism is immediately activated, reducing power output or safely shutting down according to a preset strategy to ensure system safety. Based on this control process, the actual output power of the energy storage battery can quickly and accurately track the commanded value.

[0076] Another embodiment of the present invention provides a battery charging and discharging power regulation system. For details, please refer to [link to relevant documentation]. Figure 2 , Figure 2 The diagram shown illustrates a battery charging and discharging power regulation system according to one embodiment of the present invention, which includes: The feature extraction module 11 is used to acquire the real-time operating data of the target energy storage battery and extract the battery state evolution features of the target energy storage battery from the real-time operating data. The health status assessment module 12 is used to determine the battery health status of the target energy storage battery based on the battery state evolution characteristics. The power grid status sensing module 13 is used to acquire the operating status data of the power grid side and determine the power grid stability requirements based on the operating status data. The strategy generation module 14 is used to generate a power regulation strategy based on grid stability requirements and battery health status, with grid stability requirements as a constraint and the charging and discharging power dynamically adjusted according to the battery health status. The power control execution module 15 is used to control the charging and discharging power of the target energy storage battery based on the power regulation strategy.

[0077] See Figure 3 This is a structural block diagram of a battery charging and discharging power regulation device provided in an embodiment of the present invention. The battery charging and discharging power regulation device provided in this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, it implements the steps as described in the above-described battery charging and discharging power regulation method embodiment, for example... Figure 1 The steps S1 to S5 described above; or, when the processor 21 executes the computer program, it implements the functions of each module in the above-described device embodiments, such as the feature extraction module 11.

[0078] For example, the computer program can be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the battery charging and discharging power regulation device. For example, the computer program can be divided into a feature extraction module 11, a health status assessment module 12, a power grid status perception module 13, a strategy generation module 14, and a power control execution module 15.

[0079] The battery charging and discharging power regulation device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of a battery charging and discharging power regulation device and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the battery charging and discharging power regulation device may also include input / output devices, network access devices, buses, etc.

[0080] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the battery charging and discharging power regulation device, connecting various parts of the device via various interfaces and lines.

[0081] The memory 22 can be used to store the computer program and / or modules. The processor 21 implements various functions of the battery charging and discharging power regulation device by running or executing the computer program and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0082] If the module integrated into the battery charging and discharging power regulation device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0083] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0084] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform steps in the battery charging and discharging power adjustment method of the above embodiments, for example... Figure 1 Steps S1 to S5 as described above.

[0085] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) In actual operation, the battery aging, temperature status and usage history of different electric vehicles are different. If the same power is used to respond to the grid command, it is easy for some vehicles to exit the regulation task prematurely, and even cause safety risks. This invention uses the battery health status as the direct basis for power adjustment, so that the dispatch command is "tailored to the vehicle". This not only improves the availability of the overall regulation capacity, but also reduces abnormal alarms or protection shutdowns caused by forced output.

[0086] (2) Traditional methods often ignore the battery's capacity during emergency grid regulation, resulting in long-term cumulative damage. This invention sets the grid stability requirement as a hard boundary rather than an optimization target. Under the premise of ensuring system instability, it prioritizes the protection of the battery itself, so that the energy storage resources can still maintain a high available capacity after participating in high-frequency tasks such as frequency regulation and peak shaving multiple times, thereby reducing the battery replacement cost for operators.

[0087] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for regulating the charging and discharging power of a battery, characterized in that, include: Acquire real-time operating data of the target energy storage battery, and extract the battery state evolution characteristics of the target energy storage battery from the real-time operating data; Based on the battery state evolution characteristics, the battery health status of the target energy storage battery is determined. Obtain operational status data from the power grid side, and determine power grid stability requirements based on the operational status data; Based on the grid stability requirements and the battery health status, a power regulation strategy is generated that takes the grid stability requirements as a constraint and dynamically adjusts the charging and discharging power according to the battery health status. Based on the power regulation strategy, the charging and discharging power of the target energy storage battery is controlled.

2. The battery charging and discharging power adjustment method as described in claim 1, characterized in that, Determining the battery health status of the target energy storage battery based on the battery state evolution characteristics includes: The battery state evolution characteristics are input into the constructed battery health prediction model; The battery health prediction model is used to map and calculate the battery state evolution characteristics, and output the battery health state of the target energy storage battery.

3. The battery charging and discharging power adjustment method as described in claim 2, characterized in that, The process of constructing the battery health prediction model includes: Acquire historical operating data and corresponding actual values ​​of battery health status of sample energy storage batteries, and extract historical health features from the historical operating data; Based on the historical health characteristics and the actual value of the battery health status, the optimized parameter set of the battery health prediction model is calculated through an optimization algorithm; Configure the battery health prediction model using the optimized parameter set.

4. The battery charging and discharging power adjustment method as described in claim 3, characterized in that, The optimization algorithm is an improved sparrow search algorithm. The calculation of the optimized parameter set for the battery health prediction model using the optimization algorithm includes: The historical health characteristics are input into the battery health prediction model to obtain the predicted health status value of the sample energy storage battery; A fitness function is constructed based on the deviation between the actual value of the battery health status and the predicted value of the health status. Based on the fitness function, the parameters of the battery health prediction model are optimized using the improved sparrow search algorithm. When the result of the optimization calculation meets the convergence condition, the parameters of the optimized battery health prediction model are output as the optimized parameter set.

5. The battery charging and discharging power adjustment method as described in claim 1, characterized in that, The process of acquiring operational status data from the power grid side and determining power grid stability requirements based on the operational status data includes: Real-time monitoring of electrical operating parameters of power grid nodes, and calculation of the stability margin index of the power grid side based on the electrical operating parameters; The power grid stability requirement is determined based on the deviation between the stability margin index and the preset stability threshold.

6. The battery charging and discharging power adjustment method as described in claim 1, characterized in that, The generation of a power regulation strategy based on the grid stability requirements and the battery health status, which uses the grid stability requirements as a constraint and dynamically adjusts the charging and discharging power according to the battery health status, includes: The power grid stability requirements and the battery health status are input into the constructed charge-discharge control model; The power regulation strategy is obtained by processing the grid stability requirements and the battery health status based on the charge-discharge regulation model.

7. The battery charging and discharging power adjustment method as described in claim 6, characterized in that, The charge / discharge control model is a differential game model. The power control strategy is derived by processing the grid stability requirements and the battery health status based on the charge / discharge control model, including: The power grid stability requirement is used as the uncertainty input variable of the differential game model, and the battery health status is used as the regulation cost variable of the differential game model. Solve the differential game model to obtain the power regulation solution of the differential game model; The power regulation solution is output as the power regulation strategy.

8. A battery charging and discharging power regulation system, characterized in that, include: The feature extraction module is used to acquire real-time operating data of the target energy storage battery and extract the battery state evolution features of the target energy storage battery from the real-time operating data. A health status assessment module is used to determine the battery health status of the target energy storage battery based on the battery state evolution characteristics. The power grid status sensing module is used to acquire the operating status data of the power grid side and determine the power grid stability requirements based on the operating status data. The strategy generation module is used to generate a power regulation strategy based on the grid stability requirements and the battery health status, with the grid stability requirements as a constraint and the charging and discharging power dynamically adjusted according to the battery health status. A power control execution module is used to control the charging and discharging power of the target energy storage battery based on the power regulation strategy.

9. A battery charging and discharging power regulation device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the battery charge / discharge power regulation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the battery charging and discharging power adjustment method as described in any one of claims 1 to 7.

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